feat: update barcode mappings and improve build script robustness

README.md

@@ -7,6 +7,10 @@
 
 ## 核心功能
 - 图片/Excel处理：拖拽或选择文件，生成银豹采购单（`templates/银豹-采购单模板.xls`）
+- **无界面自动化接口** (headless_api.py)：支持与 OpenClaw 等自动化平台对接。
+  - `python headless_api.py [图片路径]`：处理指定图片。
+  - `python headless_api.py`：自动处理 `data/input` 目录下最新的图片。
+  - 输出：成功时在标准输出打印最终 Excel 的绝对路径，失败时在标准错误打印错误信息。
 - 供应商管理（系统设置 → 供应商管理）：
   - 基本信息、文件名匹配、表头行号
   - 列映射与表头：预览前30行、表头选行、加载列、智能映射、导入/导出

app/services/order_service.py

@@ -108,3 +108,77 @@ class OrderService:
             logger.info("OrderService开始合并所有采购单")
         
         return self.order_merger.process(file_paths, progress_cb)
+
+    def validate_unit_price(self, result_path: str) -> List[str]:
+        """
+        校验采购单单价与商品资料进货价的差异
+        
+        Args:
+            result_path: 待校验的采购单路径
+            
+        Returns:
+            差异信息列表，无差异返回空列表
+        """
+        try:
+            import pandas as pd
+            import os
+            
+            def _read_df(path):
+                ap = os.path.abspath(path)
+                if ap.lower().endswith('.xlsx'):
+                    return pd.read_excel(ap, engine='openpyxl')
+                else:
+                    return pd.read_excel(ap, engine='xlrd')
+                    
+            item_path = os.path.join('templates', '商品资料.xlsx')
+            if not os.path.exists(item_path):
+                logger.warning(f"未找到商品资料文件: {item_path}")
+                return []
+                
+            df_item = _read_df(item_path)
+            df_res = _read_df(result_path)
+            
+            def _find_col(df, candidates, contains=None):
+                cols = list(df.columns)
+                for c in candidates:
+                    if c in cols:
+                        return c
+                if contains:
+                    for c in cols:
+                        if contains in str(c):
+                            return c
+                return None
+                
+            item_barcode_col = _find_col(df_item, ['商品条码','商品条码（小条码）','条码','barcode'], contains='条码')
+            item_price_col = _find_col(df_item, ['进货价','进货价（必填）'], contains='进货价')
+            res_barcode_col = _find_col(df_res, ['条码','barcode'], contains='条码')
+            res_price_col = _find_col(df_res, ['采购单价','unit_price','单价'], contains='单价')
+            
+            if not all([item_barcode_col, item_price_col, res_barcode_col, res_price_col]):
+                logger.warning("未能在文件和商品资料中找到完整的校验列（条码、单价）")
+                return []
+                
+            item_map = df_item[[item_barcode_col, item_price_col]].dropna()
+            item_map[item_price_col] = pd.to_numeric(item_map[item_price_col], errors='coerce')
+            item_map = item_map.dropna()
+            imap = dict(zip(item_map[item_barcode_col].astype(str).str.strip(), item_map[item_price_col]))
+            
+            df_res['_bc_'] = df_res[res_barcode_col].astype(str).str.strip()
+            df_res['_res_price_'] = pd.to_numeric(df_res[res_price_col], errors='coerce')
+            df_res['_item_price_'] = df_res['_bc_'].map(imap)
+            
+            df_check = df_res.dropna(subset=['_res_price_','_item_price_'])
+            df_check['_diff_'] = (df_check['_res_price_'] - df_check['_item_price_']).abs()
+            bad = df_check[df_check['_diff_'] > 1.0]
+            
+            results = []
+            if not bad.empty:
+                for i in range(len(bad)):
+                    r = bad.iloc[i]
+                    results.append(f"条码 {r['_bc_']}: 采购单价={r['_res_price_']} vs 进货价={r['_item_price_']} 差异={r['_diff_']:.2f}")
+                    
+            return results
+            
+        except Exception as e:
+            logger.error(f"单价校验过程中发生错误: {e}")
+            return []

build_exe.py

@@ -201,8 +201,18 @@ def create_portable_package():
     # 创建发布目录
     release_dir = Path('release')
     if release_dir.exists():
+        try:
             shutil.rmtree(release_dir)
-    release_dir.mkdir()
+        except Exception as e:
+            print(f"警告: 无法完全清理发布目录 (可能文件被占用): {e}")
+            # 如果目录还在，尝试清理能清理的部分
+            for item in release_dir.iterdir():
+                try:
+                    if item.is_dir(): shutil.rmtree(item)
+                    else: item.unlink()
+                except Exception: pass
+    
+    release_dir.mkdir(exist_ok=True)
     
