{"id":67235,"date":"2026-01-28T15:51:56","date_gmt":"2026-01-28T07:51:56","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/67235.html"},"modified":"2026-01-28T15:51:56","modified_gmt":"2026-01-28T07:51:56","slug":"http-%e7%8a%b6%e6%80%81%e7%a0%81%ef%bc%9a%e5%ae%a2%e6%88%b7%e7%ab%af%e4%b8%8e%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%9a%84%e9%80%9a%e4%bf%a1%e8%af%ad%e8%a8%80-%e7%ac%ac%e4%b8%83%e9%83%a8-2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/67235.html","title":{"rendered":"HTTP \u72b6\u6001\u7801\uff1a\u5ba2\u6237\u7aef\u4e0e\u670d\u52a1\u5668\u7684\u901a\u4fe1\u8bed\u8a00\u2014\u2014\u7b2c\u4e03\u90e8\u5206\uff1a\u9ad8\u7ea7\u4e3b\u9898\u4e0e\u672a\u6765\u5c55\u671b\uff08\u4e09\uff09"},"content":{"rendered":"

\u7b2c40\u7ae0：\u672a\u6765\u53d1\u5c55\u8d8b\u52bf<\/h3>\n

40.1 \u534f\u8bae\u6f14\u8fdb\u4e0e\u6807\u51c6\u5316<\/h4>\n

40.1.1 HTTP\u534f\u8bae\u7684\u672a\u6765<\/h5>\n

python<\/p>\n

# HTTP\u534f\u8bae\u6f14\u8fdb\u6a21\u62df\u5668
\nfrom typing import Dict, List, Optional, Set
\nfrom dataclasses import dataclass
\nfrom datetime import datetime
\nimport json<\/p>\n

@dataclass
\nclass ProtocolFeature:
\n """\u534f\u8bae\u7279\u6027"""
\n name: str
\n description: str
\n status: str # draft, experimental, standard, deprecated
\n http_versions: List[str] # \u652f\u6301\u7684HTTP\u7248\u672c
\n adoption_rate: float # \u91c7\u7528\u7387 0-1
\n impact_level: str # low, medium, high, transformative<\/p>\n

@property
\n def is_ready_for_production(self) -> bool:
\n return self.status in ['standard', 'experimental'] and self.adoption_rate > 0.3<\/p>\n

class HTTPProtocolEvolution:
\n """HTTP\u534f\u8bae\u6f14\u8fdb\u5206\u6790"""<\/p>\n

def __init__(self):
\n self.features = self._load_protocol_features()
\n self.trends = self._analyze_trends()<\/p>\n

def _load_protocol_features(self) -> List[ProtocolFeature]:
\n """\u52a0\u8f7d\u534f\u8bae\u7279\u6027"""
\n return [
\n # \u5df2\u6807\u51c6\u5316\u7279\u6027
\n ProtocolFeature(
\n name="HTTP\/2 Server Push",
\n description="Server-initiated resource\u63a8\u9001",
\n status="standard",
\n http_versions=["HTTP\/2"],
\n adoption_rate=0.6,
\n impact_level="medium"
\n ),
\n ProtocolFeature(
\n name="HTTP\/3 QUIC Transport",
\n description="QUIC-based transport layer",
\n status="standard",
\n http_versions=["HTTP\/3"],
\n adoption_rate=0.3,
\n impact_level="high"
\n ),
\n ProtocolFeature(
\n name="103 Early Hints",
\n description="Early informational status code",
\n status="standard",
\n http_versions=["HTTP\/1.1", "HTTP\/2", "HTTP\/3"],
\n adoption_rate=0.2,
\n impact_level="low"
\n ),<\/p>\n

# \u5b9e\u9a8c\u6027\u7279\u6027
\n ProtocolFeature(
\n name="Binary HTTP",
\n description="Fully binary HTTP protocol",
\n status="experimental",
\n http_versions=["Future"],
\n adoption_rate=0.05,
\n impact_level="transformative"
\n ),
\n ProtocolFeature(
\n name="HTTP over QUIC DATAGRAM",
\n description="Unreliable datagram transport",
\n status="experimental",
\n http_versions=["HTTP\/3"],
\n adoption_rate=0.1,
\n impact_level="medium"
\n ),
\n ProtocolFeature(
\n name="Extended Status Codes",
\n description="Standardized extended status code ranges",
\n status="experimental",
\n http_versions=["HTTP\/1.1+", "HTTP\/2", "HTTP\/3"],
\n adoption_rate=0.15,
\n impact_level="high"
\n ),<\/p>\n

# \u8349\u6848\u7279\u6027
\n ProtocolFeature(
\n name="HTTP State Tokens",
\n description="Stateless session tokens",
\n status="draft",
\n http_versions=["Future"],
\n adoption_rate=0.01,
\n impact_level="high"
\n ),
\n ProtocolFeature(
\n name="Opportunistic Security",
\n description="Automatically upgrade to secure protocols",
\n status="draft",
\n http_versions=["Future"],
\n adoption_rate=0.02,
\n impact_level="medium"
\n ),
\n ProtocolFeature(
\n name="Adaptive Compression",
\n description="Context-aware compression algorithms",
\n status="draft",
\n http_versions=["Future"],
\n adoption_rate=0.03,
\n impact_level="medium"
\n )
\n ]<\/p>\n

def _analyze_trends(self) -> Dict[str, List]:
\n """\u5206\u6790\u8d8b\u52bf"""
\n trends = {
\n 'adoption_growth': [],
\n 'emerging_standards': [],
\n 'declining_features': [],
\n 'cross_protocol_synergy': []
\n }<\/p>\n

# \u5206\u6790\u91c7\u7528\u589e\u957f
\n for feature in self.features:
\n if feature.adoption_rate > 0.2 and feature.status in ['experimental', 'standard']:
\n growth_potential = self._calculate_growth_potential(feature)
\n if growth_potential > 0.5:
\n trends['adoption_growth'].append({
\n 'feature': feature.name,
\n 'current_adoption': feature.adoption_rate,
\n 'growth_potential': growth_potential,
\n 'time_to_mainstream': self._estimate_time_to_mainstream(feature)
\n })<\/p>\n

# \u8bc6\u522b\u65b0\u5174\u6807\u51c6
\n draft_features = [f for f in self.features if f.status == 'draft']
\n for feature in draft_features:
\n standardization_likelihood = self._assess_standardization_likelihood(feature)
\n if standardization_likelihood > 0.7:
\n trends['emerging_standards'].append({
\n 'feature': feature.name,
\n 'standardization_likelihood': standardization_likelihood,
\n 'estimated_standardization_year': self._estimate_standardization_year(feature)
\n })<\/p>\n

# \u8bc6\u522b\u8870\u9000\u7279\u6027
\n standard_features = [f for f in self.features if f.status == 'standard']
\n for feature in standard_features:
\n if feature.adoption_rate < 0.4:
\n decline_risk = self._assess_decline_risk(feature)
\n if decline_risk > 0.6:
\n trends['declining_features'].append({
\n 'feature': feature.name,
\n 'decline_risk': decline_risk,
\n 'replacement_candidates': self._find_replacement_candidates(feature)
\n })<\/p>\n

# \u8de8\u534f\u8bae\u534f\u540c
\n trends['cross_protocol_synergy'] = self._identify_cross_protocol_synergies()<\/p>\n

return trends<\/p>\n

def _calculate_growth_potential(self, feature: ProtocolFeature) -> float:
\n """\u8ba1\u7b97\u589e\u957f\u6f5c\u529b"""
\n # \u57fa\u4e8e\u591a\u4e2a\u56e0\u7d20
\n factors = {
\n 'status': {'draft': 0.3, 'experimental': 0.7, 'standard': 1.0}.get(feature.status, 0.5),
\n 'impact': {'low': 0.3, 'medium': 0.6, 'high': 0.8, 'transformative': 0.9}.get(feature.impact_level, 0.5),
\n 'current_adoption': 1 – feature.adoption_rate, # \u4f4e\u91c7\u7528\u7387\u610f\u5473\u7740\u9ad8\u589e\u957f\u6f5c\u529b
\n 'http_version_support': len(feature.http_versions) \/ 3 # \u652f\u6301\u8d8a\u591a\u7248\u672c\u6f5c\u529b\u8d8a\u5927
\n }<\/p>\n

return sum(factors.values()) \/ len(factors)<\/p>\n

def _estimate_time_to_mainstream(self, feature: ProtocolFeature) -> str:
\n """\u4f30\u8ba1\u6210\u4e3a\u4e3b\u6d41\u7684\u65f6\u95f4"""
\n adoption = feature.adoption_rate
\n impact = {'low': 1, 'medium': 2, 'high': 3, 'transformative': 4}[feature.impact_level]<\/p>\n

# \u7b80\u5316\u7684\u4f30\u7b97\u516c\u5f0f
\n years_to_mainstream = (0.8 – adoption) * impact * 2<\/p>\n

if years_to_mainstream <= 1:
\n return "1-2 years"
\n elif years_to_mainstream <= 3:
\n return "2-3 years"
\n elif years_to_mainstream <= 5:
\n return "3-5 years"
\n else:
\n return "5+ years"<\/p>\n

def _assess_standardization_likelihood(self, feature: ProtocolFeature) -> float:
\n """\u8bc4\u4f30\u6807\u51c6\u5316\u53ef\u80fd\u6027"""
\n factors = {
\n 'industry_need': 0.8, # \u884c\u4e1a\u9700\u6c42
\n 'specification_maturity': 0.6, # \u89c4\u8303\u6210\u719f\u5ea6
\n 'vendor_support': 0.5, # \u5382\u5546\u652f\u6301
\n 'backward_compatibility': 0.7, # \u5411\u540e\u517c\u5bb9\u6027
\n 'security_implications': 0.9 # \u5b89\u5168\u6027\u5f71\u54cd
\n }<\/p>\n

# \u8c03\u6574\u57fa\u4e8e\u5f71\u54cd\u7ea7\u522b
\n impact_multiplier = {
\n 'low': 0.8,
\n 'medium': 1.0,
\n 'high': 1.2,
\n 'transformative': 1.5
\n }.get(feature.impact_level, 1.0)<\/p>\n

base_likelihood = sum(factors.values()) \/ len(factors)
\n return min(1.0, base_likelihood * impact_multiplier)<\/p>\n

def _estimate_standardization_year(self, feature: ProtocolFeature) -> int:
\n """\u4f30\u8ba1\u6807\u51c6\u5316\u5e74\u4efd"""
\n likelihood = self._assess_standardization_likelihood(feature)
\n current_year = datetime.now().year<\/p>\n

if likelihood > 0.9:
\n return current_year + 1
\n elif likelihood > 0.7:
\n return current_year + 2
\n elif likelihood > 0.5:
\n return current_year + 3
\n else:
\n return current_year + 5<\/p>\n

def _assess_decline_risk(self, feature: ProtocolFeature) -> float:
\n """\u8bc4\u4f30\u8870\u9000\u98ce\u9669"""
\n factors = {
\n 'low_adoption': 1 – feature.adoption_rate,
\n 'competing_features': 0.6, # \u7ade\u4e89\u7279\u6027
\n 'complexity': 0.4, # \u5b9e\u73b0\u590d\u6742\u5ea6
\n 'maintenance_burden': 0.5 # \u7ef4\u62a4\u8d1f\u62c5
\n }<\/p>\n

return sum(factors.values()) \/ len(factors)<\/p>\n

def _find_replacement_candidates(self, feature: ProtocolFeature) -> List[str]:
\n """\u67e5\u627e\u66ff\u4ee3\u5019\u9009"""
\n candidates = []<\/p>\n

# \u57fa\u4e8e\u529f\u80fd\u76f8\u4f3c\u6027\u67e5\u627e
\n feature_keywords = set(feature.name.lower().split())<\/p>\n

for other in self.features:
\n if other.name == feature.name:
\n continue<\/p>\n

other_keywords = set(other.name.lower().split())
\n similarity = len(feature_keywords.intersection(other_keywords)) \/ len(feature_keywords)<\/p>\n

if similarity > 0.3 and other.status in ['experimental', 'standard']:
\n candidates.append(other.name)<\/p>\n

return candidates[:3]<\/p>\n

def _identify_cross_protocol_synergies(self) -> List[Dict]:
\n """\u8bc6\u522b\u8de8\u534f\u8bae\u534f\u540c"""
\n synergies = []<\/p>\n

# \u6309HTTP\u7248\u672c\u5206\u7ec4\u7279\u6027
\n features_by_version = {}
\n for feature in self.features:
\n for version in feature.http_versions:
\n if version not in features_by_version:
\n features_by_version[version] = []
\n features_by_version[version].append(feature)<\/p>\n

# \u5bfb\u627e\u8de8\u7248\u672c\u534f\u540c
\n versions = list(features_by_version.keys())
\n for i in range(len(versions)):
\n for j in range(i + 1, len(versions)):
\n version1, version2 = versions[i], versions[j]<\/p>\n

common_features = set(
\n f.name for f in features_by_version[version1]
\n ).intersection(
\n f.name for f in features_by_version[version2]
\n )<\/p>\n

if common_features:
\n synergies.append({
\n 'protocols': [version1, version2],
\n 'shared_features': list(common_features),
\n 'synergy_potential': len(common_features) \/ min(
\n len(features_by_version[version1]),
\n len(features_by_version[version2])
\n )
\n })<\/p>\n

return synergies<\/p>\n

def generate_evolution_roadmap(self, years: int = 5) -> Dict[str, any]:
\n """\u751f\u6210\u6f14\u8fdb\u8def\u7ebf\u56fe"""
\n roadmap = {
\n 'timeframe': f"{datetime.now().year}-{datetime.now().year + years}",
\n 'phases': [],
\n 'key_milestones': [],
\n 'adoption_targets': {},
\n 'risk_assessment': {}
\n }<\/p>\n

# \u5b9a\u4e49\u9636\u6bb5
\n phases = [
\n {
\n 'name': 'Immediate (0-1 years)',
\n 'focus': 'Stabilization and optimization',
\n 'features': self._get_features_for_timeframe(0, 1)
\n },
\n {
\n 'name': 'Short-term (1-2 years)',
\n 'focus': 'Adoption and integration',
\n 'features': self._get_features_for_timeframe(1, 2)
\n },
\n {
\n 'name': 'Medium-term (2-3 years)',
\n 'focus': 'Innovation and expansion',
\n 'features': self._get_features_for_timeframe(2, 3)
\n },
\n {
\n 'name': 'Long-term (3-5 years)',
\n 'focus': 'Transformation and convergence',
\n 'features': self._get_features_for_timeframe(3, 5)
\n }
\n ]<\/p>\n

roadmap['phases'] = phases<\/p>\n

# \u5173\u952e\u91cc\u7a0b\u7891
\n roadmap['key_milestones'] = self._identify_key_milestones(years)<\/p>\n

# \u91c7\u7528\u76ee\u6807
\n roadmap['adoption_targets'] = self._set_adoption_targets(years)<\/p>\n

# \u98ce\u9669\u8bc4\u4f30
\n roadmap['risk_assessment'] = self._assess_risks(years)<\/p>\n

return roadmap<\/p>\n

def _get_features_for_timeframe(self, start_year: int, end_year: int) -> List[Dict]:
\n """\u83b7\u53d6\u65f6\u95f4\u8303\u56f4\u5185\u7684\u7279\u6027"""
\n features_in_timeframe = []<\/p>\n

for feature in self.features:
\n # \u57fa\u4e8e\u5f53\u524d\u72b6\u6001\u548c\u91c7\u7528\u7387\u4f30\u7b97\u65f6\u95f4
\n time_to_maturity = self._estimate_time_to_feature_maturity(feature)<\/p>\n

if start_year <= time_to_maturity <= end_year:
\n features_in_timeframe.append({
\n 'feature': feature.name,
\n 'estimated_maturity_year': datetime.now().year + time_to_maturity,
\n 'expected_impact': feature.impact_level,
\n 'preparation_actions': self._get_preparation_actions(feature)
\n })<\/p>\n

return sorted(features_in_timeframe, key=lambda x: x['estimated_maturity_year'])<\/p>\n

def _estimate_time_to_feature_maturity(self, feature: ProtocolFeature) -> int:
\n """\u4f30\u8ba1\u7279\u6027\u6210\u719f\u65f6\u95f4"""
\n base_time = {
\n 'draft': 3,
\n 'experimental': 2,
\n 'standard': 0
\n }.get(feature.status, 1)<\/p>\n

# \u8c03\u6574\u57fa\u4e8e\u91c7\u7528\u7387
\n adoption_adjustment = (1 – feature.adoption_rate) * 2<\/p>\n

return base_time + int(adoption_adjustment)<\/p>\n

def _get_preparation_actions(self, feature: ProtocolFeature) -> List[str]:
\n """\u83b7\u53d6\u51c6\u5907\u884c\u52a8"""
\n actions = []<\/p>\n

if feature.status == 'draft':
\n actions.extend([
\n "Monitor specification development",
\n "Participate in standardization discussions",
\n "Conduct feasibility studies"
\n ])<\/p>\n

elif feature.status == 'experimental':
\n actions.extend([
\n "Implement proof-of-concept",
\n "Test with early adopter user base",
\n "Gather performance metrics"
\n ])<\/p>\n

elif feature.status == 'standard':
\n if feature.adoption_rate < 0.5:
\n actions.extend([
\n "Plan migration strategy",
\n "Update infrastructure support",
\n "Train development teams"
\n ])<\/p>\n

return actions<\/p>\n

def _identify_key_milestones(self, years: int) -> List[Dict]:
\n """\u8bc6\u522b\u5173\u952e\u91cc\u7a0b\u7891"""
\n milestones = []<\/p>\n

# \u534f\u8bae\u7248\u672c\u91cc\u7a0b\u7891
\n protocol_milestones = [
\n {
\n 'year': 2024,
\n 'milestone': 'HTTP\/3 reaches 50% global adoption',
\n 'confidence': 'high',
\n 'impact': 'major'
\n },
\n {
\n 'year': 2025,
\n 'milestone': 'Binary HTTP specification finalized',
\n 'confidence': 'medium',
\n 'impact': 'transformative'
\n },
\n {
\n 'year': 2026,
\n 'milestone': 'Extended status codes standardized',
\n 'confidence': 'high',
\n 'impact': 'significant'
\n }
\n ]<\/p>\n

# \u6280\u672f\u91cc\u7a0b\u7891
\n tech_milestones = [
\n {
\n 'year': 2024,
\n 'milestone': 'AI-driven protocol optimization becomes mainstream',
\n 'confidence': 'medium',
\n 'impact': 'high'
\n },
\n {
\n 'year': 2025,
\n 'milestone': 'Quantum-safe HTTP extensions proposed',
\n 'confidence': 'low',
\n 'impact': 'critical'
\n }
\n ]<\/p>\n

milestones.extend(protocol_milestones)
\n milestones.extend(tech_milestones)<\/p>\n

return sorted(milestones, key=lambda x: x['year'])<\/p>\n

def _set_adoption_targets(self, years: int) -> Dict[str, Dict]:
\n """\u8bbe\u5b9a\u91c7\u7528\u76ee\u6807"""
\n targets = {}<\/p>\n

for version in ['HTTP\/1.1', 'HTTP\/2', 'HTTP\/3', 'Future']:
\n current_adoption = self._estimate_current_adoption(version)<\/p>\n

targets[version] = {
\n 'current': current_adoption,
\n 'target_1_year': min(1.0, current_adoption * 1.3),
\n 'target_3_years': min(1.0, current_adoption * 1.8),
\n 'target_5_years': min(1.0, current_adoption * 2.5),
\n 'confidence': self._estimate_adoption_confidence(version)
\n }<\/p>\n

return targets<\/p>\n

def _estimate_current_adoption(self, http_version: str) -> float:
\n """\u4f30\u8ba1\u5f53\u524d\u91c7\u7528\u7387"""
\n # \u7b80\u5316\u4f30\u8ba1
\n estimates = {
\n 'HTTP\/1.1': 0.4,
\n 'HTTP\/2': 0.5,
\n 'HTTP\/3': 0.1,
\n 'Future': 0.0
\n }
\n return estimates.get(http_version, 0.0)<\/p>\n

def _estimate_adoption_confidence(self, http_version: str) -> str:
\n """\u4f30\u8ba1\u91c7\u7528\u4fe1\u5fc3"""
\n confidences = {
\n 'HTTP\/1.1': 'high', # \u7a33\u5b9a\u4f46\u4e0b\u964d
\n 'HTTP\/2': 'high', # \u7a33\u5b9a
\n 'HTTP\/3': 'medium', # \u589e\u957f\u4e2d
\n 'Future': 'low' # \u4e0d\u786e\u5b9a
\n }
\n return confidences.get(http_version, 'low')<\/p>\n

def _assess_risks(self, years: int) -> Dict[str, List]:
\n """\u8bc4\u4f30\u98ce\u9669"""
\n risks = {
\n 'technical': [],
\n 'adoption': [],
\n 'security': [],
\n 'strategic': []
\n }<\/p>\n

# \u6280\u672f\u98ce\u9669
\n risks['technical'].extend([
\n {
\n 'risk': 'Protocol fragmentation',
\n 'likelihood': 'medium',
\n 'impact': 'high',
\n 'mitigation': 'Active participation in standardization'
\n },
\n {
\n 'risk': 'Backward compatibility breaks',
\n 'likelihood': 'low',
\n 'impact': 'critical',
\n 'mitigation': 'Comprehensive testing and fallback strategies'
\n }
\n ])<\/p>\n

# \u91c7\u7528\u98ce\u9669
\n risks['adoption'].extend([
\n {
\n 'risk': 'Slow enterprise adoption',
\n 'likelihood': 'high',
\n 'impact': 'medium',
\n 'mitigation': 'Education and gradual migration paths'
\n }
\n ])<\/p>\n

# \u5b89\u5168\u98ce\u9669
\n risks['security'].extend([
\n {
\n 'risk': 'Quantum computing threats',
\n 'likelihood': 'low',
\n 'impact': 'critical',
\n 'mitigation': 'Research and prepare quantum-safe algorithms'
\n }
\n ])<\/p>\n

# \u6218\u7565\u98ce\u9669
\n risks['strategic'].extend([
\n {
\n 'risk': 'Competing protocol ecosystems',
\n 'likelihood': 'medium',
\n 'impact': 'high',
\n 'mitigation': 'Monitor alternatives and maintain interoperability'
\n }
\n ])<\/p>\n

return risks<\/p>\n

def generate_recommendations(self) -> Dict[str, List]:
\n """\u751f\u6210\u5efa\u8bae"""
\n recommendations = {
\n 'immediate': [],
\n 'strategic': [],
\n 'research': []
\n }<\/p>\n

# \u7acb\u5373\u884c\u52a8
\n for feature in self.features:
\n if feature.is_ready_for_production:
\n recommendations['immediate'].append({
\n 'action': f'Evaluate adoption of {feature.name}',
\n 'priority': 'high' if feature.impact_level in ['high', 'transformative'] else 'medium',
\n 'effort': 'low' if feature.adoption_rate > 0.5 else 'medium'
\n })<\/p>\n

# \u6218\u7565\u884c\u52a8
\n emerging_standards = [f for f in self.features if f.status == 'draft']
\n for feature in emerging_standards:
\n if feature.impact_level in ['high', 'transformative']:
\n recommendations['strategic'].append({
\n 'action': f'Engage with {feature.name} standardization process',
\n 'timeframe': '1-2 years',
\n 'benefit': 'Influence specification and early adoption advantage'
\n })<\/p>\n

# \u7814\u7a76\u884c\u52a8
\n transformative_features = [f for f in self.features
\n if f.impact_level == 'transformative']
\n for feature in transformative_features:
\n recommendations['research'].append({
\n 'action': f'Research implications of {feature.name}',
\n 'focus': 'Technical feasibility and business impact',
\n 'deliverable': 'Research report and prototype'
\n })<\/p>\n

return recommendations<\/p>\n

# \u4f7f\u7528\u793a\u4f8b
\nevolution = HTTPProtocolEvolution()<\/p>\n

# \u5206\u6790\u8d8b\u52bf
\nprint("=== HTTP Protocol Evolution Trends ===")
\nfor category, items in evolution.trends.items():
\n print(f"\\\\n{category.upper()}:")
\n for item in items[:2]: # \u663e\u793a\u524d\u4e24\u9879
\n if isinstance(item, dict):
\n print(f" – {item.get('feature', 'Unknown')}")
\n else:
\n print(f" – {item}")<\/p>\n

# \u751f\u6210\u8def\u7ebf\u56fe
\nroadmap = evolution.generate_evolution_roadmap(years=5)
\nprint(f"\\\\n=== Evolution Roadmap ({roadmap['timeframe']}) ===")<\/p>\n

for phase in roadmap['phases']:
\n print(f"\\\\n{phase['name']}: {phase['focus']}")
\n for feature in phase['features'][:2]: # \u663e\u793a\u524d\u4e24\u4e2a\u7279\u6027
\n print(f" \u2022 {feature['feature']} ({feature['estimated_maturity_year']})")<\/p>\n

# \u751f\u6210\u5efa\u8bae
\nrecommendations = evolution.generate_recommendations()
\nprint(f"\\\\n=== Recommendations ===")
\nfor category, items in recommendations.items():
\n print(f"\\\\n{category.upper()}:")
\n for item in items[:2]: # \u663e\u793a\u524d\u4e24\u9879
\n print(f" \u2022 {item['action']}")<\/p>\n

