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基于Apriori算法的煤矿安全事故分析

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为分析煤矿事故报告中的危险致因因素,统计分析了 2018-2022年全国煤矿事故报告数据,采用Apriori关联规则算法,并利用Gephi进行关联规则可视化,探究各个致因之间的复杂关系。首先对数据进行预处理,计算词频-逆向文件频率(Term Frequency-Inverse Document Frequency,TF-IDF),提取了 78个煤矿事故致因因素,其中人因层包括31个因素,设备层包括9个因素,管理层包括31个因素,环境层包括7个因素;然后,经过关联规则挖掘算法,得到了 585条关联规则,绘制了其支持度、置信度和提升度的散点图;最后,根据Gephi生成的事故致因复杂网络图,分别分析了高支持度、高置信度和高提升度关联规则致因因素。结果表明:基于Apriori算法的煤矿事故致因分析,得到了人因层、管理层、设备层和环境层4个方面的关键致因因素;对煤矿关键致因因素进行直观、多视图的展现,有助于提高煤矿安全管理水平。
Coal mine safety accident analysis based on the Apriori algorithm
This paper conducts a statistical analysis of the national coal mine accident report data from 2018 to 2022,and uses the Apriori association rule algorithm to find all the rules that satisfy the minimum support and confidence by generating candidate itemsets and pruning based on the downward closure property of frequent itemsets.The paper also uses Gephi to visualize the association rules and further analyzes the high-support,high-confidence,and high-lift rules to reveal the complex relationships between various causes.The data show that from 2018 to 2022,there were 424 coal mine accidents and 914 deaths in China,and the types of accidents with a large proportion of occurrences were roof accidents,transportation accidents,and other accidents.Through data preprocessing and calculating Term Frequency-Inverse Document Frequency(TF-IDF)values,this paper extracts 80 factors causing coal mine accidents,including 33 factors in the human layer,9 factors in the equipment layer,31 factors in the management layer,and 7 factors in the environmental layer.According to the complex network graph of accident causes generated by Gephi,after preliminary analysis,the key factors for accident occurrence are weak safety awareness,on-site workers,inadequate safety education and training,transportation accidents,roof accidents,poor self-protection and mutual protection awareness,illegal operations,inadequate safety supervision and inspection,inadequate hidden danger investigation and management,etc.The paper then analyzes the factors causing high support,high confidence,and high-lift association rules separately.In terms of the human layer:the more important frequent itemsets are on-site workers and weak safety awareness,employees and weak safety awareness,no temporary support and weak safety awareness;in terms of the management layer:the more important frequent itemsets are roof accidents and poor safety management,three violations and transportation accidents,inadequate local outburst prevention measures and regional outburst prevention measures not eliminating media outburst hazards in coal uncovering areas;in terms of the equipment layer:the more important frequent itemsets are wire rope and transportation accidents,anchor cable and roof accidents,high-pressure filter quality defects and improper operation handling;in terms of the environmental layer:the more important frequent itemsets are fault and roof accidents,over-deep over-boundary and water accumulation in goaf areas,project department and other accidents.The paper reveals the key points of accident prevention and the causal relationships before and after the accident and points out the direction for the controllability prevention of accidents.

safety engineeringcoal mine accidentaccident causeApriori algorithmcomplex network graph

景国勋、秦洪利、蒋方

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河南理工大学安全科学与工程学院,河南焦作 454000

安阳工学院,河南安阳 455000

安全工程 煤矿事故 事故原因 Apriori算法 复杂网络图

国家自然科学基金重点项目国家自然科学基金河南省科技攻关计划

U190421051774120232102320239

2024

安全与环境学报
北京理工大学 中国环境科学学会 中国职业安全健康协会

安全与环境学报

CSTPCD北大核心
影响因子:0.943
ISSN:1009-6094
年,卷(期):2024.24(6)