     # 复制exe文件
     exe_file = Path('dist/OCR订单处理系统.exe')

config.ini

@@ -27,4 +27,4 @@ max_file_size_mb = 4
 purchase_order = 银豹-采购单模板.xls
 
 [App]
-version = 2026.03.25.1945
+version = 2026.03.25.2048

config/barcode_mappings.json

@@ -187,6 +187,50 @@
     "map_to": "6907992513195",
     "description": "条码映射：6907992513157 -> 6907992513195"
   },
+  "6902083893842": {
+    "map_to": "6902083907150",
+    "description": "条码映射：6902083893842 -> 6902083907150"
+  },
+  "6902083904685": {
+    "map_to": "6902083905217",
+    "description": "条码映射：6902083904685 -> 6902083905217"
+  },
+  "6917878036849": {
+    "map_to": "6917878036847",
+    "description": "条码映射：6917878036849 -> 6917878036847"
+  },
+  "6903979000078": {
+    "map_to": "6903979000061",
+    "description": "条码映射：6903979000078 -> 6903979000061"
+  },
+  "6937003706353": {
+    "map_to": "6937003706360",
+    "description": "条码映射：6937003706353 -> 6937003706360"
+  },
+  "6923644242961": {
+    "map_to": "6907992100043",
+    "description": "条码映射：6923644242961 -> 6907992100043"
+  },
+  "6923644258382": {
+    "map_to": "6923644252823",
+    "description": "条码映射：6923644258382 -> 6923644252823"
+  },
+  "6923450657430": {
+    "map_to": "69029110",
+    "description": "条码映射：6923450657430 -> 69029110"
+  },
+  "6923450660232": {
+    "map_to": "6923450690123",
+    "description": "条码映射：6923450660232 -> 6923450690123"
+  },
+  "6923450657614": {
+    "map_to": "6923450657607",
+    "description": "条码映射：6923450657614 -> 6923450657607"
+  },
+  "6972556000022": {
+    "map_to": "6977826050028",
+    "description": "条码映射：6972556000022 -> 6977826050028"
+  },
   "6925019900087": {
     "multiplier": 10,
     "target_unit": "瓶",

headless_api.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+OCR订单处理系统 - 无界面自动化接口
+-----------------------------
+专为与 openclaw 等自动化平台对接设计。
+处理流程：输入图片 -> OCR识别 -> 数据清洗 -> 价格校验 -> 输出结果路径。
+"""
+
+import os
+import sys
+import logging
+import time
+from pathlib import Path
+from typing import Optional
+
+# 添加当前目录到路径
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from app.config.settings import ConfigManager
+from app.services.ocr_service import OCRService
+from app.services.order_service import OrderService
+from app.core.utils.log_utils import set_log_level
+
+# 配置日志输出到 stderr，以免干扰 stdout 的路径输出
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    stream=sys.stderr
+)
+logger = logging.getLogger("HeadlessAPI")
+
+def get_latest_input_image() -> Optional[str]:
+    """获取 input 目录中最新的图片文件"""
+    input_dir = Path("data/input")
+    if not input_dir.exists():
+        return None
+    
+    extensions = ['.jpg', '.jpeg', '.png', '.bmp']
+    files = []
+    for ext in extensions:
+        files.extend(input_dir.glob(f"*{ext}"))
+        files.extend(input_dir.glob(f"*{ext.upper()}"))
+    
+    if not files:
+        return None
+        
+    # 按修改时间排序
+    latest_file = max(files, key=lambda p: p.stat().st_mtime)
+    return str(latest_file)
+
+def run_pipeline(image_path: Optional[str] = None):
+    """运行处理流水线"""
+    try:
+        # 1. 确定输入文件
+        if not image_path:
+            image_path = get_latest_input_image()
+            
+        if not image_path:
+            print("ERROR: No input image found.", file=sys.stderr)
+            return None
+            
+        logger.info(f"开始处理图片: {image_path}")
+        
+        # 2. 初始化服务
+        config_manager = ConfigManager()
+        ocr_service = OCRService(config_manager)
+        order_service = OrderService(config_manager)
+        
+        # 3. OCR 识别
+        start_time = time.perf_counter()
+        excel_intermediate = ocr_service.process_image(image_path)
+        if not excel_intermediate:
+            print(f"ERROR: OCR failed for {image_path}", file=sys.stderr)
+            return None
+            
+        # 4. Excel 处理与清洗
+        final_excel = order_service.process_excel(excel_intermediate)
+        if not final_excel:
+            print(f"ERROR: Excel processing failed for {excel_intermediate}", file=sys.stderr)
+            return None
+            
+        # 5. 单价校验 (输出到 stderr)
+        discrepancies = order_service.validate_unit_price(final_excel)
+        if discrepancies:
+            print(f"WARNING: Price validation found {len(discrepancies)} issues:", file=sys.stderr)
+            for d in discrepancies:
+                print(f"  - {d}", file=sys.stderr)
+        else:
+            logger.info("单价校验通过")
+            
+        duration = time.perf_counter() - start_time
+        logger.info(f"处理完成，耗时: {duration:.2f}s")
+        
+        # 6. 输出最终结果路径到 stdout
+        # 确保是绝对路径
+        abs_path = os.path.abspath(final_excel)
+        print(abs_path)
+        return abs_path
+
+    except Exception as e:
+        import traceback
+        print(f"CRITICAL ERROR: {str(e)}", file=sys.stderr)
+        traceback.print_exc(file=sys.stderr)
+        return None
+
+if __name__ == "__main__":
+    # 支持命令行传入图片路径
+    input_path = sys.argv[1] if len(sys.argv) > 1 else None
+    result = run_pipeline(input_path)
+    sys.exit(0 if result else 1)

