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新增 _TRIGGER_ZH / _AGENT_ZH / _ACTION_ZH 翻譯表： - trigger_type 英文代碼 → 繁中標籤（價格下滑警報、市場機會偵測等） - agent 名稱 → 繁中（Hermes 分析師、NemoTron 監控、OpenClaw 策略師） - action 代碼 → 繁中（競品價格分析、派送告警通知等） - 升級審核觸發類型、參與模組、執行步驟全面繁中化 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
746 lines
31 KiB
Python
746 lines
31 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Elephant Alpha Autonomous Decision Engine
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AI 3.0 Autonomous Operations:
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- Self-learning from outcomes
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- Predictive decision making
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- Autonomous resource optimization
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- Continuous improvement loop
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ADR-012 Compliance:
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§③ 單一 audit trail — 所有執行完畢後必發 triaged_alert Telegram
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§⑤ 雙寫強制 — ai_insights (由 orchestrator._log_decision) + Telegram
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ADR-013 Compliance:
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resource_optimization trigger → auto_heal_service.handle_exception
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"""
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import asyncio
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import json
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from datetime import datetime, timedelta
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from typing import Dict, List, Any, Optional
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from dataclasses import dataclass, asdict
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from enum import Enum
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import numpy as np
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from services.logger_manager import SystemLogger
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from services.elephant_alpha_orchestrator import elephant_orchestrator, StrategicDecision
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from database.manager import get_session
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from sqlalchemy import text
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logger = SystemLogger("ElephantAlphaEngine").get_logger()
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# trigger_type → DecisionType 映射（B3 修正：避免 .upper() ValueError）
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_TRIGGER_TO_DECISION_TYPE: Dict[str, "DecisionType"] = {} # populated after class definition
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class DecisionType(Enum):
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PRICE_OPTIMIZATION = "price_optimization"
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THREAT_RESPONSE = "threat_response"
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MARKET_OPPORTUNITY = "market_opportunity"
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RESOURCE_ALLOCATION = "resource_allocation"
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STRATEGIC_PLANNING = "strategic_planning"
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_TRIGGER_TO_DECISION_TYPE = {
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"price_drop_alert": DecisionType.PRICE_OPTIMIZATION,
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"market_opportunity": DecisionType.MARKET_OPPORTUNITY,
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"threat_escalation": DecisionType.THREAT_RESPONSE,
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"resource_optimization": DecisionType.RESOURCE_ALLOCATION,
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}
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# 告警繁中翻譯表（所有 Telegram 告警內容統一使用繁體中文）
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_TRIGGER_ZH: Dict[str, str] = {
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"price_drop_alert": "價格下滑警報",
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"market_opportunity": "市場機會偵測",
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"threat_escalation": "威脅升級通報",
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"resource_optimization": "資源調配優化",
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"sales_anomaly": "銷售異常偵測",
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"ea_escalation": "EA 升級審核",
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}
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_AGENT_ZH: Dict[str, str] = {
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"hermes": "Hermes 分析師",
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"nemotron": "NemoTron 監控",
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"openclaw": "OpenClaw 策略師",
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"scheduler": "排程器",
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"?": "未知模組",
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}
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_ACTION_ZH: Dict[str, str] = {
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"analyze_price_competition": "競品價格分析",
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"dispatch_alert": "派送告警通知",
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"generate_strategic_analysis": "產出策略分析報告",
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"adjust_price": "調整定價",
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"send_alert": "發送告警",
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}
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def _zh_trigger(trigger_type: str) -> str:
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return _TRIGGER_ZH.get(trigger_type, trigger_type)
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def _zh_step(step: dict) -> str:
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agent = _AGENT_ZH.get(step.get("agent", "?"), step.get("agent", "?"))
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action = _ACTION_ZH.get(step.get("action", ""), step.get("action", "?"))
