1301 lines
52 KiB
Python
1301 lines
52 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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services/openclaw_strategist_service.py
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OpenClaw 戰略分析師（Gemini 2.5 Flash）
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完整電商情報分析管線：
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DB 爬蟲數據 + MCP 外部情報 → Gemini 深度分析 → ai_insights 持久化 → Telegram 推播
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提供：
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generate_weekly_strategy_report() — 週報（每週一 06:00）
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generate_meta_analysis_report() — AI 系統效能自我審視（每 6 小時）
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分析維度：
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1. 業績趨勢（MoM / WoW）
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2. 競品價格比對
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3. 定價策略建議
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4. 行銷活動洞察
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5. 季節性 / 節日機會
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6. TOP 威脅 / 機會品項
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7. 具體行動清單（48h 優先事項）
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"""
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import json
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import logging
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import os
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import requests
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional
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from database.manager import get_session
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from sqlalchemy import text
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logger = logging.getLogger(__name__)
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GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
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STRATEGY_MODEL = os.getenv("OPENCLAW_MODEL", "gemini-2.5-flash")
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NVIDIA_API_KEY = os.getenv("NVIDIA_API_KEY", "")
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NVIDIA_NIM_URL = "https://integrate.api.nvidia.com/v1/chat/completions"
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NVIDIA_FALLBACK_MODEL = "meta/llama-3.3-70b-instruct"
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TAIPEI_TZ_OFFSET = 8 # UTC+8
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__all__ = [
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"generate_daily_report",
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"generate_weekly_strategy_report",
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"generate_monthly_report",
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"generate_meta_analysis_report",
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"generate_strategy_response",
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]
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# ═══════════════════════════════════════════════════════════════════════════════
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# Telegram NLP 互動入口（輕量查詢，不走完整報告管線）
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# ═══════════════════════════════════════════════════════════════════════════════
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def generate_strategy_response(query: str, context: Optional[Dict[str, Any]] = None) -> str:
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"""給 Telegram NLP 使用的輕量策略回覆。
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Contract:
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query: 使用者自然語言訊息（繁體中文）
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context: 可選，{"intent": str, "user_id": int, ...}
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Returns:
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繁體中文回覆字串。GEMINI_API_KEY 未設或呼叫失敗時，回降級訊息
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（永遠回字串，不拋例外，由呼叫端顯示於 Telegram）。
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"""
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q = (query or "").strip()
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if not q:
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return "請輸入您的問題，例如：本週業績趨勢、競品價差分析、產出週報 PPT。"
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system_prompt = (
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"你是 MOMO Pro 電商情報策略師「OpenClaw」。以繁體中文（台灣用語）回覆使用者。"
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"嚴禁簡體字，嚴禁空洞套話。若使用者要求的資料需即時查詢，"
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"請告知使用者相關可用指令（例如 /daily、/weekly、/threats）。"
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"回覆長度控制在 500 字內，可用 Markdown 條列。"
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)
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user_prompt = f"使用者問題：{q}\n上下文：{json.dumps(context or {}, ensure_ascii=False)}"
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# 優先 Gemini；無 key 或失敗時自動備援 NVIDIA NIM
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text_reply = None
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if GEMINI_API_KEY:
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try:
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text_reply = _call_gemini(system_prompt, user_prompt, temperature=0.5)
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except Exception as e:
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logger.warning("[OpenClaw] Gemini 呼叫失敗，備援 NVIDIA NIM：%s", e)
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if not text_reply and NVIDIA_API_KEY:
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try:
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text_reply = _call_nvidia_nim(system_prompt, user_prompt)
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except Exception as e:
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logger.error("[OpenClaw] NVIDIA NIM 備援也失敗：%s", e)
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if not text_reply:
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return (
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"策略師暫時無法回覆（Gemini 與 NVIDIA NIM 均離線）。\n"
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"請改用：/daily、/weekly、/threats 取得結構化報告。"
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)
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return text_reply
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# ═══════════════════════════════════════════════════════════════════════════════
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# DB 數據讀取層
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# ═══════════════════════════════════════════════════════════════════════════════
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def _fetch_sales_summary(days: int = 14) -> Dict[str, Any]:
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"""近 N 天業績彙總（本期 / 前期 對比）"""
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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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snapshot_date::date AS dt,
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SUM(COALESCE("銷售金額"::numeric, 0)) AS revenue,
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COUNT(DISTINCT "商品ID") AS sku_count
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FROM daily_sales_snapshot
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WHERE snapshot_date::date >= CURRENT_DATE - :days
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GROUP BY dt
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ORDER BY dt DESC
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"""), {"days": days}).fetchall()
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data = [{"date": str(r[0]), "revenue": float(r[1] or 0), "sku_count": int(r[2] or 0)}
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for r in rows]
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mid = len(data) // 2
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curr_rev = sum(d["revenue"] for d in data[:mid]) if mid else 0
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prev_rev = sum(d["revenue"] for d in data[mid:]) if mid else 0
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wow = ((curr_rev - prev_rev) / prev_rev * 100) if prev_rev else 0
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return {
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"daily": data[:7],
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"current_7d_revenue": curr_rev,
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"prev_7d_revenue": prev_rev,
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"wow_pct": round(wow, 1),
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}
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except Exception as e:
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logger.warning("[OpenClaw] 業績數據讀取失敗: %s", e)
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return {}
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finally:
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session.close()
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def _fetch_top_threats(limit: int = 10) -> List[Dict]:
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"""最新 TOP N 競價威脅（來自 Hermes 分析）"""
