This study delved into the classification and attributes of potential hazards within coal mining opera-tions,utilizing a genetic algorithm to develop an association rule mining model.By integrating text mining and topic mining algorithms,it uncovered the intrinsic relationships and association rules among identified hazards,leading to the creation of an association rule database.Utilizing safety hazard inspection records from a mining company in Shandong Province as a data source,the model underwent rigorous validation.Furthermore,a com-parative analysis of the performance between the enhanced genetic algorithm and both the original genetic and Apriori algorithms was conducted.The findings demonstrate that the refined genetic algorithm is markedly effi-cient in uncovering the association rules within hidden danger data,thereby significantly enriching safety manag-ers'understanding of the underlying patterns among these data points.This enhanced insight serves as a solid foundation for the detection and remediation of safety hazards in coal mines,ultimately contributing to the ad-vancement of safety management practices in coal mining operations.
coal facehidden danger of accidentsassociation rulesgenetic algorithmearly warning rule library
国家科技期刊平台
NSTL
维普
万方数据