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return f"[{agent}] {action}"
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@dataclass
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class DecisionOutcome:
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"""Track decision outcomes for learning"""
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decision_id: str
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decision_type: DecisionType
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prediction: Dict[str, Any]
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actual_outcome: Dict[str, Any]
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accuracy_score: float
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business_impact: float
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timestamp: datetime
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lessons_learned: List[str]
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@dataclass
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class AutonomousTrigger:
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"""Autonomous decision trigger conditions"""
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trigger_type: str
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conditions: Dict[str, Any]
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threshold: float
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enabled: bool
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last_triggered: Optional[datetime] = None
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class ElephantAlphaAutonomousEngine:
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"""
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Elephant Alpha Autonomous Decision Engine
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ADR-012: all execution results → triaged_alert Telegram (audit trail)
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ADR-013: resource_optimization → auto_heal_service
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"""
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# 各 trigger 的 escalation cooldown（分鐘）
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ESCALATION_COOLDOWN: Dict[str, int] = {
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"price_drop_alert": 30, # 同一類型 30 分鐘只發一次
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"market_opportunity": 60,
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"threat_escalation": 15,
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"resource_optimization": 60,
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}
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DEFAULT_COOLDOWN_MIN = 30
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def __init__(self):
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self.decision_history: List[DecisionOutcome] = [] # 最近 100 筆快取；持久化到 DB
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self.triggers: List[AutonomousTrigger] = []
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self.learning_rate = 0.1
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self.confidence_threshold = 0.7
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self.max_autonomous_decisions_per_hour = 10
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self.decision_count_hour = 0
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self.last_hour_reset = datetime.now()
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# W3-B 護欄 3：每小時費用上限
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self.hourly_cost_usd = 0.0
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self.max_hourly_cost_usd = 5.0 # $5/hr 硬上限
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self._cost_per_ea_call = 0.002 # ~100B 模型每次約 $0.002 (1K tokens)
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# Escalation dedup：記錄每個 trigger 最後一次 escalate 時間
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self._last_escalated: Dict[str, datetime] = {}
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self._initialize_triggers()
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def _initialize_triggers(self):
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"""Initialize autonomous decision triggers"""
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self.triggers = [
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AutonomousTrigger(
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trigger_type="price_drop_alert",
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conditions={"competitor_price_drop_pct": 15, "sales_velocity": "decreasing"},
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threshold=0.8,
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enabled=True
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),
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AutonomousTrigger(
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trigger_type="market_opportunity",
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conditions={"competitor_price_premium": ">5%", "our_stock": "available"},
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threshold=0.7,
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enabled=True
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),
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AutonomousTrigger(
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trigger_type="threat_escalation",
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conditions={"threat_score": 0.9, "trend": "worsening"},
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threshold=0.85,
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enabled=True
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),
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AutonomousTrigger(
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trigger_type="resource_optimization",
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conditions={"system_load": "high", "queue_size": ">10"},
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threshold=0.6,
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enabled=True
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)
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]
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async def start_autonomous_monitoring(self):
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"""Start continuous autonomous monitoring loop"""
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logger.info("[ElephantAlpha] Starting autonomous monitoring engine")
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while True:
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try:
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self._reset_hourly_counter_if_needed()
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await self._check_triggers()
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await self._continuous_learning()
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await self._optimize_resources()
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await asyncio.sleep(60)
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except Exception as e:
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logger.error(f"[ElephantAlpha] Autonomous monitoring error: {e}")
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await asyncio.sleep(300)
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async def _check_triggers(self):
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"""Check all autonomous triggers"""
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if self.decision_count_hour >= self.max_autonomous_decisions_per_hour:
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logger.warning("[ElephantAlpha] Hourly decision limit reached, skipping cycle")
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return
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for trigger in self.triggers:
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if not trigger.enabled:
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continue
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# Cooldown guard：同一 trigger 在 cooldown 期間內不重複執行
