重构: 模型类别配置动态化，适配 v2+ (5 类) 模型

- settings.py: 删除 80 类 COCO_CLASS_NAMES 硬编码，改为 MODEL_CLASS_NAMES
  + 新增 MODEL_NUM_CLASSES / MODEL_OUTPUT_CHANNELS 辅助常量
  + 新增 ALGO_INTENT_CLASSES 字典 + get_algo_target_classes() 辅助函数
  + COCO_CLASS_NAMES 保留名称向后兼容，指向 MODEL_CLASS_NAMES

- postprocessor.py: 4 处硬编码 84 → MODEL_OUTPUT_CHANNELS
  + 支持不同类别数模型切换不改代码

- algorithms.py: 4 处硬编码 target_classes 默认值 → get_algo_target_classes()
  + IllegalParkingAlgorithm / VehicleCongestionAlgorithm /
    NonMotorVehicleParkingAlgorithm / GarbageDetectionAlgorithm
  + 自动过滤当前模型不支持的类（truck/bus 等）

以后换模型只需修改 settings.py 一处 MODEL_CLASS_NAMES。

.dockerignore

@@ -0,0 +1,29 @@
+.git
+__pycache__
+*.pyc
+*.pyo
+.idea
+.vscode
+.env
+.env.*
+!.env.example
+
+# 模型和数据通过卷挂载
+models/
+data/
+logs/
+
+# 测试文件
+tests/
+test_*.py
+pytest.ini
+
+# 文档
+docs/
+*.md
+!CLAUDE.md
+
+# 临时文件
+*.engine
+*.onnx
+*.pt

.gitignore

@@ -15,8 +15,10 @@ build/
 logs/
 *.log
 
-# 模型文件（忽略中间产物）
+# 模型文件（二进制大文件，不入库，防止 git stash --include-untracked 误删）
 models/*.onnx
+models/*.engine
+models/*.pt
 
 # 环境配置
 .env

Dockerfile

@@ -0,0 +1,51 @@
+# ============================================================
+# 基础镜像：NVIDIA TensorRT 23.08
+# 内含：CUDA 12.1.1 | cuDNN 8.9.3 | TensorRT 8.6.1.6 | Python 3.10
+# ============================================================
+FROM nvcr.io/nvidia/tensorrt:23.08-py3
+
+LABEL maintainer="AI Edge Architecture Team"
+LABEL description="Edge AI Inference Service - YOLOv11n + TensorRT"
+
+# 设置时区
+ENV TZ=Asia/Shanghai
+RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
+
+# 系统依赖（视频解码、OpenCV 运行时）
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    libgl1-mesa-glx \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# 先复制依赖文件，利用 Docker 层缓存
+COPY requirements.txt .
+
+# 安装 PyTorch（CUDA 12.1 版本）+ 其余依赖
+RUN pip install --no-cache-dir \
+    torch==2.1.2 torchvision==0.16.2 \
+    --index-url https://download.pytorch.org/whl/cu121 \
+    && pip install --no-cache-dir -r requirements.txt
+
+# 复制项目代码
+COPY __init__.py .
+COPY main.py .
+COPY algorithms.py .
+COPY build_engine.py .
+COPY config/ ./config/
+COPY core/ ./core/
+COPY utils/ ./utils/
+
+# 模型和数据通过卷挂载，不打入镜像
+# -v /path/to/models:/app/models
+# -v /path/to/data:/app/data
+
+# 日志目录
+RUN mkdir -p /app/logs /app/data
+
+EXPOSE 9001
+
+CMD ["python", "main.py"]

algorithms.py

@@ -11,6 +11,8 @@ import cv2
 import numpy as np
 import redis
 
+from config.settings import get_algo_target_classes
+
 logger = logging.getLogger(__name__)
 
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -800,7 +802,7 @@ class IllegalParkingAlgorithm(BaseAlgorithm):
         self.parking_countdown_sec = parking_countdown_sec
         self.confirm_clear_sec = confirm_clear_sec
         self.cooldown_sec = cooldown_sec
-        self.target_classes = target_classes or ["car", "truck", "bus", "motorcycle"]
+        self.target_classes = target_classes or get_algo_target_classes("illegal_parking")
         self._alarm_level = alarm_level if alarm_level is not None else self.DEFAULT_ALARM_LEVEL
 
         # 状态变量
@@ -1070,7 +1072,7 @@ class VehicleCongestionAlgorithm(BaseAlgorithm):
         self.confirm_congestion_sec = confirm_congestion_sec
         self.confirm_clear_sec = confirm_clear_sec
         self.cooldown_sec = cooldown_sec
-        self.target_classes = target_classes or ["car", "truck", "bus", "motorcycle"]
+        self.target_classes = target_classes or get_algo_target_classes("vehicle_congestion")
         self._alarm_level = alarm_level if alarm_level is not None else self.DEFAULT_ALARM_LEVEL
         self.dissipation_ratio = dissipation_ratio  # Step 6
 
@@ -1283,12 +1285,549 @@ class VehicleCongestionAlgorithm(BaseAlgorithm):
         return state_info
 
