预警图片检测触发修复

apps/server/api/detection.py

@@ -130,7 +130,8 @@ async def detect_image(
                     "detections": result['detections'],
                     "image_base64": img_base64,
                     "stats": result['stats'],
-                    "alerts": result.get('alerts', []),
+                    "alerts": result.get('alerts', []) or result.get('alert_events', []),
+                    "alert_events": result.get('alert_events', []),
                     "behavior_stats": result.get('behavior_stats', {})
                 }
             )

apps/server/services/detection_service.py

@@ -12,15 +12,6 @@ import torch
 from .loitering_service import get_loitering_service
 from .adapters import DetectionAdapter
 from .event import AlertRuleEngine, EventAggregator, EventDecisionEngine
-from .event.frame_accumulator import MultiFrameAccumulator
-from .event.llm_trigger import LLMTrigger
-from .llm_analysis_service import (
-    LLMAnalysisService,
-    LLMAnalysisRequest,
-    create_llm_service_from_settings,
-)
-from .llm_cost_tracker import LLMCostTracker, get_global_tracker
-from .result_fusion import ResultFusion
 from core.settings import get_settings
 from models.event_schemas import DetectionSource
 
@@ -51,49 +42,13 @@ class DetectionService:
             max_active_events=settings.event_engine.max_active_events,
         )
     
-        # LLM 二次判断管道 (MVP-3 / D26-D30)
-        self._llm_enabled = settings.llm.enabled
-        if self._llm_enabled:
-            self._frame_accumulator = MultiFrameAccumulator(
-                window_seconds=settings.llm_trigger.window_seconds,
-            )
-            self._llm_trigger = LLMTrigger(
-                accumulator=self._frame_accumulator,
-                min_consecutive_hits=settings.llm_trigger.min_consecutive_hits,
-                min_avg_confidence=settings.llm_trigger.min_avg_confidence,
-                cooldown_seconds=settings.llm_trigger.cooldown_seconds,
-                severity_bypass=settings.llm_trigger.severity_bypass,
-            )
-            self._llm_service = create_llm_service_from_settings(settings)
-            self._cost_tracker = get_global_tracker()
-            self._result_fusion = ResultFusion(
-                strategy=settings.fusion.strategy,
-                yolo_weight=settings.fusion.yolo_weight,
-                llm_weight=settings.fusion.llm_weight,
-                suppress_on_llm_negative=settings.fusion.suppress_on_llm_negative,
-                fallback_to_yolo=settings.fusion.fallback_to_yolo,
-            )
-            logger.info(
-                "LLM 管道已初始化: provider=%s model=%s strategy=%s",
-                settings.llm.provider,
-                settings.llm.model,
-                settings.fusion.strategy,
-            )
-        else:
-            self._frame_accumulator = None
-            self._llm_trigger = None
-            self._llm_service = None
-            self._cost_tracker = None
-            self._result_fusion = None
-    
     async def detect_image(
         self,
         image: np.ndarray,
         model_id: str,
         confidence: float = 0.5,
         iou: float = 0.45,
-        algorithm_config: Optional[Dict] = None,
+        algorithm_config: Optional[Dict] = None
-        region_polygon: Optional[List[List[int]]] = None
     ) -> Dict:
         start_time = time.time()
 
@@ -107,40 +62,6 @@ class DetectionService:
             }
 
         try:
-            # 违停检测特殊处理：调用专门的违停检测方法
-            if model_id == 'illegal_parking_detection' and hasattr(model, 'detect_illegal_parking'):
-                # 如果提供了禁停区域，单张图片模式下即时判定（时间阈值设为0）
-                parking_time = 0 if region_polygon else None
-                parking_result = model.detect_illegal_parking(
-                    image, conf=confidence, illegal_parking_time=parking_time, region_polygon=region_polygon
-                )
-                detections = []
-                for vehicle in parking_result.get('illegal_parking', []):
-                    detections.append({
-                        'class': 'illegal_parking',
-                        'label': '违停车辆',
-                        'confidence': 1.0,
-                        'bbox': vehicle['bbox'],
-                        'track_id': vehicle.get('track_id'),
-                        'parking_duration': vehicle.get('parking_duration', 0)
-                    })
-                
-                processing_time = time.time() - start_time
-                result_data = {
-                    'success': parking_result['success'],
-                    'message': parking_result.get('message', '违停检测完成'),
-                    'detections': detections,
-                    'stats': {
-                        **parking_result.get('stats', {}),
-                        'total_detections': len(detections),
-                        'processing_time': round(processing_time, 3),
-                        'model_used': model_id
-                    }
-                }
-                result_data = self._apply_event_pipeline(result_data, model_id)
-                result_data = await self._apply_llm_pipeline(result_data)
-                return result_data
-            
             results = model(image, conf=confidence, iou=iou, verbose=False)
             
             detections = []
@@ -230,8 +151,6 @@ class DetectionService:
 
