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基于集对分析农村燃气工程施工质量预警管理 被引量:4

Early Warning Management of Rural Gas Engineering Construction Quality Based on Set Pair Analysis
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摘要 介绍集对分析理论。基于集对分析理论,建立了燃气管道施工质量预警管理模型,即通过三元联系数中a与c的大小关系将工程施工质量预警划分为红、橙、蓝、绿4个预警级别,并分别给出应对措施。预警管理模型中,筛选出6个一级评价指标、20个二级评价指标并制定评价标准。采用专家打分方法并归一化后确定各评价指标的权重。采取抽查或普查的方式,调查并计算得出各二级评价指标的三元联系数,结合各指标的权重,计算得到总计三元联系数,从而判断施工质量的预警级别。针对两个村庄的燃气工程案例,根据总计三元联系数的计算方法计算得到总计三元联系数。判断两个村庄的施工质量分别处于Ⅳ级绿色和Ⅲ级蓝色预警区域内,通过分析各评价指标的三元联系数,指出后续管理改进的方向。 The theory of set pair analysis is introduced.Based on the theory of set pair analysis,an early warning management model for gas pipeline construction quality is established,namely,the engineering construction quality early warning is divided into four early warning levels of red,orange,blue and green by the size relation of a and c in the ternary connection number,and corresponding countermeasures are given respectively.In the early warning management model,6 first-level evaluation indexes and 20 second-level evaluation indexes are selected,and the evaluation standards are developed.The expert scoring method was used to determine the weight of each evaluation index after normalization.The method of random check or general survey is adopted to investigate and calculate the ternary connection number of each secondary evaluation index,and the total ternary connection is calculated in combination with the weight of each index,so as to judge the early-warning level of the construction quality.For the gas project cases in two villages,the total ternary connection number is calculated based on the calculation method of the total ternary connection number.It is judged that the construction quality of the two villages is in the level IV green and level III blue early-warning areas,and through the analysis of the ternary connection number of each evaluation index,the direction of subsequent management improvement is pointed out.
作者 周兴慧 ZHOU Xinghui
出处 《煤气与热力》 2020年第12期37-42,10044,共7页 Gas & Heat
关键词 农村燃气工程 煤改气 施工质量 集对分析 预警管理 rural gas engineering coal-to-gas construction quality set pair analysis early warning management
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