40.1.2 \u72b6\u6001\u7801\u6807\u51c6\u7684\u672a\u6765\u6269\u5c55<\/h5>\n

python<\/p>\n

# \u72b6\u6001\u7801\u6807\u51c6\u6269\u5c55\u6a21\u62df
\nfrom typing import Dict, List, Optional, Set
\nfrom enum import Enum
\nfrom dataclasses import dataclass
\nfrom datetime import datetime
\nimport json<\/p>\n

class StatusCodeCategory(Enum):
\n """\u72b6\u6001\u7801\u7c7b\u522b"""
\n INFORMATIONAL = "1xx"
\n SUCCESS = "2xx"
\n REDIRECTION = "3xx"
\n CLIENT_ERROR = "4xx"
\n SERVER_ERROR = "5xx"
\n EXTENDED = "6xx" # \u672a\u6765\u6269\u5c55
\n CUSTOM = "9xx" # \u6c38\u4e45\u81ea\u5b9a\u4e49\u8303\u56f4<\/p>\n

@dataclass
\nclass StatusCodeProposal:
\n """\u72b6\u6001\u7801\u63d0\u6848"""
\n code: int
\n name: str
\n category: StatusCodeCategory
\n description: str
\n proposed_by: str
\n proposed_date: datetime
\n use_cases: List[str]
\n adoption_requirements: Dict[str, str]
\n estimated_impact: str # low, medium, high
\n standardization_status: str # draft, review, accepted, rejected<\/p>\n

@property
\n def is_valid_range(self) -> bool:
\n """\u68c0\u67e5\u662f\u5426\u5728\u6709\u6548\u8303\u56f4\u5185"""
\n ranges = {
\n StatusCodeCategory.INFORMATIONAL: (100, 199),
\n StatusCodeCategory.SUCCESS: (200, 299),
\n StatusCodeCategory.REDIRECTION: (300, 399),
\n StatusCodeCategory.CLIENT_ERROR: (400, 499),
\n StatusCodeCategory.SERVER_ERROR: (500, 599),
\n StatusCodeCategory.EXTENDED: (600, 699),
\n StatusCodeCategory.CUSTOM: (900, 999)
\n }<\/p>\n

if self.category in ranges:
\n start, end = ranges[self.category]
\n return start <= self.code <= end
\n return False<\/p>\n

class StatusCodeStandardization:
\n """\u72b6\u6001\u7801\u6807\u51c6\u5316\u7ba1\u7406"""<\/p>\n

def __init__(self):
\n self.existing_codes = self._load_existing_codes()
\n self.proposals = self._load_active_proposals()
\n self.adoption_tracking = {}<\/p>\n

def _load_existing_codes(self) -> Dict[int, Dict]:
\n """\u52a0\u8f7d\u73b0\u6709\u72b6\u6001\u7801"""
\n # \u6807\u51c6HTTP\u72b6\u6001\u7801
\n return {
\n 100: {'name': 'Continue', 'category': '1xx', 'standard': 'RFC 9110'},
\n 200: {'name': 'OK', 'category': '2xx', 'standard': 'RFC 9110'},
\n 404: {'name': 'Not Found', 'category': '4xx', 'standard': 'RFC 9110'},
\n 500: {'name': 'Internal Server Error', 'category': '5xx', 'standard': 'RFC 9110'},
\n # \u5df2\u77e5\u7684\u975e\u6807\u51c6\u4f46\u5e7f\u6cdb\u4f7f\u7528\u7684\u4ee3\u7801
\n 420: {'name': 'Enhance Your Calm', 'category': '4xx', 'standard': 'Twitter API'},
\n 429: {'name': 'Too Many Requests', 'category': '4xx', 'standard': 'RFC 6585'},
\n 460: {'name': 'Out of Stock', 'category': '4xx', 'standard': 'E-commerce Custom'},
\n 520: {'name': 'Web Server Returned an Unknown Error',
\n 'category': '5xx', 'standard': 'Cloudflare Custom'},
\n 529: {'name': 'Site is overloaded', 'category': '5xx', 'standard': 'Qualys Custom'}
\n }<\/p>\n

def _load_active_proposals(self) -> List[StatusCodeProposal]:
\n """\u52a0\u8f7d\u6d3b\u8dc3\u63d0\u6848"""
\n return [
\n StatusCodeProposal(
\n code=460,
\n name="Out of Stock",
\n category=StatusCodeCategory.CLIENT_ERROR,
\n description="The requested product is temporarily unavailable",
\n proposed_by="E-commerce Standards Body",
\n proposed_date=datetime(2023, 1, 15),
\n use_cases=[
\n "E-commerce product availability",
\n "Inventory management systems",
\n "Booking and reservation systems"
\n ],
\n adoption_requirements={
\n "minimum_implementations": 3,
\n "specification_clarity": "high",
\n "backward_compatibility": "required"
\n },
\n estimated_impact="high",
\n standardization_status="review"
\n ),
\n StatusCodeProposal(
\n code=521,
\n name="Service Unavailable – Maintenance",
\n category=StatusCodeCategory.SERVER_ERROR,
\n description="Service is temporarily unavailable due to maintenance",
\n proposed_by="Infrastructure Working Group",
\n proposed_date=datetime(2023, 3, 10),
\n use_cases=[
\n "Planned maintenance notifications",
\n "Scheduled downtime communication",
\n "Graceful degradation"
\n ],
\n adoption_requirements={
\n "minimum_implementations": 2,
\n "specification_clarity": "medium",
\n "backward_compatibility": "required"
\n },
\n estimated_impact="medium",
\n standardization_status="draft"
\n ),
\n StatusCodeProposal(
\n code=630,
\n name="AI Processing Required",
\n category=StatusCodeCategory.EXTENDED,
\n description="Request requires AI\/ML processing which may take additional time",
\n proposed_by="AI\/ML Standards Initiative",
\n proposed_date=datetime(2023, 6, 1),
\n use_cases=[
\n "AI-enhanced APIs",
\n "Machine learning inference endpoints",
\n "Real-time processing pipelines"
\n ],
\n adoption_requirements={
\n "minimum_implementations": 5,
\n "specification_clarity": "low",
\n "backward_compatibility": "optional"
\n },
\n estimated_impact="transformative",
\n standardization_status="draft"
\n ),
\n StatusCodeProposal(
\n code=910,
\n name="Quantum Computation Detected",
\n category=StatusCodeCategory.CUSTOM,
\n description="Request patterns suggest quantum computing activity",
\n proposed_by="Quantum Security Working Group",
\n proposed_date=datetime(2023, 8, 20),
\n use_cases=[
\n "Quantum threat detection",
\n "Advanced security systems",
\n "Cryptographic protocol monitoring"
\n ],
\n adoption_requirements={
\n "minimum_implementations": 1,
\n "specification_clarity": "low",
\n "backward_compatibility": "not_required"
\n },
\n estimated_impact="high",
\n standardization_status="draft"
\n )
\n ]<\/p>\n

def evaluate_proposal(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u8bc4\u4f30\u63d0\u6848"""
\n evaluation = {
\n 'proposal': {
\n 'code': proposal.code,
\n 'name': proposal.name,
\n 'category': proposal.category.value
\n },
\n 'validation': self._validate_proposal(proposal),
\n 'impact_assessment': self._assess_impact(proposal),
\n 'adoption_potential': self._estimate_adoption_potential(proposal),
\n 'recommendation': self._generate_recommendation(proposal),
\n 'next_steps': []
\n }<\/p>\n

# \u6839\u636e\u8bc4\u4f30\u7ed3\u679c\u786e\u5b9a\u4e0b\u4e00\u6b65
\n if evaluation['validation']['is_valid']:
\n if evaluation['impact_assessment']['overall_score'] > 0.7:
\n evaluation['next_steps'].append("Move to standardization review")
\n else:
\n evaluation['next_steps'].append("Require more implementation evidence")
\n else:
\n evaluation['next_steps'].append("Address validation issues")<\/p>\n

return evaluation<\/p>\n

def _validate_proposal(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u9a8c\u8bc1\u63d0\u6848"""
\n issues = []<\/p>\n

# \u68c0\u67e5\u8303\u56f4\u6709\u6548\u6027
\n if not proposal.is_valid_range:
\n issues.append(f"Code {proposal.code} outside valid range for category {proposal.category}")<\/p>\n

# \u68c0\u67e5\u51b2\u7a81
\n if proposal.code in self.existing_codes:
\n issues.append(f"Code {proposal.code} already assigned to {self.existing_codes[proposal.code]['name']}")<\/p>\n

# \u68c0\u67e5\u8bed\u4e49\u6e05\u6670\u5ea6
\n if len(proposal.description) < 20:
\n issues.append("Description too brief")<\/p>\n

# \u68c0\u67e5\u7528\u4f8b\u5145\u5206\u6027
\n if len(proposal.use_cases) < 2:
\n issues.append("Insufficient use cases provided")<\/p>\n

return {
\n 'is_valid': len(issues) == 0,
\n 'issues': issues,
\n 'completeness_score': self._calculate_completeness_score(proposal)
\n }<\/p>\n

def _calculate_completeness_score(self, proposal: StatusCodeProposal) -> float:
\n """\u8ba1\u7b97\u5b8c\u6574\u5ea6\u5206\u6570"""
\n factors = {
\n 'description_length': min(1.0, len(proposal.description) \/ 100),
\n 'use_cases_count': min(1.0, len(proposal.use_cases) \/ 5),
\n 'requirements_specified': len(proposal.adoption_requirements) \/ 3,
\n 'clarity': 0.7 if len(proposal.description.split()) > 10 else 0.3
\n }<\/p>\n

return sum(factors.values()) \/ len(factors)<\/p>\n

def _assess_impact(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u8bc4\u4f30\u5f71\u54cd"""
\n impact_factors = {
\n 'technical': self._assess_technical_impact(proposal),
\n 'business': self._assess_business_impact(proposal),
\n 'ecosystem': self._assess_ecosystem_impact(proposal)
\n }<\/p>\n

overall_score = sum(factor['score'] for factor in impact_factors.values()) \/ 3<\/p>\n

return {
\n 'factors': impact_factors,
\n 'overall_score': overall_score,
\n 'risk_level': 'low' if overall_score < 0.4 else 'medium' if overall_score < 0.7 else 'high'
\n }<\/p>\n

def _assess_technical_impact(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u8bc4\u4f30\u6280\u672f\u5f71\u54cd"""
\n # \u57fa\u4e8e\u7c7b\u522b\u548c\u4ee3\u7801\u8303\u56f4
\n technical_considerations = []
\n score = 0.5 # \u57fa\u7840\u5206\u6570<\/p>\n

if proposal.category == StatusCodeCategory.EXTENDED:
\n technical_considerations.append("New range (6xx) requires protocol updates")
\n score += 0.2
\n elif proposal.category == StatusCodeCategory.CUSTOM:
\n technical_considerations.append("Custom range (9xx) for permanent extensions")
\n score += 0.3<\/p>\n

# \u68c0\u67e5\u5411\u540e\u517c\u5bb9\u6027
\n requirements = proposal.adoption_requirements
\n if requirements.get('backward_compatibility') == 'required':
\n technical_considerations.append("Backward compatibility required")
\n score += 0.1<\/p>\n

return {
\n 'score': min(1.0, score),
\n 'considerations': technical_considerations,
\n 'implementation_complexity': self._estimate_implementation_complexity(proposal)
\n }<\/p>\n

def _estimate_implementation_complexity(self, proposal: StatusCodeProposal) -> str:
\n """\u4f30\u7b97\u5b9e\u73b0\u590d\u6742\u5ea6"""
\n if proposal.category in [StatusCodeCategory.CLIENT_ERROR, StatusCodeCategory.SERVER_ERROR]:
\n return "low" # \u9519\u8bef\u5904\u7406\u5df2\u6210\u719f
\n elif proposal.category == StatusCodeCategory.EXTENDED:
\n return "high" # \u9700\u8981\u534f\u8bae\u652f\u6301
\n else:
\n return "medium"<\/p>\n

def _assess_business_impact(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u8bc4\u4f30\u4e1a\u52a1\u5f71\u54cd"""
\n # \u57fa\u4e8e\u7528\u4f8b\u548c\u91c7\u7528\u8981\u6c42
\n business_value = len(proposal.use_cases) * 0.2
\n adoption_barrier = 1 – (len(proposal.adoption_requirements) \/ 5)<\/p>\n

score = business_value * (1 – adoption_barrier * 0.5)<\/p>\n

return {
\n 'score': min(1.0, score),
\n 'potential_users': self._estimate_potential_users(proposal),
\n 'industry_relevance': self._assess_industry_relevance(proposal)
\n }<\/p>\n

def _estimate_potential_users(self, proposal: StatusCodeProposal) -> str:
\n """\u4f30\u7b97\u6f5c\u5728\u7528\u6237"""
\n use_cases = len(proposal.use_cases)<\/p>\n

if use_cases > 4:
\n return "widespread"
\n elif use_cases > 2:
\n return "industry_specific"
\n else:
\n return "niche"<\/p>\n

def _assess_industry_relevance(self, proposal: StatusCodeProposal) -> List[str]:
\n """\u8bc4\u4f30\u884c\u4e1a\u76f8\u5173\u6027"""
\n industries = []<\/p>\n

# \u57fa\u4e8e\u7528\u4f8b\u5173\u952e\u8bcd
\n keywords_to_industries = {
\n 'e-commerce': ['product', 'inventory', 'stock', 'shopping'],
\n 'finance': ['payment', 'transaction', 'banking', 'financial'],
\n 'healthcare': ['medical', 'patient', 'health', 'clinical'],
\n 'iot': ['device', 'sensor', 'iot', 'embedded'],
\n 'ai': ['ai', 'machine learning', 'neural', 'inference']
\n }<\/p>\n

all_text = ' '.join(proposal.use_cases).lower()<\/p>\n

for industry, keywords in keywords_to_industries.items():
\n if any(keyword in all_text for keyword in keywords):
\n industries.append(industry)<\/p>\n

return industries if industries else ['general']<\/p>\n

def _assess_ecosystem_impact(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u8bc4\u4f30\u751f\u6001\u7cfb\u7edf\u5f71\u54cd"""
\n # \u68c0\u67e5\u4e0e\u73b0\u6709\u4ee3\u7801\u7684\u5173\u7cfb
\n conflicts = self._find_potential_conflicts(proposal)<\/p>\n

# \u8bc4\u4f30\u6269\u5c55\u6027
\n extensibility = self._assess_extensibility(proposal)<\/p>\n

score = 0.6 # \u57fa\u7840\u5206\u6570
\n if not conflicts:
\n score += 0.2
\n if extensibility['score'] > 0.7:
\n score += 0.1<\/p>\n

return {
\n 'score': min(1.0, score),
\n 'conflicts': conflicts,
\n 'extensibility': extensibility,
\n 'standardization_path': self._determine_standardization_path(proposal)
\n }<\/p>\n

def _find_potential_conflicts(self, proposal: StatusCodeProposal) -> List[str]:
\n """\u67e5\u627e\u6f5c\u5728\u51b2\u7a81"""
\n conflicts = []<\/p>\n

# \u68c0\u67e5\u8bed\u4e49\u91cd\u53e0
\n for code, existing in self.existing_codes.items():
\n if existing['name'].lower() in proposal.name.lower() or \\\\
\n proposal.name.lower() in existing['name'].lower():
\n conflicts.append(f"Semantic overlap with {code} ({existing['name']})")<\/p>\n

return conflicts<\/p>\n

def _assess_extensibility(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u8bc4\u4f30\u6269\u5c55\u6027"""
\n extensibility_factors = {
\n 'range_availability': 1.0 if proposal.category == StatusCodeCategory.EXTENDED else 0.5,
\n 'semantic_clarity': 0.8 if len(proposal.description.split()) > 15 else 0.4,
\n 'parameter_support': 0.3, # \u5047\u8bbe\u9700\u8981\u53c2\u6570\u652f\u6301
\n 'substatus_capability': 0.5 # \u5b50\u72b6\u6001\u7801\u80fd\u529b
\n }<\/p>\n

score = sum(extensibility_factors.values()) \/ len(extensibility_factors)<\/p>\n

return {
\n 'score': score,
\n 'factors': extensibility_factors,
\n 'recommendations': [
\n "Consider supporting extended error details",
\n "Define clear substatus code ranges if needed"
\n ] if score < 0.7 else []
\n }<\/p>\n

def _determine_standardization_path(self, proposal: StatusCodeProposal) -> str:
\n """\u786e\u5b9a\u6807\u51c6\u5316\u8def\u5f84"""
\n if proposal.category == StatusCodeCategory.EXTENDED:
\n return "RFC standardization with IETF"
\n elif proposal.category == StatusCodeCategory.CUSTOM:
\n return "Industry consortium specification"
\n elif proposal.estimated_impact == "high":
\n return "Fast-track standardization"
\n else:
\n return "Standard RFC process"<\/p>\n

def _estimate_adoption_potential(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u4f30\u7b97\u91c7\u7528\u6f5c\u529b"""
\n # \u57fa\u4e8e\u591a\u4e2a\u56e0\u7d20
\n factors = {
\n 'current_need': 0.7,
\n 'ease_of_implementation': 0.8 if self._estimate_implementation_complexity(proposal) == 'low' else 0.4,
\n 'industry_support': len(self._assess_industry_relevance(proposal)) * 0.2,
\n 'specification_quality': self._calculate_completeness_score(proposal)
\n }<\/p>\n

adoption_score = sum(factors.values()) \/ len(factors)<\/p>\n

# \u4f30\u7b97\u65f6\u95f4\u7ebf
\n if adoption_score > 0.8:
\n timeline = "1-2 years"
\n elif adoption_score > 0.6:
\n timeline = "2-3 years"
\n elif adoption_score > 0.4:
\n timeline = "3-5 years"
\n else:
\n timeline = "5+ years"<\/p>\n

return {
\n 'score': adoption_score,
\n 'factors': factors,
\n 'estimated_timeline': timeline,
\n 'key_adopters': self._identify_key_adopters(proposal)
\n }<\/p>\n

def _identify_key_adopters(self, proposal: StatusCodeProposal) -> List[str]:
\n """\u8bc6\u522b\u5173\u952e\u91c7\u7528\u8005"""
\n industries = self._assess_industry_relevance(proposal)<\/p>\n

adopters = []
\n industry_to_adopters = {
\n 'e-commerce': ['Amazon', 'Shopify', 'Alibaba'],
\n 'finance': ['Stripe', 'PayPal', 'Plaid'],
\n 'cloud': ['AWS', 'Google Cloud', 'Microsoft Azure'],
\n 'ai': ['OpenAI', 'Anthropic', 'Cohere']
\n }<\/p>\n

for industry in industries:
\n if industry in industry_to_adopters:
\n adopters.extend(industry_to_adopters[industry][:2]) # \u6bcf\u4e2a\u884c\u4e1a\u53d6\u524d2\u4e2a<\/p>\n

return list(set(adopters))[:5] # \u53bb\u91cd\u5e76\u9650\u5236\u6570\u91cf<\/p>\n

def _generate_recommendation(self, proposal: StatusCodeProposal) -> Dict[str, any]:
\n """\u751f\u6210\u63a8\u8350"""
\n evaluation = self.evaluate_proposal(proposal)<\/p>\n

if not evaluation['validation']['is_valid']:
\n return {
\n 'decision': 'REJECT',
\n 'reason': 'Validation failed',
\n 'issues': evaluation['validation']['issues']
\n }<\/p>\n

impact_score = evaluation['impact_assessment']['overall_score']
\n adoption_score = evaluation['adoption_potential']['score']<\/p>\n

overall_score = (impact_score * 0.6 + adoption_score * 0.4)<\/p>\n

if overall_score > 0.8:
\n return {
\n 'decision': 'ACCEPT',
\n 'priority': 'HIGH',
\n 'next_phase': 'Standardization',
\n 'confidence': 'high'
\n }
\n elif overall_score > 0.6:
\n return {
\n 'decision': 'ACCEPT_WITH_REVISIONS',
\n 'priority': 'MEDIUM',
\n 'revisions_needed': ['Clarify specification', 'Gather more use cases'],
\n 'next_phase': 'Revised proposal review'
\n }
\n elif overall_score > 0.4:
\n return {
\n 'decision': 'DEFER',
\n 'reason': 'Requires more ecosystem support',
\n 'actions': ['Pilot implementations', 'Industry outreach'],
\n 'review_timeline': '1 year'
\n }
\n else:
\n return {
\n 'decision': 'REJECT',
\n 'reason': 'Low impact and adoption potential',
\n 'suggestions': ['Reformulate proposal', 'Find more compelling use cases']
\n }<\/p>\n

def generate_standardization_roadmap(self) -> Dict[str, any]:
\n """\u751f\u6210\u6807\u51c6\u5316\u8def\u7ebf\u56fe"""
\n # \u8bc4\u4f30\u6240\u6709\u63d0\u6848
\n evaluated_proposals = []
\n for proposal in self.proposals:
\n evaluation = self.evaluate_proposal(proposal)
\n evaluated_proposals.append({
\n 'proposal': proposal,
\n 'evaluation': evaluation
\n })<\/p>\n

# \u5206\u7c7b\u63d0\u6848
\n accepted = [ep for ep in evaluated_proposals
\n if ep['evaluation']['recommendation']['decision'] == 'ACCEPT']<\/p>\n

revisions_needed = [ep for ep in evaluated_proposals
\n if ep['evaluation']['recommendation']['decision'] == 'ACCEPT_WITH_REVISIONS']<\/p>\n

deferred = [ep for ep in evaluated_proposals
\n if ep['evaluation']['recommendation']['decision'] == 'DEFER']<\/p>\n

# \u5236\u5b9a\u8def\u7ebf\u56fe
\n roadmap = {
\n 'timeframe': f"{datetime.now().year}-{datetime.now().year + 3}",
\n 'phases': [
\n {
\n 'name': 'Immediate Standardization',
\n 'duration': '6-12 months',
\n 'proposals': [{
\n 'code': ep['proposal'].code,
\n 'name': ep['proposal'].name,
\n 'priority': ep['evaluation']['recommendation'].get('priority', 'MEDIUM')
\n } for ep in accepted]
\n },
\n {
\n 'name': 'Revised Proposals',
\n 'duration': '12-18 months',
\n 'proposals': [{
\n 'code': ep['proposal'].code,
\n 'name': ep['proposal'].name,
\n 'revisions_needed': ep['evaluation']['recommendation'].get('revisions_needed', [])
\n } for ep in revisions_needed]
\n },
\n {
\n 'name': 'Future Considerations',
\n 'duration': '18-36 months',
\n 'proposals': [{
\n 'code': ep['proposal'].code,
\n 'name': ep['proposal'].name,
\n 'review_timeline': ep['evaluation']['recommendation'].get('review_timeline', 'TBD')
\n } for ep in deferred]
\n }
\n ],
\n 'key_milestones': self._generate_standardization_milestones(evaluated_proposals),
\n 'resource_requirements': self._estimate_resource_requirements(evaluated_proposals)
\n }<\/p>\n

return roadmap<\/p>\n

def _generate_standardization_milestones(self, evaluated_proposals: List) -> List[Dict]:
\n """\u751f\u6210\u6807\u51c6\u5316\u91cc\u7a0b\u7891"""
\n milestones = []
\n current_year = datetime.now().year<\/p>\n

# \u57fa\u4e8e\u63d0\u6848\u72b6\u6001\u548c\u590d\u6742\u5ea6
\n for ep in evaluated_proposals:
\n proposal = ep['proposal']
\n evaluation = ep['evaluation']<\/p>\n

complexity = self._estimate_implementation_complexity(proposal)<\/p>\n

if evaluation['recommendation']['decision'] == 'ACCEPT':
\n if complexity == 'low':
\n milestone_year = current_year + 1
\n elif complexity == 'medium':
\n milestone_year = current_year + 2
\n else:
\n milestone_year = current_year + 3<\/p>\n

milestones.append({
\n 'year': milestone_year,
\n 'milestone': f"{proposal.code} {proposal.name} standardized",
\n 'confidence': 'high' if complexity == 'low' else 'medium'
\n })<\/p>\n

return sorted(milestones, key=lambda x: x['year'])<\/p>\n

def _estimate_resource_requirements(self, evaluated_proposals: List) -> Dict[str, any]:
\n """\u4f30\u7b97\u8d44\u6e90\u9700\u6c42"""
\n total_proposals = len(evaluated_proposals)<\/p>\n

# \u4f30\u7b97\u5de5\u4f5c\u91cf
\n low_complexity = sum(1 for ep in evaluated_proposals
\n if self._estimate_implementation_complexity(ep['proposal']) == 'low')
\n medium_complexity = sum(1 for ep in evaluated_proposals
\n if self._estimate_implementation_complexity(ep['proposal']) == 'medium')
\n high_complexity = sum(1 for ep in evaluated_proposals
\n if self._estimate_implementation_complexity(ep['proposal']) == 'high')<\/p>\n

# \u4eba\u6708\u4f30\u7b97
\n effort_months = low_complexity * 1 + medium_complexity * 3 + high_complexity * 6<\/p>\n

return {
\n 'total_proposals': total_proposals,
\n 'complexity_distribution': {
\n 'low': low_complexity,
\n 'medium': medium_complexity,
\n 'high': high_complexity
\n },
\n 'estimated_effort_months': effort_months,
\n 'recommended_team_size': max(2, effort_months \/\/ 6),
\n 'timeline_estimate': f"{max(6, effort_months \/\/ 2)}-{max(12, effort_months)} months"
\n }<\/p>\n

def track_adoption(self, code: int, implementation_data: Dict) -> None:
\n """\u8ddf\u8e2a\u91c7\u7528\u60c5\u51b5"""
\n if code not in self.adoption_tracking:
\n self.adoption_tracking[code] = {
\n 'implementations': [],
\n 'metrics': {
\n 'total_implementations': 0,
\n 'first_implementation': None,
\n 'latest_implementation': None,
\n 'adoption_growth_rate': 0
\n }
\n }<\/p>\n

tracking = self.adoption_tracking[code]
\n tracking['implementations'].append({
\n 'timestamp': datetime.now(),
\n 'data': implementation_data
\n })<\/p>\n

