51 lines
2.4 KiB
Python
51 lines
2.4 KiB
Python
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def combine_technical_and_business(data, target_values1, target_values2):
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extracted_data = {} # 根级别存储所有数据
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technical_found = False
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business_found = False
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def extract_nested(data, parent_key='', is_technical=False, is_business=False):
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nonlocal technical_found, business_found
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if isinstance(data, dict):
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for key, value in data.items():
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current_key = f"{parent_key}.{key}" if parent_key else key
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# 检查是否为技术标的内容
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if any(target in key for target in target_values1):
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if not is_technical:
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# 直接存储在根级别
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extracted_data[key] = value
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technical_found = True
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# 标记为技术标内容并停止进一步处理这个分支
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continue
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# 检查是否为商务标的内容
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elif any(target in key for target in target_values2):
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if not is_business:
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# 存储在'商务标'分类下
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if '商务标' not in extracted_data:
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extracted_data['商务标'] = {}
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extracted_data['商务标'][key] = value
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business_found = True
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# 标记为商务标内容并停止进一步处理这个分支
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continue
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# 如果当前值是字典或列表，且不在技术或商务分类下，继续递归搜索
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if isinstance(value, dict) or isinstance(value, list):
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extract_nested(value, current_key, is_technical, is_business)
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elif isinstance(data, list):
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for index, item in enumerate(data):
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extract_nested(item, f"{parent_key}[{index}]", is_technical, is_business)
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# 开始从顶级递归搜索
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extract_nested(data)
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# 处理未找到匹配的情况
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if not technical_found:
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extracted_data['技术标'] = ''
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if not business_found:
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extracted_data['商务标'] = ''
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return extracted_data
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target_values2=['投标报价','商务标','商务部分','报价部分','业绩','信誉','分值','计算公式','信用','人员','资格','奖项','认证','荣誉']
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# update_json=combine_technical_and_business(clean_json_string(evaluation_res),target_values1,target_values2)
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