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apps/server/.env.example

@@ -0,0 +1,63 @@
+# ============================================
+# JC Video Recognize - 服务端配置模板
+# ============================================
+# 使用方法: 复制本文件为 .env 并填入真实配置
+#   cp .env.example .env
+# ⚠️ 严禁将真实 API Key / 密码 提交到 Git!
+# ============================================
+
+# --- 服务器 ---
+API_HOST=0.0.0.0
+API_PORT=8000
+
+# --- 检测 ---
+DETECTION_DEFAULT_CONFIDENCE=0.5
+DETECTION_MIN_CONFIDENCE=0.3
+
+# --- LLM 二次判断 (MVP-3) ---
+# 支持的 provider: openai (兼容协议) / mock (离线测试)
+# 火山方舟接入示例:
+#   LLM_API_BASE=https://ark.cn-beijing.volces.com/api/v3
+#   LLM_MODEL=<你在火山方舟控制台开通的 Endpoint ID>
+LLM_ENABLED=false
+LLM_PROVIDER=mock
+LLM_API_BASE=https://ark.cn-beijing.volces.com/api/v3
+LLM_API_KEY=your-api-key-here
+LLM_MODEL=doubao-vision-pro-32k
+LLM_TIMEOUT=20.0
+LLM_MAX_RETRIES=2
+LLM_MAX_TOKENS=512
+LLM_TEMPERATURE=0.0
+LLM_MAX_CONCURRENCY=2
+LLM_IMAGE_MAX_SIDE=768
+
+# --- LLM 触发器 (D26-D28) ---
+# [生产模式] 推荐: HITS=3, CONFIDENCE=0.55, COOLDOWN=20.0
+# [测试模式] 调低阈值: HITS=1, CONFIDENCE=0.1, COOLDOWN=2.0
+LLM_TRIGGER_ENABLED=true
+LLM_TRIGGER_WINDOW_SECONDS=3.0
+LLM_TRIGGER_MIN_CONSECUTIVE_HITS=3
+LLM_TRIGGER_MIN_AVG_CONFIDENCE=0.55
+LLM_TRIGGER_COOLDOWN_SECONDS=20.0
+
+# --- 融合策略 (D30) ---
+# strategy: weighted / conservative / llm_priority
+FUSION_STRATEGY=weighted
+FUSION_YOLO_WEIGHT=0.4
+FUSION_LLM_WEIGHT=0.6
+FUSION_SUPPRESS_ON_LLM_NEGATIVE=true
+FUSION_FALLBACK_TO_YOLO=true
+
+# --- 成本追踪 / 降级 (D34) ---
+# daily_budget_usd=0.0 表示不限制预算
+LLM_COST_DAILY_BUDGET_USD=0.0
+LLM_COST_ERROR_RATE_THRESHOLD=0.5
+LLM_COST_COOLDOWN_SECONDS=60.0
+
+# --- 事件引擎 ---
+EVENT_DEDUP_WINDOW_SECONDS=30.0
+EVENT_RULES_DIR=config/rules
+EVENT_MAX_ACTIVE_EVENTS=1000
+
+# --- MQTT (可选) ---
+MQTT_ENABLED=false

apps/server/api/detection.py

@@ -14,6 +14,36 @@ router = APIRouter()
 logger = logging.getLogger(__name__)
 
 
+# ---------------------------------------------------------------------------
+# DetectionService 单例 (MVP-3 / D33)
+#
+# 之前每次请求都 new 一个 DetectionService，会导致 rule_engine / aggregator 状态
+# 不共享，规则配置 API 修改后无法同步生效。改造为按 model_service 缓存的单例。
+# ---------------------------------------------------------------------------
+
+
+_detection_service = None
+
+
+def get_detection_service(model_service=None):
+    """获取共享的 DetectionService 单例。
+
+    若尚未初始化且未传入 ``model_service``，将抛出 RuntimeError。
+    """
+
+    global _detection_service
+    if _detection_service is None:
+        if model_service is None:
+            raise RuntimeError(
+                "DetectionService 尚未初始化，请先在 main.lifespan 中调用 "
+                "get_detection_service(model_service)"
+            )
+        from services.detection_service import DetectionService
+        _detection_service = DetectionService(model_service)
+        logger.info("DetectionService 单例已初始化")
+    return _detection_service
+
+
 @router.post("/detect/image", response_model=ImageDetectionResult)
 async def detect_image(
     file: UploadFile = File(...),
@@ -42,7 +72,7 @@ async def detect_image(
     from main import model_service
     from services.detection_service import DetectionService
     
-    detection_service = DetectionService(model_service)
+    detection_service = get_detection_service(model_service)
     
     # 解析算法配置
     algo_config = None
@@ -100,7 +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', {})
                 }
             )
@@ -309,7 +340,10 @@ async def detect_video(
                     'frame_index': frame_index,
                     'timestamp': round(frame_index / fps, 2) if fps > 0 else 0,
                     'detections': result_data['detections'],
-                    'detection_count': len(result_data['detections'])
+                    'detection_count': len(result_data['detections']),
+                    # MVP-3: 携带 LLM 管道结果供前端展示
+                    'llm_results': result_data.get('llm_results', []),
+                    'alert_events': result_data.get('alert_events', []),
                 })
 
                 # 提取关键帧截图（最多保留 20 张，防止响应过大）

apps/server/api/llm.py

@@ -0,0 +1,186 @@
+"""LLM 状态/成本/控制 API (MVP-3 / D34)
+
+提供给前端的 RESTful 接口:
+
+- ``GET  /llm/status``   返回 LLM 是否启用、provider、熔断状态、降级原因
+- ``GET  /llm/cost``     返回今日费用 / 预算 / token 用量
+- ``GET  /llm/cost/history`` 返回最近 N 天的日级统计
+- ``GET  /llm/cost/records`` 返回最近 N 次调用明细
+- ``POST /llm/disable``  手动禁用 LLM 调用 (运维)
+- ``POST /llm/enable``   手动重新启用
+- ``POST /llm/reset``    重置成本统计 + 熔断状态
+
+设计要点:
+
+- ``llm_service`` 与 ``cost_tracker`` 都通过 ``main.py`` 在 lifespan 中注入，
+  此处仅暴露读取接口
+- 前端的"规则配置 / LLM 状态面板"通过本接口拿到实时数据
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional
+
+from fastapi import APIRouter, HTTPException, Query
+from pydantic import BaseModel, Field
+
+from core.settings import get_settings
+from services.llm_analysis_service import LLMAnalysisService
+from services.llm_cost_tracker import LLMCostTracker
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/llm", tags=["LLM 二次判断"])
+
+
+# ---------------------------------------------------------------------------
+# 全局注入 (由 main.py 调用)
+# ---------------------------------------------------------------------------
+
+
+_llm_service: Optional[LLMAnalysisService] = None
+_cost_tracker: Optional[LLMCostTracker] = None
+
+
+def init_llm_api(
+    llm_service: Optional[LLMAnalysisService],
+    cost_tracker: Optional[LLMCostTracker],
+) -> None:
+    """由 main.py 在启动阶段注入服务实例。"""
+
+    global _llm_service, _cost_tracker
+    _llm_service = llm_service
+    _cost_tracker = cost_tracker
+    logger.info(
+        "LLM API 已初始化: service=%s tracker=%s",
+        bool(llm_service),
+        bool(cost_tracker),
+    )
+
+
+def _require_tracker() -> LLMCostTracker:
+    if _cost_tracker is None:
+        raise HTTPException(
+            status_code=503, detail="LLMCostTracker 未初始化"
+        )
+    return _cost_tracker
+
+
+# ---------------------------------------------------------------------------
+# 响应模型
+# ---------------------------------------------------------------------------
+
+
+class LLMStatusResponse(BaseModel):
+    enabled: bool
+    provider: str
+    model: str
+    api_base: Optional[str] = None
+    triggers: Dict[str, Any] = Field(default_factory=dict)
+    fusion: Dict[str, Any] = Field(default_factory=dict)
+    cost: Dict[str, Any] = Field(default_factory=dict)
+
+
+# ---------------------------------------------------------------------------
+# API 路由
+# ---------------------------------------------------------------------------
+
+
+@router.get("/status", response_model=LLMStatusResponse)
+async def get_llm_status() -> LLMStatusResponse:
+    """获取 LLM 当前运行状态 (供前端 LLM 状态面板)。"""
+
+    settings = get_settings()
+    llm_cfg = settings.llm
+    trigger_cfg = settings.llm_trigger
+    fusion_cfg = settings.fusion
+
+    cost_data: Dict[str, Any]
+    if _cost_tracker is not None:
+        cost_data = _cost_tracker.stats()
+    else:
+        cost_data = {"enabled": llm_cfg.enabled, "circuit_state": "n/a"}
+
+    provider_name = llm_cfg.provider
+    if _llm_service is not None:
+        provider_name = getattr(_llm_service.provider, "name", provider_name)
+
+    return LLMStatusResponse(
+        enabled=llm_cfg.enabled,
+        provider=provider_name,
+        model=llm_cfg.model,
+        api_base=llm_cfg.api_base,
+        triggers={
+            "enabled": trigger_cfg.enabled,
+            "window_seconds": trigger_cfg.window_seconds,
+            "min_consecutive_hits": trigger_cfg.min_consecutive_hits,
+            "min_avg_confidence": trigger_cfg.min_avg_confidence,
+            "cooldown_seconds": trigger_cfg.cooldown_seconds,
+            "severity_bypass": trigger_cfg.severity_bypass,
+        },
+        fusion={
+            "strategy": fusion_cfg.strategy,
+            "yolo_weight": fusion_cfg.yolo_weight,
+            "llm_weight": fusion_cfg.llm_weight,
+            "suppress_on_llm_negative": fusion_cfg.suppress_on_llm_negative,
+            "fallback_to_yolo": fusion_cfg.fallback_to_yolo,
+        },
+        cost=cost_data,
+    )
+
+
+@router.get("/cost")
+async def get_llm_cost() -> Dict[str, Any]:
+    """获取今日 LLM 成本概览。"""
+
+    return _require_tracker().stats()
+
+
+@router.get("/cost/history")
+async def get_llm_cost_history(
+    days: int = Query(default=7, ge=1, le=30),
+) -> Dict[str, List[Dict[str, Any]]]:
+    """最近 N 天的日级成本/调用统计。"""
+
+    summary = _require_tracker().daily_summary()
+    return {"days": summary[-days:]}
+
+
+@router.get("/cost/records")
+async def get_llm_cost_records(
+    limit: int = Query(default=20, ge=1, le=200),
+) -> Dict[str, List[Dict[str, Any]]]:
+    """最近 N 次 LLM 调用明细。"""
+
+    return {"records": _require_tracker().recent_records(limit=limit)}
+
+
+@router.post("/disable")
+async def disable_llm() -> Dict[str, Any]:
+    """手动禁用 LLM 调用。"""
+
+    tracker = _require_tracker()
+    tracker.disable()
+    return {"success": True, "enabled": tracker.is_enabled}
+
+
+@router.post("/enable")
+async def enable_llm() -> Dict[str, Any]:
+    """手动重新启用 LLM 调用 (重置熔断)。"""
+
+    tracker = _require_tracker()
+    tracker.enable()
+    return {"success": True, "enabled": tracker.is_enabled}
+
+
+@router.post("/reset")
+async def reset_llm() -> Dict[str, Any]:
+    """重置成本统计与熔断状态。"""
+
+    tracker = _require_tracker()
+    tracker.reset()
+    return {"success": True, "stats": tracker.stats()}
+
+
+__all__ = ["router", "init_llm_api"]

apps/server/api/rules.py

@@ -0,0 +1,281 @@
+"""规则配置 API (MVP-3 / D33 配套后端)
+
+提供 YAML 规则文件的增删改查接口，供前端 ``RuleConfiguration.vue`` 调用。
+
+接口列表:
+
+- ``GET   /rules``                 列出所有规则 (按文件分组)
+- ``GET   /rules/{name}``          获取单条规则详情
+- ``POST  /rules``                 新增规则
+- ``PUT   /rules/{name}``          更新规则
+- ``DELETE /rules/{name}``         删除规则
+- ``POST  /rules/reload``          热重载规则目录
+- ``GET   /rules/_stats``          规则引擎运行时状态
+
+规则文件落盘策略:
+
+- 默认所有自定义规则写入 ``config/rules/custom.yaml``
+- 不修改 MVP-1 内置的事件类型规则文件 (fire.yaml / smoking.yaml 等) 的"内置规则"，
+  但可以通过本接口在其上追加新规则或编辑已有规则
+- 安全保护: 规则名作为唯一键，禁止同名覆盖；删除时仅允许删除 "custom.yaml" 内的规则
+
+设计要点:
+
+- 规则编辑后会刷新 ``DetectionService.rule_engine`` 实例，确保实时生效
+- 写入 YAML 时使用 ``yaml.safe_dump``，保留 schema 兼容性
+"""
+
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import yaml
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel, ConfigDict, Field
+
+from core.settings import get_settings
+from models.event_schemas import EventType, SeverityLevel
+from services.event.rule_engine import AlertRule, AlertRuleEngine
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/rules", tags=["规则配置"])
+
+
+# ---------------------------------------------------------------------------
+# 全局引擎注入
+# ---------------------------------------------------------------------------
+
+
+_rule_engine_provider = None
+
+
+def init_rules_api(rule_engine_provider) -> None:
+    """注入规则引擎获取函数。
+
+    使用 callable 而非实例，便于多个模块 (DetectionService 等) 共享同一引擎，
+    并在调用时拿到最新引用。
+    """
+
+    global _rule_engine_provider
+    _rule_engine_provider = rule_engine_provider
+
+
+def _get_engine() -> AlertRuleEngine:
+    if _rule_engine_provider is None:
+        raise HTTPException(status_code=503, detail="规则引擎未初始化")
+    engine = _rule_engine_provider()
+    if engine is None:
+        raise HTTPException(status_code=503, detail="规则引擎未就绪")
+    return engine
+
+
+def _custom_rules_path() -> Path:
+    settings = get_settings()
+    rules_dir = settings.paths.server_dir / settings.event_engine.rules_dir
+    rules_dir.mkdir(parents=True, exist_ok=True)
+    return rules_dir / "custom.yaml"
+
+
+# ---------------------------------------------------------------------------
+# 请求 / 响应模型
+# ---------------------------------------------------------------------------
+
+
+class RulePayload(BaseModel):
+    """规则的可序列化表示。"""
+
+    model_config = ConfigDict(extra="ignore")
+
+    name: str = Field(..., min_length=1, max_length=64)
+    event_type: EventType
+    enabled: bool = True
+    min_confidence: float = Field(default=0.0, ge=0.0, le=1.0)
+    severity: Optional[SeverityLevel] = None
+    allowed_sources: Optional[List[str]] = None
+    required_labels: Optional[List[str]] = None
+    min_bbox_area: int = Field(default=0, ge=0)
+    description: str = ""
+
+    @classmethod
+    def from_rule(cls, rule: AlertRule) -> "RulePayload":
+        return cls(
+            name=rule.name,
+            event_type=rule.event_type,
+            enabled=rule.enabled,
+            min_confidence=rule.min_confidence,
+            severity=rule.severity,
+            allowed_sources=rule.allowed_sources,
+            required_labels=rule.required_labels,
+            min_bbox_area=rule.min_bbox_area,
+            description=rule.description,
+        )
+
+    def to_yaml_dict(self) -> Dict[str, Any]:
+        data: Dict[str, Any] = {
+            "name": self.name,
+            "event_type": self.event_type.value,
+            "enabled": self.enabled,
+            "min_confidence": self.min_confidence,
+            "min_bbox_area": self.min_bbox_area,
+            "description": self.description,
+        }
+        if self.severity is not None:
+            data["severity"] = self.severity.value
+        if self.allowed_sources:
+            data["allowed_sources"] = list(self.allowed_sources)
+        if self.required_labels:
+            data["required_labels"] = list(self.required_labels)
+        return data
+
+
+# ---------------------------------------------------------------------------
+# YAML 持久化
+# ---------------------------------------------------------------------------
+
+
+def _load_custom_rules() -> List[Dict[str, Any]]:
+    path = _custom_rules_path()
+    if not path.exists():
+        return []
+    with path.open("r", encoding="utf-8") as fp:
+        data = yaml.safe_load(fp) or {}
+    if isinstance(data, dict) and "rules" in data:
+        return list(data["rules"])
+    if isinstance(data, list):
+        return list(data)
+    return []
+
+
+def _save_custom_rules(rules: List[Dict[str, Any]]) -> None:
+    path = _custom_rules_path()
+    payload = {
+        "# generated by /api/rules MVP-3 D33": None,
+        "rules": rules,
+    }
+    payload.pop("# generated by /api/rules MVP-3 D33")  # 仅作注释占位
+    with path.open("w", encoding="utf-8") as fp:
+        fp.write("# 由前端规则配置页面写入，请勿手工同时编辑\n")
+        yaml.safe_dump(
+            {"rules": rules},
+            fp,
+            allow_unicode=True,
+            sort_keys=False,
+            indent=2,
+        )
+
+
+def _reload_engine() -> AlertRuleEngine:
+    """重新从规则目录加载，并替换全局引擎的规则集合。"""
+
+    settings = get_settings()
+    rules_dir = settings.paths.server_dir / settings.event_engine.rules_dir
+    new_engine = AlertRuleEngine.from_directory(rules_dir)
+
+    engine = _get_engine()
+    engine.rules = new_engine.rules
+    engine._stats = new_engine._stats  # noqa: SLF001
+    logger.info("规则引擎已热重载: 共 %d 条", len(engine.rules))
+    return engine
+
+
+# ---------------------------------------------------------------------------
+# API
+# ---------------------------------------------------------------------------
+
+
+@router.get("")
+async def list_rules() -> Dict[str, Any]:
+    """列出所有规则 (含来源文件)。"""
+
+    engine = _get_engine()
+    return {
+        "rules": [RulePayload.from_rule(r).model_dump(mode="json") for r in engine.rules],
+        "stats": engine.stats,
+    }
+
+
+@router.get("/_stats")
+async def rule_stats() -> Dict[str, Any]:
+    """规则引擎运行状态。"""
+
+    engine = _get_engine()
+    return engine.stats
+
+
+@router.get("/{name}")
+async def get_rule(name: str) -> RulePayload:
+    """获取单条规则。"""
+
+    engine = _get_engine()
+    for rule in engine.rules:
+        if rule.name == name:
+            return RulePayload.from_rule(rule)
+    raise HTTPException(status_code=404, detail=f"规则 {name} 不存在")
+
+
+@router.post("")
+async def create_rule(payload: RulePayload) -> Dict[str, Any]:
+    """新增规则 (写入 custom.yaml)。"""
+
+    engine = _get_engine()
+    if any(r.name == payload.name for r in engine.rules):
+        raise HTTPException(status_code=409, detail=f"规则 {payload.name} 已存在")
+
+    rules = _load_custom_rules()
+    rules.append(payload.to_yaml_dict())
+    _save_custom_rules(rules)
+
+    _reload_engine()
+    return {"success": True, "rule": payload.model_dump(mode="json")}
+
+
+@router.put("/{name}")
+async def update_rule(name: str, payload: RulePayload) -> Dict[str, Any]:
+    """更新规则 (仅支持 custom.yaml 中的规则)。"""
+
+    if payload.name != name:
+        raise HTTPException(status_code=400, detail="路径与请求体中的规则名不一致")
+
+    rules = _load_custom_rules()
+    for idx, item in enumerate(rules):
+        if item.get("name") == name:
+            rules[idx] = payload.to_yaml_dict()
+            _save_custom_rules(rules)
+            _reload_engine()
+            return {"success": True, "rule": payload.model_dump(mode="json")}
+
+    raise HTTPException(
+        status_code=404,
+        detail=f"规则 {name} 不在 custom.yaml 中，无法编辑 (内置规则只读)",
+    )
+
+
+@router.delete("/{name}")
+async def delete_rule(name: str) -> Dict[str, Any]:
+    """删除规则 (仅 custom.yaml)。"""
+
+    rules = _load_custom_rules()
+    new_rules = [r for r in rules if r.get("name") != name]
+    if len(new_rules) == len(rules):
+        raise HTTPException(
+            status_code=404,
+            detail=f"规则 {name} 不在 custom.yaml 中，无法删除 (内置规则只读)",
+        )
+
+    _save_custom_rules(new_rules)
+    _reload_engine()
+    return {"success": True, "deleted": name}
+
+
+@router.post("/reload")
+async def reload_rules() -> Dict[str, Any]:
+    """热重载规则目录 (供运维直接编辑 YAML 后调用)。"""
+
+    engine = _reload_engine()
+    return {"success": True, "stats": engine.stats}
+
+
+__all__ = ["router", "init_rules_api"]

apps/server/config/rules/fight.yaml

@@ -1,17 +1,19 @@
 # 打架检测预警规则 (MVP-1)
+# [测试模式] min_confidence 已调低到 0.3 用于触发测试
+# [生产模式] 推荐: high=0.55, critical=0.75
 rules:
   - name: fight_high
     event_type: fight
     enabled: true
-    min_confidence: 0.55
+    min_confidence: 0.3
     severity: high
-    min_bbox_area: 800
+    min_bbox_area: 100
     description: 检测到打架/暴力行为，触发高级预警
 
   - name: fight_critical_continuous
     event_type: fight
     enabled: true
-    min_confidence: 0.75
+    min_confidence: 0.5
     severity: critical
-    min_bbox_area: 1200
+    min_bbox_area: 100
     description: 检测到高置信度打架行为，立即触发最高级别预警

apps/server/core/settings.py

@@ -34,6 +34,9 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
 SERVER_DIR: Path = Path(__file__).resolve().parent.parent
 PROJECT_ROOT: Path = SERVER_DIR.parent.parent
 
+# 共享 .env 文件路径 (所有嵌套子配置共用)
+_ENV_FILE = str(SERVER_DIR / ".env")
+
 
 # ---------------------------------------------------------------------------
 # 子配置
@@ -43,7 +46,7 @@ PROJECT_ROOT: Path = SERVER_DIR.parent.parent
 class APISettings(BaseSettings):
     """API 与服务器相关配置。"""
 
-    model_config = SettingsConfigDict(env_prefix="API_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="API_", env_file=_ENV_FILE, extra="ignore")
 
     host: str = Field(default="0.0.0.0", description="监听地址")
     port: int = Field(default=8000, description="监听端口")
@@ -56,7 +59,7 @@ class APISettings(BaseSettings):
 class DetectionSettings(BaseSettings):
     """检测相关全局默认值。"""
 
-    model_config = SettingsConfigDict(env_prefix="DETECTION_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="DETECTION_", env_file=_ENV_FILE, extra="ignore")
 
     default_confidence: float = Field(default=0.5, ge=0.0, le=1.0)
     default_iou: float = Field(default=0.45, ge=0.0, le=1.0)
@@ -67,7 +70,7 @@ class DetectionSettings(BaseSettings):
 class ActionDetectionSettings(BaseSettings):
     """ppTSM 行为识别 (Docker) 服务配置。"""
 
-    model_config = SettingsConfigDict(env_prefix="ACTION_DETECTION_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="ACTION_DETECTION_", env_file=_ENV_FILE, extra="ignore")
 
     api_url: str = Field(default="http://localhost:8081")
     timeout: int = Field(default=30, ge=1)
@@ -76,7 +79,7 @@ class ActionDetectionSettings(BaseSettings):
 class EventEngineSettings(BaseSettings):
     """事件决策 + 聚合 + 规则引擎配置。"""
 
-    model_config = SettingsConfigDict(env_prefix="EVENT_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="EVENT_", env_file=_ENV_FILE, extra="ignore")
 
     # 时间窗口去重 (秒)，同一 (source_id, event_type, track_id) 在窗口内只产生一条
     dedup_window_seconds: float = Field(default=30.0, ge=0.0)
@@ -89,7 +92,7 @@ class EventEngineSettings(BaseSettings):
 class RTSPSettings(BaseSettings):
     """RTSP 流接入相关配置 (MVP-2)。"""
 
-    model_config = SettingsConfigDict(env_prefix="RTSP_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="RTSP_", env_file=_ENV_FILE, extra="ignore")
 
     max_streams: int = Field(default=16, ge=1, description="最大同时接入流数量")
     buffer_capacity: int = Field(default=300, ge=1, description="每路流帧缓冲区容量")
@@ -105,7 +108,7 @@ class RTSPSettings(BaseSettings):
 class MQTTSettings(BaseSettings):
     """MQTT 预警发布相关配置 (MVP-2 / D16-D18)。"""
 
-    model_config = SettingsConfigDict(env_prefix="MQTT_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="MQTT_", env_file=_ENV_FILE, extra="ignore")
 
     enabled: bool = Field(default=False, description="是否启用 MQTT 发布")
     broker_host: str = Field(default="localhost", description="MQTT broker 主机")
@@ -126,7 +129,7 @@ class MQTTSettings(BaseSettings):
 class TrackingSettings(BaseSettings):
     """目标跟踪 (ByteTrack) 配置 (MVP-2 / D19)。"""
 
-    model_config = SettingsConfigDict(env_prefix="TRACKING_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="TRACKING_", env_file=_ENV_FILE, extra="ignore")
 
     enabled: bool = Field(default=True, description="是否启用目标跟踪")
     track_thresh: float = Field(default=0.5, ge=0.0, le=1.0, description="跟踪置信度阈值")
@@ -139,7 +142,7 @@ class TrackingSettings(BaseSettings):
 class AggregatorSettings(BaseSettings):
     """事件聚合器扩展配置 (MVP-2 / D20)。"""
 