# \u66f4\u65b0\u6307\u6807
\n tracking['metrics']['total_implementations'] = len(tracking['implementations'])<\/p>\n

if tracking['metrics']['first_implementation'] is None:
\n tracking['metrics']['first_implementation'] = datetime.now()<\/p>\n

tracking['metrics']['latest_implementation'] = datetime.now()<\/p>\n

# \u8ba1\u7b97\u589e\u957f\u7387（\u7b80\u5316）
\n if len(tracking['implementations']) >= 2:
\n first = tracking['implementations'][0]['timestamp']
\n last = tracking['implementations'][-1]['timestamp']
\n days = (last – first).days
\n if days > 0:
\n tracking['metrics']['adoption_growth_rate'] = \\\\
\n len(tracking['implementations']) \/ days<\/p>\n

def get_adoption_report(self) -> Dict[str, any]:
\n """\u83b7\u53d6\u91c7\u7528\u62a5\u544a"""
\n report = {
\n 'summary': {},
\n 'by_status_code': {},
\n 'trends': {},
\n 'predictions': {}
\n }<\/p>\n

# \u6c47\u603b\u7edf\u8ba1
\n total_codes = len(self.adoption_tracking)
\n total_implementations = sum(
\n data['metrics']['total_implementations']
\n for data in self.adoption_tracking.values()
\n )<\/p>\n

report['summary'] = {
\n 'tracked_codes': total_codes,
\n 'total_implementations': total_implementations,
\n 'avg_implementations_per_code': total_implementations \/ total_codes if total_codes > 0 else 0
\n }<\/p>\n

# \u6309\u72b6\u6001\u7801\u8be6\u7ec6\u6570\u636e
\n for code, data in self.adoption_tracking.items():
\n report['by_status_code'][code] = {
\n 'implementations': data['metrics']['total_implementations'],
\n 'first_seen': data['metrics']['first_implementation'],
\n 'growth_rate': data['metrics']['adoption_growth_rate'],
\n 'adoption_level': self._classify_adoption_level(
\n data['metrics']['total_implementations'],
\n data['metrics']['adoption_growth_rate']
\n )
\n }<\/p>\n

# \u8d8b\u52bf\u5206\u6790
\n report['trends'] = self._analyze_adoption_trends()<\/p>\n

# \u9884\u6d4b
\n report['predictions'] = self._predict_future_adoption()<\/p>\n

return report<\/p>\n

def _classify_adoption_level(self, implementations: int, growth_rate: float) -> str:
\n """\u5206\u7c7b\u91c7\u7528\u6c34\u5e73"""
\n if implementations >= 10 and growth_rate > 0.1:
\n return "rapid_growth"
\n elif implementations >= 5:
\n return "steady_adoption"
\n elif implementations >= 2:
\n return "early_adoption"
\n else:
\n return "experimental"<\/p>\n

def _analyze_adoption_trends(self) -> Dict[str, any]:
\n """\u5206\u6790\u91c7\u7528\u8d8b\u52bf"""
\n # \u7b80\u5316\u5b9e\u73b0
\n return {
\n 'fastest_growing': sorted(
\n self.adoption_tracking.items(),
\n key=lambda x: x[1]['metrics']['adoption_growth_rate'],
\n reverse=True
\n )[:3],
\n 'most_widely_adopted': sorted(
\n self.adoption_tracking.items(),
\n key=lambda x: x[1]['metrics']['total_implementations'],
\n reverse=True
\n )[:3]
\n }<\/p>\n

def _predict_future_adoption(self) -> Dict[str, any]:
\n """\u9884\u6d4b\u672a\u6765\u91c7\u7528"""
\n predictions = {}<\/p>\n

for code, data in self.adoption_tracking.items():
\n current = data['metrics']['total_implementations']
\n growth = data['metrics']['adoption_growth_rate']<\/p>\n

if growth > 0:
\n # \u7b80\u5355\u7ebf\u6027\u9884\u6d4b
\n predictions[code] = {
\n 'current': current,
\n 'predicted_6_months': int(current + growth * 180),
\n 'predicted_1_year': int(current + growth * 365),
\n 'confidence': 'high' if current >= 5 else 'medium' if current >= 2 else 'low'
\n }<\/p>\n

return predictions<\/p>\n

# \u4f7f\u7528\u793a\u4f8b
\nstandardization = StatusCodeStandardization()<\/p>\n

# \u8bc4\u4f30\u63d0\u6848
\nprint("=== Status Code Proposal Evaluations ===")
\nfor proposal in standardization.proposals:
\n evaluation = standardization.evaluate_proposal(proposal)
\n print(f"\\\\n{proposal.code} {proposal.name}:")
\n print(f" Decision: {evaluation['recommendation']['decision']}")
\n print(f" Impact Score: {evaluation['impact_assessment']['overall_score']:.2f}")
\n print(f" Adoption Potential: {evaluation['adoption_potential']['score']:.2f}")<\/p>\n

# \u751f\u6210\u8def\u7ebf\u56fe
\nroadmap = standardization.generate_standardization_roadmap()
\nprint(f"\\\\n=== Standardization Roadmap ({roadmap['timeframe']}) ===")<\/p>\n

for phase in roadmap['phases']:
\n print(f"\\\\n{phase['name']} ({phase['duration']}):")
\n for prop in phase['proposals'][:3]: # \u663e\u793a\u524d3\u4e2a
\n print(f" \u2022 {prop['code']} {prop['name']}")<\/p>\n

# \u8ddf\u8e2a\u91c7\u7528
\nstandardization.track_adoption(460, {
\n 'organization': 'Shopify',
\n 'version': '1.0',
\n 'usage_volume': 'high'
\n})<\/p>\n

standardization.track_adoption(460, {
\n 'organization': 'WooCommerce',
\n 'version': '2.3',
\n 'usage_volume': 'medium'
\n})<\/p>\n

# \u83b7\u53d6\u91c7\u7528\u62a5\u544a
\nreport = standardization.get_adoption_report()
\nprint(f"\\\\n=== Adoption Report ===")
\nprint(f"Total tracked codes: {report['summary']['tracked_codes']}")
\nprint(f"Total implementations: {report['summary']['total_implementations']}")<\/p>\n

if 460 in report['by_status_code']:
\n print(f"\\\\nCode 460 adoption: {report['by_status_code'][460]['adoption_level']}")<\/p>\n

40.2 \u6280\u672f\u878d\u5408\u4e0e\u521b\u65b0<\/h4>\n

40.2.1 AI\u4e0e\u72b6\u6001\u7801\u7684\u878d\u5408<\/h5>\n

python<\/p>\n

"""
\nAI\u589e\u5f3a\u72b6\u6001\u7801\u7cfb\u7edf
\n\u5c06\u4f20\u7edfHTTP\u72b6\u6001\u7801\u4e0e\u4eba\u5de5\u667a\u80fd\u6280\u672f\u878d\u5408，\u63d0\u4f9b\u667a\u80fd\u5316\u7684\u72b6\u6001\u5206\u6790\u3001\u9884\u6d4b\u548c\u4fee\u590d\u5efa\u8bae\u3002
\n"""<\/p>\n

from typing import Dict, List, Optional, Any, Tuple, Union
\nfrom dataclasses import dataclass, field
\nfrom datetime import datetime, timedelta
\nfrom enum import Enum, auto
\nimport json
\nimport hashlib
\nimport statistics
\nfrom collections import Counter, defaultdict
\nimport numpy as np
\nimport pandas as pd
\nfrom dataclasses_json import dataclass_json
\nfrom abc import ABC, abstractmethod
\nimport threading
\nimport time<\/p>\n

# ============================================================================
\n# \u57fa\u7840\u7c7b\u578b\u5b9a\u4e49
\n# ============================================================================<\/p>\n

class AIStatusCodeCategory(Enum):
\n\u00a0 \u00a0 """AI\u72b6\u6001\u7801\u5206\u7c7b"""
\n\u00a0 \u00a0 PREDICTIVE = auto() \u00a0 \u00a0 \u00a0# \u9884\u6d4b\u6027 – \u9884\u6d4b\u53ef\u80fd\u53d1\u751f\u7684\u95ee\u9898
\n\u00a0 \u00a0 ADAPTIVE = auto() \u00a0 \u00a0 \u00a0 \u00a0# \u9002\u5e94\u6027 – \u6839\u636e\u4e0a\u4e0b\u6587\u8c03\u6574
\n\u00a0 \u00a0 EXPLAINABLE = auto() \u00a0 \u00a0 # \u53ef\u89e3\u91ca\u6027 – \u63d0\u4f9b\u4eba\u7c7b\u53ef\u7406\u89e3\u7684\u89e3\u91ca
\n\u00a0 \u00a0 PRESCRIPTIVE = auto() \u00a0 \u00a0# \u6307\u5bfc\u6027 – \u63d0\u4f9b\u5177\u4f53\u884c\u52a8\u5efa\u8bae
\n\u00a0 \u00a0 DIAGNOSTIC = auto() \u00a0 \u00a0 \u00a0# \u8bca\u65ad\u6027 – \u6839\u56e0\u5206\u6790<\/p>\n

class SeverityLevel(Enum):
\n\u00a0 \u00a0 """\u4e25\u91cd\u6027\u7b49\u7ea7"""
\n\u00a0 \u00a0 CRITICAL = auto() \u00a0 \u00a0# \u5173\u952e – \u7cfb\u7edf\u4e0d\u53ef\u7528
\n\u00a0 \u00a0 HIGH = auto() \u00a0 \u00a0 \u00a0 \u00a0# \u9ad8 – \u6838\u5fc3\u529f\u80fd\u53d7\u5f71\u54cd
\n\u00a0 \u00a0 MEDIUM = auto() \u00a0 \u00a0 \u00a0# \u4e2d – \u6b21\u8981\u529f\u80fd\u53d7\u5f71\u54cd
\n\u00a0 \u00a0 LOW = auto() \u00a0 \u00a0 \u00a0 \u00a0 # \u4f4e – \u8f7b\u5fae\u5f71\u54cd
\n\u00a0 \u00a0 INFO = auto() \u00a0 \u00a0 \u00a0 \u00a0# \u4fe1\u606f – \u4ec5\u4f9b\u53c2\u8003<\/p>\n

class PriorityLevel(Enum):
\n\u00a0 \u00a0 """\u4f18\u5148\u7ea7\u7b49\u7ea7"""
\n\u00a0 \u00a0 P0 = auto() \u00a0# \u7acb\u5373\u5904\u7406
\n\u00a0 \u00a0 P1 = auto() \u00a0# 1\u5c0f\u65f6\u5185\u5904\u7406
\n\u00a0 \u00a0 P2 = auto() \u00a0# 24\u5c0f\u65f6\u5185\u5904\u7406
\n\u00a0 \u00a0 P3 = auto() \u00a0# \u8ba1\u5212\u5185\u5904\u7406<\/p>\n

# ============================================================================
\n# \u6570\u636e\u7c7b\u5b9a\u4e49
\n# ============================================================================<\/p>\n

@dataclass_json
\n@dataclass
\nclass RequestContext:
\n\u00a0 \u00a0 """\u8bf7\u6c42\u4e0a\u4e0b\u6587"""
\n\u00a0 \u00a0 request_id: str
\n\u00a0 \u00a0 method: str
\n\u00a0 \u00a0 endpoint: str
\n\u00a0 \u00a0 headers: Dict[str, str] = field(default_factory=dict)
\n\u00a0 \u00a0 query_params: Dict[str, str] = field(default_factory=dict)
\n\u00a0 \u00a0 body: Optional[Dict] = None
\n\u00a0 \u00a0 user_id: Optional[str] = None
\n\u00a0 \u00a0 user_agent: Optional[str] = None
\n\u00a0 \u00a0 client_ip: Optional[str] = None
\n\u00a0 \u00a0 timestamp: datetime = field(default_factory=datetime.now)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def fingerprint(self) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u8bf7\u6c42\u6307\u7eb9\u7528\u4e8e\u76f8\u4f3c\u6027\u5339\u914d"""
\n\u00a0 \u00a0 \u00a0 \u00a0 fingerprint_str = f"{self.method}:{self.endpoint}:{self.user_id}"
\n\u00a0 \u00a0 \u00a0 \u00a0 return hashlib.md5(fingerprint_str.encode()).hexdigest()<\/p>\n

@dataclass_json
\n@dataclass
\nclass AIAnalysisResult:
\n\u00a0 \u00a0 """AI\u5206\u6790\u7ed3\u679c"""
\n\u00a0 \u00a0 category: AIStatusCodeCategory
\n\u00a0 \u00a0 insights: List[str]
\n\u00a0 \u00a0 confidence: float
\n\u00a0 \u00a0 metrics: Dict[str, Any]
\n\u00a0 \u00a0 model_version: str
\n\u00a0 \u00a0 analysis_time: datetime = field(default_factory=datetime.now)<\/p>\n

@dataclass_json
\n@dataclass
\nclass AIStatusCode:
\n\u00a0 \u00a0 """AI\u589e\u5f3a\u7684\u72b6\u6001\u7801"""
\n\u00a0 \u00a0 # \u57fa\u7840\u4fe1\u606f
\n\u00a0 \u00a0 base_code: int
\n\u00a0 \u00a0 code_description: str
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 # AI\u6269\u5c55\u4fe1\u606f
\n\u00a0 \u00a0 ai_categories: List[AIStatusCodeCategory]
\n\u00a0 \u00a0 analysis_results: List[AIAnalysisResult]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 # \u5143\u6570\u636e
\n\u00a0 \u00a0 confidence: float
\n\u00a0 \u00a0 context: RequestContext
\n\u00a0 \u00a0 timestamp: datetime = field(default_factory=datetime.now)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 # \u8ba1\u7b97\u5c5e\u6027
\n\u00a0 \u00a0 @property
\n\u00a0 \u00a0 def severity(self) -> SeverityLevel:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u4e25\u91cd\u6027\u7b49\u7ea7"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if self.base_code >= 500:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return SeverityLevel.CRITICAL
\n\u00a0 \u00a0 \u00a0 \u00a0 elif self.base_code >= 400:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return SeverityLevel.HIGH
\n\u00a0 \u00a0 \u00a0 \u00a0 elif self.base_code >= 300:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return SeverityLevel.MEDIUM
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return SeverityLevel.LOW
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 @property
\n\u00a0 \u00a0 def extended_code(self) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u6269\u5c55\u72b6\u6001\u7801"""
\n\u00a0 \u00a0 \u00a0 \u00a0 ai_suffix = "AI" if self.confidence > 0.7 else "ai"
\n\u00a0 \u00a0 \u00a0 \u00a0 return f"{self.base_code}_{ai_suffix}_{int(self.confidence * 100)}"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 @property
\n\u00a0 \u00a0 def primary_insights(self) -> List[str]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u4e3b\u8981\u6d1e\u5bdf"""
\n\u00a0 \u00a0 \u00a0 \u00a0 insights = []
\n\u00a0 \u00a0 \u00a0 \u00a0 for result in self.analysis_results:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if result.confidence > 0.6: \u00a0# \u53ea\u663e\u793a\u9ad8\u7f6e\u4fe1\u5ea6\u6d1e\u5bdf
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.extend(result.insights[:2]) \u00a0# \u6bcf\u4e2a\u7c7b\u522b\u6700\u591a2\u6761
\n\u00a0 \u00a0 \u00a0 \u00a0 return insights[:5] \u00a0# \u603b\u5171\u6700\u591a5\u6761
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def to_response(self) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8f6c\u6362\u4e3aAPI\u54cd\u5e94\u683c\u5f0f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "code": self.base_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": self.code_description,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "extended_code": self.extended_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "severity": self.severity.name.lower()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "ai_analysis": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "overall_confidence": self.confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "categories": [cat.name.lower() for cat in self.ai_categories],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "insights": self.primary_insights,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "detailed_analysis": [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "category": result.category.name.lower(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": result.confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "key_metrics": result.metrics,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "model_version": result.model_version
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for result in self.analysis_results
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "context": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "request_id": self.context.request_id,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "endpoint": self.context.endpoint,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_id": self.context.user_id,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": self.timestamp.isoformat()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendations": self._generate_recommendations()
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_recommendations(self) -> List[Dict[str, Any]]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 recommendations = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6839\u636e\u72b6\u6001\u7801\u751f\u6210\u57fa\u7840\u5efa\u8bae
\n\u00a0 \u00a0 \u00a0 \u00a0 base_recs = self._get_base_recommendations()
\n\u00a0 \u00a0 \u00a0 \u00a0 recommendations.extend(base_recs)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6839\u636eAI\u5206\u6790\u6dfb\u52a0\u989d\u5916\u5efa\u8bae
\n\u00a0 \u00a0 \u00a0 \u00a0 for result in self.analysis_results:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if result.confidence > 0.7:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if result.category == AIStatusCodeCategory.PRESCRIPTIVE:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for insight in result.insights[:2]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "prescriptive",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "high",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": insight,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": result.confidence
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return recommendations[:5] \u00a0# \u6700\u591a5\u6761\u5efa\u8bae
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _get_base_recommendations(self) -> List[Dict[str, Any]]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u57fa\u7840\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 recommendations = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if self.base_code == 404:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "immediate",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "medium",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": "\u68c0\u67e5\u8bf7\u6c42\u8def\u5f84\u662f\u5426\u6b63\u786e",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.9
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0 elif self.base_code == 500:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "immediate",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "high",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": "\u68c0\u67e5\u670d\u52a1\u5668\u65e5\u5fd7\u548c\u4f9d\u8d56\u670d\u52a1\u72b6\u6001",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.9
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0 elif self.base_code == 429:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "immediate",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "medium",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": "\u5b9e\u65bd\u6307\u6570\u9000\u907f\u91cd\u8bd5\u7b56\u7565",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.8
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return recommendations<\/p>\n

# ============================================================================
\n# AI\u6a21\u578b\u57fa\u7c7b
\n# ============================================================================<\/p>\n

class AIModel(ABC):
\n\u00a0 \u00a0 """AI\u6a21\u578b\u57fa\u7c7b"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self, name: str, version: str = "1.0"):
\n\u00a0 \u00a0 \u00a0 \u00a0 self.name = name
\n\u00a0 \u00a0 \u00a0 \u00a0 self.version = version
\n\u00a0 \u00a0 \u00a0 \u00a0 self.training_data = []
\n\u00a0 \u00a0 \u00a0 \u00a0 self.model_state = {}
\n\u00a0 \u00a0 \u00a0 \u00a0 self.performance_metrics = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "accuracy": [],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "response_time": [],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence_scores": []
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 @abstractmethod
\n\u00a0 \u00a0 def analyze(self, context: RequestContext, history: List[Dict]) -> AIAnalysisResult:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u8bf7\u6c42\u4e0a\u4e0b\u6587\u548c\u5386\u53f2\u6570\u636e"""
\n\u00a0 \u00a0 \u00a0 \u00a0 pass
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def train(self, training_data: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8bad\u7ec3\u6a21\u578b"""
\n\u00a0 \u00a0 \u00a0 \u00a0 self.training_data.extend(training_data)
\n\u00a0 \u00a0 \u00a0 \u00a0 return {"status": "trained", "samples": len(self.training_data)}
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def optimize(self, feedback_data: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u6839\u636e\u53cd\u9988\u4f18\u5316\u6a21\u578b"""
\n\u00a0 \u00a0 \u00a0 \u00a0 improvement = self._calculate_improvement(feedback_data)
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "model": self.name,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "version": self.version,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "improvement": improvement
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_improvement(self, feedback_data: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u6539\u8fdb\u7a0b\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not feedback_data:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.0
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 scores = []
\n\u00a0 \u00a0 \u00a0 \u00a0 for feedback in feedback_data:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "accuracy_score" in feedback:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 scores.append(feedback["accuracy_score"])
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return statistics.mean(scores) if scores else 0.0<\/p>\n

# ============================================================================
\n# \u9884\u6d4b\u6027AI\u6a21\u578b
\n# ============================================================================<\/p>\n

class PredictiveModel(AIModel):
\n\u00a0 \u00a0 """\u9884\u6d4b\u6027AI\u6a21\u578b – \u9884\u6d4b\u53ef\u80fd\u7684\u95ee\u9898\u548c\u8d8b\u52bf"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 super().__init__("predictive", "2.0")
\n\u00a0 \u00a0 \u00a0 \u00a0 self.patterns = {}
\n\u00a0 \u00a0 \u00a0 \u00a0 self.trends = {}
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def analyze(self, context: RequestContext, history: List[Dict]) -> AIAnalysisResult:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u6267\u884c\u9884\u6d4b\u6027\u5206\u6790"""
\n\u00a0 \u00a0 \u00a0 \u00a0 insights = []
\n\u00a0 \u00a0 \u00a0 \u00a0 metrics = {}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 1. \u9884\u6d4b\u53ef\u80fd\u7684\u9519\u8bef
\n\u00a0 \u00a0 \u00a0 \u00a0 predicted_errors = self._predict_errors(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if predicted_errors:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u9884\u6d4b\u53ef\u80fd\u9519\u8bef: {', '.join(predicted_errors[:3])}")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 metrics["predicted_errors"] = predicted_errors
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 2. \u9884\u6d4b\u54cd\u5e94\u65f6\u95f4
\n\u00a0 \u00a0 \u00a0 \u00a0 predicted_response_time = self._predict_response_time(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u9884\u6d4b\u54cd\u5e94\u65f6\u95f4: {predicted_response_time:.1f}ms")
\n\u00a0 \u00a0 \u00a0 \u00a0 metrics["predicted_response_time"] = predicted_response_time
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 3. \u9884\u6d4b\u7cfb\u7edf\u8d1f\u8f7d
\n\u00a0 \u00a0 \u00a0 \u00a0 load_prediction = self._predict_system_load(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u9884\u6d4b\u7cfb\u7edf\u8d1f\u8f7d: {load_prediction['level']}")
\n\u00a0 \u00a0 \u00a0 \u00a0 metrics.update(load_prediction)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 4. \u9884\u6d4b\u8d8b\u52bf
\n\u00a0 \u00a0 \u00a0 \u00a0 trends = self._analyze_trends(history)
\n\u00a0 \u00a0 \u00a0 \u00a0 metrics["trends"] = trends
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 confidence = self._calculate_confidence(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return AIAnalysisResult(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 category=AIStatusCodeCategory.PREDICTIVE,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights=insights,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence=confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 metrics=metrics,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 model_version=self.version
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _predict_errors(self, context: RequestContext, history: List[Dict]) -> List[str]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u9884\u6d4b\u53ef\u80fd\u7684\u9519\u8bef"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u5386\u53f2\u76f8\u4f3c\u8bf7\u6c42
\n\u00a0 \u00a0 \u00a0 \u00a0 similar_requests = self._find_similar_requests(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 error_types = []
\n\u00a0 \u00a0 \u00a0 \u00a0 for req in similar_requests:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "error_type" in req:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_types.append(req["error_type"])
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u7edf\u8ba1\u5e38\u89c1\u9519\u8bef
\n\u00a0 \u00a0 \u00a0 \u00a0 error_counts = Counter(error_types)
\n\u00a0 \u00a0 \u00a0 \u00a0 return [error for error, count in error_counts.most_common(3)]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _predict_response_time(self, context: RequestContext, history: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u9884\u6d4b\u54cd\u5e94\u65f6\u95f4"""
\n\u00a0 \u00a0 \u00a0 \u00a0 similar_requests = self._find_similar_requests(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not similar_requests:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u9ed8\u8ba4\u9884\u6d4b
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if context.method == "GET":
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 100.0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif context.method == "POST":
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 200.0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 150.0
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u5e73\u5747\u54cd\u5e94\u65f6\u95f4
\n\u00a0 \u00a0 \u00a0 \u00a0 response_times = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 req.get("response_time", 100)\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for req in similar_requests\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "response_time" in req
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return statistics.mean(response_times) if response_times else 100.0
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _predict_system_load(self, context: RequestContext, history: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u9884\u6d4b\u7cfb\u7edf\u8d1f\u8f7d"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not history:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {"level": "low", "requests_per_minute": 0}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u6700\u8fd1\u8bf7\u6c42\u9891\u7387
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_cutoff = datetime.now() – timedelta(minutes=5)
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_requests = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if datetime.fromisoformat(h.get("timestamp", "")) > recent_cutoff
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 requests_per_minute = len(recent_requests) \/ 5
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u786e\u5b9a\u8d1f\u8f7d\u7b49\u7ea7
\n\u00a0 \u00a0 \u00a0 \u00a0 if requests_per_minute > 100:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 level = "critical"
\n\u00a0 \u00a0 \u00a0 \u00a0 elif requests_per_minute > 50:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 level = "high"
\n\u00a0 \u00a0 \u00a0 \u00a0 elif requests_per_minute > 20:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 level = "medium"
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 level = "low"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "level": level,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "requests_per_minute": requests_per_minute,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "prediction_confidence": 0.8
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_trends(self, history: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u8d8b\u52bf"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(history) < 10:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {"error_trend": "stable", "performance_trend": "stable"}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5206\u6790\u9519\u8bef\u8d8b\u52bf
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_errors = sum(1 for h in history[-10:] if h.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0 older_errors = sum(1 for h in history[-20:-10] if h.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if recent_errors > older_errors * 1.5:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_trend = "increasing"
\n\u00a0 \u00a0 \u00a0 \u00a0 elif recent_errors < older_errors * 0.5:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_trend = "decreasing"
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_trend = "stable"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5206\u6790\u6027\u80fd\u8d8b\u52bf
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_times = [h.get("response_time", 0) for h in history[-10:] if "response_time" in h]
\n\u00a0 \u00a0 \u00a0 \u00a0 older_times = [h.get("response_time", 0) for h in history[-20:-10] if "response_time" in h]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if recent_times and older_times:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 avg_recent = statistics.mean(recent_times)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 avg_older = statistics.mean(older_times)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if avg_recent > avg_older * 1.2:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 performance_trend = "degrading"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif avg_recent < avg_older * 0.8:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 performance_trend = "improving"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 performance_trend = "stable"
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 performance_trend = "stable"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_trend": error_trend,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "performance_trend": performance_trend,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "analysis_window": "last_20_requests"
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _find_similar_requests(self, context: RequestContext, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u67e5\u627e\u76f8\u4f3c\u8bf7\u6c42"""
\n\u00a0 \u00a0 \u00a0 \u00a0 similar = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for req in history:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 similarity = self._calculate_similarity(context, req)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if similarity > 0.6:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 req["similarity_score"] = similarity
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 similar.append(req)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6309\u76f8\u4f3c\u5ea6\u6392\u5e8f
\n\u00a0 \u00a0 \u00a0 \u00a0 similar.sort(key=lambda x: x.get("similarity_score", 0), reverse=True)
\n\u00a0 \u00a0 \u00a0 \u00a0 return similar[:10] \u00a0# \u6700\u591a\u8fd4\u56de10\u4e2a\u76f8\u4f3c\u8bf7\u6c42
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_similarity(self, context: RequestContext, historical_req: Dict) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u8bf7\u6c42\u76f8\u4f3c\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 score = 0
\n\u00a0 \u00a0 \u00a0 \u00a0 max_score = 4
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 1. \u7aef\u70b9\u76f8\u4f3c\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.endpoint == historical_req.get("endpoint"):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 score += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 2. \u65b9\u6cd5\u76f8\u4f3c\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.method == historical_req.get("method"):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 score += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 3. \u7528\u6237\u76f8\u4f3c\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.user_id and context.user_id == historical_req.get("user_id"):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 score += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 4. \u65f6\u95f4\u76f8\u4f3c\u5ea6（\u540c\u4e00\u5929\u76f8\u540c\u65f6\u6bb5）
\n\u00a0 \u00a0 \u00a0 \u00a0 req_time = datetime.fromisoformat(historical_req.get("timestamp", ""))
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.timestamp.hour == req_time.hour:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 score += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return score \/ max_score
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_confidence(self, context: RequestContext, history: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u9884\u6d4b\u7f6e\u4fe1\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 similar_requests = self._find_similar_requests(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not similar_requests:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.5 \u00a0# \u4e2d\u7b49\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u76f8\u4f3c\u8bf7\u6c42\u6570\u91cf\u548c\u8d28\u91cf
\n\u00a0 \u00a0 \u00a0 \u00a0 similarity_scores = [req.get("similarity_score", 0) for req in similar_requests]
\n\u00a0 \u00a0 \u00a0 \u00a0 avg_similarity = statistics.mean(similarity_scores)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6837\u672c\u6570\u91cf\u56e0\u5b50
\n\u00a0 \u00a0 \u00a0 \u00a0 count_factor = min(len(similar_requests) \/ 10, 1.0)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u7f6e\u4fe1\u5ea6\u8ba1\u7b97
\n\u00a0 \u00a0 \u00a0 \u00a0 confidence = 0.3 + (avg_similarity * 0.5) + (count_factor * 0.2)
\n\u00a0 \u00a0 \u00a0 \u00a0 return min(max(confidence, 0.1), 0.95) \u00a0# \u9650\u5236\u57280.1-0.95\u4e4b\u95f4<\/p>\n

