{"id":101208,"date":"2026-09-06T12:41:30","date_gmt":"2026-09-06T04:41:30","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/101208.html"},"modified":"2026-09-06T12:41:30","modified_gmt":"2026-09-06T04:41:30","slug":"%e6%8a%80%e6%9c%af%e4%ba%a7%e5%93%81%e7%ac%ac%e4%b8%80%e7%89%88%e8%af%a5%e4%bf%9d%e7%95%99%e5%93%aa%e4%ba%9b%e6%a0%b8%e5%bf%83%e8%83%bd%e5%8a%9b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/101208.html","title":{"rendered":"\u6280\u672f\u4ea7\u54c1\u7b2c\u4e00\u7248\u8be5\u4fdd\u7559\u54ea\u4e9b\u6838\u5fc3\u80fd\u529b"},"content":{"rendered":"

\u6280\u672f\u4ea7\u54c1\u7b2c\u4e00\u7248\u8be5\u4fdd\u7559\u54ea\u4e9b\u6838\u5fc3\u80fd\u529b<\/h2>\n

\"\u5c01\u9762\u4fe1\u606f\u56fe\"<\/p>\n

\u5728\u4f7f\u7528\u5927\u8bed\u8a00\u6a21\u578b\u8f85\u52a9\u9879\u76ee\u89c4\u5212\u4e0e\u9700\u6c42\u62c6\u89e3\u65f6，\u5e38\u89c1\u504f\u8bef\u5728\u4e8e\u201c\u76f4\u63a5\u5c06 AI \u4ea7\u51fa\u7684\u529f\u80fd\u6e05\u5355\u4f5c\u4e3a\u4ea7\u54c1\u9996\u7248\u9700\u6c42\u6587\u6863\u201d\u3002\u5f53\u5411\u6a21\u578b\u8f93\u5165\u201c\u8bbe\u8ba1\u4e00\u6b3e\u9879\u76ee\u7ba1\u7406\u5e94\u7528\u201d\u65f6，\u6a21\u578b\u901a\u5e38\u8f93\u51fa\u5305\u542b\u201cAI WBS \u81ea\u52a8\u62c6\u89e3\u3001\u591a\u9879\u76ee\u8d44\u6e90\u6c60\u8c03\u5ea6\u3001\u7518\u7279\u56fe\u8054\u52a8\u3001\u667a\u80fd\u98ce\u9669\u9884\u8b66\u3001\u81ea\u52a8\u5316\u5468\u62a5\u751f\u6210\u201d\u7b49\u5927\u800c\u5168\u7684\u529f\u80fd\u7ec4\u5408\u3002<\/p>\n

\u82e5\u672a\u52a0\u7504\u522b\u5168\u91cf\u91c7\u7eb3，\u5bb9\u6613\u5f15\u53d1\u201c\u529f\u80fd\u8513\u5ef6（Feature Creep）\u201d\u3002\u539f\u672c\u8ba1\u5212\u77ed\u671f\u4ea4\u4ed8\u7684\u521d\u59cb\u7248\u672c（V1.0），\u53ef\u80fd\u6f14\u53d8\u6210\u5f00\u53d1\u5468\u671f\u62c9\u957f\u3001\u8d44\u6e90\u6d88\u8017\u4e0a\u5347\u7684\u9879\u76ee，\u5728\u83b7\u53d6\u771f\u5b9e\u7528\u6237\u53cd\u9988\u524d\u63a8\u9ad8\u9879\u76ee\u4ea4\u4ed8\u98ce\u9669\u3002<\/p>\n

\u5728 AI \u964d\u4f4e\u60f3\u6cd5\u751f\u6210\u95e8\u69db\u7684\u80cc\u666f\u4e0b，\u67b6\u6784\u4e0e\u9879\u76ee\u7ba1\u7406\u7684\u6838\u5fc3\u80fd\u529b\u5728\u4e8e**\u201c\u8fd0\u7528\u5de5\u7a0b\u539f\u5219\u88c1\u526a\u8fc7\u5269\u9700\u6c42\u201d**，\u7cbe\u51c6\u6536\u655b\u9996\u4e2a\u7248\u672c\u7684\u6700\u5c0f\u53ef\u884c\u8fb9\u754c\u3002<\/p>\n

