feat: upgrade客服多店铺分流、批量报价与稳定性防护

api/http_server.py

@@ -275,6 +275,32 @@ def health_check():
     })
 
 
+@app.route('/api/metrics', methods=['GET'])
+def metrics_dashboard():
+    """
+    运行与业务看板
+    Query:
+      - hours: 统计窗口小时数，默认24
+    """
+    try:
+        hours = int(request.args.get('hours', 24))
+        if hours <= 0:
+            hours = 24
+        from utils.metrics_tracker import get_dashboard
+        data = get_dashboard(hours=hours)
+        return jsonify({
+            'code': 200,
+            'message': 'OK',
+            'data': data,
+        })
+    except Exception as e:
+        logger.error(f"指标看板异常：{e}")
+        return jsonify({
+            'code': 500,
+            'message': f'服务器错误：{str(e)}'
+        }), 500
+
+
 def start_http_server(host='0.0.0.0', port=6060, debug=False):
     """启动 HTTP 服务器"""
     global task_manager, task_scheduler

config/README.md

@@ -62,3 +62,31 @@ python scripts/init_designer_roster.py list      # 查看当前数据
 ### 对接方要求（外部 AI 服务）
 
 对端需实现：企微群消息 → 解析「上线」/「下线」→ 存库 → 提供 GET `/online` 接口，按上述格式返回在线名单。
+
+---
+
+## system_inquiry_rules.json
+
+按店铺识别“系统客服询单”消息（非普通买家咨询），并配置处理策略。
+
+```json
+{
+  "enabled": true,
+  "default_action": "silent",
+  "default_reply": "您好，这边已收到询单消息，稍后由人工客服跟进处理。",
+  "sender_keywords": ["系统客服", "官方客服", "平台客服", "机器人客服"],
+  "message_keywords": ["询单", "代客咨询", "平台代问", "系统代发"],
+  "shops": {
+    "test_shop": {
+      "enabled": true,
+      "action": "reply",
+      "reply": "收到，已登记，稍后人工给您回。",
+      "sender_keywords": ["系统客服"],
+      "message_keywords": ["询单"]
+    }
+  }
+}
+```
+
+- `action` 支持: `silent` / `reply` / `transfer`
+- `shops.<acc_id>` 可覆盖全局规则（店铺维度）

config/config.py

@@ -40,6 +40,7 @@ IMAGE_MODULE_ENABLED = os.getenv("IMAGE_MODULE_ENABLED", "false").lower() in ("1
 
 # ========== 防抖配置 ==========
 MESSAGE_DEBOUNCE_SECONDS = int(os.getenv("MESSAGE_DEBOUNCE_SECONDS", "8"))
+OUTBOUND_PER_CUSTOMER_COOLDOWN_SECONDS = int(os.getenv("OUTBOUND_PER_CUSTOMER_COOLDOWN_SECONDS", "5"))
 
 # ========== AI 上下文加载 ==========
 CHAT_CONTEXT_LIMIT = int(os.getenv("CHAT_CONTEXT_LIMIT", "30"))
@@ -47,3 +48,27 @@ CHAT_CONTEXT_TRUNCATE_LEN = int(os.getenv("CHAT_CONTEXT_TRUNCATE_LEN", "160"))
 
 # ========== 报价底线 ==========
 MIN_PRICE_FLOOR = int(os.getenv("MIN_PRICE_FLOOR", "15"))
+MAX_SERVICE_SIZE_LONGEST_METERS = float(os.getenv("MAX_SERVICE_SIZE_LONGEST_METERS", "10"))
+MAX_SERVICE_SIZE_AREA_SQM = float(os.getenv("MAX_SERVICE_SIZE_AREA_SQM", "20"))
+
+# ========== 批量报价/自动处理 ==========
+BATCH_MAX_IMAGES = int(os.getenv("BATCH_MAX_IMAGES", "12"))  # 单次最多处理图片数
+BATCH_ANALYZE_CONCURRENCY = int(os.getenv("BATCH_ANALYZE_CONCURRENCY", "3"))  # 批量识别并发数
+
+# ========== 灰度开关 ==========
+FEATURE_BATCH_QUOTE_ENABLED = os.getenv("FEATURE_BATCH_QUOTE_ENABLED", "true").lower() in ("1", "true", "yes")
+FEATURE_BATCH_QUOTE_PERCENT = int(os.getenv("FEATURE_BATCH_QUOTE_PERCENT", "100"))  # 0-100
+FEATURE_BATCH_QUOTE_SHOPS = os.getenv("FEATURE_BATCH_QUOTE_SHOPS", "")  # 逗号分隔 acc_id 白名单，空=全量
+
+# ========== 系统客服询单识别 ==========
+SYSTEM_INQUIRY_ENABLED = os.getenv("SYSTEM_INQUIRY_ENABLED", "true").lower() in ("1", "true", "yes")
+SYSTEM_INQUIRY_DEFAULT_ACTION = os.getenv("SYSTEM_INQUIRY_DEFAULT_ACTION", "silent").strip().lower()
+SYSTEM_INQUIRY_DEFAULT_REPLY = os.getenv(
+    "SYSTEM_INQUIRY_DEFAULT_REPLY",
+    "您好，这边已收到询单消息，稍后由人工客服跟进处理。",
+)
+SYSTEM_INQUIRY_SHOPS = os.getenv("SYSTEM_INQUIRY_SHOPS", "")  # 逗号分隔店铺白名单，空=全店铺
+SYSTEM_INQUIRY_RULES_FILE = os.getenv(
+    "SYSTEM_INQUIRY_RULES_FILE",
+    str(CONFIG_DIR / "system_inquiry_rules.json"),
+)

config/system_inquiry_rules.json

@@ -0,0 +1,20 @@
+{
+  "enabled": true,
+  "default_action": "silent",
+  "default_reply": "您好，这边已收到询单消息，稍后由人工客服跟进处理。",
+  "sender_keywords": [
+    "系统客服",
+    "官方客服",
+    "平台客服",
+    "机器人客服",
+    "商家客服系统"
+  ],
+  "message_keywords": [
+    "系统询单",
+    "代客咨询",
+    "平台代问",
+    "系统代发",
+    "客服询单"
+  ],
+  "shops": {}
+}

core/pydantic_ai_agent.py

@@ -8,13 +8,16 @@
 import os
 import glob
 import asyncio
-from typing import Optional, Dict
+import random
+import hashlib
+from typing import Optional, Dict, List, Any
 from datetime import datetime
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from pydantic_ai import Agent, RunContext
 from pydantic_ai.models.openai import OpenAIChatModel
 from pydantic_ai.providers.openai import OpenAIProvider
 from dotenv import load_dotenv
+from utils.metrics_tracker import emit as metrics_emit
 
 load_dotenv()
 
@@ -87,6 +90,8 @@ class ConversationState(BaseModel):
     order_status: Optional[str] = None  # 订单状态
     discount_count: int = 0  # 让价次数
     image_count: int = 0  # 图片数量
+    pending_image_urls: List[str] = Field(default_factory=list)  # 待统一报价图片
+    pending_requirements: List[str] = Field(default_factory=list)  # 待统一报价需求
     last_update: str = ""
     last_reply_at: Optional[datetime] = None   # 最后一次回复客户的时间
 
@@ -150,6 +155,12 @@ def load_skill_md(skills_dir: str = "skills") -> str:
 
 class CustomerServiceAgent:
     """客服 Agent - 支持 SKILL.md + 工作流"""
+    C_RESET = "\033[0m"
+    C_PROMPT = "\033[96m"      # cyan
+    C_THINK = "\033[95m"       # magenta
+    C_TOOL = "\033[93m"        # yellow
+    C_REPLY = "\033[92m"       # green
+    C_MUTED = "\033[90m"       # gray
 
     def __init__(self, skills_dir: str = "skills"):
         self.api_key = os.getenv("OPENAI_API_KEY")
@@ -218,6 +229,23 @@ class CustomerServiceAgent:
         # 注册工具
         self._register_tools()
 
+    @staticmethod
+    def _log_block(title: str, content: str):
+        """统一的控制台分层日志输出。"""
+        print(f"{CustomerServiceAgent.C_PROMPT}[{title}]{CustomerServiceAgent.C_RESET}")
+        print(content)
+        print(f"{CustomerServiceAgent.C_MUTED}────────────────────{CustomerServiceAgent.C_RESET}")
+
+    @staticmethod
+    def _normalize_reply_text(text: Optional[str]) -> str:
+        """清洗模型输出，避免把占位词直接发给客户。"""
+        if text is None:
+            return ""
+        cleaned = str(text).strip()
+        if cleaned.lower() in {"无", "none", "null", "n/a"}:
+            return ""
+        return cleaned
+
     def _register_tools(self):
         """注册所有 Tool，让 Agent 可以主动调用"""
 
@@ -802,11 +830,15 @@ class CustomerServiceAgent:
                         self.message_histories.pop(customer_id, None)
                 except Exception:
                     pass
+            # 进程内状态为空时，尝试从持久化恢复
+            if not state.pending_image_urls and not state.pending_requirements:
+                self._restore_pending_quote_state(customer_id, state)
         else:
             self.conversations[customer_id] = ConversationState(
                 customer_id=customer_id,
                 last_update=now.isoformat()
             )
+            self._restore_pending_quote_state(customer_id, self.conversations[customer_id])
 
         # 定期清理长期不活跃客户（超过 7 天）
         self._cleanup_inactive(now)
@@ -827,6 +859,63 @@ class CustomerServiceAgent:
             self.conversations.pop(cid, None)
             self.message_histories.pop(cid, None)
 
