基于组合关联分析模型的电力人身事故关键致因链筛选

Key cause chains selection of electric personal accidents based on combinatorial association analysis model

为提高电力企业安全治理能力和安全生产水平,以国内2015—2019年的208起电力人身事故为样本,探究电力人身事故的发生路径和内在致因机制。首先针对关联规则挖掘阈值难以确定的问题和灰色关联分析无法发现数据隐藏联系的缺陷,提出关联规则挖掘和灰色关联分析相结合的关键致因链筛选模型;然后在实证分析中,利用关联规则挖掘出致因间较强关联规则,进而通过提高灰色关联度阈值,逐步筛选出最能反映事故发生路径的成对致因,形成电力人身事故的关键致因链;最后通过Jaccard相似度计算方法检验所得关键致因链与事故样本的相似度,结果表明:关键致因链与事故样本具有高相似度,同时也证明了组合模型的有效性。

In order to improve security management ability and production safety of power enterprises,with 208 electric personal accidents in China from 2015 to 2019 as samples,their occurrence path and inherent cause law were explored.Firstly,a screening model of key cause chains based on association rule mining and grey association analysis was proposed considering the two methods'defects,namely the former was difficult to determine thresholds and the latter can not find hidden connection of data.Then,empirical analysis was conducted,strong association rules among accident causes were discovered by using association rules,and pairwise causes which could reflect accident paths were selected by increasing threshold of grey relational degree to identify key cause chains.Finally,the similarity between these key chains and accident samples was verified by Jaccard similarity calculation method.The results show that they are highly similar,which meanwhile demonstrates the effectiveness of the proposed model.