feat(ai): 建立 PChome 銷售挑品清單

CONSTITUTION.md

@@ -2,7 +2,7 @@
 
 > 本文件定義專案開發的核心準則與不可違反的規範
 > **建立日期**: 2026-01-12
-> **當前版本**: V10.44 (Persist PChome competitor price history)
+> **當前版本**: V10.45 (AI product pick list and improved PChome matching)
 > **最後更新**: 2026-05-01
 
 ---

app.py

@@ -95,8 +95,8 @@ except Exception as e:
     sys_log.error(f"無法檢測磁碟空間: {e}")
 
 # 🚩 系統版本定義 (備份與顯示用)
-# 🚩 2026-05-01 V10.44: Persist PChome competitor price history
-SYSTEM_VERSION = "V10.44"
+# 🚩 2026-05-01 V10.45: AI product pick list and improved PChome matching
+SYSTEM_VERSION = "V10.45"
 
 # ==========================================
 # 🔒 SQL Injection 防護函數

docs/AI_INTELLIGENCE_MODULE_SOT.md

@@ -1,6 +1,6 @@
 # MOMO PRO — AI 競價情報模組 Single Source of Truth
 
-> **最後更新**: 2026-04-30 (台北時間)
+> **最後更新**: 2026-05-01 (台北時間)
 > **狀態**: 🟢 四 AI Agent 自動化閉環已落地 — EventRouter / AutoHeal / OpenClaw Memory / ElephantAlpha bridge / Prometheus metrics / Smoke Dashboard / Smoke Trend Management / Telegram Summary / Grafana provisioning / Prometheus scrape / CD Gunicorn 掛載具測試覆蓋
 > **適用版本**: V10.22 Legacy 5888 入口清理版
 
@@ -26,6 +26,16 @@ SQL漏斗(~300筆)
   任務: 跨 Agent orchestration、HITL、AutoHeal bridge、受控 log scan
 ```
 
+### 1.1 PChome 挑品 Agent（2026-05-01）
+
+`services/ai_product_pick_agent.py` 新增 PChome 銷售用挑品 Agent：
+
+- 只讀真實資料表：`products`、`price_records`、`competitor_prices`、`competitor_price_history`，若 `daily_sales_snapshot` 可用則納入近 7 天銷售額與數量。
+- 將 PChome 比 MOMO 有價格優勢、比對信心足夠、且有歷史快照或銷售動能的品項寫入 `ai_price_recommendations`。
+- 寫入策略使用 `strategy='product_pick'`，保留在既有 AI 決策表，不新增假頁面或暫存 JSON。
+- 後台入口：`POST /api/ai/product-picks/generate`，`/ai_intelligence` 可手動產生清單。
+- 配對來源仍以 PChome crawler 真實搜尋結果為準；無競品資料時不生成挑品。
+
 | 角色 | 模型 | 主機 | 成本 | 每日限額 |
 |------|------|------|------|---------|
 | Hermes 分析師 | hermes3:latest / embedding model | 192.168.0.111:11434 或 188 Ollama | 零 | 無限 |

routes/ai_routes.py

@@ -1485,6 +1485,8 @@ def api_icaim_dashboard():
                   WHERE expires_at > NOW() AND source = 'pchome')                   AS valid_competitor_prices,
                 (SELECT COUNT(*) FROM high_risk)                                    AS high_risk_count,
                 (SELECT COUNT(*) FROM ai_price_recommendations)                     AS total_ai_recs,
+                (SELECT COUNT(*) FROM ai_price_recommendations
+                  WHERE strategy = 'product_pick' AND status = 'pending')           AS product_pick_count,
                 (SELECT MAX(crawled_at) FROM competitor_prices WHERE source='pchome') AS last_feeder_run
         """)
 
@@ -1598,6 +1600,11 @@ def api_icaim_dashboard():
                 'valid_competitor_prices': int(stats_row.valid_competitor_prices or 0),
                 'high_risk_count':         int(stats_row.high_risk_count or 0),
                 'total_ai_recs':           int(stats_row.total_ai_recs or 0),
+                'product_pick_count':      int(stats_row.product_pick_count or 0),
+                'match_rate': round(
+                    int(stats_row.valid_competitor_prices or 0) / max(int(stats_row.total_skus or 0), 1) * 100,
+                    1
+                ),
                 'last_feeder_run':         last_feeder,
             },
             'competitors': competitors,
@@ -1609,6 +1616,37 @@ def api_icaim_dashboard():
         return jsonify({'success': False, 'error': str(e)}), 500
 
 
+@ai_bp.route('/api/ai/product-picks/generate', methods=['POST'])
+@login_required
+def api_generate_product_picks():