启动器.py

@@ -2503,59 +2503,25 @@ def select_excel_file(log_widget):
 
 def validate_unit_price_against_item_data(result_path: str, log_widget=None):
     try:
-        import pandas as pd
+        from app.services.order_service import OrderService
-        import os
+        service = OrderService()
-        def _read_df(path):
+        bad_results = service.validate_unit_price(result_path)
-            ap = os.path.abspath(path)
+        
-            if ap.lower().endswith('.xlsx'):
+        if bad_results:
-                return pd.read_excel(ap, engine='openpyxl')
-            else:
-                return pd.read_excel(ap, engine='xlrd')
-        item_path = os.path.join('templates', '商品资料.xlsx')
-        if not os.path.exists(item_path):
-            return
-        df_item = _read_df(item_path)
-        df_res = _read_df(result_path)
-        def _find_col(df, candidates, contains=None):
-            cols = list(df.columns)
-            for c in candidates:
-                if c in cols:
-                    return c
-            if contains:
-                for c in cols:
-                    if contains in str(c):
-                        return c
-            return None
-        item_barcode_col = _find_col(df_item, ['商品条码','商品条码（小条码）','条码','barcode'], contains='条码')
-        item_price_col = _find_col(df_item, ['进货价','进货价（必填）'], contains='进货价')
-        res_barcode_col = _find_col(df_res, ['条码','barcode'], contains='条码')
-        res_price_col = _find_col(df_res, ['采购单价','unit_price','单价'], contains='单价')
-        if not all([item_barcode_col, item_price_col, res_barcode_col, res_price_col]):
-            return
-        item_map = df_item[[item_barcode_col, item_price_col]].dropna()
-        item_map[item_price_col] = pd.to_numeric(item_map[item_price_col], errors='coerce')
-        item_map = item_map.dropna()
-        imap = dict(zip(item_map[item_barcode_col].astype(str).str.strip(), item_map[item_price_col]))
-        df_res['_bc_'] = df_res[res_barcode_col].astype(str).str.strip()
-        df_res['_res_price_'] = pd.to_numeric(df_res[res_price_col], errors='coerce')
-        df_res['_item_price_'] = df_res['_bc_'].map(imap)
-        df_check = df_res.dropna(subset=['_res_price_','_item_price_'])
-        df_check['_diff_'] = (df_check['_res_price_'] - df_check['_item_price_']).abs()
-        bad = df_check[df_check['_diff_'] > 1.0]
-        if not bad.empty:
-            lines = []
-            for i in range(min(len(bad), 10)):
-                r = bad.iloc[i]
-                lines.append(f"条码 {r['_bc_']}: 采购单价={r['_res_price_']} vs 进货价={r['_item_price_']} 差异={r['_diff_']:.2f}")
             import tkinter.messagebox as mb
-            mb.showwarning("单价校验提示", f"存在{len(bad)}条单价与商品资料进货价差异超过1元:\n" + "\n".join(lines))
+            display_count = min(len(bad_results), 10)
+            msg = f"存在{len(bad_results)}条单价与商品资料进货价差异超过1元:\n" + "\n".join(bad_results[:display_count])
+            if len(bad_results) > 10:
+                msg += f"\n...(其余 {len(bad_results)-10} 条已省略)"
+            mb.showwarning("单价校验提示", msg)
             if log_widget is not None:
-                add_to_log(log_widget, f"单价校验发现{len(bad)}条差异>1元\n", "warning")
+                add_to_log(log_widget, f"单价校验发现{len(bad_results)}条差异>1元\n", "warning")
         else:
             if log_widget is not None:
                 add_to_log(log_widget, "单价校验通过（差异<=1元）\n", "success")
-    except Exception:
+    except Exception as e:
-        pass
+        if log_widget is not None:
+            add_to_log(log_widget, f"单价校验出错: {str(e)}\n", "error")
 
 def clean_cache(log_widget):
     """清除处理缓存"""