 
+class NonMotorVehicleParkingAlgorithm(BaseAlgorithm):
+    """
+    非机动车违停检测算法（状态机版本 v1.0）
+
+    状态机：
+    IDLE → CONFIRMING_VEHICLE → PARKED_COUNTDOWN → ALARMED → CONFIRMING_CLEAR → IDLE
+
+    业务流程：
+    1. 检测到非机动车进入禁停区 → 车辆确认期（confirm_vehicle_sec，默认10秒，ratio>=0.6）
+    2. 确认有车 → 违停倒计时（parking_countdown_sec，默认180秒/3分钟）
+    3. 倒计时结束仍有车 → 触发告警（ALARMED状态）
+    4. 车辆离开 → 消失确认期（confirm_clear_sec，默认60秒，ratio<0.2）
+    5. 确认车辆离开 → 发送resolve事件 → 回到空闲状态
+
+    使用滑动窗口（10秒）抗抖动，检测自行车和摩托车。
+    """
+
+    # 状态定义
+    STATE_IDLE = "IDLE"
+    STATE_CONFIRMING_VEHICLE = "CONFIRMING_VEHICLE"
+    STATE_PARKED_COUNTDOWN = "PARKED_COUNTDOWN"
+    STATE_ALARMED = "ALARMED"
+    STATE_CONFIRMING_CLEAR = "CONFIRMING_CLEAR"
+
+    # 告警级别常量（默认值，可通过 params 覆盖）
+    DEFAULT_ALARM_LEVEL = 2  # 普通
+
+    # 滑动窗口参数
+    WINDOW_SIZE_SEC = 10
+
+    # 阈值常量（与 IllegalParkingAlgorithm 一致）
+    RATIO_CONFIRMING_DROP = 0.3
+    RATIO_CONFIRM_VEHICLE = 0.6
+    RATIO_PARKED_LEAVE = 0.2
+    RATIO_ALARMED_CLEAR = 0.15
+    RATIO_CLEAR_RETURN = 0.5
+    RATIO_CLEAR_CONFIRM = 0.2
+
+    def __init__(
+        self,
+        confirm_vehicle_sec: int = 10,
+        parking_countdown_sec: int = 180,
+        confirm_clear_sec: int = 60,
+        cooldown_sec: int = 900,
+        target_classes: Optional[List[str]] = None,
+        alarm_level: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.confirm_vehicle_sec = confirm_vehicle_sec
+        self.parking_countdown_sec = parking_countdown_sec
+        self.confirm_clear_sec = confirm_clear_sec
+        self.cooldown_sec = cooldown_sec
+        self.target_classes = target_classes or get_algo_target_classes("non_motor_vehicle_parking")
+        self._alarm_level = alarm_level if alarm_level is not None else self.DEFAULT_ALARM_LEVEL
+
+        # 状态变量
+        self.state: str = self.STATE_IDLE
+        self.state_start_time: Optional[datetime] = None
+
+        # 滑动窗口：存储 (timestamp, has_vehicle: bool)
+        self._detection_window: deque = deque(maxlen=1000)
+
+        # 告警追踪
+        self._parking_start_time: Optional[datetime] = None
+
+        # 冷却期管理
+        self.alert_cooldowns: Dict[str, datetime] = {}
+
+    def _check_target_classes(self, detection: Dict) -> bool:
+        """检查检测目标是否属于非机动车类别"""
+        det_class = detection.get("class", "")
+        return det_class in self.target_classes
+
+    def _update_window(self, current_time: datetime, has_vehicle: bool):
+        """更新滑动窗口"""
+        self._detection_window.append((current_time, has_vehicle))
+        cutoff = current_time - timedelta(seconds=self.WINDOW_SIZE_SEC)
+        while self._detection_window and self._detection_window[0][0] < cutoff:
+            self._detection_window.popleft()
+
+    def _get_window_ratio(self) -> float:
+        """获取滑动窗口内的检测命中率"""
+        if not self._detection_window:
+            return 0.0
+        hits = sum(1 for _, has in self._detection_window if has)
+        return hits / len(self._detection_window)
+
+    def _scan_tracks(self, tracks: List[Dict], roi_id: str) -> Tuple[bool, int, List[float], float]:
+        """
+        一次遍历 tracks，返回 (has_target, count, latest_bbox, max_confidence)。
+        过滤 target_classes。
+        """
+        has_target = False
+        count = 0
+        latest_bbox: List[float] = []
+        max_confidence = 0.0
+        for det in tracks:
+            if self._check_detection_in_roi(det, roi_id) and self._check_target_classes(det):
+                has_target = True
+                count += 1
+                if not latest_bbox:
+                    latest_bbox = det.get("bbox", [])
+                conf = det.get("confidence", 0.0)
+                if conf > max_confidence:
+                    max_confidence = conf
+        return has_target, count, latest_bbox, max_confidence
+
+    def _get_latest_bbox(self, tracks: List[Dict], roi_id: str) -> List[float]:
+        for det in tracks:
+            if self._check_detection_in_roi(det, roi_id) and self._check_target_classes(det):
+                return det.get("bbox", [])
+        return []
+
+    def _get_max_confidence(self, tracks: List[Dict], roi_id: str) -> float:
+        """获取ROI内非机动车的最高置信度"""
+        max_conf = 0.0
+        for det in tracks:
+            if self._check_detection_in_roi(det, roi_id) and self._check_target_classes(det):
+                max_conf = max(max_conf, det.get("confidence", 0.0))
+        return max_conf
+
+    def process(
+        self,
+        roi_id: str,
+        camera_id: str,
+        tracks: List[Dict],
+        current_time: Optional[datetime] = None,
+    ) -> List[Dict]:
+        """处理单帧检测结果"""
+        current_time = current_time or datetime.now()
+        alerts = []
+
+        # 一次遍历获取所有信息
+        roi_has_vehicle, vehicle_count, scan_bbox, scan_confidence = self._scan_tracks(tracks, roi_id)
+
+        # 更新滑动窗口
+        self._update_window(current_time, roi_has_vehicle)
+
+        # 计算一次比率，后续分支复用
+        ratio = self._get_window_ratio()
+
+        # === 状态机处理 ===
+
+        if self.state == self.STATE_IDLE:
+            if roi_has_vehicle:
+                self.state = self.STATE_CONFIRMING_VEHICLE
+                self.state_start_time = current_time
+                logger.debug(f"ROI {roi_id}: IDLE → CONFIRMING_VEHICLE (非机动车)")
+
+        elif self.state == self.STATE_CONFIRMING_VEHICLE:
+            if self.state_start_time is None:
+                self.state = self.STATE_IDLE
+                return alerts
+
+            elapsed = (current_time - self.state_start_time).total_seconds()
+
+            if ratio < self.RATIO_CONFIRMING_DROP:
+                self.state = self.STATE_IDLE
+                self.state_start_time = None
+                logger.debug(f"ROI {roi_id}: CONFIRMING_VEHICLE → IDLE (ratio={ratio:.2f}<{self.RATIO_CONFIRMING_DROP})")
+            elif elapsed >= self.confirm_vehicle_sec and ratio >= self.RATIO_CONFIRM_VEHICLE:
+                self._parking_start_time = self.state_start_time
+                self.state = self.STATE_PARKED_COUNTDOWN
+                self.state_start_time = current_time
+                logger.info(f"ROI {roi_id}: CONFIRMING_VEHICLE → PARKED_COUNTDOWN (非机动车, ratio={ratio:.2f})")
+
+        elif self.state == self.STATE_PARKED_COUNTDOWN:
+            if self.state_start_time is None:
+                self.state = self.STATE_IDLE
+                return alerts
+
+            elapsed = (current_time - self.state_start_time).total_seconds()
+
+            if ratio < self.RATIO_PARKED_LEAVE:
+                self.state = self.STATE_IDLE
+                self.state_start_time = None
+                self._parking_start_time = None
+                logger.debug(f"ROI {roi_id}: PARKED_COUNTDOWN → IDLE (非机动车离开, ratio={ratio:.2f})")
+            elif elapsed >= self.parking_countdown_sec:
+                cooldown_key = f"{camera_id}_{roi_id}"
+                if cooldown_key not in self.alert_cooldowns or \
+                   (current_time - self.alert_cooldowns[cooldown_key]).total_seconds() > self.cooldown_sec:
+
+                    alerts.append({
+                        "roi_id": roi_id,
+                        "camera_id": camera_id,
+                        "bbox": scan_bbox,
+                        "alert_type": "non_motor_vehicle_parking",
+                        "alarm_level": self._alarm_level,
+                        "confidence": scan_confidence,
+                        "message": f"检测到非机动车违停（已停留{int(elapsed / 60)}分钟）",
+                        "first_frame_time": self._parking_start_time.strftime('%Y-%m-%d %H:%M:%S') if self._parking_start_time else None,
+                        "duration_minutes": elapsed / 60,
+                    })
+
+                    self.alert_cooldowns[cooldown_key] = current_time
+                    self.state = self.STATE_ALARMED
+                    logger.warning(f"ROI {roi_id}: PARKED_COUNTDOWN → ALARMED (非机动车违停告警触发)")
+                else:
+                    self.state = self.STATE_IDLE
+                    self.state_start_time = None
+                    self._parking_start_time = None
+                    logger.debug(f"ROI {roi_id}: PARKED_COUNTDOWN → IDLE (冷却期内)")
+
+        elif self.state == self.STATE_ALARMED:
+            if ratio < self.RATIO_ALARMED_CLEAR:
+                self.state = self.STATE_CONFIRMING_CLEAR
+                self.state_start_time = current_time
+                logger.debug(f"ROI {roi_id}: ALARMED → CONFIRMING_CLEAR (ratio={ratio:.2f}<{self.RATIO_ALARMED_CLEAR})")
+
+        elif self.state == self.STATE_CONFIRMING_CLEAR:
+            if self.state_start_time is None:
+                self.state = self.STATE_IDLE
+                return alerts
+
+            elapsed = (current_time - self.state_start_time).total_seconds()
+
+            if ratio >= self.RATIO_CLEAR_RETURN:
+                self.state = self.STATE_ALARMED
+                self.state_start_time = None
+                logger.debug(f"ROI {roi_id}: CONFIRMING_CLEAR → ALARMED (非机动车仍在)")
+            elif elapsed >= self.confirm_clear_sec and ratio < self.RATIO_CLEAR_CONFIRM:
+                if self._last_alarm_id and self._parking_start_time:
+                    duration_ms = int((current_time - self._parking_start_time).total_seconds() * 1000)
+                    alerts.append({
+                        "alert_type": "alarm_resolve",
+                        "resolve_alarm_id": self._last_alarm_id,
+                        "duration_ms": duration_ms,
+                        "last_frame_time": current_time.strftime('%Y-%m-%d %H:%M:%S'),
+                        "resolve_type": "vehicle_left",
+                    })
+                    logger.info(f"ROI {roi_id}: 非机动车违停告警已解决（车辆离开）")
+
+                self.state = self.STATE_IDLE
+                self.state_start_time = None
+                self._last_alarm_id = None
+                self._parking_start_time = None
+                self.alert_cooldowns.clear()
+                logger.debug(f"ROI {roi_id}: CONFIRMING_CLEAR → IDLE")
+
+        return alerts
+
+    def reset(self):
+        """重置算法状态"""
+        self.state = self.STATE_IDLE
+        self.state_start_time = None
+        self._last_alarm_id = None
+        self._parking_start_time = None
+        self._detection_window.clear()
+        self.alert_cooldowns.clear()
+
+    def get_state(self, current_time: Optional[datetime] = None) -> Dict[str, Any]:
+        """获取当前状态"""
+        current_time = current_time or datetime.now()
+        window_ratio = self._get_window_ratio()
+        state_info = {
+            "state": self.state,
+            "state_start_time": self.state_start_time.isoformat() if self.state_start_time else None,
+            "window_ratio": window_ratio,
+        }
+        if self.state in (self.STATE_ALARMED, self.STATE_PARKED_COUNTDOWN) and self._parking_start_time:
+            state_info["parking_duration_sec"] = (current_time - self._parking_start_time).total_seconds()
+            state_info["alarm_id"] = self._last_alarm_id
+        return state_info
+
+
+class GarbageDetectionAlgorithm(BaseAlgorithm):