+    def _sync_pending_quote_state(self, customer_id: str, state: ConversationState):
+        """把待报价队列同步到客户库，避免重启丢失。"""
+        try:
+            from db.customer_db import db
+            db.update_pending_quote_state(
+                customer_id,
+                state.pending_image_urls,
+                state.pending_requirements,
+            )
+        except Exception:
+            pass
+
+    def _restore_pending_quote_state(self, customer_id: str, state: ConversationState):
+        """从客户库恢复待报价队列。"""
+        try:
+            from db.customer_db import db
+            profile = db.get_customer(customer_id)
+            state.pending_image_urls = list(getattr(profile, "pending_quote_images", []) or [])
+            state.pending_requirements = list(getattr(profile, "pending_quote_requirements", []) or [])
+            state.image_count = len(state.pending_image_urls)
+        except Exception:
+            pass
+
+    def _build_reject_message(self, reason: str = "") -> str:
+        templates = [
+            "这类图文字内容太密了，我们这边不接这单哈，建议精简后再发我看看。",
+            "这种密集文字/宣传栏类图片暂时做不了，抱歉啦，换一版简化内容我可以继续帮你看。",
+            "这张文字信息太多，处理风险高，我们先不接，您可以先筛重点文字再发我。",
+        ]
+        msg = random.choice(templates)
+        if reason:
+            msg += f"（{reason}）"
+        return msg
+
+    def _is_batch_quote_enabled(self, customer_id: str, acc_id: str) -> bool:
+        """灰度开关：按店铺白名单 + 客户哈希百分比控制新策略是否生效。"""
+        try:
+            from config.config import (
+                FEATURE_BATCH_QUOTE_ENABLED,
+                FEATURE_BATCH_QUOTE_PERCENT,
+                FEATURE_BATCH_QUOTE_SHOPS,
+            )
+            if not FEATURE_BATCH_QUOTE_ENABLED:
+                return False
+            pct = max(0, min(100, int(FEATURE_BATCH_QUOTE_PERCENT)))
+            if pct == 0:
+                return False
+            shops = [s.strip() for s in (FEATURE_BATCH_QUOTE_SHOPS or "").split(",") if s.strip()]
+            if shops and (acc_id or "") not in shops:
+                return False
+            if pct >= 100:
+                return True
+            h = int(hashlib.md5((customer_id or "").encode("utf-8")).hexdigest()[:8], 16) % 100
+            return h < pct
+        except Exception:
+            return True
+
     def _detect_stage(self, message: str) -> str:
         """检测售前/售后"""
         # 系统订单通知不属于售后，单独处理
@@ -845,7 +934,7 @@ class CustomerServiceAgent:
 核心原则：快、准、狠。**回复要像真人聊天，自然多变，禁止套模板、背台词。**
 
 【你拥有的工具，按需调用】
-- analyze_image(url)：收到图片必须调用，分析复杂度获取报价依据
+- analyze_image(url)：客户确认“图片发完”后调用，分析复杂度用于统一报价
 - process_image_gemini(customer_id)：客户付款或说「安排/处理」时调用，走完整流程
 - remove_background(image_url)：只要去背景时单独调用
 - perspective_correct(image_url)：只要透视矫正时调用（需白底图）
@@ -864,9 +953,9 @@ class CustomerServiceAgent:
 【报价规则】
 - 价格必须为5的整数倍（10/15/20/25/30），禁止报12、17、23等
 - 客户只是文字询价，没发图 → 自然引导发图，不报价
-- 收到图片 → 立刻调用 analyze_image() → 工具返回结果后【必须】立刻回复客户报价
+- 收到图片先收集，不立刻报单张价；等客户明确“发完了/统一报价”后，再统一报价
 - 报价和推成交的话术要自然多变，跟着客户语气走，不要每次都一样
-- analyze_image 工具调用完成后，你的下一句话一定是报价，不能是内部说明
+- 客户确认发完后，分析完成的下一句话必须是明确报价
 - 报价后立刻推成交，不等客户反应
 
 【文字加价规则】⚠️ 重要
@@ -1215,6 +1304,7 @@ class CustomerServiceAgent:
 
     async def process_message(self, message: CustomerMessage) -> AgentResponse:
         """处理客户消息并生成回复"""
+        metrics_emit("inbound_msg", customer_id=message.from_id, acc_id=message.acc_id)
         # 获取或创建对话状态
         state = self._get_conversation_state(message.from_id)
 
@@ -1267,6 +1357,62 @@ class CustomerServiceAgent:
                 print(f"[Agent] 订单通知静默（{pay_status or order_status}），跳过回复")
                 return AgentResponse(reply="", should_reply=False, need_transfer=False)
 
+        # 找图店：先收集图片和需求，等客户确认“发完”后统一报价
+        customer_text, _ = self._split_customer_text(message.msg)
+        shop_type = _get_shop_type(message.acc_id or "", message.goods_name or "")
+        if shop_type == "find_image" and self._is_batch_quote_enabled(message.from_id, message.acc_id):
+            incoming_urls = self._extract_image_urls(customer_text)
+            text_without_urls = self._strip_urls_from_text(customer_text)
+
+            if incoming_urls:
+                for u in incoming_urls:
+                    if u not in state.pending_image_urls:
+                        state.pending_image_urls.append(u)
+                if text_without_urls:
+                    self._append_requirement(state, text_without_urls)
+                state.image_count = len(state.pending_image_urls)
+                self._sync_pending_quote_state(message.from_id, state)
+
+                if self._is_batch_finish_signal(customer_text):
+                    quote_res = await self._quote_pending_images(state, message)
+                    reply_text = quote_res.get("reply", "")
+                    need_transfer = bool(quote_res.get("need_transfer"))
+                    state.last_reply_at = datetime.now()
+                    print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply_text}")
+                    return AgentResponse(
+                        reply=reply_text,
+                        should_reply=not need_transfer,
+                        need_transfer=need_transfer,
+                        transfer_msg=TRANSFER_MESSAGE if need_transfer else "",
+                    )
+
+                ack = self._build_collect_ack(len(state.pending_image_urls))
+                state.last_reply_at = datetime.now()
+                print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {ack}")
+                return AgentResponse(reply=ack, should_reply=True, need_transfer=False)
+
+            if state.pending_image_urls:
+                if text_without_urls:
+                    self._append_requirement(state, text_without_urls)
+                    self._sync_pending_quote_state(message.from_id, state)
+                if self._is_batch_finish_signal(customer_text):
+                    quote_res = await self._quote_pending_images(state, message)
+                    reply_text = quote_res.get("reply", "")
+                    need_transfer = bool(quote_res.get("need_transfer"))
+                    state.last_reply_at = datetime.now()
+                    print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply_text}")
+                    return AgentResponse(
+                        reply=reply_text,
+                        should_reply=not need_transfer,
+                        need_transfer=need_transfer,
+                        transfer_msg=TRANSFER_MESSAGE if need_transfer else "",
+                    )
+
+                remind = self._build_collect_remind(len(state.pending_image_urls))
+                state.last_reply_at = datetime.now()
+                print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {remind}")
+                return AgentResponse(reply=remind, should_reply=True, need_transfer=False)
+
         # 构建提示词（包含对话状态 + 客户画像）
         user_prompt = self._build_prompt(message, state)
 
@@ -1300,7 +1446,7 @@ class CustomerServiceAgent:
         # 取出该客户的历史对话，传给 AI 保持上下文
         history = self.message_histories.get(message.from_id, [])
 
-        print(f"[Agent] ── 发送给AI的提示词 ──\n{user_prompt}\n────────────────────")
+        self._log_block("PROMPT->AI 前置提示词", user_prompt)
 
         try:
             msg_lower = message.msg.lower()
@@ -1323,7 +1469,7 @@ class CustomerServiceAgent:
             result = await target_agent.run(user_prompt, deps=deps, message_history=history)
             # 更新历史，最多保留最近 30 条消息防止 token 超限
             self.message_histories[message.from_id] = result.all_messages()[-30:]
-            reply_text = result.output
+            reply_text = self._normalize_reply_text(result.output)
             # 拦截超低杀价：客户报价低于底线时，统一礼貌拒绝
             try:
                 from config.config import MIN_PRICE_FLOOR
@@ -1371,16 +1517,17 @@ class CustomerServiceAgent:
                 for part in getattr(msg, 'parts', []):
                     part_type = type(part).__name__
                     if 'ToolCall' in part_type:
-                        print(f"[Agent] 工具调用: {getattr(part, 'tool_name', '')}({getattr(part, 'args', '')})")
+                        print(f"{self.C_TOOL}[THINK/TOOL_CALL]{self.C_RESET} {getattr(part, 'tool_name', '')}({getattr(part, 'args', '')})")
                     elif 'ToolReturn' in part_type:
                         ret = str(getattr(part, 'content', ''))[:120]
-                        print(f"[Agent] 工具返回: {ret}")
+                        print(f"{self.C_TOOL}[THINK/TOOL_RETURN]{self.C_RESET} {ret}")
 
-            print(f"[Agent] AI原始输出: {repr(reply_text)}")
+            print(f"{self.C_THINK}[THINK/RAW_OUTPUT]{self.C_RESET} {repr(reply_text)}")
 
         except Exception as e:
             err_str = str(e)
             print(f"[Agent] AI 调用失败: {e}，使用兜底回复")
+            metrics_emit("ai_call_failed", customer_id=message.from_id, acc_id=message.acc_id)
             if "AccountOverdueError" in err_str or "overdue" in err_str.lower():
                 asyncio.create_task(_notify_wechat_overdue())
             else:
@@ -1392,6 +1539,8 @@ class CustomerServiceAgent:
                     tag="AI异常"
                 ))
             reply_text = None
+        else:
+            metrics_emit("ai_call_success", customer_id=message.from_id, acc_id=message.acc_id)
 
         # AI 失败兜底：给一个不出错的万能回复
         if not reply_text:
@@ -1434,6 +1583,7 @@ class CustomerServiceAgent:
         if reply_text and any(kw in reply_text for kw in transfer_keywords):
             need_transfer = True
             transfer_msg = TRANSFER_MESSAGE
+            metrics_emit("transfer_to_human", customer_id=message.from_id, acc_id=message.acc_id)
 
