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-36
@@ -116,6 +116,13 @@ class UnitConverter:
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if not text or not isinstance(text, str):
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return None
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# 处理XX入白膜格式，如"550纯净水24入白膜"
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match = re.search(r'.*?(\d+)入白膜', text)
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if match:
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result = f"1*{match.group(1)}"
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logger.info(f"提取规格(入白膜): {text} -> {result}")
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return result
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# 尝试所有模式
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for pattern, replacement in self.spec_patterns:
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match = re.search(pattern, text)
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@@ -149,6 +156,7 @@ class UnitConverter:
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3. "xx纸箱" -> 1*xx (如"15纸箱" -> 1*15)
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4. "xx白膜" -> 1*xx (如"12白膜" -> 1*12)
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5. "xxL" 容量单位特殊处理
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6. "xx(g|ml|毫升|克)*数字" -> 1*数字 (如"450g*15" -> 1*15)
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Args:
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name: 商品名称
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@@ -162,6 +170,23 @@ class UnitConverter:
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# 记录原始商品名称，用于日志
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original_name = name
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# 新增模式: 处理重量/容量*数字格式，如"450g*15", "450ml*15"
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# 忽略重量/容量值，只提取后面的数量作为规格
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weight_volume_pattern = r'.*?\d+(?:g|ml|毫升|克)[*xX×](\d+)'
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match = re.search(weight_volume_pattern, name)
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if match:
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inferred_spec = f"1*{match.group(1)}"
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logger.info(f"从名称推断规格(重量/容量*数量): {original_name} -> {inferred_spec}")
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return inferred_spec
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# 特殊模式1.1: "xx入白膜" 格式，如"550纯净水24入白膜" -> "1*24"
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pattern1_1 = r'.*?(\d+)入白膜'
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match = re.search(pattern1_1, name)
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if match:
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inferred_spec = f"1*{match.group(1)}"
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logger.info(f"从名称推断规格(入白膜): {original_name} -> {inferred_spec}")
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return inferred_spec
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# 特殊模式1: "xx入纸箱" 格式，如"445水溶C血橙15入纸箱" -> "1*15"
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pattern1 = r'.*?(\d+)入纸箱'
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match = re.search(pattern1, name)
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@@ -223,43 +248,51 @@ class UnitConverter:
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if not spec or not isinstance(spec, str):
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return 1, 1, None
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# 处理三级包装，如1*5*12
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three_level_match = re.match(r'(\d+)[*xX×](\d+)[*xX×](\d+)', spec)
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if three_level_match:
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try:
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level1 = int(three_level_match.group(1))
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level2 = int(three_level_match.group(2))
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level3 = int(three_level_match.group(3))
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logger.info(f"解析三级规格: {spec} -> {level1}*{level2}*{level3}")
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return level1, level2, level3
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except ValueError:
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pass
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try:
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# 清理规格字符串，确保格式统一
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spec = re.sub(r'\s+', '', spec) # 移除所有空白
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spec = re.sub(r'[xX×]', '*', spec) # 统一分隔符为*
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# 处理三级包装，如1*5*12
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three_level_match = re.match(r'(\d+)[*](\d+)[*](\d+)', spec)
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if three_level_match:
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try:
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level1 = int(three_level_match.group(1))
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level2 = int(three_level_match.group(2))
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level3 = int(three_level_match.group(3))
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logger.info(f"解析三级规格: {spec} -> {level1}*{level2}*{level3}")