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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, content, confidence, metadata_json, created_at
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FROM ai_insights
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WHERE insight_type = 'price_alert'
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AND status = 'approved'
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AND created_at >= NOW() - INTERVAL '48 hours'
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ORDER BY confidence DESC
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LIMIT :lim
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"""), {"lim": limit}).fetchall()
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result = []
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for r in rows:
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meta = {}
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try:
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meta = json.loads(r[3]) if r[3] else {}
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except Exception:
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pass
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result.append({
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"sku": r[0],
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"summary": (r[1] or "")[:200],
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"confidence": float(r[2] or 0),
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"gap_pct": meta.get("gap_pct", 0),
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"sales_delta": meta.get("sales_7d_delta_pct", 0),
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"momo_price": meta.get("momo_price"),
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"pchome_price": meta.get("pchome_price"),
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})
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return result
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except Exception as e:
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logger.warning("[OpenClaw] 威脅數據讀取失敗: %s", e)
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return []
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finally:
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session.close()
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def _fetch_top_recommendations(limit: int = 10) -> List[Dict]:
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"""最新定價建議"""
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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 sku, name, reason, strategy, confidence,
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momo_price, pchome_price, gap_pct, sales_7d_delta
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FROM ai_price_recommendations
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WHERE status = 'pending'
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AND created_at >= NOW() - INTERVAL '48 hours'
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ORDER BY confidence DESC
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LIMIT :lim
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"""), {"lim": limit}).fetchall()
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return [dict(zip(
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["sku","name","reason","strategy","confidence","momo_price","pchome_price","gap_pct","sales_delta"],
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r
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)) for r in rows]
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except Exception as e:
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logger.warning("[OpenClaw] 建議數據讀取失敗: %s", e)
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return []
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finally:
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session.close()
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def _fetch_category_breakdown(days: int = 7) -> List[Dict]:
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"""品類業績分佈"""
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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.category,
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SUM(COALESCE(s."銷售金額"::numeric, 0)) AS revenue,
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COUNT(DISTINCT p.i_code) AS sku_count
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FROM daily_sales_snapshot s
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JOIN products p ON p.name = s."商品名稱"
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WHERE s.snapshot_date::date >= CURRENT_DATE - :days
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AND p.status = 'ACTIVE'
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GROUP BY p.category
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ORDER BY revenue DESC
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LIMIT 10
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"""), {"days": days}).fetchall()
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return [{"category": r[0], "revenue": float(r[1] or 0), "sku_count": int(r[2] or 0)}
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for r in rows]
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except Exception as e:
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logger.warning("[OpenClaw] 品類數據讀取失敗: %s", e)
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return []
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finally:
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session.close()
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def _fetch_competitor_summary() -> Dict[str, Any]:
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"""競品價格整體概況"""
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session = get_session()
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try:
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row = session.execute(text("""
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SELECT
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COUNT(*) AS total,
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AVG((cp.price - pr.price) / pr.price * 100) AS avg_gap_pct,
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SUM(CASE WHEN cp.price < pr.price * 0.9 THEN 1 ELSE 0 END) AS undercut_count,
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SUM(CASE WHEN cp.price > pr.price * 1.1 THEN 1 ELSE 0 END) AS premium_count
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FROM competitor_prices cp
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JOIN products p ON p.i_code = cp.sku
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JOIN (
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SELECT DISTINCT ON (product_id) product_id, price
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FROM price_records ORDER BY product_id, timestamp DESC
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) pr ON pr.product_id = p.id
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WHERE cp.expires_at > NOW()
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""")).fetchone()
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if row and row[0]:
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return {
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"total_skus": int(row[0]),
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"avg_gap_pct": round(float(row[1] or 0), 1),
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"undercut_count": int(row[2] or 0),
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"premium_count": int(row[3] or 0),
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}
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return {}
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except Exception as e:
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logger.warning("[OpenClaw] 競品概況讀取失敗: %s", e)
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return {}
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finally:
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session.close()
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# ═══════════════════════════════════════════════════════════════════════════════
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# DB 寫入層
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# ═══════════════════════════════════════════════════════════════════════════════
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def _save_to_ai_insights(
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insight_type: str,
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content: str,
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confidence: float,
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metadata: Dict[str, Any],
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period: Optional[str] = None,
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) -> Optional[int]:
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"""將分析結果持久化到 ai_insights"""
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session = get_session()
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try:
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row = session.execute(text("""
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INSERT INTO ai_insights
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(insight_type, content, confidence, created_by, status,