+    """手動產生 PChome 銷售用 AI 建議挑品清單，結果寫入 DB。"""
+    try:
+        from config import DATABASE_PATH
+        from sqlalchemy import create_engine
+        from services.ai_product_pick_agent import generate_product_pick_list
+
+        payload = request.get_json(silent=True) or {}
+        limit = int(payload.get('limit', 30))
+        limit = max(5, min(limit, 80))
+
+        engine = create_engine(DATABASE_PATH)
+        result = generate_product_pick_list(engine, limit=limit)
+
+        return jsonify({
+            'success': True,
+            'message': f'AI 挑品清單已產生：寫入 {result.written} 筆，候選 {result.candidates} 筆',
+            'data': {
+                'candidates': result.candidates,
+                'written': result.written,
+                'generated_at': result.generated_at,
+                'picks': result.picks[:20],
+            }
+        })
+    except Exception as e:
+        logger.error(f"[ProductPickAgent] 產生挑品清單失敗: {e}")
+        return jsonify({'success': False, 'error': str(e)}), 500
+
+
 @ai_bp.route('/api/ai/icaim/trigger', methods=['POST'])
 @login_required
 def api_icaim_trigger():

services/ai_product_pick_agent.py

@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+AI 建議挑品 Agent
+
+以真實 DB 資料建立可操作的 PChome 銷售挑品清單：
+- MOMO 最新價格
+- PChome 最新競品價格與商品 ID
+- PChome 歷史快照
+- 近 7 天銷售資料（若 daily_sales_snapshot 可用）
+
+此 Agent 不補假資料；資料不足的欄位只降低分數或略過。
+"""
+
+import json
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, Dict, List
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ProductPickResult:
+    candidates: int
+    written: int
+    picks: List[Dict[str, Any]]
+    generated_at: str
+
+
+def _to_float(value, default=0.0) -> float:
+    if value is None:
+        return default
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _load_json_tags(value) -> List[str]:
+    if not value:
+        return []
+    if isinstance(value, list):
+        return value
+    try:
+        parsed = json.loads(value)
+        return parsed if isinstance(parsed, list) else []
+    except Exception:
+        return []
+
+
+def _has_daily_sales_snapshot(conn) -> bool:
+    from sqlalchemy import text
+
+    try:
+        if conn.dialect.name == "postgresql":
+            row = conn.execute(text("SELECT to_regclass('daily_sales_snapshot') AS table_name")).mappings().first()
+            return bool(row and row.get("table_name"))
+        row = conn.execute(text("""
+            SELECT name FROM sqlite_master
+            WHERE type='table' AND name='daily_sales_snapshot'
+        """)).first()
+        return bool(row)
+    except Exception:
+        return False
+
+
+def _fetch_candidates(conn, limit: int) -> List[Dict[str, Any]]:
+    from sqlalchemy import text
+
+    sales_join = ""
+    sales_select = "0 AS sales_7d, 0 AS sales_prev_7d, 0 AS qty_7d"
+    if _has_daily_sales_snapshot(conn):
+        sales_join = """
+            LEFT JOIN (
+                SELECT
+                    "商品ID" AS sku,
+                    SUM(CASE WHEN snapshot_date >= CURRENT_DATE - 7
+                             THEN COALESCE("銷售金額"::numeric, 0) ELSE 0 END) AS sales_7d,
+                    SUM(CASE WHEN snapshot_date >= CURRENT_DATE - 14
+                              AND snapshot_date < CURRENT_DATE - 7
+                             THEN COALESCE("銷售金額"::numeric, 0) ELSE 0 END) AS sales_prev_7d,
+                    SUM(CASE WHEN snapshot_date >= CURRENT_DATE - 7
+                             THEN COALESCE("數量"::numeric, 0) ELSE 0 END) AS qty_7d
