基于IPSO-SVM的煤与瓦斯突出预测模型研究与应用

oal and gas outbursts are major safety hazards existing in China's mine production, seriously threatening the lives of underground personnel and restricting the mining efficiency. To solve this problem, this paper comprehensively explores the occurrence mechanism and key influencing factors of coal and gas outburst by integrating theoretical analysis, model construction and practical application methods. By using the grey relational analysis method and combining with the real-time monitoring data of the mine, the correlation degree of the 10 initially selected influencing factors and the target value of outburst intensity was quantitatively calculated. The calculation proved the effect weight of each factor on the outburst intensity, providing data support for the optimization of the index. Based on this, an improved particle swarm optimization algorithm (IPSO) is innovatively proposed. By dynamically adjusting the inertia weight and learning factor, the global optimization ability is enhanced, providing a certain degree of optimization support for the parameter configuration of the vector Machine model (SVM), and forming the IPSO-SVM coupled prediction model. To a certain extent, this model solves the defect of insufficient accuracy of traditional prediction methods and improves the ability of dynamic identification and early warning of prominent risks. The research results provide theoretical basis and technical means for the scientific prevention and control of coal and gas outbursts. The research results can also reduce the accident rate through risk prediction, promote the intelligent upgrade of the coal mine safety management system, and achieve a dual improvement in safety benefits and resource exploitation efficiency.