407 lines
17 KiB
Python
407 lines
17 KiB
Python
"""
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订单服务模块
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---------
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提供订单处理服务，协调Excel处理和订单合并流程。
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"""
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import os
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import json
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import re
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import shutil
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from datetime import datetime
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Union, Any, Callable
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from ..config.settings import ConfigManager
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from ..core.utils.log_utils import get_logger
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from ..core.excel.processor import ExcelProcessor
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from ..core.excel.merger import PurchaseOrderMerger
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from ..core.db.product_db import ProductDatabase
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from ..core.db.order_metadata_db import OrderMetadataDB
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from ..core.ocr.metadata_extractor import OrderMetadataExtractor, sanitize_for_filename
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logger = get_logger(__name__)
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class OrderService:
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"""
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订单服务：协调Excel处理和订单合并流程
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"""
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def __init__(self, config: Optional[ConfigManager] = None):
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"""
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初始化订单服务
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Args:
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config: 配置管理器，如果为None则创建新的
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"""
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logger.info("初始化OrderService")
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self.config = config or ConfigManager()
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# 创建共享的商品数据库实例
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db_path = self.config.get_path('Paths', 'product_db', fallback='data/product_cache.db') if hasattr(self.config, 'get_path') else 'data/product_cache.db'
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tpl_folder = self.config.get('Paths', 'template_folder', fallback='templates')
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item_data = self.config.get('Templates', 'item_data', fallback='商品资料.xlsx')
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tpl_path = os.path.join(tpl_folder, item_data)
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self.product_db = ProductDatabase(db_path, tpl_path)
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# 创建Excel处理器和采购单合并器
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self.excel_processor = ExcelProcessor(self.config, product_db=self.product_db)
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self.order_merger = PurchaseOrderMerger(self.config)
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# 元信息识别器 + 单据元信息库
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self.extractor = OrderMetadataExtractor()
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self.metadata_db = OrderMetadataDB(db_path)
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logger.info("OrderService初始化完成")
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def get_latest_excel(self) -> Optional[str]:
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"""
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获取最新的Excel文件
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Returns:
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最新Excel文件路径，如果未找到则返回None
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"""
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return self.excel_processor.get_latest_excel()
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def process_excel(self, file_path: Optional[str] = None, progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
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"""
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处理Excel订单文件，生成标准采购单
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Args:
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file_path: Excel文件路径，如果为None则处理最新的文件
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Returns:
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输出采购单文件路径，如果处理失败则返回None
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"""
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if not file_path:
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file_path = self.excel_processor.get_latest_excel()
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if not file_path:
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logger.warning("未找到可处理的Excel文件")
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return None
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logger.info("OrderService开始处理最新Excel文件")
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else:
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logger.info(f"OrderService开始处理指定Excel文件: {file_path}")
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# 检查是否需要特殊的供应商预处理（如杨碧月）
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try:
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from .special_suppliers_service import SpecialSuppliersService
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special_service = SpecialSuppliersService(self.config)
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# 尝试识别并预处理（注意：这里不再传入 progress_cb 避免无限递归或重复进度条，
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# 或者我们在 special_service 内部逻辑中处理完后直接返回结果）
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# 为了避免循环调用，我们在 SpecialSuppliersService 内部不再调用 process_excel，
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# 而是让 process_excel 识别后自己决定是否处理预处理后的文件。
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# 我们新增一个 check_and_preprocess 方法
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preprocessed_path = self._check_special_preprocess(file_path)
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if preprocessed_path:
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logger.info(f"检测到特殊供应商，已生成预处理文件: {preprocessed_path}")
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file_path = preprocessed_path
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except Exception as e:
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logger.error(f"检查特殊预处理时出错: {e}")
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result_path = self.excel_processor.process_specific_file(file_path, progress_cb=progress_cb)
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if not result_path:
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return None
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# 应用单据元信息识别 + 重命名 result 与原图
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try:
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meta = self._extract_and_save_metadata(file_path)
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if meta:
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result_path = self._apply_metadata_to_filenames(
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result_path, file_path, meta
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)
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except Exception as e:
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logger.error(f"应用单据元信息失败（不影响 result 文件）: {e}")
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return result_path
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def _check_special_preprocess(self, file_path: str) -> Optional[str]:
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"""检查并执行特殊的预处理（支持杨碧月、烟草公司、蓉城易购）"""
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try:
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from app.core.utils.file_utils import smart_read_excel
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import pandas as pd
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import re
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# 仅读取前 50 行进行智能识别 (header=None 确保能读到第一行内容)
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df_head = smart_read_excel(file_path, nrows=50, header=None)
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df_str = df_head.astype(str)
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# 1. 识别：烟草公司 (Tobacco)
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# 特征：内容中包含“专卖证号”或特定证号“510109104938”
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is_tobacco = df_str.apply(lambda x: x.str.contains('专卖证号|510109104938')).any().any()
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if is_tobacco:
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logger.info("识别到烟草公司订单，执行专用预处理...")