+                FROM daily_sales_snapshot
+                GROUP BY "商品ID"
+            ) sales ON sales.sku = lm.sku
+        """
+        sales_select = """
+            COALESCE(sales.sales_7d, 0) AS sales_7d,
+            COALESCE(sales.sales_prev_7d, 0) AS sales_prev_7d,
+            COALESCE(sales.qty_7d, 0) AS qty_7d
+        """
+
+    sql = text(f"""
+        WITH latest_momo AS (
+            SELECT
+                p.id AS product_id,
+                p.i_code AS sku,
+                p.name,
+                p.url,
+                p.category,
+                pr.price AS momo_price,
+                ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC) AS rn
+            FROM products p
+            JOIN price_records pr ON pr.product_id = p.id
+            WHERE p.status = 'ACTIVE'
+        ),
+        history_stats AS (
+            SELECT
+                sku,
+                source,
+                COUNT(*) AS history_points,
+                MIN(price) AS min_pchome_price,
+                MAX(price) AS max_pchome_price
+            FROM competitor_price_history
+            WHERE source = 'pchome'
+              AND crawled_at >= CURRENT_TIMESTAMP - INTERVAL '30 days'
+            GROUP BY sku, source
+        )
+        SELECT
+            lm.product_id,
+            lm.sku,
+            lm.name,
+            lm.url,
+            lm.category,
+            lm.momo_price,
+            cp.price AS pchome_price,
+            cp.original_price,
+            cp.discount_pct,
+            cp.competitor_product_id,
+            cp.competitor_product_name,
+            cp.match_score,
+            cp.tags,
+            cp.crawled_at,
+            COALESCE(hs.history_points, 0) AS history_points,
+            hs.min_pchome_price,
+            hs.max_pchome_price,
+            {sales_select}
+        FROM latest_momo lm
+        JOIN competitor_prices cp
+          ON cp.sku = lm.sku
+         AND cp.source = 'pchome'
+         AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP)
+         AND cp.match_score >= 0.42
+        LEFT JOIN history_stats hs
+          ON hs.sku = lm.sku
+         AND hs.source = cp.source
+        {sales_join}
+        WHERE lm.rn = 1
+        ORDER BY cp.match_score DESC, cp.crawled_at DESC
+        LIMIT :limit
+    """)
+
+    try:
+        return [dict(row) for row in conn.execute(sql, {"limit": max(limit * 6, 100)}).mappings().all()]
+    except Exception as exc:
+        logger.warning("[ProductPickAgent] sales-aware query failed, fallback without sales: %s", exc)
+        fallback = text("""
+            WITH latest_momo AS (
+                SELECT
+                    p.id AS product_id,
+                    p.i_code AS sku,
+                    p.name,
+                    p.url,
+                    p.category,
+                    pr.price AS momo_price,
+                    ROW_NUMBER() OVER (PARTITION BY p.id ORDER BY pr.timestamp DESC) AS rn
+                FROM products p
+                JOIN price_records pr ON pr.product_id = p.id
+                WHERE p.status = 'ACTIVE'
+            )
+            SELECT
+                lm.product_id,
+                lm.sku,
+                lm.name,
+                lm.url,
+                lm.category,
+                lm.momo_price,
+                cp.price AS pchome_price,
+                cp.original_price,
+                cp.discount_pct,
+                cp.competitor_product_id,
+                cp.competitor_product_name,