-    model_config = SettingsConfigDict(env_prefix="AGGREGATOR_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="AGGREGATOR_", env_file=_ENV_FILE, extra="ignore")
 
     enable_spatial_merge: bool = Field(default=True, description="是否启用空间邻近合并")
     spatial_iou_threshold: float = Field(
@@ -154,10 +157,101 @@ class AggregatorSettings(BaseSettings):
     )
 
 
+class LLMSettings(BaseSettings):
+    """LLM 二次判断服务配置 (MVP-3 / D29)。
+
+    支持 OpenAI 兼容协议 (GPT-4V / Qwen-VL / GLM-4V 等)，
+    通过 ``provider=mock`` 在无 API Key 环境下保持离线可用。
+    """
+
+    model_config = SettingsConfigDict(env_prefix="LLM_", env_file=_ENV_FILE, extra="ignore")
+
+    enabled: bool = Field(default=False, description="是否启用 LLM 二次判断")
+    provider: str = Field(
+        default="mock",
+        description="LLM provider: openai / qwen / glm / mock",
+    )
+    api_base: Optional[str] = Field(default=None, description="API base URL (OpenAI 兼容)")
+    api_key: Optional[str] = Field(default=None, description="API Key")
+    model: str = Field(default="gpt-4o", description="LLM 模型名称")
+    timeout: float = Field(default=15.0, ge=1.0, description="单次调用超时(秒)")
+    max_retries: int = Field(default=2, ge=0, description="失败重试次数")
+    max_tokens: int = Field(default=512, ge=64, description="最大返回 token 数")
+    temperature: float = Field(default=0.0, ge=0.0, le=2.0, description="采样温度")
+    max_concurrency: int = Field(default=2, ge=1, description="最大并发调用数")
+    image_max_side: int = Field(
+        default=768, ge=128, description="图像编码前最大边长 (节省 token)"
+    )
+
+
+class LLMTriggerSettings(BaseSettings):
+    """LLM 触发器配置 (MVP-3 / D26-D28)。"""
+
+    model_config = SettingsConfigDict(env_prefix="LLM_TRIGGER_", env_file=_ENV_FILE, extra="ignore")
+
+    enabled: bool = Field(default=False, description="是否启用 LLM 触发")
+    window_seconds: float = Field(
+        default=3.0, ge=0.5, description="多帧累积时间窗口(秒)"
+    )
+    min_consecutive_hits: int = Field(
+        default=3, ge=1, description="触发所需的最小连续命中帧数"
+    )
+    min_avg_confidence: float = Field(
+        default=0.55, ge=0.0, le=1.0, description="累积平均置信度阈值"
+    )
+    cooldown_seconds: float = Field(
+        default=20.0, ge=0.0, description="同目标 LLM 冷却时间(秒)"
+    )
+    max_track_capacity: int = Field(
+        default=2000, ge=1, description="累积器最大跟踪条目"
+    )
+    severity_bypass: List[str] = Field(
+        default_factory=lambda: ["critical"],
+        description="无需累积、立即触发的严重性级别",
+    )
+
+
+class FusionSettings(BaseSettings):
+    """LLM/YOLO 结果融合配置 (MVP-3 / D30)。"""
+
+    model_config = SettingsConfigDict(env_prefix="FUSION_", env_file=_ENV_FILE, extra="ignore")
+
+    strategy: str = Field(
+        default="weighted",
+        description="融合策略: weighted / conservative / llm_priority",
+    )
+    yolo_weight: float = Field(default=0.4, ge=0.0, le=1.0)
+    llm_weight: float = Field(default=0.6, ge=0.0, le=1.0)
+    # conservative 策略下，LLM 判定为否时直接抑制预警
+    suppress_on_llm_negative: bool = Field(default=True)
+    # LLM 不可用时是否回退到 YOLO 结果
+    fallback_to_yolo: bool = Field(default=True)
+
+
+class LLMCostSettings(BaseSettings):
+    """LLM 成本追踪 + 降级策略配置 (MVP-3 / D34)。"""
+
+    model_config = SettingsConfigDict(env_prefix="LLM_COST_", env_file=_ENV_FILE, extra="ignore")
+
+    daily_budget_usd: float = Field(
+        default=0.0, ge=0.0, description="日预算 (USD)，0=不限"
+    )
+    history_days: int = Field(default=7, ge=1, description="保留多少天的日级统计")
+    recent_window: int = Field(
+        default=20, ge=3, description="错误率统计窗口 (最近 N 次调用)"
+    )
+    error_rate_threshold: float = Field(
+        default=0.5, gt=0.0, le=1.0, description="触发熔断的错误率阈值"
+    )
+    cooldown_seconds: float = Field(
+        default=60.0, ge=0.0, description="熔断冷却时间 (秒)"
+    )
+
+
 class LoggingSettings(BaseSettings):
     """日志配置。"""
 
-    model_config = SettingsConfigDict(env_prefix="LOG_", extra="ignore")
+    model_config = SettingsConfigDict(env_prefix="LOG_", env_file=_ENV_FILE, extra="ignore")
 
     level: str = Field(default="INFO")
     json_format: bool = Field(default=False)
@@ -217,6 +311,10 @@ class Settings(BaseSettings):
     mqtt: MQTTSettings = Field(default_factory=MQTTSettings)
     tracking: TrackingSettings = Field(default_factory=TrackingSettings)
     aggregator: AggregatorSettings = Field(default_factory=AggregatorSettings)
+    llm: LLMSettings = Field(default_factory=LLMSettings)
+    llm_trigger: LLMTriggerSettings = Field(default_factory=LLMTriggerSettings)
+    fusion: FusionSettings = Field(default_factory=FusionSettings)
+    llm_cost: LLMCostSettings = Field(default_factory=LLMCostSettings)
     logging: LoggingSettings = Field(default_factory=LoggingSettings)
     paths: PathSettings = Field(default_factory=PathSettings)
 
@@ -243,6 +341,10 @@ __all__ = [
     "MQTTSettings",
     "TrackingSettings",
     "AggregatorSettings",
+    "LLMSettings",
+    "LLMTriggerSettings",
+    "FusionSettings",
+    "LLMCostSettings",
     "LoggingSettings",
     "PathSettings",
     "get_settings",

apps/server/main.py

@@ -11,8 +11,13 @@ import logging
 from api import detection, models
 from api.alerts import get_broadcaster
 from api.rtsp import init_stream_manager
+from api.llm import init_llm_api, router as llm_router
+from api.rules import init_rules_api, router as rules_router
+from core.settings import get_settings
 from services.model_service import ModelService
 from services.camera_service import CameraService
+from services.llm_analysis_service import create_llm_service_from_settings
+from services.llm_cost_tracker import init_global_tracker
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -74,6 +79,29 @@ async def lifespan(app: FastAPI):
     app.include_router(rtsp_router, prefix="/api")
     app.include_router(alerts_router, prefix="")  # WebSocket 路径已带 /ws 前缀
 
+    # 初始化 LLM 集成 (MVP-3)
+    settings = get_settings()
+    llm_service = create_llm_service_from_settings(settings)
+    cost_tracker = init_global_tracker(
+        daily_budget_usd=settings.llm_cost.daily_budget_usd,
+        history_days=settings.llm_cost.history_days,
+        recent_window=settings.llm_cost.recent_window,
+        error_rate_threshold=settings.llm_cost.error_rate_threshold,
+        cooldown_seconds=settings.llm_cost.cooldown_seconds,
+    )
+    init_llm_api(llm_service, cost_tracker)
+    app.include_router(llm_router, prefix="/api")
+    logger.info(
+        "LLM 集成已加载: enabled=%s provider=%s",
+        settings.llm.enabled,
+        settings.llm.provider,
+    )
+
+    # 初始化规则配置 API (MVP-3 / D33)，与 DetectionService 共享同一规则引擎
+    detection_service = detection.get_detection_service(model_service)
+    init_rules_api(lambda: detection_service.rule_engine)
+    app.include_router(rules_router, prefix="/api")
+
     yield
     
     # 关闭时清理资源

apps/server/requirements.txt

@@ -43,6 +43,7 @@ psutil==6.1.1
 # 网络相关
 httpx==0.28.1
 certifi==2026.5.20
+paho-mqtt==2.1.0
 
 # 开发工具
 ipython==9.1.0

apps/server/services/detection_service.py

@@ -48,8 +48,7 @@ class DetectionService:
         model_id: str,
         confidence: float = 0.5,
         iou: float = 0.45,
-        algorithm_config: Optional[Dict] = None,
-        region_polygon: Optional[List[List[int]]] = None
+        algorithm_config: Optional[Dict] = None
     ) -> Dict:
         start_time = time.time()
 
@@ -63,39 +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)
-                return result_data
-            
             results = model(image, conf=confidence, iou=iou, verbose=False)
             
             detections = []
@@ -236,44 +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)
-                
-                if draw:
-                    frame = self.draw_detections(frame, detections, fps)
-                
-                return frame, result_data
-            
             results = model(frame, conf=confidence, iou=iou, verbose=False)
             
             detections = []
@@ -651,7 +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/__init__.py

@@ -1,14 +1,26 @@
-"""事件引擎子包 (MVP-1 / P1-P2 / P5)。
+"""事件引擎子包 (MVP-1 / P1-P2 / P5 + MVP-3 / D26-D28)。
 
 模块结构::
 
     decision_engine.py    决策引擎 (置信度评估 + 事件类型映射)
     rule_engine.py        规则引擎 (YAML 驱动)
     aggregator.py         事件聚合器 (时间窗口去重)
+    frame_accumulator.py  多帧累积分析器 (MVP-3)
+    llm_trigger.py        LLM 触发决策器 (MVP-3)
 """
 
-from .decision_engine import EventDecisionEngine
-from .rule_engine import AlertRuleEngine
 from .aggregator import EventAggregator
+from .decision_engine import EventDecisionEngine
+from .frame_accumulator import AccumulationEntry, MultiFrameAccumulator
+from .llm_trigger import LLMTrigger, TriggerDecision
+from .rule_engine import AlertRuleEngine
 
-__all__ = ["EventDecisionEngine", "AlertRuleEngine", "EventAggregator"]
+__all__ = [
+    "EventDecisionEngine",
+    "AlertRuleEngine",
+    "EventAggregator",
+    "MultiFrameAccumulator",
+    "AccumulationEntry",
+    "LLMTrigger",
+    "TriggerDecision",
+]

apps/server/services/event/decision_engine.py

@@ -34,13 +34,16 @@ 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,
     # 打架
     "fight": EventType.FIGHT,
     "fighting": EventType.FIGHT,
+    "violence": EventType.FIGHT,          # YOLO打架模型 (fight_detection) 正样本
     # 行为
     "loitering": EventType.LOITERING,
     "stationary": EventType.STATIONARY,

apps/server/services/event/frame_accumulator.py

@@ -0,0 +1,295 @@
+"""多帧累积分析器 (MVP-3 / D26-D27)
+
+针对同一目标在时间窗口内的连续帧候选事件进行累积，
+为 ``LLMTrigger`` 提供"是否值得调用 LLM 二次判断"的决策依据。
+
+设计要点:
+
+1. 以 ``(source_id, event_type, target_identity)`` 作为唯一累积键，
+   ``target_identity`` 优先采用 ``track_id``，缺失时回退到 bbox 网格哈希
+   (与 ``EventAggregator`` 一致，确保两个模块对"同一目标"的认定口径相同)。
+2. 累积条目记录连续命中帧数、累积平均置信度、首末时间戳、最大严重性。
+3. 按时间窗口自动淘汰过期条目，并使用 OrderedDict 实现 LRU 容量保护，
+   避免多路摄像头长时间运行导致内存无限增长。
+4. 当某次新候选事件与已有键的 ``last_seen`` 间隔超出窗口时，
+   认为序列中断，自动重置 ``consecutive_hits`` 计数。
+
+Thread-safety: 当前实现为非线程安全，调用方应在单事件循环中按序使用。
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, TypeAlias
+
+from models.event_schemas import (
+    BBox,
+    CandidateEvent,
+    EventType,
+    SeverityLevel,
+    UnifiedDetection,
+)
+
+logger = logging.getLogger(__name__)
+
+
+_AccKey: TypeAlias = Tuple[Optional[str], str, str]
+
+_SEVERITY_ORDER: Tuple[str, ...] = (
+    SeverityLevel.INFO.value,
+    SeverityLevel.LOW.value,
+    SeverityLevel.MEDIUM.value,
+    SeverityLevel.HIGH.value,
+    SeverityLevel.CRITICAL.value,
+)
+
+
+# ---------------------------------------------------------------------------
+# 累积条目
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class AccumulationEntry:
+    """单个目标在窗口内的累积统计。
+
+    Attributes:
+        key: (source_id, event_type, target_identity)
+        event_type: 事件类型
+        source_id: 摄像头/视频流标识
+        first_seen: 首次出现时间戳
+        last_seen: 最近一次出现时间戳
+        total_hits: 窗口内累计命中次数 (含被时间窗口截断重置前的不计)
+        consecutive_hits: 当前连续命中帧数
+        confidence_sum: 置信度累积和 (用于计算平均值)
+        max_confidence: 窗口内最高置信度
+        max_severity: 窗口内最高严重性
+        last_event: 最近一次的候选事件 (用于 LLM 抽帧)
+        last_bbox: 最近一次目标 bbox
+    """
+
+    key: _AccKey
+    event_type: EventType
+    source_id: Optional[str]
+    first_seen: float
+    last_seen: float
+    total_hits: int = 1
+    consecutive_hits: int = 1
+    confidence_sum: float = 0.0
+    max_confidence: float = 0.0
+    max_severity: SeverityLevel = SeverityLevel.INFO
+    last_event: Optional[CandidateEvent] = None
+    last_bbox: Optional[BBox] = None
+    metadata: Dict[str, float] = field(default_factory=dict)
+
+    @property
+    def avg_confidence(self) -> float:
+        """窗口内平均置信度 (按累积命中数平均)。"""
+
+        if self.total_hits <= 0:
+            return 0.0
+        return self.confidence_sum / self.total_hits
+
+    @property
+    def duration(self) -> float:
+        """累积持续时间 (秒)。"""
+
+        return max(0.0, self.last_seen - self.first_seen)
+
+    def to_dict(self) -> Dict[str, object]:
+        return {
+            "source_id": self.source_id,
+            "event_type": self.event_type.value,
+            "target_identity": self.key[2],
+            "total_hits": self.total_hits,
+            "consecutive_hits": self.consecutive_hits,
+            "avg_confidence": round(self.avg_confidence, 4),
+            "max_confidence": round(self.max_confidence, 4),
+            "max_severity": self.max_severity.value,
+            "first_seen": self.first_seen,
+            "last_seen": self.last_seen,
+            "duration": round(self.duration, 3),
+        }
+
+
+# ---------------------------------------------------------------------------
+# MultiFrameAccumulator
+# ---------------------------------------------------------------------------
+
+
+class MultiFrameAccumulator:
+    """多帧候选事件累积器。
+
+    Args:
+        window_seconds: 累积时间窗口，超过此值的条目会被淘汰；
+            同一 key 两次命中间隔超出窗口时，连续帧计数会被重置
+        max_capacity: 最大跟踪条目数，超出时按 LRU 淘汰
+        grid_size: 缺失 track_id 时用于构造目标 hash 的网格尺寸 (像素)
+    """
+
+    def __init__(
+        self,
+        window_seconds: float = 3.0,
+        max_capacity: int = 2000,
+        grid_size: int = 50,
+    ) -> None:
+        if window_seconds <= 0:
+            raise ValueError("window_seconds 必须 > 0")
+        if max_capacity < 1:
+            raise ValueError("max_capacity 必须 >= 1")
+        if grid_size < 1:
+            raise ValueError("grid_size 必须 >= 1")
+
+        self.window_seconds = window_seconds
+        self.max_capacity = max_capacity
+        self.grid_size = grid_size
+
+        self._entries: "OrderedDict[_AccKey, AccumulationEntry]" = OrderedDict()
+
+    # ------------------------------------------------------------------
+    # 主入口
+    # ------------------------------------------------------------------
+
+    def accumulate(
+        self,
+        events: List[CandidateEvent],
+        now: Optional[float] = None,
+    ) -> List[AccumulationEntry]:
+        """累积一批候选事件，返回受影响的累积条目快照列表。
+
+        Args:
+            events: 当前帧的候选事件
+            now: 当前时间戳 (供测试注入)，默认 ``time.time()``
+        """
+
+        if now is None:
+            now = time.time()
+
+        self._evict_expired(now)
+
+        affected: List[AccumulationEntry] = []
+        for event in events:
+            entry = self._update_one(event, now)
+            if entry is not None:
+                affected.append(entry)
+
+        # LRU 容量保护
+        while len(self._entries) > self.max_capacity:
+            dropped_key, _ = self._entries.popitem(last=False)
+            logger.debug("MultiFrameAccumulator LRU 淘汰: %s", dropped_key)
+
+        return affected
+
+    # ------------------------------------------------------------------
+    # 单事件累积
+    # ------------------------------------------------------------------
+
+    def _update_one(
+        self,
+        event: CandidateEvent,
+        now: float,
+    ) -> Optional[AccumulationEntry]:
+        key = self._make_key(event)
+        existing = self._entries.get(key)
+
+        if existing is None:
+            entry = AccumulationEntry(
+                key=key,
+                event_type=event.event_type,
+                source_id=event.source_id,
+                first_seen=now,
+                last_seen=now,
+                total_hits=1,
+                consecutive_hits=1,
+                confidence_sum=event.confidence,
+                max_confidence=event.confidence,
+                max_severity=event.severity,
+                last_event=event,
+                last_bbox=event.detection.bbox,
+            )
+            self._entries[key] = entry
+            return entry
+
+        # 序列中断检测：超出窗口则重置连续计数与平均累积
+        if now - existing.last_seen > self.window_seconds:
+            existing.first_seen = now
+            existing.total_hits = 0
+            existing.consecutive_hits = 0
+            existing.confidence_sum = 0.0
+
+        existing.last_seen = now
+        existing.total_hits += 1
+        existing.consecutive_hits += 1
+        existing.confidence_sum += event.confidence
+        existing.max_confidence = max(existing.max_confidence, event.confidence)
+        existing.max_severity = self._max_severity(
+            existing.max_severity, event.severity
+        )
+        existing.last_event = event
+        existing.last_bbox = event.detection.bbox
+
+        # 移到队尾保持 LRU
+        self._entries.move_to_end(key)
+        return existing
+
+    # ------------------------------------------------------------------
+    # 工具
+    # ------------------------------------------------------------------
+
+    def _make_key(self, event: CandidateEvent) -> _AccKey:
+        return (
+            event.source_id,
+            event.event_type.value,
+            self._target_identity(event.detection),
+        )
+
+    def _target_identity(self, det: UnifiedDetection) -> str:
+        """构造目标稳定标识：优先 track_id，否则 bbox 网格哈希。"""
+
+        if det.track_id is not None:
+            return f"t{det.track_id}"
+        cx, cy = det.bbox.center
+        gx = int(cx) // self.grid_size
+        gy = int(cy) // self.grid_size
+        return f"g{gx}_{gy}_{det.class_name}"
+
+    @staticmethod
+    def _max_severity(a: SeverityLevel, b: SeverityLevel) -> SeverityLevel:
+        try:
+            ai = _SEVERITY_ORDER.index(a.value)
+            bi = _SEVERITY_ORDER.index(b.value)
+        except ValueError:
+            return a
+        return a if ai >= bi else b
+
+    # ------------------------------------------------------------------
+    # 淘汰 / 自省
+    # ------------------------------------------------------------------
+
+    def _evict_expired(self, now: float) -> None:
+        expired = [
+            key
+            for key, entry in self._entries.items()
+            if now - entry.last_seen > self.window_seconds
+        ]
+        for key in expired:
+            self._entries.pop(key, None)
+
+    def get(self, event: CandidateEvent) -> Optional[AccumulationEntry]:
+        return self._entries.get(self._make_key(event))
+
+    def clear(self) -> None:
+        self._entries.clear()
+
+    @property
+    def active_count(self) -> int:
+        return len(self._entries)
+
+    def snapshot(self) -> List[Dict[str, object]]:
+        return [entry.to_dict() for entry in self._entries.values()]
+
+
+__all__ = ["MultiFrameAccumulator", "AccumulationEntry"]

apps/server/services/event/llm_trigger.py

@@ -0,0 +1,244 @@
+"""LLM 触发决策器 (MVP-3 / D28)
+
+基于 ``MultiFrameAccumulator`` 的累积统计，决定哪些目标值得调用 LLM 二次判断。
+
+触发策略 (任一满足即触发):
+
+1. ``severity_bypass``: 累积条目最大严重性命中白名单 (默认 critical) 立即触发，
+   无需累积窗口
+2. ``连续命中帧数 >= min_consecutive_hits`` 且 ``avg_confidence >= min_avg_confidence``
+
+冷却机制:
+
+- 触发后记录该目标最近一次的触发时间，``cooldown_seconds`` 内不再重复触发，
+  避免对同一可疑目标短时间多次调用 LLM 造成成本浪费。
+
+可观测性:
+
+- ``stats`` 暴露 evaluated / triggered / cooled / bypassed 计数，便于监控
+
+线程安全: 与 ``MultiFrameAccumulator`` 一致，单事件循环串行使用即可。
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from collections import OrderedDict
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple, TypeAlias
+
+from models.event_schemas import CandidateEvent, SeverityLevel
+
+from .frame_accumulator import AccumulationEntry, MultiFrameAccumulator
+
+logger = logging.getLogger(__name__)
+
+
+_TriggerKey: TypeAlias = Tuple[Optional[str], str, str]
+
+
+# ---------------------------------------------------------------------------
+# 触发结果
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class TriggerDecision:
+    """LLM 触发决策结果。"""
+
+    candidate: CandidateEvent
+    entry: AccumulationEntry
+    reason: str
+
+    def to_dict(self) -> Dict[str, object]:
+        return {
+            "event_type": self.candidate.event_type.value,
+            "source_id": self.candidate.source_id,
+            "confidence": round(self.candidate.confidence, 4),
+            "consecutive_hits": self.entry.consecutive_hits,
+            "avg_confidence": round(self.entry.avg_confidence, 4),
+            "max_severity": self.entry.max_severity.value,
+            "duration": round(self.entry.duration, 3),
+            "reason": self.reason,
+        }
+
+
+# ---------------------------------------------------------------------------
+# LLMTrigger
+# ---------------------------------------------------------------------------
+
+
+class LLMTrigger:
+    """LLM 触发器。
+
+    Args:
+        accumulator: 多帧累积分析器 (由调用方共享，便于状态一致)
+        min_consecutive_hits: 触发所需的最小连续命中帧数
+        min_avg_confidence: 累积平均置信度下限
+        cooldown_seconds: 同目标 LLM 冷却时间 (秒)，0 表示不冷却
+        severity_bypass: 立即触发的严重性级别集合
+        max_cooldown_entries: 冷却记录最大容量 (LRU 淘汰)
+    """
+
+    def __init__(
+        self,
+        accumulator: MultiFrameAccumulator,
+        min_consecutive_hits: int = 3,
+        min_avg_confidence: float = 0.55,
+        cooldown_seconds: float = 20.0,
+        severity_bypass: Optional[List[str]] = None,
+        max_cooldown_entries: int = 5000,
+    ) -> None:
+        if min_consecutive_hits < 1:
+            raise ValueError("min_consecutive_hits 必须 >= 1")
+        if not 0.0 <= min_avg_confidence <= 1.0:
+            raise ValueError("min_avg_confidence 必须在 [0, 1]")
+        if cooldown_seconds < 0:
+            raise ValueError("cooldown_seconds 必须 >= 0")
+        if max_cooldown_entries < 1:
+            raise ValueError("max_cooldown_entries 必须 >= 1")
+
+        self.accumulator = accumulator
+        self.min_consecutive_hits = min_consecutive_hits
+        self.min_avg_confidence = min_avg_confidence
+        self.cooldown_seconds = cooldown_seconds
+        self.severity_bypass = {
+            SeverityLevel(s) for s in (severity_bypass or [])
+        } if severity_bypass else set()
+        self.max_cooldown_entries = max_cooldown_entries
+
+        self._cooldowns: "OrderedDict[_TriggerKey, float]" = OrderedDict()
+
+        # 统计
+        self._evaluated = 0
+        self._triggered = 0
+        self._cooled = 0
+        self._bypassed = 0
+
+    # ------------------------------------------------------------------
+    # 主入口
+    # ------------------------------------------------------------------
+
+    def evaluate(
+        self,
+        candidates: List[CandidateEvent],
+        now: Optional[float] = None,
+    ) -> List[TriggerDecision]:
+        """评估候选事件，返回需要触发 LLM 复审的决策列表。
+
+        Args:
+            candidates: 当前批次候选事件 (通常来自规则引擎之前的决策结果)
+            now: 当前时间戳 (供测试注入)
+        """
+
+        if now is None:
+            now = time.time()
+
+        # 1. 先把候选事件喂给累积器 (统一时间戳，确保统计与触发判定基于相同 now)
+        self.accumulator.accumulate(candidates, now=now)
+
+        decisions: List[TriggerDecision] = []
+        for candidate in candidates:
+            self._evaluated += 1
+            entry = self.accumulator.get(candidate)
+            if entry is None:
+                continue
+
+            decision = self._make_decision(candidate, entry, now)
+            if decision is not None:
+                decisions.append(decision)
+
+        # 维护冷却表容量
+        self._evict_cooldowns(now)
+        return decisions
+
+    # ------------------------------------------------------------------
+    # 内部
+    # ------------------------------------------------------------------
+
+    def _make_decision(
+        self,
+        candidate: CandidateEvent,
+        entry: AccumulationEntry,
+        now: float,
+    ) -> Optional[TriggerDecision]:
+        cooldown_key: _TriggerKey = entry.key
+
+        # 冷却检查
+        last_fire = self._cooldowns.get(cooldown_key)
+        if last_fire is not None and (now - last_fire) < self.cooldown_seconds:
+            self._cooled += 1
+            return None
+
+        # 严重性快速通道
+        if entry.max_severity in self.severity_bypass:
+            self._bypassed += 1
+            self._mark_cooldown(cooldown_key, now)
+            return TriggerDecision(
+                candidate=candidate,
+                entry=entry,
+                reason=f"severity_bypass:{entry.max_severity.value}",
+            )
+
+        # 多帧累积阈值
+        if (
+            entry.consecutive_hits >= self.min_consecutive_hits
+            and entry.avg_confidence >= self.min_avg_confidence
+        ):
+            self._triggered += 1
+            self._mark_cooldown(cooldown_key, now)
+            return TriggerDecision(
+                candidate=candidate,
+                entry=entry,
+                reason=(
+                    f"hits={entry.consecutive_hits}>={self.min_consecutive_hits},"
+                    f"avg_conf={entry.avg_confidence:.3f}>="
+                    f"{self.min_avg_confidence:.2f}"
+                ),
+            )
+
+        return None
+
+    def _mark_cooldown(self, key: _TriggerKey, now: float) -> None:
+        self._cooldowns[key] = now
+        self._cooldowns.move_to_end(key)
+        while len(self._cooldowns) > self.max_cooldown_entries:
+            self._cooldowns.popitem(last=False)
+
+    def _evict_cooldowns(self, now: float) -> None:
+        if self.cooldown_seconds <= 0:
+            self._cooldowns.clear()
+            return
+        expired = [
+            key
+            for key, ts in self._cooldowns.items()
+            if (now - ts) >= self.cooldown_seconds
+        ]
+        for key in expired:
+            self._cooldowns.pop(key, None)
+
+    # ------------------------------------------------------------------
+    # 自省
+    # ------------------------------------------------------------------
+
+    @property
+    def stats(self) -> Dict[str, int]:
+        return {
+            "evaluated": self._evaluated,
+            "triggered": self._triggered,
+            "cooled": self._cooled,
+            "bypassed": self._bypassed,
+            "active_cooldowns": len(self._cooldowns),
+            "active_accumulations": self.accumulator.active_count,
+        }
+
+    def reset(self) -> None:
+        self._cooldowns.clear()
+        self._evaluated = 0
+        self._triggered = 0
+        self._cooled = 0
+        self._bypassed = 0
+
+
+__all__ = ["LLMTrigger", "TriggerDecision"]