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from .tobacco_service import TobaccoService
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tobacco_svc = TobaccoService(self.config)
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return tobacco_svc.preprocess_tobacco_order(file_path)
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# 2. 识别：蓉城易购 (Rongcheng Yigou)
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# 特征：内容中包含单号标识“RCDH”
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is_rongcheng = df_str.apply(lambda x: x.str.contains('RCDH')).any().any()
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if is_rongcheng:
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logger.info("识别到蓉城易购订单，执行专用预处理...")
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from .special_suppliers_service import SpecialSuppliersService
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special_svc = SpecialSuppliersService(self.config)
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return special_svc.preprocess_rongcheng_yigou(file_path)
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# 3. 识别：杨碧月 (Yang Biyue)
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# 特征：经手人列包含“杨碧月”
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handler_col = None
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for col in df_head.columns:
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# 在前50行中搜索“经手人”关键字
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if df_head[col].astype(str).str.contains('经手人').any():
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handler_col = col
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break
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if handler_col is not None:
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# 检查该列是否有“杨碧月”
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if df_head[handler_col].astype(str).str.contains('杨碧月').any():
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logger.info("识别到杨碧月订单，执行专用预处理...")
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from .special_suppliers_service import SpecialSuppliersService
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special_svc = SpecialSuppliersService(self.config)
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return special_svc.process_yang_biyue_only(file_path)
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except Exception as e:
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logger.warning(f"智能预处理识别失败: {e}")
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return None
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def get_purchase_orders(self) -> List[str]:
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"""
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获取采购单文件列表
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Returns:
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采购单文件路径列表
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"""
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return self.order_merger.get_purchase_orders()
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def merge_purchase_orders(self, file_paths: List[str], progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
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"""
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合并指定的采购单文件
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Args:
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file_paths: 采购单文件路径列表
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Returns:
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合并后的采购单文件路径，如果合并失败则返回None
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"""
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logger.info(f"OrderService开始合并指定采购单: {file_paths}")
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return self.merge_orders(file_paths, progress_cb)
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def merge_all_purchase_orders(self, progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
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"""
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合并所有可用的采购单文件
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Returns:
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合并后的采购单文件路径，如果合并失败则返回None
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"""
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logger.info("OrderService开始合并所有采购单")
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return self.merge_orders(None, progress_cb)
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def merge_orders(self, file_paths: Optional[List[str]] = None, progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
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"""
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合并采购单
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Args:
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file_paths: 采购单文件路径列表，如果为None则处理所有采购单
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Returns:
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合并后的采购单文件路径，如果合并失败则返回None
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"""
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if file_paths:
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logger.info(f"OrderService开始合并指定采购单: {file_paths}")
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else:
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logger.info("OrderService开始合并所有采购单")
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return self.order_merger.process(file_paths, progress_cb)
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def validate_unit_price(self, result_path: str) -> List[str]:
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"""
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校验采购单单价与商品资料进货价的差异
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Args:
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result_path: 待校验的采购单路径
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Returns:
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差异信息列表，无差异返回空列表
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"""
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try:
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import pandas as pd
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from app.core.utils.file_utils import smart_read_excel
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from app.core.handlers.column_mapper import ColumnMapper as CM
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# 使用共享的商品数据库实例
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product_db = self.product_db
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# 读取待校验的采购单
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df_res = smart_read_excel(result_path)
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res_barcode_col = CM.find_column(list(df_res.columns), 'barcode')
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res_price_col = CM.find_column(list(df_res.columns), 'unit_price')
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if not res_barcode_col or not res_price_col:
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logger.warning("未能在采购单中找到条码或单价列")
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return []
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# 批量查询进货价
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barcodes = df_res[res_barcode_col].astype(str).str.strip().tolist()
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item_prices = product_db.get_prices(barcodes)
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results = []
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for _, row in df_res.iterrows():
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bc = str(row[res_barcode_col]).strip()
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if bc not in item_prices:
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continue
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try:
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res_price = float(row[res_price_col])
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except (ValueError, TypeError):
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continue
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item_price = item_prices[bc]
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diff = abs(res_price - item_price)
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if diff > 1.0:
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results.append(f"条码 {bc}: 采购单价={res_price} vs 进货价={item_price} 差异={diff:.2f}")
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return results
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except Exception as e:
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logger.error(f"单价校验过程中发生错误: {e}")
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return []
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# ══════════════════════════════════════════════════════════════
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# 单据元信息识别 + 文件重命名
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# ══════════════════════════════════════════════════════════════
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def _extract_and_save_metadata(self, ocr_excel_path: str) -> Optional[Any]:
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"""从 OCR 输出的 xlsx 同目录的 .meta.json 提取元信息，写入 SQLite。
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Returns:
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OrderMetadata 或 None（失败时）