# ============================================================================
\n# \u9002\u5e94\u6027AI\u6a21\u578b
\n# ============================================================================<\/p>\n

class AdaptiveModel(AIModel):
\n\u00a0 \u00a0 """\u9002\u5e94\u6027AI\u6a21\u578b – \u6839\u636e\u4e0a\u4e0b\u6587\u52a8\u6001\u8c03\u6574"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 super().__init__("adaptive", "1.5")
\n\u00a0 \u00a0 \u00a0 \u00a0 self.context_rules = self._initialize_rules()
\n\u00a0 \u00a0 \u00a0 \u00a0 self.adaptation_history = []
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _initialize_rules(self) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u521d\u59cb\u5316\u9002\u5e94\u89c4\u5219"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "name": "high_load_adjustment",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "condition": lambda ctx, hist: self._get_system_load(hist) > 70,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": lambda: {"timeout": "reduce", "cache_ttl": "increase"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "high"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "name": "error_rate_adjustment",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "condition": lambda ctx, hist: self._get_error_rate(hist) > 0.1,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": lambda: {"retry_strategy": "exponential_backoff", "circuit_breaker": "enable"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "high"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "name": "time_of_day_adjustment",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "condition": lambda ctx, hist: 2 <= ctx.timestamp.hour <= 6,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": lambda: {"maintenance_mode": "allow", "backup_window": "active"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "medium"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def analyze(self, context: RequestContext, history: List[Dict]) -> AIAnalysisResult:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u6267\u884c\u9002\u5e94\u6027\u5206\u6790"""
\n\u00a0 \u00a0 \u00a0 \u00a0 adaptations = []
\n\u00a0 \u00a0 \u00a0 \u00a0 metrics = {}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8bc4\u4f30\u5f53\u524d\u4e0a\u4e0b\u6587
\n\u00a0 \u00a0 \u00a0 \u00a0 context_metrics = self._evaluate_context(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 metrics.update(context_metrics)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5e94\u7528\u9002\u5e94\u89c4\u5219
\n\u00a0 \u00a0 \u00a0 \u00a0 for rule in self.context_rules:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 try:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if rule["condition"](context, history):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 adaptation = rule["action"]()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 adaptations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "rule": rule["name"],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "adaptation": adaptation,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": rule["priority"]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 except Exception as e:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 continue
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u6d1e\u5bdf
\n\u00a0 \u00a0 \u00a0 \u00a0 insights = self._generate_insights(adaptations, context_metrics)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 confidence = self._calculate_confidence(context_metrics, adaptations)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return AIAnalysisResult(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 category=AIStatusCodeCategory.ADAPTIVE,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights=insights,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence=confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 metrics=metrics,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 model_version=self.version
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _evaluate_context(self, context: RequestContext, history: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8bc4\u4f30\u5f53\u524d\u4e0a\u4e0b\u6587"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "system_load": self._get_system_load(history),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_rate": self._get_error_rate(history),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_experience_score": self._calculate_user_experience(context, history),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "time_of_day": context.timestamp.hour,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "day_of_week": context.timestamp.weekday(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "is_peak_hours": self._is_peak_hours(context.timestamp)
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _get_system_load(self, history: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u7cfb\u7edf\u8d1f\u8f7d"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not history:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.0
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_cutoff = datetime.now() – timedelta(minutes=5)
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_count = sum(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 1 for h in history\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if datetime.fromisoformat(h.get("timestamp", "")) > recent_cutoff
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5047\u8bbe\u6700\u5927\u5bb9\u91cf\u4e3a1000\u8bf7\u6c42\/5\u5206\u949f
\n\u00a0 \u00a0 \u00a0 \u00a0 return min((recent_count \/ 1000) * 100, 100.0)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _get_error_rate(self, history: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u9519\u8bef\u7387"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(history) < 10:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.0
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_history = history[-50:] \u00a0# \u6700\u8fd150\u4e2a\u8bf7\u6c42
\n\u00a0 \u00a0 \u00a0 \u00a0 error_count = sum(1 for h in recent_history if h.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return error_count \/ len(recent_history) if recent_history else 0.0
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_user_experience(self, context: RequestContext, history: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u7528\u6237\u4f53\u9a8c\u5206\u6570"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not context.user_id:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.7 \u00a0# \u9ed8\u8ba4\u5206\u6570
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 user_requests = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("user_id") == context.user_id
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not user_requests:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.7 \u00a0# \u9ed8\u8ba4\u5206\u6570
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u7528\u6237\u9519\u8bef\u7387
\n\u00a0 \u00a0 \u00a0 \u00a0 error_count = sum(1 for req in user_requests if req.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0 error_rate = error_count \/ len(user_requests)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u5e73\u5747\u54cd\u5e94\u65f6\u95f4
\n\u00a0 \u00a0 \u00a0 \u00a0 response_times = [req.get("response_time", 100) for req in user_requests if "response_time" in req]
\n\u00a0 \u00a0 \u00a0 \u00a0 avg_response_time = statistics.mean(response_times) if response_times else 100
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u5206\u6570 (0-1)
\n\u00a0 \u00a0 \u00a0 \u00a0 error_score = max(0, 1 – (error_rate * 2)) \u00a0# \u9519\u8bef\u7387\u6743\u91cd\u8f83\u9ad8
\n\u00a0 \u00a0 \u00a0 \u00a0 response_score = max(0, 1 – (avg_response_time \/ 1000)) \u00a0# \u8d85\u8fc71\u79d2\u5f00\u59cb\u6263\u5206
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return (error_score * 0.6 + response_score * 0.4)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _is_peak_hours(self, timestamp: datetime) -> bool:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5224\u65ad\u662f\u5426\u662f\u9ad8\u5cf0\u65f6\u6bb5"""
\n\u00a0 \u00a0 \u00a0 \u00a0 hour = timestamp.hour
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5047\u8bbe9-12\u70b9\u548c14-18\u70b9\u662f\u9ad8\u5cf0\u65f6\u6bb5
\n\u00a0 \u00a0 \u00a0 \u00a0 return (9 <= hour <= 12) or (14 <= hour <= 18)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_insights(self, adaptations: List[Dict], context_metrics: Dict) -> List[str]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u9002\u5e94\u6027\u6d1e\u5bdf"""
\n\u00a0 \u00a0 \u00a0 \u00a0 insights = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if adaptations:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for adapt in adaptations[:2]: \u00a0# \u6700\u591a2\u6761
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u5e94\u7528\u9002\u5e94\u6027\u8c03\u6574: {adapt['rule']}")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if context_metrics.get("system_load", 0) > 70:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append("\u7cfb\u7edf\u8d1f\u8f7d\u8f83\u9ad8，\u5efa\u8bae\u4f18\u5316\u8d44\u6e90\u5206\u914d")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if context_metrics.get("error_rate", 0) > 0.1:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append("\u9519\u8bef\u7387\u8f83\u9ad8，\u5efa\u8bae\u68c0\u67e5\u7cfb\u7edf\u7a33\u5b9a\u6027")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if context_metrics.get("user_experience_score", 1) < 0.6:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append("\u7528\u6237\u4f53\u9a8c\u5206\u6570\u8f83\u4f4e，\u5efa\u8bae\u4f18\u5316\u670d\u52a1\u6027\u80fd")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return insights[:3] \u00a0# \u6700\u591a3\u6761\u6d1e\u5bdf
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_confidence(self, context_metrics: Dict, adaptations: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u9002\u5e94\u6027\u7f6e\u4fe1\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u4e0a\u4e0b\u6587\u4e30\u5bcc\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 context_factors = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 0.3 if context_metrics.get("system_load", 0) > 0 else 0.1,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 0.3 if context_metrics.get("error_rate", 0) > 0 else 0.1,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 0.2 if context_metrics.get("user_experience_score", 0) > 0 else 0.1,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 0.2 if adaptations else 0.1
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return statistics.mean(context_factors)<\/p>\n

# ============================================================================
\n# \u53ef\u89e3\u91ca\u6027AI\u6a21\u578b
\n# ============================================================================<\/p>\n

class ExplainableModel(AIModel):
\n\u00a0 \u00a0 """\u53ef\u89e3\u91ca\u6027AI\u6a21\u578b – \u63d0\u4f9b\u4eba\u7c7b\u53ef\u7406\u89e3\u7684\u89e3\u91ca"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 super().__init__("explainable", "1.2")
\n\u00a0 \u00a0 \u00a0 \u00a0 self.explanation_templates = self._load_templates()
\n\u00a0 \u00a0 \u00a0 \u00a0 self.explanation_patterns = {}
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _load_templates(self) -> Dict[str, Dict[str, str]]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u52a0\u8f7d\u89e3\u91ca\u6a21\u677f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "404": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "simple": "\u8bf7\u6c42\u7684\u8d44\u6e90\u4e0d\u5b58\u5728",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "technical": "\u670d\u52a1\u5668\u672a\u627e\u5230\u4e0e\u8bf7\u6c42URI\u5339\u914d\u7684\u8d44\u6e90",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_action": "\u68c0\u67e5URL\u662f\u5426\u6b63\u786e\u6216\u8054\u7cfb\u7ba1\u7406\u5458",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "developer_action": "\u68c0\u67e5\u8def\u7531\u914d\u7f6e\u548c\u8d44\u6e90\u5b58\u5728\u6027"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "500": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "simple": "\u670d\u52a1\u5668\u5185\u90e8\u9519\u8bef",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "technical": "\u670d\u52a1\u5668\u9047\u5230\u610f\u5916\u60c5\u51b5，\u65e0\u6cd5\u5b8c\u6210\u8bf7\u6c42",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_action": "\u8bf7\u7a0d\u540e\u91cd\u8bd5\u6216\u8054\u7cfb\u6280\u672f\u652f\u6301",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "developer_action": "\u68c0\u67e5\u670d\u52a1\u5668\u65e5\u5fd7\u548c\u5e94\u7528\u4ee3\u7801"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "429": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "simple": "\u8bf7\u6c42\u8fc7\u4e8e\u9891\u7e41",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "technical": "\u5ba2\u6237\u7aef\u5728\u7ed9\u5b9a\u65f6\u95f4\u5185\u53d1\u9001\u4e86\u592a\u591a\u8bf7\u6c42",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_action": "\u8bf7\u7a0d\u540e\u91cd\u8bd5",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "developer_action": "\u8c03\u6574\u901f\u7387\u9650\u5236\u7b56\u7565\u6216\u4f18\u5316API\u8bbe\u8ba1"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "401": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "simple": "\u672a\u6388\u6743\u8bbf\u95ee",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "technical": "\u8bf7\u6c42\u9700\u8981\u7528\u6237\u8ba4\u8bc1",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_action": "\u8bf7\u5148\u767b\u5f55\u6216\u68c0\u67e5\u51ed\u8bc1",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "developer_action": "\u9a8c\u8bc1\u8ba4\u8bc1\u4e2d\u95f4\u4ef6\u548c\u4ee4\u724c\u6709\u6548\u6027"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "403": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "simple": "\u7981\u6b62\u8bbf\u95ee",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "technical": "\u670d\u52a1\u5668\u7406\u89e3\u8bf7\u6c42\u4f46\u62d2\u7edd\u6388\u6743",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_action": "\u68c0\u67e5\u6743\u9650\u6216\u8054\u7cfb\u7ba1\u7406\u5458",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "developer_action": "\u68c0\u67e5\u6388\u6743\u903b\u8f91\u548c\u89d2\u8272\u6743\u9650"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def analyze(self, context: RequestContext, history: List[Dict]) -> AIAnalysisResult:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u53ef\u89e3\u91ca\u6027\u5206\u6790"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u786e\u5b9a\u72b6\u6001\u7801（\u4ece\u5386\u53f2\u6216\u4e0a\u4e0b\u6587\u63a8\u65ad）
\n\u00a0 \u00a0 \u00a0 \u00a0 status_code = self._determine_status_code(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u591a\u7ea7\u89e3\u91ca
\n\u00a0 \u00a0 \u00a0 \u00a0 explanations = self._generate_explanations(status_code, context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 confidence = self._calculate_confidence(status_code, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u6d1e\u5bdf
\n\u00a0 \u00a0 \u00a0 \u00a0 insights = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 f"\u72b6\u6001\u7801 {status_code} \u89e3\u91ca: {explanations.get('simple', '\u672a\u77e5\u9519\u8bef')}",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 f"\u7528\u6237\u5efa\u8bae: {explanations.get('user_action', '\u8bf7\u7a0d\u540e\u91cd\u8bd5')}"
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if "technical" in explanations:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u6280\u672f\u7ec6\u8282: {explanations['technical']}")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return AIAnalysisResult(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 category=AIStatusCodeCategory.EXPLAINABLE,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights=insights,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence=confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 metrics={
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status_code": status_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "explanations": explanations,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "explanation_depth": "multi_level",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_friendly": True
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 model_version=self.version
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _determine_status_code(self, context: RequestContext, history: List[Dict]) -> int:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u786e\u5b9a\u72b6\u6001\u7801"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5982\u679c\u6709\u5386\u53f2\u76f8\u4f3c\u8bf7\u6c42，\u4f7f\u7528\u5386\u53f2\u72b6\u6001\u7801
\n\u00a0 \u00a0 \u00a0 \u00a0 similar_requests = self._find_similar_requests(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if similar_requests:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_codes = [req.get("status_code", 200) for req in similar_requests]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if status_codes:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return statistics.mode(status_codes)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6839\u636e\u4e0a\u4e0b\u6587\u63a8\u65ad
\n\u00a0 \u00a0 \u00a0 \u00a0 if "error" in context.headers or "error" in str(context.body):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "not_found" in str(context.body).lower():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 404
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "unauthorized" in str(context.body).lower():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 401
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 400
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return 200 \u00a0# \u9ed8\u8ba4\u6210\u529f
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _find_similar_requests(self, context: RequestContext, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u67e5\u627e\u76f8\u4f3c\u8bf7\u6c42"""
\n\u00a0 \u00a0 \u00a0 \u00a0 similar = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for req in history[-100:]: \u00a0# \u53ea\u68c0\u67e5\u6700\u8fd1100\u4e2a\u8bf7\u6c42
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 similarity = self._calculate_request_similarity(context, req)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if similarity > 0.7:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 similar.append(req)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return similar[:5] \u00a0# \u6700\u591a\u8fd4\u56de5\u4e2a
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_request_similarity(self, context: RequestContext, historical_req: Dict) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u8bf7\u6c42\u76f8\u4f3c\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 score = 0
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u7aef\u70b9\u76f8\u4f3c\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.endpoint == historical_req.get("endpoint"):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 score += 2
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u65b9\u6cd5\u76f8\u4f3c\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.method == historical_req.get("method"):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 score += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u7528\u6237\u76f8\u4f3c\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.user_id and context.user_id == historical_req.get("user_id"):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 score += 2
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return score \/ 5 \u00a0# \u5f52\u4e00\u5316\u52300-1
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_explanations(self, status_code: int, context: RequestContext, history: List[Dict]) -> Dict[str, str]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u591a\u7ea7\u89e3\u91ca"""
\n\u00a0 \u00a0 \u00a0 \u00a0 explanations = {}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u7840\u6a21\u677f\u89e3\u91ca
\n\u00a0 \u00a0 \u00a0 \u00a0 template_key = str(status_code)
\n\u00a0 \u00a0 \u00a0 \u00a0 if template_key in self.explanation_templates:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 explanations.update(self.explanation_templates[template_key])
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6dfb\u52a0\u4e0a\u4e0b\u6587\u7279\u5b9a\u89e3\u91ca
\n\u00a0 \u00a0 \u00a0 \u00a0 explanations["context_specific"] = self._generate_context_specific_explanation(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_code, context, history
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6dfb\u52a0\u5386\u53f2\u6a21\u5f0f\u89e3\u91ca
\n\u00a0 \u00a0 \u00a0 \u00a0 explanations["historical_pattern"] = self._generate_historical_pattern_explanation(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_code, context, history
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return explanations
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_context_specific_explanation(self, status_code: int, context: RequestContext, history: List[Dict]) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u4e0a\u4e0b\u6587\u7279\u5b9a\u89e3\u91ca"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if status_code == 404:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return f"\u8bf7\u6c42\u7684\u7aef\u70b9 '{context.endpoint}' \u53ef\u80fd\u4e0d\u5b58\u5728\u6216\u5df2\u88ab\u79fb\u9664"
\n\u00a0 \u00a0 \u00a0 \u00a0 elif status_code == 500:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "\u670d\u52a1\u5668\u5904\u7406\u8bf7\u6c42\u65f6\u53d1\u751f\u5185\u90e8\u9519\u8bef，\u53ef\u80fd\u4e0e\u6700\u8fd1\u90e8\u7f72\u7684\u4ee3\u7801\u76f8\u5173"
\n\u00a0 \u00a0 \u00a0 \u00a0 elif status_code == 429:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return f"\u7528\u6237 {context.user_id or '\u533f\u540d\u7528\u6237'} \u5728\u77ed\u65f6\u95f4\u5185\u53d1\u9001\u4e86\u8fc7\u591a\u8bf7\u6c42"
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "\u57fa\u4e8e\u5f53\u524d\u8bf7\u6c42\u4e0a\u4e0b\u6587\u7684\u5206\u6790\u7ed3\u679c"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_historical_pattern_explanation(self, status_code: int, context: RequestContext, history: List[Dict]) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u5386\u53f2\u6a21\u5f0f\u89e3\u91ca"""
\n\u00a0 \u00a0 \u00a0 \u00a0 similar_requests = self._find_similar_requests(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not similar_requests:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "\u65e0\u8db3\u591f\u5386\u53f2\u6570\u636e\u8fdb\u884c\u5206\u6790"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5206\u6790\u76f8\u4f3c\u8bf7\u6c42\u7684\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 error_count = sum(1 for req in similar_requests if req.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0 total_count = len(similar_requests)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if total_count > 0:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_rate = error_count \/ total_count
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if error_rate > 0.5:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return f"\u7c7b\u4f3c\u8bf7\u6c42\u6709{error_rate:.0%}\u7684\u6982\u7387\u5931\u8d25，\u53ef\u80fd\u5b58\u5728\u7cfb\u7edf\u6027\u95ee\u9898"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return f"\u7c7b\u4f3c\u8bf7\u6c42\u7684\u6210\u529f\u7387\u4e3a{(1-error_rate):.0%}"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return "\u5386\u53f2\u6570\u636e\u663e\u793a\u6b63\u5e38\u6a21\u5f0f"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_confidence(self, status_code: int, history: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u89e3\u91ca\u7f6e\u4fe1\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 base_confidence = 0.7
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5982\u679c\u6709\u5386\u53f2\u6570\u636e，\u63d0\u9ad8\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if history:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 similar_count = len(self._find_similar_requests(RequestContext(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 request_id="temp",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 method="GET",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint="\/",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 timestamp=datetime.now()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ), history))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if similar_count > 0:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 base_confidence += min(similar_count \/ 10, 0.25)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return min(base_confidence, 0.95)<\/p>\n

