综合信用评价 信用分析数据
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from company.db import find_data_in_tfse, conserve_data_to_tfse
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def credit_analysis(param1, param2):
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"""
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存储综合评价分析中信用分析数据
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Parameters:
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param1: 企业ID
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param2: 评价ID
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Returns:
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result: 保存信用分析数据结果
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"""
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# Parameters 查询text_model数据
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data = find_data_in_tfse('评价', '报告数据', {"企业ID": param1, "评价ID": param2})[0]
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# Returns
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result = dict()
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# 经营分析
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def business_analysis():
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describe = data['报告内容'][1]['章节内容'][0]['小节内容'][1]['段落']
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return describe
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# 财务分析
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def financial_analysis():
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if data['行业选择'][0] == '制造业':
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describe = list()
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describe.append(data['报告内容'][3]['章节内容'][0]['小节内容'][1]['段落'])
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describe.append(data['报告内容'][3]['章节内容'][1]['小节内容'][2]['段落'])
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describe = ''.join(describe)
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else:
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describe = data['报告内容'][3]['章节内容'][0]['小节内容'][2]['段落']
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return describe
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# 风险分析
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def risk_analysis():
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risk_01 = data['报告内容'][4]['章节内容'][0]['小节内容'][0]['段落']
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list_01 = risk_01.split(',')
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risk_02 = data['报告内容'][4]['章节内容'][1]['小节内容'][0]['段落']
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list_02 = risk_02.split(',')
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risk_03 = data['报告内容'][4]['章节内容'][2]['小节内容'][0]['段落']
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list_03 = risk_03.split(',')
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describe = list()
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describe.append(list_01[::-1][0])
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describe.append(list_02[::-1][0])
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describe.append(list_03[::-1][0])
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describe = ','.join(describe)
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res = describe.replace('。', '', 2)
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return res
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# 评价意见
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def evaluation_comments():
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describe = list()
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eva_01 = data['报告内容'][5]['章节内容'][0]['小节内容'][0]['段落']
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eva_02 = data['报告内容'][5]['章节内容'][0]['小节内容'][1]['段落']
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eva_03 = data['报告内容'][5]['章节内容'][0]['小节内容'][2]['段落']
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describe.append(eva_01)
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describe.append(eva_02)
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describe.append(eva_03)
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describe = ''.join(describe)
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return describe
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result['信用分析.经营分析'] = business_analysis()
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result['信用分析.财务分析'] = financial_analysis()
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result['信用分析.风险分析'] = risk_analysis()
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result['信用分析.评价意见'] = evaluation_comments()
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return result
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from flask import Blueprint, request
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from common.scripts import verify_token
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from company.analysis.scripts import credit_analysis
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from company.scripts import *
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company_route = Blueprint('company', __name__)
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@ -54,22 +53,3 @@ def general_rating():
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risk_analysis_etl(rid)
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return {"info": "数据准备完成"}
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@company_route.route('/company_credit_etl', methods=['POST'])
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@verify_token
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def credit_etl():
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"""
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清洗综合信用分析所需要的数据
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Parameters:
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rid str 评价ID
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cid str 企业ID
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Returns:
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清洗好的信用分析数据
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"""
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# Parameters
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rid = request.json['rid']
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cid = request.json['cid']
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data = credit_analysis(cid, rid)
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return {"data": data}, 200
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@ -126,9 +126,11 @@ def general_rating_etl(rid):
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"""
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# Parameters
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rating_record = find_data_in_tfse('评价', '评价记录', {"评价ID": rid})[0]
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rating_result = find_data_in_tfse('评价', '评价结果', {"评价ID": rid})[0]
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rating_record = find_data_in_tfse('评价', '评价结果', {"企业ID": rating_result['企业ID']})