         # 未成交记录：客户表达放弃且已报价过（转人工不记录）
         customer_text, _ = self._split_customer_text(message.msg)
@@ -1451,6 +1601,9 @@ class CustomerServiceAgent:
         # 记录本次回复时间，供冷却期判断
         if should_reply:
             state.last_reply_at = datetime.now()
+            print(f"{self.C_REPLY}[REPLY->CUSTOMER]{self.C_RESET} {reply_text}")
+        else:
+            print(f"{self.C_MUTED}[REPLY->CUSTOMER]{self.C_RESET} <静默/不发送>")
 
         return AgentResponse(reply=reply_text, should_reply=should_reply, need_transfer=need_transfer, transfer_msg=transfer_msg)
 
@@ -1461,6 +1614,7 @@ class CustomerServiceAgent:
         if numbers:
             price = round(int(numbers[0]) / 5) * 5  # 强制为5的整数倍
             state.last_price = price
+            metrics_emit("quote_generated", customer_id=state.customer_id, price=price)
             # 持久化到客户数据库，重启后仍可读取
             try:
                 from db.customer_db import db
@@ -1698,24 +1852,323 @@ class CustomerServiceAgent:
 
     def _extract_image_url(self, msg: str) -> str:
         """从消息中提取图片URL，兼容纯URL和 text#*#url 两种格式"""
+        urls = self._extract_image_urls(msg)
+        return urls[0] if urls else ""
+
+    def _extract_image_urls(self, msg: str) -> List[str]:
+        """提取消息中的所有图片URL（去重保序）。"""
+        import re
+        if not msg:
+            return []
+        image_exts = (".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp")
+        image_hosts = ("alicdn.com", "imgextra", "taobao.com", "jd.com", "pinduoduo.com", "suning.com")
+        candidates = re.findall(r'https?://[^\s#]+', msg)
+        urls: List[str] = []
+        for u in candidates:
+            low = u.lower()
+            if any(ext in low for ext in image_exts) or any(h in low for h in image_hosts):
+                if u not in urls:
+                    urls.append(u)
+        return urls
+
+    def _strip_urls_from_text(self, msg: str) -> str:
+        """去掉 URL 后的纯文本，用于提取额外需求。"""
         import re
         if not msg:
             return ""
-        # 处理 "有吗#*#https://..." 格式
-        if "#*#" in msg:
-            parts = msg.split("#*#", 1)
-            candidate = parts[1].strip()
-            if candidate.startswith(("http://", "https://")):
-                return candidate
-        # 纯URL或URL在任意位置
-        m = re.search(r'https?://\S+', msg)
-        if m:
-            url = m.group()
-            image_exts = (".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp")
-            image_hosts = ("alicdn.com", "imgextra", "taobao.com", "jd.com", "pinduoduo.com", "suning.com")
-            if any(ext in url.lower() for ext in image_exts) or any(h in url.lower() for h in image_hosts):
-                return url
-        return ""
+        text = re.sub(r'https?://\S+', ' ', msg)
+        text = text.replace("#*#", " ").strip()
+        text = re.sub(r'\s+', ' ', text)
+        return text.strip("，,。.!！？?；;：: ")
+
+    def _is_batch_finish_signal(self, text: str) -> bool:
+        """客户是否表达“图发完了，可以统一报价”。"""
+        if not text:
+            return False
+        finish_keywords = [
+            "发完了", "都发完了", "发齐了", "齐了", "先这些", "就这些", "全部", "一起报", "统一报价",
+            "总共多少钱", "一共多少钱", "打包价", "总价", "报价吧", "报个总价", "给个总价",
+        ]
+        return any(k in text for k in finish_keywords)
+
+    def _build_collect_ack(self, count: int) -> str:
+        templates = [
+            "收到，这边先记下了（已收{n}张）。你继续发，等你发完我再一起给你打包报价。",
+            "好的，当前这批先收到了（第{n}张）。还有图就继续发，发齐我一次性给你总价。",
+            "没问题，已记录到第{n}张。你把需求和图片都发完，我统一给你报更合适的价格。",
+        ]
+        return random.choice(templates).format(n=count)
+
+    def _build_collect_remind(self, count: int) -> str:
+        templates = [
+            "需求我记下了（当前共{n}张图）。你继续发齐，发完回我“发完了”，我一次性给你总价。",
+            "好的，这条需求也加上了（现在{n}张）。等你说发完，我立刻统一报价。",
+            "收到，这个要求我也记住了（共{n}张）。你发完我就给你打包价。",
+        ]
+        return random.choice(templates).format(n=count)
+
+    def _append_requirement(self, state: ConversationState, text: str):
+        """追加需求并做去重/截断，减少上下文噪音。"""
+        t = (text or "").strip()
+        if not t:
+            return
+        t = t[:120]
+        if state.pending_requirements and state.pending_requirements[-1] == t:
+            return
+        if t in state.pending_requirements[-5:]:
+            return
+        state.pending_requirements.append(t)
+        if len(state.pending_requirements) > 20:
+            state.pending_requirements = state.pending_requirements[-20:]
+
+    def _calc_requirement_surcharge(self, requirements: List[str]) -> Dict[str, Any]:
+        """
+        把客户补充需求做成结构化加价，避免纯靠模型自由发挥导致价格波动。
+        返回:
+            {"extra": int, "hits": List[str]}
+        """
+        text = " ".join(requirements or [])
+        rules = [
+            (["分层", "psd", "源文件"], 30, "分层/源文件"),
+            (["去背景", "抠图", "透明底", "白底"], 5, "去背景"),
+            (["换背景", "换场景", "合成"], 10, "合成/换背景"),
+            (["改字", "改文字", "替换文字", "排版"], 10, "改文字/排版"),
+            (["调色", "改色", "换色", "配色"], 5, "调色"),
+            (["多版本", "多个版本", "两版", "三版"], 10, "多版本"),
+            (["加急", "今天要", "马上要", "尽快"], 10, "加急"),
+        ]
+        total = 0
+        hits: List[str] = []
+        for keywords, fee, label in rules:
+            if any(k in text for k in keywords):
+                total += fee
+                hits.append(f"{label}+{fee}")
+        # 防止需求加价过高，做个上限保护
+        total = min(total, 60)
+        # 金额统一 5 的倍数
+        total = round(total / 5) * 5
+        return {"extra": total, "hits": hits}
+
+    def _build_batch_quote_reply(
+        self,
+        results: List[Tuple[str, Dict[str, Any]]],
+        total_suggest: int,
+        bundle_price: int,
+        req_fee: Dict[str, Any],
+    ) -> str:
+        """构建分图明细 + 单条总报价可选项回复。"""
+        complexity_map = {
+            "simple": "简单",
+            "normal": "常规",
+            "complex": "复杂",
+            "hard": "高难",
+        }
+        detail_lines: List[str] = []
+        for i, (_, r) in enumerate(results, 1):
+            p = int(r.get("price_suggest", 20) or 20)
+            cx = complexity_map.get(str(r.get("complexity", "normal")), "常规")
+            reason = str(r.get("reason", "常规处理")).replace("\n", " ").strip()
+            if len(reason) > 18:
+                reason = reason[:18] + "..."
+            detail_lines.append(f"图{i}：{p}元（{cx}，{reason}）")
+
+        extra = int(req_fee.get("extra", 0) or 0)
+        single_total = round((total_suggest + extra) / 5) * 5
+        req_hit = "、".join(req_fee.get("hits", [])) if req_fee.get("hits") else ""
+
+        lines = ["先给你分图报下："]
+        lines.extend(detail_lines)
+        if req_hit:
+            lines.append(f"需求加价：+{extra}元（{req_hit}）")
+        option_line = f"可选：A按单张做共{single_total}元；B打包一起做{bundle_price}元（更划算）。"
+        lines.append(option_line)
+        lines.append("你定一个方案，我这边马上安排。")
+        return "\n".join(lines)
+
+    async def _quote_pending_images(self, state: ConversationState, message: CustomerMessage) -> Dict[str, Any]:
+        """
+        批量识别待处理图片并统一处理：
+        - find_image 意图且可自动处理：直接 Gemini 处理 + 上传图绘 + 回链接
+        - 高风险/不可做：转人工
+        - 其他：统一报价
+        """
+        from image.image_analyzer import image_analyzer
+
+        urls = list(state.pending_image_urls)
+        if not urls:
+            return {"reply": "你先把图片发我，我看完再给你统一报价。", "need_transfer": False}
+        try:
+            from config.config import BATCH_MAX_IMAGES, BATCH_ANALYZE_CONCURRENCY
+            max_images = max(1, int(BATCH_MAX_IMAGES))
+            analyze_concurrency = max(1, int(BATCH_ANALYZE_CONCURRENCY))
+        except Exception:
+            max_images = 12
+            analyze_concurrency = 3
+        if len(urls) > max_images:
+            return {
+                "reply": f"这次图片有点多（{len(urls)}张），我先按前{max_images}张处理报价，剩下的下一批继续发我。",
+                "need_transfer": False,
+            }
+        urls = urls[:max_images]
+
+        sem = asyncio.Semaphore(analyze_concurrency)
+        async def _analyze_one(url: str):
+            async with sem:
+                try:
+                    r = await image_analyzer.analyze(url)
+                except Exception:
+                    r = {
+                        "complexity": "normal",
+                        "reason": "识别异常，按常规估价",
+                        "price_min": 15,
+                        "price_max": 25,
+                        "price_suggest": 20,
+                        "success": False,
+                    }
+                return url, r
+
+        results = list(await asyncio.gather(*[_analyze_one(u) for u in urls]))
+        for url, r in results:
+            # 与单图流程一致：识别后写入 workflow 任务
+            try:
+                from core.workflow import workflow
+                await workflow.image_analysis_result(
+                    customer_id=message.from_id,
+                    image_url=url,
+                    complexity=r.get("complexity", "normal"),
+                    acc_id=message.acc_id,
+                    acc_type=message.acc_type,
+                    gemini_prompt=r.get("gemini_prompt", ""),
+                    aspect_ratio=r.get("aspect_ratio", "1:1"),
+                    perspective=r.get("perspective", "no"),
+                    proc_type=r.get("proc_type", ""),
+                    subject=r.get("subject", ""),
+                    quality=r.get("quality", ""),
+                )
+            except Exception as e:
+                print(f"[Agent] Workflow 批量任务创建失败: {e}")
+
+        total_min = sum(int(r.get("price_min", 15) or 15) for _, r in results)
+        total_max = sum(int(r.get("price_max", 25) or 25) for _, r in results)
+        total_suggest = sum(int(r.get("price_suggest", 20) or 20) for _, r in results)
+        req_fee = self._calc_requirement_surcharge(state.pending_requirements)
+
+        # 打包优惠：2 张减 5，3 张及以上按 9 折（四舍五入到 5 元）
+        if len(results) == 2:
+            bundle_price = max(10, total_suggest - 5)
+        elif len(results) >= 3:
+            bundle_price = max(10, round(total_suggest * 0.9 / 5) * 5)
+        else:
+            bundle_price = total_suggest
+        bundle_price += int(req_fee.get("extra", 0) or 0)
+        bundle_price = round(bundle_price / 5) * 5
+
+        # 先分流：高风险/不可做 -> 转人工
+        unsafe = []
+        dense_text_reject = []
+        for i, (_, r) in enumerate(results, 1):
+            if r.get("feasibility") == "no" or r.get("risk") == "high":
+                unsafe.append(f"图{i}")
+                note = str(r.get("note", "") or "")
+                if "文字内容过于密集" in note or "密集文字" in note:
+                    dense_text_reject.append(f"图{i}")
+        if unsafe:
+            state.pending_image_urls.clear()
+            state.pending_requirements.clear()
+            self._sync_pending_quote_state(message.from_id, state)
+            if dense_text_reject and len(dense_text_reject) == len(unsafe):
+                return {
+                    "reply": self._build_reject_message("文字密集类图片暂不接单"),
+                    "need_transfer": False,
+                }
+            return {
+                "reply": f"这批里{'、'.join(unsafe)}处理风险较高，我先帮你转人工设计师跟进会更稳妥。",
+                "need_transfer": True,
+            }
+
+        # 查找图片意图：直接自动处理并返回图绘链接
+        intent_text = (message.msg or "") + " " + " ".join(state.pending_requirements[-5:])
+        workflow_type, _ = self.workflow_router.detect_workflow(intent_text)
+        if workflow_type == "find_image":
+            links = []
+            try:
+                from image.image_processor import image_processor
+                from utils.image_queue import run_with_queue
+                for idx, (url, r) in enumerate(results, 1):
+                    req_parts = [f"complexity:{r.get('complexity', 'normal')}"]
+                    if r.get("gemini_prompt"):
+                        req_parts.append(f"prompt:{r.get('gemini_prompt')}")
+                    if r.get("aspect_ratio"):
+                        req_parts.append(f"ratio:{r.get('aspect_ratio')}")
+                    if r.get("perspective") and r.get("perspective") != "no":
+                        req_parts.append(f"perspective:{r.get('perspective')}")
+                    if r.get("proc_type"):
+                        req_parts.append(f"proc_type:{r.get('proc_type')}")
+                    if r.get("subject"):
+                        req_parts.append(f"subject:{r.get('subject')}")
+                    if r.get("quality"):
+                        req_parts.append(f"quality:{r.get('quality')}")
+
+                    process_res = await run_with_queue(image_processor.process_image(
+                        url,
+                        "enhance",
+                        requirements="|".join(req_parts),
+                        gemini_prompt=r.get("gemini_prompt", ""),
+                        aspect_ratio=r.get("aspect_ratio", "1:1"),
+                        perspective=r.get("perspective", "no"),
+                        proc_type=r.get("proc_type", ""),
+                        subject=r.get("subject", ""),
+                        quality=r.get("quality", ""),
+                    ))
+                    if not process_res.get("success"):
+                        raise RuntimeError(process_res.get("message", "图片处理失败"))
+
+                    ok, link, _ = await upload_to_tuhui(
+                        process_res["result_path"],
+                        title=f"客户{message.from_id[-4:]}-图片{idx}",
+                        description="AI自动处理结果",
+                        price=max(10, int(r.get("price_suggest", 20) or 20) + int(req_fee.get("extra", 0) or 0) // max(1, len(results))),
+                    )
+                    if not ok:
+                        raise RuntimeError(str(link))
+                    links.append(link)
+            except Exception as e:
+                print(f"[Agent] 自动处理并上传失败，回退统一报价: {e}")
+            else:
+                lines = [f"这批我先给你处理好了，按打包 {bundle_price} 元。"]
+                for i, link in enumerate(links, 1):
+                    lines.append(f"链接{i}：{link}")
+                lines.append("你先看下效果，没问题我就按这个标准继续给你做。")
+                state.last_price = bundle_price
+                try:
+                    from db.customer_db import db
+                    db.update_last_price(message.from_id, bundle_price)
+                except Exception:
+                    pass
+                state.pending_image_urls.clear()
+                state.pending_requirements.clear()
+                self._sync_pending_quote_state(message.from_id, state)
+                return {"reply": "\n".join(lines), "need_transfer": False}
+
+        reply_text = self._build_batch_quote_reply(
+            results=results,
+            total_suggest=total_suggest,
+            bundle_price=bundle_price,
+            req_fee=req_fee,
+        )
+
+        state.last_price = bundle_price
+        try:
+            from db.customer_db import db
+            db.update_last_price(message.from_id, bundle_price)
+        except Exception:
+            pass
+
+        # 清空待报价队列（本轮已统一报价）
+        state.pending_image_urls.clear()
+        state.pending_requirements.clear()
+        self._sync_pending_quote_state(message.from_id, state)
+        return {"reply": reply_text, "need_transfer": False}
 
