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@@ -201,117 +201,158 @@ def _format_project_notification_message(df_tier2: pd.DataFrame, df_tier1: pd.Da
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return "\n".join(lines)
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+def _score_top_decisions(df_tier2: pd.DataFrame, top_n: int = 5) -> pd.DataFrame:
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+ """对 Tier 2/3 决策按"影响力"评分，选出 top N 展示给运营。
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+
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+ 评分公式：
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+ score = normalize(cost_7d_avg) * 0.6
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+ + action_weight * 0.3
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+ + confidence_weight * 0.1
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+
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+ 权重理由：
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+ - 消耗权重最高（0.6）：决策影响的金额越大越该优先看
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+ - 动作权重其次（0.3）：pause/bid_down 是风险型操作，优先展示
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+ - 置信度权重最低（0.1）：仅作为 tiebreaker
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+ """
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+ if df_tier2.empty:
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+ return df_tier2
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+
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+ df = df_tier2.copy()
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+
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+ # 消耗归一化（0-1）
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+ cost = pd.to_numeric(df.get("cost_7d_avg", 0), errors="coerce").fillna(0)
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+ cost_max = cost.max()
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+ cost_norm = cost / cost_max if cost_max > 0 else cost * 0
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+
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+ # 动作权重：pause/bid_down = 1.0（风险型），bid_up/scale_up = 0.7（机会型），其他 = 0.5
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+ def _action_weight(a: str) -> float:
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+ a = str(a).strip()
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+ if a in ("pause", "bid_down"):
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+ return 1.0
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+ if a in ("bid_up", "scale_up"):
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+ return 0.7
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+ return 0.5
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+
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+ action_col = df.get("final_action", df.get("action", ""))
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+ action_weight = action_col.apply(_action_weight)
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+
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+ # 置信度权重
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+ def _conf_weight(c: str) -> float:
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+ c = str(c).strip().lower()
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+ if c == "high":
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+ return 1.0
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+ if c == "medium":
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+ return 0.6
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+ return 0.3
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+
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+ conf_weight = df.get("confidence", "medium").apply(_conf_weight) if "confidence" in df.columns else 0.6
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+
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+ df["_top_score"] = cost_norm * 0.6 + action_weight * 0.3 + conf_weight * 0.1
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+ df = df.sort_values("_top_score", ascending=False)
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+
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+ # 如果总量 <= top_n，全部返回
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+ if len(df) <= top_n:
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+ return df.drop(columns=["_top_score"])
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+
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+ return df.head(top_n).drop(columns=["_top_score"])
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+
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+
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def _format_approval_message(df_tier2: pd.DataFrame, df_tier1: pd.DataFrame, df_tier0: pd.DataFrame, request_id: str) -> str:
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+ """格式化审批消息（瘦身版，目标：单屏可读，≤ 2 KB）。
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+
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+ 设计原则：
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+ - 删除 Tier 0 逐条列表（由飞书表格兜底）
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+ - 删除 Tier 1 "自动执行" 段落（TIER1_MAX_CHANGE_PCT=0.00 已禁用该通路）
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+ - 用 Top 5 高置信/高消耗决策替代"前 5 个示例"的无排序列表
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+ - 展示"影响金额"让运营感知决策规模
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+ - 明确告知"30 分钟无回复 = 默认拒绝"，无隐式自动通过
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+ """
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+ total = len(df_tier2)
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+
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+ # 统计各 action 数量
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+ action_col = df_tier2.get("final_action", df_tier2.get("action", ""))
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+ action_counts = action_col.value_counts().to_dict() if total > 0 else {}
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+ n_pause = action_counts.get("pause", 0)
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+ n_down = action_counts.get("bid_down", 0)
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+ n_up = action_counts.get("bid_up", 0)
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+ n_scale = action_counts.get("scale_up", 0)
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+ n_observe = sum(v for k, v in action_counts.items() if k in ("observe", "hold", "creative_adjust"))
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+
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+ # 影响金额（7日均消耗合计 + 昨日总消耗）
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+ cost_7d = pd.to_numeric(df_tier2.get("cost_7d_avg", 0), errors="coerce").fillna(0).sum() if total > 0 else 0.0