apps/server/services/llm_analysis_service.py

@@ -0,0 +1,526 @@
+"""LLM 分析服务 (MVP-3 / D29)
+
+调用多模态大模型 (GPT-4V / Qwen-VL / GLM-4V) 对可疑事件进行二次判断。
+
+设计要点:
+
+1. **provider 抽象**: ``BaseLLMProvider`` 定义统一接口，
+   ``OpenAICompatibleProvider`` 覆盖所有 OpenAI 协议兼容的 vendor，
+   ``MockLLMProvider`` 提供完全离线的可预测行为，便于测试与本地开发。
+2. **并发控制**: 使用 ``asyncio.Semaphore`` 限制同时进行的 LLM 调用数量，
+   避免大量并发请求拖垮 API 配额。
+3. **降级策略**: 上层调用方可通过 ``analyze_with_fallback`` 在 LLM 失败时
+   返回 ``confirmed=None`` 的"未确认"结果，由 ``ResultFusion`` 决定如何处理。
+4. **图像编码**: 自动按最大边长缩放后编码为 base64 JPEG，控制 token 成本。
+5. **结构化输出**: prompt 强制 LLM 输出 JSON，解析失败时降级为
+   ``confirmed=None`` 而不是抛异常。
+
+依赖说明:
+
+- HTTP 客户端使用 ``httpx`` (已在 requirements 中)
+- 图像编码使用 ``opencv-python`` 已存在的 cv2.imencode
+
+无需新增 Python 依赖即可运行 (mock provider)，仅当 ``provider != mock`` 时
+才会真正发起 HTTP 请求。
+"""
+
+from __future__ import annotations
+
+import asyncio
+import base64
+import json
+import logging
+import re
+import time
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Sequence
+
+import numpy as np
+
+from models.event_schemas import CandidateEvent, EventType
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# 数据结构
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class LLMAnalysisRequest:
+    """LLM 分析请求。
+
+    Attributes:
+        candidate: 触发分析的候选事件 (含事件类型 / bbox)
+        frames: 关键帧序列 (BGR ``np.ndarray``)，
+            通常取累积窗口内的 1-3 张代表帧
+        prompt_extra: 业务侧附加提示词 (可选)
+        request_id: 请求标识 (用于日志关联)
+    """
+
+    candidate: CandidateEvent
+    frames: Sequence[np.ndarray]
+    prompt_extra: Optional[str] = None
+    request_id: Optional[str] = None
+
+
+@dataclass
+class LLMAnalysisResult:
+    """LLM 分析结果。
+
+    Attributes:
+        confirmed: True=LLM 确认事件成立，False=否决，None=未知/调用失败
+        confidence: LLM 给出的置信度 [0, 1]
+        reasoning: LLM 的简短推理说明
+        provider: 实际调用的 provider 名称
+        model: 实际调用的模型名
+        latency_ms: 调用耗时 (毫秒)
+        error: 失败时的错误信息
+        raw: 原始返回 (调试用)
+    """
+
+    confirmed: Optional[bool]
+    confidence: float = 0.0
+    reasoning: str = ""
+    provider: str = ""
+    model: str = ""
+    latency_ms: float = 0.0
+    error: Optional[str] = None
+    raw: Optional[str] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+# ---------------------------------------------------------------------------
+# Provider 抽象
+# ---------------------------------------------------------------------------
+
+
+class BaseLLMProvider:
+    """LLM provider 抽象。"""
+
+    name: str = "base"
+
+    async def analyze(self, request: LLMAnalysisRequest) -> LLMAnalysisResult:
+        raise NotImplementedError
+
+
+class MockLLMProvider(BaseLLMProvider):
+    """离线 Mock provider。
+
+    根据候选事件置信度生成可预测的判定结果，便于测试与无 API Key 环境。
+
+    规则:
+    - confidence >= 0.7  -> confirmed=True
+    - confidence <  0.3  -> confirmed=False
+    - 其余 -> confirmed=None
+    """
+
+    name = "mock"
+
+    def __init__(self, model: str = "mock-vlm") -> None:
+        self.model = model
+
+    async def analyze(self, request: LLMAnalysisRequest) -> LLMAnalysisResult:
+        start = time.time()
+        await asyncio.sleep(0)  # 模拟异步
+        conf = float(request.candidate.confidence)
+        if conf >= 0.7:
+            confirmed: Optional[bool] = True
+        elif conf < 0.3:
+            confirmed = False
+        else:
+            confirmed = None
+
+        reasoning = (
+            f"mock provider: candidate confidence={conf:.3f}, "
+            f"event_type={request.candidate.event_type.value}"
+        )
+        return LLMAnalysisResult(
+            confirmed=confirmed,
+            confidence=conf,
+            reasoning=reasoning,
+            provider=self.name,
+            model=self.model,
+            latency_ms=(time.time() - start) * 1000.0,
+        )
+
+
+class OpenAICompatibleProvider(BaseLLMProvider):
+    """OpenAI 协议兼容 provider (覆盖 GPT-4V / Qwen-VL / GLM-4V 等)。"""
+
+    name = "openai"
+
+    def __init__(
+        self,
+        api_base: str,
+        api_key: str,
+        model: str,
+        timeout: float = 15.0,
+        max_retries: int = 2,
+        max_tokens: int = 512,
+        temperature: float = 0.0,
+        provider_name: Optional[str] = None,
+    ) -> None:
+        if not api_base:
+            raise ValueError("api_base 不能为空")
+        if not api_key:
+            raise ValueError("api_key 不能为空")
+        self.api_base = api_base.rstrip("/")
+        self.api_key = api_key
+        self.model = model
+        self.timeout = timeout
+        self.max_retries = max_retries
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        if provider_name:
+            self.name = provider_name
+
+    async def analyze(self, request: LLMAnalysisRequest) -> LLMAnalysisResult:
+        try:
+            import httpx  # noqa: WPS433
+        except ImportError as exc:  # pragma: no cover - 依赖在 requirements 已声明
+            return LLMAnalysisResult(
+                confirmed=None,
+                provider=self.name,
+                model=self.model,
+                error=f"httpx 未安装: {exc}",
+            )
+
+        url = f"{self.api_base}/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        payload = {
+            "model": self.model,
+            "messages": _build_messages(request),
+            "max_tokens": self.max_tokens,
+            "temperature": self.temperature,
+        }
+
+        attempt = 0
+        last_error: Optional[str] = None
+        start = time.time()
+        while attempt <= self.max_retries:
+            attempt += 1
+            try:
+                async with httpx.AsyncClient(timeout=self.timeout) as client:
+                    resp = await client.post(url, headers=headers, json=payload)
+                    resp.raise_for_status()
+                    data = resp.json()
+                content = (
+                    data.get("choices", [{}])[0]
+                    .get("message", {})
+                    .get("content", "")
+                )
+                parsed = _parse_llm_json(content)
+                return LLMAnalysisResult(
+                    confirmed=parsed.get("confirmed"),
+                    confidence=float(parsed.get("confidence", 0.0) or 0.0),
+                    reasoning=str(parsed.get("reasoning", "")),
+                    provider=self.name,
+                    model=self.model,
+                    latency_ms=(time.time() - start) * 1000.0,
+                    raw=content,
+                )
+            except Exception as exc:  # noqa: BLE001
+                last_error = f"{type(exc).__name__}: {exc}"
+                logger.warning(
+                    "LLM 调用失败 attempt=%d/%d err=%s",
+                    attempt,
+                    self.max_retries + 1,
+                    last_error,
+                )
+                if attempt > self.max_retries:
+                    break
+                await asyncio.sleep(min(2 ** (attempt - 1), 5))
+
+        return LLMAnalysisResult(
+            confirmed=None,
+            provider=self.name,
+            model=self.model,
+            latency_ms=(time.time() - start) * 1000.0,
+            error=last_error,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Service
+# ---------------------------------------------------------------------------
+
+
+class LLMAnalysisService:
+    """LLM 二次判断服务。
+
+    Args:
+        provider: LLM provider 实例 (Mock / OpenAI 兼容)
+        max_concurrency: 同时进行的最大调用数
+        image_max_side: 编码前图像最大边长 (像素)
+    """
+
+    def __init__(
+        self,
+        provider: BaseLLMProvider,
+        max_concurrency: int = 2,
+        image_max_side: int = 768,
+    ) -> None:
+        if max_concurrency < 1:
+            raise ValueError("max_concurrency 必须 >= 1")
+        if image_max_side < 64:
+            raise ValueError("image_max_side 必须 >= 64")
+        self.provider = provider
+        self._semaphore = asyncio.Semaphore(max_concurrency)
+        self.image_max_side = image_max_side
+
+        # 统计
+        self._call_count = 0
+        self._success_count = 0
+        self._failure_count = 0
+        self._total_latency_ms = 0.0
+
+    async def analyze(self, request: LLMAnalysisRequest) -> LLMAnalysisResult:
+        """调用 LLM 分析单个事件 (并发受信号量限制)。"""
+
+        # 图像缩放预处理 (mock provider 也走一遍，保持行为一致)
+        prepared_frames = [
+            _resize_frame(f, self.image_max_side) for f in request.frames
+        ]
+        if any(p is not o for p, o in zip(prepared_frames, request.frames)):
+            request = LLMAnalysisRequest(
+                candidate=request.candidate,
+                frames=prepared_frames,
+                prompt_extra=request.prompt_extra,
+                request_id=request.request_id,
+            )
+
+        async with self._semaphore:
+            self._call_count += 1
+            try:
+                result = await self.provider.analyze(request)
+            except Exception as exc:  # noqa: BLE001
+                logger.error("LLM provider 异常: %s", exc)
+                result = LLMAnalysisResult(
+                    confirmed=None,
+                    provider=getattr(self.provider, "name", "unknown"),
+                    model=getattr(self.provider, "model", ""),
+                    error=f"{type(exc).__name__}: {exc}",
+                )
+
+        if result.error or result.confirmed is None:
+            self._failure_count += 1
+        else:
+            self._success_count += 1
+        self._total_latency_ms += result.latency_ms
+        return result
+
+    async def analyze_with_fallback(
+        self,
+        request: LLMAnalysisRequest,
+    ) -> LLMAnalysisResult:
+        """带降级的分析: 任意异常都不会向上抛出。"""
+
+        try:
+            return await self.analyze(request)
+        except Exception as exc:  # noqa: BLE001
+            logger.error("LLM 分析降级: %s", exc)
+            return LLMAnalysisResult(
+                confirmed=None,
+                provider=getattr(self.provider, "name", "unknown"),
+                model=getattr(self.provider, "model", ""),
+                error=f"{type(exc).__name__}: {exc}",
+            )
+
+    @property
+    def stats(self) -> Dict[str, Any]:
+        avg_latency = (
+            self._total_latency_ms / self._call_count
+            if self._call_count > 0
+            else 0.0
+        )
+        return {
+            "provider": getattr(self.provider, "name", "unknown"),
+            "model": getattr(self.provider, "model", ""),
+            "call_count": self._call_count,
+            "success_count": self._success_count,
+            "failure_count": self._failure_count,
+            "avg_latency_ms": round(avg_latency, 2),
+        }
+
+
+# ---------------------------------------------------------------------------
+# Factory
+# ---------------------------------------------------------------------------
+
+
+def create_llm_service_from_settings(settings: Any) -> Optional[LLMAnalysisService]:
+    """根据 ``Settings.llm`` 配置构造 LLMAnalysisService。
+
+    返回 None 表示未启用，调用方应跳过 LLM 分析。
+    """
+
+    llm_cfg = getattr(settings, "llm", None)
+    if llm_cfg is None or not getattr(llm_cfg, "enabled", False):
+        return None
+
+    provider_name = (llm_cfg.provider or "mock").lower()
+    if provider_name == "mock":
+        provider: BaseLLMProvider = MockLLMProvider(model=llm_cfg.model or "mock-vlm")
+    else:
+        api_base = llm_cfg.api_base
+        api_key = llm_cfg.api_key
+        if not api_base or not api_key:
+            logger.warning(
+                "LLM provider=%s 缺少 api_base/api_key，回退到 mock", provider_name
+            )
+            provider = MockLLMProvider(model=llm_cfg.model or "mock-vlm")
+        else:
+            provider = OpenAICompatibleProvider(
+                api_base=api_base,
+                api_key=api_key,
+                model=llm_cfg.model,
+                timeout=llm_cfg.timeout,
+                max_retries=llm_cfg.max_retries,
+                max_tokens=llm_cfg.max_tokens,
+                temperature=llm_cfg.temperature,
+                provider_name=provider_name,
+            )
+
+    return LLMAnalysisService(
+        provider=provider,
+        max_concurrency=llm_cfg.max_concurrency,
+        image_max_side=llm_cfg.image_max_side,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+_EVENT_TYPE_DESCRIPTIONS: Dict[EventType, str] = {
+    EventType.FIRE: "fire / open flame",
+    EventType.SMOKE: "smoke / haze",
+    EventType.SMOKING: "a person smoking a cigarette",
+    EventType.FIGHT: "a fight or physical altercation",
+    EventType.LOITERING: "a person loitering / lingering suspiciously",
+    EventType.STATIONARY: "a stationary person (possibly fallen or unconscious)",
+    EventType.INTRUSION: "an intrusion into a restricted area",
+    EventType.ILLEGAL_PARKING: "a vehicle illegally parked",
+    EventType.VEHICLE: "a vehicle of interest",
+    EventType.PERSON: "a person of interest",
+    EventType.UNKNOWN: "an unspecified suspicious event",
+}
+
+
+def _build_messages(request: LLMAnalysisRequest) -> List[Dict[str, Any]]:
+    """构造 OpenAI ChatCompletion messages。"""
+
+    candidate = request.candidate
+    event_desc = _EVENT_TYPE_DESCRIPTIONS.get(candidate.event_type, "an event")
+    bbox = candidate.detection.bbox.to_list()
+
+    system_prompt = (
+        "You are a strict video surveillance auditor. "
+        "Given one or more frames and a candidate event reported by an AI detector, "
+        "decide whether the event is truly present. "
+        "Respond with a single JSON object: "
+        '{"confirmed": true|false, "confidence": 0.0-1.0, "reasoning": "..."} '
+        "and nothing else."
+    )
+
+    user_text_parts: List[str] = [
+        f"Candidate event: {event_desc}.",
+        f"Detector confidence: {candidate.confidence:.3f}.",
+        f"Bounding box (x1,y1,x2,y2): {bbox}.",
+    ]
+    if request.prompt_extra:
+        user_text_parts.append(request.prompt_extra)
+    user_text_parts.append(
+        "Please answer strictly in JSON: "
+        '{"confirmed": <bool>, "confidence": <float>, "reasoning": <str>}'
+    )
+
+    content: List[Dict[str, Any]] = [
+        {"type": "text", "text": "\n".join(user_text_parts)},
+    ]
+    for frame in request.frames:
+        b64 = _encode_frame_base64(frame)
+        if b64:
+            content.append(
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/jpeg;base64,{b64}"},
+                }
+            )
+
+    return [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": content},
+    ]
+
+
+def _encode_frame_base64(frame: np.ndarray) -> Optional[str]:
+    """将 BGR 帧编码为 base64 JPEG 字符串。"""
+
+    try:
+        import cv2  # noqa: WPS433
+    except ImportError:  # pragma: no cover
+        return None
+    if frame is None or frame.size == 0:
+        return None
+    ok, buf = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 80])
+    if not ok:
+        return None
+    return base64.b64encode(buf.tobytes()).decode("ascii")
+
+
+def _resize_frame(frame: np.ndarray, max_side: int) -> np.ndarray:
+    """按最大边长等比缩放 (节省 token)。"""
+
+    if frame is None or frame.size == 0:
+        return frame
+    try:
+        import cv2  # noqa: WPS433
+    except ImportError:  # pragma: no cover
+        return frame
+    h, w = frame.shape[:2]
+    longest = max(h, w)
+    if longest <= max_side:
+        return frame
+    scale = max_side / float(longest)
+    new_size = (max(1, int(w * scale)), max(1, int(h * scale)))
+    return cv2.resize(frame, new_size, interpolation=cv2.INTER_AREA)
+
+
+_JSON_PATTERN = re.compile(r"\{.*\}", re.DOTALL)
+
+
+def _parse_llm_json(content: str) -> Dict[str, Any]:
+    """从 LLM 文本输出中尽力解析 JSON。"""
+
+    if not content:
+        return {}
+    try:
+        return json.loads(content)
+    except json.JSONDecodeError:
+        match = _JSON_PATTERN.search(content)
+        if match:
+            try:
+                return json.loads(match.group(0))
+            except json.JSONDecodeError:
+                logger.debug("LLM JSON 解析失败: %s", content[:200])
+    return {}
+
+
+__all__ = [
+    "LLMAnalysisService",
+    "LLMAnalysisRequest",
+    "LLMAnalysisResult",
+    "BaseLLMProvider",
+    "MockLLMProvider",
+    "OpenAICompatibleProvider",
+    "create_llm_service_from_settings",
+]
+
+