     def _split_customer_text(self, msg: str) -> tuple:
         """
@@ -1823,11 +2276,11 @@ class CustomerServiceAgent:
             if shop_type == "gemini_api":
                 prompt += "\n【Gemini API 店铺】客户问账号/pro/续费/套餐等，按 API 客服自然回复，不要求发图。"
             elif image_url:
-                prompt += f"\n客户发来图片（URL: {image_url}）。必须：① 调用 analyze_image('{image_url}') ② 拿到结果后直接回复报价，话术自然多变。分析完必须回复，不能不回复。"
+                prompt += "\n客户在继续发图阶段：先确认“已收图”，并引导客户把图和要求一次发完；等客户明确“发完了/统一报价”后再统一报价。"
             elif any(kw in customer_text for kw in price_keywords):
                 last_url = self._extract_image_url(msg_content)
                 if last_url:
-                    prompt += f"\n客户在询问上面那张图的价格，图片URL是 {last_url}。调用 analyze_image('{last_url}') 后直接回复报价，不能不回复。"
+                    prompt += "\n客户在询问价格：若客户已确认发完，则给总报价；若还在发图，先引导发完后统一报价。"
                 else:
                     prompt += "\n客户在询问价格但未发图，回复「发图来我看看」。"
             elif any(kw in customer_text for kw in progress_keywords):

core/websocket_client.py

@@ -3,10 +3,12 @@ import websockets
 import json
 import re
 import logging
+import random
+import time
 from collections import deque
 from datetime import datetime
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Dict, Any, List
 
 # ========== 转接分组映射 ==========
 def _get_transfer_group(acc_id: str) -> str:
@@ -49,6 +51,7 @@ import os
 logger = setup_logger()
 
 from db.chat_log_db import log_message as _chat_log
+from utils.metrics_tracker import emit as metrics_emit
 
 # 导入 Agent 模块
 try:
@@ -94,6 +97,8 @@ class QingjianAPIClient:
         self._agent_semaphore = asyncio.Semaphore(8)
         self._pending_images: dict = {}
         self._pending_image_tasks: dict = {}
+        self._system_inquiry_rules = self._load_system_inquiry_rules()
+        self._last_reply_sent_at: dict = {}  # customer_key -> monotonic ts
         
         # 延迟加载任务模块（避免循环导入）
         self.task_scheduler = None
@@ -289,7 +294,7 @@ class QingjianAPIClient:
                     if self._is_transfer_msg(data):
                         # 会话转交 → 主动打招呼
                         print(f"[{self.get_time()}] 收到转交消息，发送问候")
-                        greeting = "在呢，发图来我看看"
+                        greeting = self._pick_transfer_greeting()
                         await self.send_reply(data, greeting)
                         try:
                             from utils.wechat_chat_log import push_chat_to_wechat
@@ -324,6 +329,8 @@ class QingjianAPIClient:
                                 ))
                             except Exception:
                                 pass
+                    elif await self._handle_system_inquiry(data):
+                        print(f"[{self.get_time()}] 系统客服询单消息，已按规则处理")
                     elif self._should_ignore(data):
                         print(f"[{self.get_time()}] 系统通知，跳过回复")
                     else:
@@ -529,6 +536,12 @@ class QingjianAPIClient:
                 except Exception:
                     pass
 
+            # 超大尺寸（米制）直接拒单，避免进入报价/处理流程
+            oversize_reply = self._oversize_reply_if_needed(msg_text)
+            if oversize_reply:
+                await self.send_reply(data, oversize_reply)
+                return
+
             # 消息含图片URL：累积到待处理列表，先询问要求
             if self._msg_has_image_url(msg_text):
                 urls = self._extract_image_urls(msg_text)
@@ -543,6 +556,7 @@ class QingjianAPIClient:
                     await self._flush_pending_images(capture_key, capture_data)
                 task = asyncio.create_task(_delay_flush(key, data))
                 self._pending_image_tasks[key] = task
+                return
             elif self._msg_refers_images(msg_text):
                 urls = self._collect_recent_image_urls(_cid, data.get('acc_id', ''), max_count=6)
                 if urls:
@@ -550,20 +564,24 @@ class QingjianAPIClient:
                     self._add_pending_images(key, urls)
                     await self.send_reply(data, "稍等，我找找刚才那几张")
                     await self._flush_pending_images(key, data)
+                    return
                 else:
                     status = self._detect_order_status(msg_text)
                     if status == "paid":
                         ack = "收到付款，我马上安排处理，有需要第一时间联系您"
                         await self.send_reply(data, ack)
+                        return
                     elif status in ("waiting", "order"):
                         ack = "订单我看到了哈，方便的话请完成付款，我好安排处理"
                         await self.send_reply(data, ack)
+                        return
             else:
                 urls = self._extract_image_urls(msg_text)
                 if len(urls) == 1:
                     key = self._customer_key(data)
                     self._add_pending_images(key, urls)
                     await self.send_reply(data, "收到，我看看哈")
+                    return
                 else:
                     if self._msg_requests_external_contact(msg_text):
                         reply = "这里沟通就可以哦，其他联系方式不方便"
@@ -603,9 +621,11 @@ class QingjianAPIClient:
                     if status == "paid":
                         ack = "收到付款，我马上安排处理，有需要第一时间联系您"
                         await self.send_reply(data, ack)
+                        return
                     elif status in ("waiting", "order"):
                         ack = "订单我看到了哈，方便的话请完成付款，我好安排处理"
                         await self.send_reply(data, ack)
+                        return
 
