修正競品簡報覆蓋率與營運視角

routes/openclaw_bot_routes.py

@@ -2424,24 +2424,20 @@ def _ppt_ai_analysis(prompt_data: str, report_type: str = '') -> str:
             "台灣消費者行為與美妝/保健品類購買決策模式。\n\n"
             "═══════════════════════════════\n"
             "角色設定（絕對不可違反）\n"
-            "  我方 = PChome　　競品 = momo\n"
-            "  price_diff = momo售價 − PChome售價\n"
-            "  正值 → momo偏貴 → PChome定價優勢 → 加強曝光與轉換\n"
-            "  負值 → momo較便宜 → 競品威脅 → 研擬差異化因應策略\n"
+            "  視角 = EwoooC 商品營運決策，不預設單一平台永遠正確\n"
+            "  price_diff = PChome售價 − MOMO售價\n"
+            "  正值 → PChome較貴 / MOMO具價格優勢 → 可放大 MOMO 價格賣點\n"
+            "  負值 → PChome較便宜 / PChome低價壓力 → 需評估促銷、組合或服務差異化\n"
+            "  待補資料不可當成成功配對；必須明確列為資料品質風險\n"
             "═══════════════════════════════\n\n"
-            f"請以 PChome 視角，針對以下{report_type}輸出一份專業競品分析報告：\n\n"
+            f"請以 EwoooC 商品營運視角，針對以下{report_type}輸出一份專業競品分析報告：\n\n"
             "【整體競爭態勢】（3-4句）\n"
-            "從 PChome 角度評估本期整體定價競爭力，引用平均價差百分比，"
-            "指出最關鍵亮點與警訊，並結合美妝/保健/母嬰市場脈動說明意涵。\n\n"
-            "【PChome 定價優勢商品深度解析】（4-5句）\n"
-            "點名具體優勢商品與品類（如施巴、ESTEE LAUDER 等），"
-            "推測 PChome 具優勢的可能原因（進貨量優勢、活動定價、品牌授權），"
-            "給出具體的曝光放大策略：首頁置頂、搜尋關鍵字投放、會員專案推廣。\n\n"
-            "【momo 威脅商品應對方案】（4-5句）\n"
-            "點名具體威脅商品，判斷 momo 低價策略來源（平台補貼/獨家代理/批量進貨），"
-            "提出 PChome 差異化應對：加值服務（快速到貨/禮盒包裝）、"
-            "組合促銷、PChome 幣回饋、VIP 會員專屬折扣，"
-            "避免純粹降價而犧牲毛利。\n\n"
+            "引用平均價差、比對成功件數、待補資料件數，指出價格壓力、MOMO 優勢與資料覆蓋風險。\n\n"
+            "【PChome 低價壓力商品深度解析】（4-5句）\n"
+            "點名 PChome 較便宜的具體商品與品類，判斷可能原因（活動定價、組合包、會員回饋或清庫存），"
+            "提出 MOMO 端可採取的促銷、組合、內容曝光或服務差異化做法，避免只用降價犧牲毛利。\n\n"
+            "【MOMO 價格優勢商品放大策略】（4-5句）\n"
+            "點名 MOMO 較便宜的商品，提出可放大的搜尋關鍵字、站內陳列、檔期素材與推薦理由。\n\n"
             "【美妝/保健/母嬰品類專項洞察】（3-4句）\n"
             "針對本期出現的具體商品，結合台灣市場趨勢深度分析：\n"
             "美妝：成分透明化趨勢、敏感肌/無添加需求、社群口碑行銷；\n"
@@ -3336,23 +3332,27 @@ def _generate_ppt_cmd(sub_type: str, sub_arg: str, _chat_id: int, target: str,
         )
 
         found_c = [r for r in results if r.get('found')]
-        pc_wins_c = [r for r in found_c if r.get('price_diff', 0) < -10]
-        mo_wins_c = [r for r in found_c if r.get('price_diff', 0) > 10]
+        pchome_low_price_c = [r for r in found_c if r.get('price_diff', 0) < -10]
+        momo_low_price_c = [r for r in found_c if r.get('price_diff', 0) > 10]
+        not_found_c = [r for r in results if not r.get('found')]
         avg_diff_c = (sum(r.get('price_diff_pct', 0) for r in found_c) / len(found_c)
                       if found_c else 0)
 
