houhuan
6f96bf50ac
当从外部目录(如D:\ccc)拖入文件时，输出文件会错误地写入源目录。 根因是所有路径使用相对路径 + os.getcwd() 解析，CWD不同则路径错误。 修复方案： - ConfigManager.get_path() 改为使用 app_root (exe所在目录/脚本所在目录) - 将 22 处裸硬编码 "data/result"/"data/output" 替换为 config.get_path() - 添加 result_folder 到默认配置和 config.ini - 修复 error_utils.py 中的路径匹配字符串 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
336 lines
13 KiB
Python
336 lines
13 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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烟草公司订单处理服务
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----------------
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处理烟草公司特定格式的订单明细文件，生成银豹采购单
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"""
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import os
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import glob
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import datetime
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import pandas as pd
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import xlrd
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import xlwt
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import re
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from xlutils.copy import copy
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from openpyxl import load_workbook
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from typing import Optional, Dict, Any, List, Tuple
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from app.core.utils.log_utils import get_logger
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from app.core.utils.string_utils import parse_monetary_string
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from app.core.utils.dialog_utils import show_custom_dialog # 导入自定义弹窗工具
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from ..config.settings import ConfigManager
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logger = get_logger(__name__)
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class TobaccoService:
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"""烟草公司订单处理服务"""
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def __init__(self, config: Dict[str, Any]):
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"""
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初始化服务
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Args:
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config: 配置信息
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"""
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self.config = config
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# 修复配置获取方式，使用fallback机制
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self.output_dir = config.get_path('Paths', 'output_folder', fallback='data/output', create=True) if hasattr(config, 'get_path') else os.path.abspath('data/output')
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self.template_file = config.get('Paths', 'template_file', fallback='templates/银豹-采购单模板.xls')
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# 将烟草订单保存到result目录
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result_dir = config.get_path('Paths', 'result_folder', fallback='data/result', create=True) if hasattr(config, 'get_path') else os.path.abspath('data/result')
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os.makedirs(result_dir, exist_ok=True)
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self.output_file = os.path.join(result_dir, '银豹采购单_烟草公司.xls')
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def get_latest_tobacco_order(self) -> Optional[str]:
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"""
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获取最新的烟草订单明细文件
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Returns:
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文件路径或None
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"""
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# 获取今日开始时间戳
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today = datetime.date.today()
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today_start = datetime.datetime.combine(today, datetime.time.min).timestamp()
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# 查找订单明细文件
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file_pattern = os.path.join(self.output_dir, "订单明细*.xlsx")
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candidates = glob.glob(file_pattern)
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if not candidates:
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logger.warning("未找到烟草公司订单明细文件")
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return None
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# 按创建时间排序
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candidates.sort(key=os.path.getctime, reverse=True)
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latest_file = candidates[0]
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# 检查是否是今天的文件
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if os.path.getctime(latest_file) >= today_start:
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logger.info(f"找到最新烟草订单明细文件: {latest_file}")
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return latest_file
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else:
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logger.warning(f"找到的烟草订单明细文件不是今天创建的: {latest_file}")
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return latest_file # 仍然返回最新文件，但给出警告
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def preprocess_tobacco_order(self, file_path: str) -> Optional[str]:
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"""
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烟草订单预处理：按用户提供的 B, E, G, H 列索引进行强制清洗
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"""
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try:
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logger.info(f"执行烟草订单专用预处理: {file_path}")
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from app.core.utils.file_utils import smart_read_excel
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# 烟草格式：Row 0是专卖证号，Row 1是表头，Row 2是合计，Row 3开始是数据
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df_raw = smart_read_excel(file_path, header=None)
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if len(df_raw) <= 3:
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logger.error("烟草订单文件数据行数不足")
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return None
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# 提取数据部分 (Row 3开始)
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df_data = df_raw.iloc[3:].reset_index(drop=True)
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# 用户指定列映射：
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# A列 (Index 0) -> 商品名称
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# B列 (Index 1) -> 商品条码 (盒码)
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# E列 (Index 4) -> 批发价 (单价)
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# G列 (Index 6) -> 订单量 (数量)
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# H列 (Index 7) -> 金额
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idx_map = {
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0: '商品名称',
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1: '商品条码',
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4: '批发价',
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6: '数量',
