houhuan
32d41244e5
- 新增智能识别功能，自动检测蓉城易购、烟草公司、杨碧月订单特征 - 修改订单服务流程，在Excel处理前自动执行专用预处理 - 更新无界面API，支持智能识别模式，简化OpenClaw集成 - 完善供应商专用预处理逻辑，修复数量计算和单位换算问题 - 添加变更日志和最终更新报告文档，记录v2.1版本变更
207 lines
8.5 KiB
Python
207 lines
8.5 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import re
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import time
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import pandas as pd
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import logging
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from typing import Optional, Callable
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logger = logging.getLogger(__name__)
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class SpecialSuppliersService:
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"""
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处理特殊供应商逻辑的服务类，如蓉城易购等
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"""
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def __init__(self, config_manager=None):
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self.config_manager = config_manager
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def process_yang_biyue_only(self, src_path: str) -> Optional[str]:
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"""
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仅执行杨碧月订单的预处理，返回预处理后的文件路径
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"""
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try:
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from app.core.utils.file_utils import smart_read_excel
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# 读取原始数据
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df = smart_read_excel(src_path)
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# 检查是否包含“杨碧月”
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handler_col = None
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for col in df.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 None or not df[handler_col].astype(str).str.contains('杨碧月').any():
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return None
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logger.info("识别到杨碧月订单，正在执行专用清洗...")
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# 定义列映射关系
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column_map = {
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'商品条码': 'barcode',
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'商品名称': 'name',
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'商品规格': 'specification',
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'单位': 'unit',
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'数量': 'quantity',
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'含税单价': 'unit_price',
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'含税金额': 'total_price'
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}
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# 提取并重命名列
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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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if target_zh in str(col):
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found_cols[col] = std_name
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break
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if len(found_cols) < 4:
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logger.error(f"杨碧月订单列匹配不足: 找到 {list(found_cols.values())}")
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return None
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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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# 过滤掉空的条码行
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df_clean = df_clean.dropna(subset=['barcode'])
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# 保存预处理文件
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out_dir = os.path.dirname(src_path)
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base = os.path.basename(src_path)
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final_path = os.path.join(out_dir, f"预处理之后_{base}")
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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.error(f"预处理杨碧月订单出错: {e}")
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return None
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def process_yang_biyue(self, src_path: str, progress_cb: Optional[Callable[[int, str], None]] = None) -> Optional[str]:
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"""
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处理杨碧月经手的订单（预处理+处理）
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"""
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try:
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if progress_cb: progress_cb(10, "正在进行杨碧月订单预处理...")
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preprocessed_path = self.process_yang_biyue_only(src_path)
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if not preprocessed_path:
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return None
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if progress_cb: progress_cb(60, "预处理文件已保存，开始标准转换流程...")
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# 延迟导入以避免循环依赖
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from app.services.order_service import OrderService
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order_service = OrderService(self.config_manager)
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result = order_service.process_excel(preprocessed_path, progress_cb=lambda p: progress_cb(60 + int(p*0.4), "生成采购单中...") if progress_cb else None)
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return result
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except Exception as e:
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logger.error(f"处理杨碧月订单出错: {e}")
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return None
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def preprocess_rongcheng_yigou(self, src_path: str, progress_cb: Optional[Callable[[int, str], None]] = None) -> Optional[str]:
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"""
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蓉城易购订单预处理：按用户提供的 E, N, Q, S 列索引进行强制清洗
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"""
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try:
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if progress_cb: progress_cb(10, "正在处理蓉城易购预处理...")
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from app.core.utils.file_utils import smart_read_excel
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# 蓉城易购格式：Row 0是单号，Row 1是联系人，Row 2是表头，Row 3开始是数据
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df_raw = smart_read_excel(src_path, header=None)
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# 检查数据行数
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if len(df_raw) <= 3:
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logger.error("蓉城易购文件数据行数不足")
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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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# E列 (Index 4) -> 商品条码
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# N列 (Index 13) -> 数量
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# Q列 (Index 16) -> 单价
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# S列 (Index 18) -> 金额
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# C列 (Index 2) -> 商品名称 (通用需求)
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idx_map = {
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2: '商品名称',
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4: '商品条码',
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13: '数量',
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16: '单价',
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18: '金额'
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}
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# 确保列索引不越界
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available_indices = [i for i in idx_map.keys() if i < df_data.shape[1]]
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df2 = df_data.iloc[:, available_indices].copy()
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df2.columns = [idx_map[i] for i in available_indices]
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# 强制转换类型
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for c in ['数量', '单价', '金额']:
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if c in df2.columns:
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df2[c] = pd.to_numeric(df2[c], errors='coerce').fillna(0)
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# 过滤掉空的条码行
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df2 = df2.dropna(subset=['商品条码'])
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df2['商品条码'] = df2['商品条码'].astype(str).str.strip()
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df2 = df2[df2['商品条码'] != '']
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# 核心逻辑：分裂多条码行并均分数量
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if '商品条码' in df2.columns and '数量' in df2.columns:
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rows = []
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for _, row in df2.iterrows():
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bc_val = str(row.get('商品条码', '')).strip()
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# 识别分隔符：/ , ， 、
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if any(sep in bc_val for sep in ['/', ',', '，', '、']):
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parts = re.split(r'[/,，、]+', bc_val)
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parts = [p.strip() for p in parts if p.strip()]
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if len(parts) >= 2:
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q_total = float(row.get('数量', 0) or 0)
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if q_total > 0:
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n = len(parts)
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base_qty = int(q_total // n)
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remainder = int(q_total % n)
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for i, p_bc in enumerate(parts):
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new_row = row.copy()
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new_row['商品条码'] = p_bc
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current_qty = base_qty + (1 if i < remainder else 0)
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new_row['数量'] = current_qty
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if '单价' in new_row:
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try:
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up = float(new_row['单价'] or 0)
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new_row['金额'] = up * current_qty
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except: pass
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rows.append(new_row)
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continue
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rows.append(row)
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df2 = pd.DataFrame(rows)
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# 保存预处理文件
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out_dir = os.path.dirname(src_path)
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base = os.path.basename(src_path)
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final_path = os.path.join(out_dir, f"预处理之后_{base}")
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df2.to_excel(final_path, index=False)
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if progress_cb: progress_cb(100, "蓉城易购预处理完成")
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return final_path
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except Exception as e:
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logger.error(f"预处理蓉城易购订单出错: {e}")
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return None
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def process_rongcheng_yigou(self, src_path: str, progress_cb: Optional[Callable[[int, str], None]] = None) -> Optional[str]:
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"""
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兼容性方法：处理蓉城易购订单并执行后续转换
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"""
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cleaned_path = self.preprocess_rongcheng_yigou(src_path, progress_cb)
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if cleaned_path:
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return self.order_service.process_excel(cleaned_path, progress_cb=lambda p: progress_cb(60 + int(p*0.4), "生成采购单中...") if progress_cb else None)
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return None
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