270 lines
12 KiB
Python
270 lines
12 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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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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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Excel处理器和采购单合并器
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self.excel_processor = ExcelProcessor(self.config)
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self.order_merger = PurchaseOrderMerger(self.config)
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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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return self.excel_processor.process_specific_file(file_path, progress_cb=progress_cb)
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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
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df_head = smart_read_excel(file_path, nrows=50)
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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.process_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.process_rongcheng_yigou(file_path)
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# 3. 识别：杨碧月 (Yang Biyue)
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handler_col = None
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for col in df_head.columns:
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if '经手人' in str(col):
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handler_col = col
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break
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if handler_col is not None and 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，执行通用预处理...")
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df = smart_read_excel(file_path)
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column_map = {
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'商品条码': '商品条码', '商品名称': '商品名称', '规格': '规格',
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'单位': '单位', '数量': '数量', '单价': '单价', '金额': '金额'
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}
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found_cols = {}
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for target_zh, std_name in column_map.items():
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for col in df.columns:
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col_str = str(col)
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if target_zh == col_str: found_cols[col] = std_name; break
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if std_name not in found_cols.values():
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for col in df.columns:
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if target_zh in str(col): found_cols[col] = std_name; break
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if len(found_cols) >= 4:
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df_clean = df[list(found_cols.keys())].copy()
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df_clean = df_clean.rename(columns=found_cols)
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for c in ['数量', '单价', '金额']:
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if c in df_clean.columns:
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df_clean[c] = pd.to_numeric(df_clean[c], errors='coerce').fillna(0)
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df_clean = df_clean.dropna(subset=['商品条码'])
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out_dir = os.path.dirname(file_path)
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final_path = os.path.join(out_dir, "预处理之后.xlsx")
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df_clean.to_excel(final_path, index=False)
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return final_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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import os
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from app.core.utils.file_utils import smart_read_excel
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item_path = os.path.join('templates', '商品资料.xlsx')
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if not os.path.exists(item_path):
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logger.warning(f"未找到商品资料文件: {item_path}")
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return []
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df_item = smart_read_excel(item_path)
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df_res = smart_read_excel(result_path)
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def _find_col(df, candidates, contains=None):
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cols = list(df.columns)
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for c in candidates:
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if c in cols:
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return c
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if contains:
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for c in cols:
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if contains in str(c):
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return c
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return None
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item_barcode_col = _find_col(df_item, ['商品条码','商品条码（小条码）','条码','barcode'], contains='条码')
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item_price_col = _find_col(df_item, ['进货价','进货价（必填）'], contains='进货价')
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res_barcode_col = _find_col(df_res, ['条码','barcode'], contains='条码')
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res_price_col = _find_col(df_res, ['采购单价','unit_price','单价'], contains='单价')
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if not all([item_barcode_col, item_price_col, res_barcode_col, res_price_col]):
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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）")
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return []
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item_map = df_item[[item_barcode_col, item_price_col]].dropna()
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item_map[item_price_col] = pd.to_numeric(item_map[item_price_col], errors='coerce')
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item_map = item_map.dropna()
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imap = dict(zip(item_map[item_barcode_col].astype(str).str.strip(), item_map[item_price_col]))
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df_res['_bc_'] = df_res[res_barcode_col].astype(str).str.strip()
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df_res['_res_price_'] = pd.to_numeric(df_res[res_price_col], errors='coerce')
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df_res['_item_price_'] = df_res['_bc_'].map(imap)
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df_check = df_res.dropna(subset=['_res_price_','_item_price_'])
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df_check['_diff_'] = (df_check['_res_price_'] - df_check['_item_price_']).abs()
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bad = df_check[df_check['_diff_'] > 1.0]
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results = []
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if not bad.empty:
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for i in range(len(bad)):
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r = bad.iloc[i]
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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}")
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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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