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metadata_json, period, created_at)
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VALUES (:type, :content, :conf, 'openclaw', 'active', :meta, :period, NOW())
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RETURNING id
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"""), {
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"type": insight_type,
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"content": content[:8000],
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"conf": confidence,
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"meta": json.dumps(metadata, ensure_ascii=False),
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"period": period or datetime.now().strftime("%Y-%m-%d"),
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}).fetchone()
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session.commit()
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insight_id = row[0] if row else None
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if insight_id:
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try:
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from services.openclaw_learning_service import enqueue_insight_embedding
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enqueue_insight_embedding(insight_id, insight_type, content[:8000], period or datetime.now().strftime("%Y-%m-%d"))
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except Exception as embed_err:
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logger.warning("[OpenClaw] embedding queue enqueue failed id=%s: %s", insight_id, embed_err)
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logger.info("[OpenClaw] ai_insights 寫入成功 id=%s type=%s", insight_id, insight_type)
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return insight_id
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except Exception as e:
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logger.error("[OpenClaw] ai_insights 寫入失敗: %s", e)
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session.rollback()
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return None
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finally:
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session.close()
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def _save_action_items(actions: List[str], source_insight_id: Optional[int]) -> None:
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"""將 AI 建議的行動項目寫入 action_plans"""
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if not actions:
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return
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session = get_session()
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try:
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for i, action in enumerate(actions[:10]):
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session.execute(text("""
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INSERT INTO action_plans
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(action_type, description, status, priority, metadata_json, created_at)
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VALUES ('openclaw_recommendation', :desc, 'pending', :priority, :meta, NOW())
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"""), {
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"desc": action[:500],
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"priority": i + 1,
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"meta": json.dumps({"source_insight_id": source_insight_id, "created_by": "openclaw"}),
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})
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session.commit()
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logger.info("[OpenClaw] action_plans 寫入 %d 筆", len(actions[:10]))
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except Exception as e:
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logger.warning("[OpenClaw] action_plans 寫入失敗: %s", e)
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session.rollback()
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finally:
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session.close()
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# ═══════════════════════════════════════════════════════════════════════════════
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# Gemini 呼叫層
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# ═══════════════════════════════════════════════════════════════════════════════
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def _call_gemini(system_prompt: str, user_prompt: str, temperature: float = 0.4) -> Optional[str]:
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"""呼叫 Gemini，回傳文字；失敗回傳 None"""
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if not GEMINI_API_KEY:
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logger.warning("[OpenClaw] GEMINI_API_KEY 未設定")
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return None
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try:
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import google.generativeai as genai
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genai.configure(api_key=GEMINI_API_KEY)
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model = genai.GenerativeModel(
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model_name=STRATEGY_MODEL,
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generation_config=genai.types.GenerationConfig(
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temperature=temperature,
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max_output_tokens=4096,
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),
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system_instruction=system_prompt,
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)
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response = model.generate_content(
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user_prompt,
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request_options={"timeout": 180},
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)
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return response.text or ""
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except Exception as e:
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logger.error("[OpenClaw] Gemini 呼叫失敗: %s", e)
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return None
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def _call_nvidia_nim(system_prompt: str, user_prompt: str, temperature: float = 0.5) -> Optional[str]:
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"""Gemini 離線時備援 NVIDIA NIM，回傳文字；失敗回傳 None"""
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if not NVIDIA_API_KEY:
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return None
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try:
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resp = requests.post(
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NVIDIA_NIM_URL,
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headers={
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"Authorization": f"Bearer {NVIDIA_API_KEY}",
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"Content-Type": "application/json",
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},
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json={
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"model": NVIDIA_FALLBACK_MODEL,
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": temperature,
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"max_tokens": 1024,
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},
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timeout=60,
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)
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resp.raise_for_status()
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return resp.json()["choices"][0]["message"]["content"]
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except Exception as e:
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logger.error("[OpenClaw] NVIDIA NIM 呼叫失敗: %s", e)
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return None
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# ═══════════════════════════════════════════════════════════════════════════════
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# Telegram 推播
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# ═══════════════════════════════════════════════════════════════════════════════
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def _send_strategy_telegram(title: str, report_type: str, period: str, content: str) -> None:
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try:
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from services.telegram_templates import report as tpl_report, _send_telegram_raw
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# Telegram 訊息長度限制 4096，分段發送
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header = tpl_report(title, report_type, period, "")
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chunks = _split_message(content, max_len=3800 - len(header))
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for i, chunk in enumerate(chunks):
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msg = tpl_report(title, report_type, period, chunk) if i == 0 else chunk