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7: '金额'
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}
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available_indices = [i for i in idx_map.keys() if i < df_data.shape[1]]
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df = df_data.iloc[:, available_indices].copy()
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df.columns = [idx_map[i] for i in available_indices]
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# 1. 过滤订单量不为0的数据
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df['数量'] = pd.to_numeric(df['数量'], errors='coerce').fillna(0)
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df = df[df['数量'] != 0].copy()
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if df.empty:
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logger.warning("烟草订单无有效订单量记录")
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return None
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# 2. 核心清洗逻辑：
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# 数量 = 订单量 * 10 (G列)
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# 单价 = 批发价 / 10 (E列)
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df['单价'] = pd.to_numeric(df['批发价'], errors='coerce').fillna(0) / 10
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df['数量'] = df['数量'] * 10
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# 3. 校验金额 (H列)
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df['金额'] = pd.to_numeric(df['金额'], errors='coerce').fillna(0)
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# 4. 只保留需要的列
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final_cols = ['商品条码', '商品名称', '数量', '单价', '金额']
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df_final = df[final_cols].copy()
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# 保存预处理文件
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out_dir = os.path.dirname(file_path)
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base = os.path.basename(file_path)
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final_path = os.path.join(out_dir, f"预处理之后_{base}")
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df_final.to_excel(final_path, index=False)
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logger.info(f"烟草订单预处理完成: {final_path}")
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return final_path
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except Exception as e:
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logger.error(f"烟草订单预处理失败: {e}")
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return None
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def process_tobacco_order(self, input_file=None):
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"""
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处理烟草订单
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Args:
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input_file: 输入文件路径，如果为None则自动查找最新文件
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Returns:
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输出文件路径或None（如果处理失败）
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"""
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try:
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# 如果没有指定输入文件，查找最新的文件
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if input_file is None:
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input_file = self.get_latest_tobacco_order()
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if input_file is None:
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logger.warning("未找到烟草公司订单明细文件")
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logger.error("未找到可处理的烟草订单明细文件")
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return None
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logger.info(f"开始处理烟草公司订单: {input_file}")
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# 读取订单时间和总金额
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order_info = self._read_order_info(input_file)
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if not order_info:
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logger.error(f"读取订单信息失败: {input_file}")
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return None
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order_time, total_amount = order_info
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# 读取订单数据
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order_data = self._read_order_data(input_file)
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if order_data is None or order_data.empty:
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logger.error(f"读取订单数据失败: {input_file}")
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return None
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# 生成银豹采购单
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output_file = self._generate_pospal_order(order_data, order_time)
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if not output_file:
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logger.error("生成银豹采购单失败")
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return None
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# 获取处理条目数
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total_count = len(order_data)
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# 输出处理结果
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logger.info(f"烟草公司订单处理成功，订单时间: {order_time}, 总金额: {total_amount}, 处理条目: {total_count}")
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logger.info(f"采购单已生成: {output_file}")
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# 显示处理结果对话框
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self.show_result_dialog(output_file, order_time, total_count, total_amount)
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return output_file
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except Exception as e:
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logger.error(f"处理烟草公司订单时发生错误: {e}", exc_info=True)
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return None
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def _read_order_info(self, file_path: str) -> Optional[Tuple[str, float]]:
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"""
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读取订单信息（时间和总金额）
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Args:
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file_path: 文件路径
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Returns:
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包含订单时间和总金额的元组或None
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"""
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try:
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wb_info = load_workbook(file_path, data_only=True)
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ws_info = wb_info.active
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order_time = ws_info["H1"].value or "（空）"
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total_amount = ws_info["H3"].value or 0
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return (order_time, total_amount)
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except Exception as e:
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logger.error(f"读取订单信息出错: {e}")
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return None