apps/server/services/llm_cost_tracker.py

@@ -0,0 +1,555 @@
+"""LLM 成本追踪器 + 增强降级策略 (MVP-3 / D34)
+
+职责
+----
+
+1. 记录每次 LLM 调用的 token / 费用 / 延迟，提供按时段聚合的统计
+2. 实现"硬熔断"降级策略: 当达到日预算 / 错误率阈值时自动禁用 LLM
+3. 提供成本仪表盘所需的导出接口（供 ``api/llm.py`` 暴露给前端）
+
+设计要点
+--------
+
+* **职责分离**：成本追踪本身只负责"记账 + 状态机"，不直接决定能否调用 LLM；
+  调用方 (例如检测管道) 在每次调用前调用 ``can_call()``，根据结果决定降级
+* **滑动窗口**：使用按 UTC 日历日的桶 (``deque``) 保存最近 N 天的统计，
+  避免长时间运行内存无限增长
+* **熔断**：达到错误率阈值 (默认连续 ``error_threshold`` 次调用失败率 >50%)
+  自动进入 ``CIRCUIT_OPEN`` 状态，``cooldown_seconds`` 后自动半开尝试恢复
+* **Token 估算**：未拿到真实 usage 时按"图像 + prompt 长度"做粗估，
+  保证账单不会因为 provider 不返回 usage 而归零
+
+接入方式
+--------
+
+::
+
+    tracker = LLMCostTracker(daily_budget_usd=5.0)
+    if not tracker.can_call():
+        return None  # 降级处理
+    result = await llm_service.analyze_with_fallback(req)
+    tracker.record(result, request=req)
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import time
+from collections import deque
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Any, Deque, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# 常量 / 数据结构
+# ---------------------------------------------------------------------------
+
+
+# 不同 provider 的近似定价 (USD per 1M tokens)，仅供估算
+DEFAULT_PRICING: Dict[str, Dict[str, float]] = {
+    # OpenAI GPT-4o
+    "openai": {"prompt": 5.0, "completion": 15.0, "image": 0.005},
+    # 豆包 / Qwen / GLM 国内 VLM 大致价位（仅估算）
+    "qwen": {"prompt": 0.8, "completion": 2.0, "image": 0.001},
+    "glm": {"prompt": 1.0, "completion": 3.0, "image": 0.001},
+    "doubao": {"prompt": 0.8, "completion": 2.0, "image": 0.001},
+    # mock provider 不计费
+    "mock": {"prompt": 0.0, "completion": 0.0, "image": 0.0},
+    # 兜底
+    "default": {"prompt": 1.0, "completion": 3.0, "image": 0.002},
+}
+
+
+class CircuitState:
+    CLOSED = "closed"  # 正常
+    OPEN = "open"  # 熔断
+    HALF_OPEN = "half_open"  # 试探恢复
+
+
+@dataclass
+class CallRecord:
+    """单次 LLM 调用记录。"""
+
+    timestamp: float
+    provider: str
+    model: str
+    success: bool
+    confirmed: Optional[bool]
+    latency_ms: float
+    prompt_tokens: int
+    completion_tokens: int
+    image_count: int
+    cost_usd: float
+    error: Optional[str] = None
+
+
+@dataclass
+class _DailyBucket:
+    """按 UTC 日历日聚合的桶。"""
+
+    date: str
+    call_count: int = 0
+    success_count: int = 0
+    failure_count: int = 0
+    total_prompt_tokens: int = 0
+    total_completion_tokens: int = 0
+    total_image_count: int = 0
+    total_cost_usd: float = 0.0
+    total_latency_ms: float = 0.0
+
+    def add(self, record: CallRecord) -> None:
+        self.call_count += 1
+        if record.success:
+            self.success_count += 1
+        else:
+            self.failure_count += 1
+        self.total_prompt_tokens += record.prompt_tokens
+        self.total_completion_tokens += record.completion_tokens
+        self.total_image_count += record.image_count
+        self.total_cost_usd += record.cost_usd
+        self.total_latency_ms += record.latency_ms
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "date": self.date,
+            "call_count": self.call_count,
+            "success_count": self.success_count,
+            "failure_count": self.failure_count,
+            "prompt_tokens": self.total_prompt_tokens,
+            "completion_tokens": self.total_completion_tokens,
+            "image_count": self.total_image_count,
+            "cost_usd": round(self.total_cost_usd, 6),
+            "avg_latency_ms": round(
+                self.total_latency_ms / self.call_count, 2
+            )
+            if self.call_count
+            else 0.0,
+        }
+
+
+# ---------------------------------------------------------------------------
+# LLMCostTracker
+# ---------------------------------------------------------------------------
+
+
+class LLMCostTracker:
+    """LLM 成本追踪器 + 熔断降级。
+
+    Args:
+        daily_budget_usd: 日预算，0 表示不限
+        history_days: 保留多少天的日级统计
+        recent_window: 用于错误率计算的最近调用窗口
+        error_rate_threshold: 触发熔断的错误率阈值 [0, 1]
+        cooldown_seconds: 熔断进入半开状态的冷却时间
+        pricing_overrides: 自定义价格表 (覆盖默认)
+    """
+
+    def __init__(
+        self,
+        daily_budget_usd: float = 0.0,
+        history_days: int = 7,
+        recent_window: int = 20,
+        error_rate_threshold: float = 0.5,
+        cooldown_seconds: float = 60.0,
+        pricing_overrides: Optional[Dict[str, Dict[str, float]]] = None,
+    ) -> None:
+        if daily_budget_usd < 0:
+            raise ValueError("daily_budget_usd 必须 >= 0")
+        if history_days < 1:
+            raise ValueError("history_days 必须 >= 1")
+        if recent_window < 1:
+            raise ValueError("recent_window 必须 >= 1")
+        if not 0.0 < error_rate_threshold <= 1.0:
+            raise ValueError("error_rate_threshold 必须在 (0, 1]")
+        if cooldown_seconds < 0:
+            raise ValueError("cooldown_seconds 必须 >= 0")
+
+        self.daily_budget_usd = daily_budget_usd
+        self.history_days = history_days
+        self.recent_window = recent_window
+        self.error_rate_threshold = error_rate_threshold
+        self.cooldown_seconds = cooldown_seconds
+
+        # 合并定价
+        self._pricing: Dict[str, Dict[str, float]] = {
+            k: dict(v) for k, v in DEFAULT_PRICING.items()
+        }
+        if pricing_overrides:
+            for provider, table in pricing_overrides.items():
+                self._pricing.setdefault(provider, {})
+                self._pricing[provider].update(table)
+
+        # 状态
+        self._buckets: Deque[_DailyBucket] = deque(maxlen=history_days)
+        self._recent: Deque[bool] = deque(maxlen=recent_window)
+        self._records: Deque[CallRecord] = deque(maxlen=200)
+        self._circuit_state = CircuitState.CLOSED
+        self._circuit_opened_at: Optional[float] = None
+        self._manually_disabled = False
+        self._lock = asyncio.Lock()
+
+    # ------------------------------------------------------------------
+    # 准入检查
+    # ------------------------------------------------------------------
+
+    def can_call(self, now: Optional[float] = None) -> bool:
+        """同步判断当前是否允许调用 LLM。
+
+        触发拒绝的条件按优先级:
+        1. 手动禁用
+        2. 日预算超限
+        3. 熔断器处于 OPEN 且未到冷却结束
+        """
+
+        if self._manually_disabled:
+            return False
+        if self._budget_exhausted():
+            return False
+
+        ts = now if now is not None else time.time()
+        if self._circuit_state == CircuitState.OPEN:
+            if (
+                self._circuit_opened_at is not None
+                and (ts - self._circuit_opened_at) >= self.cooldown_seconds
+            ):
+                # 进入半开，允许一次试探
+                self._circuit_state = CircuitState.HALF_OPEN
+                logger.info("LLMCostTracker 熔断器进入 HALF_OPEN")
+                return True
+            return False
+        return True
+
+    def reason_for_block(self, now: Optional[float] = None) -> Optional[str]:
+        """返回当前不可调用的原因 (调试用)，None 表示可以调用。"""
+
+        if self._manually_disabled:
+            return "manually_disabled"
+        if self._budget_exhausted():
+            return f"daily_budget_exceeded({self._today_cost():.4f}/{self.daily_budget_usd})"
+        ts = now if now is not None else time.time()
+        if self._circuit_state == CircuitState.OPEN:
+            if (
+                self._circuit_opened_at is not None
+                and (ts - self._circuit_opened_at) < self.cooldown_seconds
+            ):
+                return "circuit_open"
+        return None
+
+    # ------------------------------------------------------------------
+    # 记录
+    # ------------------------------------------------------------------
+
+    def record(
+        self,
+        result: Any,
+        prompt_tokens: Optional[int] = None,
+        completion_tokens: Optional[int] = None,
+        image_count: int = 1,
+        provider_override: Optional[str] = None,
+    ) -> CallRecord:
+        """记录一次 LLM 调用。
+
+        ``result`` 可以是 ``LLMAnalysisResult`` 实例或鸭子类型 (含 confirmed
+        / provider / model / latency_ms / error / metadata)。
+        """
+
+        provider = provider_override or getattr(result, "provider", "default")
+        model = getattr(result, "model", "")
+        success = getattr(result, "error", None) is None and getattr(
+            result, "confirmed", None
+        ) is not None
+        latency_ms = float(getattr(result, "latency_ms", 0.0) or 0.0)
+
+        usage = self._extract_usage(result)
+        prompt_tokens = (
+            int(prompt_tokens)
+            if prompt_tokens is not None
+            else int(usage.get("prompt_tokens") or 0)
+        )
+        completion_tokens = (
+            int(completion_tokens)
+            if completion_tokens is not None
+            else int(usage.get("completion_tokens") or 0)
+        )
+
+        # token 估算兜底
+        if prompt_tokens == 0 and completion_tokens == 0 and provider != "mock":
+            prompt_tokens = self._estimate_prompt_tokens(image_count)
+            completion_tokens = self._estimate_completion_tokens(result)
+
+        cost = self._calc_cost(provider, prompt_tokens, completion_tokens, image_count)
+
+        record = CallRecord(
+            timestamp=time.time(),
+            provider=provider,
+            model=model,
+            success=success,
+            confirmed=getattr(result, "confirmed", None),
+            latency_ms=latency_ms,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+            image_count=image_count,
+            cost_usd=cost,
+            error=getattr(result, "error", None),
+        )
+
+        self._append_record(record)
+        self._update_circuit(record)
+        return record
+
+    # ------------------------------------------------------------------
+    # 控制
+    # ------------------------------------------------------------------
+
+    def disable(self) -> None:
+        """手动禁用 LLM 调用 (运维场景)。"""
+
+        self._manually_disabled = True
+        logger.warning("LLMCostTracker 已被手动禁用")
+
+    def enable(self) -> None:
+        """手动重新启用。"""
+
+        self._manually_disabled = False
+        self._circuit_state = CircuitState.CLOSED
+        self._circuit_opened_at = None
+        logger.info("LLMCostTracker 已重新启用")
+
+    def reset(self) -> None:
+        """清空全部统计 (谨慎使用)。"""
+
+        self._buckets.clear()
+        self._recent.clear()
+        self._records.clear()
+        self._circuit_state = CircuitState.CLOSED
+        self._circuit_opened_at = None
+
+    # ------------------------------------------------------------------
+    # 导出
+    # ------------------------------------------------------------------
+
+    @property
+    def is_enabled(self) -> bool:
+        return not self._manually_disabled
+
+    @property
+    def circuit_state(self) -> str:
+        return self._circuit_state
+
+    def daily_summary(self) -> List[Dict[str, Any]]:
+        return [bucket.to_dict() for bucket in self._buckets]
+
+    def today_summary(self) -> Dict[str, Any]:
+        bucket = self._today_bucket(create=False)
+        if bucket is None:
+            return {
+                "date": _today_str(),
+                "call_count": 0,
+                "cost_usd": 0.0,
+                "budget_usd": self.daily_budget_usd,
+                "budget_used_ratio": 0.0,
+            }
+        data = bucket.to_dict()
+        data["budget_usd"] = self.daily_budget_usd
+        data["budget_used_ratio"] = (
+            data["cost_usd"] / self.daily_budget_usd
+            if self.daily_budget_usd > 0
+            else 0.0
+        )
+        return data
+
+    def recent_records(self, limit: int = 20) -> List[Dict[str, Any]]:
+        records = list(self._records)[-limit:]
+        return [
+            {
+                "timestamp": r.timestamp,
+                "provider": r.provider,
+                "model": r.model,
+                "success": r.success,
+                "confirmed": r.confirmed,
+                "latency_ms": round(r.latency_ms, 2),
+                "prompt_tokens": r.prompt_tokens,
+                "completion_tokens": r.completion_tokens,
+                "image_count": r.image_count,
+                "cost_usd": round(r.cost_usd, 6),
+                "error": r.error,
+            }
+            for r in reversed(records)
+        ]
+
+    def stats(self) -> Dict[str, Any]:
+        recent_total = len(self._recent)
+        recent_failed = sum(1 for ok in self._recent if not ok)
+        recent_error_rate = (
+            recent_failed / recent_total if recent_total > 0 else 0.0
+        )
+
+        return {
+            "enabled": self.is_enabled,
+            "circuit_state": self._circuit_state,
+            "circuit_opened_at": self._circuit_opened_at,
+            "block_reason": self.reason_for_block(),
+            "today": self.today_summary(),
+            "history_days": self.history_days,
+            "recent_window": self.recent_window,
+            "recent_error_rate": round(recent_error_rate, 4),
+            "error_rate_threshold": self.error_rate_threshold,
+        }
+
+    # ------------------------------------------------------------------
+    # 内部
+    # ------------------------------------------------------------------
+
+    def _append_record(self, record: CallRecord) -> None:
+        bucket = self._today_bucket(create=True)
+        assert bucket is not None
+        bucket.add(record)
+        self._recent.append(record.success)
+        self._records.append(record)
+
+    def _today_bucket(self, *, create: bool) -> Optional[_DailyBucket]:
+        today = _today_str()
+        if self._buckets and self._buckets[-1].date == today:
+            return self._buckets[-1]
+        if not create:
+            for b in self._buckets:
+                if b.date == today:
+                    return b
+            return None
+        bucket = _DailyBucket(date=today)
+        self._buckets.append(bucket)
+        return bucket
+
+    def _today_cost(self) -> float:
+        bucket = self._today_bucket(create=False)
+        return bucket.total_cost_usd if bucket else 0.0
+
+    def _budget_exhausted(self) -> bool:
+        if self.daily_budget_usd <= 0:
+            return False
+        return self._today_cost() >= self.daily_budget_usd
+
+    def _update_circuit(self, record: CallRecord) -> None:
+        # HALF_OPEN: 一次结果决定回到 CLOSED 还是 OPEN
+        if self._circuit_state == CircuitState.HALF_OPEN:
+            if record.success:
+                self._circuit_state = CircuitState.CLOSED
+                self._circuit_opened_at = None
+                logger.info("LLMCostTracker 熔断器恢复 CLOSED")
+            else:
+                self._circuit_state = CircuitState.OPEN
+                self._circuit_opened_at = time.time()
+                logger.warning("LLMCostTracker 半开试探失败，回到 OPEN")
+            return
+
+        # CLOSED: 检查最近窗口的错误率
+        if (
+            self._circuit_state == CircuitState.CLOSED
+            and len(self._recent) >= max(3, self.recent_window // 2)
+        ):
+            failures = sum(1 for ok in self._recent if not ok)
+            error_rate = failures / len(self._recent)
+            if error_rate >= self.error_rate_threshold:
+                self._circuit_state = CircuitState.OPEN
+                self._circuit_opened_at = time.time()
+                logger.warning(
+                    "LLMCostTracker 触发熔断: 最近 %d 次调用错误率 %.2f >= %.2f",
+                    len(self._recent),
+                    error_rate,
+                    self.error_rate_threshold,
+                )
+
+    def _calc_cost(
+        self,
+        provider: str,
+        prompt_tokens: int,
+        completion_tokens: int,
+        image_count: int,
+    ) -> float:
+        rates = self._pricing.get(provider) or self._pricing["default"]
+        cost = (
+            prompt_tokens / 1_000_000.0 * rates.get("prompt", 0.0)
+            + completion_tokens / 1_000_000.0 * rates.get("completion", 0.0)
+            + image_count * rates.get("image", 0.0)
+        )
+        return max(0.0, cost)
+
+    @staticmethod
+    def _estimate_prompt_tokens(image_count: int) -> int:
+        # 经验值：每张图 ~ 200 token，prompt 模板 ~ 120 token
+        return 120 + 200 * max(0, image_count)
+
+    @staticmethod
+    def _estimate_completion_tokens(result: Any) -> int:
+        reasoning = getattr(result, "reasoning", "") or ""
+        # 粗略 1.3 字符 = 1 token
+        return max(16, int(len(reasoning) / 1.3))
+
+    @staticmethod
+    def _extract_usage(result: Any) -> Dict[str, Any]:
+        """从结果对象上尽力获取 usage (provider 可能放 metadata 里)。"""
+
+        usage = getattr(result, "usage", None)
+        if isinstance(usage, dict):
+            return usage
+        metadata = getattr(result, "metadata", None)
+        if isinstance(metadata, dict):
+            md_usage = metadata.get("usage")
+            if isinstance(md_usage, dict):
+                return md_usage
+        return {}
+
+
+# ---------------------------------------------------------------------------
+# 全局单例
+# ---------------------------------------------------------------------------
+
+
+_global_tracker: Optional[LLMCostTracker] = None
+
+
+def init_global_tracker(
+    daily_budget_usd: float = 0.0,
+    history_days: int = 7,
+    recent_window: int = 20,
+    error_rate_threshold: float = 0.5,
+    cooldown_seconds: float = 60.0,
+) -> LLMCostTracker:
+    """初始化并返回全局成本追踪器。"""
+
+    global _global_tracker
+    _global_tracker = LLMCostTracker(
+        daily_budget_usd=daily_budget_usd,
+        history_days=history_days,
+        recent_window=recent_window,
+        error_rate_threshold=error_rate_threshold,
+        cooldown_seconds=cooldown_seconds,
+    )
+    return _global_tracker
+
+
+def get_global_tracker() -> Optional[LLMCostTracker]:
+    return _global_tracker
+
+
+# ---------------------------------------------------------------------------
+# 工具
+# ---------------------------------------------------------------------------
+
+
+def _today_str() -> str:
+    return datetime.now(tz=timezone.utc).strftime("%Y-%m-%d")
+
+
+__all__ = [
+    "LLMCostTracker",
+    "CallRecord",
+    "CircuitState",
+    "DEFAULT_PRICING",
+    "init_global_tracker",
+    "get_global_tracker",
+]

apps/server/services/result_fusion.py

@@ -0,0 +1,375 @@
+"""结果融合器 (MVP-3 / D30)
+
+将 YOLO 候选事件 (含规则引擎产出的 ``AlertEvent``) 与 LLM 分析结果
+按指定策略融合，产出最终对外发出的 ``AlertEvent``。
+
+策略说明
+--------
+
+1. ``weighted`` (加权平均):
+   final_conf = yolo_weight * yolo_conf + llm_weight * llm_conf
+   - LLM 明确否决时 (confirmed=False) 同样适用，但置信度向 0 收敛
+   - LLM 不确定 (confirmed=None) 时，若 ``fallback_to_yolo`` 为 True
+     直接保留 YOLO 结果
+
+2. ``conservative`` (保守):
+   - LLM 否决 + ``suppress_on_llm_negative`` -> 抑制预警 (返回 None)
+   - LLM 确认 -> 取 ``min(yolo_conf, llm_conf)`` 与原值的最小值
+   - LLM 不确定 -> 抑制 (除非 ``fallback_to_yolo``)
+
+3. ``llm_priority`` (LLM 优先):
+   - LLM 确认 -> 直接使用 LLM 置信度
+   - LLM 否决 -> 抑制
+   - LLM 不确定 -> 回退到 YOLO 置信度 (若 ``fallback_to_yolo``)
+
+融合后会在 ``AlertEvent.metadata`` 中写入 ``llm`` 子对象，便于审计。
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional
+
+from models.event_schemas import AlertEvent, SeverityLevel
+from services.llm_analysis_service import LLMAnalysisResult
+
+logger = logging.getLogger(__name__)
+
+
+SUPPORTED_STRATEGIES = ("weighted", "conservative", "llm_priority")
+
+
+# ---------------------------------------------------------------------------
+# 融合输出
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class FusionOutcome:
+    """融合结果。"""
+
+    alert: Optional[AlertEvent]
+    suppressed: bool = False
+    reason: str = ""
+    final_confidence: float = 0.0
+    yolo_confidence: float = 0.0
+    llm_confidence: float = 0.0
+    strategy: str = ""
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+# ---------------------------------------------------------------------------
+# ResultFusion
+# ---------------------------------------------------------------------------
+
+
+class ResultFusion:
+    """YOLO + LLM 结果融合器。
+
+    Args:
+        strategy: 融合策略，见模块 docstring
+        yolo_weight: weighted 策略下 YOLO 权重
+        llm_weight: weighted 策略下 LLM 权重
+        suppress_on_llm_negative: LLM 明确否决时是否抑制预警
+        fallback_to_yolo: LLM 未确认 / 不可用时是否回退到 YOLO 结果
+        promote_severity_on_high_confidence: 高融合置信度是否提升严重性
+    """
+
+    def __init__(
+        self,
+        strategy: str = "weighted",
+        yolo_weight: float = 0.4,
+        llm_weight: float = 0.6,
+        suppress_on_llm_negative: bool = True,
+        fallback_to_yolo: bool = True,
+        promote_severity_on_high_confidence: bool = False,
+        high_confidence_threshold: float = 0.85,
+    ) -> None:
+        if strategy not in SUPPORTED_STRATEGIES:
+            raise ValueError(
+                f"不支持的融合策略: {strategy}, 支持: {SUPPORTED_STRATEGIES}"
+            )
+        if not 0.0 <= yolo_weight <= 1.0:
+            raise ValueError("yolo_weight 必须在 [0, 1]")
+        if not 0.0 <= llm_weight <= 1.0:
+            raise ValueError("llm_weight 必须在 [0, 1]")
+        total = yolo_weight + llm_weight
+        if total <= 0:
+            raise ValueError("yolo_weight + llm_weight 必须 > 0")
+
+        self.strategy = strategy
+        self.yolo_weight = yolo_weight / total
+        self.llm_weight = llm_weight / total
+        self.suppress_on_llm_negative = suppress_on_llm_negative
+        self.fallback_to_yolo = fallback_to_yolo
+        self.promote_severity_on_high_confidence = promote_severity_on_high_confidence
+        self.high_confidence_threshold = high_confidence_threshold
+
+    # ------------------------------------------------------------------
+    # 主入口
+    # ------------------------------------------------------------------
+
+    def fuse(
+        self,
+        alert: AlertEvent,
+        llm_result: Optional[LLMAnalysisResult],
+    ) -> FusionOutcome:
+        """融合单条预警事件与 LLM 结果。
+
+        Args:
+            alert: 规则引擎/聚合器产出的预警事件 (尚未发布)
+            llm_result: LLM 分析结果，None 表示未触发 LLM
+        """
+
+        yolo_conf = float(alert.confidence)
+
+        # 未调用 LLM 或 LLM 不可用
+        if llm_result is None:
+            return self._fallback_outcome(
+                alert,
+                yolo_conf=yolo_conf,
+                reason="llm_skipped",
+            )
+
+        if llm_result.error or llm_result.confirmed is None:
+            return self._fallback_outcome(
+                alert,
+                yolo_conf=yolo_conf,
+                llm_conf=llm_result.confidence,
+                reason=f"llm_unavailable:{llm_result.error or 'unknown'}",
+                llm_metadata=self._llm_metadata(llm_result),
+            )
+
+        # 至此 LLM 给出明确判定
+        if self.strategy == "weighted":
+            outcome = self._fuse_weighted(alert, yolo_conf, llm_result)
+        elif self.strategy == "conservative":
+            outcome = self._fuse_conservative(alert, yolo_conf, llm_result)
+        else:  # llm_priority
+            outcome = self._fuse_llm_priority(alert, yolo_conf, llm_result)
+
+        if outcome.alert is not None:
+            self._apply_metadata(outcome.alert, llm_result, outcome)
+            if self.promote_severity_on_high_confidence:
+                self._maybe_promote_severity(outcome.alert, outcome.final_confidence)
+
+        return outcome
+
+    # ------------------------------------------------------------------
+    # 策略实现
+    # ------------------------------------------------------------------
+
+    def _fuse_weighted(
+        self,
+        alert: AlertEvent,
+        yolo_conf: float,
+        llm_result: LLMAnalysisResult,
+    ) -> FusionOutcome:
+        # 否决降权: confirmed=False 时 LLM 置信度按 1-conf 反转
+        llm_conf = (
+            float(llm_result.confidence)
+            if llm_result.confirmed
+            else max(0.0, 1.0 - float(llm_result.confidence))
+        )
+        # confirmed=False 直接将 LLM 端贡献当作"反对票"
+        if not llm_result.confirmed:
+            # 反转后 llm 端置信度越高代表越反对，因此对最终值取 (yolo*w_y) - llm*w_l
+            final = self.yolo_weight * yolo_conf - self.llm_weight * llm_conf
+            final = max(0.0, min(1.0, final))
+        else:
+            final = self.yolo_weight * yolo_conf + self.llm_weight * llm_conf
+            final = max(0.0, min(1.0, final))
+
+        # 反对足够强且策略允许时抑制
+        if (
+            not llm_result.confirmed
+            and self.suppress_on_llm_negative
+            and final < 0.2
+        ):
+            return FusionOutcome(
+                alert=None,
+                suppressed=True,
+                reason="weighted_suppressed_by_llm_negative",
+                final_confidence=final,
+                yolo_confidence=yolo_conf,
+                llm_confidence=float(llm_result.confidence),
+                strategy=self.strategy,
+                metadata=self._llm_metadata(llm_result),
+            )
+
+        alert.confidence = round(final, 4)
+        return FusionOutcome(
+            alert=alert,
+            suppressed=False,
+            reason="weighted",
+            final_confidence=final,
+            yolo_confidence=yolo_conf,
+            llm_confidence=float(llm_result.confidence),
+            strategy=self.strategy,
+            metadata=self._llm_metadata(llm_result),
+        )
+
+    def _fuse_conservative(
+        self,
+        alert: AlertEvent,
+        yolo_conf: float,
+        llm_result: LLMAnalysisResult,
+    ) -> FusionOutcome:
+        if not llm_result.confirmed:
+            if self.suppress_on_llm_negative:
+                return FusionOutcome(
+                    alert=None,
+                    suppressed=True,
+                    reason="conservative_llm_negative",
+                    final_confidence=0.0,
+                    yolo_confidence=yolo_conf,
+                    llm_confidence=float(llm_result.confidence),
+                    strategy=self.strategy,
+                    metadata=self._llm_metadata(llm_result),
+                )
+            # 不抑制时也大幅降权
+            final = min(yolo_conf, 1.0 - float(llm_result.confidence))
+        else:
+            # 双方都确认: 取较小者，体现保守
+            final = min(yolo_conf, float(llm_result.confidence))
+
+        final = max(0.0, min(1.0, final))
+        alert.confidence = round(final, 4)
+        return FusionOutcome(
+            alert=alert,
+            suppressed=False,
+            reason="conservative",
+            final_confidence=final,
+            yolo_confidence=yolo_conf,
+            llm_confidence=float(llm_result.confidence),
+            strategy=self.strategy,
+            metadata=self._llm_metadata(llm_result),
+        )
+
+    def _fuse_llm_priority(
+        self,
+        alert: AlertEvent,
+        yolo_conf: float,
+        llm_result: LLMAnalysisResult,
+    ) -> FusionOutcome:
+        if not llm_result.confirmed:
+            if self.suppress_on_llm_negative:
+                return FusionOutcome(
+                    alert=None,
+                    suppressed=True,
+                    reason="llm_priority_negative",
+                    final_confidence=0.0,
+                    yolo_confidence=yolo_conf,
+                    llm_confidence=float(llm_result.confidence),
+                    strategy=self.strategy,
+                    metadata=self._llm_metadata(llm_result),
+                )
+            final = max(0.0, 1.0 - float(llm_result.confidence))
+        else:
+            final = float(llm_result.confidence)
+
+        final = max(0.0, min(1.0, final))
+        alert.confidence = round(final, 4)
+        return FusionOutcome(
+            alert=alert,
+            suppressed=False,
+            reason="llm_priority",
+            final_confidence=final,
+            yolo_confidence=yolo_conf,
+            llm_confidence=float(llm_result.confidence),
+            strategy=self.strategy,
+            metadata=self._llm_metadata(llm_result),
+        )
+
+    # ------------------------------------------------------------------
+    # 降级
+    # ------------------------------------------------------------------
+
+    def _fallback_outcome(
+        self,
+        alert: AlertEvent,
+        yolo_conf: float,
+        llm_conf: float = 0.0,
+        reason: str = "fallback",
+        llm_metadata: Optional[Dict[str, Any]] = None,
+    ) -> FusionOutcome:
+        if not self.fallback_to_yolo:
+            return FusionOutcome(
+                alert=None,
+                suppressed=True,
+                reason=f"{reason}:fallback_disabled",
+                final_confidence=0.0,
+                yolo_confidence=yolo_conf,
+                llm_confidence=llm_conf,
+                strategy=self.strategy,
+                metadata=llm_metadata or {},
+            )
+
+        alert.metadata.setdefault("llm", {})
+        if llm_metadata:
+            alert.metadata["llm"].update(llm_metadata)
+        alert.metadata["llm"]["fusion_reason"] = reason
+        return FusionOutcome(
+            alert=alert,
+            suppressed=False,
+            reason=reason,
+            final_confidence=yolo_conf,
+            yolo_confidence=yolo_conf,
+            llm_confidence=llm_conf,
+            strategy=self.strategy,
+            metadata=llm_metadata or {},
+        )
+
+    # ------------------------------------------------------------------
+    # 元数据 / 严重性
+    # ------------------------------------------------------------------
+
+    @staticmethod
+    def _llm_metadata(llm_result: LLMAnalysisResult) -> Dict[str, Any]:
+        return {
+            "provider": llm_result.provider,
+            "model": llm_result.model,
+            "confirmed": llm_result.confirmed,
+            "confidence": round(float(llm_result.confidence), 4),
+            "reasoning": llm_result.reasoning,
+            "latency_ms": round(llm_result.latency_ms, 2),
+            "error": llm_result.error,
+            "evaluated_at": time.time(),
+        }
+
+    def _apply_metadata(
+        self,
+        alert: AlertEvent,
+        llm_result: LLMAnalysisResult,
+        outcome: FusionOutcome,
+    ) -> None:
+        alert.metadata.setdefault("llm", {})
+        alert.metadata["llm"].update(self._llm_metadata(llm_result))
+        alert.metadata["llm"]["fusion_strategy"] = self.strategy
+        alert.metadata["llm"]["fusion_reason"] = outcome.reason
+        alert.metadata["llm"]["final_confidence"] = round(
+            outcome.final_confidence, 4
+        )
+
+    @staticmethod
+    def _maybe_promote_severity(alert: AlertEvent, final_confidence: float) -> None:
+        order = [
+            SeverityLevel.INFO,
+            SeverityLevel.LOW,
+            SeverityLevel.MEDIUM,
+            SeverityLevel.HIGH,
+            SeverityLevel.CRITICAL,
+        ]
+        try:
+            idx = order.index(alert.severity)
+        except ValueError:
+            return
+        if final_confidence >= 0.95 and idx < len(order) - 1:
+            alert.severity = order[idx + 1]
+
+
+__all__ = ["ResultFusion", "FusionOutcome", "SUPPORTED_STRATEGIES"]
+
+