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_send_telegram_raw(msg)
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except Exception as e:
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logger.error("[OpenClaw] Telegram 推播失敗: %s", e)
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def _split_message(text: str, max_len: int = 3800) -> List[str]:
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if len(text) <= max_len:
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return [text]
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chunks = []
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while text:
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chunks.append(text[:max_len])
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text = text[max_len:]
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return chunks
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# ═══════════════════════════════════════════════════════════════════════════════
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# 主要公開函式
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# ═══════════════════════════════════════════════════════════════════════════════
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def generate_weekly_strategy_report(
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context: Optional[Any] = None,
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force_tg_alert: bool = False,
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) -> dict:
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"""
|
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OpenClaw 全景電商週報（每週一 06:00）
|
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流程：
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1. 讀取 DB：業績 / 競品 / 威脅 / 建議 / 品類
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2. MCP 收集：外部市場趨勢 / 節日 / 競品動態
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3. Gemini 2.5 Flash 深度分析
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4. 持久化 → ai_insights + action_plans
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5. Telegram 推播
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"""
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now = datetime.now()
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period = f"{now.strftime('%Y年第%W週')} ({now.strftime('%m/%d')})"
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||
logger.info("[OpenClaw] 週報任務啟動 period=%s", period)
|
||
|
||
# ── Step 1：DB 數據收集 ──────────────────────────────────────────────────
|
||
sales = _fetch_sales_summary(14)
|
||
threats = _fetch_top_threats(10)
|
||
recommendations = _fetch_top_recommendations(10)
|
||
categories = _fetch_category_breakdown(7)
|
||
competitor_summary = _fetch_competitor_summary()
|
||
|
||
# ── Step 2：MCP 外部情報 ─────────────────────────────────────────────────
|
||
mcp_data: Dict[str, str] = {}
|
||
try:
|
||
from services.mcp_collector_service import mcp_collector
|
||
mcp_data = mcp_collector.collect_all()
|
||
holiday_ctx = mcp_collector.get_holiday_context()
|
||
seasonal_ctx = mcp_collector.get_seasonal_context()
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] MCP 收集失敗（非阻塞）: %s", e)
|
||
holiday_ctx = ""
|
||
seasonal_ctx = ""
|
||
|
||
# ── Step 3：組建 Gemini Prompt ───────────────────────────────────────────
|
||
system_prompt = """你是 OpenClaw，一位台灣頂尖電商戰略分析師，專精於 momo 購物平台。
|
||
你的任務是根據真實業績數據、競品情報、外部市場趨勢，產出一份具體可執行的週報。
|
||
|
||
語言規定：
|
||
- 所有輸出必須使用繁體中文（台灣用語）
|
||
- 數字格式：金額用 NT$ 標示，百分比保留1位小數
|
||
- 語氣：專業但不失親切，適合匯報給電商運營主管
|
||
|
||
分析原則：
|
||
- 每個洞察必須有數據支撐，禁止憑空推測
|
||
- 建議必須具體（時間、對象、行動、預期效益）
|
||
- 優先關注「可在 48 小時內執行」的行動項目"""
|
||
|
||
db_section = f"""
|
||
【DB 即時數據】
|
||
業績概況：
|
||
本週營收：NT${sales.get('current_7d_revenue', 0):,.0f}
|
||
前週營收：NT${sales.get('prev_7d_revenue', 0):,.0f}
|
||
週成長率：{sales.get('wow_pct', 0):+.1f}%
|
||
|
||
競品比對概況：
|
||
監控SKU總數：{competitor_summary.get('total_skus', 0)}
|
||
平均價差：{competitor_summary.get('avg_gap_pct', 0):+.1f}%
|
||
被競品削價數：{competitor_summary.get('undercut_count', 0)} 個
|
||
我方具優勢數：{competitor_summary.get('premium_count', 0)} 個
|
||
|
||
TOP 威脅品項（近48h Hermes 偵測）：
|
||
{_format_threats(threats)}
|
||
|
||
待處理定價建議：
|
||
{_format_recommendations(recommendations)}
|
||
|
||
品類業績分佈（本週）：
|
||
{_format_categories(categories)}
|
||
"""
|
||
|
||
mcp_section = f"""
|
||
【MCP 外部情報】
|
||
市場趨勢：
|
||
{mcp_data.get('market_trends', '（未取得）')[:600]}
|
||
|
||
競品動態：
|
||
{mcp_data.get('competitor_intel', '（未取得）')[:500]}
|
||
|
||
消費者情緒：
|
||
{mcp_data.get('consumer_sentiment', '（未取得）')[:400]}
|
||
|
||
定價策略參考：
|
||
{mcp_data.get('pricing_strategy', '（未取得）')[:400]}
|
||
|
||
節日行事曆：
|
||
{holiday_ctx}
|
||
{mcp_data.get('holiday_calendar', '')[:300]}
|
||
|
||
季節性洞察：
|
||
{seasonal_ctx}
|
||
{mcp_data.get('seasonal_insights', '')[:300]}
|
||
"""
|
||
|
||
user_prompt = f"""請根據以下數據，產出本週電商全景戰略週報：
|
||
|
||
{db_section}
|
||
{mcp_section}
|
||
|
||
請按以下結構輸出（每節使用 HTML <b> 標題，內容精簡扼要）：
|
||
|
||
<b>📊 本週業績總結</b>
|
||
（關鍵指標 + WoW 變化 + 異常警示）
|
||
|
||
<b>🏆 TOP 機會品項</b>
|
||
（具備提價或強推空間的品項，3-5個，含具體建議）
|
||
|
||
<b>⚠️ TOP 威脅品項</b>
|
||
（最需緊急處理的競品削價風險，3-5個，含建議行動）
|
||
|
||
<b>💰 本週定價策略建議</b>
|
||
（整體定價方向 + 品類重點調整 + 心理定價應用）
|
||
|
||
<b>📢 行銷活動洞察</b>
|
||
（節日/季節機會 + 推薦活動形式 + 投放時機）
|
||
|
||
<b>📦 品類熱度分析</b>
|
||
（成長品類 vs 衰退品類 + 庫存備貨建議）
|
||
|
||
<b>🔮 市場競爭洞察</b>
|
||
（競品最新動態 + 平台策略差異 + 我方應對）
|
||
|
||
<b>🎯 48小時優先行動清單</b>
|
||
（5-8條具體可執行任務，格式：[優先度] 行動說明 → 預期效益）
|
||
|
||
<b>📈 下週展望</b>
|
||
（風險提示 + 機會預告 + 需人工決策事項）
|
||
|
||
重要：語言必須是繁體中文，數據必須引用上方提供的實際數字。
|
||
"""
|
||
|
||
# ── Step 4：Gemini 生成 ───────────────────────────────────────────────────
|
||
logger.info("[OpenClaw] 呼叫 Gemini %s 生成週報...", STRATEGY_MODEL)
|
||
report_content = _call_gemini(system_prompt, user_prompt, temperature=0.35)
|
||
|
||
if not report_content:
|
||
logger.error("[OpenClaw] Gemini 週報生成失敗")
|
||
return {"status": "error", "report_type": "weekly_strategy", "error": "Gemini 呼叫失敗"}
|
||
|
||
# ── Step 5：解析行動清單 ─────────────────────────────────────────────────
|
||
action_items = _extract_action_items(report_content)
|
||
|
||
# ── Step 6：持久化 DB ────────────────────────────────────────────────────
|
||
metadata = {
|
||
"period": period,
|
||
"model": STRATEGY_MODEL,
|
||
"wow_pct": sales.get("wow_pct", 0),
|
||
"threat_count": len(threats),
|
||
"recommendation_count": len(recommendations),
|
||
"mcp_topics_collected": sum(1 for v in mcp_data.values() if v),
|
||
"action_count": len(action_items),
|
||
"generated_at": now.isoformat(),
|
||
}
|
||
insight_id = _save_to_ai_insights(
|
||
insight_type="weekly_strategy",
|
||
content=report_content,
|
||
confidence=0.88,
|
||
metadata=metadata,
|
||
period=now.strftime("%Y-%W"),
|
||
)
|
||
_save_action_items(action_items, insight_id)
|
||
|
||
# ── Step 7：Telegram 推播 ────────────────────────────────────────────────
|
||
if force_tg_alert or True:
|
||
_send_strategy_telegram(
|
||
title="OpenClaw 電商全景週報",
|
||
report_type="weekly_strategy",
|
||
period=period,
|
||
content=report_content,
|
||
)
|
||
|
||
logger.info("[OpenClaw] 週報完成 insight_id=%s actions=%d", insight_id, len(action_items))
|
||
return {
|
||
"status": "ok",
|
||
"report_type": "weekly_strategy",
|
||
"insight_id": insight_id,
|
||
"period": period,
|
||
"action_count": len(action_items),
|
||
"summary": report_content[:300],
|
||
}
|
||
|
||
|
||
def generate_daily_report() -> dict:
|
||
"""
|
||
OpenClaw 電商日報（每日 09:00）
|
||
|
||
流程：
|
||
1. 讀取昨日業績快照 + TOP 競品威脅 + 定價建議
|
||
2. Gemini 快速日報分析（溫度 0.3，精簡版）
|
||
3. 生成圖表：近7日營收趨勢 + 競品價差柱圖
|
||
4. 持久化 ai_insights（type='daily_report'）
|
||
5. Telegram 圖文推播
|
||
"""
|
||
now = datetime.now()
|
||
yesterday = now - timedelta(days=1)
|
||
period = yesterday.strftime("%Y年%m月%d日")
|
||
logger.info("[OpenClaw] 日報任務啟動 period=%s", period)
|
||
|
||
# ── Step 1：DB 數據收集 ──────────────────────────────────────────────────
|
||
sales = _fetch_sales_summary(7)
|
||
threats = _fetch_top_threats(5)
|
||
recommendations = _fetch_top_recommendations(5)
|
||
competitor_summary = _fetch_competitor_summary()
|
||
|
||
# 昨日單日業績
|
||
yesterday_sales = _fetch_yesterday_sales()
|
||
|
||
# ── Step 2：組建 Gemini Prompt ───────────────────────────────────────────
|
||
system_prompt = """你是 OpenClaw 日報分析師，負責每日電商業績快報。
|
||
語言：繁體中文（台灣用語）。風格：精簡、數字導向、可執行。
|
||
每個洞察必須有數字支撐，禁止空泛描述。"""
|
||
|
||
user_prompt = f"""請根據以下數據，產出今日電商日報（{period}）：
|
||
|
||
【昨日業績】
|
||
銷售金額：NT${yesterday_sales.get('revenue', 0):,.0f}
|
||
成交SKU數：{yesterday_sales.get('sku_count', 0)} 個
|
||
訂單數：{yesterday_sales.get('order_count', 0)} 筆
|
||
|
||
【近7日趨勢】
|
||
本週累計：NT${sales.get('current_7d_revenue', 0):,.0f}
|
||
前週同期：NT${sales.get('prev_7d_revenue', 0):,.0f}
|
||
WoW變化：{sales.get('wow_pct', 0):+.1f}%
|
||
|
||
【競品警示（近24h Hermes偵測）】
|
||
{_format_threats(threats)}
|
||
|
||
【待處理定價建議（TOP 5）】
|
||
{_format_recommendations(recommendations)}
|
||
|
||
【競品整體概況】
|
||
監控SKU：{competitor_summary.get('total_skus', 0)} 個
|
||
被削價風險：{competitor_summary.get('undercut_count', 0)} 個（價差超過10%）
|
||
平均價差：{competitor_summary.get('avg_gap_pct', 0):+.1f}%
|
||
|
||
請按以下結構輸出（使用 HTML <b> 標題）：
|
||
|
||
<b>📅 {period} 電商日報</b>
|
||
|
||
<b>📊 昨日業績快報</b>
|
||
（昨日關鍵數字 + 與近期均值比較 + 異常點說明）
|
||
|
||
<b>⚠️ 今日最高優先威脅</b>
|
||
（最緊急的 1-3 個競品削價威脅，含具體行動建議）
|
||
|
||
<b>💰 今日定價行動建議</b>
|
||
（1-3 條今日應執行的調價動作，格式：SKU → 建議行動 → 預期效果）
|
||
|
||
<b>🎯 今日 3 件事</b>
|
||
（最重要的 3 件可執行任務，24h內完成）
|
||
|
||
語言：繁體中文，全文200字以內，精準扼要。
|
||
"""
|
||
|
||
# ── Step 3：Gemini 生成 ───────────────────────────────────────────────────
|
||
logger.info("[OpenClaw] 呼叫 Gemini 生成日報...")