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cooldown_min = self.ESCALATION_COOLDOWN.get(
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trigger.trigger_type, self.DEFAULT_COOLDOWN_MIN
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)
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if trigger.last_triggered:
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elapsed = (datetime.now() - trigger.last_triggered).total_seconds() / 60
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if elapsed < cooldown_min:
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logger.debug(
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f"[ElephantAlpha] Trigger {trigger.trigger_type} in cooldown "
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f"({elapsed:.1f}/{cooldown_min} min), skip"
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)
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continue
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if await self._evaluate_trigger(trigger):
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await self._execute_autonomous_decision(trigger)
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async def _evaluate_trigger(self, trigger: AutonomousTrigger) -> bool:
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if trigger.trigger_type == "price_drop_alert":
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return await self._check_price_drop_trigger(trigger)
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elif trigger.trigger_type == "market_opportunity":
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return await self._check_market_opportunity_trigger(trigger)
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elif trigger.trigger_type == "threat_escalation":
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return await self._check_threat_escalation_trigger(trigger)
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elif trigger.trigger_type == "resource_optimization":
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return await self._check_resource_optimization_trigger(trigger)
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return False
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# ── Trigger checkers ──────────────────────────────────────────────
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async def _check_price_drop_trigger(self, trigger: AutonomousTrigger) -> bool:
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session = get_session()
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try:
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rows = session.execute(text("""
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SELECT
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p.i_code AS sku,
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cp.price AS competitor_price,
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pr.price AS momo_price,
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((pr.price - cp.price) / pr.price * 100) AS price_gap_pct
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FROM products p
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JOIN (
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SELECT DISTINCT ON (product_id) product_id, price
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FROM price_records
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ORDER BY product_id, timestamp DESC
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) pr ON pr.product_id = p.id
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JOIN competitor_prices cp ON cp.sku = p.i_code
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WHERE cp.expires_at > NOW()
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AND cp.price < pr.price * 0.85
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AND cp.crawled_at >= NOW() - INTERVAL '2 hours'
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LIMIT 10
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""")).fetchall()
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return len(rows) >= 3
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finally:
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session.close()
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async def _check_market_opportunity_trigger(self, trigger: AutonomousTrigger) -> bool:
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session = get_session()
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try:
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rows = session.execute(text("""
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SELECT p.i_code AS sku
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FROM products p
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JOIN (
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SELECT DISTINCT ON (product_id) product_id, price
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FROM price_records
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ORDER BY product_id, timestamp DESC
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) pr ON pr.product_id = p.id
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JOIN competitor_prices cp ON cp.sku = p.i_code
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WHERE cp.expires_at > NOW()
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AND cp.price > pr.price * 1.05
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AND cp.crawled_at >= NOW() - INTERVAL '1 hour'
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LIMIT 5
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""")).fetchall()
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return bool(rows)
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finally:
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session.close()
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async def _check_threat_escalation_trigger(self, trigger: AutonomousTrigger) -> bool:
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session = get_session()
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try:
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rows = session.execute(text("""
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SELECT product_sku
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FROM ai_insights
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WHERE insight_type = 'price_alert'
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AND confidence >= 0.9
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AND created_at >= NOW() - INTERVAL '30 minutes'
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AND metadata_json LIKE '%worsening%'
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LIMIT 3
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""")).fetchall()
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return len(rows) >= 2
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finally:
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session.close()
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async def _check_resource_optimization_trigger(self, trigger: AutonomousTrigger) -> bool:
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return (self._get_action_queue_size() > 10
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or self._get_system_load_percentage() > 80)
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# ── Decision execution ────────────────────────────────────────────
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async def _execute_autonomous_decision(self, trigger: AutonomousTrigger):
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"""
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Execute autonomous decision.
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W3-B: cost guard — abort if hourly spend would exceed $5.
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Price actions require confidence ≥ 0.85 (護欄 1).
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ADR-012 §⑤: successful execution → triaged_alert Telegram.
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ADR-013: resource_optimization → auto_heal_service.