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return level1, level2, level3
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except ValueError:
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pass
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# 处理二级包装，如1*12
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two_level_match = re.match(r'(\d+)[*](\d+)', spec)
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if two_level_match:
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try:
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level1 = int(two_level_match.group(1))
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level2 = int(two_level_match.group(2))
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logger.info(f"解析二级规格: {spec} -> {level1}*{level2}")
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return level1, level2, None
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except ValueError:
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pass
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# 处理二级包装，如1*12
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two_level_match = re.match(r'(\d+)[*xX×](\d+)', spec)
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if two_level_match:
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try:
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level1 = int(two_level_match.group(1))
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level2 = int(two_level_match.group(2))
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logger.info(f"解析二级规格: {spec} -> {level1}*{level2}")
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return level1, level2, None
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except ValueError:
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pass
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# 特殊处理L/升为单位的规格，如12.5L*1
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volume_match = re.match(r'([\d\.]+)[L升][*xX×](\d+)', spec)
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if volume_match:
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try:
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volume = float(volume_match.group(1))
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quantity = int(volume_match.group(2))
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logger.info(f"解析容量规格: {spec} -> {volume}L*{quantity}")
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return 1, quantity, None
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except ValueError:
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pass
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# 默认值
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logger.warning(f"无法解析规格: {spec}，使用默认值1*1")
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return 1, 1, None
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# 特殊处理L/升为单位的规格，如12.5L*1
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volume_match = re.match(r'([\d\.]+)[L升][*xX×](\d+)', spec)
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if volume_match:
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try:
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volume = float(volume_match.group(1))
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quantity = int(volume_match.group(2))
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logger.info(f"解析容量规格: {spec} -> {volume}L*{quantity}")
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return 1, quantity, None
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except ValueError:
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pass
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# 默认值
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logger.warning(f"无法解析规格: {spec}，使用默认值1*1")
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return 1, 1, None
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except Exception as e:
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logger.error(f"解析规格时出错: {e}")
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return 1, 1, None
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def process_unit_conversion(self, product: Dict) -> Dict:
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"""
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+304
-112
@@ -210,121 +210,109 @@ class ExcelProcessor:
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def extract_product_info(self, df: pd.DataFrame) -> List[Dict]:
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"""
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从数据帧中提取商品信息
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从处理后的数据框中提取商品信息
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支持处理不同格式的Excel文件
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Args:
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df: 数据帧
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df: 数据框
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Returns:
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商品信息列表
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商品信息列表，每个商品为一个字典
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"""
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# 清理数据：移除全空行
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df = df.dropna(how='all')
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logger.info(f"移除空行后，有效数据行数: {len(df)}")
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# 提取有用的列
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barcode_cols = self.extract_barcode(df)
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# 如果没有找到条码列，无法继续处理
