252 lines
11 KiB
Python
252 lines
11 KiB
Python
"""
|
||
订单服务模块
|
||
---------
|
||
提供订单处理服务，协调Excel处理和订单合并流程。
|
||
"""
|
||
|
||
import os
|
||
from typing import Dict, List, Optional, Tuple, Union, Any, Callable
|
||
|
||
from ..config.settings import ConfigManager
|
||
from ..core.utils.log_utils import get_logger
|
||
from ..core.excel.processor import ExcelProcessor
|
||
from ..core.excel.merger import PurchaseOrderMerger
|
||
|
||
logger = get_logger(__name__)
|
||
|
||
class OrderService:
|
||
"""
|
||
订单服务：协调Excel处理和订单合并流程
|
||
"""
|
||
|
||
def __init__(self, config: Optional[ConfigManager] = None):
|
||
"""
|
||
初始化订单服务
|
||
|
||
Args:
|
||
config: 配置管理器，如果为None则创建新的
|
||
"""
|
||
logger.info("初始化OrderService")
|
||
self.config = config or ConfigManager()
|
||
|
||
# 创建Excel处理器和采购单合并器
|
||
self.excel_processor = ExcelProcessor(self.config)
|
||
self.order_merger = PurchaseOrderMerger(self.config)
|
||
|
||
logger.info("OrderService初始化完成")
|
||
|
||
def get_latest_excel(self) -> Optional[str]:
|
||
"""
|
||
获取最新的Excel文件
|
||
|
||
Returns:
|
||
最新Excel文件路径，如果未找到则返回None
|
||
"""
|
||
return self.excel_processor.get_latest_excel()
|
||
|
||
def process_excel(self, file_path: Optional[str] = None, progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
|
||
"""
|
||
处理Excel订单文件，生成标准采购单
|
||
|
||
Args:
|
||
file_path: Excel文件路径，如果为None则处理最新的文件
|
||
|
||
Returns:
|
||
输出采购单文件路径，如果处理失败则返回None
|
||
"""
|
||
if not file_path:
|
||
file_path = self.excel_processor.get_latest_excel()
|
||
if not file_path:
|
||
logger.warning("未找到可处理的Excel文件")
|
||
return None
|
||
logger.info("OrderService开始处理最新Excel文件")
|
||
else:
|
||
logger.info(f"OrderService开始处理指定Excel文件: {file_path}")
|
||
|
||
# 检查是否需要特殊的供应商预处理（如杨碧月）
|
||
try:
|
||
from .special_suppliers_service import SpecialSuppliersService
|
||
special_service = SpecialSuppliersService(self.config)
|
||
|
||
# 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，
|
||
# 或者我们在 special_service 内部逻辑中处理完后直接返回结果）
|
||
# 为了避免循环调用，我们在 SpecialSuppliersService 内部不再调用 process_excel，
|
||
# 而是让 process_excel 识别后自己决定是否处理预处理后的文件。
|
||
|
||
# 我们新增一个 check_and_preprocess 方法
|
||
preprocessed_path = self._check_special_preprocess(file_path)
|
||
if preprocessed_path:
|
||
logger.info(f"检测到特殊供应商，已生成预处理文件: {preprocessed_path}")
|
||
file_path = preprocessed_path
|
||
except Exception as e:
|
||
logger.error(f"检查特殊预处理时出错: {e}")
|
||
|
||
return self.excel_processor.process_specific_file(file_path, progress_cb=progress_cb)
|
||
|
||
def _check_special_preprocess(self, file_path: str) -> Optional[str]:
|
||
"""检查并执行特殊的预处理（支持杨碧月、烟草公司、蓉城易购）"""
|
||
try:
|
||
from app.core.utils.file_utils import smart_read_excel
|
||
import pandas as pd
|
||
import re
|
||
|
||
# 仅读取前 50 行进行智能识别 (header=None 确保能读到第一行内容)
|
||
df_head = smart_read_excel(file_path, nrows=50, header=None)
|
||
df_str = df_head.astype(str)
|
||
|
||
# 1. 识别：烟草公司 (Tobacco)
|
||
# 特征：内容中包含“专卖证号”或特定证号“510109104938”
|
||
is_tobacco = df_str.apply(lambda x: x.str.contains('专卖证号|510109104938')).any().any()
|
||
if is_tobacco:
|
||
logger.info("识别到烟草公司订单，执行专用预处理...")
|
||
from .tobacco_service import TobaccoService
|
||
tobacco_svc = TobaccoService(self.config)
|
||
return tobacco_svc.preprocess_tobacco_order(file_path)
|
||
|
||
# 2. 识别：蓉城易购 (Rongcheng Yigou)
|
||
# 特征：内容中包含单号标识“RCDH”
|
||
is_rongcheng = df_str.apply(lambda x: x.str.contains('RCDH')).any().any()
|
||
if is_rongcheng:
|
||
logger.info("识别到蓉城易购订单，执行专用预处理...")
|
||
from .special_suppliers_service import SpecialSuppliersService
|
||
special_svc = SpecialSuppliersService(self.config)
|
||
return special_svc.preprocess_rongcheng_yigou(file_path)
|
||
|
||
# 3. 识别：杨碧月 (Yang Biyue)
|
||
# 特征：经手人列包含“杨碧月”
|
||
handler_col = None
|
||
for col in df_head.columns:
|
||
# 在前50行中搜索“经手人”关键字
|
||
if df_head[col].astype(str).str.contains('经手人').any():
|
||
handler_col = col
|
||
break
|
||
|
||
if handler_col is not None:
|
||
# 检查该列是否有“杨碧月”
|
||
if df_head[handler_col].astype(str).str.contains('杨碧月').any():
|
||
logger.info("识别到杨碧月订单，执行专用预处理...")