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"""
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# W3-B: 費用護欄
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if self.hourly_cost_usd + self._cost_per_ea_call > self.max_hourly_cost_usd:
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logger.warning(
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f"[ElephantAlpha] Hourly cost limit ${self.max_hourly_cost_usd} reached, "
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f"skipping trigger: {trigger.trigger_type}"
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)
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return
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# ADR-013: resource_optimization → AIOps autoheal loop
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if trigger.trigger_type == "resource_optimization":
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await self._handle_resource_via_autoheal(trigger)
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trigger.last_triggered = datetime.now()
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return
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context = await self._build_trigger_context(trigger)
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decision = await elephant_orchestrator.analyze_and_coordinate(context)
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# ── API Key 未設定時 fallback decision 的偵測 ──────────────
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# fallback_decision 的 reasoning 固定字串，偵測到就靜默跳過，
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# 不發 Telegram、不計費、只 log warning。
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if "Elephant Alpha unavailable" in (decision.reasoning or ""):
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logger.warning(
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f"[ElephantAlpha] API Key 未設定，trigger={trigger.trigger_type} "
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"跳過本次決策（不發 Telegram）。請在 momo-scheduler 環境變數注入 OPENROUTER_API_KEY。"
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)
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trigger.last_triggered = datetime.now() # 仍更新以避免無限重試
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return
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self.hourly_cost_usd += self._cost_per_ea_call
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# 護欄 1: price 行動信心閾值 0.85
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price_triggers = {"price_drop_alert", "market_opportunity"}
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effective_threshold = (
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0.85 if trigger.trigger_type in price_triggers else self.confidence_threshold
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)
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if decision.confidence >= effective_threshold:
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await self._execute_decision(decision, trigger)
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trigger.last_triggered = datetime.now()
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self.decision_count_hour += 1
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logger.info(f"[ElephantAlpha] Autonomous decision executed: {trigger.trigger_type}")
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# W2-B: ADR-012 §⑤ — 執行完畢後強制發 Telegram audit trail
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await self._notify_telegram_executed(decision, trigger)
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else:
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# W2-A: ADR-012 §⑤ — 升級人工 + Telegram（有 dedup 保護）
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trigger.last_triggered = datetime.now()
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await self._escalate_to_human(decision, trigger)
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async def _handle_resource_via_autoheal(self, trigger: AutonomousTrigger):
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"""ADR-013: resource_optimization → auto_heal_service.handle_exception"""
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try:
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from services.auto_heal_service import AutoHealService
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heal_service = AutoHealService()
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await asyncio.get_event_loop().run_in_executor(
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None,
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heal_service.handle_exception,
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"resource_pressure",
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{
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"queue_size": self._get_action_queue_size(),
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"system_load": self._get_system_load_percentage(),
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"source": "elephant_alpha_autonomous_engine",
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}
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)
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logger.info("[ElephantAlpha] Resource optimization handed off to AutoHealService")
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except Exception as e:
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logger.error(f"[ElephantAlpha] AutoHeal handoff failed: {e}")
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async def _build_trigger_context(self, trigger: AutonomousTrigger) -> Dict[str, Any]:
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context = {
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"trigger_type": trigger.trigger_type,
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"urgency": "high",
