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feat: 益选 OCR 订单处理系统初始提交

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- 智能供应商识别（蓉城易购/烟草/杨碧月/通用）
- 百度 OCR 表格识别集成
- 规则引擎（列映射/数据清洗/单位转换/规格推断）
- 条码映射管理与云端同步（Gitea REST API）
- 云端同步支持：条码映射、供应商配置、商品资料、采购模板
- 拖拽一键处理（图片→OCR→Excel→合并）
- 191 个单元测试
- 移除无用的模板管理功能
- 清理 IDE 产物目录

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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2026-05-04 19:51:13 +08:00
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app/services/special_suppliers_service.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import re
import time
import pandas as pd
from typing import Optional, Callable
from ..core.utils.log_utils import get_logger
logger = get_logger(__name__)
class SpecialSuppliersService:
"""
处理特殊供应商逻辑的服务类，如蓉城易购等
"""
def __init__(self, config_manager=None):
self.config_manager = config_manager
def process_yang_biyue_only(self, src_path: str) -> Optional[str]:
"""
仅执行杨碧月订单的预处理，返回预处理后的文件路径
"""
try:
from app.core.utils.file_utils import smart_read_excel
# 读取原始数据
df = smart_read_excel(src_path)
# 检查是否包含“杨碧月”
handler_col = None
for col in df.columns:
if '经手人' in str(col):
handler_col = col
break
if handler_col is None or not df[handler_col].astype(str).str.contains('杨碧月').any():
return None
# 识别到杨碧月订单，执行专用清洗
logger.info("识别到杨碧月订单，正在执行专用清洗...")
# 定义列映射关系 (映射到 ExcelProcessor 期望的中文列名)
# 使用精确匹配优先，防止“结算单位”匹配到“单位”
column_map = {
'商品条码': '商品条码',
'商品名称': '商品名称',
'商品规格': '规格',
'单位': '单位',
'数量': '数量',
'含税单价': '单价',
'含税金额': '金额'
}
found_cols = {}
# 1. 第一遍：尝试精确匹配
for target_zh, std_name in column_map.items():
for col in df.columns:
if str(col).strip() == target_zh:
found_cols[col] = std_name
break
# 2. 第二遍：对未匹配成功的列尝试模糊匹配（但要排除特定干扰词）
for target_zh, std_name in column_map.items():
if std_name in found_cols.values():
continue
for col in df.columns:
col_str = str(col)
if target_zh in col_str:
# 排除干扰列
if target_zh == '单位' and '结算单位' in col_str:
continue
if target_zh == '数量' and '基本单位数量' in col_str:
continue
found_cols[col] = std_name
break
if len(found_cols) < 4:
logger.error(f"杨碧月订单列匹配不足: 找到 {list(found_cols.values())}")
return None
df_clean = df[list(found_cols.keys())].copy()
df_clean = df_clean.rename(columns=found_cols)
# 过滤掉空的条码行
df_clean = df_clean.dropna(subset=['商品条码'])
# 保存预处理文件
out_dir = os.path.dirname(src_path)
base = os.path.basename(src_path)
final_path = os.path.join(out_dir, f"预处理之后_{base}")
df_clean.to_excel(final_path, index=False)
return final_path
except Exception as e:
logger.error(f"预处理杨碧月订单出错: {e}")
return None
def process_yang_biyue(self, src_path: str, progress_cb: Optional[Callable[[int, str], None]] = None) -> Optional[str]:
"""
处理杨碧月经手的订单（预处理+处理）
"""
try:
if progress_cb: progress_cb(10, "正在进行杨碧月订单预处理...")
preprocessed_path = self.process_yang_biyue_only(src_path)
if not preprocessed_path:
return None
if progress_cb: progress_cb(60, "预处理文件已保存，开始标准转换流程...")
# 延迟导入以避免循环依赖
from app.services.order_service import OrderService
order_service = OrderService(self.config_manager)
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)
return result
except Exception as e:
logger.error(f"处理杨碧月订单出错: {e}")
return None
def preprocess_rongcheng_yigou(self, src_path: str, progress_cb: Optional[Callable[[int, str], None]] = None) -> Optional[str]:
"""
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
"""
try:
if progress_cb: progress_cb(10, "正在处理蓉城易购预处理...")
from app.core.utils.file_utils import smart_read_excel
# 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
df_raw = smart_read_excel(src_path, header=None)
# 检查数据行数
if len(df_raw) <= 3:
logger.error("蓉城易购文件数据行数不足")
return None
# 提取数据部分 (Row 3开始)
df_data = df_raw.iloc[3:].reset_index(drop=True)
# 用户指定列映射：
# E列 (Index 4) -> 商品条码
# N列 (Index 13) -> 数量
# Q列 (Index 16) -> 单价
# S列 (Index 18) -> 金额
# C列 (Index 2) -> 商品名称 (通用需求)
idx_map = {
2: '商品名称',
4: '商品条码',
13: '数量',
16: '单价',
18: '金额'
}
# 确保列索引不越界
available_indices = [i for i in idx_map.keys() if i < df_data.shape[1]]
df2 = df_data.iloc[:, available_indices].copy()
df2.columns = [idx_map[i] for i in available_indices]
# 强制转换类型
for c in ['数量', '单价', '金额']:
if c in df2.columns:
df2[c] = pd.to_numeric(df2[c], errors='coerce').fillna(0)
# 过滤掉空的条码行
df2 = df2.dropna(subset=['商品条码'])
df2['商品条码'] = df2['商品条码'].astype(str).str.strip()
df2 = df2[df2['商品条码'] != '']
# 核心逻辑：分裂多条码行并均分数量
if '商品条码' in df2.columns and '数量' in df2.columns:
rows = []
for _, row in df2.iterrows():
bc_val = str(row.get('商品条码', '')).strip()
# 识别分隔符：/ , ， 、
if any(sep in bc_val for sep in ['/', ',', '，', '、']):
parts = re.split(r'[/,，、]+', bc_val)
parts = [p.strip() for p in parts if p.strip()]
if len(parts) >= 2:
q_total = float(row.get('数量', 0) or 0)
if q_total > 0:
n = len(parts)
base_qty = int(q_total // n)
remainder = int(q_total % n)
for i, p_bc in enumerate(parts):
new_row = row.copy()
new_row['商品条码'] = p_bc
current_qty = base_qty + (1 if i < remainder else 0)
new_row['数量'] = current_qty
if '单价' in new_row:
try:
up = float(new_row['单价'] or 0)
new_row['金额'] = up * current_qty
except Exception:
pass
rows.append(new_row)
continue
rows.append(row)
df2 = pd.DataFrame(rows)
# 保存预处理文件
out_dir = os.path.dirname(src_path)
base = os.path.basename(src_path)
final_path = os.path.join(out_dir, f"预处理之后_{base}")
df2.to_excel(final_path, index=False)
if progress_cb: progress_cb(100, "蓉城易购预处理完成")
return final_path
except Exception as e:
logger.error(f"预处理蓉城易购订单出错: {e}")
return None
def process_rongcheng_yigou(self, src_path: str, progress_cb: Optional[Callable[[int, str], None]] = None) -> Optional[str]:
"""
兼容性方法：处理蓉城易购订单并执行后续转换
"""
cleaned_path = self.preprocess_rongcheng_yigou(src_path, progress_cb)
if cleaned_path:
from app.services.order_service import OrderService
order_service = OrderService(self.config_manager)
return 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)
return None