\n

1. \u4e3a\u4ec0\u4e48 AI \u5efa\u8bae\u5bb9\u6613\u5f15\u53d1\u529f\u80fd\u8513\u5ef6？<\/h3>\n

\u5927\u8bed\u8a00\u6a21\u578b（LLM）\u57fa\u4e8e\u6982\u7387\u8865\u5168\u673a\u5236\u3002\u5f53\u8981\u6c42\u5176\u8bbe\u8ba1\u201c\u9879\u76ee\u7ba1\u7406\u65b9\u6848\u201d\u65f6，\u6a21\u578b\u503e\u5411\u4e8e\u6c47\u96c6\u884c\u4e1a\u901a\u7528\u529f\u80fd，\u4ee5\u6ee1\u8db3\u7edf\u8ba1\u610f\u4e49\u4e0a\u7684\u5b8c\u6574\u5ea6\u3002<\/p>\n

\u4f46\u8fd9\u79cd\u5b8c\u6574\u5ea6\u5728\u521d\u59cb\u9636\u6bb5\u53ef\u80fd\u5e26\u6765\u4ee5\u4e0b\u9690\u60a3：<\/p>\n

  • \u975e\u6838\u5fc3\u529f\u80fd\u5206\u6563\u7814\u53d1\u6ce8\u610f\u529b：\u8fc7\u5ea6\u5173\u6ce8\u8f85\u52a9\u529f\u80fd（\u5982\u4e2a\u6027\u5316\u4e3b\u9898\u6216\u590d\u6742\u6743\u9650），\u524a\u5f31\u5bf9\u6838\u5fc3\u4ef7\u503c\u94fe\u8def（\u5982 AI \u4efb\u52a1\u62c6\u89e3\u51c6\u786e\u7387）\u7684\u6295\u5165\u3002<\/li>\n
  • \u6d4b\u8bd5\u4e0e\u7ef4\u62a4\u590d\u6742\u5ea6\u4e0a\u5347：\u529f\u80fd\u6570\u91cf\u589e\u591a\u5bfc\u81f4\u6a21\u5757\u95f4\u4ea4\u4e92\u903b\u8f91\u590d\u6742\u5316，\u6d4b\u8bd5\u4e0e\u7f3a\u9677\u4fee\u590d\u6210\u672c\u5448\u9636\u68af\u5f0f\u589e\u957f\u3002<\/li>\n
  • \u62c9\u957f\u53cd\u9988\u9a8c\u8bc1\u5468\u671f：\u4ea4\u4ed8\u4e0a\u7ebf\u8282\u70b9\u5ef6\u540e，\u4f1a\u76f8\u5e94\u63a8\u8fdf\u83b7\u53d6\u771f\u5b9e\u5e02\u573a\u4e0e\u7528\u6237\u53cd\u9988\u7684\u65f6\u95f4\u3002<\/li>\n
    \n

    2. \u7b2c\u4e00\u7248（V1.0）\u7684\u786e\u5b9a\u6027\u88c1\u526a\u6cd5\u5219<\/h3>\n

    \u4e3a\u9632\u6b62\u9700\u6c42\u6c60\u65e0\u5e8f\u6269\u5c55，\u5efa\u8bae\u5efa\u7acb\u786e\u5b9a\u6027\u7684\u9700\u6c42\u88c1\u526a\u673a\u5236：<\/p>\n

    \u6838\u5fc3\u6cd5\u5219\u4e00：\u805a\u7126\u5355\u4e00\u6838\u5fc3\u4ef7\u503c\u94fe\u6761（Single Core Value Loop）<\/h4>\n

    V1.0 \u81f4\u529b\u4e8e\u89e3\u51b3\u6838\u5fc3\u8bc9\u6c42\u3002\u4ee5\u201cAI \u4ee3\u7801\u68c0\u67e5\u5de5\u5177\u201d\u4e3a\u4f8b，V1.0 \u7684\u6838\u5fc3\u76ee\u6807\u662f\u201c\u5728 Git Commit \u65f6\u51c6\u786e\u8bc6\u522b\u7279\u5b9a\u7c7b\u578b\u7684\u5b89\u5168\u98ce\u9669\u201d\u3002\u5bf9\u4e8e PDF \u62a5\u544a\u5bfc\u51fa\u3001\u56e2\u961f\u6548\u80fd\u5206\u6790\u7b49\u6269\u5c55\u9879，\u53ef\u6682\u7f13\u81f3\u540e\u7eed\u8fed\u4ee3\u3002<\/p>\n