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"autonomous_mode": True,
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"timestamp": datetime.now().isoformat()
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}
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if trigger.trigger_type == "price_drop_alert":
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context.update(await self._get_price_drop_context())
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elif trigger.trigger_type == "market_opportunity":
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context.update(await self._get_market_opportunity_context())
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elif trigger.trigger_type == "threat_escalation":
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context.update(await self._get_threat_escalation_context())
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return context
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# ── Context builders ──────────────────────────────────────────────
|
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async def _get_price_drop_context(self) -> Dict[str, Any]:
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session = get_session()
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try:
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rows = session.execute(text("""
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SELECT
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p.i_code AS sku, p.name, p.category,
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cp.price AS competitor_price, cp.source AS competitor_name,
|
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pr.price AS momo_price,
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((pr.price - cp.price) / pr.price * 100) AS price_gap_pct
|
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FROM products p
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JOIN (
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SELECT DISTINCT ON (product_id) product_id, price
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FROM price_records
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ORDER BY product_id, timestamp DESC
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) pr ON pr.product_id = p.id
|
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JOIN competitor_prices cp ON cp.sku = p.i_code
|
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WHERE cp.expires_at > NOW()
|
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AND cp.price < pr.price * 0.85
|
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AND cp.crawled_at >= NOW() - INTERVAL '2 hours'
|
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ORDER BY price_gap_pct DESC
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LIMIT 10
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""")).fetchall()
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||
return {
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"affected_products": [
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{"sku": r.sku, "name": r.name, "category": r.category,
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||
"current_price": float(r.momo_price),
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||
"competitor_price": float(r.competitor_price),
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||
"price_gap_pct": float(r.price_gap_pct),
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||
"competitor": r.competitor_name}
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||
for r in rows
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||
],
|
||
"decision_type": "price_optimization",
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||
"business_impact": "revenue_protection"
|
||
}
|
||
finally:
|
||
session.close()
|
||
|
||
async def _get_market_opportunity_context(self) -> Dict[str, Any]:
|
||
session = get_session()
|
||
try:
|
||
rows = session.execute(text("""
|
||
SELECT
|
||
p.i_code AS sku, p.name, p.category,
|
||
pr.price AS momo_price, cp.price AS competitor_price,
|
||
cp.source AS competitor_name
|
||
FROM products p
|
||
JOIN (
|
||
SELECT DISTINCT ON (product_id) product_id, price
|
||
FROM price_records
|
||
ORDER BY product_id, timestamp DESC
|
||
) pr ON pr.product_id = p.id
|
||
JOIN competitor_prices cp ON cp.sku = p.i_code
|
||
WHERE cp.expires_at > NOW()
|
||
AND cp.price > pr.price * 1.05
|
||
AND cp.crawled_at >= NOW() - INTERVAL '1 hour'
|
||
LIMIT 5
|
||
""")).fetchall()
|
||
return {
|
||
"opportunity_products": [
|
||
{"sku": r.sku, "name": r.name, "category": r.category,
|
||
"current_price": float(r.momo_price),
|
||
"competitor_price": float(r.competitor_price),
|
||
"competitor": r.competitor_name}
|
||
for r in rows
|
||
],
|
||
"decision_type": "market_opportunity",
|
||
"business_impact": "revenue_growth"
|
||
}
|
||
finally:
|
||
session.close()
|
||
|
||
async def _get_threat_escalation_context(self) -> Dict[str, Any]:
|
||
session = get_session()
|
||
try:
|
||
rows = session.execute(text("""
|
||
SELECT product_sku AS sku, confidence, content, created_at
|
||
FROM ai_insights
|
||
WHERE insight_type = 'price_alert'
|
||
AND confidence >= 0.9
|
||
AND created_at >= NOW() - INTERVAL '30 minutes'
|
||
LIMIT 5
|
||
""")).fetchall()
|
||
return {
|
||
"threat_insights": [
|
||
{"sku": r.sku,
|
||
"confidence": float(r.confidence) if r.confidence else 0.0,
|
||
"content": r.content,
|
||
"created_at": r.created_at.isoformat()}
|
||
for r in rows
|
||
],
|
||
"decision_type": "threat_response",
|
||
"business_impact": "risk_mitigation"
|
||
}
|
||
finally:
|
||
session.close()
|
||
|
||
# ── Execution & persistence ───────────────────────────────────────
|
||
|
||
async def _execute_decision(self, decision: StrategicDecision, trigger: AutonomousTrigger):
|
||
"""Execute each step; persist outcome to DB (B5); Telegram handled by caller."""