apps/web/src/api/detection.js

@@ -68,3 +68,97 @@ export const detectionApi = {
     })
   }
 }
+
+// ============================================================
+// MVP-2 / MVP-3 扩展 API: 摄像头管理 / 规则配置 / LLM 状态
+// ============================================================
+
+/**
+ * 摄像头 (RTSP 流) 管理 API (MVP-2 / D11-D14)
+ */
+export const cameraApi = {
+  list() {
+    return api.get('/rtsp/streams')
+  },
+  get(streamId) {
+    return api.get(`/rtsp/streams/${encodeURIComponent(streamId)}`)
+  },
+  add(payload) {
+    return api.post('/rtsp/streams', payload)
+  },
+  remove(streamId) {
+    return api.delete(`/rtsp/streams/${encodeURIComponent(streamId)}`)
+  },
+  start(streamId) {
+    return api.post(`/rtsp/streams/${encodeURIComponent(streamId)}/start`)
+  },
+  stop(streamId) {
+    return api.post(`/rtsp/streams/${encodeURIComponent(streamId)}/stop`)
+  },
+  updateConfig(streamId, payload) {
+    return api.put(`/rtsp/streams/${encodeURIComponent(streamId)}/config`, payload)
+  },
+  startAll() {
+    return api.post('/rtsp/start-all')
+  },
+  stopAll() {
+    return api.post('/rtsp/stop-all')
+  },
+  health() {
+    return api.get('/rtsp/health')
+  }
+}
+
+/**
+ * 规则配置 API (MVP-3 / D33)
+ */
+export const ruleApi = {
+  list() {
+    return api.get('/rules')
+  },
+  get(name) {
+    return api.get(`/rules/${encodeURIComponent(name)}`)
+  },
+  create(payload) {
+    return api.post('/rules', payload)
+  },
+  update(name, payload) {
+    return api.put(`/rules/${encodeURIComponent(name)}`, payload)
+  },
+  remove(name) {
+    return api.delete(`/rules/${encodeURIComponent(name)}`)
+  },
+  reload() {
+    return api.post('/rules/reload')
+  },
+  stats() {
+    return api.get('/rules/_stats')
+  }
+}
+
+/**
+ * LLM 状态 / 成本 / 控制 API (MVP-3 / D29-D34)
+ */
+export const llmApi = {
+  status() {
+    return api.get('/llm/status')
+  },
+  cost() {
+    return api.get('/llm/cost')
+  },
+  costHistory(days = 7) {
+    return api.get('/llm/cost/history', { params: { days } })
+  },
+  costRecords(limit = 20) {
+    return api.get('/llm/cost/records', { params: { limit } })
+  },
+  enable() {
+    return api.post('/llm/enable')
+  },
+  disable() {
+    return api.post('/llm/disable')
+  },
+  reset() {
+    return api.post('/llm/reset')
+  }
+}

apps/web/src/components/ImageDetection.vue

@@ -5,7 +5,7 @@
       <DetectionConfig
         :models="models"
         :default-type="'image'"
-        :default-model="config.model"
+        :default-model="selectedModel || (models.length > 0 ? models[0].id : '')"
         @type-change="onTypeChange"
         @config-change="onConfigChange"
       />
@@ -346,18 +346,25 @@ import {
 } from '@element-plus/icons-vue'
 import { detectionApi } from '@/api/detection'
 import DetectionConfig from './DetectionConfig.vue'
+import { useAlertStore } from '@/stores/alertStore'
+
+const alertStore = useAlertStore()
 
 const props = defineProps({
   models: {
     type: Array,
     default: () => []
+  },
+  selectedModel: {
+    type: String,
+    default: ''
   }
 })
 
-const emit = defineEmits(['type-change'])
+const emit = defineEmits(['type-change', 'model-change'])
 
 const config = ref({
-  model: props.models.length > 0 ? props.models[0].id : 'fire_detection',
+  model: props.selectedModel || (props.models.length > 0 ? props.models[0].id : 'fire_detection'),
   confidence: 0.5,
   iou: 0.45,
   composite: false,
@@ -369,7 +376,12 @@ const onTypeChange = (type) => {
 }
 
 const onConfigChange = (newConfig) => {
+  const oldModel = config.value.model
   config.value = { ...newConfig }
+  // 模型变化时通知父组件
+  if (newConfig.model && newConfig.model !== oldModel) {
+    emit('model-change', newConfig.model)
+  }
 }
 
 // 可拖拽调整宽度相关
@@ -571,8 +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} - ${alertMessage}`,
           type: 'warning',
           duration: 3000
         })
@@ -778,6 +795,23 @@ const handleFightVideoSuccess = (response) => {
     const detCount = response.data.stats?.total_detections || 0
     ElMessage.success(`检测完成：${detCount} 个目标，已提取 ${kfCount} 张关键帧`)
 
+    // MVP-3: 将 LLM 管道产出的预警推送到 AlertList
+    const frameResults = response.data.frame_results || []
+    let llmAlertCount = 0
+    for (const fr of frameResults) {
+      const alerts = fr.alert_events || []
+      for (const alert of alerts) {
+        alertStore.addAlert({
+          ...alert,
+          llm_results: fr.llm_results || [],
+        })
+        llmAlertCount++
+      }
+    }
+    if (llmAlertCount > 0) {
+      ElMessage.success(`LLM 二次判断完成，已生成 ${llmAlertCount} 条预警记录`)
+    }
+
     // 如果后端提示编码不兼容，自动标记视频错误
     if (response.data.video_codec_warning) {
       console.warn('视频编码不兼容浏览器，将以关键帧形式展示')
@@ -1088,8 +1122,6 @@ onUnmounted(() => {
 
 .image-container {
   width: 100%;
-  aspect-ratio: 16 / 9;
-  min-height: 400px;
   max-height: 600px;
   display: flex;
   align-items: center;
@@ -1101,8 +1133,8 @@ onUnmounted(() => {
 }
 
 .display-image {
-  width: 100%;
-  height: 100%;
+  max-width: 100%;
+  max-height: 600px;
   object-fit: contain;
   background: #000;
 }

apps/web/src/components/VideoDetection.vue

@@ -5,7 +5,7 @@
       <DetectionConfig
         :models="models"
         :default-type="'video'"
-        :default-model="config.model"
+        :default-model="selectedModel || (models.length > 0 ? models[0].id : '')"
         @type-change="onTypeChange"
         @config-change="onConfigChange"
       />
@@ -223,13 +223,17 @@ const props = defineProps({
   models: {
     type: Array,
     default: () => []
+  },
+  selectedModel: {
+    type: String,
+    default: ''
   }
 })
 
-const emit = defineEmits(['type-change'])
+const emit = defineEmits(['type-change', 'model-change'])
 
 const config = ref({
-  model: props.models.length > 0 ? props.models[0].id : 'fire_detection',
+  model: props.selectedModel || (props.models.length > 0 ? props.models[0].id : 'fire_detection'),
   confidence: 0.5,
   iou: 0.45,
   composite: false,
@@ -241,8 +245,13 @@ const onTypeChange = (type) => {
 }
 
 const onConfigChange = (newConfig) => {
+  const oldModel = config.value.model
   config.value = { ...newConfig }
-  
+  // 模型变化时通知父组件
+  if (newConfig.model && newConfig.model !== oldModel) {
+    emit('model-change', newConfig.model)
+  }
+
   // 如果摄像头已连接，实时更新配置
   if (websocket.value && cameraConnected.value) {
     updateCameraConfig()

apps/web/src/layouts/MainLayout.vue

@@ -99,7 +99,9 @@ import {
   WarningFilled,
   Bell,
   Fold,
-  Expand
+  Expand,
+  Connection,
+  SetUp
 } from '@element-plus/icons-vue'
 import { useAlertStore } from '@/stores/alertStore'
 import { connectAlertSocket, disconnectAlertSocket } from '@/services/mqtt.client'
@@ -111,6 +113,8 @@ const alertStore = useAlertStore()
 
 const menuItems = [
   { path: '/', title: '模型检测', icon: VideoCameraFilled },
+  { path: '/cameras', title: '摄像头管理', icon: Connection },
+  { path: '/rules', title: '规则配置', icon: SetUp },
   { path: '/alerts', title: '预警列表', icon: WarningFilled }
 ]
 

apps/web/src/router/index.js

@@ -1,6 +1,8 @@
 import { createRouter, createWebHistory } from 'vue-router'
 import Home from '@/views/Home.vue'
 import AlertList from '@/views/AlertList.vue'
+import CameraManagement from '@/views/CameraManagement.vue'
+import RuleConfiguration from '@/views/RuleConfiguration.vue'
 import Layout from '@/layouts/MainLayout.vue'
 
 const routes = [
@@ -14,6 +16,18 @@ const routes = [
         component: Home,
         meta: { title: '模型检测', icon: 'VideoCamera', keepAlive: true }
       },
+      {
+        path: '/cameras',
+        name: 'CameraManagement',
+        component: CameraManagement,
+        meta: { title: '摄像头管理', icon: 'Connection', keepAlive: true }
+      },
+      {
+        path: '/rules',
+        name: 'RuleConfiguration',
+        component: RuleConfiguration,
+        meta: { title: '规则配置', icon: 'SetUp', keepAlive: true }
+      },
       {
         path: '/alerts',
         name: 'AlertList',
@@ -29,4 +43,4 @@ const router = createRouter({
   routes
 })
 
-export default router
+export default router

apps/web/src/stores/alertStore.js

@@ -84,6 +84,17 @@ export const useAlertStore = defineStore('alerts', () => {
   }
 
   // ---- 桌面通知 ----
+  const activeNotifyOffsets = new Set()
+  const NOTIFY_STEP = 80
+
+  function getAvailableOffset() {
+    let offset = 0
+    while (activeNotifyOffsets.has(offset)) {
+      offset += NOTIFY_STEP
+    }
+    return offset
+  }
+
   function showDesktopNotification(alert) {
     const severityLabels = {
       critical: '严重',
@@ -94,6 +105,8 @@ export const useAlertStore = defineStore('alerts', () => {
     }
     const label = severityLabels[alert.severity] || alert.severity
     const typeName = alert.event_type || '未知事件'
+    const offset = getAvailableOffset()
+    activeNotifyOffsets.add(offset)
 
     ElNotification({
       title: `[${label}] ${typeName}`,
@@ -103,10 +116,15 @@ export const useAlertStore = defineStore('alerts', () => {
       type: alert.severity === 'critical' || alert.severity === 'high'
         ? 'warning'
         : 'info',
-      duration: alert.severity === 'critical' ? 0 : 4500,
+      duration: 5000,
+      offset,
+      showClose: true,
       dangerouslyUseHTMLString: false,
       onClick: () => {
         markAsRead(alert.alert_id)
+      },
+      onClose: () => {
+        activeNotifyOffsets.delete(offset)
       }
     })
   }

apps/web/src/views/AlertList.vue

@@ -29,6 +29,7 @@
           class="filter-control"
           @change="applyFilter"
         >
+          <el-option label="全部" value="" />
           <el-option label="严重" value="critical" />
           <el-option label="高危" value="high" />
           <el-option label="中等" value="medium" />
@@ -44,6 +45,7 @@
           class="filter-control"
           @change="applyFilter"
         >
+          <el-option label="全部" value="" />
           <el-option label="火灾" value="fire" />
           <el-option label="烟雾" value="smoke" />
           <el-option label="抽烟" value="smoking" />
@@ -156,6 +158,28 @@
               <span v-if="det.track_id" class="track-id">#{{ det.track_id }}</span>
             </span>
           </div>
+
+          <!-- MVP-3: LLM 二次判断结果 -->
+          <div v-if="alert.llm_results && alert.llm_results.length > 0" class="llm-result">
+            <el-tag
+              :type="alert.llm_results[0].llm_confirmed ? 'success' : 'danger'"
+              effect="light"
+              round
+              size="small"
+            >
+              {{ alert.llm_results[0].llm_confirmed ? 'LLM确认' : 'LLM否决' }}
+            </el-tag>
+            <span class="llm-confidence">
+              LLM置信度: {{ ((alert.llm_results[0].llm_confidence || 0) * 100).toFixed(0) }}%
+            </span>
+            <span
+              v-if="alert.llm_results[0].llm_reasoning"
+              class="llm-reasoning"
+              :title="alert.llm_results[0].llm_reasoning"
+            >
+              {{ alert.llm_results[0].llm_reasoning.slice(0, 50) }}...
+            </span>
+          </div>
         </div>
 
         <div class="alert-actions">
@@ -534,6 +558,33 @@ onMounted(() => {
   opacity: 0.8;
 }
 
+/* MVP-3: LLM 二次判断结果样式 */
+.llm-result {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  margin-top: 8px;
+  padding: 6px 10px;
+  background: rgba(99, 102, 241, 0.06);
+  border-left: 3px solid #818CF8;
+  border-radius: 4px;
+}
+
+.llm-confidence {
+  font-size: 12px;
+  color: #A5B4FC;
+}
+
+.llm-reasoning {
+  font-size: 12px;
+  color: #9CA3AF;
+  max-width: 200px;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  cursor: help;
+}
+
 .alert-actions {
   display: flex;
   align-items: center;

apps/web/src/views/CameraManagement.vue

@@ -0,0 +1,683 @@
+<template>
+  <div class="camera-mgmt-page">
+    <!-- 顶部统计 -->
+    <div class="stat-grid">
+      <div
+        v-for="stat in stats"
+        :key="stat.key"
+        class="stat-card"
+        :class="`stat-card--${stat.tone}`"
+      >
+        <div class="stat-icon">
+          <el-icon :size="20"><component :is="stat.icon" /></el-icon>
+        </div>
+        <div class="stat-content">
+          <div class="stat-label">{{ stat.label }}</div>
+          <div class="stat-value">{{ stat.value }}</div>
+        </div>
+      </div>
+    </div>
+
+    <!-- 工具栏 -->
+    <div class="toolbar">
+      <div class="toolbar-left">
+        <el-input
+          v-model="filter.keyword"
+          placeholder="按 ID/URL 搜索"
+          size="small"
+          clearable
+          class="filter-control"
+        >
+          <template #prefix>
+            <el-icon><Search /></el-icon>
+          </template>
+        </el-input>
+
+        <el-select
+          v-model="filter.status"
+          placeholder="状态"
+          clearable
+          size="small"
+          class="filter-control"
+        >
+          <el-option label="已连接" value="connected" />
+          <el-option label="重连中" value="reconnecting" />
+          <el-option label="错误" value="error" />
+          <el-option label="未启动" value="idle" />
+          <el-option label="已停止" value="stopped" />
+        </el-select>
+      </div>
+
+      <div class="toolbar-right">
+        <el-button size="small" @click="loadStreams">
+          <el-icon><Refresh /></el-icon>
+          刷新
+        </el-button>
+        <el-button size="small" type="success" plain @click="handleStartAll">
+          <el-icon><VideoPlay /></el-icon>
+          全部启动
+        </el-button>
+        <el-button size="small" type="warning" plain @click="handleStopAll">
+          <el-icon><VideoPause /></el-icon>
+          全部停止
+        </el-button>
+        <el-button size="small" type="primary" @click="openAddDialog">
+          <el-icon><Plus /></el-icon>
+          添加摄像头
+        </el-button>
+      </div>
+    </div>
+
+    <!-- 摄像头列表 -->
+    <div class="camera-list">
+      <el-empty
+        v-if="filteredStreams.length === 0"
+        description="尚未配置摄像头，点击右上角添加"
+        class="empty-state"
+      />
+
+      <div
+        v-for="stream in filteredStreams"
+        :key="stream.stream_id"
+        class="camera-card"
+        :class="`status-${stream.status}`"
+      >
+        <div class="status-bar"></div>
+
+        <div class="camera-body">
+          <div class="camera-head">
+            <div class="camera-title">
+              <el-tag :type="statusTagType(stream.status)" size="small" effect="dark" round>
+                {{ statusLabel(stream.status) }}
+              </el-tag>
+              <span class="camera-id">{{ stream.stream_id }}</span>
+            </div>
+            <div class="camera-actions">
+              <el-button
+                v-if="!isRunning(stream)"
+                text
+                type="success"
+                size="small"
+                @click="handleStart(stream)"
+              >
+                <el-icon><VideoPlay /></el-icon>
+                启动
+              </el-button>
+              <el-button
+                v-else
+                text
+                type="warning"
+                size="small"
+                @click="handleStop(stream)"
+              >
+                <el-icon><VideoPause /></el-icon>
+                停止
+              </el-button>
+              <el-button text size="small" @click="openEditDialog(stream)">
+                <el-icon><Edit /></el-icon>
+                配置
+              </el-button>
+              <el-popconfirm
+                title="确认移除该摄像头？"
+                confirm-button-text="移除"
+                cancel-button-text="取消"
+                @confirm="handleRemove(stream)"
+              >
+                <template #reference>
+                  <el-button text type="danger" size="small">
+                    <el-icon><Delete /></el-icon>
+                    移除
+                  </el-button>
+                </template>
+              </el-popconfirm>
+            </div>
+          </div>
+
+          <div class="camera-url">{{ stream.rtsp_url }}</div>
+
+          <div class="camera-meta">
+            <div class="meta-item">
+              <el-icon><Cpu /></el-icon>
+              <span>{{ stream.config?.model_id || '-' }}</span>
+            </div>
+            <div class="meta-item">
+              <el-icon><DataLine /></el-icon>
+              <span>置信度 {{ formatNumber(stream.config?.confidence) }}</span>
+            </div>
+            <div class="meta-item">
+              <el-icon><PictureFilled /></el-icon>
+              <span>{{ stream.frame_count || 0 }} 帧</span>
+            </div>
+            <div class="meta-item">
+              <el-icon><Timer /></el-icon>
+              <span>{{ formatLastFrame(stream.last_frame_at) }}</span>
+            </div>
+            <div v-if="stream.error_message" class="meta-item meta-error">
+              <el-icon><WarningFilled /></el-icon>
+              <span>{{ stream.error_message }}</span>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <!-- 添加 / 编辑摄像头弹窗 -->
+    <el-dialog
+      v-model="dialogVisible"
+      :title="editingStreamId ? '配置摄像头' : '添加摄像头'"
+      width="520px"
+      :close-on-click-modal="false"
+      class="camera-dialog"
+    >
+      <el-form
+        ref="formRef"
+        :model="formState"
+        :rules="formRules"
+        label-position="top"
+        size="default"
+      >
+        <el-form-item label="摄像头 ID" prop="stream_id">
+          <el-input
+            v-model="formState.stream_id"
+            placeholder="例如 cam-01"
+            :disabled="!!editingStreamId"
+          />
+        </el-form-item>
+        <el-form-item label="RTSP URL" prop="rtsp_url">
+          <el-input
+            v-model="formState.rtsp_url"
+            placeholder="rtsp://user:pass@host:554/stream"
+            :disabled="!!editingStreamId"
+          />
+        </el-form-item>
+        <el-form-item label="检测模型" prop="model_id">
+          <el-select v-model="formState.model_id" placeholder="选择检测模型" style="width: 100%">
+            <el-option label="火灾检测" value="fire_detection" />
+            <el-option label="烟雾检测" value="smoke_detection" />
+            <el-option label="抽烟检测" value="smoking_detection" />
+            <el-option label="打架斗殴" value="fight_detection" />
+            <el-option label="徘徊检测" value="loitering_detection" />
+            <el-option label="违章停车" value="illegal_parking_detection" />
+          </el-select>
+        </el-form-item>
+        <el-form-item label="置信度阈值">
+          <el-slider
+            v-model="formState.confidence"
+            :min="0.1"
+            :max="0.95"
+            :step="0.05"
+            show-stops
+            :format-tooltip="(v) => v.toFixed(2)"
+          />
+        </el-form-item>
+        <el-form-item label="IOU 阈值">
+          <el-slider
+            v-model="formState.iou"
+            :min="0.2"
+            :max="0.9"
+            :step="0.05"
+            show-stops
+            :format-tooltip="(v) => v.toFixed(2)"
+          />
+        </el-form-item>
+        <el-form-item label="帧采样间隔">
+          <el-input-number
+            v-model="formState.frame_skip"
+            :min="0"
+            :max="20"
+            :step="1"
+            controls-position="right"
+          />
+          <span class="form-hint">0 = 每帧检测，N = 每 N+1 帧检测一次</span>
+        </el-form-item>
+      </el-form>
+
+      <template #footer>
+        <el-button @click="dialogVisible = false">取消</el-button>
+        <el-button type="primary" :loading="submitting" @click="handleSubmit">
+          {{ editingStreamId ? '保存' : '添加' }}
+        </el-button>
+      </template>
+    </el-dialog>
+  </div>
+</template>
+
+<script setup>
+import { computed, onMounted, onUnmounted, reactive, ref } from 'vue'
+import { ElMessage } from 'element-plus'
+import {
+  Search,
+  Refresh,
+  Plus,
+  VideoPlay,
+  VideoPause,
+  Edit,
+  Delete,
+  Cpu,
+  DataLine,
+  PictureFilled,
+  Timer,
+  WarningFilled,
+  CircleCheckFilled,
+  Connection,
+  Bell
+} from '@element-plus/icons-vue'
+import { cameraApi } from '@/api/detection'
+
+const streams = ref([])
+const filter = reactive({ keyword: '', status: '' })
+const dialogVisible = ref(false)
+const submitting = ref(false)
+const editingStreamId = ref('')
+const formRef = ref(null)
+const formState = reactive(makeEmptyForm())
+let pollTimer = null
+
+const formRules = {
+  stream_id: [{ required: true, message: '请输入摄像头 ID', trigger: 'blur' }],
+  rtsp_url: [{ required: true, message: '请输入 RTSP 地址', trigger: 'blur' }],
+  model_id: [{ required: true, message: '请选择检测模型', trigger: 'change' }]
+}
+
+function makeEmptyForm() {
+  return {
+    stream_id: '',
+    rtsp_url: '',
+    model_id: 'fire_detection',
+    confidence: 0.5,
+    iou: 0.45,
+    frame_skip: 0
+  }
+}
+
+const stats = computed(() => {
+  const total = streams.value.length
+  const active = streams.value.filter((s) => s.status === 'connected').length
+  const errored = streams.value.filter((s) => s.status === 'error').length
+  const reconnecting = streams.value.filter((s) => s.status === 'reconnecting').length
+  return [
+    { key: 'total', label: '摄像头总数', value: total, icon: Connection, tone: 'neutral' },
+    { key: 'active', label: '运行中', value: active, icon: CircleCheckFilled, tone: 'success' },
+    { key: 'reconnecting', label: '重连中', value: reconnecting, icon: Bell, tone: 'warning' },
+    { key: 'error', label: '故障', value: errored, icon: WarningFilled, tone: 'danger' }
+  ]
+})
+
+const filteredStreams = computed(() => {
+  return streams.value.filter((s) => {
+    if (filter.status && s.status !== filter.status) return false
+    if (filter.keyword) {
+      const kw = filter.keyword.toLowerCase()
+      const text = `${s.stream_id} ${s.rtsp_url || ''}`.toLowerCase()
+      if (!text.includes(kw)) return false
+    }
+    return true
+  })
+})
+
+function isRunning(stream) {
+  return ['connecting', 'connected', 'reconnecting'].includes(stream.status)
+}
+
+function statusLabel(status) {
+  const map = {
+    idle: '未启动',
+    connecting: '连接中',
+    connected: '已连接',
+    reconnecting: '重连中',
+    stopped: '已停止',
+    error: '错误'
+  }
+  return map[status] || status
+}
+
+function statusTagType(status) {
+  const map = {
+    connected: 'success',
+    connecting: 'warning',
+    reconnecting: 'warning',
+    error: 'danger',
+    idle: 'info',
+    stopped: 'info'
+  }
+  return map[status] || 'info'
+}
+
+function formatNumber(v) {
+  if (v === undefined || v === null) return '-'
+  return Number(v).toFixed(2)
+}
+
+function formatLastFrame(ts) {
+  if (!ts) return '无数据'
+  const date = new Date(ts * 1000)
+  const diff = (Date.now() - date.getTime()) / 1000
+  if (diff < 60) return `${Math.floor(diff)} 秒前`
+  if (diff < 3600) return `${Math.floor(diff / 60)} 分钟前`
+  return date.toLocaleString('zh-CN', { hour12: false })
+}
+
+async function loadStreams() {
+  try {
+    const { data } = await cameraApi.list()
+    streams.value = data?.streams || []
+  } catch (err) {
+    ElMessage.error(`加载摄像头列表失败: ${err.message || err}`)
+  }
+}
+
+function openAddDialog() {
+  editingStreamId.value = ''
+  Object.assign(formState, makeEmptyForm())
+  dialogVisible.value = true
+}
+
+function openEditDialog(stream) {
+  editingStreamId.value = stream.stream_id
+  Object.assign(formState, {
+    stream_id: stream.stream_id,
+    rtsp_url: stream.rtsp_url,
+    model_id: stream.config?.model_id || 'fire_detection',
+    confidence: Number(stream.config?.confidence ?? 0.5),
+    iou: Number(stream.config?.iou ?? 0.45),
+    frame_skip: Number(stream.config?.frame_skip ?? 0)
+  })
+  dialogVisible.value = true
+}
+
+async function handleSubmit() {
+  if (!formRef.value) return
+  await formRef.value.validate(async (valid) => {
+    if (!valid) return
+    submitting.value = true
+    try {
+      if (editingStreamId.value) {
+        await cameraApi.updateConfig(editingStreamId.value, {
+          model_id: formState.model_id,
+          confidence: formState.confidence,
+          iou: formState.iou,
+          frame_skip: formState.frame_skip
+        })
+        ElMessage.success('已保存配置')
+      } else {
+        await cameraApi.add({
+          stream_id: formState.stream_id,
+          rtsp_url: formState.rtsp_url,
+          model_id: formState.model_id,
+          confidence: formState.confidence,
+          iou: formState.iou,
+          frame_skip: formState.frame_skip
+        })
+        ElMessage.success('摄像头已添加')
+      }
+      dialogVisible.value = false
+      await loadStreams()
+    } catch (err) {
+      const msg = err.response?.data?.detail || err.message || String(err)
+      ElMessage.error(`操作失败: ${msg}`)
+    } finally {
+      submitting.value = false
+    }
+  })
+}
+
+async function handleStart(stream) {
+  try {
+    await cameraApi.start(stream.stream_id)
+    ElMessage.success(`已启动 ${stream.stream_id}`)
+    await loadStreams()
+  } catch (err) {
+    ElMessage.error(`启动失败: ${err.response?.data?.detail || err.message}`)
+  }
+}
+
+async function handleStop(stream) {
+  try {
+    await cameraApi.stop(stream.stream_id)
+    ElMessage.success(`已停止 ${stream.stream_id}`)
+    await loadStreams()
+  } catch (err) {
+    ElMessage.error(`停止失败: ${err.response?.data?.detail || err.message}`)
+  }
+}
+
+async function handleRemove(stream) {
+  try {
+    await cameraApi.remove(stream.stream_id)
+    ElMessage.success(`已移除 ${stream.stream_id}`)
+    await loadStreams()
+  } catch (err) {
+    ElMessage.error(`移除失败: ${err.response?.data?.detail || err.message}`)
+  }
+}
+
+async function handleStartAll() {
+  try {
+    await cameraApi.startAll()
+    ElMessage.success('已启动全部摄像头')
+    await loadStreams()
+  } catch (err) {
+    ElMessage.error(`操作失败: ${err.message}`)
+  }
+}
+
+async function handleStopAll() {
+  try {
+    await cameraApi.stopAll()
+    ElMessage.success('已停止全部摄像头')
+    await loadStreams()
+  } catch (err) {
+    ElMessage.error(`操作失败: ${err.message}`)
+  }
+}
+
+onMounted(() => {
+  loadStreams()
+  // 每 5 秒刷新一次状态
+  pollTimer = setInterval(loadStreams, 5000)
+})
+
+onUnmounted(() => {
+  if (pollTimer) clearInterval(pollTimer)
+})
+</script>
+
+<style scoped>
+.camera-mgmt-page {
+  padding: 24px;
+  min-height: 100%;
+  background: #020617;
+}
+
+.stat-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+  gap: 16px;
+  margin-bottom: 24px;
+}
+
+.stat-card {
+  display: flex;
+  align-items: center;
+  gap: 14px;
+  padding: 18px 20px;
+  border-radius: 12px;
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  transition: border-color 200ms ease;
+}
+
+.stat-card:hover {
+  border-color: rgba(34, 197, 94, 0.3);
+}
+
+.stat-icon {
+  width: 44px;
+  height: 44px;
+  border-radius: 10px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  background: rgba(148, 163, 184, 0.1);
+  color: #94A3B8;
+  flex-shrink: 0;
+}
+
+.stat-card--success .stat-icon { background: rgba(34, 197, 94, 0.12); color: #22C55E; }
+.stat-card--warning .stat-icon { background: rgba(245, 158, 11, 0.12); color: #F59E0B; }
+.stat-card--danger .stat-icon  { background: rgba(239, 68, 68, 0.12); color: #EF4444; }
+
+.stat-label {
+  font-size: 12px;
+  color: #94A3B8;
+  margin-bottom: 4px;
+}
+
+.stat-value {
+  font-size: 22px;
+  font-weight: 600;
+  color: #F8FAFC;
+  font-family: 'Fira Code', monospace;
+}
+
+.toolbar {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 16px;
+  padding: 12px 16px;
+  margin-bottom: 16px;
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  border-radius: 12px;
+  flex-wrap: wrap;
+}
+
+.toolbar-left {
+  display: flex;
+  gap: 12px;
+  flex-wrap: wrap;
+  align-items: center;
+}
+
+.toolbar-right {
+  display: flex;
+  gap: 8px;
+  align-items: center;
+}
+
+.filter-control {
+  width: 200px;
+}
+
+.camera-list {
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+.camera-card {
+  display: flex;
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  border-radius: 12px;
+  overflow: hidden;
+  transition: border-color 200ms ease;
+}
+
+.camera-card:hover {
+  border-color: rgba(34, 197, 94, 0.3);
+}
+
+.status-bar {
+  width: 4px;
+  background: #64748B;
+  flex-shrink: 0;
+}
+
+.status-connected   .status-bar { background: #22C55E; }
+.status-connecting  .status-bar { background: #F59E0B; }
+.status-reconnecting .status-bar { background: #F59E0B; }
+.status-error       .status-bar { background: #EF4444; }
+.status-idle        .status-bar { background: #94A3B8; }
+.status-stopped     .status-bar { background: #64748B; }
+
+.camera-body {
+  flex: 1;
+  padding: 14px 16px;
+  min-width: 0;
+}
+
+.camera-head {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 12px;
+  flex-wrap: wrap;
+  margin-bottom: 8px;
+}
+
+.camera-title {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.camera-id {
+  font-size: 15px;
+  font-weight: 600;
+  color: #F8FAFC;
+  font-family: 'Fira Code', monospace;
+}
+
+.camera-actions {
+  display: flex;
+  gap: 4px;
+}
+
+.camera-url {
+  font-size: 12px;
+  color: #64748B;
+  font-family: 'Fira Code', monospace;
+  margin-bottom: 8px;
+  word-break: break-all;
+}
+
+.camera-meta {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 16px;
+}
+
+.meta-item {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  font-size: 13px;
+  color: #94A3B8;
+}
+
+.meta-item .el-icon { font-size: 14px; }
+
+.meta-error {
+  color: #EF4444;
+}
+
+.empty-state {
+  padding: 60px 0;
+}
+
+.form-hint {
+  margin-left: 12px;
+  font-size: 12px;
+  color: #64748B;
+}
+
+:deep(.camera-dialog .el-dialog) {
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+}
+
+:deep(.camera-dialog .el-dialog__title) { color: #F8FAFC; }
+:deep(.camera-dialog .el-form-item__label) { color: #CBD5E1; }
+</style>