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+ yesterday_cost = pd.to_numeric(df_tier2.get("yesterday_cost", 0), errors="coerce").fillna(0).sum() if "yesterday_cost" in df_tier2.columns else 0.0
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+
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lines = [
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- "📊 广告调控审批请求",
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- f"请求ID: {request_id}",
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- f"时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}",
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+ f"📊 广告调控审批请求 {request_id}",
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+ "─" * 40,
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+ f"📌 决策总数: {total} （需全部人工审批，无自动执行）",
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+ f" ⏸ 暂停 {n_pause} / ⬇ 降价 {n_down} / ⬆ 提价 {n_up} / 🚀 扩量 {n_scale} / 👀 观察 {n_observe}",
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+ "",
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+ "💰 影响金额",
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+ f" 受影响广告 7 日均消耗合计: {cost_7d:,.0f} 元",
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+ f" 昨日总消耗: {yesterday_cost:,.0f} 元",
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"",
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]
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- # Tier 2/3: 需审批
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- if not df_tier2.empty:
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- total_count = len(df_tier2)
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- lines.append(f"🔶 需审批操作（{total_count} 个）：")
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- lines.append("-" * 40)
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+ # Top N 高置信/高消耗决策
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+ if total > 0:
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+ top_df = _score_top_decisions(df_tier2, top_n=5)
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+ header = f"🔝 Top {len(top_df)} 高置信/高消耗决策" if total > 5 else f"🔝 全部决策（{total} 个）"
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+ lines.append(header)
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- # 统计各操作类型
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- action_counts = df_tier2.get("final_action", df_tier2.get("action", "")).value_counts().to_dict()
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- for action, count in action_counts.items():
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+ for idx, (_, row) in enumerate(top_df.iterrows(), start=1):
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+ ad_id = row.get("ad_id", "")
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+ action = str(row.get("final_action", row.get("action", ""))).strip()
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+ ad_name = str(row.get("ad_name", ""))[:20]
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+ reason = str(row.get("reason", "")).replace("\n", " ")[:60]
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+
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+ # ROI（优先使用 7 日均值，回退到单日动态 ROI）
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+ roi_val = row.get("动态ROI_7日均值", row.get("动态ROI", row.get("yesterday_roi", 0)))
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+ try:
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+ roi_val = float(roi_val)
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+ roi_str = f"ROI {roi_val:.2f}"
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+ except (ValueError, TypeError):
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+ roi_str = "ROI -"
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+
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+ # 动作标签
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if action == "pause":
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- lines.append(f" ⏸️ 暂停: {count} 个")
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+ action_label = "⏸ 暂停"
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elif action == "bid_down":
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- lines.append(f" ⬇️ 降价: {count} 个")
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+ pct = row.get("recommended_change_pct", 0)
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+ try:
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+ pct = float(pct)
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+ except (ValueError, TypeError):
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+ pct = 0
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+ action_label = f"⬇ 降价 {abs(pct)*100:.0f}%"
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elif action == "bid_up":
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- lines.append(f" ⬆️ 提价: {count} 个")
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+ pct = row.get("recommended_change_pct", 0)
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+ try:
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+ pct = float(pct)
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+ except (ValueError, TypeError):
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+ pct = 0
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+ action_label = f"⬆ 提价 {pct*100:.0f}%"
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+ elif action == "scale_up":
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+ action_label = "🚀 扩量"
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else:
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- lines.append(f" {action}: {count} 个")
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- lines.append("")
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+ action_label = action
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- # 如果数量过多（>20），只显示摘要统计，不逐条列出
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- if total_count > 20:
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- lines.append(f"⚠️ 广告数量较多（{total_count} 个），详情请查看在线表格")
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- # 只展示前 5 个示例
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- lines.append("")
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- lines.append("前 5 个示例：")
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- for i, (_, row) in enumerate(df_tier2.head(5).iterrows()):
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- ad_id = row.get("ad_id", "")
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- action = row.get("final_action", row.get("action", ""))
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- ad_name = str(row.get("ad_name", ""))[:20]
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- lines.append(f" [{ad_id}] {ad_name} → {action}")
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- lines.append(" ...")
