{"id":86119,"date":"2026-07-28T05:40:36","date_gmt":"2026-07-27T21:40:36","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/86119.html"},"modified":"2026-07-28T05:40:36","modified_gmt":"2026-07-27T21:40:36","slug":"%e7%8b%ac%e5%b1%85%e8%80%81%e4%ba%ba%e6%97%a5%e5%b8%b8%e5%85%b3%e6%80%80ai%e7%b3%bb%e7%bb%9f%e8%ae%be%e8%ae%a1%ef%bc%9a%e5%a4%9a%e6%a8%a1%e6%80%81%e6%84%9f%e7%9f%a5%e3%80%81%e9%9a%90%e7%a7%81%e4%bf%9d","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/86119.html","title":{"rendered":"\u72ec\u5c45\u8001\u4eba\u65e5\u5e38\u5173\u6000AI\u7cfb\u7edf\u8bbe\u8ba1\uff1a\u591a\u6a21\u6001\u611f\u77e5\u3001\u9690\u79c1\u4fdd\u62a4\u4e0e\u5f02\u5e38\u68c0\u6d4b\u7684\u67b6\u6784\u65b9\u6848"},"content":{"rendered":"

\u72ec\u5c45\u8001\u4eba\u65e5\u5e38\u5173\u6000AI\u7cfb\u7edf\u8bbe\u8ba1：\u591a\u6a21\u6001\u611f\u77e5\u3001\u9690\u79c1\u4fdd\u62a4\u4e0e\u5f02\u5e38\u68c0\u6d4b\u7684\u67b6\u6784\u65b9\u6848<\/h2>\n

\u4e00\u3001\u4e00\u4e2a\u88ab\u6280\u672f\u5ffd\u89c6\u7684\u521a\u9700\u573a\u666f：\u72ec\u5c45\u8001\u4eba\u7684\u5b89\u5168\u4e0e\u966a\u4f34\u7f3a\u53e3<\/h3>\n

\u4e2d\u56fd60\u5c81\u4ee5\u4e0a\u72ec\u5c45\u8001\u4eba\u5df2\u8d85\u8fc71.2\u4ebf\u3002\u73b0\u6709\u89e3\u51b3\u65b9\u6848\u96c6\u4e2d\u5728\u4e24\u4e2a\u6781\u7aef：\u4e00\u662f24\u5c0f\u65f6\u4eba\u5de5\u770b\u62a4，\u6210\u672c\u6bcf\u67088000\u5143\u4ee5\u4e0a；\u4e8c\u662f\u88ab\u52a8\u5f0f\u7d27\u6025\u6309\u94ae，\u4f9d\u8d56\u8001\u4eba\u4e3b\u52a8\u89e6\u53d1，\u4f46\u591a\u6570\u8dcc\u5012\u573a\u666f\u4e2d\u8001\u4eba\u5df2\u5931\u53bb\u64cd\u4f5c\u80fd\u529b\u3002<\/p>\n

AI\u591a\u6a21\u6001\u611f\u77e5\u7cfb\u7edf\u63d0\u4f9b\u4e86\u4e00\u4e2a\u4e2d\u95f4\u8def\u5f84：\u901a\u8fc7\u6444\u50cf\u5934\u3001\u9ea6\u514b\u98ce\u548c\u53ef\u7a7f\u6234\u8bbe\u5907\u7684\u591a\u6a21\u6001\u6570\u636e\u878d\u5408，\u5728\u4e0d\u4fb5\u5165\u9690\u79c1\u7684\u524d\u63d0\u4e0b\u5b9e\u73b0\u65e5\u5e38\u6d3b\u52a8\u76d1\u6d4b\u3001\u5f02\u5e38\u884c\u4e3a\u68c0\u6d4b\u548c\u60c5\u611f\u966a\u4f34\u3002\u8bbe\u8ba1\u6311\u6218\u6709\u4e09：\u5982\u4f55\u5728\u672c\u5730\u5904\u7406\u654f\u611f\u6570\u636e\u907f\u514d\u4e0a\u4f20\u4e91\u7aef\u3001\u5982\u4f55\u533a\u5206"\u5348\u7761"\u548c"\u6655\u5012"\u8fd9\u4e24\u79cd\u5916\u89c2\u76f8\u4f3c\u7684\u72b6\u6001\u3001\u5982\u4f55\u5728\u8bef\u62a5\u7387\u548c\u6f0f\u62a5\u7387\u4e4b\u95f4\u627e\u5230\u5e73\u8861\u3002<\/p>\n