+    """
+    垃圾检测算法（状态机版本 v1.0）
+
+    状态机：
+    IDLE → CONFIRMING_GARBAGE → ALARMED → CONFIRMING_CLEAR → IDLE
+
+    业务流程：
+    1. 检测到垃圾 → 垃圾确认期（confirm_garbage_sec，默认60秒，ratio>=0.6）
+    2. 确认有垃圾 → 触发告警（ALARMED 状态）
+    3. 垃圾消失（ratio<0.15）→ 消失确认期（confirm_clear_sec，默认60秒）
+    4. 消失确认期内持续 ratio<0.2 → 发送 resolve 事件 → 回到 IDLE
+
+    与 IllegalParking 的差异：无 PARKED_COUNTDOWN 阶段（垃圾无"临时停留"概念）。
+    使用滑动窗口（10秒）抗抖动，只检测 garbage 类。
+    """
+
+    # 状态定义
+    STATE_IDLE = "IDLE"
+    STATE_CONFIRMING_GARBAGE = "CONFIRMING_GARBAGE"
+    STATE_ALARMED = "ALARMED"
+    STATE_CONFIRMING_CLEAR = "CONFIRMING_CLEAR"
+
+    # 告警级别常量（默认值，可通过 params 覆盖）
+    DEFAULT_ALARM_LEVEL = 2  # 普通
+
+    # 滑动窗口参数
+    WINDOW_SIZE_SEC = 10
+
+    # 阈值常量
+    RATIO_CONFIRMING_DROP = 0.3       # 确认期内命中率低于此值则回到 IDLE
+    RATIO_CONFIRM_GARBAGE = 0.6       # 确认有垃圾的命中率阈值
+    RATIO_ALARMED_CLEAR = 0.15        # 已告警状态下进入消失确认的阈值
+    RATIO_CLEAR_RETURN = 0.5          # 消失确认期间垃圾再次出现的阈值
+    RATIO_CLEAR_CONFIRM = 0.2         # 消失确认完成的阈值
+
+    def __init__(
+        self,
+        confirm_garbage_sec: int = 60,
+        confirm_clear_sec: int = 60,
+        cooldown_sec: int = 1800,
+        target_classes: Optional[List[str]] = None,
+        alarm_level: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.confirm_garbage_sec = confirm_garbage_sec
+        self.confirm_clear_sec = confirm_clear_sec
+        self.cooldown_sec = cooldown_sec
+        self.target_classes = target_classes or get_algo_target_classes("garbage")
+        self._alarm_level = alarm_level if alarm_level is not None else self.DEFAULT_ALARM_LEVEL
+
+        # 状态变量
+        self.state: str = self.STATE_IDLE
+        self.state_start_time: Optional[datetime] = None
+
+        # 滑动窗口：存储 (timestamp, has_garbage: bool)
+        self._detection_window: deque = deque(maxlen=1000)
+
+        # 告警追踪
+        self._garbage_start_time: Optional[datetime] = None
+
+        # 冷却期管理
+        self.alert_cooldowns: Dict[str, datetime] = {}
+
+    def _check_target_classes(self, detection: Dict) -> bool:
+        """检查检测目标是否属于垃圾类别"""
+        det_class = detection.get("class", "")
+        return det_class in self.target_classes
+
+    def _update_window(self, current_time: datetime, has_garbage: bool):
+        """更新滑动窗口"""
+        self._detection_window.append((current_time, has_garbage))
+        cutoff = current_time - timedelta(seconds=self.WINDOW_SIZE_SEC)
+        while self._detection_window and self._detection_window[0][0] < cutoff:
+            self._detection_window.popleft()
+
+    def _get_window_ratio(self) -> float:
+        """获取滑动窗口内的检测命中率"""
+        if not self._detection_window:
+            return 0.0
+        hits = sum(1 for _, has in self._detection_window if has)
+        return hits / len(self._detection_window)
+
+    def _scan_tracks(self, tracks: List[Dict], roi_id: str) -> Tuple[bool, int, List[float], float]:
+        """
+        一次遍历 tracks，返回 (has_target, count, latest_bbox, max_confidence)。
+        过滤 target_classes。
+        """
+        has_target = False
+        count = 0
+        latest_bbox: List[float] = []
+        max_confidence = 0.0
+        for det in tracks:
+            if self._check_detection_in_roi(det, roi_id) and self._check_target_classes(det):
+                has_target = True
+                count += 1
+                if not latest_bbox:
+                    latest_bbox = det.get("bbox", [])
+                conf = det.get("confidence", 0.0)
+                if conf > max_confidence:
+                    max_confidence = conf
+        return has_target, count, latest_bbox, max_confidence
+
+    def process(
+        self,
+        roi_id: str,
+        camera_id: str,
+        tracks: List[Dict],
+        current_time: Optional[datetime] = None,
+    ) -> List[Dict]:
+        """处理单帧检测结果"""
+        current_time = current_time or datetime.now()
+        alerts = []
+
+        # 一次遍历获取所有信息
+        roi_has_garbage, garbage_count, scan_bbox, scan_confidence = self._scan_tracks(tracks, roi_id)
+
+        # 更新滑动窗口
+        self._update_window(current_time, roi_has_garbage)
+
+        # 计算一次比率，后续分支复用
+        ratio = self._get_window_ratio()
+
+        # === 状态机处理 ===
+
+        if self.state == self.STATE_IDLE:
+            if roi_has_garbage:
+                self.state = self.STATE_CONFIRMING_GARBAGE
+                self.state_start_time = current_time
+                logger.debug(f"ROI {roi_id}: IDLE → CONFIRMING_GARBAGE")
+
+        elif self.state == self.STATE_CONFIRMING_GARBAGE:
+            if self.state_start_time is None:
+                self.state = self.STATE_IDLE
+                return alerts
+
+            elapsed = (current_time - self.state_start_time).total_seconds()
+
+            if ratio < self.RATIO_CONFIRMING_DROP:
+                # 命中率过低，可能只是闪现
+                self.state = self.STATE_IDLE
+                self.state_start_time = None
+                logger.debug(
+                    f"ROI {roi_id}: CONFIRMING_GARBAGE → IDLE "
+                    f"(ratio={ratio:.2f}<{self.RATIO_CONFIRMING_DROP})"
+                )
+            elif elapsed >= self.confirm_garbage_sec and ratio >= self.RATIO_CONFIRM_GARBAGE:
+                # 确认有垃圾持续存在，检查冷却期
+                cooldown_key = f"{camera_id}_{roi_id}"
+                if cooldown_key not in self.alert_cooldowns or \
+                   (current_time - self.alert_cooldowns[cooldown_key]).total_seconds() > self.cooldown_sec:
+
+                    self._garbage_start_time = self.state_start_time
+                    alerts.append({
+                        "roi_id": roi_id,
+                        "camera_id": camera_id,
+                        "bbox": scan_bbox,
+                        "alert_type": "garbage",
+                        "alarm_level": self._alarm_level,
+                        "confidence": scan_confidence,
+                        "message": f"检测到垃圾（持续{int(elapsed)}秒，{garbage_count}处）",
+                        "first_frame_time": self._garbage_start_time.strftime('%Y-%m-%d %H:%M:%S'),
+                        "garbage_count": garbage_count,
+                    })
+
+                    self.alert_cooldowns[cooldown_key] = current_time
+                    self.state = self.STATE_ALARMED
+                    logger.warning(f"ROI {roi_id}: CONFIRMING_GARBAGE → ALARMED (垃圾告警触发)")
+                else:
+                    self.state = self.STATE_IDLE
+                    self.state_start_time = None
+                    logger.debug(f"ROI {roi_id}: CONFIRMING_GARBAGE → IDLE (冷却期内)")
+
+        elif self.state == self.STATE_ALARMED:
+            if ratio < self.RATIO_ALARMED_CLEAR:
+                self.state = self.STATE_CONFIRMING_CLEAR
+                self.state_start_time = current_time
+                logger.debug(
+                    f"ROI {roi_id}: ALARMED → CONFIRMING_CLEAR "
+                    f"(ratio={ratio:.2f}<{self.RATIO_ALARMED_CLEAR})"
+                )
+
+        elif self.state == self.STATE_CONFIRMING_CLEAR:
+            if self.state_start_time is None:
+                self.state = self.STATE_IDLE
+                return alerts
+
+            elapsed = (current_time - self.state_start_time).total_seconds()
+
+            if ratio >= self.RATIO_CLEAR_RETURN:
+                # 垃圾又出现（或清扫者挡住片刻），回到 ALARMED
+                self.state = self.STATE_ALARMED
+                self.state_start_time = None
+                logger.debug(f"ROI {roi_id}: CONFIRMING_CLEAR → ALARMED (垃圾仍在)")
+            elif elapsed >= self.confirm_clear_sec and ratio < self.RATIO_CLEAR_CONFIRM:
+                # 确认垃圾已被清理
+                if self._last_alarm_id and self._garbage_start_time:
+                    duration_ms = int((current_time - self._garbage_start_time).total_seconds() * 1000)
+                    alerts.append({
+                        "alert_type": "alarm_resolve",
+                        "resolve_alarm_id": self._last_alarm_id,
+                        "duration_ms": duration_ms,
+                        "last_frame_time": current_time.strftime('%Y-%m-%d %H:%M:%S'),
+                        "resolve_type": "garbage_removed",
+                    })
+                    logger.info(f"ROI {roi_id}: 垃圾告警已解决（垃圾被清理）")
+
+                self.state = self.STATE_IDLE
+                self.state_start_time = None
+                self._last_alarm_id = None
+                self._garbage_start_time = None
+                self.alert_cooldowns.clear()  # 清理后清空冷却，新垃圾可正常告警
+                logger.debug(f"ROI {roi_id}: CONFIRMING_CLEAR → IDLE")
+
+        return alerts
+
+    def reset(self):
+        """重置算法状态"""
+        self.state = self.STATE_IDLE
+        self.state_start_time = None
+        self._last_alarm_id = None
+        self._garbage_start_time = None
+        self._detection_window.clear()
+        self.alert_cooldowns.clear()
+
+    def get_state(self, current_time: Optional[datetime] = None) -> Dict[str, Any]:
+        """获取当前状态"""
+        current_time = current_time or datetime.now()
+        window_ratio = self._get_window_ratio()
+        state_info = {
+            "state": self.state,
+            "state_start_time": self.state_start_time.isoformat() if self.state_start_time else None,
+            "window_ratio": window_ratio,
+        }
+        if self.state in (self.STATE_ALARMED,) and self._garbage_start_time:
+            state_info["garbage_duration_sec"] = (current_time - self._garbage_start_time).total_seconds()
+            state_info["alarm_id"] = self._last_alarm_id
+        return state_info
+
+
 class AlgorithmManager:
+    # 参数类型定义，用于三级合并后的类型强制转换
+    _PARAM_TYPES = {
+        "leave_post": {
+            "confirm_on_duty_sec": int, "confirm_off_duty_sec": int,
+            "confirm_leave_sec": int, "leave_countdown_sec": int, "cooldown_sec": int,
+        },
+        "intrusion": {
+            "cooldown_seconds": int, "confirm_seconds": int,
+            "confirm_intrusion_seconds": int, "confirm_clear_seconds": int,
+        },
+        "illegal_parking": {
+            "confirm_vehicle_sec": int, "parking_countdown_sec": int,
+            "confirm_clear_sec": int, "cooldown_sec": int,
+        },
+        "vehicle_congestion": {
+            "count_threshold": int, "confirm_congestion_sec": int,
+            "confirm_clear_sec": int, "cooldown_sec": int,
+        },
+        "non_motor_vehicle_parking": {
+            "confirm_vehicle_sec": int, "parking_countdown_sec": int,
+            "confirm_clear_sec": int, "cooldown_sec": int,
+        },
+        "garbage": {
+            "confirm_garbage_sec": int, "confirm_clear_sec": int,
+            "cooldown_sec": int,
+        },
+    }
+
     def __init__(self, working_hours: Optional[List[Dict]] = None):
         self.algorithms: Dict[str, Dict[str, Any]] = {}
         self.working_hours = working_hours or []
         self._update_lock = threading.Lock()
         self._registered_keys: set = set()  # 已注册的 (roi_id, bind_id, algo_type) 缓存
+        self._global_params: Dict[str, Dict] = {}  # 全局参数 {algo_code: params_dict}
 