|
||
report_content = _call_gemini(system_prompt, user_prompt, temperature=0.3)
|
||
|
||
if not report_content:
|
||
logger.error("[OpenClaw] 日報 Gemini 呼叫失敗")
|
||
return {"status": "error", "report_type": "daily_report", "error": "Gemini 呼叫失敗"}
|
||
|
||
# ── Step 4：生成圖表 ─────────────────────────────────────────────────────
|
||
charts = []
|
||
try:
|
||
from services.chart_generator_service import (
|
||
revenue_trend_chart,
|
||
price_gap_bar_chart,
|
||
)
|
||
rev_chart = revenue_trend_chart(7, "近7日")
|
||
if rev_chart:
|
||
charts.append(("revenue_7d.png", rev_chart, "📈 近7日營收趨勢"))
|
||
|
||
if threats:
|
||
gap_chart = price_gap_bar_chart(threats, "競品價差警示（TOP 5）")
|
||
if gap_chart:
|
||
charts.append(("price_gap.png", gap_chart, "⚠️ 競品價差分析"))
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] 日報圖表生成失敗（非阻塞）: %s", e)
|
||
|
||
# ── Step 5：持久化 DB ────────────────────────────────────────────────────
|
||
metadata = {
|
||
"period": period,
|
||
"model": STRATEGY_MODEL,
|
||
"yesterday_revenue": yesterday_sales.get("revenue", 0),
|
||
"wow_pct": sales.get("wow_pct", 0),
|
||
"threat_count": len(threats),
|
||
"chart_count": len(charts),
|
||
"generated_at": now.isoformat(),
|
||
}
|
||
insight_id = _save_to_ai_insights(
|
||
insight_type="daily_report",
|
||
content=report_content,
|
||
confidence=0.85,
|
||
metadata=metadata,
|
||
period=yesterday.strftime("%Y-%m-%d"),
|
||
)
|
||
action_items = _extract_action_items_daily(report_content)
|
||
_save_action_items(action_items, insight_id)
|
||
|
||
# ── Step 6：Telegram 推播（圖文）────────────────────────────────────────
|
||
try:
|
||
from services.telegram_templates import (
|
||
daily_report_header,
|
||
send_report_with_charts,
|
||
_get_chat_ids,
|
||
)
|
||
header = daily_report_header(
|
||
date_str=period,
|
||
revenue=yesterday_sales.get("revenue", 0),
|
||
wow=sales.get("wow_pct", 0),
|
||
threat_count=len(threats),
|
||
opportunity_count=competitor_summary.get("premium_count", 0),
|
||
)
|
||
full_msg = header + "\n\n" + report_content
|
||
if charts:
|
||
send_report_with_charts(full_msg, charts, _get_chat_ids())
|
||
else:
|
||
from services.telegram_templates import _send_telegram_raw
|
||
_send_telegram_raw(full_msg)
|
||
except Exception as e:
|
||
logger.error("[OpenClaw] 日報 Telegram 推播失敗: %s", e)
|
||
|
||
logger.info("[OpenClaw] 日報完成 insight_id=%s charts=%d", insight_id, len(charts))
|
||
return {
|
||
"status": "ok",
|
||
"report_type": "daily_report",
|
||
"insight_id": insight_id,
|
||
"period": period,
|
||
"chart_count": len(charts),
|
||
"action_count": len(action_items),
|
||
}
|
||
|
||
|
||
def generate_monthly_report() -> dict:
|
||
"""
|
||
OpenClaw 電商月報（每月1日 07:00）
|
||
|
||
流程：
|
||
1. 讀取上月完整業績 + 品類分佈 + 競品趨勢
|
||
2. MCP 收集月度外部情報
|
||
3. Gemini 深度月度分析（完整版）
|
||
4. 生成圖表：月度概覽 + 品類橫條 + 價格熱圖
|
||
5. 持久化 ai_insights（type='monthly_report'）
|
||
6. Telegram 圖文推播
|
||
"""
|
||
now = datetime.now()
|
||
# 上個月
|
||
first_of_this_month = now.replace(day=1)
|
||
last_month_end = first_of_this_month - timedelta(days=1)
|
||
last_month_start = last_month_end.replace(day=1)
|
||
period = last_month_end.strftime("%Y年%m月")
|
||
logger.info("[OpenClaw] 月報任務啟動 period=%s", period)
|
||
|
||
# ── Step 1：DB 數據收集（上月完整數據）─────────────────────────────────
|
||
days_in_month = (first_of_this_month - last_month_start).days
|
||
sales = _fetch_monthly_sales_summary(last_month_start, last_month_end)
|
||
categories = _fetch_category_breakdown(days_in_month)
|
||
threats = _fetch_top_threats(10)
|
||
competitor_summary = _fetch_competitor_summary()
|
||
price_trend_data = _fetch_price_trend_summary(days_in_month)
|
||
|
||
# ── Step 2：MCP 外部情報（月度版）───────────────────────────────────────
|
||
mcp_data: Dict[str, str] = {}
|
||
try:
|
||
from services.mcp_collector_service import mcp_collector
|
||
mcp_data = mcp_collector.collect_all()
|
||
holiday_ctx = mcp_collector.get_holiday_context()
|
||
seasonal_ctx = mcp_collector.get_seasonal_context()
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] 月報 MCP 收集失敗（非阻塞）: %s", e)
|
||
holiday_ctx = ""
|
||
seasonal_ctx = ""
|
||
|
||
# ── Step 3：組建 Gemini Prompt ───────────────────────────────────────────
|
||
system_prompt = """你是 OpenClaw 月報首席分析師，負責 momo 平台電商月度深度報告。
|
||
語言：繁體中文（台灣用語）。格式：HTML標題 + 條列式數據。
|
||
每個洞察必須有月度數字支撐，並與上月/去年同期比較。
|
||
重點：月度趨勢、品類策略、定價最佳化、下月行動計畫。"""
|
||
|
||
db_section = f"""
|
||
【{period} 業績總覽】
|
||
月營收：NT${sales.get('revenue', 0):,.0f}
|
||
MoM 變化：{sales.get('mom_pct', 0):+.1f}%
|
||
YoY 變化：{sales.get('yoy_pct', 0):+.1f}%
|
||
活躍SKU數：{sales.get('sku_count', 0)} 個
|
||
平均客單價：NT${sales.get('avg_order_value', 0):,.0f}
|
||
|
||
【品類業績分佈（TOP 10）】
|
||
{_format_categories(categories)}
|
||
|
||
【競品整體概況】
|
||
監控SKU：{competitor_summary.get('total_skus', 0)} 個
|
||
月均價差：{competitor_summary.get('avg_gap_pct', 0):+.1f}%
|
||
被削價風險SKU：{competitor_summary.get('undercut_count', 0)} 個
|
||
|
||
【價格變動概況】
|
||
本月調價次數：{price_trend_data.get('price_changes', 0)} 次
|
||
平均調幅：{price_trend_data.get('avg_change_pct', 0):+.1f}%
|
||