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- else:
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- # 数量较少，逐条列出
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- for _, row in df_tier2.iterrows():
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- ad_id = row.get("ad_id", "")
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- action = row.get("final_action", row.get("action", ""))
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- ad_name = str(row.get("ad_name", ""))[:20]
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- reason = str(row.get("reason", ""))[:60]
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- cost_avg = row.get("cost_7d_avg", 0)
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-
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- if action == "pause":
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- action_label = "⏸️ 暂停"
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- elif action == "bid_down":
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- pct = row.get("recommended_change_pct", 0)
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- if isinstance(pct, str):
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- try:
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- pct = float(pct)
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- except ValueError:
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- pct = 0
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- action_label = f"⬇️ 降价{abs(pct)*100:.0f}%"
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- elif action == "bid_up":
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- pct = row.get("recommended_change_pct", 0)
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- if isinstance(pct, str):
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- try:
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- pct = float(pct)
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- except ValueError:
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- pct = 0
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- action_label = f"⬆️ 提价{pct*100:.0f}%"
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- else:
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- action_label = action
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-
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- lines.append(f" [{ad_id}] {ad_name}")
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- lines.append(f" 操作: {action_label} | 日均消耗: {cost_avg:.0f}元")
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- lines.append(f" 原因: {reason}")
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- lines.append("")
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-
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- # Tier 0: 无需操作（observe/hold/creative_adjust）
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- if not df_tier0.empty:
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- lines.append(f"ℹ️ 无需操作（{len(df_tier0)} 个，仅通知）：")
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- for _, row in df_tier0.iterrows():
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- ad_id = row.get("ad_id", "")
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- action = row.get("final_action", row.get("action", ""))
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- action_label = {
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- "observe": "观察等待",
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- "hold": "保持不变",
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- "creative_adjust": "需人工调整素材",
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- "scale_up": "建议扩量（新增广告/创意）"
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- }.get(action, action)
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- lines.append(f" [{ad_id}] {action_label}")
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- lines.append("")
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+ lines.append(f" {idx}. [{ad_id}] {ad_name} | {action_label} | {roi_str} | {reason}")
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- # Tier 1: 小幅调价（自动执行）
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- if not df_tier1.empty:
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- lines.append(f"✅ 自动执行（{len(df_tier1)} 个小幅调价）：")
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- for _, row in df_tier1.iterrows():
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- ad_id = row.get("ad_id", "")
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- action = row.get("final_action", row.get("action", ""))
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- change_pct = row.get("recommended_change_pct", 0)
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- lines.append(f" [{ad_id}] {action} {change_pct:+.1%}")
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+ if total > 5:
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+ lines.append(f" （完整列表见下方飞书在线表格链接）")
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lines.append("")
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- # 回复指令
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+ # 回复方式（多轮协商）
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lines.extend([
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- "-" * 40,
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- "📝 直接回复即可，示例：",
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- " \"批准\" / \"通过\" — 全部批准",
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- " \"拒绝\" / \"不行\" — 全部拒绝",
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- " \"广告 12345 不要暂停\" — 修改指定广告",
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- " \"只批准降价的\" — 部分批准",
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- " \"降幅改小一点\" — 调整后重新审批",