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"""
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try:
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base = Path(ocr_excel_path)
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file_hash = base.stem
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meta_path = base.with_suffix('.meta.json')
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ocr_text = ''
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ocr_rows: List[List[str]] = []
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source_image = ''
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if meta_path.exists():
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try:
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payload = json.loads(meta_path.read_text(encoding='utf-8'))
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# 优先用通用识别文本（含手写抬头/日期）
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ocr_text = payload.get('general_text') or payload.get('ocr_text', '') or ''
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ocr_rows = payload.get('ocr_rows', []) or []
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source_image = payload.get('image_path', '') or ''
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except Exception as e:
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logger.warning(f"读 meta.json 失败: {e}")
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# 兜底：从 xlsx 拼文本（与 OCRService._write_meta_json 的兜底一致）
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if not ocr_text:
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try:
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import xlrd
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rb = xlrd.open_workbook(str(ocr_excel_path))
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ws = rb.sheet_by_index(0)
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lines = []
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for r in range(ws.nrows):
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row_vals = [str(ws.cell_value(r, c)) for c in range(ws.ncols)]
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ocr_rows.append(row_vals)
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lines.append(' '.join(row_vals))
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ocr_text = '\n'.join(lines)
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except Exception as e:
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logger.debug(f"从 xlsx 拼 OCR 文本失败: {e}")
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meta = self.extractor.extract(ocr_text, ocr_rows)
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self.metadata_db.save(
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file_hash=file_hash,
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supplier=meta.supplier,
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bill_date=meta.bill_date,
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total_amount=meta.total_amount,
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raw_supplier_text=meta.raw_supplier_text,
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source_image=source_image,
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)
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logger.info(
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f"元信息识别: hash={file_hash} supplier={meta.supplier!r} "
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f"bill_date={meta.bill_date!r} total_amount={meta.total_amount:.2f}"
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)
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return meta
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except Exception as e:
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logger.error(f"_extract_and_save_metadata 失败: {e}")
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return None
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def _apply_metadata_to_filenames(self, result_path: str,
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ocr_excel_path: str,
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meta) -> str:
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"""应用新文件名规则：
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- result: 采购单_{YYYYMMDD}_{供应商}_{hash}.xls
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- 原图: {原stem}_{YYYYMMDD}_{供应商}_{hash}.{ext}
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任一步骤失败不影响 result 文件本身。
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Returns:
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新 result 路径（无论重命名是否成功都返回；失败时返回原路径）
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"""
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try:
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file_hash = Path(ocr_excel_path).stem
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supplier_clean = sanitize_for_filename(meta.supplier) or '未知供应商'
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date_part = meta.bill_date or '未知日期'
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# ── 1. result 重命名 ──
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new_result_name = f"采购单_{date_part}_{supplier_clean}_{file_hash}.xls"
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result_dir = Path(result_path).parent
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new_result_path = result_dir / new_result_name
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try:
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# 冲突时加 _N
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if new_result_path.exists() and str(new_result_path) != str(result_path):
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new_result_path = self._dedup_path(new_result_path)
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if str(new_result_path) != str(result_path):
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os.rename(result_path, str(new_result_path))
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logger.info(f"result 重命名: {result_path} -> {new_result_path}")
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result_path = str(new_result_path)
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except Exception as e:
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logger.warning(f"result 重命名失败: {e}")
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# ── 2. 原图重命名 ──
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try:
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meta_row = self.metadata_db.get(file_hash)
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src_image = (meta_row or {}).get('source_image', '')
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if src_image and os.path.exists(src_image):
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src_p = Path(src_image)
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stem = src_p.stem
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ext = src_p.suffix
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new_image_name = f"{stem}_{date_part}_{supplier_clean}_{file_hash}{ext}"
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new_image_path = src_p.parent / new_image_name
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# 不覆盖已重命名的图片
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if str(new_image_path) != str(src_p) and not new_image_path.exists():
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os.rename(src_p, new_image_path)
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logger.info(f"原图重命名: {src_p.name} -> {new_image_path.name}")
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# 更新 source_image 路径
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self.metadata_db.save(
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file_hash=file_hash,
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supplier=meta.supplier,
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bill_date=meta.bill_date,
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total_amount=meta.total_amount,
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raw_supplier_text=meta.raw_supplier_text,
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source_image=str(new_image_path),
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)
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except Exception as e:
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logger.warning(f"原图重命名失败: {e}")
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return result_path
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except Exception as e:
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logger.error(f"_apply_metadata_to_filenames 失败: {e}")
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return result_path
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@staticmethod
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def _dedup_path(p: Path) -> Path:
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"""路径冲突时加 _N 后缀。"""
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stem, suffix = p.stem, p.suffix
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parent = p.parent
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n = 1
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while True:
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cand = parent / f"{stem}_{n}{suffix}"
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if not cand.exists():
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return cand
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n += 1
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