主動降價SKU：{price_trend_data.get('price_cuts', 0)} 個
|
||
主動提價SKU：{price_trend_data.get('price_raises', 0)} 個
|
||
"""
|
||
|
||
mcp_section = f"""
|
||
【MCP 外部情報（月度）】
|
||
市場趨勢：
|
||
{mcp_data.get('market_trends', '（未取得）')[:600]}
|
||
|
||
競品動態：
|
||
{mcp_data.get('competitor_intel', '（未取得）')[:500]}
|
||
|
||
下月節日行事曆：
|
||
{holiday_ctx}
|
||
{mcp_data.get('holiday_calendar', '')[:400]}
|
||
|
||
季節性洞察：
|
||
{seasonal_ctx}
|
||
{mcp_data.get('seasonal_insights', '')[:400]}
|
||
"""
|
||
|
||
user_prompt = f"""請根據以下數據，產出 {period} 電商月度策略報告：
|
||
|
||
{db_section}
|
||
{mcp_section}
|
||
|
||
請按以下結構輸出（使用 HTML <b> 標題，詳細分析）：
|
||
|
||
<b>📅 {period} 電商月度報告</b>
|
||
|
||
<b>📊 月度業績總結</b>
|
||
（月營收 + MoM/YoY 變化 + 超出/低於預期分析 + 關鍵驅動因素）
|
||
|
||
<b>🏆 本月品類贏家 vs 輸家</b>
|
||
（成長最快3個品類 vs 衰退最嚴重3個品類，含原因分析）
|
||
|
||
<b>💰 本月定價策略回顧</b>
|
||
（調價效果評估 + 最佳定價案例 + 失誤案例 + 改進建議）
|
||
|
||
<b>⚔️ 競品月度分析</b>
|
||
（主要競爭對手動態 + 我方優劣勢 + 市場份額評估）
|
||
|
||
<b>📢 行銷效益評估</b>
|
||
（本月活動效果 + ROI 估算 + 最佳行銷時機分析）
|
||
|
||
<b>🔮 下月趨勢預測</b>
|
||
（季節性機會 + 節日活動規劃 + 風險預警 + 庫存建議）
|
||
|
||
<b>🎯 下月優先行動計畫</b>
|
||
（8-10條具體可執行任務，含時間節點和負責方向，
|
||
格式：[週次/日期] 行動說明 → 預期效益）
|
||
|
||
<b>📈 Q{((now.month-1)//3)+1} 策略展望</b>
|
||
（季度目標設定 + 關鍵里程碑 + 需人工決策事項）
|
||
|
||
語言：繁體中文，數據必須引用上方提供的實際數字。
|
||
"""
|
||
|
||
# ── Step 4：Gemini 生成 ───────────────────────────────────────────────────
|
||
logger.info("[OpenClaw] 呼叫 Gemini 生成月報...")
|
||
report_content = _call_gemini(system_prompt, user_prompt, temperature=0.35)
|
||
|
||
if not report_content:
|
||
logger.error("[OpenClaw] 月報 Gemini 呼叫失敗")
|
||
return {"status": "error", "report_type": "monthly_report", "error": "Gemini 呼叫失敗"}
|
||
|
||
# ── Step 5：生成圖表 ─────────────────────────────────────────────────────
|
||
charts = []
|
||
try:
|
||
from services.chart_generator_service import (
|
||
monthly_overview_chart,
|
||
category_revenue_chart,
|
||
price_history_heatmap,
|
||
)
|
||
overview_chart = monthly_overview_chart(6)
|
||
if overview_chart:
|
||
charts.append(("monthly_overview.png", overview_chart, f"📊 近6個月營收趨勢"))
|
||
|
||
cat_chart = category_revenue_chart(days_in_month, period)
|
||
if cat_chart:
|
||
charts.append(("category_revenue.png", cat_chart, "🏆 品類業績分佈"))
|
||
|
||
heatmap = price_history_heatmap(days_in_month)
|
||
if heatmap:
|
||
charts.append(("price_heatmap.png", heatmap, "🔥 品類價格熱圖"))
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] 月報圖表生成失敗（非阻塞）: %s", e)
|
||
|
||
# ── Step 6：持久化 DB ────────────────────────────────────────────────────
|
||
action_items = _extract_action_items(report_content)
|
||
metadata = {
|
||
"period": period,
|
||
"model": STRATEGY_MODEL,
|
||
"monthly_revenue": sales.get("revenue", 0),
|
||
"mom_pct": sales.get("mom_pct", 0),
|
||
"yoy_pct": sales.get("yoy_pct", 0),
|
||
"category_count": len(categories),
|
||
"chart_count": len(charts),
|
||
"mcp_topics_collected": sum(1 for v in mcp_data.values() if v),
|
||
"action_count": len(action_items),
|
||
"generated_at": now.isoformat(),
|
||
}
|
||
insight_id = _save_to_ai_insights(
|
||
insight_type="monthly_report",
|
||
content=report_content,
|
||
confidence=0.90,
|
||
metadata=metadata,
|
||
period=last_month_end.strftime("%Y-%m"),
|
||
)
|
||
_save_action_items(action_items, insight_id)
|
||
|
||
# ── Step 7：Telegram 推播（圖文）────────────────────────────────────────
|
||
try:
|
||
from services.telegram_templates import (
|
||
monthly_report_header,
|
||
send_report_with_charts,
|
||
_get_chat_ids,
|
||
)
|
||
top3 = [c.get("category", "N/A") for c in categories[:3]] or ["N/A"]
|
||
header = monthly_report_header(
|
||
month_str=period,
|
||
revenue=sales.get("revenue", 0),
|
||
mom=sales.get("mom_pct", 0),
|
||
yoy=sales.get("yoy_pct", 0),
|
||
top3_categories=top3,
|
||
)
|
||
full_msg = header + "\n\n" + report_content
|
||
if charts:
|
||
send_report_with_charts(full_msg, charts, _get_chat_ids())
|
||
else:
|
||
from services.telegram_templates import _send_telegram_raw
|
||
_send_telegram_raw(full_msg)
|
||
except Exception as e:
|
||
logger.error("[OpenClaw] 月報 Telegram 推播失敗: %s", e)
|
||
|
||
logger.info("[OpenClaw] 月報完成 insight_id=%s charts=%d actions=%d",
|
||
insight_id, len(charts), len(action_items))
|
||
return {
|
||
"status": "ok",
|
||
"report_type": "monthly_report",
|
||
"insight_id": insight_id,
|
||
"period": period,
|
||
"chart_count": len(charts),
|
||
"action_count": len(action_items),
|
||
}
|
||
|
||
|
||
def generate_meta_analysis_report() -> str:
|
||
"""
|
||
AI 系統效能自我審視（每 6 小時 run_openclaw_meta_analysis_task 呼叫）
|
||
|
||
分析 ai_insights 近期累積資料，評估：
|
||
- 各 Agent 預測準確率
|
||
- 價格建議執行率
|
||
- 告警品質與誤報率
|
||
- 系統盲區與改進方向
|
||
|
||
結果持久化至 ai_insights（type='meta_analysis'），並推播 Telegram。
|
||
"""
|
||
now = datetime.now()
|
||
period = now.strftime("%Y-%m-%d %H:00")
|
||
logger.info("[OpenClaw] Meta-Analysis 任務啟動 %s", period)
|
||
|
||
# ── 讀取近期 ai_insights 摘要 ────────────────────────────────────────────
|
||
session = get_session()
|
||
try:
|
||
stats = session.execute(text("""
|
||
SELECT
|
||
insight_type,
|
||
created_by,
|
||