         data_summary = (
             f"【可信資料源=competitor_prices 高信心配對，MOMO vs PChome】\n"
             f"分析週期：{period_label}\n"
-            f"掃描商品：{len(results)} 件  |  比對成功：{len(found_c)} 件\n"
-            f"PChome定價優勢（比momo便宜）：{len(pc_wins_c)} 件  |  momo威脅（比PChome便宜）：{len(mo_wins_c)} 件\n"
+            f"掃描商品：{len(results)} 件  |  高信心比對：{len(found_c)} 件  |  待補身份/價格：{len(not_found_c)} 件\n"
+            f"PChome 低價壓力（PChome 比 MOMO 便宜）：{len(pchome_low_price_c)} 件  |  MOMO 價格優勢（MOMO 比 PChome 便宜）：{len(momo_low_price_c)} 件\n"
             f"平均價差：{avg_diff_c:+.1f}%（正值=PChome較貴、MOMO具價格優勢；負值=PChome較便宜）\n\n"
-            f"PChome優勢 TOP3（應加強曝光宣傳）：" + " / ".join(
+            f"PChome低價壓力 TOP3（需研擬因應）：" + " / ".join(
                 f"{r['momo_name'][:15]}（PChome便宜NT${abs(r['price_diff']):,.0f}）"
-                for r in pc_wins_c[:3]) + "\n"
-            f"momo威脅 TOP3（需研擬因應策略）：" + " / ".join(
+                for r in pchome_low_price_c[:3]) + "\n"
+            f"MOMO價格優勢 TOP3（可加強曝光）：" + " / ".join(
                 f"{r['momo_name'][:15]}（MOMO便宜NT${r['price_diff']:,.0f}）"
-                for r in mo_wins_c[:3]) + "\n\n"
+                for r in momo_low_price_c[:3]) + "\n"
+            f"待補資料樣本：" + " / ".join(
+                f"{r['momo_name'][:12]}（{r.get('match_status', 'no_valid_match')}）"
+                for r in not_found_c[:3]) + "\n\n"
             f"外部情報：{mcp_text_c[:400]}"
         )
         ai_text = cached_ai or _ppt_ai_analysis(data_summary, f'競品比較簡報（{period_label}）')

services/competitor_intel_repository.py

@@ -406,11 +406,36 @@ def fetch_competitor_comparison_results(
 ) -> list[dict]:
     """輸出與 legacy competitor PPT 相容的比價結果，不再 live crawl。"""
     limit = max(1, min(int(limit or 30), 100))
-    has_daily_sales = inspect(engine).has_table("daily_sales")
+    inspector = inspect(engine)
+    if not (
+        inspector.has_table("products")
+        and inspector.has_table("price_records")
+        and inspector.has_table("competitor_prices")
+    ):
+        return []
+
+    has_daily_sales = inspector.has_table("daily_sales")
+    has_match_attempts = inspector.has_table("competitor_match_attempts")
     sales_cte = ""
     sales_join = ""
     sales_select = "0 AS momo_revenue,"
-    order_expr = "ABS((lm.momo_price - vc.pchome_price) / vc.pchome_price * 100) DESC NULLS LAST"
+    attempt_cte = """
+        latest_attempt AS (
+            SELECT
+                NULL AS sku,
+                NULL AS attempt_status,
+                NULL AS candidate_count,
+                NULL AS best_match_score,
+                NULL AS error_message,
+                NULL AS attempted_at
+            WHERE FALSE
+        )
+    """
+    order_expr = (
+        "lm.momo_price DESC NULLS LAST, "
+        "(vc.pchome_price IS NULL), "
+        "ABS((lm.momo_price - vc.pchome_price) / vc.pchome_price * 100) DESC NULLS LAST"
+    )
     params: dict[str, Any] = {"limit": limit}
 
     if has_daily_sales:
@@ -434,7 +459,27 @@ def fetch_competitor_comparison_results(
         """
         sales_join = "LEFT JOIN sales_rank sr ON sr.product_id = lm.product_id"
         sales_select = "COALESCE(sr.momo_revenue, 0) AS momo_revenue,"
-        order_expr = "COALESCE(sr.momo_revenue, 0) DESC, " + order_expr
+        order_expr = (
+            "COALESCE(sr.momo_revenue, 0) DESC, "
+            "(vc.pchome_price IS NULL), "
+            "ABS((lm.momo_price - vc.pchome_price) / vc.pchome_price * 100) DESC NULLS LAST"
+        )
+
+    if has_match_attempts:
+        attempt_cte = """
+        latest_attempt AS (
+            SELECT DISTINCT ON (cma.sku)
+                cma.sku,
+                cma.attempt_status,
+                cma.candidate_count,
+                cma.best_match_score,
+                cma.error_message,
+                cma.attempted_at
+            FROM competitor_match_attempts cma
+            WHERE cma.source = 'pchome'
+            ORDER BY cma.sku, cma.attempted_at DESC NULLS LAST
+        )
+        """
 