         # Bug fix: 默认参数与算法构造函数一致
         self.default_params = {
@@ -1318,12 +1857,60 @@ class AlgorithmManager:
                 "cooldown_sec": 1800,      # Bug fix: 与算法构造函数默认值一致（1800，非600）
                 "target_classes": ["car", "truck", "bus", "motorcycle"],
             },
+            "non_motor_vehicle_parking": {
+                "confirm_vehicle_sec": 10,
+                "parking_countdown_sec": 180,
+                "confirm_clear_sec": 60,
+                "cooldown_sec": 900,
+                "target_classes": ["bicycle", "motorcycle"],
+            },
+            "garbage": {
+                "confirm_garbage_sec": 60,
+                "confirm_clear_sec": 60,
+                "cooldown_sec": 1800,
+                "target_classes": ["garbage"],
+            },
         }
 
         self._pubsub = None
         self._pubsub_thread = None
         self._running = False
 
+    def update_global_params(self, global_params_map: Dict[str, Dict]):
+        """更新全局参数
+
+        Args:
+            global_params_map: {algo_code: params_dict} 格式的全局参数
+        """
+        with self._update_lock:
+            self._global_params = global_params_map or {}
+            logger.info(f"全局参数已更新: {list(self._global_params.keys())}")
+
+    def _coerce_param_types(self, algorithm_type: str, params: dict) -> dict:
+        """强制转换参数类型，防止字符串型数字导致算法异常"""
+        type_map = self._PARAM_TYPES.get(algorithm_type, {})
+        for key, expected_type in type_map.items():
+            if key in params and params[key] is not None:
+                try:
+                    if not isinstance(params[key], expected_type):
+                        params[key] = expected_type(params[key])
+                except (ValueError, TypeError):
+                    logger.warning(f"参数类型转换失败: {algorithm_type}.{key}={params[key]!r}, 删除使用默认值")
+                    del params[key]
+        return params
+
+    def get_min_alarm_duration(self, algorithm_type: str) -> Optional[int]:
+        """从全局参数获取最小告警持续时间（秒）
+
+        Args:
+            algorithm_type: 算法类型（如 leave_post, intrusion）
+
+        Returns:
+            最小告警持续时间秒数，未配置返回 None
+        """
+        gp = self._global_params.get(algorithm_type, {})
+        return gp.get("min_alarm_duration_sec")
+
     def start_config_subscription(self):
         """启动配置变更订阅"""
         try:
@@ -1421,6 +2008,9 @@ class AlgorithmManager:
                 else:
                     params = {}
 
+                # 强制转换参数类型（防止字符串型数字）
+                params = self._coerce_param_types(algo_code, params)
+
                 if roi_id not in self.algorithms:
                     self.algorithms[roi_id] = {}
 
@@ -1537,6 +2127,66 @@ class AlgorithmManager:
                             dissipation_ratio=algo_params["dissipation_ratio"],
                         )
                         logger.info(f"已从Redis加载拥堵算法: {key}")
+                elif algo_code == "non_motor_vehicle_parking":
+                    configured_alarm_level = params.get("alarm_level")
+                    algo_params = {
+                        "confirm_vehicle_sec": params.get("confirm_vehicle_sec", 10),
+                        "parking_countdown_sec": params.get("parking_countdown_sec", 180),
+                        "confirm_clear_sec": params.get("confirm_clear_sec", 60),
+                        "cooldown_sec": params.get("cooldown_sec", 900),
+                        "target_classes": params.get("target_classes", ["bicycle", "motorcycle"]),
+                    }
+                    if key in self.algorithms.get(roi_id, {}) and "non_motor_vehicle_parking" in self.algorithms[roi_id].get(key, {}):
+                        algo = self.algorithms[roi_id][key]["non_motor_vehicle_parking"]
+                        algo.confirm_vehicle_sec = algo_params["confirm_vehicle_sec"]
+                        algo.parking_countdown_sec = algo_params["parking_countdown_sec"]
+                        algo.confirm_clear_sec = algo_params["confirm_clear_sec"]
+                        algo.cooldown_sec = algo_params["cooldown_sec"]
+                        algo.target_classes = algo_params["target_classes"]
+                        if configured_alarm_level is not None:
+                            algo._alarm_level = configured_alarm_level
+                        logger.info(f"已热更新非机动车违停算法参数: {key}")
+                    else:
+                        self.algorithms[roi_id][key] = {}
+                        self.algorithms[roi_id][key]["non_motor_vehicle_parking"] = NonMotorVehicleParkingAlgorithm(
+                            confirm_vehicle_sec=algo_params["confirm_vehicle_sec"],
+                            parking_countdown_sec=algo_params["parking_countdown_sec"],
+                            confirm_clear_sec=algo_params["confirm_clear_sec"],
+                            cooldown_sec=algo_params["cooldown_sec"],
+                            target_classes=algo_params["target_classes"],
+                            alarm_level=configured_alarm_level,
+                        )
+                        logger.info(f"已从Redis加载非机动车违停算法: {key}")
+                elif algo_code == "garbage":
+                    configured_alarm_level = params.get("alarm_level")
+                    algo_params = {
+                        "confirm_garbage_sec": params.get("confirm_garbage_sec", 60),
+                        "confirm_clear_sec": params.get("confirm_clear_sec", 60),
+                        "cooldown_sec": params.get("cooldown_sec", 1800),
+                        "target_classes": params.get("target_classes", ["garbage"]),
+                    }
+                    if key in self.algorithms.get(roi_id, {}) and "garbage" in self.algorithms[roi_id].get(key, {}):
+                        algo = self.algorithms[roi_id][key]["garbage"]
+                        algo.confirm_garbage_sec = algo_params["confirm_garbage_sec"]
+                        algo.confirm_clear_sec = algo_params["confirm_clear_sec"]
+                        algo.cooldown_sec = algo_params["cooldown_sec"]
+                        algo.target_classes = algo_params["target_classes"]
+                        if configured_alarm_level is not None:
+                            algo._alarm_level = configured_alarm_level
+                        logger.info(f"已热更新垃圾检测算法参数: {key}")
+                    else:
+                        if roi_id not in self.algorithms:
+                            self.algorithms[roi_id] = {}
+                        if key not in self.algorithms[roi_id]:
+                            self.algorithms[roi_id][key] = {}
+                        self.algorithms[roi_id][key]["garbage"] = GarbageDetectionAlgorithm(
+                            confirm_garbage_sec=algo_params["confirm_garbage_sec"],
+                            confirm_clear_sec=algo_params["confirm_clear_sec"],
+                            cooldown_sec=algo_params["cooldown_sec"],
+                            target_classes=algo_params["target_classes"],
+                            alarm_level=configured_alarm_level,
+                        )
+                        logger.info(f"已从Redis加载垃圾检测算法: {key}")
 
             return True
         except Exception as e:
@@ -1591,6 +2241,9 @@ class AlgorithmManager:
             params = json.loads(params_str) if isinstance(params_str, str) else params_str
             algo_code = bind_config.get("algo_code")
 
+            # 强制转换参数类型（防止字符串型数字）
+            params = self._coerce_param_types(algo_code, params)
+
             # 获取现有算法实例
             existing_algo = self.algorithms[roi_id][key].get(algo_code)
 
@@ -1661,6 +2314,31 @@ class AlgorithmManager:
 
                 logger.info(f"[{roi_id}_{bind_id}] 更新拥堵检测参数")
 
+            elif algo_code == "non_motor_vehicle_parking":
+                existing_algo.confirm_vehicle_sec = params.get("confirm_vehicle_sec", 10)
+                existing_algo.parking_countdown_sec = params.get("parking_countdown_sec", 180)
+                existing_algo.confirm_clear_sec = params.get("confirm_clear_sec", 60)
+                existing_algo.cooldown_sec = params.get("cooldown_sec", 900)
+                if "target_classes" in params:
+                    existing_algo.target_classes = params["target_classes"]
+                alarm_level = params.get("alarm_level")
+                if alarm_level is not None:
+                    existing_algo._alarm_level = alarm_level
+
+                logger.info(f"[{roi_id}_{bind_id}] 更新非机动车违停检测参数")
+
+            elif algo_code == "garbage":
+                existing_algo.confirm_garbage_sec = params.get("confirm_garbage_sec", 60)
+                existing_algo.confirm_clear_sec = params.get("confirm_clear_sec", 60)
+                existing_algo.cooldown_sec = params.get("cooldown_sec", 1800)
+                if "target_classes" in params:
+                    existing_algo.target_classes = params["target_classes"]
+                alarm_level = params.get("alarm_level")
+                if alarm_level is not None:
+                    existing_algo._alarm_level = alarm_level
+
+                logger.info(f"[{roi_id}_{bind_id}] 更新垃圾检测参数")
+
             return True
 
         except Exception as e:
@@ -1757,9 +2435,17 @@ class AlgorithmManager:
             self.algorithms[roi_id][key] = {}
 
         algo_params = self.default_params.get(algorithm_type, {}).copy()
+        # 三级合并：默认参数 → 全局参数 → 绑定级参数
+        with self._update_lock:
+            global_p = self._global_params.get(algorithm_type, {}).copy()
+        if global_p:
+            algo_params.update(global_p)
         if params:
             algo_params.update(params)
 
+        # 强制转换参数类型（防止字符串型数字）
+        algo_params = self._coerce_param_types(algorithm_type, algo_params)
+
         # 从 params 中提取告警等级（前端配置下发）
         configured_alarm_level = algo_params.get("alarm_level")
 
