Coal Mine Accident Risk Analysis with Large Language Models and Bayesian Networks

Coal mining, characterized by its complex operational environment and significant management challenges, is a prototypical high-risk industry with frequent accidents. Accurate identification of the key risk factors influencing coal mine safety is critical for reducing accident rates and enhancing operational safety. Comprehensive analyses of coal mine accident investigation reports provide invaluable insights into latent risk factors and the underlying mechanisms of accidents. In this study, we construct an integrated research framework that synthesizes large language models, association rule mining, and Bayesian networks to systematically analyze 700 coal mine accident investigation reports. First, a large language model is employed to extract risk factors, identifying multiple layers of risks, including 14 direct, 38 composite, and 75 specific factors. Next, the Apriori algorithm is applied to mine 281 strong association rules, which serve as the foundation for constructing a Bayesian network model comprising 127 nodes. Finally, sensitivity analysis and critical path analysis are conducted on the Bayesian network to reveal seven primary risk factors primarily related to on-site safety management, the execution of operational procedures, and insufficient safety supervision. The novelty of our framework lies in its efficient processing of unstructured text data via large language models, which significantly enhances the accuracy and comprehensiveness of risk factor identification compared to traditional methods. The findings provide robust theoretical and practical support for coal mine safety risk management and offer valuable insights for risk management practices in other high-risk industries. From a policy perspective, we recommend that the government strengthen legislation and supervision of coal mine safety with a particular focus on the enforcement of operational procedures and on-site safety management, promote comprehensive safety education and training to enhance frontline personnel’s awareness and emergency response capabilities, and leverage data-driven technologies to develop intelligent risk early-warning systems. These measures will improve the precision and efficiency of safety management and provide a scientific basis for accident prevention and control.