摘要
为科学防治煤矿瓦斯事故,系统分析我国煤矿瓦斯事故风险因素以及因素耦合关系,采用Python软件,建立基于粒子群优化(PSO)算法的关联规则挖掘模型,并进行验证;结合人因分析与分类系统(HFACS)事故风险模型,对煤矿瓦斯事故风险因素进行分类,并使用PSO-频繁模式增长(FP-growth)算法挖掘煤矿瓦斯事故调查报告的关联规则。结果表明:PSO-FP-growth算法相较于PSO-Apriori算法运行速度及关联规则效果更优;根据瓦斯事故风险因素关联规则可视化及高支持度关联因素显示,我国煤矿瓦斯事故发生的主要风险因素是煤矿企业安全监督管理存在缺陷、瓦斯防治技术不到位、员工安全意识淡薄以及现场管理人员管理意识和技术不到位造成的。
In order to further scientifically prevent and control coal mine gas accidents and systematically analyze the risk factors and coupling relationships of coal mine gas accidents in my country,an association rule mining model based on the PSO algorithm using Python software was established and verified.The risk factors of coal mine gas accidents were classified in combination with the HFACS accident risk model,and the constructed PSO-FP(Freguent Pattern)-growth algorithm was used to mine association rules for coal mine gas accident investigation reports.The results show that the PSO-FP-growth algorithm has better running speed and association rule effect than the PSO-Apriori algorithm.According to the visualization of association rules of gas accident risk factors and high-support association factors,the main risk factors for coal mine gas accidents in my country are defects in safety supervision and management of coal mine enterprises,inadequate gas prevention and control technology,weak safety awareness of employees,and inadequate management awareness and technology of on-site managers.
作者
张洽
憨瑞东
陈涛
ZHANG Qia;HAN Ruidong;CHEN Tao(School of Management,Xi'an University of Science and Technology,Xi'an Shaanxi 710600,China)
出处
《中国安全科学学报》
北大核心
2025年第2期104-110,共7页
China Safety Science Journal