apps/web/src/views/Home.vue

@@ -4,14 +4,18 @@
     <ImageDetection
       v-if="activeTab === 'image'"
       :models="models"
+      :selected-model="selectedModel"
       @type-change="handleTypeChange"
+      @model-change="handleModelChange"
     />
 
     <!-- 视频检测模块 -->
     <VideoDetection
       v-if="activeTab === 'video'"
       :models="models"
+      :selected-model="selectedModel"
       @type-change="handleTypeChange"
+      @model-change="handleModelChange"
     />
   </div>
 </template>
@@ -26,15 +30,24 @@ import VideoDetection from '@/components/VideoDetection.vue'
 
 const activeTab = ref('image')
 const models = ref([])
+const selectedModel = ref('')
 
 const handleTypeChange = (type) => {
   activeTab.value = type
 }
 
+const handleModelChange = (modelId) => {
+  selectedModel.value = modelId
+}
+
 const loadModels = async () => {
   try {
     const response = await detectionApi.getModels()
     models.value = response.data
+    // 初始化默认选中第一个模型
+    if (models.value.length > 0 && !selectedModel.value) {
+      selectedModel.value = models.value[0].id
+    }
   } catch (error) {
     ElMessage.error('加载模型列表失败')
   }

apps/web/src/views/RuleConfiguration.vue

@@ -0,0 +1,903 @@
+<template>
+  <div class="rule-config-page">
+    <!-- LLM 状态面板 (MVP-3 / D34) -->
+    <section class="llm-panel" v-if="llmStatus">
+      <div class="panel-header">
+        <div class="panel-title">
+          <el-icon :size="18"><Cpu /></el-icon>
+          <span>LLM 二次判断</span>
+          <el-tag
+            :type="llmStatus.enabled ? 'success' : 'info'"
+            effect="dark"
+            round
+            size="small"
+          >
+            {{ llmStatus.enabled ? '已启用' : '未启用' }}
+          </el-tag>
+          <el-tag
+            v-if="llmCircuitState"
+            :type="circuitTagType"
+            effect="plain"
+            round
+            size="small"
+          >
+            熔断: {{ circuitLabel }}
+          </el-tag>
+        </div>
+        <div class="panel-actions">
+          <el-button size="small" @click="loadLLMStatus">
+            <el-icon><Refresh /></el-icon>
+            刷新
+          </el-button>
+          <el-button
+            v-if="llmTrackerEnabled"
+            size="small"
+            type="warning"
+            plain
+            @click="handleLLMDisable"
+          >
+            <el-icon><VideoPause /></el-icon>
+            紧急停用
+          </el-button>
+          <el-button
+            v-else
+            size="small"
+            type="success"
+            plain
+            @click="handleLLMEnable"
+          >
+            <el-icon><VideoPlay /></el-icon>
+            重新启用
+          </el-button>
+          <el-popconfirm title="确认重置成本统计与熔断？" @confirm="handleLLMReset">
+            <template #reference>
+              <el-button size="small" type="danger" plain>
+                <el-icon><Refresh /></el-icon>
+                重置统计
+              </el-button>
+            </template>
+          </el-popconfirm>
+        </div>
+      </div>
+
+      <div class="llm-grid">
+        <div class="llm-card">
+          <div class="llm-label">Provider</div>
+          <div class="llm-value">{{ llmStatus.provider }}</div>
+          <div class="llm-sub">{{ llmStatus.model || '-' }}</div>
+        </div>
+        <div class="llm-card">
+          <div class="llm-label">融合策略</div>
+          <div class="llm-value">{{ fusionLabel }}</div>
+          <div class="llm-sub">
+            YOLO {{ formatRatio(llmStatus.fusion?.yolo_weight) }} /
+            LLM {{ formatRatio(llmStatus.fusion?.llm_weight) }}
+          </div>
+        </div>
+        <div class="llm-card">
+          <div class="llm-label">触发阈值</div>
+          <div class="llm-value">
+            {{ llmStatus.triggers?.min_consecutive_hits }} 连续帧
+          </div>
+          <div class="llm-sub">
+            avg≥{{ formatRatio(llmStatus.triggers?.min_avg_confidence) }} ·
+            冷却{{ llmStatus.triggers?.cooldown_seconds }}s
+          </div>
+        </div>
+        <div class="llm-card">
+          <div class="llm-label">今日调用</div>
+          <div class="llm-value">{{ todayCost?.call_count ?? 0 }} 次</div>
+          <div class="llm-sub">
+            成功 {{ todayCost?.success_count ?? 0 }} /
+            失败 {{ todayCost?.failure_count ?? 0 }}
+          </div>
+        </div>
+        <div class="llm-card">
+          <div class="llm-label">今日费用</div>
+          <div class="llm-value">${{ formatCost(todayCost?.cost_usd) }}</div>
+          <div class="llm-sub">
+            预算
+            {{ todayCost?.budget_usd ? `$${formatCost(todayCost.budget_usd)}` : '∞' }}
+            <span v-if="todayCost?.budget_usd > 0" class="budget-bar-wrap">
+              <span
+                class="budget-bar"
+                :class="{ 'is-danger': (todayCost?.budget_used_ratio ?? 0) >= 0.8 }"
+                :style="{ width: `${Math.min(100, (todayCost?.budget_used_ratio ?? 0) * 100)}%` }"
+              ></span>
+            </span>
+          </div>
+        </div>
+        <div class="llm-card">
+          <div class="llm-label">最近错误率</div>
+          <div class="llm-value">
+            {{ formatRatio(llmStatus.cost?.recent_error_rate) }}
+          </div>
+          <div class="llm-sub">
+            阈值 {{ formatRatio(llmStatus.cost?.error_rate_threshold) }}
+          </div>
+        </div>
+      </div>
+
+      <div v-if="llmStatus.cost?.block_reason" class="llm-warning">
+        <el-icon><WarningFilled /></el-icon>
+        当前不会调用 LLM，原因：{{ llmStatus.cost.block_reason }}
+      </div>
+    </section>
+
+    <!-- 工具栏 -->
+    <div class="toolbar">
+      <div class="toolbar-left">
+        <el-input
+          v-model="filter.keyword"
+          placeholder="按规则名/描述搜索"
+          size="small"
+          clearable
+          class="filter-control"
+        >
+          <template #prefix>
+            <el-icon><Search /></el-icon>
+          </template>
+        </el-input>
+        <el-select
+          v-model="filter.event_type"
+          placeholder="事件类型"
+          clearable
+          size="small"
+          class="filter-control"
+        >
+          <el-option
+            v-for="opt in eventTypeOptions"
+            :key="opt.value"
+            :label="opt.label"
+            :value="opt.value"
+          />
+        </el-select>
+      </div>
+      <div class="toolbar-right">
+        <el-button size="small" @click="loadRules">
+          <el-icon><Refresh /></el-icon>
+          刷新
+        </el-button>
+        <el-button size="small" type="warning" plain @click="handleReload">
+          <el-icon><MagicStick /></el-icon>
+          热重载
+        </el-button>
+        <el-button size="small" type="primary" @click="openCreateDialog">
+          <el-icon><Plus /></el-icon>
+          新增规则
+        </el-button>
+      </div>
+    </div>
+
+    <!-- 规则列表 -->
+    <div class="rule-list">
+      <el-empty
+        v-if="filteredRules.length === 0"
+        description="暂无规则"
+        class="empty-state"
+      />
+
+      <div
+        v-for="rule in filteredRules"
+        :key="rule.name"
+        class="rule-card"
+        :class="{ 'is-disabled': !rule.enabled }"
+      >
+        <div class="rule-bar" :class="`severity-${rule.severity || 'info'}`"></div>
+        <div class="rule-body">
+          <div class="rule-head">
+            <div class="rule-title">
+              <el-tag
+                :type="severityTagType(rule.severity)"
+                size="small"
+                effect="dark"
+                round
+              >
+                {{ severityLabel(rule.severity) }}
+              </el-tag>
+              <span class="rule-name">{{ rule.name }}</span>
+              <el-tag size="small" effect="plain">
+                {{ eventTypeLabel(rule.event_type) }}
+              </el-tag>
+              <el-switch
+                :model-value="rule.enabled"
+                size="small"
+                @change="(v) => handleToggle(rule, v)"
+              />
+            </div>
+            <div class="rule-actions">
+              <el-button text size="small" @click="openEditDialog(rule)">
+                <el-icon><Edit /></el-icon>
+                编辑
+              </el-button>
+              <el-popconfirm
+                title="确认删除该规则？(仅 custom.yaml 中的规则可删除)"
+                @confirm="handleDelete(rule)"
+              >
+                <template #reference>
+                  <el-button text type="danger" size="small">
+                    <el-icon><Delete /></el-icon>
+                    删除
+                  </el-button>
+                </template>
+              </el-popconfirm>
+            </div>
+          </div>
+
+          <div v-if="rule.description" class="rule-desc">{{ rule.description }}</div>
+
+          <div class="rule-meta">
+            <div class="meta-item">
+              <el-icon><DataLine /></el-icon>
+              <span>min_conf ≥ {{ formatRatio(rule.min_confidence) }}</span>
+            </div>
+            <div class="meta-item">
+              <el-icon><Crop /></el-icon>
+              <span>min_area ≥ {{ rule.min_bbox_area || 0 }}</span>
+            </div>
+            <div v-if="rule.allowed_sources?.length" class="meta-item">
+              <el-icon><VideoCamera /></el-icon>
+              <span>限定: {{ rule.allowed_sources.join(', ') }}</span>
+            </div>
+            <div v-if="rule.required_labels?.length" class="meta-item">
+              <el-icon><Collection /></el-icon>
+              <span>标签: {{ rule.required_labels.join(', ') }}</span>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <!-- 编辑/新增规则弹窗 -->
+    <el-dialog
+      v-model="dialogVisible"
+      :title="editingName ? '编辑规则' : '新增规则'"
+      width="560px"
+      :close-on-click-modal="false"
+      class="rule-dialog"
+    >
+      <el-form
+        ref="formRef"
+        :model="formState"
+        :rules="formRules"
+        label-position="top"
+      >
+        <el-form-item label="规则名称" prop="name">
+          <el-input
+            v-model="formState.name"
+            placeholder="例如 fire_high_priority"
+            :disabled="!!editingName"
+          />
+        </el-form-item>
+        <el-form-item label="事件类型" prop="event_type">
+          <el-select v-model="formState.event_type" placeholder="选择事件类型" style="width: 100%">
+            <el-option
+              v-for="opt in eventTypeOptions"
+              :key="opt.value"
+              :label="opt.label"
+              :value="opt.value"
+            />
+          </el-select>
+        </el-form-item>
+        <el-form-item label="严重级别">
+          <el-select v-model="formState.severity" placeholder="使用默认" clearable style="width: 100%">
+            <el-option label="信息" value="info" />
+            <el-option label="低危" value="low" />
+            <el-option label="中等" value="medium" />
+            <el-option label="高危" value="high" />
+            <el-option label="严重" value="critical" />
+          </el-select>
+        </el-form-item>
+        <el-form-item label="最低置信度">
+          <el-slider
+            v-model="formState.min_confidence"
+            :min="0"
+            :max="1"
+            :step="0.05"
+            :format-tooltip="(v) => Number(v).toFixed(2)"
+          />
+        </el-form-item>
+        <el-form-item label="最小边界框面积">
+          <el-input-number
+            v-model="formState.min_bbox_area"
+            :min="0"
+            :step="100"
+            controls-position="right"
+          />
+          <span class="form-hint">单位：像素²，0 表示不限</span>
+        </el-form-item>
+        <el-form-item label="限定摄像头 ID（可选）">
+          <el-select
+            v-model="formState.allowed_sources"
+            multiple
+            allow-create
+            filterable
+            default-first-option
+            placeholder="留空表示所有摄像头"
+            style="width: 100%"
+          >
+            <el-option
+              v-for="opt in cameraOptions"
+              :key="opt"
+              :label="opt"
+              :value="opt"
+            />
+          </el-select>
+        </el-form-item>
+        <el-form-item label="必备标签（可选）">
+          <el-select
+            v-model="formState.required_labels"
+            multiple
+            allow-create
+            filterable
+            default-first-option
+            placeholder="检测项 class_name 或 label 必须包含其中之一"
+            style="width: 100%"
+          />
+        </el-form-item>
+        <el-form-item label="描述">
+          <el-input
+            v-model="formState.description"
+            type="textarea"
+            :rows="2"
+            placeholder="说明该规则的业务用途"
+          />
+        </el-form-item>
+        <el-form-item label="启用">
+          <el-switch v-model="formState.enabled" />
+        </el-form-item>
+      </el-form>
+      <template #footer>
+        <el-button @click="dialogVisible = false">取消</el-button>
+        <el-button type="primary" :loading="submitting" @click="handleSubmit">
+          {{ editingName ? '保存' : '创建' }}
+        </el-button>
+      </template>
+    </el-dialog>
+  </div>
+</template>
+
+<script setup>
+import { computed, onMounted, onUnmounted, reactive, ref } from 'vue'
+import { ElMessage } from 'element-plus'
+import {
+  Search,
+  Refresh,
+  Plus,
+  Edit,
+  Delete,
+  Cpu,
+  DataLine,
+  Crop,
+  Collection,
+  VideoCamera,
+  VideoPlay,
+  VideoPause,
+  WarningFilled,
+  MagicStick
+} from '@element-plus/icons-vue'
+import { ruleApi, llmApi, cameraApi } from '@/api/detection'
+
+const eventTypeOptions = [
+  { label: '火灾', value: 'fire' },
+  { label: '烟雾', value: 'smoke' },
+  { label: '抽烟', value: 'smoking' },
+  { label: '打架', value: 'fight' },
+  { label: '徘徊', value: 'loitering' },
+  { label: '滞留', value: 'stationary' },
+  { label: '入侵', value: 'intrusion' },
+  { label: '违章停车', value: 'illegal_parking' },
+  { label: '车辆', value: 'vehicle' },
+  { label: '人员', value: 'person' }
+]
+
+const rules = ref([])
+const llmStatus = ref(null)
+const cameraOptions = ref([])
+const filter = reactive({ keyword: '', event_type: '' })
+const dialogVisible = ref(false)
+const submitting = ref(false)
+const editingName = ref('')
+const formRef = ref(null)
+const formState = reactive(makeEmptyForm())
+let pollTimer = null
+
+const formRules = {
+  name: [
+    { required: true, message: '请输入规则名称', trigger: 'blur' },
+    { pattern: /^[A-Za-z0-9_\-]+$/, message: '仅允许字母、数字、下划线和短横线', trigger: 'blur' }
+  ],
+  event_type: [{ required: true, message: '请选择事件类型', trigger: 'change' }]
+}
+
+function makeEmptyForm() {
+  return {
+    name: '',
+    event_type: 'fire',
+    enabled: true,
+    min_confidence: 0.5,
+    severity: '',
+    allowed_sources: [],
+    required_labels: [],
+    min_bbox_area: 0,
+    description: ''
+  }
+}
+
+const filteredRules = computed(() => {
+  return rules.value.filter((r) => {
+    if (filter.event_type && r.event_type !== filter.event_type) return false
+    if (filter.keyword) {
+      const kw = filter.keyword.toLowerCase()
+      const text = `${r.name} ${r.description || ''}`.toLowerCase()
+      if (!text.includes(kw)) return false
+    }
+    return true
+  })
+})
+
+const todayCost = computed(() => llmStatus.value?.cost?.today)
+const llmCircuitState = computed(() => llmStatus.value?.cost?.circuit_state)
+const llmTrackerEnabled = computed(() => llmStatus.value?.cost?.enabled !== false)
+
+const circuitLabel = computed(() => {
+  const map = { closed: '正常', open: '熔断', half_open: '半开试探' }
+  return map[llmCircuitState.value] || llmCircuitState.value || '-'
+})
+
+const circuitTagType = computed(() => {
+  if (llmCircuitState.value === 'open') return 'danger'
+  if (llmCircuitState.value === 'half_open') return 'warning'
+  return 'success'
+})
+
+const fusionLabel = computed(() => {
+  const map = {
+    weighted: '加权融合',
+    conservative: '保守策略',
+    llm_priority: 'LLM 优先'
+  }
+  return map[llmStatus.value?.fusion?.strategy] || llmStatus.value?.fusion?.strategy || '-'
+})
+
+function severityTagType(severity) {
+  const map = {
+    critical: 'danger',
+    high: 'warning',
+    medium: 'primary',
+    low: 'info',
+    info: 'success'
+  }
+  return map[severity] || 'info'
+}
+
+function severityLabel(severity) {
+  const map = {
+    critical: '严重',
+    high: '高危',
+    medium: '中等',
+    low: '低危',
+    info: '信息'
+  }
+  return map[severity] || severity || '默认'
+}
+
+function eventTypeLabel(type) {
+  const opt = eventTypeOptions.find((o) => o.value === type)
+  return opt ? opt.label : type
+}
+
+function formatRatio(v) {
+  if (v === undefined || v === null) return '-'
+  return Number(v).toFixed(2)
+}
+
+function formatCost(v) {
+  if (v === undefined || v === null) return '0.0000'
+  return Number(v).toFixed(4)
+}
+
+async function loadRules() {
+  try {
+    const { data } = await ruleApi.list()
+    rules.value = data?.rules || []
+  } catch (err) {
+    ElMessage.error(`加载规则失败: ${err.message || err}`)
+  }
+}
+
+async function loadLLMStatus() {
+  try {
+    const { data } = await llmApi.status()
+    llmStatus.value = data
+  } catch (err) {
+    // LLM 未启用时 cost 接口可能 503，状态接口仍可用 - 失败时静默
+    console.warn('加载 LLM 状态失败:', err.message)
+  }
+}
+
+async function loadCameras() {
+  try {
+    const { data } = await cameraApi.list()
+    cameraOptions.value = (data?.streams || []).map((s) => s.stream_id)
+  } catch (err) {
+    // 摄像头列表加载失败不阻塞规则页
+    cameraOptions.value = []
+  }
+}
+
+function openCreateDialog() {
+  editingName.value = ''
+  Object.assign(formState, makeEmptyForm())
+  dialogVisible.value = true
+}
+
+function openEditDialog(rule) {
+  editingName.value = rule.name
+  Object.assign(formState, {
+    name: rule.name,
+    event_type: rule.event_type,
+    enabled: rule.enabled,
+    min_confidence: Number(rule.min_confidence ?? 0),
+    severity: rule.severity || '',
+    allowed_sources: rule.allowed_sources || [],
+    required_labels: rule.required_labels || [],
+    min_bbox_area: Number(rule.min_bbox_area ?? 0),
+    description: rule.description || ''
+  })
+  dialogVisible.value = true
+}
+
+async function handleSubmit() {
+  if (!formRef.value) return
+  await formRef.value.validate(async (valid) => {
+    if (!valid) return
+    submitting.value = true
+    const payload = {
+      name: formState.name,
+      event_type: formState.event_type,
+      enabled: formState.enabled,
+      min_confidence: formState.min_confidence,
+      severity: formState.severity || null,
+      allowed_sources: formState.allowed_sources?.length ? formState.allowed_sources : null,
+      required_labels: formState.required_labels?.length ? formState.required_labels : null,
+      min_bbox_area: formState.min_bbox_area || 0,
+      description: formState.description || ''
+    }
+    try {
+      if (editingName.value) {
+        await ruleApi.update(editingName.value, payload)
+        ElMessage.success('规则已保存')
+      } else {
+        await ruleApi.create(payload)
+        ElMessage.success('规则已创建')
+      }
+      dialogVisible.value = false
+      await loadRules()
+    } catch (err) {
+      const msg = err.response?.data?.detail || err.message || String(err)
+      ElMessage.error(`操作失败: ${msg}`)
+    } finally {
+      submitting.value = false
+    }
+  })
+}
+
+async function handleToggle(rule, enabled) {
+  try {
+    await ruleApi.update(rule.name, { ...rule, enabled })
+    ElMessage.success(`已${enabled ? '启用' : '停用'} ${rule.name}`)
+    await loadRules()
+  } catch (err) {
+    const msg = err.response?.data?.detail || err.message
+    ElMessage.error(`切换失败: ${msg}`)
+    await loadRules()
+  }
+}
+
+async function handleDelete(rule) {
+  try {
+    await ruleApi.remove(rule.name)
+    ElMessage.success('已删除')
+    await loadRules()
+  } catch (err) {
+    ElMessage.error(`删除失败: ${err.response?.data?.detail || err.message}`)
+  }
+}
+
+async function handleReload() {
+  try {
+    await ruleApi.reload()
+    ElMessage.success('规则已热重载')
+    await loadRules()
+  } catch (err) {
+    ElMessage.error(`重载失败: ${err.message}`)
+  }
+}
+
+async function handleLLMEnable() {
+  try {
+    await llmApi.enable()
+    ElMessage.success('LLM 已重新启用')
+    await loadLLMStatus()
+  } catch (err) {
+    ElMessage.error(`操作失败: ${err.message}`)
+  }
+}
+
+async function handleLLMDisable() {
+  try {
+    await llmApi.disable()
+    ElMessage.success('LLM 已停用')
+    await loadLLMStatus()
+  } catch (err) {
+    ElMessage.error(`操作失败: ${err.message}`)
+  }
+}
+
+async function handleLLMReset() {
+  try {
+    await llmApi.reset()
+    ElMessage.success('成本统计已重置')
+    await loadLLMStatus()
+  } catch (err) {
+    ElMessage.error(`操作失败: ${err.message}`)
+  }
+}
+
+onMounted(() => {
+  loadRules()
+  loadLLMStatus()
+  loadCameras()
+  pollTimer = setInterval(loadLLMStatus, 10000)
+})
+
+onUnmounted(() => {
+  if (pollTimer) clearInterval(pollTimer)
+})
+</script>
+
+<style scoped>
+.rule-config-page {
+  padding: 24px;
+  min-height: 100%;
+  background: #020617;
+}
+
+/* ---- LLM 状态面板 ---- */
+.llm-panel {
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  border-radius: 12px;
+  padding: 18px 20px;
+  margin-bottom: 20px;
+}
+
+.panel-header {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 12px;
+  flex-wrap: wrap;
+  margin-bottom: 14px;
+}
+
+.panel-title {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  color: #F8FAFC;
+  font-size: 15px;
+  font-weight: 600;
+}
+
+.panel-actions {
+  display: flex;
+  gap: 8px;
+}
+
+.llm-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(180px, 1fr));
+  gap: 12px;
+}
+
+.llm-card {
+  padding: 12px 14px;
+  background: #131E33;
+  border: 1px solid rgba(255, 255, 255, 0.04);
+  border-radius: 8px;
+}
+
+.llm-label {
+  font-size: 12px;
+  color: #94A3B8;
+  margin-bottom: 6px;
+}
+
+.llm-value {
+  font-size: 18px;
+  font-weight: 600;
+  color: #F8FAFC;
+  font-family: 'Fira Code', monospace;
+}
+
+.llm-sub {
+  font-size: 11px;
+  color: #64748B;
+  margin-top: 4px;
+  display: flex;
+  align-items: center;
+  gap: 8px;
+}
+
+.budget-bar-wrap {
+  display: inline-block;
+  width: 80px;
+  height: 4px;
+  background: rgba(255, 255, 255, 0.06);
+  border-radius: 999px;
+  overflow: hidden;
+}
+
+.budget-bar {
+  display: block;
+  height: 100%;
+  background: #22C55E;
+  transition: width 200ms ease;
+}
+
+.budget-bar.is-danger {
+  background: #EF4444;
+}
+
+.llm-warning {
+  margin-top: 12px;
+  padding: 8px 12px;
+  background: rgba(239, 68, 68, 0.1);
+  border: 1px solid rgba(239, 68, 68, 0.3);
+  border-radius: 8px;
+  color: #FCA5A5;
+  font-size: 13px;
+  display: flex;
+  align-items: center;
+  gap: 6px;
+}
+
+/* ---- 工具栏 ---- */
+.toolbar {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 16px;
+  padding: 12px 16px;
+  margin-bottom: 16px;
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  border-radius: 12px;
+  flex-wrap: wrap;
+}
+
+.toolbar-left {
+  display: flex;
+  gap: 12px;
+  flex-wrap: wrap;
+  align-items: center;
+}
+
+.toolbar-right {
+  display: flex;
+  gap: 8px;
+}
+
+.filter-control {
+  width: 200px;
+}
+
+/* ---- 规则列表 ---- */
+.rule-list {
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+
+.rule-card {
+  display: flex;
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+  border-radius: 12px;
+  overflow: hidden;
+  transition: border-color 200ms ease;
+}
+
+.rule-card:hover {
+  border-color: rgba(34, 197, 94, 0.3);
+}
+
+.rule-card.is-disabled {
+  opacity: 0.55;
+}
+
+.rule-bar {
+  width: 4px;
+  flex-shrink: 0;
+  background: #64748B;
+}
+
+.rule-bar.severity-critical { background: #EF4444; }
+.rule-bar.severity-high     { background: #F59E0B; }
+.rule-bar.severity-medium   { background: #3B82F6; }
+.rule-bar.severity-low      { background: #94A3B8; }
+.rule-bar.severity-info     { background: #22C55E; }
+
+.rule-body {
+  flex: 1;
+  padding: 14px 16px;
+  min-width: 0;
+}
+
+.rule-head {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 12px;
+  flex-wrap: wrap;
+  margin-bottom: 6px;
+}
+
+.rule-title {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.rule-name {
+  font-size: 15px;
+  font-weight: 600;
+  color: #F8FAFC;
+  font-family: 'Fira Code', monospace;
+}
+
+.rule-actions {
+  display: flex;
+  gap: 4px;
+}
+
+.rule-desc {
+  font-size: 13px;
+  color: #94A3B8;
+  margin-bottom: 8px;
+}
+
+.rule-meta {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 16px;
+}
+
+.meta-item {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  font-size: 13px;
+  color: #94A3B8;
+}
+
+.meta-item .el-icon { font-size: 14px; }
+
+.empty-state { padding: 60px 0; }
+
+.form-hint {
+  margin-left: 12px;
+  font-size: 12px;
+  color: #64748B;
+}
+
+:deep(.rule-dialog .el-dialog) {
+  background: #0F172A;
+  border: 1px solid rgba(255, 255, 255, 0.06);
+}
+
+:deep(.rule-dialog .el-dialog__title) { color: #F8FAFC; }
+:deep(.rule-dialog .el-form-item__label) { color: #CBD5E1; }
+</style>