             # 事件管道 (MVP-1): 决策 → 规则 → 聚合
             result_data = self._apply_event_pipeline(result_data, model_id)
-            # LLM 二次判断管道 (MVP-3)
-            result_data = await self._apply_llm_pipeline(result_data, frame=image)
 
             return result_data
         except Exception as e:
@@ -283,45 +202,6 @@ class DetectionService:
                     'stats': None
                 }
             
-            # 违停检测特殊处理：调用专门的违停检测方法
-            if model_id == 'illegal_parking_detection' and hasattr(model, 'detect_illegal_parking'):
-                parking_result = model.detect_illegal_parking(
-                    frame, conf=confidence
-                )
-                detections = []
-                for vehicle in parking_result.get('illegal_parking', []):
-                    detections.append({
-                        'class': 'illegal_parking',
-                        'label': '违停车辆',
-                        'confidence': 1.0,
-                        'bbox': vehicle['bbox'],
-                        'track_id': vehicle.get('track_id'),
-                        'parking_duration': vehicle.get('parking_duration', 0)
-                    })
-                
-                processing_time = time.time() - start_time
-                fps = 1.0 / processing_time if processing_time > 0 else 0
-                
-                result_data = {
-                    'success': parking_result['success'],
-                    'message': parking_result.get('message', '违停检测完成'),
-                    'detections': detections,
-                    'stats': {
-                        **parking_result.get('stats', {}),
-                        'total_detections': len(detections),
-                        'fps': round(fps, 2),
-                        'processing_time': round(processing_time, 3),
-                        'model_used': model_id
-                    }
-                }
-                result_data = self._apply_event_pipeline(result_data, model_id)
-                result_data = await self._apply_llm_pipeline(result_data, frame=frame)
-                
-                if draw:
-                    frame = self.draw_detections(frame, detections, fps)
-                
-                return frame, result_data
-            
             results = model(frame, conf=confidence, iou=iou, verbose=False)
             
             detections = []
@@ -436,8 +316,6 @@ class DetectionService:
 
             # 事件管道 (MVP-1): 决策 → 规则 → 聚合
             result_data = self._apply_event_pipeline(result_data, model_id=model_id)
-            # LLM 二次判断管道 (MVP-3)
-            result_data = await self._apply_llm_pipeline(result_data, frame=frame)
 
             return frame, result_data
         except Exception as e:
@@ -552,8 +430,6 @@ class DetectionService:
                 model_id='fire_composite',
                 source=DetectionSource.COMPOSITE,
             )
-            # LLM 二次判断管道 (MVP-3)
-            result_data = await self._apply_llm_pipeline(result_data, frame=image)
             return result_data
 
         except Exception as e:
@@ -631,15 +507,13 @@ class DetectionService:
         model_id: Optional[str] = None,
         source_id: Optional[str] = None,
         source: DetectionSource = DetectionSource.YOLO,
-        frame: Optional[np.ndarray] = None,
     ) -> Dict:
-        """对检测结果执行 决策 → 规则 → 聚合 → LLM 二次判断 → 融合 管道。
+        """对检测结果执行 决策 → 规则 → 聚合 三段管道。
 
-        在 ``result_data`` 中追加以下字段:
+        在 ``result_data`` 中追加两个字段:
 
         - ``candidate_events``: List[dict]  决策引擎产出的候选事件
         - ``alert_events``:    List[dict]  规则命中后经聚合的预警事件
-        - ``llm_results``:     List[dict]  LLM 分析结果 (仅 LLM 启用时)
         """
 
         if not result_data.get('success') or not result_data.get('detections'):
@@ -669,185 +543,6 @@ class DetectionService:
 