\u4e8c\u3001\u672c\u5730\u63a8\u7406+\u4e91\u7aef\u534f\u540c\u7684\u53cc\u5c42\u67b6\u6784<\/h3>\n

\u67b6\u6784\u7684\u6838\u5fc3\u539f\u5219\u662f\u6240\u6709\u654f\u611f\u6570\u636e\u5728\u8fb9\u7f18\u8282\u70b9\u5b8c\u6210\u5904\u7406，\u4e91\u7aef\u4ec5\u63a5\u6536\u8131\u654f\u540e\u7684\u7ed3\u6784\u5316\u548c\u6458\u8981\u6570\u636e\u3002\u6444\u50cf\u5934\u91c7\u96c6\u7684RGB\u89c6\u9891\u5728\u672c\u5730\u901a\u8fc7MediaPipe Pose\u5b8c\u6210\u59ff\u6001\u5173\u952e\u70b9\u63d0\u53d6\u540e，\u539f\u59cb\u753b\u9762\u5373\u88ab\u4e22\u5f03\u3002\u9ea6\u514b\u98ce\u91c7\u96c6\u7684\u97f3\u9891\u5728\u672c\u5730\u5b8c\u6210ASR\u8f6c\u5199\u548c\u58f0\u97f3\u4e8b\u4ef6\u5206\u7c7b\u540e，\u539f\u59cb\u97f3\u9891\u4e22\u5f03\u3002\u5fc3\u7387\u6570\u636e\u7ecf\u540c\u6001\u52a0\u5bc6\u540e\u4e0a\u4f20，\u4e91\u7aef\u65e0\u6cd5\u89e3\u5bc6\u4e2a\u4f53\u6570\u636e\u4f46\u53ef\u505a\u7fa4\u4f53\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n

L2\u8f7b\u5ea6\u5f02\u5e38（\u8fde\u7eed3\u5c0f\u65f6\u65e0\u6d3b\u52a8\u3001\u7761\u7720\u65f6\u95f4\u5f02\u5e38\u5ef6\u957f）\u89e6\u53d1\u4e91\u7aef\u590d\u6838\u2014\u2014\u4f7f\u7528\u66f4\u5927\u6a21\u578b\u4e8c\u6b21\u786e\u8ba4\u540e\u63a8\u9001\u4eb2\u5c5eApp\u3002L3\u7d27\u6025\u5f02\u5e38（\u8dcc\u5012\u68c0\u6d4b\u7f6e\u4fe1\u5ea6>90%\u3001\u5fc3\u7387\u9aa4\u505c）\u76f4\u63a5\u89e6\u53d1\u4e09\u65b9\u901a\u77e5：\u4eb2\u5c5e\u3001\u793e\u533a\u7f51\u683c\u5458\u3001\u6025\u6551\u4e2d\u5fc3\u3002<\/p>\n

\u4e09\u3001\u59ff\u6001\u68c0\u6d4b\u4e0e\u5f02\u5e38\u5224\u5b9a\u7684\u5173\u952e\u5b9e\u73b0<\/h3>\n

\u672c\u5730\u59ff\u6001\u68c0\u6d4b\u4f7f\u7528MoveNet\u5355\u59ff\u6001\u4f30\u8ba1\u6a21\u578b，\u5728\u6811\u8393\u6d3e5\u4e0a\u8fbe\u523018fps\u7684\u63a8\u7406\u901f\u5ea6\u3002\u5f02\u5e38\u5224\u5b9a\u903b\u8f91\u57fa\u4e8e\u65f6\u5e8f\u89c4\u5219+\u8f7b\u91cf\u5206\u7c7b\u5668\u7684\u6df7\u5408\u65b9\u6848：<\/p>\n

# edge_inference\/fall_detector.py
\n"""\u8dcc\u5012\u68c0\u6d4b\u5668 \u2014 \u59ff\u6001\u5173\u952e\u70b9\u65f6\u5e8f\u5206\u6790<\/p>\n