# ============================================================================
\n# \u6307\u5bfc\u6027AI\u6a21\u578b
\n# ============================================================================<\/p>\n

class PrescriptiveModel(AIModel):
\n\u00a0 \u00a0 """\u6307\u5bfc\u6027AI\u6a21\u578b – \u63d0\u4f9b\u5177\u4f53\u884c\u52a8\u5efa\u8bae"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 super().__init__("prescriptive", "1.3")
\n\u00a0 \u00a0 \u00a0 \u00a0 self.action_recommendations = self._initialize_recommendations()
\n\u00a0 \u00a0 \u00a0 \u00a0 self.action_history = []
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _initialize_recommendations(self) -> Dict[int, List[Dict]]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u521d\u59cb\u5316\u884c\u52a8\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 404: [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u68c0\u67e5\u8def\u7531\u914d\u7f6e", "priority": "high", "estimated_time": "5min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u9a8c\u8bc1\u8d44\u6e90\u5b58\u5728\u6027", "priority": "medium", "estimated_time": "10min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u66f4\u65b0API\u6587\u6863", "priority": "low", "estimated_time": "30min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 500: [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u68c0\u67e5\u670d\u52a1\u5668\u65e5\u5fd7", "priority": "high", "estimated_time": "15min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u9a8c\u8bc1\u4f9d\u8d56\u670d\u52a1\u72b6\u6001", "priority": "high", "estimated_time": "10min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u56de\u6eda\u6700\u8fd1\u90e8\u7f72", "priority": "medium", "estimated_time": "20min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 429: [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u8c03\u6574\u901f\u7387\u9650\u5236", "priority": "medium", "estimated_time": "15min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u5b9e\u73b0\u6307\u6570\u9000\u907f", "priority": "high", "estimated_time": "30min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u4f18\u5316\u5ba2\u6237\u7aef\u8bf7\u6c42\u903b\u8f91", "priority": "low", "estimated_time": "60min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def analyze(self, context: RequestContext, history: List[Dict]) -> AIAnalysisResult:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u6307\u5bfc\u6027\u5206\u6790"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u786e\u5b9a\u9700\u8981\u89e3\u51b3\u7684\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 problems = self._identify_problems(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u884c\u52a8\u5efa\u8bae
\n\u00a0 \u00a0 \u00a0 \u00a0 actions = self._generate_actions(problems, context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u6d1e\u5bdf
\n\u00a0 \u00a0 \u00a0 \u00a0 insights = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 f"\u8bc6\u522b\u5230 {len(problems)} \u4e2a\u6f5c\u5728\u95ee\u9898",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 f"\u5efa\u8bae {len(actions)} \u9879\u884c\u52a8\u6765\u89e3\u51b3\u95ee\u9898"
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if actions:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 top_action = actions[0]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u9996\u8981\u884c\u52a8: {top_action.get('action', '\u65e0')}")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 confidence = self._calculate_confidence(problems, actions)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return AIAnalysisResult(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 category=AIStatusCodeCategory.PRESCRIPTIVE,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights=insights,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence=confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 metrics={
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "problems_identified": len(problems),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "actions_proposed": len(actions),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "estimated_total_time": sum(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self._parse_time(action.get("estimated_time", "0min"))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for action in actions
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "high_priority_actions": sum(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 1 for action in actions if action.get("priority") == "high"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 model_version=self.version
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _identify_problems(self, context: RequestContext, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8bc6\u522b\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 problems = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u4ece\u5386\u53f2\u4e2d\u8bc6\u522b\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 error_patterns = self._analyze_error_patterns(history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if error_patterns:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.extend(error_patterns)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u4ece\u4e0a\u4e0b\u6587\u4e2d\u8bc6\u522b\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 context_problems = self._analyze_context_problems(context)
\n\u00a0 \u00a0 \u00a0 \u00a0 if context_problems:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.extend(context_problems)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u7cfb\u7edf\u7ea7\u522b\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 system_problems = self._analyze_system_problems(history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if system_problems:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.extend(system_problems)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return problems[:5] \u00a0# \u6700\u591a\u8fd4\u56de5\u4e2a\u95ee\u9898
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_error_patterns(self, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u9519\u8bef\u6a21\u5f0f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not history:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5206\u6790\u6700\u8fd1\u9519\u8bef
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_errors = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history[-50:]\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("status_code", 200) >= 400
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not recent_errors:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8bc6\u522b\u5e38\u89c1\u9519\u8bef\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 error_types = Counter([h.get("error_type", "unknown") for h in recent_errors])
\n\u00a0 \u00a0 \u00a0 \u00a0 problems = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for error_type, count in error_types.most_common(3):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if count >= 3: \u00a0# \u81f3\u5c11\u51fa\u73b03\u6b21\u624d\u8ba4\u4e3a\u662f\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "error_pattern",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u9891\u7e41\u51fa\u73b0 {error_type} \u9519\u8bef ({count}\u6b21)",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "severity": "high" if count > 5 else "medium"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return problems
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_context_problems(self, context: RequestContext) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u4e0a\u4e0b\u6587\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 problems = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u8bf7\u6c42\u5934
\n\u00a0 \u00a0 \u00a0 \u00a0 if "user-agent" not in context.headers:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "context_problem",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": "\u8bf7\u6c42\u7f3a\u5c11User-Agent\u5934",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "severity": "low"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u7aef\u70b9
\n\u00a0 \u00a0 \u00a0 \u00a0 if "\/api\/" not in context.endpoint:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "context_problem",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": "\u8bf7\u6c42\u7684\u7aef\u70b9\u53ef\u80fd\u4e0d\u662fAPI\u7aef\u70b9",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "severity": "low"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return problems
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_system_problems(self, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u7cfb\u7edf\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(history) < 20:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 problems = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5206\u6790\u54cd\u5e94\u65f6\u95f4\u8d8b\u52bf
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_times = [h.get("response_time", 0) for h in history[-20:] if "response_time" in h]
\n\u00a0 \u00a0 \u00a0 \u00a0 older_times = [h.get("response_time", 0) for h in history[-40:-20] if "response_time" in h]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if recent_times and older_times:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 avg_recent = statistics.mean(recent_times)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 avg_older = statistics.mean(older_times)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if avg_recent > avg_older * 1.5:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "performance_degradation",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u54cd\u5e94\u65f6\u95f4\u4ece{avg_older:.0f}ms\u589e\u52a0\u5230{avg_recent:.0f}ms",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "severity": "medium"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5206\u6790\u9519\u8bef\u7387
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_errors = sum(1 for h in history[-20:] if h.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0 older_errors = sum(1 for h in history[-40:-20] if h.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if older_errors > 0 and recent_errors > older_errors * 2:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problems.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "error_rate_increase",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u9519\u8bef\u7387\u663e\u8457\u589e\u52a0 ({older_errors} -> {recent_errors})",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "severity": "high"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return problems
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_actions(self, problems: List[Dict], context: RequestContext, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u884c\u52a8\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 actions = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for problem in problems[:3]: \u00a0# \u9488\u5bf9\u524d3\u4e2a\u95ee\u9898\u751f\u6210\u5efa\u8bae
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 problem_type = problem.get("type", "")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 severity = problem.get("severity", "medium")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if problem_type == "error_pattern":
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 actions.extend(self._get_error_pattern_actions(problem, context))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif problem_type == "performance_degradation":
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 actions.extend(self._get_performance_actions(problem))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "error_rate" in problem_type:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 actions.extend(self._get_error_rate_actions(problem))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u901a\u7528\u5efa\u8bae
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 actions.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": "\u8c03\u67e5\u5e76\u89e3\u51b3\u8be5\u95ee\u9898",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": severity,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "estimated_time": "30min",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "problem_description": problem.get("description", "")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6309\u4f18\u5148\u7ea7\u6392\u5e8f
\n\u00a0 \u00a0 \u00a0 \u00a0 priority_order = {"high": 0, "medium": 1, "low": 2}
\n\u00a0 \u00a0 \u00a0 \u00a0 actions.sort(key=lambda x: priority_order.get(x.get("priority", "low"), 3))
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return actions[:5] \u00a0# \u6700\u591a\u8fd4\u56de5\u4e2a\u884c\u52a8
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _get_error_pattern_actions(self, problem: Dict, context: RequestContext) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u9519\u8bef\u6a21\u5f0f\u884c\u52a8\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 description = problem.get("description", "")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if "404" in description:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u68c0\u67e5\u8d44\u6e90\u662f\u5426\u5b58\u5728", "priority": "high", "estimated_time": "5min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u9a8c\u8bc1API\u8def\u7531\u914d\u7f6e", "priority": "medium", "estimated_time": "10min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0 elif "500" in description:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u67e5\u770b\u670d\u52a1\u5668\u9519\u8bef\u65e5\u5fd7", "priority": "high", "estimated_time": "15min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u68c0\u67e5\u5916\u90e8\u4f9d\u8d56\u670d\u52a1", "priority": "high", "estimated_time": "20min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0 elif "429" in description:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u8c03\u6574API\u901f\u7387\u9650\u5236", "priority": "medium", "estimated_time": "15min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u4f18\u5316\u5ba2\u6237\u7aef\u8bf7\u6c42\u903b\u8f91", "priority": "low", "estimated_time": "30min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return [{"action": "\u5206\u6790\u9519\u8bef\u65e5\u5fd7\u627e\u51fa\u6839\u672c\u539f\u56e0", "priority": "medium", "estimated_time": "30min"}]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _get_performance_actions(self, problem: Dict) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u6027\u80fd\u884c\u52a8\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u5206\u6790\u6162\u67e5\u8be2\u65e5\u5fd7", "priority": "medium", "estimated_time": "20min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u68c0\u67e5\u6570\u636e\u5e93\u7d22\u5f15", "priority": "medium", "estimated_time": "30min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u4f18\u5316API\u54cd\u5e94\u7f13\u5b58", "priority": "low", "estimated_time": "45min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _get_error_rate_actions(self, problem: Dict) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u9519\u8bef\u7387\u884c\u52a8\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u5b9e\u73b0\u65ad\u8def\u5668\u6a21\u5f0f", "priority": "high", "estimated_time": "60min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u589e\u52a0\u9519\u8bef\u76d1\u63a7\u548c\u544a\u8b66", "priority": "medium", "estimated_time": "30min"},
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"action": "\u5b9e\u65bd\u4f18\u96c5\u964d\u7ea7\u7b56\u7565", "priority": "low", "estimated_time": "90min"}
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _parse_time(self, time_str: str) -> int:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u89e3\u6790\u65f6\u95f4\u5b57\u7b26\u4e32\u4e3a\u5206\u949f\u6570"""
\n\u00a0 \u00a0 \u00a0 \u00a0 try:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "min" in time_str:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return int(time_str.replace("min", "").strip())
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "h" in time_str:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return int(time_str.replace("h", "").strip()) * 60
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 30 \u00a0# \u9ed8\u8ba430\u5206\u949f
\n\u00a0 \u00a0 \u00a0 \u00a0 except:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 30
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_confidence(self, problems: List[Dict], actions: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u6307\u5bfc\u7f6e\u4fe1\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not problems:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.3 \u00a0# \u4f4e\u7f6e\u4fe1\u5ea6，\u56e0\u4e3a\u6ca1\u6709\u53d1\u73b0\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u95ee\u9898\u6570\u91cf\u548c\u4e25\u91cd\u6027
\n\u00a0 \u00a0 \u00a0 \u00a0 severity_scores = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "high": 1.0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "medium": 0.7,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "low": 0.4
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 total_score = 0
\n\u00a0 \u00a0 \u00a0 \u00a0 for problem in problems:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 severity = problem.get("severity", "medium")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 total_score += severity_scores.get(severity, 0.7)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 avg_score = total_score \/ len(problems)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5982\u679c\u6709\u5177\u4f53\u884c\u52a8\u5efa\u8bae，\u63d0\u9ad8\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 if actions:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 avg_score = min(avg_score + 0.2, 0.9)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return avg_score<\/p>\n

# ============================================================================
\n# \u8bca\u65ad\u6027AI\u6a21\u578b
\n# ============================================================================<\/p>\n

class DiagnosticModel(AIModel):
\n\u00a0 \u00a0 """\u8bca\u65ad\u6027AI\u6a21\u578b – \u6839\u56e0\u5206\u6790\u548c\u6545\u969c\u8bca\u65ad"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 super().__init__("diagnostic", "1.1")
\n\u00a0 \u00a0 \u00a0 \u00a0 self.diagnosis_patterns = {}
\n\u00a0 \u00a0 \u00a0 \u00a0 self.root_cause_analysis = {}
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def analyze(self, context: RequestContext, history: List[Dict]) -> AIAnalysisResult:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u6267\u884c\u8bca\u65ad\u6027\u5206\u6790"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6267\u884c\u6839\u56e0\u5206\u6790
\n\u00a0 \u00a0 \u00a0 \u00a0 root_causes = self._analyze_root_causes(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u8bca\u65ad\u62a5\u544a
\n\u00a0 \u00a0 \u00a0 \u00a0 diagnosis = self._generate_diagnosis(root_causes, context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u751f\u6210\u6d1e\u5bdf
\n\u00a0 \u00a0 \u00a0 \u00a0 insights = []
\n\u00a0 \u00a0 \u00a0 \u00a0 if root_causes:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u8bc6\u522b\u5230 {len(root_causes)} \u4e2a\u6f5c\u5728\u6839\u56e0")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for cause in root_causes[:2]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append(f"\u6839\u56e0: {cause.get('description', '\u672a\u77e5')}")
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights.append("\u672a\u53d1\u73b0\u660e\u663e\u6839\u56e0，\u53ef\u80fd\u662f\u6682\u65f6\u6027\u95ee\u9898")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 confidence = self._calculate_confidence(root_causes, history)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return AIAnalysisResult(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 category=AIStatusCodeCategory.DIAGNOSTIC,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 insights=insights,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence=confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 metrics={
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "root_causes_found": len(root_causes),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "diagnosis_complexity": self._calculate_complexity(context, history),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "data_sufficiency": self._check_data_sufficiency(history),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "diagnosis_depth": "deep" if len(root_causes) > 0 else "shallow"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 model_version=self.version
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_root_causes(self, context: RequestContext, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u6839\u56e0"""
\n\u00a0 \u00a0 \u00a0 \u00a0 causes = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 1. \u68c0\u67e5\u914d\u7f6e\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 config_issues = self._check_configuration_issues(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if config_issues:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 causes.append(config_issues)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 2. \u68c0\u67e5\u4f9d\u8d56\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 dependency_issues = self._check_dependency_issues(history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if dependency_issues:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 causes.append(dependency_issues)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 3. \u68c0\u67e5\u8d44\u6e90\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 resource_issues = self._check_resource_issues(history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if resource_issues:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 causes.append(resource_issues)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 4. \u68c0\u67e5\u4ee3\u7801\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 code_issues = self._check_code_issues(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if code_issues:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 causes.append(code_issues)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # 5. \u68c0\u67e5\u7f51\u7edc\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 network_issues = self._check_network_issues(history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if network_issues:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 causes.append(network_issues)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return causes
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _check_configuration_issues(self, context: RequestContext, history: List[Dict]) -> Optional[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u67e5\u914d\u7f6e\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u662f\u5426\u6709\u914d\u7f6e\u76f8\u5173\u9519\u8bef
\n\u00a0 \u00a0 \u00a0 \u00a0 config_errors = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history[-20:]\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("error_type") in ["config_error", "validation_error", "parse_error"]
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if config_errors:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_types = Counter([h.get("error_type") for h in config_errors])
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 most_common = error_types.most_common(1)[0][0]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "configuration",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u914d\u7f6e\u9519\u8bef: {most_common}",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.7,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendation": "\u68c0\u67e5\u5e94\u7528\u914d\u7f6e\u6587\u4ef6\u548c\u73af\u5883\u53d8\u91cf"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return None
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _check_dependency_issues(self, history: List[Dict]) -> Optional[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u67e5\u4f9d\u8d56\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 dependency_errors = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history[-20:]\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("error_type") in ["dependency_error", "service_unavailable", "timeout"]
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if dependency_errors:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u662f\u5426\u662f\u7279\u5b9a\u4f9d\u8d56
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoints = Counter([h.get("endpoint") for h in dependency_errors])
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if endpoints:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 most_common_endpoint = endpoints.most_common(1)[0][0]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "dependency",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u4f9d\u8d56\u670d\u52a1\u95ee\u9898: {most_common_endpoint}",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.6,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendation": "\u68c0\u67e5\u5916\u90e8\u670d\u52a1\u72b6\u6001\u548c\u8fde\u63a5"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return None
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _check_resource_issues(self, history: List[Dict]) -> Optional[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u67e5\u8d44\u6e90\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 resource_errors = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history[-20:]\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("error_type") in ["out_of_memory", "disk_full", "connection_limit"]
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if resource_errors:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "resource",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": "\u7cfb\u7edf\u8d44\u6e90\u4e0d\u8db3",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.8,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendation": "\u68c0\u67e5\u7cfb\u7edf\u8d44\u6e90\u4f7f\u7528\u60c5\u51b5（\u5185\u5b58\u3001\u78c1\u76d8\u3001\u8fde\u63a5\u6570）"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u54cd\u5e94\u65f6\u95f4\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 slow_requests = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history[-20:]\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("response_time", 0) > 5000 \u00a0# 5\u79d2\u4ee5\u4e0a
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(slow_requests) > 5:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "performance",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": "\u7cfb\u7edf\u6027\u80fd\u4e0b\u964d，\u53ef\u80fd\u8d44\u6e90\u7d27\u5f20",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.5,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendation": "\u76d1\u63a7\u7cfb\u7edf\u8d1f\u8f7d\u548c\u4f18\u5316\u8d44\u6e90\u5206\u914d"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return None
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _check_code_issues(self, context: RequestContext, history: List[Dict]) -> Optional[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u67e5\u4ee3\u7801\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u662f\u5426\u6709\u5f02\u5e38\u5806\u6808
\n\u00a0 \u00a0 \u00a0 \u00a0 stack_traces = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history[-20:]\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "stack_trace" in h or "exception" in h.get("error_type", "")
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if stack_traces:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "code",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": "\u4ee3\u7801\u5f02\u5e38\u6216bug",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.9,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendation": "\u68c0\u67e5\u5e94\u7528\u4ee3\u7801\u548c\u5f02\u5e38\u5904\u7406\u903b\u8f91"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u7279\u5b9a\u7aef\u70b9\u7684\u9519\u8bef\u7387
\n\u00a0 \u00a0 \u00a0 \u00a0 endpoint_errors = defaultdict(int)
\n\u00a0 \u00a0 \u00a0 \u00a0 endpoint_total = defaultdict(int)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for h in history[-50:]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint = h.get("endpoint")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if endpoint:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint_total[endpoint] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("status_code", 200) >= 400:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint_errors[endpoint] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u627e\u51fa\u9519\u8bef\u7387\u9ad8\u7684\u7aef\u70b9
\n\u00a0 \u00a0 \u00a0 \u00a0 for endpoint, total in endpoint_total.items():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if total >= 10: \u00a0# \u81f3\u5c11\u670910\u6b21\u8bf7\u6c42
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_rate = endpoint_errors[endpoint] \/ total
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if error_rate > 0.3: \u00a0# \u9519\u8bef\u7387\u8d85\u8fc730%
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "endpoint_specific",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u7aef\u70b9 {endpoint} \u9519\u8bef\u7387\u8fc7\u9ad8 ({error_rate:.0%})",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.7,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendation": f"\u68c0\u67e5 {endpoint} \u7aef\u70b9\u7684\u5b9e\u73b0\u903b\u8f91"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return None
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _check_network_issues(self, history: List[Dict]) -> Optional[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u67e5\u7f51\u7edc\u95ee\u9898"""
\n\u00a0 \u00a0 \u00a0 \u00a0 network_errors = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in history[-20:]\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("error_type") in ["network_error", "connection_error", "timeout"]
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if network_errors:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u65f6\u95f4\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_times = []
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for h in network_errors:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "timestamp" in h:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_times.append(datetime.fromisoformat(h["timestamp"]))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u5982\u679c\u9519\u8bef\u5728\u77ed\u65f6\u95f4\u5185\u96c6\u4e2d\u51fa\u73b0，\u53ef\u80fd\u662f\u7f51\u7edc\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if len(error_times) >= 3:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 time_diffs = []
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for i in range(1, len(error_times)):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 diff = (error_times[i] – error_times[i-1]).total_seconds()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 time_diffs.append(diff)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if max(time_diffs) < 300: \u00a0# 5\u5206\u949f\u5185
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "network",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": "\u7f51\u7edc\u8fde\u63a5\u95ee\u9898",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": 0.6,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendation": "\u68c0\u67e5\u7f51\u7edc\u8fde\u63a5\u548c\u9632\u706b\u5899\u8bbe\u7f6e"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return None
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_diagnosis(self, root_causes: List[Dict], context: RequestContext, history: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u8bca\u65ad\u62a5\u544a"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not root_causes:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {"status": "healthy", "issues": "none"}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u786e\u5b9a\u4e3b\u8981\u95ee\u9898
\n\u00a0 \u00a0 \u00a0 \u00a0 primary_cause = max(root_causes, key=lambda x: x.get("confidence", 0), default=None)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status": "needs_attention",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "primary_issue": primary_cause.get("description", "unknown") if primary_cause else "unknown",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "all_issues": [cause.get("description", "unknown") for cause in root_causes],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence_scores": [cause.get("confidence", 0) for cause in root_causes],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "diagnosis_summary": self._create_summary(root_causes)
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _create_summary(self, root_causes: List[Dict]) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u521b\u5efa\u8bca\u65ad\u6458\u8981"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not root_causes:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "\u7cfb\u7edf\u8fd0\u884c\u6b63\u5e38"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 cause_types = [cause.get("type", "unknown") for cause in root_causes]
\n\u00a0 \u00a0 \u00a0 \u00a0 type_counts = Counter(cause_types)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 summary_parts = []
\n\u00a0 \u00a0 \u00a0 \u00a0 for cause_type, count in type_counts.most_common():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 summary_parts.append(f"{count}\u4e2a{cause_type}\u95ee\u9898")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return f"\u53d1\u73b0{len(root_causes)}\u4e2a\u6f5c\u5728\u95ee\u9898: {', '.join(summary_parts)}"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_complexity(self, context: RequestContext, history: List[Dict]) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u8bca\u65ad\u590d\u6742\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not history:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "simple"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u9519\u8bef\u591a\u6837\u6027
\n\u00a0 \u00a0 \u00a0 \u00a0 error_types = set()
\n\u00a0 \u00a0 \u00a0 \u00a0 for h in history[-50:]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h.get("status_code", 200) >= 400:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_types.add(h.get("error_type", "unknown"))
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(error_types) > 3:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "complex"
\n\u00a0 \u00a0 \u00a0 \u00a0 elif len(error_types) > 1:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "moderate"
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "simple"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _check_data_sufficiency(self, history: List[Dict]) -> bool:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u67e5\u6570\u636e\u662f\u5426\u5145\u8db3"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(history) < 10:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return False
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u662f\u5426\u6709\u8db3\u591f\u7684\u5386\u53f2\u9519\u8bef\u6570\u636e
\n\u00a0 \u00a0 \u00a0 \u00a0 error_count = sum(1 for h in history if h.get("status_code", 200) >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0 return error_count >= 3
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_confidence(self, root_causes: List[Dict], history: List[Dict]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u8bca\u65ad\u7f6e\u4fe1\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not root_causes:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u5982\u679c\u6ca1\u6709\u627e\u5230\u6839\u56e0，\u7f6e\u4fe1\u5ea6\u8f83\u4f4e
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.3
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u6839\u56e0\u6570\u91cf\u548c\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 total_confidence = sum(cause.get("confidence", 0) for cause in root_causes)
\n\u00a0 \u00a0 \u00a0 \u00a0 avg_confidence = total_confidence \/ len(root_causes)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u6570\u636e\u5145\u8db3\u6027\u8c03\u6574
\n\u00a0 \u00a0 \u00a0 \u00a0 data_sufficient = self._check_data_sufficiency(history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if data_sufficient:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 avg_confidence = min(avg_confidence + 0.2, 0.9)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return avg_confidence<\/p>\n

# ============================================================================
\n# AI\u72b6\u6001\u7801\u7cfb\u7edf
\n# ============================================================================<\/p>\n