    \u6838\u5fc3\u6cd5\u5219\u4e8c：\u786c\u6027\u65f6\u95f4\u76d2\u7ea6\u675f（Hard Timeboxing）<\/h4>\n

    \u5c06\u9996\u4e2a\u7248\u672c\u7684\u5f00\u53d1\u5468\u671f\u7ea6\u675f\u5728\u9884\u8bbe\u65f6\u95f4\u76d2\u5185（\u5982 14 \u5929）\u3002\u82e5\u9700\u6c42\u5217\u8868\u8bc4\u4f30\u5de5\u65f6\u8d85\u51fa\u9884\u8bbe，\u4f18\u5148\u91c7\u53d6\u9700\u6c42\u88c1\u526a 50% \u7684\u65b9\u5f0f，\u786e\u4fdd\u9879\u76ee\u6536\u655b\u81f3\u65f6\u95f4\u76d2\u8303\u56f4\u5185\u3002<\/p>\n

    \u6838\u5fc3\u6cd5\u5219\u4e09：\u4f18\u5148\u7ec4\u4ef6\u590d\u7528\u4e0e\u6258\u7ba1\u670d\u52a1<\/h4>\n

    \u7528\u6237\u8ba4\u8bc1\u91c7\u7528 Supabase \/ Auth0，UI \u91c7\u7528\u6210\u719f\u7ec4\u4ef6\u5e93，\u6570\u636e\u5e93\u914d\u7f6e\u6258\u7ba1\u670d\u52a1\u3002V1.0 \u9636\u6bb5\u51cf\u5c11\u81ea\u7814\u975e\u6838\u5fc3\u57fa\u7840\u8bbe\u65bd，\u6709\u52a9\u4e8e\u4fdd\u969c\u7cfb\u7edf\u5e73\u7a33\u8fd0\u884c\u5e76\u7f29\u77ed\u4ea4\u4ed8\u5468\u671f\u3002<\/p>\n

    \n

    3. \u751f\u4ea7\u7ea7 Python \u4ee3\u7801\u5b9e\u73b0：WBS \u9700\u6c42\u88c1\u526a\u4e0e\u4f18\u5148\u7ea7\u8ba1\u7b97\u5668<\/h3>\n

    \u4ee5\u4e0b Python \u4ee3\u7801\u5c55\u793a\u4e86\u57fa\u4e8e\u89c4\u5219\u7684\u9700\u6c42\u88c1\u526a\u8bc4\u4f30\u6a21\u5757\u3002\u8be5\u811a\u672c\u626b\u63cf\u9700\u6c42\u5217\u8868，\u7ed3\u5408\u201c\u75db\u70b9\u76f8\u5173\u5ea6\u201d\u4e0e\u201c\u5f00\u53d1\u590d\u6742\u5ea6\u201d\u8ba1\u7b97\u6743\u91cd\u5f97\u5206，\u81ea\u52a8\u7b5b\u9009\u7b26\u5408 V1.0 \u65f6\u95f4\u76d2\u8303\u56f4\u7684\u9700\u6c42：<\/p>\n

    from typing import List, Dict, Any<\/p>\n

    class FeaturePruningEngine:
    \n def __init__(self, max_v1_days: float = 14.0):
    \n self.max_days = max_v1_days<\/p>\n

    def evaluate_and_prune(self, raw_features: List[Dict[str, Any]]) -> Dict[str, Any]:
    \n """\u5bf9\u9700\u6c42\u529f\u80fd\u6c60\u5b9e\u65bd\u786e\u5b9a\u6027\u88c1\u526a"""
    \n scored_features = []<\/p>\n

    for feat in raw_features:
    \n name = feat["name"]
    \n impact = feat["user_impact"] # 1-10 \u5206: \u5bf9\u6838\u5fc3\u75db\u70b9\u7684\u89e3\u51b3\u7a0b\u5ea6
    \n complexity = feat["complexity"] # 1-10 \u5206: \u7814\u53d1\u590d\u6742\u5ea6\u4e0e\u5de5\u65f6<\/p>\n