|
||
for step in decision.execution_plan:
|
||
try:
|
||
await self._execute_step(step)
|
||
logger.info(f"[ElephantAlpha] Step done: {step.get('agent')} → {step.get('action')}")
|
||
except Exception as e:
|
||
logger.error(f"[ElephantAlpha] Step failed: {e}")
|
||
|
||
decision_type = _TRIGGER_TO_DECISION_TYPE.get(
|
||
trigger.trigger_type, DecisionType.STRATEGIC_PLANNING
|
||
)
|
||
outcome = DecisionOutcome(
|
||
decision_id=f"{trigger.trigger_type}_{datetime.now().timestamp()}",
|
||
decision_type=decision_type,
|
||
prediction=asdict(decision),
|
||
actual_outcome={},
|
||
accuracy_score=0.0,
|
||
business_impact=0.0,
|
||
timestamp=datetime.now(),
|
||
lessons_learned=[]
|
||
)
|
||
|
||
# B5 FIX: persist to action_plans (DB)
|
||
session = get_session()
|
||
try:
|
||
session.execute(text("""
|
||
INSERT INTO action_plans
|
||
(session_id, plan_type, sku, payload, status, created_by)
|
||
VALUES (:sid, :pt, :sku, :pl, :status, :by)
|
||
"""), {
|
||
"sid": outcome.decision_id,
|
||
"pt": "elephant_alpha_decision",
|
||
"sku": None,
|
||
"pl": json.dumps({
|
||
"decision": {k: str(v) for k, v in asdict(decision).items()},
|
||
"trigger": trigger.trigger_type,
|
||
"confidence": decision.confidence,
|
||
}),
|
||
"status": "executed",
|
||
"by": "elephant_alpha",
|
||
})
|
||
session.commit()
|
||
except Exception as e:
|
||
logger.error(f"[ElephantAlpha] DB persist failed: {e}")
|
||
session.rollback()
|
||
finally:
|
||
session.close()
|
||
|
||
self.decision_history.append(outcome)
|
||
if len(self.decision_history) > 100:
|
||
self.decision_history = self.decision_history[-100:]
|
||
|
||
async def _execute_step(self, step: Dict[str, Any]):
|
||
"""Execute individual step. NemoTron gets PriceThreat list (B6 FIX)."""
|
||
from services.hermes_analyst_service import HermesAnalystService, PriceThreat
|
||
from services.nemoton_dispatcher_service import NemotronDispatcher
|
||
from services.openclaw_strategist_service import generate_weekly_strategy_report
|
||
|
||
agent_type = step.get("agent", "").lower()
|
||
action = step.get("action", "")
|
||
params = step.get("parameters", {})
|
||
|
||
logger.info(f"[ElephantAlpha] Execute: {agent_type} → {action}")
|
||
|
||
if agent_type == "hermes" and action == "analyze_price_competition":
|
||
return HermesAnalystService().run()
|
||
|
||
elif agent_type == "nemotron" and action == "dispatch_alert":
|
||
raw_threats = params.get("threats", [])
|
||
threats = [
|
||
PriceThreat(
|
||
sku=t.get("sku", ""), name=t.get("name", ""),
|
||
category=t.get("category", ""),
|
||
momo_price=float(t.get("momo_price", 0)),
|
||
pchome_price=float(t.get("competitor_price", 0)),
|
||
gap_pct=float(t.get("price_gap_pct", 0)),
|
||
sales_7d_delta_pct=float(t.get("sales_7d_delta_pct", 0)),
|
||
risk=t.get("risk", "MED"),
|
||
recommended_action=t.get("recommended_action", ""),
|
||
confidence=float(t.get("confidence", 0.5)),
|
||
)
|
||
for t in raw_threats if isinstance(t, dict)
|
||
]
|
||
if threats:
|
||
NemotronDispatcher().dispatch(threats)
|
||
|
||