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df_records = pd.DataFrame(rating_record).sort_values('评价时间', ascending=False)
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rating_results = find_data_in_tfse('评价', '评价结果', {"企业ID": rating_result['企业ID']})
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text_model = find_data_in_tfse('评价', '报告数据', {"企业ID": rating_result['企业ID'], "评价ID": rid})[0]
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df_records = pd.DataFrame(rating_results).sort_values('评价时间', ascending=False)
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# Returns
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result = dict()
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@ -203,13 +205,63 @@ def general_rating_etl(rid):
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df_risks = pd.DataFrame({'实际值': risks, '最大值': {"合规风险": 43, "经营风险": 10, "关联风险": 10}})
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result['指标表格']['风险指标'] = json.loads((df_risks['实际值'] / df_risks['最大值']).apply(lambda x: '优' if x >= 1 else ('良' if x >= 0.75 else ('中' if x >= 0.5 else ('低' if x >= 0.25 else '差')))).to_json())
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# 生成信用分析数据
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result['信用分析'] = dict()
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result['信用分析']['经营分析'] = '经营分析是利用会计核算、统计核算、业务以及其他方面提供的数据信息,。。。。'
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result['信用分析']['财务分析'] = '财务分析是以会计核算和报表资料及其他相关资料为依据,采用一系列专门的分析技术和方法,对企业等经济组织过去和现在有关筹资活动、投资活动、经营活动、分配活动的盈利能力、营运能力、偿债能力和增长能力状况等进行分析与评价的经济管理活动。'
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result['信用分析']['风险分析'] = '风险分析有狭义和广义两种,狭义的风险分析是指通过定量分析的方法给出完成任务所需的费用、进度、性能三个随机变量的可实现值的概率分布。 而广义的风险分析则是一种识别和测算风险,开发、选择和管理方案来解决这些风险的有组织的手段。 它包括风险识别、风险评估和风险管理三方面的内容。 本文中论及风险分析时,都采用后一种定义。'
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result['信用分析']['评价意见'] = '评价意见是指在掌握大量数据资料的基础上,对经过筛选后的少数方案再具体化,通过进一步的调查、研究和评价,最后选出最令人满意的方案,其评价结论是方案审批的依据。'
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result['信用分析']['查看报告'] = '/file/get_company_report?file_id=61bc4c4733120000ce000695'
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def credit_analysis_content():
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"""
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综合评价分析中信用分析数据
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"""
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# 经营分析
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def business_analysis_content():
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describe = text_model['报告内容'][1]['章节内容'][0]['小节内容'][1]['段落']
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return describe
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# 财务分析
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def financial_analysis_content():
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if text_model['行业选择'][0] == '制造业':
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describe = list()
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describe.append(text_model['报告内容'][3]['章节内容'][0]['小节内容'][1]['段落'])
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describe.append(text_model['报告内容'][3]['章节内容'][1]['小节内容'][2]['段落'])
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describe = ''.join(describe)
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else:
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describe = text_model['报告内容'][3]['章节内容'][0]['小节内容'][2]['段落']
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return describe
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# 风险分析
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def risk_analysis_content():
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risk_01 = text_model['报告内容'][4]['章节内容'][0]['小节内容'][0]['段落']
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list_01 = risk_01.split(',')
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risk_02 = text_model['报告内容'][4]['章节内容'][1]['小节内容'][0]['段落']
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list_02 = risk_02.split(',')
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risk_03 = text_model['报告内容'][4]['章节内容'][2]['小节内容'][0]['段落']
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list_03 = risk_03.split(',')
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describe = list()
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describe.append(list_01[::-1][0])
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describe.append(list_02[::-1][0])
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describe.append(list_03[::-1][0])
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describe = ','.join(describe)
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res = describe.replace('。', '', 2)
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return res
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# 评价意见
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def evaluation_comments_content():
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describe = list()
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eva_01 = text_model['报告内容'][5]['章节内容'][0]['小节内容'][0]['段落']
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eva_02 = text_model['报告内容'][5]['章节内容'][0]['小节内容'][1]['段落']
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eva_03 = text_model['报告内容'][5]['章节内容'][0]['小节内容'][2]['段落']
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describe.append(eva_01)
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describe.append(eva_02)
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describe.append(eva_03)
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describe = ''.join(describe)
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return describe
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# 生成信用分析数据
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result['信用分析'] = dict()
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result['信用分析']['经营分析'] = business_analysis_content()
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result['信用分析']['财务分析'] = financial_analysis_content()
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result['信用分析']['风险分析'] = risk_analysis_content()
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result['信用分析']['评价意见'] = evaluation_comments_content()
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result['信用分析']['查看报告'] = '/file/get_company_report?file_id={}'.format(rating_record['报告fid'])
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credit_analysis_content()
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insert_data_to_tfse('企业', '综合评价分析', result)
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result['周边风险'] = associate_risk()
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result['变更记录'] = change_log()
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insert_data_to_tfse('企业', '风险要素分析', result)
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if __name__ == '__main__':
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# general_rating_etl("kSVoCeJ1")
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# financial_analysis("bR2n0mV0")
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risk_analysis_etl("bR2n0mV0")
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