         return result_data
 
-    async def _apply_llm_pipeline(
-        self,
-        result_data: Dict,
-        frame: Optional[np.ndarray] = None,
-    ) -> Dict:
-        """LLM 二次判断管道 (MVP-3 / D35)。
-
-        在已有 ``alert_events`` 的基础上:
-        1. 将候选事件喂给 LLMTrigger 判断是否需要 LLM 复审
-        2. 对触发的决策调用 LLMAnalysisService
-        3. 用 ResultFusion 融合 YOLO + LLM 结果
-        4. 用 LLMCostTracker 记录成本
-
-        追加字段:
-        - ``llm_results``: List[dict]  LLM 分析结果列表
-        """
-
-        if not self._llm_enabled:
-            result_data['llm_results'] = []
-            return result_data
-
-        alert_events = result_data.get('alert_events', [])
-        if not alert_events:
-            result_data['llm_results'] = []
-            return result_data
-
-        logger.info(
-            "[LLM管道] 开始评估 | alert_events=%d | 有frame=%s",
-            len(alert_events),
-            frame is not None and frame.size > 0,
-        )
-
-        try:
-            # 1. 从 alert_events 重建 CandidateEvent 列表供 LLMTrigger 评估
-            from models.event_schemas import CandidateEvent, AlertEvent, EventType, SeverityLevel, UnifiedDetection
-            candidates_for_llm = []
-            for evt_dict in alert_events:
-                try:
-                    # AlertEvent 用 detections(列表), CandidateEvent 用 detection(单个) → 取首个
-                    dets = evt_dict.get('detections', [])
-                    det = dets[0] if dets else None
-
-                    candidate = CandidateEvent(
-                        event_type=EventType(evt_dict['event_type']),
-                        severity=SeverityLevel(evt_dict.get('severity', 'high')),
-                        confidence=evt_dict.get('confidence', 0.5),
-                        detection=det,
-                        source_id=evt_dict.get('source_id'),
-                        timestamp=evt_dict.get('timestamp', evt_dict.get('first_seen')),
-                        triggered_rules=[evt_dict.get('rule_name')] if evt_dict.get('rule_name') else [],
-                    )
-                    candidates_for_llm.append(candidate)
-                except Exception as exc:
-                    logger.debug("[LLM管道] 重建候选事件失败: %s | 原始数据: %s", exc, {k: v for k, v in evt_dict.items() if k != 'detections'})
-                    continue
-
-            if not candidates_for_llm:
-                logger.info("[LLM管道] 无有效候选事件，跳过")
-                result_data['llm_results'] = []
-                return result_data
-
-            # 2. LLMTrigger 评估
-            trigger_decisions = self._llm_trigger.evaluate(candidates_for_llm)
-
-            if not trigger_decisions:
-                logger.info("[LLM管道] 触发器评估: 未达到触发条件 (需连续%d帧或critical严重性)", self._llm_trigger.min_consecutive_hits)
-                result_data['llm_results'] = []
-                return result_data
-
-            logger.info(
-                "[LLM管道] 触发器命中 %d 个决策 | 详情: %s",
-                len(trigger_decisions),
-                [{d.candidate.event_type.value: d.reason} for d in trigger_decisions],
-            )
-
-            # 3. 逐个触发决策调用 LLM + 融合
-            llm_results = []
-            fused_alert_events = []
-            for decision in trigger_decisions:
-                llm_result = None
-
-                # 检查成本追踪器是否允许调用
-                if self._cost_tracker and not self._cost_tracker.can_call():
-                    logger.info(
-                        "[LLM管道] 调用被降级拦截: %s",
-                        self._cost_tracker.reason_for_block(),
-                    )
-                elif self._llm_service:
-                    # 构造 LLM 请求
-                    frames = [frame] if frame is not None and frame.size > 0 else []
-                    logger.info(
-                        "[LLM管道] 调用 LLM 分析 | 事件=%s | 置信度=%.2f | 图片=%d张",
-                        decision.candidate.event_type.value,
-                        decision.candidate.confidence,
-                        len(frames),
-                    )
-                    request = LLMAnalysisRequest(
-                        candidate=decision.candidate,
-                        frames=frames,
-                    )
-                    llm_result = await self._llm_service.analyze_with_fallback(request)
-
-                    if llm_result:
-                        logger.info(
-                            "[LLM管道] LLM 返回 | confirmed=%s | confidence=%.2f | reasoning=%.80s",
-                            llm_result.confirmed,
-                            llm_result.confidence,
-                            (llm_result.reasoning or ''),
-                        )
-                    else:
-                        logger.warning("[LLM管道] LLM 返回空结果 (可能降级到 mock)")
-
-                    # 记录成本
-                    if self._cost_tracker and llm_result:
-                        self._cost_tracker.record(
-                            llm_result,
-                            image_count=len(frames) or 1,
-                        )
-
-                # 4. 找到对应的 AlertEvent 进行融合
-                alert_dict = None
-                for evt_dict in alert_events:
-                    if (
-                        evt_dict.get('event_type') == decision.candidate.event_type.value
-                        and evt_dict.get('source_id') == decision.candidate.source_id
-                    ):
-                        alert_dict = evt_dict
-                        break
-
-                if alert_dict is not None and self._result_fusion:
-                    try:
-                        alert_event = AlertEvent(**alert_dict)
-                        outcome = self._result_fusion.fuse(alert_event, llm_result)
-                        if outcome.alert is not None and not outcome.suppressed:
-                            fused_alert_events.append(outcome.alert.model_dump(mode='json'))
-                        llm_results.append({
-                            'trigger_reason': decision.reason,
-                            'llm_confirmed': llm_result.confirmed if llm_result else None,
-                            'llm_confidence': llm_result.confidence if llm_result else None,
-                            'llm_reasoning': llm_result.reasoning if llm_result else None,
-                            'fusion_strategy': outcome.strategy,
-                            'fusion_reason': outcome.reason,
-                            'final_confidence': outcome.final_confidence,
-                            'suppressed': outcome.suppressed,
-                        })
-                        logger.info(
-                            "[LLM管道] 融合完成 | 策略=%s | 最终置信度=%.2f | 抑制=%s | 原因=%s",
-                            outcome.strategy,
-                            outcome.final_confidence,
-                            outcome.suppressed,
-                            outcome.reason,
-                        )
-                    except Exception as exc:
-                        logger.error("LLM 融合失败: %s", exc)
-                        llm_results.append({
-                            'trigger_reason': decision.reason,
-                            'llm_confirmed': None,
-                            'error': str(exc),
-                        })
-
-            # 用融合后的结果替换原始 alert_events
-            if fused_alert_events:
-                result_data['alert_events'] = fused_alert_events
-
-            result_data['llm_results'] = llm_results
-            logger.info(
-                "[LLM管道] 完成 | 总决策=%d | LLM结果=%d | 融合后事件=%d",
-                len(trigger_decisions),
-                len(llm_results),
-                len(fused_alert_events),
-            )
-
-        except Exception as e:  # noqa: BLE001
-            logger.error("LLM 管道执行失败: %s", e)
-            result_data['llm_results'] = []
-            result_data['llm_pipeline_error'] = str(e)
-
-        return result_data
-
 
