feat(ui): simplify interface by removing dedicated tobacco/rongcheng buttons and optimizing auto-routing

app/services/order_service.py

@@ -113,11 +113,21 @@ class OrderService:
                 return special_svc.preprocess_rongcheng_yigou(file_path)
 
             # 3. 识别：杨碧月 (Yang Biyue)
-            from .special_suppliers_service import SpecialSuppliersService
-            special_svc = SpecialSuppliersService(self.config)
-            # 我们直接复用 SpecialSuppliersService 里的逻辑，但要确保它只返回路径
-            # 修改 SpecialSuppliersService.process_yang_biyue 使其支持仅返回预处理路径
-            return special_svc.process_yang_biyue_only(file_path)
+            # 特征：经手人列包含“杨碧月”
+            handler_col = None
+            for col in df_head.columns:
+                # 在前50行中搜索“经手人”关键字
+                if df_head[col].astype(str).str.contains('经手人').any():
+                    handler_col = col
+                    break
+            
+            if handler_col is not None:
+                # 检查该列是否有“杨碧月”
+                if df_head[handler_col].astype(str).str.contains('杨碧月').any():
+                    logger.info("识别到杨碧月订单，执行专用预处理...")
+                    from .special_suppliers_service import SpecialSuppliersService
+                    special_svc = SpecialSuppliersService(self.config)
+                    return special_svc.process_yang_biyue_only(file_path)
                     
         except Exception as e:
             logger.warning(f"智能预处理识别失败: {e}")

app/services/special_suppliers_service.py

@@ -37,24 +37,41 @@ class SpecialSuppliersService:
             if handler_col is None or not df[handler_col].astype(str).str.contains('杨碧月').any():
                 return None
 
+            # 识别到杨碧月订单，执行专用清洗
             logger.info("识别到杨碧月订单，正在执行专用清洗...")
 
-            # 定义列映射关系
+            # 定义列映射关系 (映射到 ExcelProcessor 期望的中文列名)
+            # 使用精确匹配优先，防止“结算单位”匹配到“单位”
             column_map = {
-                '商品条码': 'barcode',
-                '商品名称': 'name',
-                '商品规格': 'specification',
-                '单位': 'unit',
-                '数量': 'quantity',
-                '含税单价': 'unit_price',
-                '含税金额': 'total_price'
+                '商品条码': '商品条码',
+                '商品名称': '商品名称',
+                '商品规格': '规格',
+                '单位': '单位',
+                '数量': '数量',
+                '含税单价': '单价',
+                '含税金额': '金额'
             }
             
-            # 提取并重命名列
             found_cols = {}
+            # 1. 第一遍：尝试精确匹配
             for target_zh, std_name in column_map.items():
                 for col in df.columns:
-                    if target_zh in str(col):
+                    if str(col).strip() == target_zh:
+                        found_cols[col] = std_name
+                        break
+            
+            # 2. 第二遍：对未匹配成功的列尝试模糊匹配（但要排除特定干扰词）
+            for target_zh, std_name in column_map.items():
+                if std_name in found_cols.values():
+                    continue
+                for col in df.columns:
+                    col_str = str(col)
+                    if target_zh in col_str:
+                        # 排除干扰列
+                        if target_zh == '单位' and '结算单位' in col_str:
+                            continue
+                        if target_zh == '数量' and '基本单位数量' in col_str:
+                            continue
                         found_cols[col] = std_name
                         break
             
@@ -66,7 +83,7 @@ class SpecialSuppliersService:
             df_clean = df_clean.rename(columns=found_cols)
             
             # 过滤掉空的条码行
-            df_clean = df_clean.dropna(subset=['barcode'])
+            df_clean = df_clean.dropna(subset=['商品条码'])
             
             # 保存预处理文件
             out_dir = os.path.dirname(src_path)