|
||
from .special_suppliers_service import SpecialSuppliersService
|
||
special_svc = SpecialSuppliersService(self.config)
|
||
return special_svc.process_yang_biyue_only(file_path)
|
||
|
||
except Exception as e:
|
||
logger.warning(f"智能预处理识别失败: {e}")
|
||
return None
|
||
|
||
def get_purchase_orders(self) -> List[str]:
|
||
"""
|
||
获取采购单文件列表
|
||
|
||
Returns:
|
||
采购单文件路径列表
|
||
"""
|
||
return self.order_merger.get_purchase_orders()
|
||
|
||
def merge_purchase_orders(self, file_paths: List[str], progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
|
||
"""
|
||
合并指定的采购单文件
|
||
|
||
Args:
|
||
file_paths: 采购单文件路径列表
|
||
|
||
Returns:
|
||
合并后的采购单文件路径，如果合并失败则返回None
|
||
"""
|
||
logger.info(f"OrderService开始合并指定采购单: {file_paths}")
|
||
return self.merge_orders(file_paths, progress_cb)
|
||
|
||
def merge_all_purchase_orders(self, progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
|
||
"""
|
||
合并所有可用的采购单文件
|
||
|
||
Returns:
|
||
合并后的采购单文件路径，如果合并失败则返回None
|
||
"""
|
||
logger.info("OrderService开始合并所有采购单")
|
||
return self.merge_orders(None, progress_cb)
|
||
|
||
def merge_orders(self, file_paths: Optional[List[str]] = None, progress_cb: Optional[Callable[[int], None]] = None) -> Optional[str]:
|
||
"""
|
||
合并采购单
|
||
|
||
Args:
|
||
file_paths: 采购单文件路径列表，如果为None则处理所有采购单
|
||
|
||
Returns:
|
||
合并后的采购单文件路径，如果合并失败则返回None
|
||
"""
|
||
if file_paths:
|
||
logger.info(f"OrderService开始合并指定采购单: {file_paths}")
|
||
else:
|
||
logger.info("OrderService开始合并所有采购单")
|
||
|
||
return self.order_merger.process(file_paths, progress_cb)
|
||
|
||
def validate_unit_price(self, result_path: str) -> List[str]:
|
||
"""
|
||
校验采购单单价与商品资料进货价的差异
|
||
|
||
Args:
|
||
result_path: 待校验的采购单路径
|
||
|
||
Returns:
|
||
差异信息列表，无差异返回空列表
|
||
"""
|
||
try:
|
||
import pandas as pd
|
||
import os
|
||
from app.core.utils.file_utils import smart_read_excel
|
||
|
||
item_path = os.path.join('templates', '商品资料.xlsx')
|
||
if not os.path.exists(item_path):
|
||
logger.warning(f"未找到商品资料文件: {item_path}")
|
||
return []
|
||
|
||
df_item = smart_read_excel(item_path)
|
||
df_res = smart_read_excel(result_path)
|
||
|
||
def _find_col(df, candidates, contains=None):
|
||
cols = list(df.columns)
|
||
for c in candidates:
|
||
if c in cols:
|
||
return c
|
||
if contains:
|
||
for c in cols:
|
||
if contains in str(c):
|
||
return c
|
||
return None
|
||
|
||
item_barcode_col = _find_col(df_item, ['商品条码','商品条码（小条码）','条码','barcode'], contains='条码')
|
||
item_price_col = _find_col(df_item, ['进货价','进货价（必填）'], contains='进货价')
|
||
res_barcode_col = _find_col(df_res, ['条码','barcode'], contains='条码')
|
||
res_price_col = _find_col(df_res, ['采购单价','unit_price','单价'], contains='单价')
|
||
|
||
if not all([item_barcode_col, item_price_col, res_barcode_col, res_price_col]):
|
||
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）")
|
||
return []
|
||
|
||
item_map = df_item[[item_barcode_col, item_price_col]].dropna()
|
||
item_map[item_price_col] = pd.to_numeric(item_map[item_price_col], errors='coerce')
|
||
item_map = item_map.dropna()
|
||
imap = dict(zip(item_map[item_barcode_col].astype(str).str.strip(), item_map[item_price_col]))
|
||
|
||
df_res['_bc_'] = df_res[res_barcode_col].astype(str).str.strip()
|
||
df_res['_res_price_'] = pd.to_numeric(df_res[res_price_col], errors='coerce')
|
||
df_res['_item_price_'] = df_res['_bc_'].map(imap)
|
||
|
||
df_check = df_res.dropna(subset=['_res_price_','_item_price_'])
|
||
df_check['_diff_'] = (df_check['_res_price_'] - df_check['_item_price_']).abs()
|
||
bad = df_check[df_check['_diff_'] > 1.0]
|
||
|
||
results = []
|
||
if not bad.empty:
|
||
for i in range(len(bad)):
|
||
r = bad.iloc[i]
|
||
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}")
|
||
|
||
return results
|
||
|
||
except Exception as e:
|
||
logger.error(f"单价校验过程中发生错误: {e}")
|
||
return []
|