COUNT(*) AS total,
|
||
AVG(confidence) AS avg_conf,
|
||
SUM(CASE WHEN status='active' THEN 1 ELSE 0 END) AS active_cnt,
|
||
SUM(CASE WHEN status='relearn' THEN 1 ELSE 0 END) AS relearn_cnt,
|
||
MAX(created_at) AS latest
|
||
FROM ai_insights
|
||
WHERE created_at >= NOW() - INTERVAL '24 hours'
|
||
GROUP BY insight_type, created_by
|
||
ORDER BY total DESC
|
||
""")).fetchall()
|
||
|
||
action_stats = session.execute(text("""
|
||
SELECT status, COUNT(*) AS cnt
|
||
FROM action_plans
|
||
WHERE created_at >= NOW() - INTERVAL '24 hours'
|
||
GROUP BY status
|
||
""")).fetchall()
|
||
|
||
reco_stats = session.execute(text("""
|
||
SELECT status, COUNT(*) AS cnt, AVG(confidence) AS avg_conf
|
||
FROM ai_price_recommendations
|
||
WHERE created_at >= NOW() - INTERVAL '24 hours'
|
||
GROUP BY status
|
||
""")).fetchall()
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] Meta 數據讀取失敗: %s", e)
|
||
stats, action_stats, reco_stats = [], [], []
|
||
finally:
|
||
session.close()
|
||
|
||
# ── 組建 Prompt ───────────────────────────────────────────────────────────
|
||
system_prompt = """你是 OpenClaw 自我審視模組，負責分析 AI 多智能體系統的近期表現。
|
||
請用繁體中文，以電商 AI 系統架構師的視角撰寫分析報告，語氣客觀、聚焦問題與改進。"""
|
||
|
||
stats_text = "\n".join([
|
||
f" {r[0]} ({r[1]}): 共{r[2]}筆, 平均信心{r[3]:.2f}, 活躍{r[4]}, 重學{r[5]}"
|
||
for r in stats
|
||
]) or " （無近期數據）"
|
||
|
||
action_text = "\n".join([
|
||
f" {r[0]}: {r[1]} 筆" for r in action_stats
|
||
]) or " （無近期數據）"
|
||
|
||
reco_text = "\n".join([
|
||
f" {r[0]}: {r[1]} 筆, 平均信心{r[2]:.2f}" for r in reco_stats
|
||
]) or " （無近期數據）"
|
||
|
||
user_prompt = f"""請分析以下 AI 系統近 24 小時運作數據，產出自我審視報告：
|
||
|
||
【ai_insights 產出統計】
|
||
{stats_text}
|
||
|
||
【action_plans 執行狀況】
|
||
{action_text}
|
||
|
||
【ai_price_recommendations 建議狀況】
|
||
{reco_text}
|
||
|
||
【分析時間】{period}
|
||
|
||
請按以下結構輸出：
|
||
|
||
<b>🤖 AI 系統效能自我審視報告</b>
|
||
時間：{period}
|
||
|
||
<b>📊 各 Agent 產出統計</b>
|
||
（逐一評估 Hermes/NemoTron/OpenClaw/ElephantAlpha 的輸出品質）
|
||
|
||
<b>⚠️ 偵測到的系統問題</b>
|
||
（誤報、漏報、重學事件分析）
|
||
|
||
<b>💡 盲區與改進建議</b>
|
||
（哪些場景 AI 表現不足？建議優化方向）
|
||
|
||
<b>✅ 本週期亮點</b>
|
||
（表現良好的分析案例）
|
||
|
||
<b>🔧 技術債與優化優先順序</b>
|
||
（1-3 項具體技術改進建議）
|
||
|
||
語言：繁體中文，200字以內，精簡扼要。
|
||
"""
|
||
|
||
# ── Gemini 生成 ──────────────────────────────────────────────────────────
|
||
report_content = _call_gemini(system_prompt, user_prompt, temperature=0.3)
|
||
if not report_content:
|
||
logger.error("[OpenClaw] Meta-Analysis Gemini 呼叫失敗")
|
||
return "（Meta-Analysis 生成失敗）"
|
||
|
||
# ── 持久化 DB ─────────────────────────────────────────────────────────────
|
||
metadata = {
|
||
"period": period,
|
||
"model": STRATEGY_MODEL,
|
||
"insight_types_analyzed": len(stats),
|
||
"generated_at": now.isoformat(),
|
||
}
|
||
insight_id = _save_to_ai_insights(
|
||
insight_type="meta_analysis",
|
||
content=report_content,
|
||
confidence=0.85,
|
||
metadata=metadata,
|
||
period=now.strftime("%Y-%m-%d"),
|
||
)
|
||
|
||
# ── Telegram 推播 ────────────────────────────────────────────────────────
|
||
try:
|
||
from services.telegram_templates import _send_telegram_raw
|
||
_send_telegram_raw(report_content)
|
||
except Exception as e:
|
||
logger.error("[OpenClaw] Meta-Analysis Telegram 推播失敗: %s", e)
|
||
|
||
logger.info("[OpenClaw] Meta-Analysis 完成 insight_id=%s", insight_id)
|
||
return report_content
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
# 輔助格式化函式
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
|
||
def _fetch_yesterday_sales() -> Dict[str, Any]:
|
||
"""昨日單日業績"""
|
||
session = get_session()
|
||
try:
|
||
row = session.execute(text("""
|
||
SELECT
|
||
SUM(COALESCE("銷售金額"::numeric, 0)) AS revenue,
|
||
COUNT(DISTINCT "商品ID") AS sku_count,
|
||
COUNT(*) AS order_count
|
||
FROM daily_sales_snapshot
|
||
WHERE snapshot_date::date = CURRENT_DATE - 1
|
||
""")).fetchone()
|
||
if row:
|
||
return {
|
||
"revenue": float(row[0] or 0),
|
||
"sku_count": int(row[1] or 0),
|
||
"order_count": int(row[2] or 0),
|
||
}
|
||
return {"revenue": 0, "sku_count": 0, "order_count": 0}
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] 昨日業績讀取失敗: %s", e)
|
||
return {"revenue": 0, "sku_count": 0, "order_count": 0}
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def _fetch_monthly_sales_summary(start_date: datetime, end_date: datetime) -> Dict[str, Any]:
|
||
"""上月業績彙總，含 MoM / YoY 比較"""
|
||
session = get_session()
|
||
try:
|
||
row = session.execute(text("""
|
||
SELECT
|
||
SUM(COALESCE("銷售金額"::numeric, 0)) AS revenue,
|
||
COUNT(DISTINCT "商品ID") AS sku_count,
|
||
COUNT(*) AS order_count,
|
||
AVG(COALESCE("銷售金額"::numeric, 0)) AS avg_order_value
|
||
FROM daily_sales_snapshot
|
||
WHERE snapshot_date::date BETWEEN :start AND :end
|
||
"""), {"start": start_date.date(), "end": end_date.date()}).fetchone()
|
||
|
||
revenue = float(row[0] or 0) if row else 0
|
||
sku_count = int(row[1] or 0) if row else 0
|
||
avg_order_value = float(row[3] or 0) if row else 0
|
||
|
||
# 上上月（MoM）
|
||
prev_start = (start_date - timedelta(days=1)).replace(day=1)
|
||