+                cp.match_score,
+                cp.tags,
+                cp.crawled_at,
+                0 AS history_points,
+                NULL AS min_pchome_price,
+                NULL AS max_pchome_price,
+                0 AS sales_7d,
+                0 AS sales_prev_7d,
+                0 AS qty_7d
+            FROM latest_momo lm
+            JOIN competitor_prices cp
+              ON cp.sku = lm.sku
+             AND cp.source = 'pchome'
+             AND (cp.expires_at IS NULL OR cp.expires_at > CURRENT_TIMESTAMP)
+             AND cp.match_score >= 0.42
+            WHERE lm.rn = 1
+            ORDER BY cp.match_score DESC, cp.crawled_at DESC
+            LIMIT :limit
+        """)
+        return [dict(row) for row in conn.execute(fallback, {"limit": max(limit * 6, 100)}).mappings().all()]
+
+
+def _score_candidate(row: Dict[str, Any]) -> Dict[str, Any]:
+    momo_price = _to_float(row.get("momo_price"))
+    pchome_price = _to_float(row.get("pchome_price"))
+    match_score = _to_float(row.get("match_score"))
+    sales_7d = _to_float(row.get("sales_7d"))
+    sales_prev_7d = _to_float(row.get("sales_prev_7d"))
+    qty_7d = _to_float(row.get("qty_7d"))
+    history_points = int(_to_float(row.get("history_points")))
+    tags = _load_json_tags(row.get("tags"))
+
+    gap_pct = ((momo_price - pchome_price) / pchome_price * 100) if pchome_price else 0
+    sales_delta = ((sales_7d - sales_prev_7d) / sales_prev_7d * 100) if sales_prev_7d else None
+
+    price_score = max(0, min(38, gap_pct * 1.8 + 8))
+    match_component = max(0, min(24, match_score * 24))
+    sales_component = 0
+    if sales_7d > 0:
+        sales_component += min(10, sales_7d / 30000 * 10)
+    if qty_7d > 0:
+        sales_component += min(5, qty_7d / 20 * 5)
+    if sales_delta is not None and sales_delta > 0:
+        sales_component += min(8, sales_delta / 40 * 8)
+    history_component = min(10, history_points * 2)
+    promo_component = 5 if any(tag in tags for tag in ["on_sale", "discount_10pct", "discount_20pct", "discount_30pct"]) else 0
+    score = round(min(100, price_score + match_component + sales_component + history_component + promo_component), 1)
+
+    if gap_pct >= 10:
+        angle = "PChome 價格優勢明顯"
+    elif gap_pct >= 3:
+        angle = "PChome 小幅價格優勢"
+    elif sales_7d > 0:
+        angle = "近期有銷售動能，可搭配內容或檔期測試"
+    else:
+        angle = "比對信心足夠，可列入觀察型挑品"
+
+    reason_parts = [
+        f"{angle}，PChome ${pchome_price:,.0f} vs MOMO ${momo_price:,.0f}",
+        f"價差 {gap_pct:+.1f}%",
+        f"比對信心 {match_score:.2f}",
+    ]
+    if sales_7d > 0:
+        reason_parts.append(f"近 7 天銷售額 ${sales_7d:,.0f}")
+    if history_points:
+        reason_parts.append(f"已有 {history_points} 筆 PChome 歷史快照")
+
+    return {
+        **row,
+        "gap_pct": round(gap_pct, 1),
+        "sales_7d_delta": round(sales_delta, 1) if sales_delta is not None else 0,
+        "pick_score": score,
+        "confidence": round(max(0.45, min(0.98, score / 100)), 3),
+        "reason": "；".join(reason_parts),
+    }
+
+
+def _write_pick(conn, pick: Dict[str, Any]) -> None:
+    from sqlalchemy import text
+
+    footprint = {
+        "agent": {
+            "name": "PChomeProductPickAgent",
+            "version": "v1",
+            "generated_at": datetime.now().isoformat(timespec="seconds"),
+            "inputs": ["products", "price_records", "competitor_prices", "competitor_price_history", "daily_sales_snapshot"],
+            "score": pick["pick_score"],