docs/项目现状与功能梳理.md

@@ -0,0 +1,821 @@
+# jc-video-recognize 项目现状与功能梳理
+
+> 基于 `event-judgment-algorithm-architecture.md` 和 `project-integration-plan.md` 两份架构设计文档，
+> 结合三个 MVP 迭代的实际代码实现，对项目当前已实现功能的全面梳理。
+
+---
+
+## 文档信息
+
+- **项目名称**: jc-video-recognize 视频模型检测平台
+- **文档版本**: v2.0
+- **更新日期**: 2026-06-16
+- **覆盖范围**: MVP-1（事件判断管道）+ MVP-2（RTSP/MQTT/实时推送）+ MVP-3（LLM 二次判断）
+
+---
+
+## 1. 项目概述
+
+本项目是一个全栈视频智能检测平台，集成了多种 AI 检测模型（YOLO 系列 + PaddlePaddle 系列），覆盖火灾、安全帽、人群、抽烟、徘徊、车辆违停、打架斗殴等场景。系统在原始检测能力基础上，构建了完整的事件判断管道、RTSP 视频流接入、MQTT 预警发布、WebSocket 实时推送、以及大模型二次判断等企业级能力。
+
+### 1.1 技术栈
+
+| 层级 | 技术栈 | 说明 |
+|------|--------|------|
+| **前端** | Vue 3 + Vite 5 + Element Plus + Pinia | 暗色主题 UI，4 个核心页面 |
+| **后端** | FastAPI + Uvicorn | REST API + WebSocket |
+| **AI 推理** | YOLOv8/v10 (Ultralytics) + PaddlePaddle 3.0 | 多模型检测服务 |
+| **视频流** | OpenCV + RTSP | 多路摄像头实时接入 |
+| **消息系统** | MQTT (paho-mqtt) | 预警事件发布 |
+| **大模型** | OpenAI 兼容协议 (GPT-4V/Qwen-VL/GLM-4V) | 二次判断与结果融合 |
+| **目标跟踪** | ByteTrack (纯 Python) | 稳定跟踪 ID 分配 |
+| **构建部署** | pnpm + Turborepo + Docker + Nginx | Monorepo 管理 |
+
+### 1.2 整体架构图
+
+```
+┌─────────────────────────────────────────────────────────────────────────────┐
+│                           前端 (Vue 3)                                      │
+│  ┌──────────┐ ┌──────────────┐ ┌──────────┐ ┌──────────────┐              │
+│  │ 模型检测  │ │ 摄像头管理    │ │ 规则配置  │ │ 预警列表     │              │
+│  │ Home.vue │ │ CameraMgmt   │ │ RuleConfig│ │ AlertList    │              │
+│  └────┬─────┘ └──────┬───────┘ └────┬─────┘ └──────┬───────┘              │
+│       │               │              │               │                      │
+│       └───────────────┴──────────────┴───────────────┘                      │
+│                       │ HTTP/REST + WebSocket                              │
+└───────────────────────┼─────────────────────────────────────────────────────┘
+                        │
+┌───────────────────────┼─────────────────────────────────────────────────────┐
+│                       ▼                                                     │
+│                  后端 (FastAPI)                                              │
+│  ┌──────────────────────────────────────────────────────────────────────┐  │
+│  │                         API 层                                       │  │
+│  │  /api/detect/*  /api/models/*  /api/rtsp/*  /api/rules/*  /api/llm/*│  │
+│  │  /ws/camera    /ws/alerts                                           │  │
+│  └──────────────────────────┬───────────────────────────────────────────┘  │
+│                             │                                              │
+│  ┌──────────────────────────▼───────────────────────────────────────────┐  │
+│  │                      服务层                                          │  │
+│  │                                                                      │  │
+│  │  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌────────────┐ │  │
+│  │  │ DetectionSvc │  │ RTSPService │  │ StreamMgr   │  │ MQTTService│ │  │
+│  │  │ (检测核心)   │  │ (流接入)    │  │ (多路调度)  │  │ (消息发布) │ │  │
+│  │  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘  └──────┬─────┘ │  │
+│  │         │                │                │                │        │  │
+│  │  ┌──────▼────────────────▼────────────────▼────────────────▼─────┐ │  │
+│  │  │                    事件判断管道                                 │ │  │
+│  │  │                                                                │ │  │
+│  │  │  DecisionEngine → AlertRuleEngine → EventAggregator            │ │  │
+│  │  │       │                                              │         │ │  │
+│  │  │       ▼                                              ▼         │ │  │
+│  │  │  ┌──────────────────────────────────────────────────────┐     │ │  │
+│  │  │  │              LLM 二次判断管道                         │     │ │  │
+│  │  │  │  FrameAccumulator → LLMTrigger → LLMAnalysisService  │     │ │  │
+│  │  │  │  ResultFusion + LLMCostTracker (熔断降级)             │     │ │  │
+│  │  │  └──────────────────────────────────────────────────────┘     │ │  │
+│  │  └───────────────────────────────────────────────────────────────┘ │  │
+│  │                                                                    │  │
+│  │  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐    │  │
+│  │  │ ByteTracker  │  │ AlertPublisher│  │ DetectionAdapter    │    │  │
+│  │  │ (目标跟踪)   │  │ (MQTT发布)   │  │ (结果格式统一)       │    │  │
+│  │  └──────────────┘  └──────────────┘  └──────────────────────┘    │  │
+│  └───────────────────────────────────────────────────────────────────┘  │
+│                                                                          │
+│  ┌───────────────────────────────────────────────────────────────────┐  │
+│  │                      推理引擎层                                    │  │
+│  │  ┌─────────┐  ┌──────────────┐  ┌────────────┐                  │  │
+│  │  │  YOLO   │  │ PaddlePaddle │  │ Docker API │                  │  │
+│  │  │(Ultralytics) │(PP-YOLOE)  │  │ (ppTSM)    │                  │  │
+│  │  └─────────┘  └──────────────┘  └────────────┘                  │  │
+│  └───────────────────────────────────────────────────────────────────┘  │
+└──────────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## 2. 已实现功能清单
+
+### 2.1 AI 检测能力（原始功能）
+
+| 模型 ID | 名称 | 框架 | 检测类别 | 图片检测 | 视频检测 |
+|---------|------|------|----------|---------|---------|
+| `fire_detection` | 火灾检测 | YOLOv10 | 火焰、烟雾 | ✅ | ❌ |
+| `helmet_detection` | 安全帽检测 | YOLOv8 | 人员、安全帽 | ✅ | ❌ |
+| `crowd_detection` | 人群检测 | YOLOv8 | 人员 | ✅ | ❌ |
+| `smoking_detection` | 抽烟检测 | YOLOv8 | 香烟、烟雾 | ✅ | ❌ |
+| `smoking_detection_paddle` | 抽烟检测 (Paddle) | PP-YOLOE-s | 香烟 | ✅ | ❌ |
+| `loitering_detection` | 徘徊检测 | YOLOv8 | 人员（含行为分析） | ✅ | ❌ |
+| `vehicle_detection` | 车辆检测 | PP-YOLOE-l | 车辆 | ✅ | ❌ |
+| `illegal_parking_detection` | 违停检测 | PP-YOLOE-l | 车辆（含违停判断） | ✅ | ❌ |
+| `fight_detection` | 打架斗殴检测 | YOLOv8 | 暴力行为、正常 | ✅ | ✅ |
+| `action_detection` | 打架检测 (Docker) | ppTSM | 打架、正常 | ✅ | ❌ |
+
+**行为分析算法**（集成在 `loitering_detection` 下）：
+
+| 算法 | 说明 | 参数 |
+|------|------|------|
+| 静止检测 | 基于位置匹配，检测人员静止停留 | 静止阈值(秒)、位置容差(像素) |
+| 徘徊检测 | 基于跟踪ID，检测人员长时间停留 | 徘徊阈值(秒)、移动阈值(像素) |
+
+**检测适配器**：通过 `DetectionAdapter` 将 YOLO、PaddlePaddle、Docker 三种不同输出格式统一为 `UnifiedDetection` / `DetectionResult`，为后续事件管道提供一致的数据入口。
+
+---
+
+### 2.2 事件判断管道（MVP-1 已实现）
+
+对应设计文档 `event-judgment-algorithm-architecture.md` 中的「决策层」。
+
+#### 2.2.1 事件决策引擎 (`EventDecisionEngine`)
+
+**文件**: `services/event/decision_engine.py`
+
+- 根据置信度阈值过滤检测结果
+- 将底层 class_name 映射为统一 `EventType` 枚举（fire/smoke/smoking/fight/loitering/illegal_parking 等）
+- 生成 `CandidateEvent` 候选事件，附带严重性级别、来源信息、检测详情
+
+#### 2.2.2 预警规则引擎 (`AlertRuleEngine`)
+
+**文件**: `services/event/rule_engine.py`
+
+- 从 YAML 配置目录加载规则（`config/rules/*.yaml`）
+- 支持规则类型：置信度阈值、事件类型、最小边界框面积、限定摄像头来源、必备标签
+- 每条规则可配置严重性级别（info/low/medium/high/critical）
+- 支持热重载（无需重启服务即可更新规则）
+- 已内置规则文件：`fire.yaml`、`smoking.yaml`、`loitering.yaml`、`fight.yaml`、`vehicle.yaml`
+
+#### 2.2.3 事件聚合器 (`EventAggregator`)
+
+**文件**: `services/event/aggregator.py`
+
+- 基于时间窗口去重：同一 (source_id, event_type, track_id) 在窗口内只产生一条预警
+- 空间邻近合并：IOU 超过阈值的同类事件自动合并
+- 置信度加权融合：历史置信度按衰减因子递减，新检测加权更新
+- 输出 `AlertEvent`，包含首次/末次出现时间、触发次数、关联检测列表
+
+#### 2.2.4 统一事件数据契约 (`event_schemas.py`)
+
+**文件**: `models/event_schemas.py`
+
+定义了完整的事件数据模型体系：
+
+| 模型 | 说明 |
+|------|------|
+| `EventType` | 统一事件类型枚举（11 种） |
+| `SeverityLevel` | 严重性级别枚举（5 级） |
+| `DetectionSource` | 检测来源枚举（YOLO/Paddle/Docker/Behavior/Composite） |
+| `BBox` | 边界框 (xyxy) |
+| `UnifiedDetection` | 统一检测结果（含 track_id） |
+| `DetectionResult` | 单帧检测结果集合 |
+| `CandidateEvent` | 候选事件（决策引擎输出） |
+| `AlertEvent` | 预警事件（聚合器输出，最终发布格式） |
+
+---
+
+### 2.3 RTSP 视频流接入（MVP-2 已实现）
+
+对应设计文档 `project-integration-plan.md` 中的「视频流接入方案」。
+
+#### 2.3.1 RTSP 流接入服务 (`RTSPService`)
+
+**文件**: `services/rtsp_service.py`
+
+- 基于 OpenCV VideoCapture 的 RTSP 接入，兼容主流 IP 摄像头
+- 后台线程解码帧，避免阻塞事件循环
+- 自动重连：断线后按指数退避策略重试（可配置最大次数、间隔、退避因子）
+- 帧回调机制：每解码一帧触发回调，由 StreamManager 分发到检测管道
+- 优雅关闭：stop() 等待解码线程退出，释放资源
+- 流状态管理：idle → connecting → connected → reconnecting → stopped → error
+
+#### 2.3.2 多路流调度管理器 (`StreamManager`)
+
+**文件**: `services/stream_manager.py`
+
+- 统一管理多路 RTSPService 实例（最大 16 路，可配置）
+- 每路流对应一个 `FrameBuffer`（环形缓冲区），解耦解码与检测
+- 检测调度：轮询帧缓冲区，按流配置的模型和参数执行检测
+- 状态监控：汇总所有流状态，提供健康检查接口
+- RESTful API 管理：添加/移除/启动/停止/配置更新
+
+#### 2.3.3 帧缓冲区 (`FrameBuffer`)
+
+**文件**: `services/frame_buffer.py`
+
+- 环形缓冲区设计，防止内存溢出
+- 支持丢帧策略（最新帧优先 / 均匀采样）
+- 线程安全：解码线程写入、检测线程读取
+
+#### 2.3.4 RTSP 管理 API
+
+**文件**: `api/rtsp.py`
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/api/rtsp/streams` | 获取所有流状态列表 |
+| `GET` | `/api/rtsp/streams/{stream_id}` | 获取单路流详情 |
+| `POST` | `/api/rtsp/streams` | 添加新摄像头流 |
+| `DELETE` | `/api/rtsp/streams/{stream_id}` | 移除摄像头流 |
+| `POST` | `/api/rtsp/streams/{stream_id}/start` | 启动指定流 |
+| `POST` | `/api/rtsp/streams/{stream_id}/stop` | 停止指定流 |
+| `PUT` | `/api/rtsp/streams/{stream_id}/config` | 更新流检测配置 |
+| `POST` | `/api/rtsp/start-all` | 启动全部流 |
+| `POST` | `/api/rtsp/stop-all` | 停止全部流 |
+| `GET` | `/api/rtsp/health` | 健康检查 |
+
+---
+
+### 2.4 MQTT 预警消息系统（MVP-2 已实现）
+
+对应设计文档 `project-integration-plan.md` 中的「MQTT预警事件消息系统」。
+
+#### 2.4.1 MQTT 客户端服务 (`MQTTService`)
+
+**文件**: `services/mqtt_service.py`
+
+- 基于 paho-mqtt 的 MQTT 客户端封装
+- 支持用户名/密码认证
+- 自动重连：按指数退避策略重试（可配置最小/最大延迟）
+- QoS 等级可配置（默认 QoS 1，确保至少送达一次）
+- 消息保留（retain）可配置
+
+#### 2.4.2 预警发布器 (`AlertPublisher`)
+
+**文件**: `services/alert_publisher.py`
+
+- 将 `AlertEvent` 格式化为标准 JSON 消息
+- 主题命名规则：`{prefix}/{event_type}/{source_id}`（单流单类型）、`{prefix}/all`（全量订阅）
+- 同时触发 WebSocket 广播（通过 AlertBroadcaster）
+- 消息包含：alert_id、event_type、severity、confidence、source_id、rule_name、detections 等
+
+#### 2.4.3 MQTT 配置
+
+通过环境变量或 `.env` 文件配置：
+
+| 配置项 | 环境变量 | 默认值 | 说明 |
+|--------|----------|--------|------|
+| 启用 | `MQTT_ENABLED` | `false` | 是否启用 MQTT |
+| Broker 地址 | `MQTT_BROKER_HOST` | `localhost` | MQTT 服务器 |
+| Broker 端口 | `MQTT_BROKER_PORT` | `1883` | 端口 |
+| 客户端 ID | `MQTT_CLIENT_ID` | `jc-video-recognize` | 客户端标识 |
+| QoS | `MQTT_QOS` | `1` | 消息质量等级 |
+| 主题前缀 | `MQTT_ALERT_TOPIC_PREFIX` | `video/alerts` | 主题前缀 |
+
+---
+
+### 2.5 目标跟踪服务（MVP-2 已实现）
+
+对应设计文档 `event-judgment-algorithm-architecture.md` 中的「目标轨迹关联」。
+
+#### 2.5.1 ByteTracker (`tracking_service.py`)
+
+**文件**: `services/tracking_service.py`
+
+- 基于 ByteTrack 论文的简化跟踪器，纯 Python 实现
+- 按置信度将检测分为 high/low 两组
+- 先用 high 检测与现有 tracks 进行 IOU 匹配
+- 未匹配的 tracks 再与 low 检测匹配（拯救低置信度真实目标）
+- 未匹配的 high 检测创建新 track
+- 超过 `max_lost_frames` 的 tracks 移除
+- 为每个检测目标分配稳定的 `track_id`，支持目标轨迹关联
+
+---
+
+### 2.6 WebSocket 实时推送（MVP-2 已实现）
+
+对应设计文档 `project-integration-plan.md` 中的「实时通信」。
+
+#### 2.6.1 预警 WebSocket (`/ws/alerts`)
+
+**文件**: `api/alerts.py`
+
+- `AlertBroadcaster` 维护 WebSocket 连接池
+- 支持按事件类型、source_id 过滤订阅
+- 心跳机制：前端每 30 秒发送 ping，后端回复 pong
+- 预警事件通过 MQTT 发布的同时广播到所有 WebSocket 订阅者
+- 消息格式：`{"type": "alert", "data": {...}}`
+
+#### 2.6.2 摄像头 WebSocket (`/ws/camera`)
+
+**文件**: `main.py` → `CameraService`
+
+- 实时视频流传输（原始帧 + 标注帧双路）
+- 支持动态切换模型、调整置信度/IOU 阈值
+- 自动清理摄像头资源
+
+---
+
+### 2.7 LLM 大模型二次判断（MVP-3 已实现）
+
+对应设计文档 `event-judgment-algorithm-architecture.md` 中的「AI增强层」和 `project-integration-plan.md` 中的「大模型二次判断架构」。
+
+#### 2.7.1 多帧累积器 (`MultiFrameAccumulator`)
+
+**文件**: `services/event/frame_accumulator.py`
+
+- 累积同一目标在时间窗口内的候选事件
+- 统计连续命中帧数、平均置信度、首次/末次出现时间
+- LRU 淘汰机制：超出最大容量时淘汰最久未更新的条目
+- 过期淘汰：超出时间窗口的条目自动清理
+- 为 LLM 触发器提供累积统计数据
+
+#### 2.7.2 LLM 触发器 (`LLMTrigger`)
+
+**文件**: `services/event/llm_trigger.py`
+
+- 基于累积统计决定是否触发 LLM 二次判断
+- 触发条件：连续命中帧数 ≥ `min_consecutive_hits` 且平均置信度 ≥ `min_avg_confidence`
+- 严重性旁路：`critical` 级别事件无需累积，立即触发 LLM
+- 冷却机制：同一目标在冷却时间内不重复触发
+- 输出 `TriggerDecision`，包含是否触发、累积帧列表、触发原因
+
+#### 2.7.3 LLM 分析服务 (`LLMAnalysisService`)
+
+**文件**: `services/llm_analysis_service.py`
+
+- **Provider 抽象**：`BaseLLMProvider` 抽象基类，支持多种 LLM 后端
+  - `MockLLMProvider`：离线测试用，返回模拟结果
+  - `OpenAICompatibleProvider`：支持 OpenAI 兼容协议（GPT-4V/Qwen-VL/GLM-4V 等）
+- 并发控制：`asyncio.Semaphore` 限制同时进行的 LLM 调用数
+- 图像预处理：自动缩放到 `image_max_side` 以节省 token
+- 结构化输出解析：从 LLM 响应中提取 confirmed/confidence/reasoning
+- 调用统计：记录调用次数、成功/失败数、总延迟
+
+#### 2.7.4 结果融合器 (`ResultFusion`)
+
+**文件**: `services/result_fusion.py`
+
+三种融合策略：
+
+| 策略 | 说明 |
+|------|------|
+| `weighted` | 加权融合：YOLO 权重 × YOLO 置信度 + LLM 权重 × LLM 置信度 |
+| `conservative` | 保守策略：LLM 明确否决时直接抑制预警 |
+| `llm_priority` | LLM 优先：LLM 给出明确判定时以 LLM 为准 |
+
+关键行为：
+- LLM 不可用/未触发时，可配置是否回退到 YOLO 结果（`fallback_to_yolo`）
+- LLM 明确否决时，可配置是否抑制预警（`suppress_on_llm_negative`）
+- 高融合置信度时可自动提升严重性级别（`promote_severity_on_high_confidence`）
+
+#### 2.7.5 LLM 成本追踪与熔断降级 (`LLMCostTracker`)
+
+**文件**: `services/llm_cost_tracker.py`
+
+- **成本追踪**：记录每次 LLM 调用的 token 用量、费用、延迟
+- **日预算控制**：超过日预算自动拒绝调用
+- **熔断降级**：
+  - 状态机：CLOSED → OPEN → HALF_OPEN → CLOSED
+  - 触发条件：最近 N 次调用错误率超过阈值
+  - 冷却恢复：OPEN 状态持续 `cooldown_seconds` 后进入 HALF_OPEN，允许一次试探
+  - 手动控制：支持紧急停用 / 重新启用 / 重置统计
+- **定价表**：内置主流模型定价，支持自定义覆盖
+- **统计接口**：提供日级统计、历史趋势、最近调用记录
+
+#### 2.7.6 LLM 管理 API
+
+**文件**: `api/llm.py`
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/api/llm/status` | 获取 LLM 运行状态（provider/模型/触发配置/融合配置/成本） |
+| `GET` | `/api/llm/cost` | 获取当前成本统计 |
+| `GET` | `/api/llm/cost/history` | 获取历史成本趋势（按天） |
+| `GET` | `/api/llm/cost/records` | 获取最近调用记录 |
+| `POST` | `/api/llm/disable` | 紧急停用 LLM |