prev_end = start_date - timedelta(days=1)
|
||
prev_row = session.execute(text("""
|
||
SELECT SUM(COALESCE("銷售金額"::numeric, 0)) AS revenue
|
||
FROM daily_sales_snapshot
|
||
WHERE snapshot_date::date BETWEEN :start AND :end
|
||
"""), {"start": prev_start.date(), "end": prev_end.date()}).fetchone()
|
||
prev_revenue = float(prev_row[0] or 0) if prev_row else 0
|
||
mom_pct = ((revenue - prev_revenue) / prev_revenue * 100) if prev_revenue else 0
|
||
|
||
# 去年同月（YoY）
|
||
yoy_start = start_date.replace(year=start_date.year - 1)
|
||
yoy_end = end_date.replace(year=end_date.year - 1)
|
||
yoy_row = session.execute(text("""
|
||
SELECT SUM(COALESCE("銷售金額"::numeric, 0)) AS revenue
|
||
FROM daily_sales_snapshot
|
||
WHERE snapshot_date::date BETWEEN :start AND :end
|
||
"""), {"start": yoy_start.date(), "end": yoy_end.date()}).fetchone()
|
||
yoy_revenue = float(yoy_row[0] or 0) if yoy_row else 0
|
||
yoy_pct = ((revenue - yoy_revenue) / yoy_revenue * 100) if yoy_revenue else 0
|
||
|
||
return {
|
||
"revenue": revenue,
|
||
"sku_count": sku_count,
|
||
"order_count": int(row[2] or 0) if row else 0,
|
||
"avg_order_value": avg_order_value,
|
||
"mom_pct": round(mom_pct, 1),
|
||
"yoy_pct": round(yoy_pct, 1),
|
||
}
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] 月度業績讀取失敗: %s", e)
|
||
return {"revenue": 0, "sku_count": 0, "order_count": 0,
|
||
"avg_order_value": 0, "mom_pct": 0, "yoy_pct": 0}
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def _fetch_price_trend_summary(days: int = 30) -> Dict[str, Any]:
|
||
"""近N天價格異動統計"""
|
||
session = get_session()
|
||
try:
|
||
row = session.execute(text("""
|
||
SELECT
|
||
COUNT(*) AS total_changes,
|
||
AVG(ABS(pr2.price - pr1.price) / pr1.price * 100) AS avg_change_pct,
|
||
SUM(CASE WHEN pr2.price < pr1.price THEN 1 ELSE 0 END) AS price_cuts,
|
||
SUM(CASE WHEN pr2.price > pr1.price THEN 1 ELSE 0 END) AS price_raises
|
||
FROM price_records pr2
|
||
JOIN price_records pr1 ON pr1.product_id = pr2.product_id
|
||
AND pr1.timestamp = (
|
||
SELECT MAX(timestamp) FROM price_records
|
||
WHERE product_id = pr2.product_id
|
||
AND timestamp < pr2.timestamp - INTERVAL '1 day'
|
||
)
|
||
WHERE pr2.timestamp >= NOW() - :days * INTERVAL '1 day'
|
||
AND ABS(pr2.price - pr1.price) / pr1.price > 0.005
|
||
"""), {"days": days}).fetchone()
|
||
if row and row[0]:
|
||
return {
|
||
"price_changes": int(row[0]),
|
||
"avg_change_pct": round(float(row[1] or 0), 1),
|
||
"price_cuts": int(row[2] or 0),
|
||
"price_raises": int(row[3] or 0),
|
||
}
|
||
return {"price_changes": 0, "avg_change_pct": 0, "price_cuts": 0, "price_raises": 0}
|
||
except Exception as e:
|
||
logger.warning("[OpenClaw] 價格趨勢統計讀取失敗: %s", e)
|
||
return {"price_changes": 0, "avg_change_pct": 0, "price_cuts": 0, "price_raises": 0}
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
def _extract_action_items_daily(report_text: str) -> List[str]:
|
||
"""從日報文字中解析「今日3件事」行動項目"""
|
||
lines = report_text.split("\n")
|
||
items = []
|
||
in_action_section = False
|
||
for line in lines:
|
||
if "今日" in line and ("3件" in line or "行動" in line or "優先" in line):
|
||
in_action_section = True
|
||
continue
|
||
if in_action_section:
|
||
stripped = line.strip()
|
||
if stripped.startswith(("•", "-", "1.", "2.", "3.", "①", "②", "③")):
|
||
items.append(stripped.lstrip("•-①②③").strip().lstrip("123.").strip())
|
||
elif stripped.startswith("<b>") and items:
|
||
break
|
||
return items[:5]
|
||
|
||
|
||
def _format_threats(threats: List[Dict]) -> str:
|
||
if not threats:
|
||
return " （無近期競價威脅）"
|
||
lines = []
|
||
for t in threats[:5]:
|
||
lines.append(
|
||
f" • SKU {t['sku']}：價差 {t.get('gap_pct', 0):+.1f}%，"
|
||
f"業績週變化 {t.get('sales_delta', 0):+.1f}%，"
|
||
f"信心 {t.get('confidence', 0):.2f}"
|
||
)
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _format_recommendations(recs: List[Dict]) -> str:
|
||
if not recs:
|
||
return " （無待處理定價建議）"
|
||
lines = []
|
||
for r in recs[:5]:
|
||
lines.append(
|
||
f" • {r.get('name', r.get('sku', ''))[:30]}：{r.get('strategy', '')}，"
|
||
f"信心 {r.get('confidence', 0):.2f}"
|
||
)
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _format_categories(cats: List[Dict]) -> str:
|
||
if not cats:
|
||
return " （無品類數據）"
|
||
lines = []
|
||
for c in cats[:5]:
|
||
lines.append(
|
||
f" • {c.get('category', '未分類')}："
|
||
f"NT${c.get('revenue', 0):,.0f}，{c.get('sku_count', 0)} 個 SKU"
|
||
)
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _extract_action_items(report_text: str) -> List[str]:
|
||
"""從報告文字中解析行動清單（48小時優先行動）"""
|
||
lines = report_text.split("\n")
|
||
items = []
|
||
in_action_section = False
|
||
for line in lines:
|
||
if "48小時" in line or "優先行動" in line:
|
||
in_action_section = True
|
||
continue
|
||
if in_action_section:
|
||
stripped = line.strip()
|
||
if stripped.startswith("•") or stripped.startswith("-") or stripped.startswith("["):
|
||
items.append(stripped.lstrip("•-").strip())
|
||
elif stripped.startswith("<b>") and items:
|
||
break
|
||
return items[:8]
|