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if not barcode_cols:
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logger.error("未找到条码列，无法处理")
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return []
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# 定义列名映射
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column_mapping = {
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'name': ['商品名称', '名称', '品名', '商品', '商品名', '商品或服务名称', '品项名', '产品名称', '品项'],
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'specification': ['规格', '规格型号', '型号', '商品规格', '产品规格', '包装规格'],
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'quantity': ['数量', '采购数量', '购买数量', '采购数量', '订单数量', '数量（必填）', '入库数', '入库数量'],
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'unit': ['单位', '采购单位', '计量单位', '单位（必填）', '单位名称', '计价单位'],
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'price': ['单价', '价格', '采购单价', '销售价', '进货价', '单价（必填）', '采购价', '参考价', '入库单价']
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}
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# 映射列名到标准名称
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mapped_columns = {'barcode': barcode_cols[0]} # 使用第一个找到的条码列
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# 记录列名映射详情
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logger.info(f"使用条码列: {mapped_columns['barcode']}")
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for target, possible_names in column_mapping.items():
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for col in df.columns:
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col_str = str(col).strip()
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for name in possible_names:
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if col_str == name:
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mapped_columns[target] = col
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logger.info(f"找到{target}列: {col}")
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break
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if target in mapped_columns:
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break
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# 如果没有找到精确匹配，尝试部分匹配
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if target not in mapped_columns:
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for col in df.columns:
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col_str = str(col).strip().lower()
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for name in possible_names:
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if name.lower() in col_str:
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mapped_columns[target] = col
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logger.info(f"找到{target}列(部分匹配): {col}")
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break
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if target in mapped_columns:
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break
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logger.info(f"列名映射结果: {mapped_columns}")
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# 检查是否有规格列
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has_specification_column = 'specification' in mapped_columns
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logger.info(f"是否存在规格列: {has_specification_column}")
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# 提取商品信息
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products = []
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# 检测表头位置和数据格式
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column_mapping = self._detect_column_mapping(df)
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logger.info(f"列名映射结果: {column_mapping}")
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# 检查是否有规格列
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has_specification_column = '规格' in df.columns
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logger.info(f"是否存在规格列: {has_specification_column}")
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# 处理每一行数据
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for idx, row in df.iterrows():
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barcode = row.get(mapped_columns['barcode'])
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# 跳过空行或无效条码
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if pd.isna(barcode) or not self.validate_barcode(barcode):
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logger.debug(f"跳过第{idx+1}行: 条码为空或无效 [{barcode}]")
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try:
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# 条码处理 - 确保条码总是字符串格式且不带小数点
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barcode_raw = row[column_mapping['barcode']] if column_mapping.get('barcode') else ''
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if pd.isna(barcode_raw) or barcode_raw == '' or str(barcode_raw).strip() in ['nan', 'None']:
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continue
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# 使用format_barcode函数处理条码，确保无小数点
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barcode = format_barcode(barcode_raw)
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# 处理数量字段，先提取数字部分再转换为浮点数
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quantity_value = 0
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quantity_str = ""
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if column_mapping.get('quantity') and not pd.isna(row[column_mapping['quantity']]):
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quantity_str = str(row[column_mapping['quantity']])
