关键词: 粒子群优化(PSO)算法, 煤矿瓦斯事故, 事故致因, 关联规则, 人因分析与分类系统(HFACS)

Abstract:

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.

Key words: particle swarm optimization (PSO) algorithm, coal mine gas accident, accident causation, association rules, human factors analysis and classification system(HFACS)