     def draw_detections(
         self,
@@ -884,7 +579,6 @@ class DetectionService:
                 'helmet': (255, 255, 0),
                 'no_helmet': (255, 0, 255),
                 'cigarette': (0, 165, 255),
-                'illegal_parking': (0, 0, 255),
                 # 兼容旧模型类别
                 'violence': (0, 0, 255),
                 'fight': (0, 0, 255),

apps/server/services/event/decision_engine.py

@@ -34,7 +34,9 @@ DEFAULT_CLASS_TO_EVENT: Dict[str, EventType] = {
     # 火灾
     "fire": EventType.FIRE,
     "flame": EventType.FIRE,
+    "火焰": EventType.FIRE,
     "smoke": EventType.SMOKE,
+    "烟雾": EventType.SMOKE,
     # 抽烟
     "smoking": EventType.SMOKING,
     "cigarette": EventType.SMOKING,

apps/web/src/components/ImageDetection.vue

@@ -583,8 +583,13 @@ const handleUploadSuccess = (response) => {
     if (response.data.alerts && response.data.alerts.length > 0) {
       alerts.value = response.data.alerts
       response.data.alerts.forEach(alert => {
+        // 同步到全局预警中心，供 AlertList 展示
+        alertStore.addAlert(alert)
+        // 兼容两种字段名: 旧格式 {type, message} / 新格式 {event_type, metadata.description}
+        const alertType = alert.type || alert.event_type
+        const alertMessage = alert.message || (alert.metadata && alert.metadata.description) || ''
         ElMessage({
-          message: `行为告警: ${alert.type} - ${alert.message}`,
+          message: `预警: ${alertType} - ${alertMessage}`,
           type: 'warning',
           duration: 3000
         })
@@ -1117,8 +1122,6 @@ onUnmounted(() => {
 
 .image-container {
   width: 100%;
-  aspect-ratio: 16 / 9;
-  min-height: 400px;
   max-height: 600px;
   display: flex;
   align-items: center;
@@ -1130,8 +1133,8 @@ onUnmounted(() => {
 }
 
 .display-image {
-  width: 100%;
+  max-width: 100%;
-  height: 100%;
+  max-height: 600px;
   object-fit: contain;
   background: #000;
 }