             # 构建 CustomerMessage
             customer_msg = CustomerMessage(
@@ -813,11 +833,68 @@ class QingjianAPIClient:
         lower = msg.lower()
         kws = ("加qq", "qq号", "vx", "微信", "加v", "联系方式", "私聊", "加一下", "加个", "手机号", "电话", "加群", "q q", "v 信")
         return any(k in lower for k in kws)
+
+    @staticmethod
+    def _extract_size_pairs_m(msg: str) -> list[tuple[float, float]]:
+        """提取消息中的米制尺寸对，如 15*6.4米 / 15米*6.4 / 15x6.4m。"""
+        if not msg:
+            return []
+        s = (msg or "").lower().replace("×", "*").replace("x", "*")
+        pairs = []
+        patterns = [
+            r'(\d+(?:\.\d+)?)\s*\*\s*(\d+(?:\.\d+)?)\s*(?:米|m)\b',
+            r'(\d+(?:\.\d+)?)\s*(?:米|m)\s*\*\s*(\d+(?:\.\d+)?)\b',
+        ]
+        for p in patterns:
+            for m in re.findall(p, s):
+                try:
+                    a = float(m[0])
+                    b = float(m[1])
+                    if a > 0 and b > 0:
+                        pairs.append((a, b))
+                except Exception:
+                    continue
+        return pairs
+
+    def _oversize_reply_if_needed(self, msg: str) -> str:
+        """
+        检测超大尺寸需求并返回拒绝话术；未命中返回空字符串。
+        规则：最长边 > 阈值 或 面积 > 阈值。
+        """
+        try:
+            from config.config import MAX_SERVICE_SIZE_LONGEST_METERS, MAX_SERVICE_SIZE_AREA_SQM
+            longest_limit = float(MAX_SERVICE_SIZE_LONGEST_METERS)
+            area_limit = float(MAX_SERVICE_SIZE_AREA_SQM)
+        except Exception:
+            longest_limit = 10.0
+            area_limit = 20.0
+
+        pairs = self._extract_size_pairs_m(msg)
+        for w, h in pairs:
+            longest = max(w, h)
+            area = w * h
+            if longest > longest_limit or area > area_limit:
+                return (
+                    f"{w:g}米*{h:g}米这个尺寸太大了，我们这边做不了。"
+                    "如果要做可以拆成几段小尺寸，我再给你按段评估。"
+                )
+        return ""
     def _is_transfer_msg(self, data: dict) -> bool:
         """判断是否是会话转交消息（需要主动打招呼）"""
         msg = self.to_chinese(data.get('msg', ''))
         return '转交给' in msg or '转接给' in msg
 
+    def _pick_transfer_greeting(self) -> str:
+        """转接后问候话术：简短自然，随机避免机械感。"""
+        choices = [
+            "在的亲，发图我看下",
+            "在呢亲，有需求直接说",
+            "我在的，您把要求发我",
+            "在的哈，你说我这边看着处理",
+            "在呢，图和需求发来我看看",
+        ]
+        return random.choice(choices)
+
     def _is_shop_card(self, data: dict) -> bool:
         """判断是否是进店卡片消息"""
         msg = self.to_chinese(data.get('msg', ''))
@@ -838,6 +915,130 @@ class QingjianAPIClient:
         except Exception:
             return False
 
+    def _load_system_inquiry_rules(self) -> Dict[str, Any]:
+        """加载系统客服询单规则（全局 + 店铺覆盖）。"""
+        from config.config import (
+            SYSTEM_INQUIRY_ENABLED,
+            SYSTEM_INQUIRY_DEFAULT_ACTION,
+            SYSTEM_INQUIRY_DEFAULT_REPLY,
+            SYSTEM_INQUIRY_RULES_FILE,
+        )
+        enabled_env = os.getenv("SYSTEM_INQUIRY_ENABLED")
+        enabled = (
+            enabled_env.lower() in ("1", "true", "yes")
+            if isinstance(enabled_env, str)
+            else bool(SYSTEM_INQUIRY_ENABLED)
+        )
+        action = (os.getenv("SYSTEM_INQUIRY_DEFAULT_ACTION") or SYSTEM_INQUIRY_DEFAULT_ACTION or "silent").strip().lower()
+        reply = os.getenv("SYSTEM_INQUIRY_DEFAULT_REPLY") or SYSTEM_INQUIRY_DEFAULT_REPLY or ""
+        rules_file = os.getenv("SYSTEM_INQUIRY_RULES_FILE") or str(SYSTEM_INQUIRY_RULES_FILE)
+        defaults: Dict[str, Any] = {
+            "enabled": bool(enabled),
+            "default_action": action,
+            "default_reply": reply,
+            "sender_keywords": ["系统客服", "官方客服", "平台客服", "机器人客服", "商家客服系统"],
+            "message_keywords": ["系统询单", "代客咨询", "平台代问", "系统代发", "客服询单"],
+            "shops": {},
+        }
+        try:
+            p = Path(rules_file)
+            if p.exists():
+                with p.open("r", encoding="utf-8") as f:
+                    loaded = json.load(f)
+                if isinstance(loaded, dict):
+                    defaults.update(loaded)
+        except Exception as e:
+            logger.warning(f"系统询单规则加载失败，使用默认规则: {e}")
+        return defaults
+
+    @staticmethod
+    def _normalize_kw_list(v: Any) -> List[str]:
+        if not isinstance(v, list):
+            return []
+        return [str(x).strip().lower() for x in v if str(x).strip()]
+
+    def _resolve_system_inquiry_policy(self, acc_id: str) -> Dict[str, Any]:
+        """根据店铺合并系统询单策略。"""
+        from config.config import SYSTEM_INQUIRY_SHOPS
+
+        rules = self._system_inquiry_rules or {}
+        if not bool(rules.get("enabled", True)):
+            return {"enabled": False}
+
+        shops_env = os.getenv("SYSTEM_INQUIRY_SHOPS", SYSTEM_INQUIRY_SHOPS or "")
+        shop_whitelist = [s.strip() for s in shops_env.split(",") if s.strip()]
+        if shop_whitelist and (acc_id or "") not in shop_whitelist:
+            return {"enabled": False}
+
+        policy: Dict[str, Any] = {
+            "enabled": True,
+            "action": str(rules.get("default_action", "silent")).strip().lower(),
+            "reply": str(rules.get("default_reply", "")).strip(),
+            "sender_keywords": self._normalize_kw_list(rules.get("sender_keywords")),
+            "message_keywords": self._normalize_kw_list(rules.get("message_keywords")),
+        }
+        shop_cfg = (rules.get("shops") or {}).get(acc_id or "", {})
+        if isinstance(shop_cfg, dict):
+            if "enabled" in shop_cfg and not bool(shop_cfg.get("enabled", True)):
+                return {"enabled": False}
+            if shop_cfg.get("action"):
+                policy["action"] = str(shop_cfg.get("action")).strip().lower()
+            if shop_cfg.get("reply"):
+                policy["reply"] = str(shop_cfg.get("reply")).strip()
+            if isinstance(shop_cfg.get("sender_keywords"), list):
+                policy["sender_keywords"] = self._normalize_kw_list(shop_cfg.get("sender_keywords"))
+            if isinstance(shop_cfg.get("message_keywords"), list):
+                policy["message_keywords"] = self._normalize_kw_list(shop_cfg.get("message_keywords"))
+        if policy["action"] not in ("silent", "reply", "transfer"):
+            policy["action"] = "silent"
+        return policy
+
+    def _match_system_inquiry(self, data: dict, policy: Dict[str, Any]) -> bool:
+        """识别是否为系统客服询单消息。"""
+        if not policy.get("enabled", False):
+            return False
+
+        from_name = self.to_chinese(data.get("from_name", "") or "").lower()
+        from_id = str(data.get("from_id", "") or "").lower()
+        msg = self.to_chinese(data.get("msg", "") or "").lower()
+
+        sender_hits = 0
+        for kw in policy.get("sender_keywords", []):
+            if kw and (kw in from_name or kw in from_id):
+                sender_hits += 1
+        message_hits = 0
+        for kw in policy.get("message_keywords", []):
+            if kw and kw in msg:
+                message_hits += 1
+
+        # 优先看发送者特征；纯文本命中时至少要求两个关键词，降低误判风险
+        return sender_hits > 0 or message_hits >= 2
+
+    async def _handle_system_inquiry(self, data: dict) -> bool:
+        """命中系统询单后按策略处理。"""
+        acc_id = data.get("acc_id", "")
+        policy = self._resolve_system_inquiry_policy(acc_id)
+        if not self._match_system_inquiry(data, policy):
+            return False
+
+        customer_id = data.get("from_id", "")
+        metrics_emit("system_inquiry_detected", customer_id=customer_id, acc_id=acc_id)
+        action = policy.get("action", "silent")
+        logger.info(f"系统询单命中 | 店铺:{acc_id} | 客户:{customer_id} | action:{action}")
+
+        if action == "reply":
+            reply = policy.get("reply") or "您好，这边已收到询单消息，稍后由人工客服跟进处理。"
+            await self.send_reply(data, reply)
+            metrics_emit("system_inquiry_auto_reply", customer_id=customer_id, acc_id=acc_id)
+            return True
+        if action == "transfer":
+            await self.transfer_to_human(data, "系统询单转人工")
+            metrics_emit("system_inquiry_transfer", customer_id=customer_id, acc_id=acc_id)
+            return True
+
+        metrics_emit("system_inquiry_ignored", customer_id=customer_id, acc_id=acc_id)
+        return True
+
     def _should_ignore(self, data: dict) -> bool:
         """判断是否应该忽略该消息（不回复）"""
         msg = self.to_chinese(data.get('msg', ''))
@@ -1108,6 +1309,23 @@ class QingjianAPIClient:
             print(f"[{self.get_time()}] 错误: 未连接到服务器")
             return
 