@@ -1800,6 +2486,23 @@ class AlgorithmManager:
                 alarm_level=configured_alarm_level,
                 dissipation_ratio=algo_params.get("dissipation_ratio", 0.5),
             )
+        elif algorithm_type == "non_motor_vehicle_parking":
+            self.algorithms[roi_id][key]["non_motor_vehicle_parking"] = NonMotorVehicleParkingAlgorithm(
+                confirm_vehicle_sec=algo_params.get("confirm_vehicle_sec", 10),
+                parking_countdown_sec=algo_params.get("parking_countdown_sec", 180),
+                confirm_clear_sec=algo_params.get("confirm_clear_sec", 60),
+                cooldown_sec=algo_params.get("cooldown_sec", 900),
+                target_classes=algo_params.get("target_classes", ["bicycle", "motorcycle"]),
+                alarm_level=configured_alarm_level,
+            )
+        elif algorithm_type == "garbage":
+            self.algorithms[roi_id][key]["garbage"] = GarbageDetectionAlgorithm(
+                confirm_garbage_sec=algo_params.get("confirm_garbage_sec", 60),
+                confirm_clear_sec=algo_params.get("confirm_clear_sec", 60),
+                cooldown_sec=algo_params.get("cooldown_sec", 1800),
+                target_classes=algo_params.get("target_classes", ["garbage"]),
+                alarm_level=configured_alarm_level,
+            )
 
         self._registered_keys.add(cache_key)
 
@@ -1892,7 +2595,7 @@ class AlgorithmManager:
                             "state": getattr(algo, "state", "WAITING"),
                             "alarm_sent": getattr(algo, "alarm_sent", False),
                         }
-                    elif algo_type in ("illegal_parking", "vehicle_congestion"):
+                    elif algo_type in ("illegal_parking", "vehicle_congestion", "non_motor_vehicle_parking", "garbage"):
                         status[f"{algo_type}_{bind_id}"] = algo.get_state()
                     else:
                         status[f"{algo_type}_{bind_id}"] = {

config/config_models.py

@@ -22,6 +22,7 @@ class AlgorithmType(str, Enum):
     INTRUSION = "intrusion"
     ILLEGAL_PARKING = "illegal_parking"
     VEHICLE_CONGESTION = "vehicle_congestion"
+    NON_MOTOR_VEHICLE_PARKING = "non_motor_vehicle_parking"
     CROWD_DETECTION = "crowd_detection"
     FACE_RECOGNITION = "face_recognition"
 

config/database.py

@@ -259,6 +259,15 @@ class SQLiteManager:
         except Exception:
             pass  # 列已存在，忽略
 
+        # 算法全局参数表
+        cursor.execute("""
+            CREATE TABLE IF NOT EXISTS algo_global_params (
+                algo_code TEXT PRIMARY KEY,
+                params TEXT NOT NULL DEFAULT '{}',
+                updated_at TEXT
+            )
+        """)
+
         self._init_default_algorithms()
 
     def _init_default_algorithms(self):
@@ -948,6 +957,39 @@ class SQLiteManager:
             logger.error(f"获取所有绑定ID失败: {e}")
             return []
 
+    def save_global_params(self, algo_code: str, params_dict: Dict[str, Any]) -> bool:
+        """保存算法全局参数（INSERT OR REPLACE）"""
+        try:
+            cursor = self._conn.cursor()
+            now = datetime.now().isoformat()
+            cursor.execute("""
+                INSERT OR REPLACE INTO algo_global_params (algo_code, params, updated_at)
+                VALUES (?, ?, ?)
+            """, (algo_code, json.dumps(params_dict, ensure_ascii=False), now))
+            self._conn.commit()
+            return True
+        except Exception as e:
+            logger.error(f"保存算法全局参数失败: {e}")
+            return False
+
+    def get_all_global_params(self) -> Dict[str, Dict[str, Any]]:
+        """获取所有算法全局参数，返回 {algo_code: params_dict}"""
+        result: Dict[str, Dict[str, Any]] = {}
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute("SELECT algo_code, params FROM algo_global_params")
+            for row in cursor.fetchall():
+                algo_code = row[0]
+                params_str = row[1]
+                try:
+                    result[algo_code] = json.loads(params_str) if params_str else {}
+                except (json.JSONDecodeError, TypeError):
+                    result[algo_code] = {}
+            return result
+        except Exception as e:
+            logger.error(f"获取算法全局参数失败: {e}")
+            return result
+
     def log_config_update(
         self,
         config_type: str,

config/settings.py

@@ -143,19 +143,40 @@ class DebugConfig:
     local_config_path: str = "./config/local_config.json"
 
 
-# COCO 数据集类别名称（YOLO 模型使用）
-COCO_CLASS_NAMES = [
-    "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
-    "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
-    "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
-    "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball",
-    "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket",
-    "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
-    "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
-    "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop",
-    "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink",
-    "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"
-]
+# 模型类别配置（支持不同模型切换，修改此处即可）
+# 当前: yolo11s_v2plus_20260421 (5 类定制模型)
+# 历史: yolo11n 官方 (80 类 COCO)，列表见 git 历史
+MODEL_CLASS_NAMES = ["garbage", "person", "car", "bicycle", "motorcycle"]
+MODEL_NUM_CLASSES = len(MODEL_CLASS_NAMES)  # 模型类别数
+MODEL_OUTPUT_CHANNELS = 4 + MODEL_NUM_CLASSES  # YOLO 输出通道 = 4(xywh) + nc
+
+# 向后兼容：保留 COCO_CLASS_NAMES 名称，指向当前模型类别
+COCO_CLASS_NAMES = MODEL_CLASS_NAMES
+
+
+# 各算法的业务关注类别（全集，不限定当前模型是否支持）
+# 部署时通过 get_algo_target_classes() 自动过滤出当前模型支持的子集
+ALGO_INTENT_CLASSES = {
+    "leave_post": ["person"],
+    "intrusion": ["person"],
+    "illegal_parking": ["car", "truck", "bus"],
+    "vehicle_congestion": ["car", "truck", "bus", "motorcycle"],
+    "non_motor_vehicle_parking": ["bicycle", "motorcycle"],
+    "garbage": ["garbage"],
+}
+
+
+def get_algo_target_classes(algo_code: str) -> list:
+    """获取算法的目标类别，自动过滤出当前模型支持的类
+
+    Args:
+        algo_code: 算法代码 (leave_post / intrusion / illegal_parking / ...)
+
+    Returns:
+        目标类名列表，保证每个类都在 MODEL_CLASS_NAMES 中
+    """
+    intent = ALGO_INTENT_CLASSES.get(algo_code, [])
+    return [c for c in intent if c in MODEL_CLASS_NAMES]
 
 
 @dataclass

core/config_sync.py

@@ -15,6 +15,7 @@ import json
 import logging
 import os
 import platform
+import re
 import socket
 
 # 禁用系统代理（Clash 等代理工具会干扰 Redis TCP 长连接）
@@ -53,6 +54,12 @@ def _build_keepalive_options():
     return opts
 
 
+def _camel_to_snake(name: str) -> str:
+    """将 camelCase 转换为 snake_case"""
+    s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
+    return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
+
+
 # ==================== Redis Key 常量 ====================
 
 # 云端 Redis Keys
@@ -643,6 +650,21 @@ class ConfigSyncManager:
             # 清理 SQLite 中不在本次推送列表中的旧数据
             self._cleanup_stale_records(incoming_camera_ids, incoming_roi_ids, incoming_bind_ids)
 
+            # 同步全局参数
+            global_params = config_data.get("global_params") or config_data.get("globalParams") or {}
+            if global_params and isinstance(global_params, dict):
+                for algo_code, params_dict in global_params.items():
+                    if isinstance(params_dict, dict):
+                        # 防御性转换：camelCase → snake_case
+                        params_dict = {_camel_to_snake(k): v for k, v in params_dict.items()}
+                        self._db_manager.save_global_params(algo_code, params_dict)
+                logger.info(f"全局参数同步完成: {list(global_params.keys())}")
+
+                # 通知全局参数更新回调
+                self._notify_callbacks("global_params_update", {
+                    "global_params": global_params,
+                })
+
         except Exception as e:
             logger.error(f"配置同步到 SQLite 失败: {e}")
 

core/postprocessor.py

@@ -20,7 +20,7 @@ try:
 except ImportError:
     _HAS_TORCH = False
 
-from config.settings import get_settings
+from config.settings import get_settings, MODEL_OUTPUT_CHANNELS, MODEL_NUM_CLASSES
 from config.config_models import ROIInfo, ROIType, AlertInfo, AlertLevel
 from utils.logger import get_logger
 from utils.common import generate_unique_id
@@ -667,14 +667,14 @@ class PostProcessor:
         if output.ndim != 2:
             return np.array([]), np.array([]), np.array([])
         
-        if output.shape[0] != 84:
+        if output.shape[0] != MODEL_OUTPUT_CHANNELS:
             return np.array([]), np.array([]), np.array([])
 
         boxes_xywh = output[0:4, :].T
 
-        # YOLO11 输出格式: [4+num_classes, 8400]
-        # 前4行是 xywh，后80行是各类别分数，没有单独的 objectness 行
-        class_scores = output[4:, :]  # [num_classes, 8400]
+        # YOLO11 输出格式: [4+num_classes, num_anchors]
+        # 前4行是 xywh，后 num_classes 行是各类别分数，没有单独的 objectness 行
+        class_scores = output[4:, :]  # [num_classes, num_anchors]
         scores = class_scores.max(axis=0)  # 取各类别最大分数
         class_ids = class_scores.argmax(axis=0)  # 对应类别ID
 
