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