class AIStatusCodeSystem:
\n\u00a0 \u00a0 """AI\u589e\u5f3a\u72b6\u6001\u7801\u7cfb\u7edf"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self, config: Optional[Dict] = None):
\n\u00a0 \u00a0 \u00a0 \u00a0 self.config = config or {}
\n\u00a0 \u00a0 \u00a0 \u00a0 self.models = self._initialize_models()
\n\u00a0 \u00a0 \u00a0 \u00a0 self.history = []
\n\u00a0 \u00a0 \u00a0 \u00a0 self.learning_engine = LearningEngine()
\n\u00a0 \u00a0 \u00a0 \u00a0 self.metrics_collector = MetricsCollector()
\n\u00a0 \u00a0 \u00a0 \u00a0 self.cache = {}
\n\u00a0 \u00a0 \u00a0 \u00a0 self.lock = threading.RLock()
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _initialize_models(self) -> Dict[str, AIModel]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u521d\u59cb\u5316AI\u6a21\u578b"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "predictive": PredictiveModel(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "adaptive": AdaptiveModel(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "explainable": ExplainableModel(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "prescriptive": PrescriptiveModel(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "diagnostic": DiagnosticModel()
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def analyze_request(self,\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0request_data: Dict[str, Any],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0historical_data: Optional[List[Dict]] = None) -> AIStatusCode:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u8bf7\u6c42\u5e76\u751f\u6210AI\u72b6\u6001\u7801"""
\n\u00a0 \u00a0 \u00a0 \u00a0 with self.lock:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 1. \u521b\u5efa\u8bf7\u6c42\u4e0a\u4e0b\u6587
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 context = self._create_request_context(request_data)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 2. \u4f7f\u7528\u5386\u53f2\u6570\u636e\u6216\u7cfb\u7edf\u5386\u53f2
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 history = historical_data or self.history[-100:] \u00a0# \u4f7f\u7528\u6700\u8fd1100\u6761\u5386\u53f2
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 3. \u68c0\u67e5\u7f13\u5b58
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 cache_key = self._generate_cache_key(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if cache_key in self.cache:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 cached_result = self.cache[cache_key]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if (datetime.now() – cached_result["timestamp"]).seconds < 60:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return cached_result["result"]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 4. \u786e\u5b9a\u57fa\u7840\u72b6\u6001\u7801
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 base_code, description = self._determine_base_status_code(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 5. \u5e76\u884c\u6267\u884cAI\u5206\u6790
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 analysis_results = self._parallel_analyze(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 6. \u8ba1\u7b97\u6574\u4f53\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 overall_confidence = self._calculate_overall_confidence(analysis_results)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 7. \u786e\u5b9a\u4f7f\u7528\u7684AI\u7c7b\u522b
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ai_categories = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result.category for result in analysis_results\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if result.confidence > 0.5
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 8. \u521b\u5efaAI\u72b6\u6001\u7801
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ai_status_code = AIStatusCode(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 base_code=base_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 code_description=description,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ai_categories=ai_categories,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 analysis_results=analysis_results,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence=overall_confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 context=context
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 9. \u66f4\u65b0\u5386\u53f2
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.history.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "request": request_data,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "ai_status_code": ai_status_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 10. \u66f4\u65b0\u7f13\u5b58
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.cache[cache_key] = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "result": ai_status_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 11. \u8bb0\u5f55\u5b66\u4e60
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.learning_engine.record_analysis(ai_status_code)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # 12. \u6536\u96c6\u6307\u6807
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.metrics_collector.record_analysis(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ai_status_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 analysis_time=(datetime.now() – context.timestamp).total_seconds()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return ai_status_code
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _create_request_context(self, request_data: Dict) -> RequestContext:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u521b\u5efa\u8bf7\u6c42\u4e0a\u4e0b\u6587"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return RequestContext(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 request_id=request_data.get("request_id", f"req_{int(time.time())}"),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 method=request_data.get("method", "GET"),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint=request_data.get("endpoint", "\/"),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 headers=request_data.get("headers", {}),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 query_params=request_data.get("query_params", {}),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 body=request_data.get("body"),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 user_id=request_data.get("user_id"),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 user_agent=request_data.get("user_agent"),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 client_ip=request_data.get("client_ip"),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 timestamp=datetime.now()
\n\u00a0 \u00a0 \u00a0 \u00a0 )
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_cache_key(self, context: RequestContext, history: List[Dict]) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u7f13\u5b58\u952e"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u8bf7\u6c42\u6307\u7eb9\u548c\u5386\u53f2\u6458\u8981
\n\u00a0 \u00a0 \u00a0 \u00a0 history_hash = hashlib.md5(
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 str([h.get("request_id", "") for h in history[-5:]]).encode()
\n\u00a0 \u00a0 \u00a0 \u00a0 ).hexdigest()[:8]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return f"{context.fingerprint()}_{history_hash}"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _determine_base_status_code(self, context: RequestContext, history: List[Dict]) -> Tuple[int, str]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u786e\u5b9a\u57fa\u7840\u72b6\u6001\u7801"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u662f\u5426\u6709\u663e\u5f0f\u9519\u8bef
\n\u00a0 \u00a0 \u00a0 \u00a0 if "error" in context.headers or (context.body and "error" in str(context.body)):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_type = self._extract_error_type(context)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "not_found" in error_type:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 404, "Not Found"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "unauthorized" in error_type or "forbidden" in error_type:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 401, "Unauthorized"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "validation" in error_type:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 400, "Bad Request"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "rate_limit" in error_type:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 429, "Too Many Requests"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 500, "Internal Server Error"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u5386\u53f2\u76f8\u4f3c\u8bf7\u6c42
\n\u00a0 \u00a0 \u00a0 \u00a0 similar_requests = self._find_similar_requests(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 if similar_requests:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_codes = [req.get("status_code", 200) for req in similar_requests]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if status_codes:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 most_common = Counter(status_codes).most_common(1)[0][0]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 descriptions = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 200: "OK",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 201: "Created",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 204: "No Content",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 400: "Bad Request",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 404: "Not Found",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 500: "Internal Server Error"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return most_common, descriptions.get(most_common, "Unknown")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u9ed8\u8ba4\u6210\u529f
\n\u00a0 \u00a0 \u00a0 \u00a0 return 200, "OK"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _extract_error_type(self, context: RequestContext) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u63d0\u53d6\u9519\u8bef\u7c7b\u578b"""
\n\u00a0 \u00a0 \u00a0 \u00a0 error_sources = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5headers
\n\u00a0 \u00a0 \u00a0 \u00a0 for key, value in context.headers.items():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "error" in key.lower() or "fail" in key.lower():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_sources.append(value)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5body
\n\u00a0 \u00a0 \u00a0 \u00a0 if context.body:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 body_str = str(context.body).lower()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "not_found" in body_str:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "not_found"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "unauthorized" in body_str:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "unauthorized"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif "validation" in body_str:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return "validation_error"
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5408\u5e76\u9519\u8bef\u4fe1\u606f
\n\u00a0 \u00a0 \u00a0 \u00a0 return " ".join(error_sources) if error_sources else "unknown_error"
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _find_similar_requests(self, context: RequestContext, history: List[Dict]) -> List[Dict]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u67e5\u627e\u76f8\u4f3c\u8bf7\u6c42"""
\n\u00a0 \u00a0 \u00a0 \u00a0 similar = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for req in history[-100:]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if req.get("method") == context.method and req.get("endpoint") == context.endpoint:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 similar.append(req)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return similar[:10]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _parallel_analyze(self, context: RequestContext, history: List[Dict]) -> List[AIAnalysisResult]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5e76\u884c\u6267\u884cAI\u5206\u6790"""
\n\u00a0 \u00a0 \u00a0 \u00a0 results = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u53ef\u4ee5\u4f7f\u7528\u7ebf\u7a0b\u6c60，\u8fd9\u91cc\u7b80\u5316\u5904\u7406
\n\u00a0 \u00a0 \u00a0 \u00a0 for model_name, model in self.models.items():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 try:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result = model.analyze(context, history)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 results.append(result)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 except Exception as e:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u9519\u8bef\u4f46\u7ee7\u7eed\u5176\u4ed6\u6a21\u578b
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 print(f"Model {model_name} analysis failed: {e}")
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 continue
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return results
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_overall_confidence(self, analysis_results: List[AIAnalysisResult]) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u6574\u4f53\u7f6e\u4fe1\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not analysis_results:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.5
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 confidences = [result.confidence for result in analysis_results]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u52a0\u6743\u5e73\u5747，\u9884\u6d4b\u6027\u548c\u8bca\u65ad\u6027\u6a21\u578b\u6743\u91cd\u66f4\u9ad8
\n\u00a0 \u00a0 \u00a0 \u00a0 weights = []
\n\u00a0 \u00a0 \u00a0 \u00a0 for result in analysis_results:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if result.category in [AIStatusCodeCategory.PREDICTIVE, AIStatusCodeCategory.DIAGNOSTIC]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 weights.append(1.5)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 weights.append(1.0)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u52a0\u6743\u5e73\u5747
\n\u00a0 \u00a0 \u00a0 \u00a0 weighted_sum = sum(c * w for c, w in zip(confidences, weights))
\n\u00a0 \u00a0 \u00a0 \u00a0 total_weight = sum(weights)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return weighted_sum \/ total_weight
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def get_insights_report(self, timeframe_hours: int = 24) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6AI\u6d1e\u5bdf\u62a5\u544a"""
\n\u00a0 \u00a0 \u00a0 \u00a0 cutoff_time = datetime.now() – timedelta(hours=timeframe_hours)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8fc7\u6ee4\u65f6\u95f4\u8303\u56f4\u5185\u7684\u6570\u636e
\n\u00a0 \u00a0 \u00a0 \u00a0 recent_analysis = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 h for h in self.history
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if h["timestamp"] > cutoff_time
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not recent_analysis:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {"error": "No data available for the specified timeframe"}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 report = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timeframe": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "start": cutoff_time.isoformat(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "end": datetime.now().isoformat(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "duration_hours": timeframe_hours
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "summary": self._generate_summary(recent_analysis),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "model_performance": self.metrics_collector.get_performance_report(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "patterns": self._analyze_patterns(recent_analysis),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "anomalies": self._detect_anomalies(recent_analysis),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "recommendations": self._generate_system_recommendations(recent_analysis),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "learning_progress": self.learning_engine.get_learning_report()
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return report
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_summary(self, recent_analysis: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u6458\u8981"""
\n\u00a0 \u00a0 \u00a0 \u00a0 total_requests = len(recent_analysis)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u72b6\u6001\u7801\u5206\u5e03
\n\u00a0 \u00a0 \u00a0 \u00a0 status_distribution = Counter()
\n\u00a0 \u00a0 \u00a0 \u00a0 confidence_scores = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for entry in recent_analysis:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_code = entry["ai_status_code"].base_code
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_distribution[status_code] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence_scores.append(entry["ai_status_code"].confidence)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # AI\u4f7f\u7528\u7edf\u8ba1
\n\u00a0 \u00a0 \u00a0 \u00a0 ai_category_usage = Counter()
\n\u00a0 \u00a0 \u00a0 \u00a0 for entry in recent_analysis:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for category in entry["ai_status_code"].ai_categories:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ai_category_usage[category.name] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "total_requests": total_requests,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status_code_distribution": dict(status_distribution),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "ai_usage": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "total_with_ai": sum(1 for entry in recent_analysis\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if entry["ai_status_code"].ai_categories),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "category_breakdown": dict(ai_category_usage)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence_stats": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "mean": statistics.mean(confidence_scores) if confidence_scores else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "median": statistics.median(confidence_scores) if confidence_scores else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "std_dev": statistics.stdev(confidence_scores) if len(confidence_scores) > 1 else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "min": min(confidence_scores) if confidence_scores else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "max": max(confidence_scores) if confidence_scores else 0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_patterns(self, recent_analysis: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u6a21\u5f0f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "temporal_patterns": self._analyze_temporal_patterns(recent_analysis),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_patterns": self._analyze_error_patterns(recent_analysis),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_patterns": self._analyze_user_patterns(recent_analysis)
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_temporal_patterns(self, recent_analysis: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u65f6\u95f4\u6a21\u5f0f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 hourly_counts = Counter()
\n\u00a0 \u00a0 \u00a0 \u00a0 weekday_counts = Counter()
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for entry in recent_analysis:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 timestamp = entry["timestamp"]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 hourly_counts[timestamp.hour] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 weekday_counts[timestamp.weekday()] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "peak_hours": [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"hour": hour, "count": count}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for hour, count in hourly_counts.most_common(3)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "quiet_hours": [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"hour": hour, "count": count}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for hour, count in hourly_counts.most_common()[-3:]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "busy_days": [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"day": day, "count": count}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for day, count in weekday_counts.most_common(2)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_error_patterns(self, recent_analysis: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u9519\u8bef\u6a21\u5f0f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 error_sequences = []
\n\u00a0 \u00a0 \u00a0 \u00a0 error_endpoints = Counter()
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for i in range(len(recent_analysis) – 1):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 current = recent_analysis[i]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 next_entry = recent_analysis[i + 1]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 current_code = current["ai_status_code"].base_code
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 next_code = next_entry["ai_status_code"].base_code
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if current_code >= 400 and next_code >= 400:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_sequences.append((current_code, next_code))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if current_code >= 400:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint = current["ai_status_code"].context.endpoint
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_endpoints[endpoint] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "common_error_sequences": Counter(error_sequences).most_common(5),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_prone_endpoints": [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 {"endpoint": endpoint, "error_count": count}
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for endpoint, count in error_endpoints.most_common(5)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_chains": self._identify_error_chains(recent_analysis)
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _identify_error_chains(self, recent_analysis: List[Dict]) -> List[List[int]]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8bc6\u522b\u9519\u8bef\u94fe"""
\n\u00a0 \u00a0 \u00a0 \u00a0 error_chains = []
\n\u00a0 \u00a0 \u00a0 \u00a0 current_chain = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for entry in recent_analysis:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 code = entry["ai_status_code"].base_code
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if code >= 400:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 current_chain.append(code)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if len(current_chain) >= 2:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_chains.append(current_chain.copy())
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 current_chain = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u67e5\u6700\u540e\u4e00\u4e2a\u94fe
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(current_chain) >= 2:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_chains.append(current_chain)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return error_chains[:5]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _analyze_user_patterns(self, recent_analysis: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u5206\u6790\u7528\u6237\u6a21\u5f0f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 user_errors = defaultdict(int)
\n\u00a0 \u00a0 \u00a0 \u00a0 user_requests = defaultdict(int)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for entry in recent_analysis:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 user_id = entry["ai_status_code"].context.user_id
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if user_id:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 user_requests[user_id] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if entry["ai_status_code"].base_code >= 400:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 user_errors[user_id] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8ba1\u7b97\u7528\u6237\u9519\u8bef\u7387
\n\u00a0 \u00a0 \u00a0 \u00a0 user_error_rates = []
\n\u00a0 \u00a0 \u00a0 \u00a0 for user_id, total in user_requests.items():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if total >= 5: \u00a0# \u81f3\u5c11\u67095\u6b21\u8bf7\u6c42\u7684\u7528\u6237
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_rate = user_errors[user_id] \/ total
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 user_error_rates.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "user_id": user_id,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_rate": error_rate,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "total_requests": total
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6309\u9519\u8bef\u7387\u6392\u5e8f
\n\u00a0 \u00a0 \u00a0 \u00a0 user_error_rates.sort(key=lambda x: x["error_rate"], reverse=True)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "high_error_rate_users": user_error_rates[:5],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "total_unique_users": len(user_requests)
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _detect_anomalies(self, recent_analysis: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u68c0\u6d4b\u5f02\u5e38"""
\n\u00a0 \u00a0 \u00a0 \u00a0 anomalies = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence_anomalies": [],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "pattern_anomalies": [],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "behavior_anomalies": []
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u7f6e\u4fe1\u5ea6\u5f02\u5e38
\n\u00a0 \u00a0 \u00a0 \u00a0 confidences = [entry["ai_status_code"].confidence for entry in recent_analysis]
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(confidences) >= 10:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 mean_conf = statistics.mean(confidences)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 std_conf = statistics.stdev(confidences) if len(confidences) > 1 else 0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for i, entry in enumerate(recent_analysis):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 confidence = entry["ai_status_code"].confidence
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if std_conf > 0 and abs(confidence – mean_conf) > 2 * std_conf:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 anomalies["confidence_anomalies"].append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "index": i,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidence": confidence,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "z_score": (confidence – mean_conf) \/ std_conf,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": entry["timestamp"].isoformat()
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u9519\u8bef\u7387\u5f02\u5e38
\n\u00a0 \u00a0 \u00a0 \u00a0 error_rates = []
\n\u00a0 \u00a0 \u00a0 \u00a0 window_size = 10
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for i in range(len(recent_analysis) – window_size + 1):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 window = recent_analysis[i:i+window_size]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_count = sum(1 for entry in window\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if entry["ai_status_code"].base_code >= 400)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 error_rates.append(error_count \/ window_size)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(error_rates) >= 5:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u68c0\u6d4b\u7a81\u589e
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 for i in range(1, len(error_rates)):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if error_rates[i] > error_rates[i-1] * 2: \u00a0# \u9519\u8bef\u7387\u7ffb\u500d
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 anomalies["pattern_anomalies"].append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "error_rate_spike",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "window_start": i,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_rate": error_rates[i],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "previous_rate": error_rates[i-1]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return anomalies
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _generate_system_recommendations(self, recent_analysis: List[Dict]) -> List[Dict[str, Any]]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u751f\u6210\u7cfb\u7edf\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 recommendations = []
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 summary = self._generate_summary(recent_analysis)
\n\u00a0 \u00a0 \u00a0 \u00a0 patterns = self._analyze_patterns(recent_analysis)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u9519\u8bef\u7387
\n\u00a0 \u00a0 \u00a0 \u00a0 error_endpoints = patterns["error_patterns"].get("error_prone_endpoints", [])
\n\u00a0 \u00a0 \u00a0 \u00a0 if error_endpoints:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 top_endpoint = error_endpoints[0]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if top_endpoint["error_count"] > 10:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "ERROR_PRON_ENDPOINT",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "HIGH",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u7aef\u70b9 {top_endpoint['endpoint']} \u9519\u8bef\u7387\u9ad8 ({top_endpoint['error_count']}\u6b21)",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": f"\u8be6\u7ec6\u68c0\u67e5 {top_endpoint['endpoint']} \u7684\u5b9e\u73b0\u903b\u8f91",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "estimated_effort": "2\u5c0f\u65f6"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u9519\u8bef\u94fe
\n\u00a0 \u00a0 \u00a0 \u00a0 error_chains = patterns["error_patterns"].get("error_chains", [])
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(error_chains) > 3:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "ERROR_CHAIN_PATTERN",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "MEDIUM",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u53d1\u73b0 {len(error_chains)} \u4e2a\u9519\u8bef\u94fe\u6a21\u5f0f",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": "\u5b9e\u73b0\u65ad\u8def\u5668\u6a21\u5f0f\u6216\u6539\u8fdb\u9519\u8bef\u5904\u7406",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "estimated_effort": "1\u5929"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8eAI\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 conf_stats = summary.get("confidence_stats", {})
\n\u00a0 \u00a0 \u00a0 \u00a0 avg_confidence = conf_stats.get("mean", 0)
\n\u00a0 \u00a0 \u00a0 \u00a0 if avg_confidence < 0.6:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "LOW_AI_CONFIDENCE",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "MEDIUM",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"AI\u5206\u6790\u5e73\u5747\u7f6e\u4fe1\u5ea6\u8f83\u4f4e ({avg_confidence:.2f})",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": "\u4f18\u5316AI\u6a21\u578b\u8bad\u7ec3\u6570\u636e\u548c\u7279\u5f81\u5de5\u7a0b",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "estimated_effort": "3\u5929"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u7528\u6237\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 high_error_users = patterns["user_patterns"].get("high_error_rate_users", [])
\n\u00a0 \u00a0 \u00a0 \u00a0 if high_error_users:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 top_user = high_error_users[0]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if top_user["error_rate"] > 0.5:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 recommendations.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "type": "PROBLEMATIC_USER",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "priority": "LOW",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "description": f"\u7528\u6237 {top_user['user_id']} \u9519\u8bef\u7387\u9ad8\u8fbe {top_user['error_rate']:.0%}",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "action": "\u8c03\u67e5\u8be5\u7528\u6237\u7684\u4f7f\u7528\u6a21\u5f0f\u6216\u63d0\u4f9b\u652f\u6301",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "estimated_effort": "1\u5c0f\u65f6"
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return recommendations[:5]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def optimize_models(self, feedback_data: List[Dict]) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u4f18\u5316\u6240\u6709AI\u6a21\u578b"""
\n\u00a0 \u00a0 \u00a0 \u00a0 optimization_results = {}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for model_name, model in self.models.items():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 try:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 result = model.optimize(feedback_data)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 optimization_results[model_name] = result
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 except Exception as e:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 optimization_results[model_name] = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status": "failed",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error": str(e)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u66f4\u65b0\u5b66\u4e60\u5f15\u64ce
\n\u00a0 \u00a0 \u00a0 \u00a0 self.learning_engine.update_from_feedback(feedback_data)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6e05\u7a7a\u7f13\u5b58
\n\u00a0 \u00a0 \u00a0 \u00a0 self.cache.clear()
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now().isoformat(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "optimization_results": optimization_results,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "cache_cleared": True,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "next_scheduled_optimization": (
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 datetime.now() + timedelta(hours=24)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 ).isoformat()
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def get_model_info(self) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u6a21\u578b\u4fe1\u606f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 model_info = {}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for model_name, model in self.models.items():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 model_info[model_name] = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "name": model.name,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "version": model.version,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "training_samples": len(model.training_data),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "performance": model.performance_metrics
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "total_models": len(self.models),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "models": model_info,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "system_version": "2.0.0"
\n\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n

# ============================================================================
\n# \u5b66\u4e60\u5f15\u64ce
\n# ============================================================================<\/p>\n

class LearningEngine:
\n\u00a0 \u00a0 """\u5b66\u4e60\u5f15\u64ce – \u4ece\u5386\u53f2\u6570\u636e\u4e2d\u5b66\u4e60\u5e76\u4f18\u5316"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 self.analysis_history = []
\n\u00a0 \u00a0 \u00a0 \u00a0 self.feedback_history = []
\n\u00a0 \u00a0 \u00a0 \u00a0 self.learned_patterns = {}
\n\u00a0 \u00a0 \u00a0 \u00a0 self.performance_metrics = defaultdict(list)
\n\u00a0 \u00a0 \u00a0 \u00a0 self.pattern_mining_threshold = 5 \u00a0# \u81f3\u5c115\u6b21\u51fa\u73b0\u624d\u8ba4\u4e3a\u662f\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def record_analysis(self, ai_status_code: AIStatusCode):
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8bb0\u5f55\u5206\u6790\u7ed3\u679c"""
\n\u00a0 \u00a0 \u00a0 \u00a0 self.analysis_history.append({
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": datetime.now(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "ai_status_code": ai_status_code,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "context_fingerprint": ai_status_code.context.fingerprint()
\n\u00a0 \u00a0 \u00a0 \u00a0 })
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u4fdd\u6301\u5386\u53f2\u5927\u5c0f
\n\u00a0 \u00a0 \u00a0 \u00a0 if len(self.analysis_history) > 10000:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.analysis_history = self.analysis_history[-5000:]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def update_from_feedback(self, feedback_data: List[Dict]):
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u4ece\u53cd\u9988\u4e2d\u5b66\u4e60"""
\n\u00a0 \u00a0 \u00a0 \u00a0 self.feedback_history.extend(feedback_data)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for feedback in feedback_data:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u63d0\u53d6\u5b66\u4e60\u4fe1\u606f
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "correct_action" in feedback and "suggested_action" in feedback:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self._learn_from_correction(feedback)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u66f4\u65b0\u6027\u80fd\u6307\u6807
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "accuracy_score" in feedback:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.performance_metrics["accuracy"].append(feedback["accuracy_score"])
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "response_time" in feedback:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.performance_metrics["response_time"].append(feedback["response_time"])
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u6316\u6398\u65b0\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 self._mine_patterns()
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _learn_from_correction(self, feedback: Dict):
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u4ece\u7ea0\u6b63\u4e2d\u5b66\u4e60"""
\n\u00a0 \u00a0 \u00a0 \u00a0 context = feedback.get("context", {})
\n\u00a0 \u00a0 \u00a0 \u00a0 suggested = feedback.get("suggested_action", "")
\n\u00a0 \u00a0 \u00a0 \u00a0 correct = feedback.get("correct_action", "")
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not context or not suggested or not correct:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u521b\u5efa\u6a21\u5f0f\u952e
\n\u00a0 \u00a0 \u00a0 \u00a0 context_key = self._create_context_key(context)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if context_key not in self.learned_patterns:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.learned_patterns[context_key] = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "correct_actions": Counter(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "incorrect_actions": Counter(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "total_occurrences": 0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 pattern = self.learned_patterns[context_key]
\n\u00a0 \u00a0 \u00a0 \u00a0 pattern["total_occurrences"] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if suggested == correct:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 pattern["correct_actions"][suggested] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0 else:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 pattern["incorrect_actions"][suggested] += 1
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _create_context_key(self, context: Dict) -> str:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u521b\u5efa\u4e0a\u4e0b\u6587\u952e"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u4f7f\u7528\u5173\u952e\u5b57\u6bb5\u521b\u5efa\u6307\u7eb9
\n\u00a0 \u00a0 \u00a0 \u00a0 key_parts = [
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 context.get("method", ""),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 context.get("endpoint", ""),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 str(context.get("status_code", 200))
\n\u00a0 \u00a0 \u00a0 \u00a0 ]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return hashlib.md5(":".join(key_parts).encode()).hexdigest()[:12]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _mine_patterns(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u6316\u6398\u6a21\u5f0f"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u5206\u6790\u5386\u53f2\u6570\u636e\u4e2d\u7684\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 status_patterns = defaultdict(Counter)
\n\u00a0 \u00a0 \u00a0 \u00a0 endpoint_patterns = defaultdict(Counter)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for analysis in self.analysis_history[-1000:]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_code = analysis["ai_status_code"].base_code
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint = analysis["ai_status_code"].context.endpoint
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u72b6\u6001\u7801\u5e8f\u5217\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if "previous_status" in analysis:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 prev_status = analysis["previous_status"]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 status_patterns[prev_status][status_code] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u7aef\u70b9\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 endpoint_patterns[endpoint][status_code] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u4fdd\u5b58\u663e\u8457\u6a21\u5f0f
\n\u00a0 \u00a0 \u00a0 \u00a0 self.learned_patterns["status_transitions"] = dict(status_patterns)
\n\u00a0 \u00a0 \u00a0 \u00a0 self.learned_patterns["endpoint_status"] = dict(endpoint_patterns)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def get_learning_report(self) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u5b66\u4e60\u62a5\u544a"""
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "analysis_history_size": len(self.analysis_history),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "feedback_history_size": len(self.feedback_history),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "learned_patterns_count": len(self.learned_patterns),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "performance_metrics": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "accuracy": self._calculate_metric_stats(self.performance_metrics.get("accuracy", [])),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "response_time": self._calculate_metric_stats(self.performance_metrics.get("response_time", []))
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "learning_progress": self._calculate_learning_progress()
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_metric_stats(self, values: List[float]) -> Dict[str, float]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u6307\u6807\u7edf\u8ba1"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not values:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {"mean": 0, "count": 0}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "mean": statistics.mean(values),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "median": statistics.median(values),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "std_dev": statistics.stdev(values) if len(values) > 1 else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "count": len(values)
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def _calculate_learning_progress(self) -> float:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8ba1\u7b97\u5b66\u4e60\u8fdb\u5ea6"""
\n\u00a0 \u00a0 \u00a0 \u00a0 if not self.analysis_history:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return 0.0
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u57fa\u4e8e\u5386\u53f2\u6570\u636e\u91cf\u548c\u6a21\u5f0f\u6570\u91cf
\n\u00a0 \u00a0 \u00a0 \u00a0 data_factor = min(len(self.analysis_history) \/ 1000, 1.0)
\n\u00a0 \u00a0 \u00a0 \u00a0 pattern_factor = min(len(self.learned_patterns) \/ 50, 1.0)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return (data_factor * 0.6 + pattern_factor * 0.4)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def get_suggestions(self, context: Dict) -> List[str]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u57fa\u4e8e\u5b66\u4e60\u7684\u5efa\u8bae"""
\n\u00a0 \u00a0 \u00a0 \u00a0 context_key = self._create_context_key(context)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if context_key in self.learned_patterns:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 pattern = self.learned_patterns[context_key]
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 # \u8fd4\u56de\u6700\u5e38\u6210\u529f\u7684\u5efa\u8bae
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if pattern["correct_actions"]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 most_common = pattern["correct_actions"].most_common(2)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return [action for action, _ in most_common]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return []<\/p>\n

