Intelligent early warning of mine safety risk is the key technical means to ensure mine safety production. In the face of challenges such as complex mining environment, strong multi-factor coupling of disasters, and large amount of multi-source heterogeneous data, systematic research was carried out on the typical application scenarios and fusion path of mathematical methods. Starting from three typical application scenarios: dynamic prediction of disaster risk, real-time perception and intelligent control of human-machine-environment total factors, and emergency decision-making optimization of accident chain traceability and simulation, the evolution rules of mine safety early warning technology "from single to multiple, from traditional to intelligent" "from discrete monitoring to closed-loop contro" and "from experience-driven to intelligent assistance" were sorted out. On this basis, the mathematical methods supporting the above application scenarios were summarized, including basic statistics and spatio-temporal analysis, machine learning and deep learning, complex system modeling, mechanism-data hybrid driving model and digital twin model. The collaborative framework of application scenarios and mathematical methods was constructed, and the selection and combination of mathematical methods in different application scenarios were clarified. The research results can provide technical reference and method support for the transformation of mine safety risk early warning to "active prevention and control".

Mine safety  /  Risk intelligent early warning  /  Big data driven  /  Multi-scenario analysis  /  Digital twin