    # \u8ba1\u7b97 ROI \u4f18\u5148\u7ea7\u5f97\u5206: \u9ad8 Impact + \u4f4e Complexity = \u9ad8\u4f18\u5148\u7ea7
    \n score = (impact * 2.0) \/ (complexity + 0.5)<\/p>\n

    scored_features.append({
    \n "name": name,
    \n "impact": impact,
    \n "complexity": complexity,
    \n "estimated_days": feat["estimated_days"],
    \n "score": round(score, 2),
    \n "category": feat.get("category", "feature")
    \n })<\/p>\n

    # \u6309\u5f97\u5206\u964d\u5e8f\u6392\u5217
    \n scored_features.sort(key=lambda x: x["score"], reverse=True)<\/p>\n

    v1_scope = []
    \n v2_backlog = []
    \n total_days = 0.0<\/p>\n

    for feat in scored_features:
    \n # \u8d85\u51fa\u65f6\u95f4\u76d2\u529f\u80fd\u7684\u5f52\u5165 V2.0 \u50a8\u5907\u6c60
    \n if total_days + feat["estimated_days"] <= self.max_days:
    \n v1_scope.append(feat)
    \n total_days += feat["estimated_days"]
    \n else:
    \n v2_backlog.append(feat)<\/p>\n

    return {
    \n "v1_total_days": round(total_days, 1),
    \n "v1_scope": [f["name"] for f in v1_scope],
    \n "v2_backlog": [f["name"] for f in v2_backlog],
    \n "detailed_v1": v1_scope
    \n }<\/p>\n

    # \u8fd0\u884c\u88c1\u526a\u8bc4\u4f30\u793a\u4f8b
    \nif __name__ == "__main__":
    \n # \u6a21\u62df\u9700\u6c42\u5217\u8868
    \n backlog_items = [
    \n {"name": "\u6838\u5fc3 API \u667a\u80fd\u5206\u6790", "user_impact": 10, "complexity": 3, "estimated_days": 4.0},
    \n {"name": "\u6781\u7b80 Web \u7ed3\u679c\u5c55\u793a", "user_impact": 8, "complexity": 2, "estimated_days": 2.0},
    \n {"name": "\u591a\u79df\u6237\u4e0e RBAC \u6743\u9650\u7ba1\u7406", "user_impact": 3, "complexity": 8, "estimated_days": 7.0},
    \n {"name": "PDF\/Word \u62a5\u544a\u4e00\u952e\u5bfc\u51fa", "user_impact": 4, "complexity": 5, "estimated_days": 3.5},
    \n {"name": "\u6d88\u606f\u901a\u77e5\u63a8\u9001", "user_impact": 5, "complexity": 4, "estimated_days": 3.0},
    \n {"name": "\u81ea\u5b9a\u4e49\u754c\u9762\u4e3b\u9898", "user_impact": 2, "complexity": 2, "estimated_days": 1.5}
    \n ]<\/p>\n

    pruner = FeaturePruningEngine(max_v1_days=10.0) # \u7ea6\u675f V1 \u7814\u53d1\u5de5\u65f6\u4e0d\u8d85\u8fc7 10 \u5929
    \n result = pruner.evaluate_and_prune(backlog_items)<\/p>\n

    print("=== V1.0 \u786e\u5b9a\u6027\u88c1\u526a\u7ed3\u679c ===")
    \n print(f"V1.0 \u9884\u4f30\u603b\u5de5\u65f6: {result['v1_total_days']} \u5929")
    \n print(f"\u2705 V1.0 \u9501\u5b9a\u4ea4\u4ed8\u8303\u56f4: {result['v1_scope']}")
    \n print(f"\u274c \u88c1\u526a\u81f3 V2.0 \u50a8\u5907\u6c60: {result['v2_backlog']}")<\/p>\n