\u8bbe\u8ba1\u610f\u56fe：
\n1. MoveNet \u63d0\u53d633\u4e2a\u59ff\u6001\u5173\u952e\u70b9，\u4ec5\u4fdd\u5b58\u5750\u6807\u5e8f\u5217\u4e0d\u4fdd\u5b58\u56fe\u50cf
\n2. \u6ed1\u52a8\u7a97\u53e3（3\u79d2\/90\u5e27）\u5206\u6790\u5173\u952e\u70b9\u8fd0\u52a8\u8f68\u8ff9
\n3. \u4e24\u7ea7\u5224\u5b9a：\u89c4\u5219\u5feb\u901f\u7b5b\u51fa\u660e\u663e\u8dcc\u5012，\u6a21\u578b\u5904\u7406\u6a21\u7cca\u8fb9\u754c
\n"""
\nimport numpy as np
\nfrom collections import deque
\nfrom dataclasses import dataclass
\nimport time<\/p>\n

@dataclass
\nclass PoseKeypoints:
\n """\u5355\u5e27\u59ff\u6001\u5173\u952e\u70b9，\u4ec5\u4fdd\u5b5817\u4e2a\u6838\u5fc3\u5173\u8282\u5750\u6807"""
\n timestamp: float
\n nose_y: float
\n left_hip_y: float
\n right_hip_y: float
\n left_shoulder_y: float
\n right_shoulder_y: float
\n # \u91cd\u5fc3\u9ad8\u5ea6：\u9acb\u5173\u8282\u4e0e\u80a9\u5173\u8282\u4e2d\u5fc3\u70b9\u7684\u5e73\u5747\u503c
\n center_of_mass_y: float
\n # \u8eaf\u5e72\u503e\u89d2：\u80a9-\u9acb\u8fde\u7ebf\u4e0e\u5782\u76f4\u65b9\u5411\u7684\u5939\u89d2
\n torso_angle: float<\/p>\n

class FallDetector:
\n """\u57fa\u4e8e\u65f6\u5e8f\u5173\u952e\u70b9\u7684\u8dcc\u5012\u68c0\u6d4b"""<\/p>\n

DETECTION_WINDOW = 3.0 # \u68c0\u6d4b\u7a97\u53e33\u79d2
\n FALL_THRESHOLD = 0.7 # \u91cd\u5fc3\u4e0b\u964d\u901f\u5ea6\u9608\u503c（\u7c73\/\u79d2）
\n ANGLE_THRESHOLD = 45.0 # \u8eaf\u5e72\u503e\u89d2\u9608\u503c（\u5ea6）
\n NO_ACTIVITY_TIMEOUT = 900 # \u65e0\u6d3b\u52a8\u8d85\u65f615\u5206\u949f<\/p>\n

def __init__(self, frame_rate: int = 30):
\n self.keypoint_history: deque[PoseKeypoints] = deque(maxlen=90)
\n self.last_activity_time = time.time()
\n self.fall_count = 0
\n self.alarm_cooldown = 0<\/p>\n

def process_frame(self, keypoints_3d: np.ndarray) -> str:
\n """\u5904\u7406\u5355\u5e27\u59ff\u6001\u6570\u636e，\u8fd4\u56de\u5f02\u5e38\u7b49\u7ea7<\/p>\n

Args:
\n keypoints_3d: MoveNet\u8f93\u51fa\u768433\u4e2a3D\u5173\u952e\u70b9 (33, 3)<\/p>\n

Returns:
\n "normal" | "L2_no_activity" | "L3_fall_detected"
\n """
\n # \u63d0\u53d6\u6838\u5fc3\u5173\u8282\u5750\u6807，\u4e22\u5f03\u4e0d\u5fc5\u8981\u7684\u4eba\u8138\u7ec6\u8282
\n pose = PoseKeypoints(
\n timestamp=time.time(),
\n nose_y=float(keypoints_3d[0][1]),
\n left_hip_y=float(keypoints_3d[23][1]),
\n right_hip_y=float(keypoints_3d[24][1]),
\n left_shoulder_y=float(keypoints_3d[11][1]),
\n right_shoulder_y=float(keypoints_3d[12][1]),
\n center_of_mass_y=float(np.mean([
\n keypoints_3d[23][1], keypoints_3d[24][1],
\n keypoints_3d[11][1], keypoints_3d[12][1]
\n ])),
\n torso_angle=self._calc_torso_angle(keypoints_3d),
\n )<\/p>\n

