系统工程理论与实践
繫統工程理論與實踐
계통공정이론여실천
Systems Engineering—Theory & Practice
2015年
4期
837~846
,共null页
社会大数据信息 农户信用借款 声誉计算模型 借款伙伴 联保因子
社會大數據信息 農戶信用藉款 聲譽計算模型 藉款夥伴 聯保因子
사회대수거신식 농호신용차관 성예계산모형 차관화반 련보인자
social big data information; farmer credit loan; reputation computing model; loan partner;UNPROFOR factor
农户信用借款信息的处理是农户信用评级中的一个重要环节.本文基于社会大数据信息研究农户信用借款声誉计算模型,分析对农户信用借款产生主要影响的七大因素:借款金额、借款期限、借款距离、联保因子、还款能力、反馈评分和近期声誉,并对这七大影响因素进行量化,然后在分别考虑农户在历史上从未借款、首次还款、当期无新还款和当期有新还款这四种不同的情形,相应建立了社会大数据信息下农户信用借款声誉计算模型,并通过两个实例对模型的应用进行有效性的检验.实例分析表明,在社会大数据信息下,采用本文所建立的农户信用借款声誉计算模型对农户声誉值的计算,结果能够很好地描述农户声誉值的变化趋势.由于这种变化趋势呈现较平稳光滑的变动,因此符合在具有社会管理功能前提下的声誉值变化特征,从而说明模型应用的有效性.该模型在实践中对农户信用借款声誉值的计算具有重要借鉴价值.
農戶信用藉款信息的處理是農戶信用評級中的一箇重要環節.本文基于社會大數據信息研究農戶信用藉款聲譽計算模型,分析對農戶信用藉款產生主要影響的七大因素:藉款金額、藉款期限、藉款距離、聯保因子、還款能力、反饋評分和近期聲譽,併對這七大影響因素進行量化,然後在分彆攷慮農戶在歷史上從未藉款、首次還款、噹期無新還款和噹期有新還款這四種不同的情形,相應建立瞭社會大數據信息下農戶信用藉款聲譽計算模型,併通過兩箇實例對模型的應用進行有效性的檢驗.實例分析錶明,在社會大數據信息下,採用本文所建立的農戶信用藉款聲譽計算模型對農戶聲譽值的計算,結果能夠很好地描述農戶聲譽值的變化趨勢.由于這種變化趨勢呈現較平穩光滑的變動,因此符閤在具有社會管理功能前提下的聲譽值變化特徵,從而說明模型應用的有效性.該模型在實踐中對農戶信用藉款聲譽值的計算具有重要藉鑒價值.
농호신용차관신식적처리시농호신용평급중적일개중요배절.본문기우사회대수거신식연구농호신용차관성예계산모형,분석대농호신용차관산생주요영향적칠대인소:차관금액、차관기한、차관거리、련보인자、환관능력、반궤평분화근기성예,병대저칠대영향인소진행양화,연후재분별고필농호재역사상종미차관、수차환관、당기무신환관화당기유신환관저사충불동적정형,상응건립료사회대수거신식하농호신용차관성예계산모형,병통과량개실례대모형적응용진행유효성적검험.실례분석표명,재사회대수거신식하,채용본문소건립적농호신용차관성예계산모형대농호성예치적계산,결과능구흔호지묘술농호성예치적변화추세.유우저충변화추세정현교평은광활적변동,인차부합재구유사회관리공능전제하적성예치변화특정,종이설명모형응용적유효성.해모형재실천중대농호신용차관성예치적계산구유중요차감개치.
Treatment of farmer credit loan information is an important problem in the credit rating. The paper mainly studies reputation calculating models on farmer credit loans based on social big data information. It analysed the seven factors affecting farmers credit loan: loan amount, loan period, loan distance, UNPROFOR factor, repayment ability, feedback rating, and recent reputation. The seven fac- tors are quantified. And then to the farmer, considering four different stations: never borrowing in the history, first repayment, no new repayment in the current, and with a new repayment in the current, it correspondingly established the credit computing models on the farmer credit loans. The two examples test the models and show that, by using the models to calculate the reputation values of the farmer credit loans, the results can well describe the change trend of the farmer's reputation values about his credit loans. Because the change trend is more smooth movements, which just fits in the change characteristic of the reputation value with the premise of the social function. Therefore, the model is proved to be valid application. The models in the practical work have the reference values to calculate the reputation values of the farmer credit loans.