    \n

    4. \u521d\u59cb\u7248\u672c\u7684\u63a8\u8fdb\u51c6\u5219<\/h3>\n

    \u63a8\u8fdb\u9996\u4e2a\u7248\u672c\u4ea4\u4ed8\u65f6，\u9879\u76ee\u7ba1\u7406\u5b9c\u5173\u6ce8\u4ee5\u4e0b\u4e09\u9879\u539f\u5219：<\/p>\n

  • V1.0 \u65e8\u5728\u9a8c\u8bc1\u6838\u5fc3\u5047\u8bbe：\u5728\u6838\u5fc3\u903b\u8f91\u901a\u7545\u7684\u524d\u63d0\u4e0b，\u4f18\u5148\u4fdd\u8bc1\u4e3b\u6d41\u7a0b\u53ef\u6b63\u5e38\u8fd0\u884c\u4e0e\u9a8c\u8bc1\u3002<\/li>\n
  • \u4fdd\u6301\u4ea4\u4ed8\u5468\u671f\u7684\u9700\u6c42\u51bb\u7ed3：\u5728\u9884\u8bbe\u65f6\u95f4\u76d2\u5f00\u53d1\u671f\u95f4，\u4fdd\u6301\u9700\u6c42\u8303\u56f4\u76f8\u5bf9\u7a33\u5b9a\u3002\u65b0\u589e\u60f3\u6cd5\u53ef\u767b\u8bb0\u5165 V2.0 Backlog \u5f85\u540e\u7eed\u8bc4\u4f30\u3002<\/li>\n
  • \u6a21\u578b\u8f85\u52a9\u751f\u6210，\u67b6\u6784\u51b3\u7b56\u628a\u5173：\u5229\u7528\u5927\u6a21\u578b\u8f85\u52a9\u7f16\u5199\u57fa\u7840\u4ee3\u7801\u4e0e\u6d4b\u8bd5\u7528\u4f8b，\u800c\u8fb9\u754c\u5212\u5b9a\u4e0e\u529f\u80fd\u88c1\u526a\u9700\u7531\u67b6\u6784\u4e0e\u9879\u76ee\u7ba1\u7406\u8005\u4e3b\u5bfc\u51b3\u7b56\u3002<\/li>\n

    \u9996\u4e2a\u7248\u672c\u7684\u6838\u5fc3\u4ef7\u503c\u5728\u4e8e\u5feb\u901f\u63a8\u5411\u771f\u5b9e\u4f7f\u7528\u573a\u666f，\u63a5\u53d7\u5e02\u573a\u4e0e\u7528\u6237\u7684\u68c0\u9a8c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

    \u6280\u672f\u4ea7\u54c1\u7b2c\u4e00\u7248\u8be5\u4fdd\u7559\u54ea\u4e9b\u6838\u5fc3\u80fd\u529b\u5728\u4f7f\u7528\u5927\u8bed\u8a00\u6a21\u578b\u8f85\u52a9\u9879\u76ee\u89c4\u5212\u4e0e\u9700\u6c42\u62c6\u89e3\u65f6，\u5e38\u89c1\u504f\u8bef\u5728\u4e8e\u201c\u76f4\u63a5\u5c06 AI \u4ea7\u51fa\u7684\u529f\u80fd\u6e05\u5355\u4f5c\u4e3a\u4ea7\u54c1\u9996\u7248\u9700\u6c42\u6587\u6863\u201d\u3002\u5f53\u5411\u6a21\u578b\u8f93\u5165\u201c\u8bbe\u8ba1\u4e00\u6b3e\u9879\u76ee\u7ba1\u7406\u5e94\u7528\u201d\u65f6，\u6a21\u578b\u901a\u5e38\u8f93\u51fa\u5305\u542b\u201cAI WBS \u81ea\u52a8\u62c6\u89e3\u3001\u591a\u9879\u76ee\u8d44\u6e90\u6c60\u8c03\u5ea6\u3001\u7518\u7279\u56fe\u8054\u52a8\u3001\u667a\u80fd\u98ce\u9669\u9884\u8b66\u3001\u81ea\u52a8\u5316\u5468\u62a5\u751f\u6210\u201d\u7b49\u5927\u800c\u5168\u7684\u529f\u80fd\u7ec4\u5408\u3002
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