基于文本挖掘的高处坠落事故致因及关联规则分析  

Analysis of causes and association rules for falling accidents based on text mining method

作　　者：李珏[1] 李世杰 LI Jue;LI Shi-jie(School of Traffic and Transportation Engineering,Changsha University of Science&Technology,Changsha 410114,China)

机构地区：[1]长沙理工大学交通运输工程学院,湖南长沙410114

出　　处：《长沙理工大学学报（自然科学版）》2020年第2期61-67,74,共8页Journal of Changsha University of Science and Technology:Natural Science

基　　金：国家自然科学基金资助项目(71371036);湖南省研究生科研创新项目(CX20190669)。

摘　　要：高处坠落事故是建筑业中最常见的事故之一。为明确高处坠落事故致因,找到有效的事故预防措施,搜集了2012~2017年国内528例高处坠落事故调查报告,以R语言为平台,结合文本挖掘技术确定了事故中的32项事故致因,其中包括7项关键事故致因。利用Apriori算法挖掘出事故致因之间的关联规则,通过可视化关联规则结果,得到了高处坠落事故发生过程中的事故致因因果关系,为高处坠落事故的预控提供参考。Falling accidents is one of the most common accidents in construction industry.In order to clarify causes of falling accidents and find effective prevention measures,528 domestic investigation reports of falling accidents from 2012 to 2017 were collected.Based on R language and text mining technology,32 causes of accident were identified,including 7 key causes.Apriori algorithm was used to mine association rules among the accident causes.By visualizing the results of the association rules,the causalities of the accident causes during the falling accidents were obtained,which could provide reference for the prevention of falling accidents.

关 键 词：文本挖掘 高处坠落事故 事故致因 R语言 TF-IDF算法 关联规则 APRIORI算法 

分 类 号：X947[环境科学与工程—安全科学]