+        # 同一客户外发限流：N 秒内最多 1 条
+        try:
+            from config.config import OUTBOUND_PER_CUSTOMER_COOLDOWN_SECONDS
+            cooldown = max(0, int(OUTBOUND_PER_CUSTOMER_COOLDOWN_SECONDS))
+        except Exception:
+            cooldown = 5
+        if cooldown > 0:
+            ckey = f"{original_msg.get('acc_id', '')}:{original_msg.get('from_id', '')}"
+            now_mono = time.monotonic()
+            last = self._last_reply_sent_at.get(ckey, 0.0)
+            if (now_mono - last) < cooldown:
+                logger.info(
+                    f"外发限流命中，跳过发送 | 客户:{ckey} | cooldown:{cooldown}s | msg:{str(reply_content)[:40]}"
+                )
+                return
+            self._last_reply_sent_at[ckey] = now_mono
+        
         shop_id = original_msg.get("acc_id", "")
 
         # 根据轻简API文档：

db/customer_db.py

@@ -65,6 +65,8 @@ class CustomerProfile:
     last_gemini_prompt: str = ""  # 最近一次图片的 Gemini 处理提示词
     last_aspect_ratio: str = "1:1"  # 最近一次图片的建议输出比例
     last_perspective: str = "no"    # 最近一次图片的透视状态
+    pending_quote_images: List[str] = None        # 待统一报价图片队列（持久化）
+    pending_quote_requirements: List[str] = None  # 待统一报价需求队列（持久化）
 
     # 当前任务状态
     processing_status: str = ""       # 待处理/处理中/等待确认/已完成
@@ -148,6 +150,10 @@ class CustomerProfile:
             self.image_type_history = []
         if self.upsell_opportunity is None:
             self.upsell_opportunity = []
+        if self.pending_quote_images is None:
+            self.pending_quote_images = []
+        if self.pending_quote_requirements is None:
+            self.pending_quote_requirements = []
 
 
 class CustomerDatabase:
@@ -423,6 +429,24 @@ class CustomerDatabase:
             profile.last_perspective = perspective
         self.save_customer(profile)
 
+    def update_pending_quote_state(
+        self,
+        customer_id: str,
+        images: List[str],
+        requirements: List[str]
+    ):
+        """持久化收图阶段状态，防止服务重启丢失。"""
+        profile = self.get_customer(customer_id)
+        profile.pending_quote_images = list(images or [])
+        profile.pending_quote_requirements = list(requirements or [])
+        self.save_customer(profile)
+
+    def clear_pending_quote_state(self, customer_id: str):
+        profile = self.get_customer(customer_id)
+        profile.pending_quote_images = []
+        profile.pending_quote_requirements = []
+        self.save_customer(profile)
+
     def update_processing_status(self, customer_id: str, status: str, image_url: str = "", expected_done_at: str = ""):
         """更新当前任务处理状态"""
         profile = self.get_customer(customer_id)

image/image_analyzer.py

@@ -239,6 +239,11 @@ class ImageAnalyzer:
     # 最短等待时间（秒）：即使AI极快返回，也等这么久，看起来像真人在找
     MIN_WAIT_SECONDS = 4
 
+    DENSE_TEXT_SUBJECT_KEYWORDS = (
+        "宣传栏", "公告栏", "展板", "海报墙", "通知栏", "知识栏", "制度牌", "公示栏", "墙报", "密密麻麻",
+        "word wall", "poster wall", "bulletin board",
+    )
+
     async def analyze(self, image_path: str) -> dict:
         """
         异步分析图片复杂度（使用火山引擎 /responses 接口）。
@@ -519,6 +524,8 @@ class ImageAnalyzer:
         sensitive    = p(content, "敏感内容:", "敏感内容：").lower().strip()
         flatness    = p(content, "平整度:", "平整度：").lower().strip()  # flat|mild|rough
         has_text    = p(content, "含文字:", "含文字：").lower().strip()
+        text_amount = p(content, "文字数量:", "文字数量：").strip()
+        text_layer_need = p(content, "文字分层需求:", "文字分层需求：").lower().strip()
         has_face    = p(content, "含人脸:", "含人脸：").lower().strip()
         has_shadow  = p(content, "阴影:", "阴影：").lower().strip()
         reason      = p(content, "原因:", "原因：")
@@ -534,11 +541,22 @@ class ImageAnalyzer:
 
         if has_face not in ("yes", "no"):
             has_face = "no"
+        valid_text_amounts = {"none", "少量 (1-10 字)", "中量 (11-50 字)", "大量 (51-200 字)", "极多 (200 字以上)"}
+        if text_amount not in valid_text_amounts:
+            text_amount = "none"
+        if text_layer_need not in ("yes", "no"):
+            text_layer_need = "no"
         if risk not in ("none", "low", "high"):
             risk = "none"
         if perspective not in ("no", "mild", "strong"):
             perspective = "no"
 
+        # 识别“密集文字场景”关键词（中文 + 英文兜底）
+        dense_text_scene = any(
+            kw in ((subject or "") + " " + (proc_type or "") + " " + (reason or "")).lower()
+            for kw in self.DENSE_TEXT_SUBJECT_KEYWORDS
+        )
+
         # 校验比例合法性
         valid_ratios = {"1:1", "9:16", "16:9", "3:4", "4:3", "3:2", "2:3", "5:4", "4:5"}
         if aspect_ratio not in valid_ratios:
@@ -556,11 +574,11 @@ class ImageAnalyzer:
         if has_text == "yes":
             if complexity == "simple":
                 # 简单但含文字 → 提升到 normal 价格
-                price_min, price_max = self.PRICE_MAP["normal"]
+                price_min, price_max, _ = self.PRICE_MAP["normal"]
                 reason = "含文字，需精细处理"
             elif complexity == "normal":
                 # normal 含文字 → 提升到 complex 价格
-                price_min, price_max = self.PRICE_MAP["complex"]
+                price_min, price_max, _ = self.PRICE_MAP["complex"]
                 reason = "含文字，需精细处理"
             # complex/hard 保持原价，已经够高
         # 建议报价：complex/hard 取固定值，simple/normal 取中间，且必须为5的整数倍
@@ -605,6 +623,14 @@ class ImageAnalyzer:
                 price_suggest = 80
             reason += " | 大量文字分层"
 
+        # 硬规则：密集文字类图片不接单（知识宣传栏/公告栏等）
+        if text_amount in ["大量 (51-200 字)", "极多 (200 字以上)"] or dense_text_scene:
+            feasibility = "no"
+            risk = "high"
+            note = "文字内容过于密集（如宣传栏/公告栏），暂不接单处理"
+            reason = (reason or "文字密集") + " | 密集文字场景不接单"
+            price_suggest = 0
+
         # 确保是 5 的倍数
         price_suggest = round(price_suggest / 5) * 5
 
@@ -649,6 +675,10 @@ class ImageAnalyzer:
     def _fallback(self, reason: str) -> dict:
         """识别失败时的默认结果（返回 normal，让人工判断）"""
         print(f"[ImageAnalyzer] 识别失败，使用默认值: {reason}")
+        text_amount = "none"
+        text_layer_need = "no"
+        text_surcharge = 0
+        layer_surcharge = 0
         return {
             "complexity":    "normal",
             "reason":        reason,

services/service_gemini.py

@@ -14,11 +14,19 @@ import time
 from datetime import datetime
 from pathlib import Path
 import logging
+from dotenv import load_dotenv
+from utils.metrics_tracker import emit as metrics_emit
 
 
 from utils.service_base import BaseService
 
 logger = logging.getLogger(__name__)
+load_dotenv()
+GEMINI_IMAGE_MODEL = os.getenv("GEMINI_IMAGE_MODEL", "gemini-3.1-flash-image-preview")
+GEMINI_IMAGE_FALLBACK_MODEL = os.getenv("GEMINI_IMAGE_FALLBACK_MODEL", "gemini-2.5-flash-image")
+GEMINI_IMAGE_SIZE = os.getenv("GEMINI_IMAGE_SIZE", "1K")
+GEMINI_THINKING_LEVEL = os.getenv("GEMINI_THINKING_LEVEL", "MINIMAL")
+GEMINI_PERSON_GENERATION = os.getenv("GEMINI_PERSON_GENERATION", "")
 
 
 class GeminiExtractV2Service(BaseService):
@@ -46,17 +54,26 @@ class GeminiExtractV2Service(BaseService):
             "name": "西风接口$0.014",
             "api_key": "sk-uRuvzLfIHsc3BiHZ2cyebk0cYsZ8NR9rLL326QqXCKIy9EpK",
             "api_url": "https://api.apiqik.online/v1beta/models",
-            "api_model": "gemini-2.5-flash-image",  # 更稳定的模型
-            "max_retries": 2,  # 贵接口少重试
+            "api_model": GEMINI_IMAGE_MODEL,
+            "max_retries": 2,
             "cost": "中",
             "use_gemini_format": True  # 使用Gemini原生API格式
         },
+        {
+            "name": "西风接口Fallback",
+            "api_key": "sk-uRuvzLfIHsc3BiHZ2cyebk0cYsZ8NR9rLL326QqXCKIy9EpK",
+            "api_url": "https://api.apiqik.online/v1beta/models",
+            "api_model": GEMINI_IMAGE_FALLBACK_MODEL,
+            "max_retries": 1,
+            "cost": "中",
+            "use_gemini_format": True
+        },
 