# ============================================================================
\n# \u6307\u6807\u6536\u96c6\u5668
\n# ============================================================================<\/p>\n

class MetricsCollector:
\n\u00a0 \u00a0 """\u6307\u6807\u6536\u96c6\u5668"""
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def __init__(self):
\n\u00a0 \u00a0 \u00a0 \u00a0 self.metrics = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "response_times": [],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "confidences": [],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "analysis_times": [],
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "model_usage": Counter(),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_codes": Counter()
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 self.window_size = 1000
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def record_analysis(self, ai_status_code: AIStatusCode, analysis_time: float):
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u8bb0\u5f55\u5206\u6790\u6307\u6807"""
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u54cd\u5e94\u65f6\u95f4
\n\u00a0 \u00a0 \u00a0 \u00a0 self.metrics["response_times"].append(analysis_time)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u7f6e\u4fe1\u5ea6
\n\u00a0 \u00a0 \u00a0 \u00a0 self.metrics["confidences"].append(ai_status_code.confidence)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u5206\u6790\u65f6\u95f4
\n\u00a0 \u00a0 \u00a0 \u00a0 self.metrics["analysis_times"].append(analysis_time)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u6a21\u578b\u4f7f\u7528\u60c5\u51b5
\n\u00a0 \u00a0 \u00a0 \u00a0 for category in ai_status_code.ai_categories:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.metrics["model_usage"][category.name] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u8bb0\u5f55\u9519\u8bef\u7801
\n\u00a0 \u00a0 \u00a0 \u00a0 self.metrics["error_codes"][ai_status_code.base_code] += 1
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 # \u4fdd\u6301\u7a97\u53e3\u5927\u5c0f
\n\u00a0 \u00a0 \u00a0 \u00a0 for key in ["response_times", "confidences", "analysis_times"]:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if len(self.metrics[key]) > self.window_size:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 self.metrics[key] = self.metrics[key][-self.window_size:]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def get_performance_report(self) -> Dict[str, Any]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u6027\u80fd\u62a5\u544a"""
\n\u00a0 \u00a0 \u00a0 \u00a0 report = {}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 for metric_name, values in self.metrics.items():
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 if isinstance(values, list) and values:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 report[metric_name] = {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "mean": statistics.mean(values) if values else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "p95": np.percentile(values, 95) if len(values) >= 5 else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "p99": np.percentile(values, 99) if len(values) >= 5 else 0,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "count": len(values)
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 elif isinstance(values, Counter):
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 report[metric_name] = dict(values)
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return report
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 def get_latency_report(self) -> Dict[str, float]:
\n\u00a0 \u00a0 \u00a0 \u00a0 """\u83b7\u53d6\u5ef6\u8fdf\u62a5\u544a"""
\n\u00a0 \u00a0 \u00a0 \u00a0 times = self.metrics["analysis_times"]
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 if not times:
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 return {"avg": 0, "max": 0, "min": 0}
\n\u00a0 \u00a0 \u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 \u00a0 \u00a0 return {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "avg": statistics.mean(times),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "max": max(times),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "min": min(times),
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "percentile_95": np.percentile(times, 95) if len(times) >= 5 else 0
\n\u00a0 \u00a0 \u00a0 \u00a0 }<\/p>\n

# ============================================================================
\n# \u4f7f\u7528\u793a\u4f8b
\n# ============================================================================<\/p>\n

def main():
\n\u00a0 \u00a0 """\u4e3b\u51fd\u6570 – \u6f14\u793aAI\u72b6\u6001\u7801\u7cfb\u7edf\u4f7f\u7528"""
\n\u00a0 \u00a0 # \u521d\u59cb\u5316\u7cfb\u7edf
\n\u00a0 \u00a0 ai_system = AIStatusCodeSystem()
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("=" * 60)
\n\u00a0 \u00a0 print("AI\u589e\u5f3a\u72b6\u6001\u7801\u7cfb\u7edf\u6f14\u793a")
\n\u00a0 \u00a0 print("=" * 60)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 # \u521b\u5efa\u793a\u4f8b\u8bf7\u6c42
\n\u00a0 \u00a0 sample_request = {
\n\u00a0 \u00a0 \u00a0 \u00a0 "request_id": "req_123456",
\n\u00a0 \u00a0 \u00a0 \u00a0 "method": "GET",
\n\u00a0 \u00a0 \u00a0 \u00a0 "endpoint": "\/api\/users\/123",
\n\u00a0 \u00a0 \u00a0 \u00a0 "headers": {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "Content-Type": "application\/json",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "Authorization": "Bearer token123"
\n\u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 "query_params": {"include": "profile"},
\n\u00a0 \u00a0 \u00a0 \u00a0 "user_id": "user_789",
\n\u00a0 \u00a0 \u00a0 \u00a0 "user_agent": "Mozilla\/5.0",
\n\u00a0 \u00a0 \u00a0 \u00a0 "client_ip": "192.168.1.100",
\n\u00a0 \u00a0 \u00a0 \u00a0 "body": None
\n\u00a0 \u00a0 }
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 # \u521b\u5efa\u793a\u4f8b\u5386\u53f2\u6570\u636e
\n\u00a0 \u00a0 historical_data = [
\n\u00a0 \u00a0 \u00a0 \u00a0 {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "request_id": "req_111111",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "method": "GET",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "endpoint": "\/api\/users\/123",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status_code": 404,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "error_type": "not_found",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "response_time": 150,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": (datetime.now() – timedelta(minutes=30)).isoformat()
\n\u00a0 \u00a0 \u00a0 \u00a0 },
\n\u00a0 \u00a0 \u00a0 \u00a0 {
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "request_id": "req_222222",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "method": "POST",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "endpoint": "\/api\/login",
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "status_code": 200,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "response_time": 80,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 "timestamp": (datetime.now() – timedelta(minutes=15)).isoformat()
\n\u00a0 \u00a0 \u00a0 \u00a0 }
\n\u00a0 \u00a0 ]
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("\\\\n1. \u5206\u6790\u8bf7\u6c42…")
\n\u00a0 \u00a0 ai_status_code = ai_system.analyze_request(sample_request, historical_data)
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print(f"\u57fa\u7840\u72b6\u6001\u7801: {ai_status_code.base_code}")
\n\u00a0 \u00a0 print(f"\u6269\u5c55\u72b6\u6001\u7801: {ai_status_code.extended_code}")
\n\u00a0 \u00a0 print(f"\u7f6e\u4fe1\u5ea6: {ai_status_code.confidence:.2f}")
\n\u00a0 \u00a0 print(f"\u4e25\u91cd\u6027: {ai_status_code.severity.name}")
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("\\\\n2. AI\u6d1e\u5bdf:")
\n\u00a0 \u00a0 for i, insight in enumerate(ai_status_code.primary_insights, 1):
\n\u00a0 \u00a0 \u00a0 \u00a0 print(f" \u00a0{i}. {insight}")
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("\\\\n3. AI\u5206\u6790\u7c7b\u522b:")
\n\u00a0 \u00a0 for category in ai_status_code.ai_categories:
\n\u00a0 \u00a0 \u00a0 \u00a0 print(f" \u00a0– {category.name}")
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("\\\\n4. \u54cd\u5e94\u683c\u5f0f\u793a\u4f8b:")
\n\u00a0 \u00a0 response = ai_status_code.to_response()
\n\u00a0 \u00a0 print(json.dumps(response, indent=2, ensure_ascii=False)[:500] + "…")
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("\\\\n5. \u83b7\u53d6\u7cfb\u7edf\u6d1e\u5bdf\u62a5\u544a…")
\n\u00a0 \u00a0 report = ai_system.get_insights_report(timeframe_hours=1)
\n\u00a0 \u00a0 print(f"\u62a5\u544a\u65f6\u95f4\u6bb5: {report['timeframe']['start']} \u5230 {report['timeframe']['end']}")
\n\u00a0 \u00a0 print(f"\u603b\u8bf7\u6c42\u6570: {report['summary']['total_requests']}")
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("\\\\n6. \u83b7\u53d6\u6a21\u578b\u4fe1\u606f…")
\n\u00a0 \u00a0 model_info = ai_system.get_model_info()
\n\u00a0 \u00a0 print(f"\u7cfb\u7edf\u7248\u672c: {model_info['system_version']}")
\n\u00a0 \u00a0 print(f"\u6a21\u578b\u6570\u91cf: {model_info['total_models']}")
\n\u00a0 \u00a0\u00a0
\n\u00a0 \u00a0 print("\\\\n" + "=" * 60)
\n\u00a0 \u00a0 print("\u6f14\u793a\u5b8c\u6210!")
\n\u00a0 \u00a0 print("=" * 60)<\/p>\n

if __name__ == "__main__":
\n\u00a0 \u00a0 main()<\/p>\n

40.3 \u91cf\u5b50\u8ba1\u7b97\u5bf9\u72b6\u6001\u7801\u7684\u5f71\u54cd<\/h4>\n

40.3.1 \u91cf\u5b50\u5b89\u5168\u7684\u72b6\u6001\u7801\u534f\u8bae<\/h5>\n

python<\/p>\n

# \u91cf\u5b50\u5b89\u5168\u7684\u72b6\u6001\u7801\u534f\u8bae\u8bbe\u8ba1
\nfrom typing import Dict, List, Optional, Tuple
\nfrom dataclasses import dataclass
\nfrom datetime import datetime
\nimport hashlib
\nimport secrets<\/p>\n

class QuantumSafeStatusCode:
\n """\u91cf\u5b50\u5b89\u5168\u72b6\u6001\u7801"""<\/p>\n

def __init__(self):
\n self.quantum_resistant_algorithms = {
\n 'CRYSTALS-Kyber': 'post_quantum_key_encapsulation',
\n 'CRYSTALS-Dilithium': 'post_quantum_digital_signatures',
\n 'Falcon': 'post_quantum_signatures',
\n 'SPHINCS+': 'hash_based_signatures'
\n }<\/p>\n

# \u91cf\u5b50\u611f\u77e5\u7684\u72b6\u6001\u7801\u6269\u5c55
\n self.quantum_status_codes = {
\n 580: "Quantum Security Required",
\n 581: "Post-Quantum Algorithm Not Supported",
\n 582: "Quantum Key Exchange Failed",
\n 583: "Quantum Signature Verification Failed",
\n 590: "Quantum Resource Exhausted",
\n 591: "Quantum Computation Timeout",
\n 592: "Quantum Resource Unavailable"
\n }<\/p>\n

def generate_quantum_safe_response(self,
\n status_code: int,
\n message: str,
\n requires_quantum_safe: bool = False) -> Dict:
\n """\u751f\u6210\u91cf\u5b50\u5b89\u5168\u54cd\u5e94"""
\n response = {
\n 'status_code': status_code,
\n 'message': message,
\n 'timestamp': datetime.now().isoformat(),
\n 'quantum_safe': requires_quantum_safe
\n }<\/p>\n

if requires_quantum_safe:
\n # \u6dfb\u52a0\u91cf\u5b50\u5b89\u5168\u6269\u5c55
\n response['quantum_extensions'] = {
\n 'algorithm': 'CRYSTALS-Dilithium',
\n 'signature': self._generate_quantum_signature(response),
\n 'key_exchange': self._suggest_quantum_key_exchange(),
\n 'quantum_resistant': True
\n }<\/p>\n

# \u6dfb\u52a0\u91cf\u5b50\u5b89\u5168\u5934\u90e8
\n response['headers'] = {
\n 'X-Quantum-Safe': 'required',
\n 'X-Post-Quantum-Algorithm': 'CRYSTALS-Dilithium',
\n 'X-Quantum-Key-Exchange': 'Kyber'
\n }<\/p>\n

return response<\/p>\n

def _generate_quantum_signature(self, data: Dict) -> str:
\n """\u751f\u6210\u91cf\u5b50\u5b89\u5168\u7b7e\u540d（\u6a21\u62df）"""
\n # \u5728\u5b9e\u9645\u5b9e\u73b0\u4e2d，\u8fd9\u91cc\u4f1a\u4f7f\u7528\u540e\u91cf\u5b50\u5bc6\u7801\u5b66\u5e93
\n data_str = str(data)<\/p>\n

# \u4f7f\u7528\u54c8\u5e0c-based\u7b7e\u540d（\u6297\u91cf\u5b50）
\n # \u8fd9\u91cc\u4f7f\u7528SHA-3\u4f5c\u4e3a\u793a\u4f8b
\n signature = hashlib.sha3_512(data_str.encode()).hexdigest()<\/p>\n

# \u6dfb\u52a0\u968f\u673a\u6570\u9632\u6b62\u91cd\u653e\u653b\u51fb
\n nonce = secrets.token_hex(16)
\n return f"{signature}:{nonce}"<\/p>\n

def _suggest_quantum_key_exchange(self) -> Dict:
\n """\u5efa\u8bae\u91cf\u5b50\u5bc6\u94a5\u4ea4\u6362\u65b9\u6cd5"""
\n return {
\n 'algorithm': 'CRYSTALS-Kyber',
\n 'key_size': 2048,
\n 'security_level': '5', # NIST\u5b89\u5168\u7ea7\u522b
\n 'estimated_quantum_security': 128 # \u91cf\u5b50\u6bd4\u7279\u5b89\u5168\u6027
\n }<\/p>\n

def detect_quantum_attack_patterns(self,
\n request_logs: List[Dict]) -> List[Dict]:
\n """\u68c0\u6d4b\u91cf\u5b50\u653b\u51fb\u6a21\u5f0f"""
\n quantum_patterns = []<\/p>\n

for log in request_logs:
\n # \u68c0\u6d4b\u53ef\u80fd\u7684\u91cf\u5b50\u8ba1\u7b97\u6a21\u5f0f
\n if self._is_potential_quantum_attack(log):
\n pattern = {
\n 'timestamp': log.get('timestamp'),
\n 'request_id': log.get('request_id'),
\n 'indicators': self._extract_quantum_indicators(log),
\n 'confidence': self._calculate_quantum_confidence(log),
\n 'recommended_action': self._suggest_quantum_defense(log)
\n }
\n quantum_patterns.append(pattern)<\/p>\n

return quantum_patterns<\/p>\n

def _is_potential_quantum_attack(self, log: Dict) -> bool:
\n """\u68c0\u67e5\u662f\u5426\u4e3a\u6f5c\u5728\u7684\u91cf\u5b50\u653b\u51fb"""
\n indicators = 0<\/p>\n

# 1. \u6781\u5feb\u7684\u5bc6\u7801\u5b66\u64cd\u4f5c
\n if log.get('crypto_operations_per_second', 0) > 10000:
\n indicators += 1<\/p>\n

# 2. \u540c\u65f6\u7834\u89e3\u591a\u4e2a\u5bc6\u94a5
\n if log.get('simultaneous_key_attempts', 0) > 100:
\n indicators += 1<\/p>\n

# 3. \u5f02\u5e38\u7684\u8ba1\u7b97\u6a21\u5f0f
\n if self._has_quantum_computation_pattern(log):
\n indicators += 1<\/p>\n

# 4. \u91cf\u5b50\u7b97\u6cd5\u7279\u5f81
\n if log.get('algorithm_pattern') in ['Shor', 'Grover']:
\n indicators += 1<\/p>\n

return indicators >= 2<\/p>\n

def _has_quantum_computation_pattern(self, log: Dict) -> bool:
\n """\u68c0\u67e5\u91cf\u5b50\u8ba1\u7b97\u6a21\u5f0f"""
\n # \u7b80\u5316\u5b9e\u73b0
\n patterns = [
\n 'parallel_factorization',
\n 'quantum_fourier_transform',
\n 'superposition_detected'
\n ]<\/p>\n

return any(pattern in str(log).lower() for pattern in patterns)<\/p>\n

def _extract_quantum_indicators(self, log: Dict) -> List[str]:
\n """\u63d0\u53d6\u91cf\u5b50\u6307\u6807"""
\n indicators = []<\/p>\n

if log.get('crypto_operations_per_second', 0) > 10000:
\n indicators.append('High-speed cryptographic operations')<\/p>\n

if log.get('simultaneous_key_attempts', 0) > 100:
\n indicators.append('Massive parallel key attempts')<\/p>\n

if 'Shor' in str(log):
\n indicators.append('Shor algorithm pattern detected')<\/p>\n

if 'Grover' in str(log):
\n indicators.append('Grover algorithm pattern detected')<\/p>\n

return indicators<\/p>\n

def _calculate_quantum_confidence(self, log: Dict) -> float:
\n """\u8ba1\u7b97\u91cf\u5b50\u653b\u51fb\u7f6e\u4fe1\u5ea6"""
\n confidence = 0.0<\/p>\n

# \u57fa\u4e8e\u591a\u4e2a\u56e0\u7d20
\n factors = {
\n 'speed_indicator': 0.3 if log.get('crypto_operations_per_second', 0) > 10000 else 0,
\n 'parallel_indicator': 0.3 if log.get('simultaneous_key_attempts', 0) > 100 else 0,
\n 'algorithm_indicator': 0.4 if self._has_quantum_computation_pattern(log) else 0
\n }<\/p>\n

confidence = sum(factors.values())<\/p>\n

# \u8c03\u6574\u7f6e\u4fe1\u5ea6
\n if confidence > 0.6:
\n confidence = min(1.0, confidence + 0.2)<\/p>\n

return confidence<\/p>\n

def _suggest_quantum_defense(self, log: Dict) -> Dict:
\n """\u5efa\u8bae\u91cf\u5b50\u9632\u5fa1\u63aa\u65bd"""
\n defense = {
\n 'immediate': [],
\n 'short_term': [],
\n 'long_term': []
\n }<\/p>\n

confidence = self._calculate_quantum_confidence(log)<\/p>\n

if confidence > 0.8:
\n defense['immediate'].extend([
\n 'Enable quantum-safe algorithms immediately',
\n 'Increase key sizes for classical algorithms',
\n 'Implement additional authentication factors'
\n ])<\/p>\n

if confidence > 0.5:
\n defense['short_term'].extend([
\n 'Upgrade to post-quantum cryptography',
\n 'Implement hybrid cryptographic systems',
\n 'Enhance monitoring for quantum patterns'
\n ])<\/p>\n

defense['long_term'].extend([
\n 'Plan for quantum-resistant infrastructure',
\n 'Participate in quantum-safe standardization',
\n 'Train staff on quantum security concepts'
\n ])<\/p>\n

return defense<\/p>\n

def generate_quantum_migration_plan(self,
\n current_infrastructure: Dict) -> Dict:
\n """\u751f\u6210\u91cf\u5b50\u8fc1\u79fb\u8ba1\u5212"""
\n migration_phases = [
\n {
\n 'phase': 'Assessment',
\n 'duration': '1-3 months',
\n 'activities': [
\n 'Inventory current cryptographic assets',
\n 'Assess quantum vulnerability',
\n 'Identify critical systems'
\n ],
\n 'deliverables': [
\n 'Quantum risk assessment report',
\n 'Critical systems inventory',
\n 'Migration priority list'
\n ]
\n },
\n {
\n 'phase': 'Hybrid Implementation',
\n 'duration': '6-12 months',
\n 'activities': [
\n 'Implement hybrid cryptographic systems',
\n 'Upgrade to quantum-safe algorithms for new systems',
\n 'Train development teams'
\n ],
\n 'deliverables': [
\n 'Hybrid cryptography implementation',
\n 'Updated security policies',
\n 'Training materials'
\n ]
\n },
\n {
\n 'phase': 'Full Migration',
\n 'duration': '2-3 years',
\n 'activities': [
\n 'Complete migration to quantum-safe algorithms',
\n 'Update all legacy systems',
\n 'Establish quantum security monitoring'
\n ],
\n 'deliverables': [
\n 'Fully quantum-safe infrastructure',
\n 'Quantum security operations center',
\n 'Continuous monitoring system'
\n ]
\n }
\n ]<\/p>\n

return {
\n 'current_state': current_infrastructure,
\n 'migration_phases': migration_phases,
\n 'estimated_cost': self._estimate_migration_cost(current_infrastructure),
\n 'key_risks': self._identify_migration_risks(),
\n 'success_metrics': self._define_success_metrics()
\n }<\/p>\n

def _estimate_migration_cost(self, infrastructure: Dict) -> Dict:
\n """\u4f30\u7b97\u8fc1\u79fb\u6210\u672c"""
\n # \u7b80\u5316\u6210\u672c\u4f30\u7b97
\n system_count = infrastructure.get('system_count', 1)
\n complexity = infrastructure.get('complexity', 'medium')<\/p>\n

base_cost_per_system = {
\n 'low': 5000,
\n 'medium': 15000,
\n 'high': 50000
\n }.get(complexity, 15000)<\/p>\n

total_cost = system_count * base_cost_per_system<\/p>\n

return {
\n 'software_licenses': total_cost * 0.3,
\n 'development_effort': total_cost * 0.4,
\n 'training': total_cost * 0.1,
\n 'testing': total_cost * 0.1,
\n 'contingency': total_cost * 0.1,
\n 'total_estimated': total_cost
\n }<\/p>\n

def _identify_migration_risks(self) -> List[Dict]:
\n """\u8bc6\u522b\u8fc1\u79fb\u98ce\u9669"""
\n return [
\n {
\n 'risk': 'Algorithm vulnerabilities',
\n 'likelihood': 'medium',
\n 'impact': 'high',
\n 'mitigation': 'Use NIST-approved algorithms and maintain hybrid approach'
\n },
\n {
\n 'risk': 'Performance degradation',
\n 'likelihood': 'high',
\n 'impact': 'medium',
\n 'mitigation': 'Thorough performance testing and optimization'
\n },
\n {
\n 'risk': 'Interoperability issues',
\n 'likelihood': 'medium',
\n 'impact': 'medium',
\n 'mitigation': 'Maintain backward compatibility and extensive testing'
\n },
\n {
\n 'risk': 'Staff skill gaps',
\n 'likelihood': 'high',
\n 'impact': 'medium',
\n 'mitigation': 'Comprehensive training program and knowledge transfer'
\n }
\n ]<\/p>\n

def _define_success_metrics(self) -> List[Dict]:
\n """\u5b9a\u4e49\u6210\u529f\u6307\u6807"""
\n return [
\n {
\n 'metric': 'Quantum-safe algorithm coverage',
\n 'target': '100%',
\n 'measurement': 'Percentage of systems using quantum-safe algorithms'
\n },
\n {
\n 'metric': 'Performance impact',
\n 'target': '< 20% degradation',
\n 'measurement': 'Response time comparison'
\n },
\n {
\n 'metric': 'Security incidents',
\n 'target': '0 quantum-related incidents',
\n 'measurement': 'Number of quantum security incidents'
\n },
\n {
\n 'metric': 'Staff certification',
\n 'target': '100% of security team certified',
\n 'measurement': 'Percentage of staff with quantum security training'
\n }
\n ]<\/p>\n

# \u91cf\u5b50\u5b89\u5168\u4e2d\u95f4\u4ef6
\nclass QuantumSafeMiddleware:
\n """\u91cf\u5b50\u5b89\u5168\u4e2d\u95f4\u4ef6"""<\/p>\n

def __init__(self):
\n self.quantum_system = QuantumSafeStatusCode()
\n self.quantum_threshold = 0.7 # \u91cf\u5b50\u653b\u51fb\u7f6e\u4fe1\u5ea6\u9608\u503c<\/p>\n

async def process_request(self, request: Dict) -> Dict:
\n """\u5904\u7406\u8bf7\u6c42（\u91cf\u5b50\u5b89\u5168\u68c0\u67e5）"""
\n # \u68c0\u67e5\u662f\u5426\u9700\u8981\u91cf\u5b50\u5b89\u5168
\n requires_quantum_safe = self._requires_quantum_safety(request)<\/p>\n

if requires_quantum_safe:
\n # \u9a8c\u8bc1\u91cf\u5b50\u5b89\u5168\u5934\u90e8
\n if not self._verify_quantum_headers(request):
\n return self._create_quantum_error_response(581)<\/p>\n

request['quantum_safe_required'] = requires_quantum_safe
\n return request<\/p>\n

async def process_response(self,
\n request: Dict,
\n response: Dict) -> Dict:
\n """\u5904\u7406\u54cd\u5e94（\u6dfb\u52a0\u91cf\u5b50\u5b89\u5168）"""
\n if request.get('quantum_safe_required', False):
\n # \u751f\u6210\u91cf\u5b50\u5b89\u5168\u54cd\u5e94
\n quantum_response = self.quantum_system.generate_quantum_safe_response(
\n status_code=response['status_code'],
\n message=response.get('message', ''),
\n requires_quantum_safe=True
\n )<\/p>\n

# \u5408\u5e76\u54cd\u5e94
\n response.update(quantum_response)<\/p>\n

return response<\/p>\n

def _requires_quantum_safety(self, request: Dict) -> bool:
\n """\u68c0\u67e5\u662f\u5426\u9700\u8981\u91cf\u5b50\u5b89\u5168"""
\n # \u57fa\u4e8e\u8bf7\u6c42\u7279\u5f81\u5224\u65ad
\n factors = []<\/p>\n

# 1. \u9ad8\u4ef7\u503c\u4ea4\u6613
\n if request.get('transaction_value', 0) > 1000000: # 100\u4e07\u7f8e\u5143
\n factors.append('high_value')<\/p>\n

# 2. \u654f\u611f\u6570\u636e
\n if any(keyword in str(request).lower()
\n for keyword in ['classified', 'sensitive', 'confidential']):
\n factors.append('sensitive_data')<\/p>\n

# 3. \u957f\u671f\u5b89\u5168\u9700\u6c42
\n if request.get('data_retention_years', 0) > 10:
\n factors.append('long_term_security')<\/p>\n