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# 使用提取数字的函数
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quantity_num = extract_number(quantity_str)
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if quantity_num is not None:
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quantity_value = quantity_num
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# 基础信息
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product = {
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'barcode': barcode,
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'name': str(row[column_mapping['name']]) if column_mapping.get('name') else '',
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'quantity': quantity_value,
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'price': float(row[column_mapping['price']]) if column_mapping.get('price') and not pd.isna(row[column_mapping['price']]) else 0,
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'unit': str(row[column_mapping['unit']]) if column_mapping.get('unit') and not pd.isna(row[column_mapping['unit']]) else '',
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'specification': '',
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'package_quantity': None
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}
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# 清理单位
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if product['unit'] == 'nan' or product['unit'] == 'None':
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product['unit'] = ''
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# 打印每行提取出的信息
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logger.info(f"第{idx+1}行: 提取商品信息 条码={product['barcode']}, 名称={product['name']}, 规格={product['specification']}, 数量={product['quantity']}, 单位={product['unit']}, 单价={product['price']}")
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# 从数量字段中提取单位（如果单位字段为空）
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if not product['unit'] and quantity_str:
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num, unit = self.unit_converter.extract_unit_from_quantity(quantity_str)
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if unit:
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product['unit'] = unit
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logger.info(f"从数量提取单位: {quantity_str} -> {unit}")
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# 如果数量被提取出来，更新数量
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if num is not None:
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product['quantity'] = num
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# 提取规格并解析包装数量
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if '规格' in df.columns and not pd.isna(row['规格']):
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product['specification'] = str(row['规格'])
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package_quantity = self.parse_specification(product['specification'])
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if package_quantity:
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product['package_quantity'] = package_quantity
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logger.info(f"解析规格: {product['specification']} -> 包装数量={package_quantity}")
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else:
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# 逻辑1: 如果规格为空，尝试从商品名称推断规格
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if product['name']:
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# 特殊处理："营养快线原味450g*15"或"娃哈哈瓶装大AD水蜜桃450ml*15"等形式的名称
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weight_volume_pattern = r'.*?\d+(?:g|ml|毫升|克)[*xX×](\d+)'
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match = re.search(weight_volume_pattern, product['name'])
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if match:
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inferred_spec = f"1*{match.group(1)}"
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inferred_qty = int(match.group(1))
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product['specification'] = inferred_spec
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product['package_quantity'] = inferred_qty
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logger.info(f"从商品名称提取重量/容量规格: {product['name']} -> {inferred_spec}, 包装数量={inferred_qty}")
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else:
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# 一般情况的规格推断
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inferred_spec, inferred_qty = self.infer_specification_from_name(product['name'])
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if inferred_spec:
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product['specification'] = inferred_spec
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product['package_quantity'] = inferred_qty
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logger.info(f"从商品名称推断规格: {product['name']} -> {inferred_spec}, 包装数量={inferred_qty}")
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# 应用单位转换规则
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product = self.unit_converter.process_unit_conversion(product)
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products.append(product)
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except Exception as e:
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logger.error(f"提取第{idx+1}行商品信息时出错: {e}", exc_info=True)
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continue
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# 创建商品信息字典
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product = {