         {
             "name": "最贵的",
             "api_key": "sk-8i7uYE0RtnQwDImV8a5f7014DcAb46F6BcEb72Df92218aC8",
             "api_url": "https://api.laozhang.ai/v1/chat/completions", 
-            "api_model": "gemini-2.5-flash-image-preview",
+            "api_model": GEMINI_IMAGE_MODEL,
             "max_retries": 1,
             "cost": "高"
         }
@@ -90,6 +107,9 @@ class GeminiExtractV2Service(BaseService):
         output_path: str,
         custom_prompt: str = None,
         aspect_ratio: str = "1:1",
+        image_size: str = "",
+        person_generation: str = "",
+        thinking_level: str = "",
     ) -> tuple[bool, str, dict]:
         """
         使用多API配置进行印花图案提取
@@ -113,6 +133,7 @@ class GeminiExtractV2Service(BaseService):
         # 按优先级逐个尝试API配置
         for config_index, config in enumerate(self.API_CONFIGS):
             logger.info(f"尝试使用API: {config['name']} (成本: {config['cost']})")
+            metrics_emit("gemini_request", model=config.get("api_model", ""), provider=config.get("name", ""))
             
             # 对每个API配置进行重试
             for attempt in range(config['max_retries']):
@@ -129,9 +150,22 @@ class GeminiExtractV2Service(BaseService):
 
                         # 有效比例列表（Auto 不传 aspectRatio）
                         valid_ratios = {"1:1", "9:16", "16:9", "3:4", "4:3", "3:2", "2:3", "5:4", "4:5"}
+                        valid_sizes = {"1K", "2K", "4K"}
+                        valid_thinking = {"MINIMAL", "LOW", "MEDIUM", "HIGH"}
                         image_config = {}
                         if aspect_ratio in valid_ratios:
                             image_config["aspectRatio"] = aspect_ratio
+                        size_val = (image_size or GEMINI_IMAGE_SIZE or "").upper().strip()
+                        if size_val in valid_sizes:
+                            image_config["imageSize"] = size_val
+                        person_val = (person_generation or GEMINI_PERSON_GENERATION or "").strip()
+                        if person_val:
+                            # 中转接口若支持该字段会生效；不设置时不发送，保证兼容
+                            image_config["personGeneration"] = person_val
+                        thinking_val = (thinking_level or GEMINI_THINKING_LEVEL or "").upper().strip()
+                        thinking_config = {}
+                        if thinking_val in valid_thinking:
+                            thinking_config["thinkingLevel"] = thinking_val
 
                         data = {
                             "contents": [
@@ -153,9 +187,13 @@ class GeminiExtractV2Service(BaseService):
                             "generationConfig": {
                                 "responseModalities": ["IMAGE"],
                                 **({"imageConfig": image_config} if image_config else {}),
+                                **({"thinkingConfig": thinking_config} if thinking_config else {}),
                             }
                         }
-                        logger.info(f"Gemini 生成配置: 比例={aspect_ratio} 格式=JPEG")
+                        logger.info(
+                            f"Gemini 生成配置: 比例={aspect_ratio} 尺寸={image_config.get('imageSize', '默认')} "
+                            f"person={image_config.get('personGeneration', '默认')} thinking={thinking_config.get('thinkingLevel', '默认')}"
+                        )
                     else:
                         # OpenAI兼容格式
                         api_url = config['api_url']
@@ -212,6 +250,25 @@ class GeminiExtractV2Service(BaseService):
                                     continue
                                 
                                 result = await response.json()
+                                # Gemini 偶发只返回文本不返回图片：NO_IMAGE 时快速重试/降级
+                                if config.get('use_gemini_format', False):
+                                    finish_reason = ""
+                                    try:
+                                        finish_reason = (
+                                            (result.get("candidates") or [{}])[0].get("finishReason", "")
+                                        )
+                                    except Exception:
+                                        finish_reason = ""
+                                    if finish_reason == "NO_IMAGE":
+                                        logger.warning(
+                                            f"{config['name']} 返回 NO_IMAGE (模型={config.get('api_model')})，第{attempt + 1}次"
+                                        )
+                                        metrics_emit("gemini_no_image", model=config.get("api_model", ""), provider=config.get("name", ""))
+                                        if attempt == config['max_retries'] - 1:
+                                            logger.warning(f"{config['name']} NO_IMAGE 重试已用完，切换下一个配置")
+                                            break
+                                        await asyncio.sleep(1 + attempt)
+                                        continue
                     
                     except (aiohttp.ClientError, asyncio.TimeoutError, AssertionError) as e:
                         logger.error(f"{config['name']} 网络连接错误 (第{attempt + 1}次): {str(e)}")
@@ -235,6 +292,7 @@ class GeminiExtractV2Service(BaseService):
                     
                     if success:
                         logger.info(f"使用 {config['name']} 成功完成印花提取")
+                        metrics_emit("gemini_success", model=config.get("api_model", ""), provider=config.get("name", ""))
                         try:
                             from utils.api_cost_tracker import record
                             record("gemini_extract", count=1)

tests/test_batch_quote_reply_format.py

@@ -0,0 +1,66 @@
+import unittest
+from unittest.mock import AsyncMock, patch
+
+from core.pydantic_ai_agent import CustomerMessage, CustomerServiceAgent
+
+
+class BatchQuoteReplyFormatTest(unittest.IsolatedAsyncioTestCase):
+    async def test_batch_reply_contains_per_image_and_options(self):
+        agent = CustomerServiceAgent()
+        cid = "__batch_quote_case__"
+        st = agent._get_conversation_state(cid)
+        st.pending_image_urls = ["https://img.alicdn.com/a.jpg", "https://img.alicdn.com/b.jpg"]
+        st.pending_requirements = ["去背景", "加急"]
+
+        msg = CustomerMessage(
+            msg_id="m-batch-1",
+            acc_id="test_shop",
+            msg="发完了，统一报价",
+            from_id=cid,
+            from_name="t",
+            cy_id=cid,
+            acc_type="AliWorkbench",
+            msg_type=0,
+            cy_name="t",
+            goods_name="专业找图",
+            goods_order="",
+        )
+
+        fake_r1 = {
+            "complexity": "normal",
+            "reason": "常规处理",
+            "price_min": 15,
+            "price_max": 25,
+            "price_suggest": 20,
+            "feasibility": "yes",
+            "risk": "low",
+            "aspect_ratio": "1:1",
+            "perspective": "no",
+        }
+        fake_r2 = {
+            "complexity": "complex",
+            "reason": "细节较多",
+            "price_min": 20,
+            "price_max": 30,
+            "price_suggest": 25,
+            "feasibility": "yes",
+            "risk": "low",
+            "aspect_ratio": "1:1",
+            "perspective": "no",
+        }
+
+        with patch("image.image_analyzer.image_analyzer.analyze", new=AsyncMock(side_effect=[fake_r1, fake_r2])):
+            with patch("core.workflow.workflow.image_analysis_result", new=AsyncMock(return_value=None)):
+                res = await agent._quote_pending_images(st, msg)
+
+        self.assertFalse(res.get("need_transfer", False))
+        reply = res.get("reply", "")
+        self.assertIn("图1", reply)
+        self.assertIn("图2", reply)
+        self.assertIn("可选", reply)
+        self.assertIn("打包", reply)
+        self.assertIn("共", reply)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

tests/test_outbound_cooldown.py

@@ -0,0 +1,40 @@
+import os
+import unittest
+
+from websockets.protocol import State
+
+from core.websocket_client import QingjianAPIClient
+
+
+class _DummyWS:
+    def __init__(self):
+        self.state = State.OPEN
+        self.sent = []
+
+    async def send(self, msg_json: str):
+        self.sent.append(msg_json)
+
+
+class OutboundCooldownTest(unittest.IsolatedAsyncioTestCase):
+    def setUp(self):
+        os.environ["OUTBOUND_PER_CUSTOMER_COOLDOWN_SECONDS"] = "5"
+
+    async def test_skip_second_reply_within_cooldown(self):
+        c = QingjianAPIClient(enable_agent=False)
+        c.websocket = _DummyWS()
+        msg = {
+            "acc_id": "shop_a",
+            "from_id": "u001",
+            "from_name": "u001",
+            "acc_type": "AliWorkbench",
+        }
+        await c.send_reply(msg, "第一条")
+        await c.send_reply(msg, "第二条")
+        self.assertEqual(len(c.websocket.sent), 1)
+
+    def tearDown(self):
+        os.environ.pop("OUTBOUND_PER_CUSTOMER_COOLDOWN_SECONDS", None)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

tests/test_oversize_guard.py

@@ -0,0 +1,34 @@
+import os
+import unittest
+
+from core.websocket_client import QingjianAPIClient
+
+
+class OversizeGuardTest(unittest.TestCase):
+    def setUp(self):
+        os.environ["MAX_SERVICE_SIZE_LONGEST_METERS"] = "10"
+        os.environ["MAX_SERVICE_SIZE_AREA_SQM"] = "20"
+
+    def test_extract_size_pairs(self):
+        c = QingjianAPIClient(enable_agent=False)
+        pairs = c._extract_size_pairs_m("15*6.4米 高度")
+        self.assertTrue(len(pairs) >= 1)
+        self.assertEqual(pairs[0], (15.0, 6.4))
+
+    def test_oversize_hits(self):
+        c = QingjianAPIClient(enable_agent=False)
+        r = c._oversize_reply_if_needed("15*6.4米")
+        self.assertIn("做不了", r)
+
+    def test_normal_size_not_hit(self):
+        c = QingjianAPIClient(enable_agent=False)
+        r = c._oversize_reply_if_needed("2.4*1.2米")
+        self.assertEqual(r, "")
+
+    def tearDown(self):
+        os.environ.pop("MAX_SERVICE_SIZE_LONGEST_METERS", None)
+        os.environ.pop("MAX_SERVICE_SIZE_AREA_SQM", None)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