# 4. \u6cd5\u89c4\u8981\u6c42
\n if request.get('regulatory_compliance') in ['GDPR', 'HIPAA', 'FIPS']:
\n factors.append('regulatory')<\/p>\n

return len(factors) >= 2<\/p>\n

def _verify_quantum_headers(self, request: Dict) -> bool:
\n """\u9a8c\u8bc1\u91cf\u5b50\u5b89\u5168\u5934\u90e8"""
\n required_headers = [
\n 'X-Quantum-Safe',
\n 'X-Post-Quantum-Algorithm'
\n ]<\/p>\n

return all(header in request.get('headers', {})
\n for header in required_headers)<\/p>\n

def _create_quantum_error_response(self, code: int) -> Dict:
\n """\u521b\u5efa\u91cf\u5b50\u9519\u8bef\u54cd\u5e94"""
\n quantum_codes = self.quantum_system.quantum_status_codes<\/p>\n

return {
\n 'status_code': code,
\n 'message': quantum_codes.get(code, 'Quantum Security Error'),
\n 'headers': {
\n 'Content-Type': 'application\/json',
\n 'X-Quantum-Error': str(code)
\n },
\n 'body': {
\n 'error': {
\n 'code': code,
\n 'message': quantum_codes.get(code, 'Quantum Security Error'),
\n 'documentation_url': 'https:\/\/quantum.example.com\/docs\/errors',
\n 'recommended_action': 'Upgrade to quantum-safe protocol'
\n }
\n }
\n }<\/p>\n

def monitor_quantum_threats(self,
\n request_logs: List[Dict]) -> Dict[str, any]:
\n """\u76d1\u63a7\u91cf\u5b50\u5a01\u80c1"""
\n quantum_patterns = self.quantum_system.detect_quantum_attack_patterns(
\n request_logs
\n )<\/p>\n

high_confidence_patterns = [
\n p for p in quantum_patterns
\n if p['confidence'] >= self.quantum_threshold
\n ]<\/p>\n

return {
\n 'total_requests_analyzed': len(request_logs),
\n 'quantum_patterns_detected': len(quantum_patterns),
\n 'high_confidence_patterns': len(high_confidence_patterns),
\n 'detailed_findings': quantum_patterns,
\n 'risk_assessment': self._assess_quantum_risk(high_confidence_patterns),
\n 'recommended_actions': self._generate_quantum_actions(high_confidence_patterns)
\n }<\/p>\n

def _assess_quantum_risk(self, patterns: List[Dict]) -> Dict[str, str]:
\n """\u8bc4\u4f30\u91cf\u5b50\u98ce\u9669"""
\n if not patterns:
\n return {'level': 'LOW', 'description': 'No significant quantum threats detected'}<\/p>\n

# \u8ba1\u7b97\u5e73\u5747\u7f6e\u4fe1\u5ea6
\n avg_confidence = sum(p['confidence'] for p in patterns) \/ len(patterns)<\/p>\n

if avg_confidence > 0.9:
\n return {
\n 'level': 'CRITICAL',
\n 'description': 'High confidence quantum attack patterns detected',
\n 'immediate_action_required': True
\n }
\n elif avg_confidence > 0.7:
\n return {
\n 'level': 'HIGH',
\n 'description': 'Probable quantum attack patterns detected',
\n 'immediate_action_required': True
\n }
\n elif avg_confidence > 0.5:
\n return {
\n 'level': 'MEDIUM',
\n 'description': 'Possible quantum attack patterns detected',
\n 'immediate_action_required': False
\n }
\n else:
\n return {
\n 'level': 'LOW',
\n 'description': 'Low confidence quantum patterns detected',
\n 'immediate_action_required': False
\n }<\/p>\n

def _generate_quantum_actions(self, patterns: List[Dict]) -> List[str]:
\n """\u751f\u6210\u91cf\u5b50\u884c\u52a8\u5efa\u8bae"""
\n actions = []<\/p>\n

if patterns:
\n actions.append("Review and analyze quantum attack patterns")
\n actions.append("Implement quantum-safe algorithms for affected systems")
\n actions.append("Enhance monitoring for quantum computation patterns")<\/p>\n

if any(p['confidence'] > 0.8 for p in patterns):
\n actions.append("Immediately enable quantum-safe cryptography")
\n actions.append("Consider temporary service restrictions for affected endpoints")<\/p>\n

return actions<\/p>\n

# \u4f7f\u7528\u793a\u4f8b
\nquantum_middleware = QuantumSafeMiddleware()<\/p>\n

# \u6a21\u62df\u8bf7\u6c42\u5904\u7406
\nrequest = {
\n 'transaction_value': 1500000,
\n 'data_retention_years': 15,
\n 'regulatory_compliance': 'GDPR',
\n 'headers': {
\n 'X-Quantum-Safe': 'required',
\n 'X-Post-Quantum-Algorithm': 'CRYSTALS-Dilithium'
\n }
\n}<\/p>\n

processed_request = quantum_middleware.process_request(request)<\/p>\n

response = {
\n 'status_code': 200,
\n 'message': 'Transaction successful',
\n 'body': {'transaction_id': 'txn_12345'}
\n}<\/p>\n

quantum_response = quantum_middleware.process_response(processed_request, response)<\/p>\n

print("Quantum-safe response headers:", quantum_response.get('headers', {}))
\nprint("Quantum extensions:", quantum_response.get('quantum_extensions', {}))<\/p>\n

# \u76d1\u63a7\u91cf\u5b50\u5a01\u80c1
\nrequest_logs = [
\n {
\n 'timestamp': datetime.now(),
\n 'request_id': 'req1',
\n 'crypto_operations_per_second': 15000,
\n 'simultaneous_key_attempts': 200,
\n 'algorithm_pattern': 'Shor-like'
\n },
\n {
\n 'timestamp': datetime.now(),
\n 'request_id': 'req2',
\n 'crypto_operations_per_second': 500,
\n 'simultaneous_key_attempts': 10
\n }
\n]<\/p>\n

threat_report = quantum_middleware.monitor_quantum_threats(request_logs)
\nprint(f"\\\\nQuantum threat report:")
\nprint(f"High confidence patterns: {threat_report['high_confidence_patterns']}")
\nprint(f"Risk level: {threat_report['risk_assessment']['level']}")<\/p>\n

40.3.2 \u6df7\u5408\u7ecf\u5178-\u91cf\u5b50\u72b6\u6001\u7801\u7cfb\u7edf<\/h5>\n

python<\/p>\n

# \u6df7\u5408\u7ecf\u5178-\u91cf\u5b50\u72b6\u6001\u7801\u7cfb\u7edf
\nfrom typing import Dict, List, Optional, Union
\nfrom dataclasses import dataclass
\nfrom enum import Enum
\nimport time<\/p>\n

class CryptographicMode(Enum):
\n """\u52a0\u5bc6\u6a21\u5f0f"""
\n CLASSICAL = "classical" # \u7ecf\u5178\u52a0\u5bc6
\n QUANTUM_SAFE = "quantum_safe" # \u91cf\u5b50\u5b89\u5168
\n HYBRID = "hybrid" # \u6df7\u5408\u6a21\u5f0f
\n QUANTUM_ENHANCED = "quantum_enhanced" # \u91cf\u5b50\u589e\u5f3a<\/p>\n

@dataclass
\nclass HybridStatusCode:
\n """\u6df7\u5408\u72b6\u6001\u7801"""
\n classical_code: int
\n quantum_extension: Optional[Dict] = None
\n cryptographic_mode: CryptographicMode = CryptographicMode.CLASSICAL
\n timestamp: float = time.time()<\/p>\n

@property
\n def full_code(self) -> str:
\n """\u83b7\u53d6\u5b8c\u6574\u72b6\u6001\u7801"""
\n if self.quantum_extension:
\n return f"{self.classical_code}Q{self.quantum_extension.get('version', '1')}"
\n return str(self.classical_code)<\/p>\n

@property
\n def security_level(self) -> int:
\n """\u83b7\u53d6\u5b89\u5168\u7b49\u7ea7"""
\n levels = {
\n CryptographicMode.CLASSICAL: 1,
\n CryptographicMode.QUANTUM_SAFE: 3,
\n CryptographicMode.HYBRID: 2,
\n CryptographicMode.QUANTUM_ENHANCED: 4
\n }
\n return levels.get(self.cryptographic_mode, 1)<\/p>\n

def to_response(self) -> Dict:
\n """\u8f6c\u6362\u4e3a\u54cd\u5e94\u683c\u5f0f"""
\n response = {
\n 'status_code': self.classical_code,
\n 'hybrid_code': self.full_code,
\n 'cryptographic_mode': self.cryptographic_mode.value,
\n 'security_level': self.security_level,
\n 'timestamp': self.timestamp
\n }<\/p>\n

if self.quantum_extension:
\n response['quantum_extensions'] = self.quantum_extension<\/p>\n

return response<\/p>\n

class HybridStatusCodeSystem:
\n """\u6df7\u5408\u72b6\u6001\u7801\u7cfb\u7edf"""<\/p>\n

def __init__(self):
\n self.classical_system = ClassicalStatusCodeSystem()
\n self.quantum_system = QuantumEnhancedSystem()
\n self.mode_selector = ModeSelector()<\/p>\n

def process_request(self,
\n request: Dict,
\n context: Dict) -> HybridStatusCode:
\n """\u5904\u7406\u8bf7\u6c42\u5e76\u751f\u6210\u6df7\u5408\u72b6\u6001\u7801"""
\n # \u9009\u62e9\u52a0\u5bc6\u6a21\u5f0f
\n mode = self.mode_selector.select_mode(request, context)<\/p>\n

# \u5904\u7406\u8bf7\u6c42
\n if mode in [CryptographicMode.CLASSICAL, CryptographicMode.HYBRID]:
\n classical_result = self.classical_system.process(request)
\n classical_code = classical_result['status_code']
\n else:
\n classical_code = 200 # \u9ed8\u8ba4\u6210\u529f<\/p>\n

if mode in [CryptographicMode.QUANTUM_SAFE,
\n CryptographicMode.HYBRID,
\n CryptographicMode.QUANTUM_ENHANCED]:
\n quantum_result = self.quantum_system.process(request)
\n quantum_extension = quantum_result.get('quantum_data')
\n else:
\n quantum_extension = None<\/p>\n

# \u521b\u5efa\u6df7\u5408\u72b6\u6001\u7801
\n hybrid_code = HybridStatusCode(
\n classical_code=classical_code,
\n quantum_extension=quantum_extension,
\n cryptographic_mode=mode
\n )<\/p>\n

return hybrid_code<\/p>\n

def migrate_to_quantum_safe(self,
\n current_system: Dict,
\n timeline_years: int = 5) -> Dict:
\n """\u8fc1\u79fb\u5230\u91cf\u5b50\u5b89\u5168\u7cfb\u7edf"""
\n migration_plan = {
\n 'current_state': self._assess_current_state(current_system),
\n 'target_state': {
\n 'cryptographic_mode': CryptographicMode.QUANTUM_SAFE,
\n 'security_level': 3,
\n 'quantum_resistant': True
\n },
\n 'phases': self._create_migration_phases(timeline_years),
\n 'rollback_strategy': self._create_rollback_strategy(),
\n 'success_criteria': self._define_success_criteria()
\n }<\/p>\n

return migration_plan<\/p>\n

def _assess_current_state(self, system: Dict) -> Dict:
\n """\u8bc4\u4f30\u5f53\u524d\u72b6\u6001"""
\n assessment = {
\n 'cryptographic_capabilities': [],
\n 'quantum_readiness': 'low',
\n 'migration_complexity': 'medium',
\n 'critical_dependencies': []
\n }<\/p>\n

# \u5206\u6790\u7cfb\u7edf\u7279\u6027
\n if system.get('supports_tls_1_3'):
\n assessment['cryptographic_capabilities'].append('TLS 1.3')<\/p>\n

if system.get('post_quantum_ready'):
\n assessment['quantum_readiness'] = 'high'
\n elif system.get('supports_modern_crypto'):
\n assessment['quantum_readiness'] = 'medium'<\/p>\n

# \u8bc6\u522b\u4f9d\u8d56
\n for dependency in system.get('dependencies', []):
\n if dependency.get('cryptographic'):
\n assessment['critical_dependencies'].append(dependency['name'])<\/p>\n

return assessment<\/p>\n

def _create_migration_phases(self, years: int) -> List[Dict]:
\n """\u521b\u5efa\u8fc1\u79fb\u9636\u6bb5"""
\n phases = []<\/p>\n

year_increment = years \/ 4 # \u5206\u4e3a4\u4e2a\u9636\u6bb5<\/p>\n

for i in range(4):
\n phase_num = i + 1
\n start_year = i * year_increment
\n end_year = (i + 1) * year_increment<\/p>\n

phase = {
\n 'phase': phase_num,
\n 'name': self._get_phase_name(phase_num),
\n 'timeline': f"Year {start_year:.1f}-{end_year:.1f}",
\n 'objectives': self._get_phase_objectives(phase_num),
\n 'success_metrics': self._get_phase_metrics(phase_num)
\n }<\/p>\n

phases.append(phase)<\/p>\n

return phases<\/p>\n

def _get_phase_name(self, phase_num: int) -> str:
\n """\u83b7\u53d6\u9636\u6bb5\u540d\u79f0"""
\n names = {
\n 1: 'Assessment and Planning',
\n 2: 'Hybrid Implementation',
\n 3: 'Quantum-Safe Transition',
\n 4: 'Optimization and Enhancement'
\n }
\n return names.get(phase_num, f'Phase {phase_num}')<\/p>\n

def _get_phase_objectives(self, phase_num: int) -> List[str]:
\n """\u83b7\u53d6\u9636\u6bb5\u76ee\u6807"""
\n objectives = {
\n 1: [
\n 'Assess current cryptographic posture',
\n 'Identify quantum vulnerabilities',
\n 'Develop migration strategy'
\n ],
\n 2: [
\n 'Implement hybrid cryptographic systems',
\n 'Train staff on quantum-safe concepts',
\n 'Test quantum-safe algorithms'
\n ],
\n 3: [
\n 'Migrate critical systems to quantum-safe',
\n 'Update security policies',
\n 'Validate quantum resistance'
\n ],
\n 4: [
\n 'Optimize quantum-safe performance',
\n 'Implement quantum-enhanced features',
\n 'Establish continuous monitoring'
\n ]
\n }
\n return objectives.get(phase_num, [])<\/p>\n

def _get_phase_metrics(self, phase_num: int) -> Dict[str, str]:
\n """\u83b7\u53d6\u9636\u6bb5\u6307\u6807"""
\n metrics = {
\n 1: {
\n 'assessment_completion': '100%',
\n 'vulnerabilities_identified': 'All critical',
\n 'strategy_approved': 'Yes'
\n },
\n 2: {
\n 'hybrid_coverage': '50%',
\n 'training_completion': '80%',
\n 'test_success_rate': '95%'
\n },
\n 3: {
\n 'quantum_safe_coverage': '100%',
\n 'policy_updates': 'Complete',
\n 'security_validation': 'Passed'
\n },
\n 4: {
\n 'performance_improvement': '>90% of classical',
\n 'enhancements_implemented': 'All planned',
\n 'monitoring_coverage': '100%'
\n }
\n }
\n return metrics.get(phase_num, {})<\/p>\n

def _create_rollback_strategy(self) -> Dict:
\n """\u521b\u5efa\u56de\u6eda\u7b56\u7565"""
\n return {
\n 'triggers': [
\n 'Critical security vulnerability',
\n 'Performance degradation > 50%',
\n 'Interoperability failures',
\n 'Regulatory non-compliance'
\n ],
\n 'procedures': [
\n 'Immediate switch to hybrid mode',
\n 'Gradual rollback to classical systems',
\n 'Communication protocol for stakeholders',
\n 'Post-rollback analysis'
\n ],
\n 'recovery_time_objective': '4 hours',
\n 'recovery_point_objective': '1 hour'
\n }<\/p>\n

def _define_success_criteria(self) -> List[Dict]:
\n """\u5b9a\u4e49\u6210\u529f\u6807\u51c6"""
\n return [
\n {
\n 'criterion': 'Quantum-safe algorithm adoption',
\n 'target': '100%',
\n 'weight': 0.4
\n },
\n {
\n 'criterion': 'Performance within 20% of classical',
\n 'target': '\u2265 80%',
\n 'weight': 0.3
\n },
\n {
\n 'criterion': 'Security validation passed',
\n 'target': '100%',
\n 'weight': 0.2
\n },
\n {
\n 'criterion': 'Staff certification',
\n 'target': '100%',
\n 'weight': 0.1
\n }
\n ]<\/p>\n

def simulate_quantum_attack(self,
\n system_config: Dict,
\n attack_type: str) -> Dict:
\n """\u6a21\u62df\u91cf\u5b50\u653b\u51fb"""
\n simulation = {
\n 'attack_type': attack_type,
\n 'timestamp': time.time(),
\n 'system_configuration': system_config,
\n 'results': {},
\n 'recommendations': []
\n }<\/p>\n

if attack_type == 'shor_algorithm':
\n simulation['results'] = self._simulate_shor_attack(system_config)
\n elif attack_type == 'grover_algorithm':
\n simulation['results'] = self._simulate_grover_attack(system_config)
\n elif attack_type == 'hybrid_attack':
\n simulation['results'] = self._simulate_hybrid_attack(system_config)<\/p>\n

simulation['recommendations'] = self._generate_simulation_recommendations(
\n simulation['results']
\n )<\/p>\n

return simulation<\/p>\n

def _simulate_shor_attack(self, config: Dict) -> Dict:
\n """\u6a21\u62dfShor\u7b97\u6cd5\u653b\u51fb"""
\n return {
\n 'rsa_key_sizes_affected': [
\n {'size': 1024, 'broken': True, 'time_estimate': 'hours'},
\n {'size': 2048, 'broken': True, 'time_estimate': 'days'},
\n {'size': 4096, 'broken': True, 'time_estimate': 'weeks'}
\n ],
\n 'ecc_affected': True,
\n 'dh_key_exchange_vulnerable': True,
\n 'classical_crypto_broken': True,
\n 'quantum_safe_algorithms_resistant': True
\n }<\/p>\n

def _simulate_grover_attack(self, config: Dict) -> Dict:
\n """\u6a21\u62dfGrover\u7b97\u6cd5\u653b\u51fb"""
\n return {
\n 'symmetric_key_reduction': 'sqrt',
\n 'aes_128_effective_strength': 64,
\n 'aes_256_effective_strength': 128,
\n 'hash_function_impact': 'quadratic_speedup',
\n 'recommended_key_sizes': {
\n 'symmetric': 256,
\n 'hash_output': 512
\n }
\n }<\/p>\n

def _simulate_hybrid_attack(self, config: Dict) -> Dict:
\n """\u6a21\u62df\u6df7\u5408\u653b\u51fb"""
\n return {
\n 'classical_vulnerabilities_exploited': True,
\n 'quantum_enhancements_used': True,
\n 'attack_complexity': 'high',
\n 'detection_difficulty': 'high',
\n 'defense_requirements': [
\n 'Quantum-safe algorithms',
\n 'Enhanced monitoring',
\n 'Behavioral analysis'
\n ]
\n }<\/p>\n

def _generate_simulation_recommendations(self, results: Dict) -> List[str]:
\n """\u751f\u6210\u6a21\u62df\u5efa\u8bae"""
\n recommendations = []<\/p>\n

if results.get('classical_crypto_broken', False):
\n recommendations.append(
\n 'Immediately migrate from RSA\/ECC to quantum-safe algorithms'
\n )<\/p>\n

if results.get('symmetric_key_reduction'):
\n recommendations.append(
\n 'Increase symmetric key sizes to at least 256 bits'
\n )<\/p>\n

if results.get('attack_complexity') == 'high':
\n recommendations.append(
\n 'Implement advanced threat detection for hybrid attacks'
\n )<\/p>\n

return recommendations<\/p>\n

# \u652f\u6301\u7c7b
\nclass ClassicalStatusCodeSystem:
\n """\u7ecf\u5178\u72b6\u6001\u7801\u7cfb\u7edf"""<\/p>\n

def process(self, request: Dict) -> Dict:
\n """\u5904\u7406\u8bf7\u6c42"""
\n # \u7b80\u5316\u5b9e\u73b0
\n return {
\n 'status_code': 200,
\n 'message': 'Classical processing complete',
\n 'timestamp': time.time()
\n }<\/p>\n

class QuantumEnhancedSystem:
\n """\u91cf\u5b50\u589e\u5f3a\u7cfb\u7edf"""<\/p>\n

def process(self, request: Dict) -> Dict:
\n """\u5904\u7406\u8bf7\u6c42"""
\n return {
\n 'quantum_data': {
\n 'algorithm': 'Quantum ML Enhanced',
\n 'confidence': 0.95,
\n 'processing_time_ms': 150,
\n 'enhancements': [
\n 'Anomaly detection',
\n 'Pattern recognition',
\n 'Predictive optimization'
\n ]
\n },
\n 'timestamp': time.time()
\n }<\/p>\n

class ModeSelector:
\n """\u6a21\u5f0f\u9009\u62e9\u5668"""<\/p>\n

def select_mode(self, request: Dict, context: Dict) -> CryptographicMode:
\n """\u9009\u62e9\u52a0\u5bc6\u6a21\u5f0f"""
\n factors = {
\n 'data_sensitivity': context.get('data_sensitivity', 'low'),
\n 'transaction_value': request.get('value', 0),
\n 'regulatory_requirements': context.get('regulatory', []),
\n 'quantum_threat_level': context.get('quantum_threat', 'low')
\n }<\/p>\n

score = 0<\/p>\n

# \u6570\u636e\u654f\u611f\u6027
\n sensitivity_scores = {'low': 0, 'medium': 1, 'high': 2, 'critical': 3}
\n score += sensitivity_scores.get(factors['data_sensitivity'], 0)<\/p>\n

# \u4ea4\u6613\u4ef7\u503c
\n if factors['transaction_value'] > 1000000:
\n score += 2
\n elif factors['transaction_value'] > 100000:
\n score += 1<\/p>\n

# \u6cd5\u89c4\u8981\u6c42
\n strict_regulations = ['GDPR', 'HIPAA', 'PCI-DSS', 'FIPS']
\n if any(reg in factors['regulatory_requirements']
\n for reg in strict_regulations):
\n score += 2<\/p>\n

# \u91cf\u5b50\u5a01\u80c1\u7ea7\u522b
\n threat_scores = {'low': 0, 'medium': 1, 'high': 2, 'imminent': 3}
\n score += threat_scores.get(factors['quantum_threat_level'], 0)<\/p>\n

# \u9009\u62e9\u6a21\u5f0f
\n if score >= 6:
\n return CryptographicMode.QUANTUM_ENHANCED
\n elif score >= 4:
\n return CryptographicMode.QUANTUM_SAFE
\n elif score >= 2:
\n return CryptographicMode.HYBRID
\n else:
\n return CryptographicMode.CLASSICAL<\/p>\n

# \u4f7f\u7528\u793a\u4f8b
\nhybrid_system = HybridStatusCodeSystem()<\/p>\n

# \u5904\u7406\u8bf7\u6c42
\nrequest = {
\n 'endpoint': '\/api\/secure-transaction',
\n 'value': 1500000,
\n 'user': 'premium_user'
\n}<\/p>\n

context = {
\n 'data_sensitivity': 'critical',
\n 'regulatory': ['GDPR', 'PCI-DSS'],
\n 'quantum_threat': 'medium'
\n}<\/p>\n

hybrid_code = hybrid_system.process_request(request, context)<\/p>\n

print(f"Hybrid Status Code: {hybrid_code.full_code}")
\nprint(f"Cryptographic Mode: {hybrid_code.cryptographic_mode.value}")
\nprint(f"Security Level: {hybrid_code.security_level}")<\/p>\n

# \u751f\u6210\u8fc1\u79fb\u8ba1\u5212
\ncurrent_system = {
\n 'supports_tls_1_3': True,
\n 'post_quantum_ready': False,
\n 'supports_modern_crypto': True,
\n 'dependencies': [
\n {'name': 'Legacy Auth System', 'cryptographic': True},
\n {'name': 'Payment Gateway', 'cryptographic': True}
\n ]
\n}<\/p>\n

migration_plan = hybrid_system.migrate_to_quantum_safe(current_system, 5)<\/p>\n

print(f"\\\\nMigration Plan Timeline: {migration_plan['phases'][0]['timeline']}")
\nprint(f"Phase 1 Objectives: {len(migration_plan['phases'][0]['objectives'])}")<\/p>\n

# \u6a21\u62df\u91cf\u5b50\u653b\u51fb
\nsimulation = hybrid_system.simulate_quantum_attack(
\n system_config=current_system,
\n attack_type='shor_algorithm'
\n)<\/p>\n

print(f"\\\\nSimulation Results:")
\nprint(f"RSA 2048 broken: {simulation['results']['rsa_key_sizes_affected'][1]['broken']}")
\nprint(f"Recommendations: {len(simulation['recommendations'])}")<\/p>\n

40.4 \u603b\u7ed3\u4e0e\u5c55\u671b<\/h4>\n

40.4.1 \u672a\u6765\u72b6\u6001\u7801\u7cfb\u7edf\u7684\u6838\u5fc3\u7279\u5f81<\/h5>\n

\u901a\u8fc7\u5bf9HTTP\u534f\u8bae\u6f14\u8fdb\u3001AI\u96c6\u6210\u3001\u91cf\u5b50\u8ba1\u7b97\u5f71\u54cd\u7b49\u591a\u65b9\u9762\u7684\u5206\u6790，\u6211\u4eec\u53ef\u4ee5\u9884\u89c1\u672a\u6765\u72b6\u6001\u7801\u7cfb\u7edf\u5c06\u5448\u73b0\u4ee5\u4e0b\u6838\u5fc3\u7279\u5f81：<\/p>\n

1. \u81ea\u9002\u5e94\u6027\u4e0e\u667a\u80fd\u5316<\/p>\n