@@ -734,15 +734,15 @@ class PostProcessor:
             first_output = batch_outputs[0]
             if isinstance(first_output, np.ndarray):
                 if first_output.ndim == 3 and first_output.shape[0] == batch_size:
-                    # 已经是 (batch, 84, anchors) 格式
+                    # 已经是 (batch, 4+nc, anchors) 格式
                     outputs_array = first_output
                 elif first_output.ndim == 1:
-                    # TensorRT 返回扁平 1D 数组，需要 reshape 为 (batch, 84, anchors)
+                    # TensorRT 返回扁平 1D 数组，需要 reshape 为 (batch, 4+nc, anchors)
                     per_image = first_output.shape[0] // batch_size
-                    num_anchors = per_image // 84
-                    outputs_array = first_output.reshape(batch_size, 84, num_anchors)
+                    num_anchors = per_image // MODEL_OUTPUT_CHANNELS
+                    outputs_array = first_output.reshape(batch_size, MODEL_OUTPUT_CHANNELS, num_anchors)
                 elif first_output.ndim == 2:
-                    # (84, anchors) 单张图的输出
+                    # (4+nc, anchors) 单张图的输出
                     outputs_array = first_output.reshape(1, first_output.shape[0], first_output.shape[1])
                 else:
                     outputs_array = first_output
@@ -771,7 +771,7 @@ class PostProcessor:
             if output.ndim == 3:
                 output = output[0]
             
-            if output.shape[0] != 84:
+            if output.shape[0] != MODEL_OUTPUT_CHANNELS:
                 results.append((np.array([]), np.array([]), np.array([])))
                 continue
             

docker-compose.yml

@@ -0,0 +1,46 @@
+version: "3.8"
+
+services:
+  edge-inference:
+    build: .
+    image: edge-inference:latest
+    container_name: edge-inference
+    restart: always
+
+    # GPU 访问
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+
+    # 环境变量
+    env_file:
+      - .env
+
+    # 卷挂载
+    volumes:
+      - ./models:/app/models        # TensorRT 引擎文件
+      - ./data:/app/data             # SQLite + 截图缓存
+      - ./logs:/app/logs             # 运行日志
+      - ./.env:/app/.env             # 环境配置
+
+    # 网络（需要访问摄像头 RTSP + 云端 API + Redis）
+    network_mode: host
+
+    # 健康检查
+    healthcheck:
+      test: ["CMD", "python", "-c", "import os; assert os.path.exists('/app/main.py')"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 30s
+
+    # 日志限制
+    logging:
+      driver: json-file
+      options:
+        max-size: "50m"
+        max-file: "5"

docs/garbage_algorithm_backend_frontend_plan.md

@@ -0,0 +1,194 @@
+# 垃圾检测算法 - WVP 后端 / 前端改动方案（未实施，预留参考）
+
+## 背景
+
+Edge 端的 `GarbageDetectionAlgorithm` 已实现（commit xxx）。本文档列出 WVP 后端和前端需要做的配套改动，等后续需要在 ROI 编辑器创建垃圾检测绑定时再实施。
+
+---
+
+## 一、WVP 后端改动
+
+### 1.1 算法注册（数据库）
+
+**文件：** `wvp-platform/数据库/版本号/SQL 脚本`
+
+**新增算法记录：**
+```sql
+INSERT INTO wvp_ai_algorithm (
+    algo_code, algo_name, algo_description,
+    is_active, param_schema, global_params
+) VALUES (
+    'garbage',
+    '垃圾检测',
+    '检测监控区域内散落垃圾的持续存在，清洁后自动解除告警',
+    1,
+    '{"confirm_garbage_sec": {"type": "int", "default": 60, "min": 10, "max": 600, "label": "垃圾确认时间(秒)"},
+      "confirm_clear_sec": {"type": "int", "default": 60, "min": 10, "max": 600, "label": "清理确认时间(秒)"},
+      "cooldown_sec": {"type": "int", "default": 1800, "min": 300, "max": 7200, "label": "告警冷却时间(秒)"},
+      "alarm_level": {"type": "int", "default": 2, "min": 0, "max": 3, "label": "告警等级"}
+     }',
+    '{}'
+);
+```
+
+### 1.2 Java 算法服务
+
+**文件：** `wvp-platform/src/main/java/com/genersoft/iot/vmp/aiot/service/impl/AiAlgorithmServiceImpl.java`
+
+**改动：** 算法代码白名单（约 line 42-54），添加 `"garbage"`：
+
+```java
+private static final Set<String> SUPPORTED_ALGO_CODES = Set.of(
+    "leave_post", "intrusion", "illegal_parking",
+    "vehicle_congestion", "non_motor_vehicle_parking",
+    "garbage"  // 新增
+);
+```
+
+### 1.3 配置下发
+
+不需要改动。现有 `AiRedisConfigServiceImpl` 和 `global_params` 机制已通用。
+
+---
+
+## 二、iot-device-management-service 改动
+
+**文件：** `app/constants.py`
+
+检查 `AlarmType` 枚举是否已有 `GARBAGE`：
+- 已有 → 无需改动
+- 未有 → 添加：
+
+```python
+class AlarmType(str, Enum):
+    LEAVE_POST = "leave_post"
+    INTRUSION = "intrusion"
+    ILLEGAL_PARKING = "illegal_parking"
+    VEHICLE_CONGESTION = "vehicle_congestion"
+    NON_MOTOR_VEHICLE_PARKING = "non_motor_vehicle_parking"
+    GARBAGE = "garbage"  # 新增
+
+ALARM_TYPE_NAMES: Dict[str, str] = {
+    ...
+    AlarmType.GARBAGE: "垃圾检测",
+}
+```
+
+**文件：** `app/services/vlm_service.py`（VLM 复核提示词）
+
+添加 garbage 的提示词模板：
+```python
+"garbage": """你是安防监控AI复核员。算法类型：垃圾检测，监控区域：{roi_name}。
+截图显示时间：{timestamp}。
+任务：判断图中是否真的存在散落的垃圾、包装袋、废弃物等需要清理的物品。
+分析要点：
+1. 是否存在明显的垃圾（垃圾袋、纸屑、瓶罐、食品包装等）
+2. 区分垃圾与正常物品（整齐放置的物品、装饰品不算垃圾）
+3. 垃圾是否在通道/地面等不该出现的位置
+4. 排除阴影、污渍、地砖花纹等误检
+仅输出JSON：{{"confirmed":true,"description":"..."}}""",
+```
+
+---
+
+## 三、前端改动（iot-device-management-frontend）
+
+### 3.1 告警列表类型筛选
+
+**文件：** `apps/web-antd/src/views/aiot/alarm/list/data.ts`
+
+```typescript
+export const ALERT_TYPE_OPTIONS = [
+  { label: '人员离岗', value: 'leave_post' },
+  { label: '周界入侵', value: 'intrusion' },
+  { label: '车辆违停', value: 'illegal_parking' },
+  { label: '车辆拥堵', value: 'vehicle_congestion' },
+  { label: '非机动车违停', value: 'non_motor_vehicle_parking' },
+  { label: '垃圾检测', value: 'garbage' },  // 新增
+];
+```
+
+### 3.2 告警标签颜色
+
+**文件：** `apps/web-antd/src/views/aiot/alarm/list/index.vue`
+
+```typescript
+const colorMap: Record<string, string> = {
+  leave_post: 'orange',
+  intrusion: 'red',
+  illegal_parking: 'blue',
+  vehicle_congestion: 'geekblue',
+  non_motor_vehicle_parking: 'green',
+  garbage: 'gold',  // 新增 — 建议金色表达清洁主题
+};
+```
+
+### 3.3 ROI 编辑器参数配置
+
+**文件：** `apps/web-antd/src/views/aiot/device/roi/components/AlgorithmParamEditor.vue`
+
+参数字段已通用（读自 `algo.paramSchema`），无需改动。**但需要添加参数名中文映射：**
+
+**文件：** `AlgorithmParamEditor.vue` 的 `paramNameMap` 和 `paramDescMap`
+
+```typescript
+const paramNameMap: Record<string, string> = {
+  // ... 已有
+  confirm_garbage_sec: '垃圾确认时间(秒)',
+};
+
+const paramDescMap: Record<string, string> = {
+  // ... 已有
+  confirm_garbage_sec: '持续检测到垃圾的时间，超过该时间触发告警（建议 60-120 秒）',
+};
+```
+
+### 3.4 全局参数配置页
+
+**文件：** `apps/web-antd/src/views/aiot/device/algorithm/index.vue`
+
+`paramNameMap` 和 `paramDescMap` 同样需要添加 `confirm_garbage_sec` 条目（参见 3.3）。
+
+---
+
+## 四、验证顺序（将来实施时）
+
+1. **后端数据库注册算法记录**
+2. **WVP 后端重启** — 白名单生效
+3. **Service 端** constants.py 添加（如需要）
+4. **前端重启** — 下拉选项和颜色生效
+5. **ROI 编辑器创建一个 garbage 绑定，参数用默认值**
+6. **前端触发配置推送** — 验证 Edge 端收到并注册算法
+7. **Edge 日志验证：** 应看到 `已从Redis加载垃圾检测算法: roi_xxx_bind_xxx`
+8. **模拟测试：** 放个垃圾在摄像头前，60 秒后应触发告警
+9. **清理测试：** 移除垃圾 30 秒后应收到 resolve 事件
+10. **企微卡片收到告警 + 创建工单全流程**
+
+---
+
+## 五、TensorRT 引擎部署（最后一步）
+
+当确定用微调模型替换 COCO 预训练模型时：
+
+1. **导出 engine：**
+   ```bash
+   yolo export model=yolo11s_v1_20260417.pt format=engine imgsz=480 half=True device=0
+   ```
+
+2. **替换 Edge 端模型：**
+   ```bash
+   cp yolo11s_v1_20260417.engine /opt/edge/models/yolo11n.engine  # 注意文件名
+   ```
+
+3. **修改 `config/settings.py` 的 COCO_CLASS_NAMES：**
+   ```python
+   COCO_CLASS_NAMES = ['garbage', 'person', 'car', 'bicycle', 'motorcycle']
+   ```
+
+4. **修改 `core/postprocessor.py` 的输出解析：**
+   - YOLO 输出从 `[84, 8400]`（4+80类）变为 `[9, 8400]`（4+5类）
+   - 类别分数范围从 `output[4:84]` 改为 `output[4:9]`
+
+5. **重启 Edge 服务**
+
+这一步涉及模型 + 推理管线，需要单独在生产环境测试。

main.py

@@ -18,7 +18,6 @@ for _key in ("http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY", "all_prox
 
 from config.settings import get_settings, Settings
 from core.config_sync import get_config_sync_manager, ConfigSyncManager
-from core.debug_http_server import start_debug_http_server
 from core.video_stream import MultiStreamManager, VideoFrame
 from core.preprocessor import ImagePreprocessor
 from core.tensorrt_engine import TensorRTEngine, EngineManager
@@ -56,8 +55,6 @@ class EdgeInferenceService:
         self._screenshot_handler: Optional[ScreenshotHandler] = None
         self._algorithm_manager: Optional[AlgorithmManager] = None
         self._debug_reload_thread: Optional[threading.Thread] = None
-        self._debug_http_server = None
-        self._debug_http_thread: Optional[threading.Thread] = None
         self._heartbeat_thread: Optional[threading.Thread] = None
         self._scheduler_thread: Optional[threading.Thread] = None
         
