Data-driven Analysis of Risk Factors for Coal Mine Accidents

Coal mine safety production management is always essential for economic and social security and stability, analyzing and exploring the causes of coal mine accidents and the intrinsic correlation between various factors can effectively realize the control and containment of coal mine accidents.Existing research on cause analysis and risk assessment in the coal mine field mostly depends on a limited number of experts rather than data.This study uses text mining methods to extract historical coal mine accident information and integrates DEMATEL, ISM, and MICMAC methods to analyze coal mine safety accidents in a data-driven manner.This paper analyzes the fundamental, direct, and key factors of coal mine accident risk, reveals the causal relationship of accidents, and proposes relevant prevention and control measures based on the characteristics of each factor.The research results show that the data-driven risk analysis model can eliminate traditional methods' dependence on experts and provide theoretical references for preventing coal mine accidents.