fix: 修复 MQTT 未连接问题

ResultReporter 创建后需调用 initialize() 方法初始化 MQTT 连接

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

main.py

@@ -123,6 +123,7 @@ class EdgeInferenceService:
     def _init_reporter(self):
         """初始化结果上报器"""
         self._reporter = ResultReporter()
+        self._reporter.initialize()  # 初始化存储和MQTT连接
         self._logger.info("结果上报器初始化成功")
     
     def _init_algorithm_manager(self):
@@ -187,6 +188,10 @@ class EdgeInferenceService:
         """处理视频帧 - 批量处理多 ROI"""
         try:
             roi_configs = self._config_manager.get_roi_configs_with_bindings(camera_id)
+
+            # 每100帧打印一次状态
+            if self._performance_stats["total_frames_processed"] % 100 == 0:
+                self._logger.info(f"[{camera_id}] 已处理 {self._performance_stats['total_frames_processed']} 帧, ROI数: {len(roi_configs)}")
             
             roi_items = []
             for roi in roi_configs:
@@ -252,6 +257,11 @@ class EdgeInferenceService:
                 conf_threshold=self._settings.inference.conf_threshold
             )
 
+            # 诊断日志：显示每个 ROI 的检测结果数量
+            total_detections = sum(len(r[0]) for r in batch_results)
+            if total_detections > 0:
+                self._logger.info(f"[推理] batch_size={batch_size}, 总检测数={total_detections}")
+
             for idx, (camera_id, roi, bind, frame, _, scale_info) in enumerate(roi_items):
                 boxes, scores, class_ids = batch_results[idx]
 
@@ -319,23 +329,30 @@ class EdgeInferenceService:
             if self._algorithm_manager is None:
                 self._logger.warning("算法管理器不可用，跳过算法处理")
                 return
-            
+
             roi_id = roi.roi_id
             algo_code = bind.algo_code
             algo_params = bind.params or {}
-            
+
+            # 诊断日志：检测到目标（使用 INFO 级别确保能看到）
+            if len(boxes) > 0:
+                self._logger.info(f"[{camera_id}] ROI={roi_id[:8]} 检测到 {len(boxes)} 个目标, algo={algo_code}")
+
             self._algorithm_manager.register_algorithm(
                 roi_id=roi_id,
                 bind_id=bind.bind_id,
                 algorithm_type=algo_code,
                 params=algo_params
             )
-            
+
             tracks = self._build_tracks(roi, boxes, scores, class_ids, scale_info)
-            
+
             if not tracks:
                 return
-            
+
+            # 诊断日志：tracks 内容（INFO 级别）
+            self._logger.info(f"[{camera_id}] tracks: {[t.get('class') for t in tracks]}, target_class={bind.target_class}")
+
             alerts = self._algorithm_manager.process(
                 roi_id=roi_id,
                 bind_id=bind.bind_id,
@@ -344,7 +361,15 @@ class EdgeInferenceService:
                 tracks=tracks,
                 current_time=frame.timestamp
             )
-            
+
+            # 诊断日志：算法处理结果
+            if alerts:
+                self._logger.info(f"[{camera_id}] 算法 {algo_code} 返回 {len(alerts)} 个告警")
+            else:
+                # 获取算法状态用于诊断
+                algo_status = self._algorithm_manager.get_status(roi_id)
+                self._logger.info(f"[{camera_id}] 算法 {algo_code} 无告警, 状态: {algo_status}")
+
             for alert in alerts:
                 self._performance_stats["total_alerts_generated"] += 1