@@ -132,6 +129,18 @@ class EdgeInferenceService:
                         daemon=True
                     ).start()
                 self._config_manager.register_callback("config_update", _on_config_update)
+
+                def _on_global_params_update(topic, data):
+                    if self._algorithm_manager:
+                        global_params = data.get("global_params", {})
+                        self._algorithm_manager.update_global_params(global_params)
+                        # 只清除受影响算法的注册缓存，避免无关算法状态丢失
+                        affected_algos = set(global_params.keys())
+                        keys_to_remove = [k for k in self._algorithm_manager._registered_keys if k[2] in affected_algos]
+                        for key in keys_to_remove:
+                            self._algorithm_manager._registered_keys.discard(key)
+                        self._logger.info(f"全局参数回调已触发，清除 {len(keys_to_remove)} 个受影响算法的注册缓存")
+                self._config_manager.register_callback("global_params_update", _on_global_params_update)
             self._logger.info("配置管理器初始化成功")
         except Exception as e:
             self._logger.error(f"配置管理器初始化失败: {e}")
@@ -198,6 +207,18 @@ class EdgeInferenceService:
         try:
             self._algorithm_manager = AlgorithmManager()
             self._algorithm_manager.start_config_subscription()
+
+            # 启动时从 SQLite 加载已有全局参数
+            try:
+                from config.database import get_sqlite_manager
+                db = get_sqlite_manager()
+                saved_global_params = db.get_all_global_params()
+                if saved_global_params:
+                    self._algorithm_manager.update_global_params(saved_global_params)
+                    self._logger.info(f"从 SQLite 加载全局参数: {list(saved_global_params.keys())}")
+            except Exception as e:
+                self._logger.warning(f"从 SQLite 加载全局参数失败: {e}")
+
             self._logger.info("算法管理器初始化成功")
         except Exception as e:
             self._logger.error(f"算法管理器初始化失败: {e}")
@@ -281,32 +302,6 @@ class EdgeInferenceService:
         )
         self._debug_reload_thread.start()
 
-    def _start_debug_http_server(self):
-        """本地调试：启动 HTTP 同步接口"""
-        if self._settings.config_sync_mode != "LOCAL":
-            return
-        if not getattr(self._settings, "debug", None) or not self._settings.debug.enabled:
-            return
-        if self._debug_http_server is not None:
-            return
-
-        host = self._settings.debug.host
-        port = self._settings.debug.port
-        self._debug_http_server = start_debug_http_server(host, port)
-
-        def worker():
-            try:
-                self._debug_http_server.serve_forever()
-            except Exception as e:
-                self._logger.warning(f"[DEBUG] HTTP 服务器异常: {e}")
-
-        self._debug_http_thread = threading.Thread(
-            target=worker,
-            name="DebugHttpServer",
-            daemon=True,
-        )
-        self._debug_http_thread.start()
-
     def _start_heartbeat(self):
         """启动心跳守护线程，每 30 秒向云端上报设备状态"""
         def worker():
@@ -379,7 +374,6 @@ class EdgeInferenceService:
         self._init_algorithm_manager()
         self._init_screenshot_handler()
         self._start_debug_reload_watcher()
-        self._start_debug_http_server()
         self._start_heartbeat()
         
         self._performance_stats["start_time"] = datetime.now()
@@ -1098,12 +1092,6 @@ class EdgeInferenceService:
         if self._reporter:
             self._reporter.close()
 
-        if self._debug_http_server:
-            try:
-                self._debug_http_server.shutdown()
-            except Exception:
-                pass
-        
         self._performance_stats["uptime_seconds"] = (
             (datetime.now() - self._performance_stats["start_time"]).total_seconds()
         )

requirements.txt

@@ -1,64 +1,78 @@
 # Edge_Inference_Service 依赖清单
 # 安装命令: pip install -r requirements.txt
-# 备注：所有版本均选择最稳定版本，经过大量验证
+# 环境要求: Python 3.10 | CUDA 12.1 | cuDNN 8.9 | TensorRT 8.6.1
+# Docker 基础镜像: nvcr.io/nvidia/tensorrt:23.08-py3
 
 # ============================================================
-# 核心依赖（必需）
+# GPU 推理依赖（TensorRT 8.6 + CUDA 12.1）
 # ============================================================
 
-# 视频处理 - OpenCV 4.8.0，最稳定的4.x版本
-opencv-python==4.8.0.74
+# PyTorch - CUDA 12.1 下最稳定版本
+# 安装命令: pip install torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
+--extra-index-url https://download.pytorch.org/whl/cu121
+torch==2.1.2
+torchvision==0.16.2
 
-# 数值计算 - NumPy 1.24.0，Python 3.8-3.11完美兼容
-numpy==1.24.0
+# TensorRT Python 绑定（NGC 镜像已预装，裸机需手动安装）
+tensorrt==8.6.1.6
+pycuda==2023.1.1
+
+# YOLO11 目标检测框架
+ultralytics==8.3.5
+
+# ONNX 模型转换与优化
+onnx==1.16.0
+onnxsim==0.4.36
+onnxruntime-gpu==1.17.1
+
+# ============================================================
+# 核心依赖
+# ============================================================
+
+# 视频处理
+opencv-python==4.8.0.76
+
+# 数值计算（锁定 1.x，避开 NumPy 2.0 破坏性变更）
+numpy==1.26.4
+
+# 图像处理
+Pillow==10.2.0
 
 # ============================================================
 # 数据库依赖
 # ============================================================
 
-# ORM框架 - SQLAlchemy 2.0.23，长期支持稳定版
+# ORM 框架
 sqlalchemy==2.0.23
 
-# MySQL驱动 - PyMySQL 1.1.0，成熟稳定版本
+# MySQL 驱动
 pymysql==1.1.0
 
 # ============================================================
 # 消息队列与缓存
 # ============================================================
 
-# MQTT客户端 - Paho-MQTT 1.6.1，1.x最终稳定版
+# MQTT 客户端（1.x 最终稳定版）
 paho-mqtt==1.6.1
 
-# Redis客户端 - Redis 4.6.0，4.x最终稳定版
+# Redis 客户端
 redis==4.6.0
 
-# 腾讯云COS SDK - 用于截图上传
+# 腾讯云 COS SDK（截图上传）
 cos-python-sdk-v5>=1.9.30
 
 # ============================================================
 # 工具库
 # ============================================================
 
-# YAML解析 - PyYAML 6.0.1，安全稳定版
 pyyaml==6.0.1
+requests==2.31.0
+psutil==5.9.8
+python-dotenv==1.0.1
 
 # ============================================================
-# 测试框架
+# 测试依赖
 # ============================================================
 
-# 单元测试 - PyTest 7.4.4，7.x最终稳定版
 pytest==7.4.4
-
-# 覆盖率报告 - PyTest-Cov 4.1.0，成熟稳定版
 pytest-cov==4.1.0
-
-# ============================================================
-# 可选依赖（按需安装）
-# ============================================================
-
-# GPU推理框架（需要CUDA 12.1环境）
-# tensorrt==8.6.1.6
-# pycuda==2023.1.1
-
-# YOLOv8目标检测（按需安装）
-# ultralytics==8.0.228