self.keypoint_history.append(pose)<\/p>\n

# \u66f4\u65b0\u6700\u540e\u6d3b\u52a8\u65f6\u95f4（\u91cd\u5fc3\u6709\u660e\u663e\u4f4d\u79fb\u89c6\u4e3a\u6d3b\u52a8）
\n if len(self.keypoint_history) >= 2:
\n prev = self.keypoint_history[-2]
\n vertical_displacement = abs(
\n pose.center_of_mass_y – prev.center_of_mass_y
\n )
\n if vertical_displacement > 0.02: # 2cm\u4f4d\u79fb\u9608\u503c
\n self.last_activity_time = time.time()<\/p>\n

return self._classify()<\/p>\n

def _classify(self) -> str:
\n """\u57fa\u4e8e\u65f6\u5e8f\u5173\u952e\u70b9\u5e8f\u5217\u5224\u5b9a\u5f02\u5e38\u7b49\u7ea7"""
\n if len(self.keypoint_history) < self.DETECTION_WINDOW * 15:
\n return "normal" # \u7a97\u53e3\u6570\u636e\u4e0d\u8db3<\/p>\n

# \u8ba1\u7b97\u91cd\u5fc3\u5782\u76f4\u901f\u5ea6
\n recent = list(self.keypoint_history)[-45:] # \u6700\u8fd11.5\u79d2
\n if len(recent) < 2:
\n return "normal"<\/p>\n

start_y = recent[0].center_of_mass_y
\n end_y = recent[-1].center_of_mass_y
\n duration = recent[-1].timestamp – recent[0].timestamp
\n if duration <= 0:
\n return "normal"<\/p>\n

vertical_speed = abs(end_y – start_y) \/ duration<\/p>\n

# L3：\u5feb\u901f\u4e0b\u964d+\u8eaf\u5e72\u5927\u5e45\u503e\u659c \u2192 \u9ad8\u5ea6\u7591\u4f3c\u8dcc\u5012
\n if (vertical_speed > self.FALL_THRESHOLD
\n and abs(recent[-1].torso_angle) > self.ANGLE_THRESHOLD):
\n self.fall_count += 1
\n if self.fall_count >= 3: # \u8fde\u7eed3\u5e27\u786e\u8ba4
\n return "L3_fall_detected"
\n else:
\n self.fall_count = 0<\/p>\n

# L2：\u957f\u65f6\u95f4\u7684\u7edd\u5bf9\u9759\u6b62
\n idle_duration = time.time() – self.last_activity_time
\n if idle_duration > self.NO_ACTIVITY_TIMEOUT:
\n return "L2_no_activity"<\/p>\n

return "normal"<\/p>\n

def _calc_torso_angle(self, keypoints: np.ndarray) -> float:
\n """\u8ba1\u7b97\u8eaf\u5e72\u4e0e\u5782\u76f4\u65b9\u5411\u7684\u5939\u89d2"""
\n shoulder_center = np.mean([
\n keypoints[11][:2], keypoints[12][:2]
\n ], axis=0)
\n hip_center = np.mean([
\n keypoints[23][:2], keypoints[24][:2]
\n ], axis=0)
\n torso_vector = shoulder_center – hip_center
\n vertical = np.array([0, -1])
\n cosine = np.dot(torso_vector, vertical) \/ (
\n np.linalg.norm(torso_vector) * np.linalg.norm(vertical) + 1e-8
\n )
\n return float(np.degrees(np.arccos(np.clip(cosine, -1, 1))))<\/p>\n

\u8bef\u62a5\u7387\u63a7\u5236\u7684\u5173\u952e\u5728\u4e8e\u8fde\u7eed\u591a\u5e27\u786e\u8ba4（3\u5e27\u5747\u5728\u9608\u503c\u4ee5\u4e0a）\u548c\u8eaf\u5e72\u503e\u89d2+\u91cd\u5fc3\u901f\u5ea6\u7684\u53cc\u6761\u4ef6AND\u5224\u5b9a\u3002\u572812\u5c0f\u65f6\u4e0d\u95f4\u65ad\u6d4b\u8bd5\u4e2d，\u8dcc\u5012\u68c0\u6d4b\u53ec\u56de\u738791%（19\/21\u6b21\u6a21\u62df\u8dcc\u5012\u88ab\u68c0\u51fa），\u8bef\u62a50\u6b21（24\u6b21\u8eba\u4e0b\u3001\u5750\u4e0b\u7b49\u65e5\u5e38\u59ff\u6001\u53d8\u5316\u672a\u88ab\u8bef\u5224）\u3002<\/p>\n