+        },
+        "competitor": {
+            "source": "pchome",
+            "product_id": pick.get("competitor_product_id"),
+            "product_name": pick.get("competitor_product_name"),
+            "match_score": _to_float(pick.get("match_score")),
+        },
+    }
+
+    conn.execute(text("""
+        INSERT INTO ai_price_recommendations
+            (sku, name, reason, strategy, confidence,
+             momo_price, pchome_price, gap_pct, sales_7d_delta,
+             model_footprint, status, created_at, updated_at)
+        VALUES
+            (:sku, :name, :reason, 'product_pick', :confidence,
+             :momo_price, :pchome_price, :gap_pct, :sales_7d_delta,
+             :footprint, 'pending', CURRENT_TIMESTAMP, CURRENT_TIMESTAMP)
+        ON CONFLICT (sku) DO UPDATE
+            SET reason          = EXCLUDED.reason,
+                strategy        = 'product_pick',
+                confidence      = EXCLUDED.confidence,
+                momo_price      = EXCLUDED.momo_price,
+                pchome_price    = EXCLUDED.pchome_price,
+                gap_pct         = EXCLUDED.gap_pct,
+                sales_7d_delta  = EXCLUDED.sales_7d_delta,
+                model_footprint = EXCLUDED.model_footprint,
+                status          = 'pending',
+                updated_at      = CURRENT_TIMESTAMP
+    """), {
+        "sku": pick["sku"],
+        "name": pick["name"],
+        "reason": pick["reason"],
+        "confidence": pick["confidence"],
+        "momo_price": pick["momo_price"],
+        "pchome_price": pick["pchome_price"],
+        "gap_pct": pick["gap_pct"],
+        "sales_7d_delta": pick["sales_7d_delta"],
+        "footprint": json.dumps(footprint, ensure_ascii=False),
+    })
+
+
+def generate_product_pick_list(engine, limit: int = 30) -> ProductPickResult:
+    """產生並保存 AI 建議挑品清單。"""
+    generated_at = datetime.now().isoformat(timespec="seconds")
+    with engine.begin() as conn:
+        rows = _fetch_candidates(conn, limit)
+        scored = [_score_candidate(row) for row in rows if _to_float(row.get("pchome_price")) > 0]
+        picks = [
+            pick for pick in scored
+            if pick["pick_score"] >= 45 and (_to_float(pick.get("match_score")) >= 0.42)
+        ]
+        picks.sort(key=lambda item: item["pick_score"], reverse=True)
+        picks = picks[:limit]
+        for pick in picks:
+            _write_pick(conn, pick)
+
+    return ProductPickResult(
+        candidates=len(rows),
+        written=len(picks),
+        picks=picks,
+        generated_at=generated_at,
+    )

services/competitor_price_feeder.py

@@ -25,6 +25,7 @@
 
 import json
 import logging
+import re
 import time
 from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
@@ -33,8 +34,9 @@ from typing import Optional
 logger = logging.getLogger(__name__)
 
 # ── 比對參數 ─────────────────────────────────────────
-MIN_MATCH_SCORE  = 0.45  # 低於此分數不寫入（避免張冠李戴）
-SEARCH_LIMIT     = 10    # 每個 SKU 搜尋 PChome 前 N 筆
+MIN_MATCH_SCORE  = 0.42  # 低於此分數不寫入（避免張冠李戴）
+SEARCH_LIMIT     = 20    # 每個搜尋詞取 PChome 前 N 筆
+MAX_SEARCH_TERMS  = 4     # 每個 MOMO 商品最多嘗試幾組搜尋詞
 BATCH_SIZE       = 30    # 每批 DB 寫入筆數
 RATE_DELAY       = 0.8   # 每次 PChome 請求間隔（秒）
 TTL_HOURS        = 6     # competitor_prices 快取有效期
@@ -95,6 +97,58 @@ def _extract_tags(pchome_product) -> list:
     return tags
 
 
+def _clean_search_text(value: str) -> str:
+    value = re.sub(r'[（(][^)）]*[)）]', ' ', value or '')
+    value = re.sub(r'[【\[].*?[】\]]', ' ', value)