elif agent_type == "openclaw" and action == "generate_strategic_analysis":
|
||
return generate_weekly_strategy_report()
|
||
|
||
# ── Telegram notifications (ADR-012 §⑤) ─────────────────────────
|
||
|
||
async def _notify_telegram_executed(
|
||
self, decision: StrategicDecision, trigger: AutonomousTrigger
|
||
):
|
||
"""W2-B: ADR-012 §⑤ — 自主執行完畢後強制發 Telegram audit trail。"""
|
||
try:
|
||
from services.telegram_templates import triaged_alert, _send_telegram_raw
|
||
msg, keyboard = triaged_alert(
|
||
base_event={
|
||
"event_type": trigger.trigger_type,
|
||
"title": f"🐘 EA 自主執行完畢 · {_zh_trigger(trigger.trigger_type)}",
|
||
"summary": decision.expected_outcome or "EA 完成自主決策",
|
||
"id": f"ea_{trigger.trigger_type}_{int(datetime.now().timestamp())}",
|
||
},
|
||
tier_label="🐘 Elephant Alpha · 自主執行",
|
||
ai_summary=(decision.reasoning or "")[:300],
|
||
ai_executed=[
|
||
_zh_step(s)
|
||
for s in decision.execution_plan[:5]
|
||
] or ["（無具體執行計畫）"],
|
||
)
|
||
_send_telegram_raw(msg)
|
||
logger.info(f"[ElephantAlpha] Telegram audit sent for: {trigger.trigger_type}")
|
||
except Exception as e:
|
||
logger.error(f"[ElephantAlpha] Telegram audit failed (non-blocking): {e}")
|
||
|
||
async def _escalate_to_human(self, decision: StrategicDecision, trigger: AutonomousTrigger):
|
||
"""
|
||
W2-A: ADR-012 §⑤ — 信心不足時雙寫：
|
||
1. ai_insights (DB)
|
||
2. triaged_alert Telegram → 統帥收到升級通知
|
||
Dedup：同一 trigger_type 在 cooldown 期間內只發一次 Telegram。
|
||
"""
|
||
logger.warning(
|
||
f"[ElephantAlpha] Escalating to human: {trigger.trigger_type} "
|
||
f"confidence={decision.confidence:.2f}"
|
||
)
|
||
|
||
# Dedup check：同一 trigger 在 cooldown 期間內只寫 DB + 發一次 Telegram
|
||
cooldown_min = self.ESCALATION_COOLDOWN.get(
|
||
trigger.trigger_type, self.DEFAULT_COOLDOWN_MIN
|
||
)
|
||
last_esc = self._last_escalated.get(trigger.trigger_type)
|
||
telegram_allowed = (
|
||
last_esc is None
|
||
or (datetime.now() - last_esc).total_seconds() / 60 >= cooldown_min
|
||
)
|
||
|
||
# 1. DB write（每次都記錄，不 dedup）
|
||
session = get_session()
|
||
try:
|
||
session.execute(text("""
|
||
INSERT INTO ai_insights
|
||
(insight_type, content, confidence, created_by, status, metadata_json)
|
||
VALUES (:type, :content, :conf, :by, :status, :meta)
|
||
"""), {
|
||
"type": "human_review",
|
||
"content": (
|
||
f"[Elephant Alpha 升級審核] {trigger.trigger_type} "
|
||
f"信心度僅 {decision.confidence:.2f}，建議人工介入。"
|
||
),
|
||
"conf": decision.confidence,
|
||
"by": "elephant_alpha",
|
||
"status": "pending",
|
||
"meta": json.dumps({
|
||
"decision": asdict(decision),
|
||
"trigger": trigger.trigger_type,
|
||
"reason": "low_confidence"
|
||
})
|
||
})
|
||
session.commit()
|
||
except Exception as e:
|
||
logger.error(f"[ElephantAlpha] DB escalation write failed: {e}")
|
||
session.rollback()
|
||
finally:
|
||
session.close()
|
||
|
||
# 2. Telegram (ADR-012 §⑤)：只在 cooldown 過後才發，避免轟炸
|
||
if not telegram_allowed:
|
||
logger.info(
|
||
f"[ElephantAlpha] Escalation Telegram suppressed (cooldown {cooldown_min} min): "
|
||