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def _read_order_data(self, file_path: str) -> Optional[pd.DataFrame]:
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"""
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读取订单数据
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Args:
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file_path: 文件路径
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Returns:
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订单数据DataFrame或None
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"""
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columns = ['商品', '盒码', '条码', '建议零售价', '批发价', '需求量', '订单量', '金额']
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try:
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from app.core.utils.file_utils import smart_read_excel
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# 读取Excel文件
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df_old = smart_read_excel(file_path, header=None, skiprows=3, names=columns)
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# 过滤订单量不为0的数据，并计算采购量和单价
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df_filtered = df_old[df_old['订单量'] != 0].copy()
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df_filtered['采购量'] = df_filtered['订单量'] * 10
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df_filtered['采购单价'] = df_filtered['金额'] / df_filtered['采购量']
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df_filtered = df_filtered.reset_index(drop=True)
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return df_filtered
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except Exception as e:
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logger.error(f"读取订单数据失败: {e}")
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return None
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def _generate_pospal_order(self, order_data: pd.DataFrame, order_time: str) -> Optional[str]:
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"""
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生成银豹采购单
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Args:
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order_data: 订单数据
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order_time: 订单时间
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Returns:
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输出文件路径或None
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"""
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try:
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# 检查模板文件是否存在
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if not os.path.exists(self.template_file):
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logger.error(f"采购单模板文件不存在: {self.template_file}")
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return None
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# 打开模板，准备写入
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template_rd = xlrd.open_workbook(self.template_file, formatting_info=True)
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template_wb = copy(template_rd)
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template_ws = template_wb.get_sheet(0)
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# 获取模板中的表头列索引
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header_row = template_rd.sheet_by_index(0).row_values(0)
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barcode_col = header_row.index("条码（必填）")
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amount_col = header_row.index("采购量（必填）")
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gift_col = header_row.index("赠送量")
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price_col = header_row.index("采购单价（必填）")
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# 写入数据到模板
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for i, row in order_data.iterrows():
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template_ws.write(i + 1, barcode_col, row['盒码']) # 商品条码
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template_ws.write(i + 1, amount_col, int(row['采购量'])) # 采购量
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template_ws.write(i + 1, gift_col, "") # 赠送量为空
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template_ws.write(i + 1, price_col, round(row['采购单价'], 2)) # 采购单价保留两位小数
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# 确保输出目录存在
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os.makedirs(os.path.dirname(self.output_file), exist_ok=True)
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# 保存输出文件
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template_wb.save(self.output_file)
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logger.info(f"采购单生成成功: {self.output_file}")
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return self.output_file
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except Exception as e:
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logger.error(f"生成银豹采购单失败: {e}")
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return None
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def show_result_dialog(self, output_file, order_time, total_count, total_amount):
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"""
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显示处理结果对话框
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Args:
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output_file: 输出文件路径
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order_time: 订单时间
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total_count: 总处理条目
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total_amount: 总金额
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"""
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# 创建附加信息
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additional_info = {
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"订单来源": "烟草公司",
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"处理时间": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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}
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# 确保 total_amount 是数字类型
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parsed = parse_monetary_string(total_amount)
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total_amount = parsed if parsed is not None else 0.0
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amount_display = f"¥{total_amount:.2f}"
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# 显示自定义对话框
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show_custom_dialog(
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title="烟草订单处理结果",
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message="烟草订单处理完成",
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result_file=output_file,
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time_info=order_time,
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count_info=f"{total_count}个商品",
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amount_info=amount_display,
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additional_info=additional_info
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)
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# 记录日志
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logger.info(f"烟草公司订单处理成功，订单时间: {order_time}, 总金额: {total_amount}, 处理条目: {total_count}") |