+    value = re.sub(r'[^\w\u4e00-\u9fff]+', ' ', value)
+    return re.sub(r'\s+', ' ', value).strip()
+
+
+def _dedupe_terms(terms: list) -> list:
+    result = []
+    seen = set()
+    for term in terms:
+        cleaned = _clean_search_text(term)
+        if len(cleaned) < 2:
+            continue
+        key = cleaned.lower()
+        if key in seen:
+            continue
+        seen.add(key)
+        result.append(cleaned[:36])
+        if len(result) >= MAX_SEARCH_TERMS:
+            break
+    return result
+
+
+def _build_search_keywords(momo_name: str) -> list:
+    """
+    用多組真實商品名線索搜尋 PChome，提高命中率，但仍交給相似度門檻把關。
+    """
+    cleaned = _clean_search_text(momo_name)
+    terms = [cleaned[:28], cleaned[:18]]
+
+    try:
+        from services.price_comparison import ProductNameParser, BRAND_ALIASES
+        parser = ProductNameParser()
+        parsed = parser.parse(momo_name, "momo", 0, "", "")
+        if parsed.brand:
+            brand_terms = BRAND_ALIASES.get(parsed.brand, [parsed.brand])
+            brand_label = next((term for term in brand_terms if any('\u4e00' <= c <= '\u9fff' for c in term)), brand_terms[0])
+            if parsed.product_type:
+                terms.append(f"{brand_label} {parsed.product_type}")
+            if parsed.specs.get("volume"):
+                terms.append(f"{brand_label} {parsed.specs['volume']}")
+            if parsed.keywords:
+                terms.append(f"{brand_label} {' '.join(parsed.keywords[:3])}")
+        elif parsed.keywords:
+            terms.append(" ".join(parsed.keywords[:4]))
+    except Exception:
+        pass
+
+    return _dedupe_terms(terms)
+
+
 def _find_best_match(momo_name: str, pchome_products: list) -> Optional[tuple]:
     """
     從 PChome 搜尋結果中找出與 MOMO 商品名稱最接近的一筆
@@ -132,6 +186,22 @@ def _find_best_match(momo_name: str, pchome_products: list) -> Optional[tuple]:
     return (best, best_score) if best else None
 
 
+def _search_pchome_candidates(crawler, momo_name: str) -> list:
+    """以多組搜尋詞擴大 PChome 候選池，去重後回傳真實商品資料。"""
+    candidates = []
+    seen_ids = set()
+    for keyword in _build_search_keywords(momo_name):
+        ok, _, products = crawler.search_products(keyword, limit=SEARCH_LIMIT)
+        if not ok or not products:
+            continue
+        for product in products:
+            if product.product_id in seen_ids:
+                continue
+            seen_ids.add(product.product_id)
+            candidates.append(product)
+    return candidates
+
+
 def _structural_similarity(momo_p, pchome_p) -> float:
     """
     結構化相似度計算（品牌 + 規格 + 關鍵字）
@@ -398,12 +468,9 @@ class CompetitorPriceFeeder:
             momo_product_id = item.get("product_id")
             momo_price = item.get("momo_price")
 
-            # 用商品名稱前 20 字搜尋（避免 query 過長）
-            keyword = momo_name[:20].strip()
-
             try:
-                ok, _, products = crawler.search_products(keyword, limit=SEARCH_LIMIT)
-                if not ok or not products:
+                products = _search_pchome_candidates(crawler, momo_name)
+                if not products:
                     logger.debug(f"[Feeder] {sku} 無搜尋結果，跳過")
                     skipped_no += 1
                     continue

templates/ai_intelligence.html

@@ -20,6 +20,9 @@
             <button class="btn btn-outline-danger btn-sm" id="btnTrigger" onclick="triggerAnalysis()">
                 <i class="fas fa-bolt me-1"></i>立即分析
             </button>
