feat: upgrade客服多店铺分流、批量报价与稳定性防护

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,