Study on Emergency Decision-Making of Mine External Fires Based on Deduction of Precursory Scenarios

External mine fires are known for their unpredictability, rapid spread, and difficulty in terms of extinguishment, often resulting in severe casualties and property damage when not managed swiftly. This study examines the progression of coal mine fire incidents through scenario deduction and presents an emergency decision-making model based on precursor scenario analysis. We classify precursor elements according to the causes of coal mine fires, organizing scenario elements into states, precursors, and emergency activities using knowledge meta-theory. A dynamic Bayesian network forms the core of the decision-making model, enabling calculation of scenario node probabilities and the development of expert-driven response strategies for critical scenarios. Additionally, we design a comprehensive evaluation index system, utilizing multi-attribute decision-making to establish decision matrices and attribute weights. An improved entropy-weighting TOPSIS method is used to select the optimal emergency decision scheme. The model’s effectiveness is demonstrated through a case study of the “9–27” fire incident at the Chongqing Songzao Coal Mine, where findings affirm the model’s practicality and accuracy in supporting timely, effective emergency responses to external coal mine fires.