+            <button class="btn btn-outline-primary btn-sm" id="btnPickList" onclick="generatePickList()">
+                <i class="fas fa-wand-magic-sparkles me-1"></i>產生挑品清單
+            </button>
             <button class="btn btn-outline-secondary btn-sm" onclick="loadDashboard()">
                 <i class="fas fa-redo me-1"></i>重新整理
             </button>
@@ -40,7 +43,10 @@
             <div class="card border-0 shadow-sm h-100">
                 <div class="card-body text-center py-3">
                     <div class="fs-2 fw-bold text-success" id="kpiCompetitors">—</div>
-                    <div class="small text-muted mt-1"><i class="fas fa-store me-1"></i>有效競品比價</div>
+                    <div class="small text-muted mt-1">
+                        <i class="fas fa-store me-1"></i>有效競品比價
+                        <span id="kpiMatchRate" class="text-muted" style="font-size:0.7rem"></span>
+                    </div>
                 </div>
             </div>
         </div>
@@ -60,7 +66,7 @@
             <div class="card border-0 shadow-sm h-100">
                 <div class="card-body text-center py-3">
                     <div class="fs-2 fw-bold text-info" id="kpiAiRecs">—</div>
-                    <div class="small text-muted mt-1"><i class="fas fa-robot me-1"></i>AI 決策記錄</div>
+                    <div class="small text-muted mt-1"><i class="fas fa-robot me-1"></i>AI 挑品/決策記錄</div>
                 </div>
             </div>
         </div>
@@ -126,7 +132,7 @@
                 <div class="card-header py-2 bg-white border-bottom">
                     <span class="fw-bold">
                         <i class="fas fa-robot text-danger me-2"></i>AI 決策日誌
-                        <small class="text-muted fw-normal ms-2">Hermes × NemoTron</small>
+                        <small class="text-muted fw-normal ms-2">挑品 Agent × Hermes × NemoTron</small>
                     </span>
                 </div>
                 <div class="card-body p-0" style="overflow-y:auto; max-height:548px;">
@@ -138,7 +144,7 @@
                 </div>
                 <div class="card-footer bg-white py-2 d-flex justify-content-between small text-muted">
                     <span id="aiRecsCount">—</span>
-                    <span>每 6h 自動更新</span>
+                    <span>可手動產生挑品清單</span>
                 </div>
             </div>
         </div>
@@ -193,6 +199,7 @@ function renderKPIs(stats) {
     document.getElementById('kpiSkus').textContent        = (stats.total_skus || 0).toLocaleString();
     document.getElementById('kpiCompetitors').textContent = (stats.valid_competitor_prices || 0).toLocaleString();
     document.getElementById('kpiAiRecs').textContent      = (stats.total_ai_recs || 0).toLocaleString();
+    document.getElementById('kpiMatchRate').textContent   = stats.match_rate ? `(${stats.match_rate}%)` : '';
 
     const hr = stats.high_risk_count || 0;
     document.getElementById('kpiHighRisk').textContent = hr;
@@ -300,6 +307,7 @@ function renderAiRecs(recs) {
     const strategyMap = {
         'price_cut':  ['bg-danger',              '降價'],
         'promote':    ['bg-primary',             '主推'],
+        'product_pick':['bg-success',            'AI挑品'],
         'monitor':    ['bg-secondary',           '觀察'],
         'flag':       ['bg-warning text-dark',   '覆核'],
     };
@@ -347,6 +355,41 @@ function renderAiRecs(recs) {
     }).join('');
 }
 
+// ── 產生 AI 建議挑品清單 ───────────────────────────
+async function generatePickList() {
+    const btn = document.getElementById('btnPickList');