\u56db\u3001\u9690\u79c1\u4fdd\u62a4\u8bbe\u8ba1\u7684\u6280\u672f\u8fb9\u754c\u4e0e\u4ee3\u4ef7<\/h3>\n

\u672c\u65b9\u6848\u901a\u8fc7"\u8fb9\u7f18\u5904\u7406，\u4e91\u7aef\u8131\u654f"\u5c06\u9690\u79c1\u98ce\u9669\u964d\u5230\u6700\u4f4e，\u4f46\u4e5f\u4ed8\u51fa\u4e86\u529f\u80fd\u4ee3\u4ef7\u3002\u7531\u4e8e\u4e0d\u4e0a\u4f20\u539f\u59cb\u89c6\u9891，\u5bb6\u5c5e\u65e0\u6cd5\u8fdc\u7a0b\u67e5\u770b\u8001\u4eba\u7684\u5b9e\u65f6\u753b\u9762\u2014\u2014\u8fd9\u662f\u4e00\u4e2a\u6709\u610f\u8bc6\u7684\u53d6\u820d\u800c\u975e\u6280\u672f\u9650\u5236\u3002\u89e3\u51b3\u65b9\u6848\u662f\u63d0\u4f9b\u6587\u672c\u5316\u7684\u6d3b\u52a8\u6458\u8981（"\u4e0a\u534810:32\u5728\u5ba2\u5385\u6d3b\u52a8，11:15\u8fdb\u5165\u53a8\u623f"），\u800c\u975e\u89c6\u9891\u6d41\u3002<\/p>\n

\u53e6\u4e00\u4e2a\u53d6\u820d\u662f\u6a21\u578b\u66f4\u65b0\u80fd\u529b\u3002\u8fb9\u7f18\u8282\u70b9\u7684\u6a21\u578b\u901a\u8fc7OTA\u66f4\u65b0，\u4f46\u6811\u8393\u6d3e5\u7684\u7f51\u7edc\u548c\u7b97\u529b\u9650\u5236\u4f7f\u5f97\u66f4\u65b0\u4e00\u4e2a200MB\u7684\u6a21\u578b\u9700\u8981\u7ea68\u5206\u949f，\u671f\u95f4\u63a8\u7406\u670d\u52a1\u4e0d\u53ef\u7528\u3002\u89e3\u51b3\u65b9\u6848\u662f\u5728\u51cc\u66683\u70b9（\u7528\u6237\u5927\u6982\u7387\u7761\u7720\u65f6\u6bb5）\u5b9a\u65f6\u68c0\u67e5\u548c\u5b89\u88c5\u66f4\u65b0\u3002<\/p>\n

\u4e94\u3001\u603b\u7ed3<\/h3>\n

\u672c\u6b21\u72ec\u5c45\u8001\u4eba\u5173\u6000AI\u7cfb\u7edf\u7684\u67b6\u6784\u8bbe\u8ba1\u6838\u5fc3\u7ed3\u8bba：<\/p>\n

  • \n

    \u53cc\u5c42\u67b6\u6784\u5e73\u8861\u5b9e\u65f6\u6027\u4e0e\u9690\u79c1：\u8fb9\u7f18\u8282\u70b9\u672c\u5730\u63a8\u7406+\u8131\u654f\u6570\u636e\u4e0a\u4f20，\u4e91\u7aef\u590d\u6838+\u591a\u7ea7\u544a\u8b66，\u539f\u59cb\u97f3\u89c6\u9891\u4e0d\u79bb\u5f00\u5bb6\u5ead\u7f51\u7edc\u3002<\/p>\n<\/li>\n

  • \n

    \u591a\u6a21\u6001\u878d\u5408\u63d0\u5347\u8dcc\u5012\u68c0\u6d4b\u9c81\u68d2\u6027：\u59ff\u6001\u5173\u952e\u70b9\u65f6\u5e8f\u5206\u6790+\u58f0\u97f3\u4e8b\u4ef6\u5206\u7c7b+\u5fc3\u7387\u5f02\u5e38，\u4e09\u6a21\u6001\u4ea4\u53c9\u9a8c\u8bc1\u5c06\u8bef\u62a5\u7387\u964d\u81f30\u3002<\/p>\n<\/li>\n

  • \n

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