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'barcode': format_barcode(barcode),
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'name': row.get(mapped_columns.get('name', ''), ''),
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'specification': row.get(mapped_columns.get('specification', ''), ''),
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'quantity': extract_number(str(row.get(mapped_columns.get('quantity', ''), 0))) or 0,
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'unit': str(row.get(mapped_columns.get('unit', ''), '')),
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'price': extract_number(str(row.get(mapped_columns.get('price', ''), 0))) or 0
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}
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logger.info(f"第{idx+1}行: 提取商品信息 条码={product['barcode']}, 名称={product['name']}, 规格={product['specification']}, 数量={product['quantity']}, 单位={product['unit']}, 单价={product['price']}")
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# 如果商品名称为空但商品条码不为空，则使用条码作为名称
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if not product['name'] and product['barcode']:
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product['name'] = f"商品 ({product['barcode']})"
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logger.info(f"商品名称为空，使用条码作为名称: {product['name']}")
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# 单位处理：如果单位为空但数量包含单位信息
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quantity_str = str(row.get(mapped_columns.get('quantity', ''), ''))
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if not product['unit'] and 'quantity' in mapped_columns:
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num, unit = self.unit_converter.extract_unit_from_quantity(quantity_str)
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if unit:
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product['unit'] = unit
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logger.info(f"从数量提取单位: {quantity_str} -> {unit}")
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# 如果数量被提取出来，更新数量
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if num is not None:
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product['quantity'] = num
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# 推断规格：如果规格为空或不存在规格列，尝试从商品名称推断
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if (not product['specification'] or not has_specification_column) and product['name']:
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inferred_spec = self.unit_converter.infer_specification_from_name(product['name'])
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if inferred_spec:
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product['specification'] = inferred_spec
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logger.info(f"从商品名称推断规格: {product['name']} -> {inferred_spec}")
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# 应用单位转换规则
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product = self.unit_converter.process_unit_conversion(product)
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products.append(product)
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logger.info(f"提取到 {len(products)} 个商品信息")
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return products
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@@ -355,6 +343,9 @@ class ExcelProcessor:
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logger.info(f"开始处理{len(products)} 个产品信息")
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for product in products:
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barcode = product.get('barcode', '')
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# 确保条码是整数字符串
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barcode = format_barcode(barcode)
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if not barcode:
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logger.warning(f"跳过无条码商品")
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continue
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@@ -573,12 +564,8 @@ class ExcelProcessor:
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self.processed_files[file_path] = output_file
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self._save_processed_files()
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# 自动打开输出目录
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try:
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os.startfile(os.path.abspath(self.output_dir))
|
||||
logger.info(f"已自动打开输出目录: {self.output_dir}")
|
||||
except Exception as e:
|
||||
logger.warning(f"无法自动打开输出目录: {e}")
|
||||
# 不再自动打开输出目录
|
||||
logger.info(f"采购单已保存到: {output_file}")
|
||||
|
||||
return output_file
|
||||
|
||||
@@ -602,4 +589,209 @@ class ExcelProcessor:
|
||||
return None
|
||||
|
||||
# 处理文件
|
||||
return self.process_specific_file(latest_file)
|
||||
return self.process_specific_file(latest_file)
|
||||
|
||||
def _detect_column_mapping(self, df: pd.DataFrame) -> Dict[str, str]:
|
||||
"""
|
||||
检测和映射Excel表头列名
|
||||
|
||||
Args:
|
||||
df: 数据框
|
||||
|
||||
Returns:
|
||||
列名映射字典，键为标准列名，值为实际列名
|
||||
"""
|
||||
# 提取有用的列
|
||||
barcode_cols = self.extract_barcode(df)
|
||||
|
||||
# 如果没有找到条码列，无法继续处理
|
||||
if not barcode_cols:
|
||||
logger.error("未找到条码列，无法处理")
|
||||
return {}
|
||||
|
||||
# 定义列名映射
|
||||
column_mapping = {
|
||||
'name': ['商品名称', '名称', '品名', '商品', '商品名', '商品或服务名称', '品项名', '产品名称', '品项'],
|
||||
'specification': ['规格', '规格型号', '型号', '商品规格', '产品规格', '包装规格'],
|
||||
'quantity': ['数量', '采购数量', '购买数量', '采购数量', '订单数量', '数量（必填）', '入库数', '入库数量'],
|
||||