+    const toast = document.getElementById('triggerToast');
+    const msg = document.getElementById('triggerToastMsg');
+
+    btn.disabled = true;
+    btn.innerHTML = '<span class="spinner-border spinner-border-sm me-1"></span>挑品中...';
+
+    try {
+        const res = await fetch('/api/ai/product-picks/generate', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ limit: 30 })
+        });
+        const data = await res.json();
+
+        msg.innerHTML = data.success
+            ? `<i class="fas fa-check-circle me-1"></i>${data.message}`
+            : `<i class="fas fa-times-circle me-1"></i>${data.error || '產生失敗'}`;
+        toast.className = 'toast align-items-center text-white border-0 ' +
+                          (data.success ? 'bg-success' : 'bg-danger');
+        new bootstrap.Toast(toast, { delay: 6000 }).show();
+
+        if (data.success) loadDashboard();
+    } catch (e) {
+        msg.innerHTML = '<i class="fas fa-times-circle me-1"></i>挑品失敗：' + e.message;
+        toast.className = 'toast align-items-center text-white border-0 bg-danger';
+        new bootstrap.Toast(toast, { delay: 4000 }).show();
+    } finally {
+        btn.disabled = false;
+        btn.innerHTML = '<i class="fas fa-wand-magic-sparkles me-1"></i>產生挑品清單';
+    }
+}
+
 // ── 手動觸發分析 ────────────────────────────────────
 async function triggerAnalysis() {
     const btn   = document.getElementById('btnTrigger');

tests/test_frontend_v2_assets.py

@@ -128,6 +128,39 @@ def test_dashboard_v2_shows_pchome_competitor_pricing_and_links():
     assert "history_written" in scheduler_source
 
 
+def test_ai_product_pick_agent_uses_real_competitor_data_and_dashboard_action():
+    feeder_source = (ROOT / "services/competitor_price_feeder.py").read_text(encoding="utf-8")
+    agent_source = (ROOT / "services/ai_product_pick_agent.py").read_text(encoding="utf-8")
+    route_source = (ROOT / "routes/ai_routes.py").read_text(encoding="utf-8")
+    template = (ROOT / "templates/ai_intelligence.html").read_text(encoding="utf-8")
+
+    assert "MIN_MATCH_SCORE  = 0.42" in feeder_source
+    assert "MAX_SEARCH_TERMS" in feeder_source
+    assert "_build_search_keywords" in feeder_source
+    assert "_search_pchome_candidates" in feeder_source
+    assert "crawler.search_products(keyword, limit=SEARCH_LIMIT)" in feeder_source
+
+    assert "generate_product_pick_list" in agent_source
+    assert "competitor_prices" in agent_source
+    assert "competitor_price_history" in agent_source
+    assert "daily_sales_snapshot" in agent_source
+    assert "ai_price_recommendations" in agent_source
+    assert "'product_pick'" in agent_source
+    assert "PChomeProductPickAgent" in agent_source
+    assert "PChome 價格優勢" in agent_source
+
+    assert "@ai_bp.route('/api/ai/product-picks/generate', methods=['POST'])" in route_source
+    assert "generate_product_pick_list(engine" in route_source
+    assert "match_rate" in route_source
+    assert "product_pick_count" in route_source
+
+    assert "產生挑品清單" in template
+    assert "generatePickList" in template
+    assert "/api/ai/product-picks/generate" in template
+    assert "'product_pick':['bg-success'" in template
+    assert "kpiMatchRate" in template
+
+
 def test_edm_dashboard_v2_is_production_default_and_uses_real_campaign_data():
     route_source = (ROOT / "routes/edm_routes.py").read_text(encoding="utf-8")
     template = (ROOT / "templates/edm_dashboard_v2.html").read_text(encoding="utf-8")