tests/test_regression_pipeline.py

@@ -0,0 +1,76 @@
+import os
+import unittest
+from unittest.mock import AsyncMock
+
+from core.pydantic_ai_agent import CustomerServiceAgent, CustomerMessage
+from db.customer_db import db
+
+
+class RegressionPipelineTest(unittest.IsolatedAsyncioTestCase):
+    def setUp(self):
+        self.customer_id = "__regression_test_customer__"
+        db.clear_pending_quote_state(self.customer_id)
+        os.environ["FEATURE_BATCH_QUOTE_ENABLED"] = "true"
+        os.environ["FEATURE_BATCH_QUOTE_PERCENT"] = "100"
+        os.environ["FEATURE_BATCH_QUOTE_SHOPS"] = ""
+
+    async def test_collect_images_then_ack(self):
+        agent = CustomerServiceAgent()
+        msg = CustomerMessage(
+            msg_id="m1",
+            acc_id="test_shop",
+            msg="https://img.alicdn.com/a.jpg#*#https://img.alicdn.com/b.jpg",
+            from_id=self.customer_id,
+            from_name="t",
+            cy_id=self.customer_id,
+            acc_type="AliWorkbench",
+            msg_type=0,
+            cy_name="t",
+            goods_name="专业找图",
+            goods_order="",
+        )
+        resp = await agent.process_message(msg)
+        self.assertTrue(resp.should_reply)
+        self.assertIn("张", resp.reply)
+        st = agent._get_conversation_state(self.customer_id)
+        self.assertEqual(len(st.pending_image_urls), 2)
+
+    async def test_finish_signal_triggers_batch_quote(self):
+        agent = CustomerServiceAgent()
+        st = agent._get_conversation_state(self.customer_id)
+        st.pending_image_urls = ["https://img.alicdn.com/a.jpg"]
+        st.pending_requirements = ["去背景"]
+        agent._sync_pending_quote_state(self.customer_id, st)
+        agent._quote_pending_images = AsyncMock(return_value={"reply": "打包15元，确认我就安排", "need_transfer": False})
+
+        msg = CustomerMessage(
+            msg_id="m2",
+            acc_id="test_shop",
+            msg="发完了，报价吧",
+            from_id=self.customer_id,
+            from_name="t",
+            cy_id=self.customer_id,
+            acc_type="AliWorkbench",
+            msg_type=0,
+            cy_name="t",
+            goods_name="专业找图",
+            goods_order="",
+        )
+        resp = await agent.process_message(msg)
+        self.assertTrue(resp.should_reply)
+        self.assertIn("15", resp.reply)
+        agent._quote_pending_images.assert_awaited()
+
+    async def test_pending_state_restore(self):
+        db.update_pending_quote_state(self.customer_id, ["u1", "u2"], ["r1"])
+        agent = CustomerServiceAgent()
+        st = agent._get_conversation_state(self.customer_id)
+        self.assertEqual(st.pending_image_urls, ["u1", "u2"])
+        self.assertEqual(st.pending_requirements, ["r1"])
+
+    def tearDown(self):
+        db.clear_pending_quote_state(self.customer_id)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

tests/test_system_inquiry_rules.py

@@ -0,0 +1,70 @@
+import os
+import unittest
+from unittest.mock import AsyncMock
+
+from core.websocket_client import QingjianAPIClient
+
+
+class SystemInquiryRulesTest(unittest.IsolatedAsyncioTestCase):
+    def setUp(self):
+        self.rules = {
+            "enabled": True,
+            "default_action": "silent",
+            "default_reply": "已收到",
+            "sender_keywords": ["系统客服", "官方客服"],
+            "message_keywords": ["系统询单", "代客咨询"],
+            "shops": {
+                "shop_reply": {
+                    "enabled": True,
+                    "action": "reply",
+                    "reply": "店铺回复模板",
+                    "sender_keywords": ["机器人客服"],
+                    "message_keywords": ["询单"],
+                }
+            },
+        }
+        os.environ["SYSTEM_INQUIRY_ENABLED"] = "true"
+        os.environ["SYSTEM_INQUIRY_SHOPS"] = ""
+
+    async def test_detect_by_sender_keyword(self):
+        client = QingjianAPIClient(enable_agent=False)
+        client._system_inquiry_rules = self.rules
+        policy = client._resolve_system_inquiry_policy("shop_a")
+        data = {"acc_id": "shop_a", "from_name": "平台系统客服", "from_id": "kefu001", "msg": "你好"}
+        self.assertTrue(client._match_system_inquiry(data, policy))
+
+    async def test_shop_rule_reply_action(self):
+        client = QingjianAPIClient(enable_agent=False)
+        client._system_inquiry_rules = self.rules
+        client.send_reply = AsyncMock()
+        client.transfer_to_human = AsyncMock()
+
+        data = {
+            "acc_id": "shop_reply",
+            "from_name": "机器人客服A",
+            "from_id": "robot_01",
+            "msg": "有个询单请处理",
+            "acc_type": "AliWorkbench",
+        }
+        handled = await client._handle_system_inquiry(data)
+        self.assertTrue(handled)
+        client.send_reply.assert_awaited_once()
+        client.transfer_to_human.assert_not_awaited()
+
+    async def test_shop_whitelist_blocks_other_shops(self):
+        os.environ["SYSTEM_INQUIRY_SHOPS"] = "shop_only"
+        client = QingjianAPIClient(enable_agent=False)
+        client._system_inquiry_rules = self.rules
+        client.send_reply = AsyncMock()
+        data = {"acc_id": "shop_other", "from_name": "系统客服", "from_id": "sys_1", "msg": "系统询单"}
+        handled = await client._handle_system_inquiry(data)
+        self.assertFalse(handled)
+        client.send_reply.assert_not_awaited()
+
+    def tearDown(self):
+        for k in ("SYSTEM_INQUIRY_ENABLED", "SYSTEM_INQUIRY_SHOPS"):
+            os.environ.pop(k, None)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

tests/test_transfer_greeting_context.py

@@ -0,0 +1,20 @@
+import unittest
+
+from core.websocket_client import QingjianAPIClient
+
+
+class TransferGreetingContextTest(unittest.TestCase):
+    def test_transfer_greeting_is_non_empty(self):
+        c = QingjianAPIClient(enable_agent=False)
+        text = c._pick_transfer_greeting()
+        self.assertTrue(isinstance(text, str) and len(text) > 0)
+
+    def test_transfer_greeting_contains_presence_phrase(self):
+        c = QingjianAPIClient(enable_agent=False)
+        for _ in range(10):
+            text = c._pick_transfer_greeting()
+            self.assertTrue(("在" in text) or ("我在" in text))
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)

utils/metrics_tracker.py

@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+"""
+轻量指标采集器
+- 事件落盘到 JSONL
+- 提供近 N 小时聚合，给 /api/metrics 使用
+"""
+from __future__ import annotations
+
+import json
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Dict, Any
+
+ROOT = Path(__file__).resolve().parent.parent
+METRICS_FILE = ROOT / "config" / ".runtime_metrics.jsonl"
+
+
+def _now_iso() -> str:
+    return datetime.now().isoformat(timespec="seconds")
+
+
+def emit(event: str, **fields: Any):
+    """记录一条事件，失败不抛异常。"""
+    try:
+        METRICS_FILE.parent.mkdir(parents=True, exist_ok=True)
+        payload = {"ts": _now_iso(), "event": event}
+        payload.update(fields or {})
+        with METRICS_FILE.open("a", encoding="utf-8") as f:
+            f.write(json.dumps(payload, ensure_ascii=False) + "\n")
+    except Exception:
+        pass
+
+
+def _iter_recent(hours: int = 24):
+    if not METRICS_FILE.exists():
+        return []
+    cutoff = datetime.now() - timedelta(hours=hours)
+    items = []
+    try:
+        with METRICS_FILE.open("r", encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    obj = json.loads(line)
+                    ts = datetime.fromisoformat(obj.get("ts", ""))
+                except Exception:
+                    continue
+                if ts >= cutoff:
+                    items.append(obj)
+    except Exception:
+        return []
+    return items
+
+
+def get_runtime_summary(hours: int = 24) -> Dict[str, Any]:
+    """近 N 小时运行指标。"""
+    rows = _iter_recent(hours=hours)
+    counts: Dict[str, int] = {}
+    for r in rows:
+        e = r.get("event", "unknown")
+        counts[e] = counts.get(e, 0) + 1
+
+    inbound = counts.get("inbound_msg", 0)
+    transfer = counts.get("transfer_to_human", 0)
+    quote = counts.get("quote_generated", 0)
+    ai_fail = counts.get("ai_call_failed", 0)
+    gemini_req = counts.get("gemini_request", 0)
+    no_image = counts.get("gemini_no_image", 0)
+
+    return {
+        "window_hours": hours,
+        "counts": counts,
+        "rates": {
+            "transfer_rate": round((transfer / inbound) * 100, 2) if inbound else 0.0,
+            "quote_rate": round((quote / inbound) * 100, 2) if inbound else 0.0,
+            "ai_fail_rate": round((ai_fail / inbound) * 100, 2) if inbound else 0.0,
+            "no_image_rate": round((no_image / gemini_req) * 100, 2) if gemini_req else 0.0,
+        },
+    }
+
+
+def get_dashboard(hours: int = 24) -> Dict[str, Any]:
+    """业务+运行看板聚合。"""
+    runtime = get_runtime_summary(hours=hours)
+    try:
+        from db.deal_outcome_db import get_daily_summary
+        daily = get_daily_summary()
+        deal_ok = int(daily.get("成交数", 0))
+        deal_fail = int(daily.get("未成交数", 0))
+        total = deal_ok + deal_fail
+        conversion = round((deal_ok / total) * 100, 2) if total else 0.0
+    except Exception:
+        deal_ok = deal_fail = 0
+        conversion = 0.0
+    return {
+        "runtime": runtime,
+        "business": {
+            "deal_success": deal_ok,
+            "deal_fail": deal_fail,
+            "conversion_rate": conversion,
+        },
+    }