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- f" ⏰ 超时时间: {IM_APPROVAL_TIMEOUT_MINUTES} 分钟",
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+ "📝 回复方式（支持多轮协商）",
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+ " \"通过\" — 全部批准",
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+ " \"拒绝\" — 全部取消",
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+ " \"广告 12345 不动\" — 保留这条，其余按建议",
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+ " \"整体太激进/保守\" — 要求重新评估",
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+ " \"只批准 pause\" — 按 action 类型过滤",
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+ f" ⏰ {IM_APPROVAL_TIMEOUT_MINUTES} 分钟无回复 = 默认拒绝",
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"",
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- "📎 决策详情请查看在线表格（自动发送链接）",
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+ "📎 详单: 飞书在线表格链接（消息 2 单独发送）",
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])
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return "\n".join(lines)
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@@ -501,14 +542,14 @@ async def send_approval_request(
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except Exception as e:
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logger.warning("发送到个人失败: %s", e)
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- # 消息 1b：发送到投放项目群聊（如果配置了）
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+ # 消息 1b：发送到投放项目群聊 — 与个人 IM 一致的完整审批文本
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if FEISHU_AD_PROJECT_CHAT_ID:
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try:
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result_project = _feishu.send_message(to=FEISHU_AD_PROJECT_CHAT_ID, text=message)
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feishu_sent_to_project_chat = True
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feishu_sent = True
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logger.info("飞书审批消息发送成功（项目群）: message_id=%s", result_project.message_id)
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- # 群聊 chat_id 本身就是 oc_xxx 格式，可直接用于轮询
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+ # 群聊 chat_id 本身就是 oc_xxx 格式，可直接用于轮询回复
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if FEISHU_AD_PROJECT_CHAT_ID not in poll_chat_ids:
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poll_chat_ids.append(FEISHU_AD_PROJECT_CHAT_ID)
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except Exception as e:
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@@ -518,30 +559,29 @@ async def send_approval_request(
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try:
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xlsx_path = _generate_approval_xlsx(df_for_review, request_id)
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- # 导入飞书在线表格并发送链接（项目群）— 临时禁用
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from feishu_doc import import_to_feishu
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- # 发送到项目群 — 临时禁用
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- # if FEISHU_AD_PROJECT_CHAT_ID:
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- # import_result = await import_to_feishu(
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- # ctx=ctx,
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- # xlsx_path=str(xlsx_path),
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- # send_im=True,
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- # chat_id=FEISHU_AD_PROJECT_CHAT_ID
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- # )
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- #
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- # if import_result.metadata and import_result.metadata.get("url"):
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- # sheet_url = import_result.metadata["url"]
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- # logger.info("飞书审批表格导入成功（项目群）: %s", sheet_url)
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- # else:
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- # logger.warning("飞书在线表格导入失败（项目群），回退到文件附件模式")
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- # # 回退：发送文件附件（项目群）
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- # file_result = _feishu.send_file(
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- # to=FEISHU_AD_PROJECT_CHAT_ID,
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- # file=str(xlsx_path),
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- # file_name=f"审批决策表_{request_id}.xlsx",
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- # )
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- # logger.info("飞书审批 Excel（文件）发送成功（项目群）: message_id=%s", file_result.message_id)
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+ # 发送到项目群（与个人一致）
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+ if FEISHU_AD_PROJECT_CHAT_ID:
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+ try:
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+ import_result = await import_to_feishu(
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+ ctx=ctx,
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+ xlsx_path=str(xlsx_path),
|
|
|
+ send_im=True,
|
|
|
+ chat_id=FEISHU_AD_PROJECT_CHAT_ID,
|
|
|
+ )
|
|
|
+ if import_result.metadata and import_result.metadata.get("url"):
|
|
|
+ logger.info("飞书审批表格发送成功（项目群）: %s", import_result.metadata["url"])
|
|
|
+ else:
|
|
|
+ logger.warning("飞书在线表格导入失败（项目群），回退到文件附件模式")
|
|
|
+ file_result = _feishu.send_file(
|
|
|
+ to=FEISHU_AD_PROJECT_CHAT_ID,
|
|
|
+ file=str(xlsx_path),
|
|
|
+ file_name=f"审批决策表_{request_id}.xlsx",
|
|
|
+ )
|
|
|
+ logger.info("飞书审批 Excel（文件）发送成功（项目群）: message_id=%s", file_result.message_id)
|
|
|
+ except Exception as e:
|
|
|
+ logger.warning("发送表格到项目群失败: %s", e)
|
|
|
|
|
|
# 发送到个人
|
|
|
if FEISHU_OPERATOR_OPEN_ID:
|
刘立冬