f"{trigger.trigger_type}"
|
||
)
|
||
return
|
||
|
||
self._last_escalated[trigger.trigger_type] = datetime.now()
|
||
try:
|
||
from services.telegram_templates import triaged_alert, _send_telegram_raw
|
||
msg, keyboard = triaged_alert(
|
||
base_event={
|
||
"event_type": "ea_escalation",
|
||
"title": f"🐘 EA 升級審核 · {_zh_trigger(trigger.trigger_type)}",
|
||
"summary": (
|
||
f"自主決策信心度 {decision.confidence:.2f} 低於門檻，需人工批准"
|
||
),
|
||
"id": f"ea_review_{int(datetime.now().timestamp())}",
|
||
},
|
||
tier_label="🐘 Elephant Alpha · L3 HITL",
|
||
ai_summary=(decision.reasoning or "")[:300],
|
||
ai_cause=(
|
||
f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
|
||
f"信心度：{decision.confidence:.2f} | "
|
||
f"參與模組：{', '.join(_AGENT_ZH.get(a, a) for a in decision.agents_required)}"
|
||
),
|
||
ai_actions=[
|
||
f"步驟 {s.get('step', i+1)}：{_zh_step(s)}"
|
||
for i, s in enumerate(decision.execution_plan[:3])
|
||
] or ["無具體執行計畫"],
|
||
)
|
||
_send_telegram_raw(msg, reply_markup=keyboard)
|
||
logger.info(
|
||
f"[ElephantAlpha] Human escalation Telegram sent: {trigger.trigger_type}"
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"[ElephantAlpha] Telegram escalation failed (non-blocking): {e}")
|
||
|
||
# ── Learning & resource management ───────────────────────────────
|
||
|
||
async def _continuous_learning(self):
|
||
recent_outcomes = [
|
||
o for o in self.decision_history
|
||
if o.timestamp >= datetime.now() - timedelta(hours=24)
|
||
]
|
||
if len(recent_outcomes) >= 5:
|
||
accuracy_scores = [o.accuracy_score for o in recent_outcomes if o.accuracy_score > 0]
|
||
if accuracy_scores:
|
||
avg_accuracy = np.mean(accuracy_scores)
|
||
if avg_accuracy > 0.8:
|
||
self.confidence_threshold = min(0.9, self.confidence_threshold + 0.05)
|
||
elif avg_accuracy < 0.6:
|
||
self.confidence_threshold = max(0.5, self.confidence_threshold - 0.05)
|
||
logger.info(
|
||
f"[ElephantAlpha] Learning: accuracy={avg_accuracy:.2f}, "
|
||
f"threshold={self.confidence_threshold:.2f}"
|
||
)
|
||
|
||
async def _optimize_resources(self):
|
||
queue_size = self._get_action_queue_size()
|
||
system_load = self._get_system_load_percentage()
|
||
if system_load > 90:
|
||
self.max_autonomous_decisions_per_hour = 5
|
||
elif system_load < 50 and queue_size < 5:
|
||
self.max_autonomous_decisions_per_hour = 15
|
||
|
||
def _get_action_queue_size(self) -> int:
|
||
session = get_session()
|
||
try:
|
||
row = session.execute(text(
|
||
"SELECT COUNT(*) as count FROM action_plans WHERE status = 'pending'"
|
||
)).fetchone()
|
||
return row.count if row else 0
|
||
finally:
|
||
session.close()
|
||
|
||
def _get_system_load_percentage(self) -> float:
|
||
return 45.0
|
||
|
||
def _reset_hourly_counter_if_needed(self):
|
||
if datetime.now().hour != self.last_hour_reset.hour:
|
||
self.decision_count_hour = 0
|
||
self.hourly_cost_usd = 0.0 # W3-B: 同步重置費用計數
|
||
self.last_hour_reset = datetime.now()
|
||
|
||
|
||
# Global autonomous engine instance
|
||
autonomous_engine = ElephantAlphaAutonomousEngine()
|