'unit': ['单位', '采购单位', '计量单位', '单位（必填）', '单位名称', '计价单位'],
|
||||
'price': ['单价', '价格', '采购单价', '销售价', '进货价', '单价（必填）', '采购价', '参考价', '入库单价']
|
||||
}
|
||||
|
||||
# 映射列名到标准名称
|
||||
mapped_columns = {'barcode': barcode_cols[0]} # 使用第一个找到的条码列
|
||||
|
||||
# 记录列名映射详情
|
||||
logger.info(f"使用条码列: {mapped_columns['barcode']}")
|
||||
|
||||
for target, possible_names in column_mapping.items():
|
||||
for col in df.columns:
|
||||
col_str = str(col).strip()
|
||||
for name in possible_names:
|
||||
if col_str == name:
|
||||
mapped_columns[target] = col
|
||||
logger.info(f"找到{target}列: {col}")
|
||||
break
|
||||
if target in mapped_columns:
|
||||
break
|
||||
|
||||
# 如果没有找到精确匹配，尝试部分匹配
|
||||
if target not in mapped_columns:
|
||||
for col in df.columns:
|
||||
col_str = str(col).strip().lower()
|
||||
for name in possible_names:
|
||||
if name.lower() in col_str:
|
||||
mapped_columns[target] = col
|
||||
logger.info(f"找到{target}列(部分匹配): {col}")
|
||||
break
|
||||
if target in mapped_columns:
|
||||
break
|
||||
|
||||
return mapped_columns
|
||||
|
||||
def infer_specification_from_name(self, product_name: str) -> Tuple[Optional[str], Optional[int]]:
|
||||
"""
|
||||
从商品名称推断规格
|
||||
根据特定的命名规则匹配规格信息
|
||||
|
||||
Args:
|
||||
product_name: 商品名称
|
||||
|
||||
Returns:
|
||||
规格字符串和包装数量的元组
|
||||
"""
|
||||
if not product_name or not isinstance(product_name, str):
|
||||
logger.warning(f"无效的商品名: {product_name}")
|
||||
return None, None
|
||||
|
||||
product_name = product_name.strip()
|
||||
|
||||
# 特殊处理：重量/容量*数字格式
|
||||
weight_volume_pattern = r'.*?\d+(?:g|ml|毫升|克)[*xX×](\d+)'
|
||||
match = re.search(weight_volume_pattern, product_name)
|
||||
if match:
|
||||
inferred_spec = f"1*{match.group(1)}"
|
||||
inferred_qty = int(match.group(1))
|
||||
logger.info(f"从商品名称提取重量/容量规格: {product_name} -> {inferred_spec}, 包装数量={inferred_qty}")
|
||||
return inferred_spec, inferred_qty
|
||||
|
||||
# 使用单位转换器推断规格
|
||||
inferred_spec = self.unit_converter.infer_specification_from_name(product_name)
|
||||
if inferred_spec:
|
||||
# 解析规格中的包装数量
|
||||
package_quantity = self.parse_specification(inferred_spec)
|
||||
if package_quantity:
|
||||
logger.info(f"从商品名称推断规格: {product_name} -> {inferred_spec}, 包装数量={package_quantity}")
|
||||
return inferred_spec, package_quantity
|
||||
|
||||
# 特定商品规则匹配
|
||||
spec_rules = [
|
||||
# XX入白膜格式，如"550纯净水24入白膜"
|
||||
(r'.*?(\d+)入白膜', lambda m: (f"1*{m.group(1)}", int(m.group(1)))),
|
||||
|
||||
# 白膜格式，如"550水24白膜"
|
||||
(r'.*?(\d+)白膜', lambda m: (f"1*{m.group(1)}", int(m.group(1)))),
|
||||
|
||||
# 445水溶C系列
|
||||
(r'445水溶C.*?(\d+)[入个]纸箱', lambda m: (f"1*{m.group(1)}", int(m.group(1)))),
|
||||
|
||||
# 东方树叶系列
|
||||
(r'东方树叶.*?(\d+\*\d+).*纸箱', lambda m: (m.group(1), int(m.group(1).split('*')[1]))),
|
||||
|
||||
# 桶装
|
||||
(r'(\d+\.?\d*L)桶装', lambda m: (f"{m.group(1)}*1", 1)),
|
||||
|
||||
# 树叶茶系
|
||||
(r'树叶.*?(\d+)[入个]纸箱', lambda m: (f"1*{m.group(1)}", int(m.group(1)))),
|
||||
|
||||
# 茶π系列
|
||||
(r'茶[πΠπ].*?(\d+)纸箱', lambda m: (f"1*{m.group(1)}", int(m.group(1)))),
|
||||
|
||||
# 通用入数匹配
|
||||
(r'.*?(\d+)[入个](?:纸箱|箱装|白膜)', lambda m: (f"1*{m.group(1)}", int(m.group(1)))),
|
||||
|
||||
# 通用数字+纸箱格式
|
||||
(r'.*?(\d+)纸箱', lambda m: (f"1*{m.group(1)}", int(m.group(1))))
|
||||
]
|
||||
|
||||
# 尝试所有规则
|
||||
for pattern, formatter in spec_rules:
|
||||
match = re.search(pattern, product_name)
|
||||
if match:
|
||||
spec, qty = formatter(match)
|
||||
logger.info(f"根据特定规则推断规格: {product_name} -> {spec}, 包装数量={qty}")
|
||||
return spec, qty
|
||||
|
||||
# 尝试直接从名称中提取数字*数字格式
|
||||
match = re.search(r'(\d+\*\d+)', product_name)
|
||||
if match:
|
||||
spec = match.group(1)
|
||||
package_quantity = self.parse_specification(spec)
|
||||
if package_quantity:
|
||||
logger.info(f"从名称中直接提取规格: {spec}, 包装数量={package_quantity}")
|
||||
return spec, package_quantity
|
||||
|
||||
# 最后尝试提取任何位置的数字，默认典型件装数
|
||||
numbers = re.findall(r'\d+', product_name)
|
||||
if numbers:
|
||||
for num in numbers:
|
||||
# 检查是否为典型的件装数(12/15/24/30)
|
||||
if num in ['12', '15', '24', '30']:
|
||||
spec = f"1*{num}"
|
||||
logger.info(f"从名称中提取可能的件装数: {spec}, 包装数量={int(num)}")
|
||||
return spec, int(num)
|
||||
|
||||
logger.warning(f"无法从商品名'{product_name}' 推断规格")
|
||||
return None, None
|
||||
|
||||
def parse_specification(self, spec_str: str) -> Optional[int]:
|
||||
"""
|
||||
解析规格字符串，提取包装数量
|
||||
支持格式：1*15, 1x15, 1*5*10
|
||||
|
||||
Args:
|
||||
spec_str: 规格字符串
|
||||
|
||||
Returns:
|
||||
包装数量，如果无法解析则返回None
|
||||
"""
|
||||
if not spec_str or not isinstance(spec_str, str):
|
||||
return None
|
||||
|
||||
try:
|
||||
# 清理规格字符串
|
||||
spec_str = clean_string(spec_str)
|
||||
|
||||
# 匹配重量/容量格式，如"450g*15"、"450ml*15"
|
||||
match = re.search(r'\d+(?:g|ml|毫升|克)[*xX×](\d+)', spec_str)
|
||||
if match:
|
||||
# 返回后面的数量
|
||||
return int(match.group(1))
|
||||
|
||||
# 匹配1*5*10 格式的三级规格
|
||||
match = re.search(r'(\d+)[\*xX×](\d+)[\*xX×](\d+)', spec_str)
|
||||
if match:
|
||||
# 取最后一个数字作为袋数量
|
||||
return int(match.group(3))
|
||||
|
||||
# 匹配1*15, 1x15 格式
|
||||
match = re.search(r'(\d+)[\*xX×](\d+)', spec_str)
|
||||
if match:
|
||||
# 取第二个数字作为包装数量
|
||||
return int(match.group(2))
|
||||
|
||||
# 匹配24瓶/件等格式
|
||||
match = re.search(r'(\d+)[瓶个支袋][/／](件|箱)', spec_str)
|
||||
if match:
|
||||
return int(match.group(1))
|
||||
|
||||
# 匹配4L格式
|
||||
match = re.search(r'(\d+(?:\.\d+)?)\s*[Ll升][*×]?(\d+)?', spec_str)
|
||||
if match:
|
||||
# 如果有第二个数字，返回它；否则返回1
|
||||
return int(match.group(2)) if match.group(2) else 1
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"解析规格'{spec_str}'时出错: {e}")
|
||||
|
||||
return None
|
||||
Reference in New Issue
Block a user