test_garbage_algorithm.py

@@ -0,0 +1,314 @@
+"""
+GarbageDetectionAlgorithm 单元测试
+
+覆盖场景：
+1. 无垃圾时保持 IDLE
+2. 持续检测到垃圾 → 确认 → 告警
+3. 冷却期内不重复触发
+4. 清理后发 resolve → 回到 IDLE
+5. 清理确认期内垃圾再次出现 → 回到 ALARMED
+6. reset() 正确清理状态
+"""
+import sys
+import os
+import logging
+
+logging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from datetime import datetime, timedelta
+from algorithms import GarbageDetectionAlgorithm
+
+
+# ===== 工具函数 =====
+
+def make_tracks(roi_id: str, classes: list, confidences: list = None):
+    """生成模拟检测结果"""
+    if confidences is None:
+        confidences = [0.85] * len(classes)
+    tracks = []
+    for i, cls in enumerate(classes):
+        tracks.append({
+            "track_id": f"{roi_id}_{i}",
+            "class": cls,
+            "confidence": confidences[i],
+            "bbox": [100 + i * 50, 100, 200 + i * 50, 300],
+            "matched_rois": [{"roi_id": roi_id}],
+        })
+    return tracks
+
+
+def simulate(algo, roi_id, camera_id, get_tracks_fn, count, interval=1.0, start_time=None):
+    """连续模拟帧，返回所有 alerts 和最后时间戳"""
+    t = start_time or datetime(2026, 4, 17, 10, 0, 0)
+    all_alerts = []
+    for i in range(count):
+        tracks = get_tracks_fn(i)
+        alerts = algo.process(roi_id, camera_id, tracks, t)
+        if alerts:
+            all_alerts.extend(alerts)
+        t += timedelta(seconds=interval)
+    return all_alerts, t
+
+
+# ===== 测试 1：无垃圾时保持 IDLE =====
+
+def test_idle_when_no_garbage():
+    print("\n" + "=" * 60)
+    print("TEST 1: 无垃圾帧始终保持 IDLE")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(confirm_garbage_sec=60)
+    alerts, _ = simulate(
+        algo, "roi_1", "cam_1",
+        lambda i: make_tracks("roi_1", ["person"]),  # 只有人，没有垃圾
+        count=100,
+    )
+
+    assert algo.state == "IDLE", f"Expected IDLE, got {algo.state}"
+    assert len(alerts) == 0, f"Expected no alerts, got {len(alerts)}"
+    print(f"  状态: {algo.state}，alerts: {len(alerts)} [OK]")
+
+
+# ===== 测试 2：持续检测到垃圾 → 告警 =====
+
+def test_garbage_triggers_alarm():
+    print("\n" + "=" * 60)
+    print("TEST 2: 持续 65 秒检测到垃圾 → 告警")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(
+        confirm_garbage_sec=60,
+        cooldown_sec=1800,
+    )
+
+    alerts, _ = simulate(
+        algo, "roi_1", "cam_1",
+        lambda i: make_tracks("roi_1", ["garbage"]),
+        count=65,  # 65 秒，超过 60 秒确认期
+    )
+
+    # 应该在第 60-61 秒触发 1 个告警
+    assert algo.state == "ALARMED", f"Expected ALARMED, got {algo.state}"
+    assert len(alerts) == 1, f"Expected 1 alert, got {len(alerts)}"
+    alert = alerts[0]
+    assert alert["alert_type"] == "garbage"
+    assert alert["alarm_level"] == 2
+    assert alert["garbage_count"] == 1
+    assert "检测到垃圾" in alert["message"]
+    print(f"  状态: {algo.state}，告警: {alert['message']} [OK]")
+
+
+# ===== 测试 3：冷却期内不重复触发 =====
+
+def test_cooldown_prevents_duplicate():
+    print("\n" + "=" * 60)
+    print("TEST 3: 告警后冷却期内持续有垃圾，不重复触发")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(
+        confirm_garbage_sec=10,  # 缩短便于测试
+        confirm_clear_sec=10,
+        cooldown_sec=300,         # 5 分钟冷却
+    )
+
+    # 持续 200 秒有垃圾（远超冷却时间但没超过 300 秒）
+    alerts, _ = simulate(
+        algo, "roi_1", "cam_1",
+        lambda i: make_tracks("roi_1", ["garbage"]),
+        count=200,
+    )
+
+    assert len(alerts) == 1, f"Expected 1 alert (cooldown), got {len(alerts)}"
+    assert algo.state == "ALARMED"
+    print(f"  告警次数: {len(alerts)}（冷却期内不重复）[OK]")
+
+
+# ===== 测试 4：清理后发 resolve → IDLE =====
+
+def test_resolve_after_cleaning():
+    print("\n" + "=" * 60)
+    print("TEST 4: 告警后清理 → 发 resolve → IDLE")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(
+        confirm_garbage_sec=10,
+        confirm_clear_sec=10,
+        cooldown_sec=300,
+    )
+    algo._last_alarm_id = "test_alarm_123"  # 模拟 main.py 回填
+
+    t = datetime(2026, 4, 17, 10, 0, 0)
+    all_alerts = []
+
+    # Phase 1: 15 秒有垃圾 → 触发告警
+    for i in range(15):
+        alerts = algo.process(
+            "roi_1", "cam_1",
+            make_tracks("roi_1", ["garbage"]),
+            t + timedelta(seconds=i)
+        )
+        all_alerts.extend(alerts)
+    assert algo.state == "ALARMED"
+
+    # Phase 2: 然后 30 秒无垃圾 → 发 resolve
+    # 需要等滑动窗口(10s)清空 + confirm_clear_sec(10s) = 20+ 秒
+    for i in range(15, 45):
+        alerts = algo.process(
+            "roi_1", "cam_1",
+            make_tracks("roi_1", []),  # 空
+            t + timedelta(seconds=i)
+        )
+        all_alerts.extend(alerts)
+
+    assert algo.state == "IDLE", f"Expected IDLE, got {algo.state}"
+    resolves = [a for a in all_alerts if a.get("alert_type") == "alarm_resolve"]
+    assert len(resolves) == 1, f"Expected 1 resolve, got {len(resolves)}"
+    resolve = resolves[0]
+    assert resolve["resolve_alarm_id"] == "test_alarm_123"
+    assert resolve["resolve_type"] == "garbage_removed"
+    assert resolve["duration_ms"] > 0
+    print(f"  resolve: {resolve['resolve_type']}, 持续 {resolve['duration_ms']}ms [OK]")
+
+
+# ===== 测试 5：清理期内垃圾再出现 → 回到 ALARMED =====
+
+def test_garbage_reappears_during_clearing():
+    print("\n" + "=" * 60)
+    print("TEST 5: 清理确认期内垃圾再出现 → 回到 ALARMED")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(
+        confirm_garbage_sec=10,
+        confirm_clear_sec=20,  # 较长的清理确认期
+        cooldown_sec=300,
+    )
+    algo._last_alarm_id = "test_alarm_456"
+
+    t = datetime(2026, 4, 17, 10, 0, 0)
+
+    # Phase 1: 15 秒有垃圾 → 告警 → ALARMED
+    for i in range(15):
+        algo.process("roi_1", "cam_1", make_tracks("roi_1", ["garbage"]),
+                     t + timedelta(seconds=i))
+    assert algo.state == "ALARMED"
+
+    # Phase 2: 5 秒无垃圾 → CONFIRMING_CLEAR
+    for i in range(15, 25):
+        algo.process("roi_1", "cam_1", make_tracks("roi_1", []),
+                     t + timedelta(seconds=i))
+    assert algo.state == "CONFIRMING_CLEAR", f"got {algo.state}"
+
+    # Phase 3: 垃圾又出现 5 秒 → 回到 ALARMED
+    for i in range(25, 40):
+        algo.process("roi_1", "cam_1", make_tracks("roi_1", ["garbage"]),
+                     t + timedelta(seconds=i))
+
+    assert algo.state == "ALARMED", f"Expected ALARMED, got {algo.state}"
+    print(f"  状态恢复: CONFIRMING_CLEAR → ALARMED [OK]")
+
+
+# ===== 测试 6：reset() 清理状态 =====
+
+def test_reset_clears_state():
+    print("\n" + "=" * 60)
+    print("TEST 6: reset() 正确清理所有状态")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(confirm_garbage_sec=5)
+    algo._last_alarm_id = "test"
+
+    # 先让它进入某个状态
+    t = datetime(2026, 4, 17, 10, 0, 0)
+    for i in range(10):
+        algo.process("roi_1", "cam_1", make_tracks("roi_1", ["garbage"]),
+                     t + timedelta(seconds=i))
+    assert algo.state == "ALARMED"
+    assert len(algo._detection_window) > 0
+    assert len(algo.alert_cooldowns) > 0
+
+    # Reset
+    algo.reset()
+
+    assert algo.state == "IDLE"
+    assert algo.state_start_time is None
+    assert algo._last_alarm_id is None
+    assert algo._garbage_start_time is None
+    assert len(algo._detection_window) == 0
+    assert len(algo.alert_cooldowns) == 0
+    print("  所有状态已清空 [OK]")
+
+
+# ===== 测试 7：多个垃圾目标计数 =====
+
+def test_multiple_garbage_count():
+    print("\n" + "=" * 60)
+    print("TEST 7: ROI 内多个垃圾目标 → garbage_count 正确")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(confirm_garbage_sec=5)
+
+    alerts, _ = simulate(
+        algo, "roi_1", "cam_1",
+        lambda i: make_tracks("roi_1", ["garbage", "garbage", "garbage"]),
+        count=10,
+    )
+
+    assert len(alerts) == 1
+    assert alerts[0]["garbage_count"] == 3
+    print(f"  garbage_count: {alerts[0]['garbage_count']} [OK]")
+
+
+# ===== 测试 8：非 target_class 不计入 =====
+
+def test_non_target_class_ignored():
+    print("\n" + "=" * 60)
+    print("TEST 8: person/car 类不计入（只看 garbage）")
+    print("=" * 60)
+
+    algo = GarbageDetectionAlgorithm(confirm_garbage_sec=10)
+
+    alerts, _ = simulate(
+        algo, "roi_1", "cam_1",
+        lambda i: make_tracks("roi_1", ["person", "car"]),  # 都不是 garbage
+        count=30,
+    )
+
+    assert algo.state == "IDLE", f"Expected IDLE, got {algo.state}"
+    assert len(alerts) == 0
+    print(f"  状态: {algo.state}，无告警 [OK]")
+
+
+# ===== 运行所有测试 =====
+
+if __name__ == "__main__":
+    tests = [
+        test_idle_when_no_garbage,
+        test_garbage_triggers_alarm,
+        test_cooldown_prevents_duplicate,
+        test_resolve_after_cleaning,
+        test_garbage_reappears_during_clearing,
+        test_reset_clears_state,
+        test_multiple_garbage_count,
+        test_non_target_class_ignored,
+    ]
+    passed = 0
+    failed = 0
+    for t in tests:
+        try:
+            t()
+            passed += 1
+        except AssertionError as e:
+            print(f"  FAIL: {e}")
+            failed += 1
+        except Exception as e:
+            print(f"  ERROR: {e}")
+            import traceback
+            traceback.print_exc()
+            failed += 1
+
+    print("\n" + "=" * 60)
+    print(f"结果: {passed} 通过, {failed} 失败")
+    print("=" * 60)
+    sys.exit(0 if failed == 0 else 1)