+| `POST` | `/api/llm/enable` | 重新启用 LLM |
+| `POST` | `/api/llm/reset` | 重置成本统计与熔断状态 |
+
+---
+
+### 2.8 规则配置管理（MVP-3 已实现）
+
+#### 2.8.1 规则管理 API
+
+**文件**: `api/rules.py`
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/api/rules` | 获取所有规则列表 |
+| `GET` | `/api/rules/{name}` | 获取单条规则详情 |
+| `POST` | `/api/rules` | 新增规则（写入 custom.yaml） |
+| `PUT` | `/api/rules/{name}` | 更新规则 |
+| `DELETE` | `/api/rules/{name}` | 删除规则 |
+| `POST` | `/api/rules/reload` | 热重载规则（从 YAML 重新加载） |
+| `GET` | `/api/rules/_stats` | 获取规则统计信息 |
+
+---
+
+### 2.9 统一配置管理（MVP-1/2/3 已实现）
+
+**文件**: `core/settings.py`
+
+基于 `pydantic-settings` 的多环境配置管理，支持环境变量、`.env` 文件、默认值三级加载。
+
+| 子配置 | 环境变量前缀 | 覆盖范围 |
+|--------|-------------|---------|
+| `APISettings` | `API_` | 服务端口、CORS |
+| `DetectionSettings` | `DETECTION_` | 默认置信度、IOU、最低置信度 |
+| `ActionDetectionSettings` | `ACTION_DETECTION_` | Docker 行为识别服务 |
+| `EventEngineSettings` | `EVENT_` | 去重窗口、规则目录、最大事件数 |
+| `RTSPSettings` | `RTSP_` | 最大流数、缓冲区、重连策略 |
+| `MQTTSettings` | `MQTT_` | Broker 配置、QoS、主题 |
+| `TrackingSettings` | `TRACKING_` | ByteTrack 参数 |
+| `AggregatorSettings` | `AGGREGATOR_` | 空间合并、置信度融合 |
+| `LLMSettings` | `LLM_` | Provider、模型、API Key、并发 |
+| `LLMTriggerSettings` | `LLM_TRIGGER_` | 触发阈值、冷却、严重性旁路 |
+| `FusionSettings` | `FUSION_` | 融合策略、权重、回退 |
+| `LLMCostSettings` | `LLM_COST_` | 日预算、熔断阈值、冷却 |
+| `LoggingSettings` | `LOG_` | 日志级别、格式 |
+| `PathSettings` | `PATH_` | 静态资源、模型路径 |
+
+---
+
+## 3. 前端功能清单
+
+### 3.1 页面结构
+
+| 页面 | 路径 | 组件 | 功能 |
+|------|------|------|------|
+| 模型检测 | `/` | `Home.vue` + `ImageDetection.vue` + `VideoDetection.vue` | 图片/视频/摄像头实时检测 |
+| 摄像头管理 | `/cameras` | `CameraManagement.vue` | RTSP 流增删改查、启停控制 |
+| 规则配置 | `/rules` | `RuleConfiguration.vue` | 规则 CRUD + LLM 状态面板 |
+| 预警列表 | `/alerts` | `AlertList.vue` | 实时预警展示、过滤、LLM 结果展示 |
+
+### 3.2 全局功能
+
+- **暗色主题 UI**：统一深色设计风格
+- **可折叠侧边栏**：4 个导航菜单项
+- **实时连接状态**：显示 WebSocket 连接状态（已连接/连接中/未连接）
+- **预警铃铛**：顶部显示未读预警数量，点击跳转预警列表
+- **全局预警弹窗**：`AlertNotification.vue` 收到新预警时弹出桌面通知
+- **WebSocket 自动重连**：指数退避策略，最多重试 10 次
+- **心跳保活**：每 30 秒发送 ping
+
+### 3.3 摄像头管理页面功能
+
+- 统计卡片：摄像头总数、运行中、重连中、故障
+- 搜索过滤：按 ID/URL 搜索、按状态过滤
+- 摄像头操作：启动/停止/配置/移除
+- 批量操作：全部启动/全部停止
+- 添加/编辑弹窗：配置 RTSP URL、检测模型、置信度/IOU 阈值、帧采样间隔
+- 自动刷新：每 5 秒轮询状态
+
+### 3.4 规则配置页面功能
+
+- **LLM 状态面板**：
+  - Provider/模型/融合策略/触发阈值展示
+  - 今日调用次数/成功/失败/费用/预算使用率
+  - 最近错误率/熔断状态
+  - 紧急停用/重新启用/重置统计操作
+- **规则列表**：
+  - 按规则名/描述搜索、按事件类型过滤
+  - 规则卡片：严重级别标签、事件类型、启停开关、编辑/删除
+  - 规则元数据：最低置信度、最小边界框面积、限定摄像头、必备标签
+- **新增/编辑规则弹窗**：
+  - 规则名称、事件类型、严重级别
+  - 最低置信度滑块、最小边界框面积
+  - 限定摄像头 ID（多选）、必备标签（多选）
+  - 描述、启用开关
+
+### 3.5 预警列表页面功能
+
+- 统计卡片：预警总数、未读、严重预警、订阅状态
+- 过滤工具栏：按严重级别/事件类型/摄像头 ID 过滤
+- 预警卡片：
+  - 严重性色条（红/橙/蓝/灰/绿）
+  - 未读标记（绿色圆点）
+  - 事件类型、置信度、触发次数、规则名称
+  - 检测标签（含 track_id）
+  - **LLM 二次判断结果**：LLM 确认/否决标签、LLM 置信度、推理摘要
+- 操作：全部标为已读、清空记录、单条删除
+
+---
+
+## 4. 完整 API 接口一览
+
+### 4.1 检测接口
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `POST` | `/api/detect/image` | 图片检测（支持所有模型） |
+| `POST` | `/api/detect/video` | 视频检测（仅 YOLO 模型，主要面向打架斗殴） |
+| `GET` | `/api/algorithms/config` | 获取算法配置选项 |
+
+### 4.2 模型管理
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/api/models` | 获取可用模型列表 |
+| `GET` | `/api/models/{model_id}` | 获取单个模型信息 |
+| `POST` | `/api/models/{model_id}/load` | 加载指定模型 |
+
+### 4.3 RTSP 流管理
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/api/rtsp/streams` | 获取所有流状态 |
+| `GET` | `/api/rtsp/streams/{stream_id}` | 获取单路流详情 |
+| `POST` | `/api/rtsp/streams` | 添加新摄像头流 |
+| `DELETE` | `/api/rtsp/streams/{stream_id}` | 移除摄像头流 |
+| `POST` | `/api/rtsp/streams/{stream_id}/start` | 启动指定流 |
+| `POST` | `/api/rtsp/streams/{stream_id}/stop` | 停止指定流 |
+| `PUT` | `/api/rtsp/streams/{stream_id}/config` | 更新流检测配置 |
+| `POST` | `/api/rtsp/start-all` | 启动全部流 |
+| `POST` | `/api/rtsp/stop-all` | 停止全部流 |
+| `GET` | `/api/rtsp/health` | 健康检查 |
+
+### 4.4 规则配置
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/api/rules` | 获取所有规则 |
+| `GET` | `/api/rules/{name}` | 获取单条规则 |
+| `POST` | `/api/rules` | 新增规则 |
+| `PUT` | `/api/rules/{name}` | 更新规则 |
+| `DELETE` | `/api/rules/{name}` | 删除规则 |
+| `POST` | `/api/rules/reload` | 热重载规则 |
+| `GET` | `/api/rules/_stats` | 规则统计 |
+
+### 4.5 LLM 管理
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/api/llm/status` | LLM 运行状态 |
+| `GET` | `/api/llm/cost` | 当前成本统计 |
+| `GET` | `/api/llm/cost/history` | 历史成本趋势 |
+| `GET` | `/api/llm/cost/records` | 最近调用记录 |
+| `POST` | `/api/llm/disable` | 紧急停用 LLM |
+| `POST` | `/api/llm/enable` | 重新启用 LLM |
+| `POST` | `/api/llm/reset` | 重置统计与熔断 |
+
+### 4.6 WebSocket
+
+| 路径 | 说明 |
+|------|------|
+| `/ws/camera` | 摄像头实时视频流 |
+| `/ws/alerts` | 预警事件实时推送（支持过滤订阅） |
+
+### 4.7 系统
+
+| 方法 | 路径 | 说明 |
+|------|------|------|
+| `GET` | `/` | 服务信息 |
+| `GET` | `/api/health` | 健康检查 |
+
+---
+
+## 5. 数据流全景
+
+### 5.1 实时检测管道（RTSP → 预警）
+
+```
+RTSP 摄像头
+    │
+    ▼
+RTSPService (解码线程)
+    │
+    ▼
+FrameBuffer (环形缓冲区)
+    │
+    ▼
+StreamManager (检测调度)
+    │
+    ├──▶ ModelService (YOLO/Paddle 推理)
+    │        │
+    │        ▼
+    │    DetectionAdapter (格式统一)
+    │        │
+    │        ▼
+    │    ByteTracker (目标跟踪, 分配 track_id)
+    │
+    ▼
+DetectionResult (统一检测结果)
+    │
+    ▼
+EventDecisionEngine (置信度过滤 + 类型映射)
+    │
+    ▼
+CandidateEvent[]
+    │
+    ├──▶ AlertRuleEngine (规则匹配)
+    │        │
+    │        ▼
+    │    AlertEvent[] (规则过滤后)
+    │
+    ├──▶ MultiFrameAccumulator (多帧累积)
+    │        │
+    │        ▼
+    │    LLMTrigger (触发判定)
+    │        │
+    │        ▼ (触发时)
+    │    LLMAnalysisService (大模型分析)
+    │        │
+    │        ▼
+    │    ResultFusion (YOLO + LLM 融合)
+    │        │
+    │        ▼
+    │    LLMCostTracker (成本追踪 + 熔断)
+    │
+    ▼
+EventAggregator (去重 + 空间合并 + 置信度融合)
+    │
+    ▼
+AlertEvent (最终预警)
+    │
+    ├──▶ AlertPublisher → MQTT (外部系统)
+    ├──▶ AlertBroadcaster → WebSocket (前端实时推送)
+    └──▶ 前端 AlertList / 桌面通知
+```
+
+### 5.2 图片/视频检测管道（上传 → 结果）
+
+```
+用户上传图片/视频
+    │
+    ▼
+POST /api/detect/image 或 /api/detect/video
+    │
+    ▼
+DetectionService
+    │
+    ├──▶ ModelService (推理)
+    │        │
+    │        ▼
+    │    检测结果 + 标注图/视频
+    │
+    ├──▶ 事件管道 (同上，但仅图片检测触发)
+    │
+    ▼
+返回检测结果 (含标注图/视频 URL/关键帧)
+```
+
+---
+
+## 6. 测试覆盖
+
+项目包含完整的单元测试和集成测试：
+
+### 6.1 单元测试
+
+| 测试文件 | 覆盖模块 |
+|----------|---------|
+| `test_decision_engine.py` | 事件决策引擎 |
+| `test_rule_engine.py` | 预警规则引擎 |
+| `test_aggregator.py` / `test_aggregator_v2.py` | 事件聚合器 |
+| `test_event_schemas.py` | 事件数据模型 |
+| `test_detection_adapter.py` | 检测适配器 |
+| `test_frame_accumulator.py` | 多帧累积器 |
+| `test_llm_trigger.py` | LLM 触发器 |
+| `test_llm_analysis_service.py` | LLM 分析服务 |
+| `test_llm_cost_tracker.py` | LLM 成本追踪 |
+| `test_result_fusion.py` | 结果融合器 |
+| `test_rtsp_service.py` | RTSP 流服务 |
+| `test_stream_manager.py` | 多路流管理器 |
+| `test_tracking_service.py` | 目标跟踪 |
+| `test_mqtt_service.py` | MQTT 服务 |
+| `test_alert_publisher.py` | 预警发布器 |
+| `test_frame_buffer.py` | 帧缓冲区 |
+| `test_settings.py` | 配置管理 |
+
+### 6.2 集成测试
+
+| 测试文件 | 覆盖场景 |
+|----------|---------|
+| `test_detect_image_integration.py` | 图片检测端到端 |
+| `test_detection_service_pipeline.py` | 检测服务管道 |
+| `test_event_pipeline.py` | 事件管道端到端 |
+
+---
+
+## 7. 设计文档对照：已实现 vs 待实现
+
+### 7.1 `event-judgment-algorithm-architecture.md` 对照
+
+| 设计模块 | 设计内容 | 实现状态 | 说明 |
+|----------|---------|---------|------|
+| 事件决策引擎 | 置信度评估、场景识别、初步筛选 | ✅ 已实现 | 置信度过滤 + 类型映射，场景识别暂未实现 |
+| 预警规则引擎 | 规则匹配、时间窗口、区域规则 | ✅ 已实现 | 置信度/面积/来源/标签规则已实现，时间窗口规则暂未实现 |
+| 事件聚合器 | 去重合并、时间窗口、事件关联 | ✅ 已实现 | 时间窗口去重 + 空间合并 + 置信度融合 |
+| 大模型触发器 | 智能决策、优先级排序、并发控制 | ✅ 已实现 | 多帧累积 + 冷却 + 严重性旁路 |
+| LLM 视觉分析 | 图像理解、场景分析、推理验证 | ✅ 已实现 | OpenAI 兼容协议 + Mock Provider |
+| 结果融合 | 置信融合、决策输出 | ✅ 已实现 | weighted/conservative/llm_priority 三策略 |
+| 严重性评估器 | 风险等级、优先级计算 | ⚠️ 部分实现 | 规则引擎指定严重性，动态评估暂未实现 |
+| 事件格式化 | 标准格式、元数据填充 | ✅ 已实现 | AlertEvent 统一格式 |
+| MQTT 发布 | 消息发布、QoS 管理 | ✅ 已实现 | AlertPublisher + MQTTService |
+
+### 7.2 `project-integration-plan.md` 对照
+
+| 设计模块 | 设计内容 | 实现状态 | 说明 |
+|----------|---------|---------|------|
+| RTSP 流接入 | 解码、缓冲、重连 | ✅ 已实现 | RTSPService + FrameBuffer + 指数退避重连 |
+| 多路流管理 | 调度、状态监控 | ✅ 已实现 | StreamManager + REST API |
+| MQTT 预警系统 | 消息发布、QoS、主题设计 | ✅ 已实现 | MQTTService + AlertPublisher |
+| AI 模型扩展 | 车辆/打架检测 | ✅ 已实现 | fight_detection + vehicle_detection_paddle |
+| 大模型二次判断 | 多模型策略、Prompt 设计 | ✅ 已实现 | Provider 抽象 + 结构化输出解析 |
+| 触发条件 | 置信度区间、去重窗口、并发限制 | ✅ 已实现 | LLMTrigger + MultiFrameAccumulator |
+| 成本控制 | 日预算、熔断降级 | ✅ 已实现 | LLMCostTracker (CLOSED/OPEN/HALF_OPEN) |
+| WebSocket 实时推送 | 预警订阅、过滤 | ✅ 已实现 | AlertBroadcaster + 前端 WebSocket 客户端 |
+| 消息持久化 | 存储与消费确认 | ❌ 未实现 | 当前仅内存存储，未做持久化 |
+| 人脸识别/属性分析 | InsightFace / PaddleFace | ❌ 未实现 | — |
+| 越界/入侵检测 | 虚拟围栏算法 | ❌ 未实现 | — |
+| 异常行为检测 | 自定义行为分析 | ⚠️ 部分实现 | 已有静止/徘徊检测，通用异常行为未实现 |
+
+---
+
+## 8. 项目文件结构
+
+```
+jc-video-recognize/
+├── apps/
+│   ├── web/                              # 前端 (Vue 3 + Vite 5)
+│   │   └── src/
+│   │       ├── api/detection.js          # API 请求封装 (检测/摄像头/规则/LLM)
+│   │       ├── components/
+│   │       │   ├── AlertNotification.vue # 全局预警弹窗
+│   │       │   ├── AlgorithmConfig.vue   # 行为分析算法配置
+│   │       │   ├── DetectionConfig.vue   # 检测参数配置
+│   │       │   ├── ImageDetection.vue    # 图片检测模块
+│   │       │   └── VideoDetection.vue    # 视频/摄像头检测模块
+│   │       ├── layouts/MainLayout.vue    # 主布局 (侧边栏+顶栏+预警铃铛)
+│   │       ├── router/index.js           # 路由 (4 个页面)
+│   │       ├── services/mqtt.client.js   # WebSocket 预警客户端
+│   │       ├── stores/alertStore.js      # Pinia 预警状态管理
+│   │       └── views/
+│   │           ├── Home.vue              # 模型检测首页
+│   │           ├── CameraManagement.vue  # 摄像头管理
+│   │           ├── RuleConfiguration.vue # 规则配置 + LLM 面板
+│   │           └── AlertList.vue         # 预警列表
+│   └── server/                           # 后端 (FastAPI)
+│       ├── main.py                       # 服务入口 + 生命周期管理
+│       ├── api/
+│       │   ├── detection.py              # 检测 API
+│       │   ├── models.py                 # 模型管理 API
+│       │   ├── rtsp.py                   # RTSP 流管理 API
+│       │   ├── alerts.py                 # 预警 WebSocket API
+│       │   ├── rules.py                  # 规则配置 API
+│       │   └── llm.py                    # LLM 管理 API
+│       ├── config/rules/                 # 规则 YAML 配置
+│       │   ├── fire.yaml
+│       │   ├── smoking.yaml
+│       │   ├── loitering.yaml
+│       │   ├── fight.yaml
+│       │   └── vehicle.yaml
+│       ├── core/settings.py              # 统一配置管理 (14 个子配置)
+│       ├── models/
+│       │   ├── event_schemas.py          # 统一事件数据契约
+│       │   └── schemas.py               # 检测请求/响应模型
+│       ├── services/
+│       │   ├── detection_service.py      # 检测核心 (管道编排)
+│       │   ├── model_service.py          # 模型加载与管理
+│       │   ├── rtsp_service.py           # RTSP 流接入
+│       │   ├── stream_manager.py         # 多路流调度
+│       │   ├── frame_buffer.py           # 帧缓冲区
+│       │   ├── mqtt_service.py           # MQTT 客户端
+│       │   ├── alert_publisher.py        # 预警发布器
+│       │   ├── tracking_service.py       # ByteTrack 目标跟踪
+│       │   ├── llm_analysis_service.py   # LLM 分析服务
+│       │   ├── llm_cost_tracker.py       # LLM 成本追踪 + 熔断
+│       │   ├── result_fusion.py          # 结果融合器
+│       │   ├── loitering_service.py      # 徘徊检测服务
+│       │   ├── camera_service.py         # 摄像头 WebSocket 服务
+│       │   ├── action_detection_service.py   # Docker 行为识别适配器
+│       │   ├── paddle_detection_service.py   # PaddlePaddle 抽烟检测适配器
+│       │   ├── vehicle_detection_service.py  # 车辆/违停检测适配器
+│       │   ├── adapters/detection_adapter.py # 检测结果格式统一适配器
+│       │   └── event/
+│       │       ├── decision_engine.py    # 事件决策引擎
+│       │       ├── rule_engine.py        # 预警规则引擎
+│       │       ├── aggregator.py         # 事件聚合器
+│       │       ├── frame_accumulator.py  # 多帧累积器
+│       │       └── llm_trigger.py        # LLM 触发器
+│       └── tests/                        # 测试 (17 单元 + 3 集成)
+├── models/                               # AI 模型文件
+│   ├── fire_detection/                   # YOLOv10 火灾检测
+│   ├── helmet_detection/                 # YOLOv8 安全帽检测
+│   ├── crowd_detection/                  # YOLOv8 人群检测
+│   ├── smoking_detection/                # YOLOv8 抽烟检测
+│   ├── smoking_detection_paddle/         # PaddlePaddle 抽烟检测
+│   ├── loitering_detection/              # YOLOv8 徒步检测
+│   ├── fight_detection/                  # YOLOv8 打架斗殴检测
+│   └── vehicle_detection_paddle/         # PaddlePaddle 车辆检测
+├── docs/                                 # 项目文档
+└── docker/                               # Docker 部署配置
+```
+
+---
+
+## 9. 待完善功能
+
+基于两份设计文档的对照，以下功能尚未实现：
+
+| 优先级 | 功能 | 来源文档 | 说明 |
+|--------|------|---------|------|
+| 高 | 预警事件持久化存储 | project-integration-plan | 当前仅内存存储，重启后丢失 |
+| 高 | 时间窗口规则 | event-judgment-algorithm | 规则引擎暂不支持工作时间/非工作时间过滤 |
+| 中 | 场景识别 | event-judgment-algorithm | 决策引擎暂未实现室内/室外/禁区场景分类 |
+| 中 | 动态严重性评估 | event-judgment-algorithm | 当前严重性由规则静态指定，未实现动态计算 |
+| 中 | 越界/入侵检测 | project-integration-plan | 虚拟围栏算法未实现 |
+| 中 | 人脸识别/属性分析 | project-integration-plan | InsightFace / PaddleFace 未集成 |
+| 低 | 区域规则 | event-judgment-algorithm | 规则引擎暂不支持区域（禁区/普通区域）过滤 |
+| 低 | 组合规则 | event-judgment-algorithm | 多条件组合规则未实现 |
+| 低 | 消息消费确认机制 | project-integration-plan | MQTT 消息持久化与消费确认未实现 |
+| 低 | 预警消息管理后台 | project-integration-plan | 消息查询与统计分析页面未实现 |