     sql = text(f"""
         WITH latest_momo AS (
@@ -463,7 +508,8 @@ def fetch_competitor_comparison_results(
               AND COALESCE(cp.match_score, 0) >= {PCHOME_MATCH_SCORE_FLOOR}
               AND COALESCE(cp.tags, '[]'::jsonb) ? 'identity_v2'
             ORDER BY cp.sku, cp.crawled_at DESC NULLS LAST
-        )
+        ),
+        {attempt_cte}
         {sales_cte}
         SELECT
             lm.sku,
@@ -473,11 +519,17 @@ def fetch_competitor_comparison_results(
             vc.competitor_product_id,
             vc.competitor_product_name,
             vc.match_score,
+            la.attempt_status,
+            la.candidate_count,
+            la.best_match_score,
+            la.error_message,
+            la.attempted_at,
             {sales_select}
             (vc.pchome_price - lm.momo_price) AS price_diff,
             ((vc.pchome_price - lm.momo_price) / lm.momo_price * 100) AS price_diff_pct
         FROM latest_momo lm
-        JOIN valid_competitor vc ON vc.sku = lm.sku
+        LEFT JOIN valid_competitor vc ON vc.sku = lm.sku
+        LEFT JOIN latest_attempt la ON la.sku = lm.sku
         {sales_join}
         WHERE lm.rn = 1
           AND lm.momo_price > 0
@@ -490,8 +542,9 @@ def fetch_competitor_comparison_results(
     results = []
     for row in rows:
         pchome_id = row.get("competitor_product_id")
+        found = bool(row.get("pchome_price"))
         results.append({
-            "found": True,
+            "found": found,
             "momo_icode": str(row.get("sku") or ""),
             "momo_name": row.get("name") or "",
             "momo_price": _num(row.get("momo_price")),
@@ -502,6 +555,10 @@ def fetch_competitor_comparison_results(
             "price_diff_pct": _num(row.get("price_diff_pct")),
             "match_score": _num(row.get("match_score")),
             "momo_revenue": _num(row.get("momo_revenue")),
+            "match_status": "matched" if found else (row.get("attempt_status") or "no_valid_match"),
+            "candidate_count": int(row.get("candidate_count") or 0),
+            "best_match_score": _num(row.get("best_match_score")),
+            "match_diagnostic": row.get("error_message") or "",
         })
     return results
 

tests/test_competitor_intel_cache.py

@@ -1,6 +1,9 @@
 from pathlib import Path
 
 
+ROOT = Path(__file__).resolve().parents[1]
+
+
 def test_competitor_intel_cache_reuses_memory_and_shared_file(tmp_path, monkeypatch):
     from services import competitor_intel_repository as repo
 
@@ -34,3 +37,24 @@ def test_clear_competitor_intel_cache_removes_shared_file(tmp_path, monkeypatch)
 
     assert repo._MEM_CACHE == {}
     assert not cache_file.exists()
+
+
+def test_competitor_ppt_results_keep_pending_diagnostics_in_export():
+    source = (ROOT / "services" / "competitor_intel_repository.py").read_text(encoding="utf-8")
+
+    assert "LEFT JOIN valid_competitor" in source
+    assert "\"found\": found" in source
+    assert "\"match_status\"" in source
+    assert "\"candidate_count\"" in source
+    assert "(vc.pchome_price IS NULL)" in source
+
+
+def test_competitor_ppt_prompt_uses_neutral_ewooc_viewpoint():
+    source = (ROOT / "routes" / "openclaw_bot_routes.py").read_text(encoding="utf-8")
+
+    assert "EwoooC 商品營運視角" in source
+    assert "待補資料不可當成成功配對" in source
+    assert "高信心比對" in source
+    assert "待補身份/價格" in source
+    assert "我方 = PChome" not in source
+    assert "請以 PChome 視角" not in source