PSO-SVM模型在掘进工作面突出预警系统中的应用
Application of PSO-SVM model in outburst warning system of heading face
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摘要: 为实现掘进工作面煤与瓦斯突出风险快速、准确预警，借助工作面瓦斯涌出特征与突出“三要素”之间变化关系建立了含地应力系数、瓦斯体积分数及瓦斯涌出系数等参数的实时预警指标体系；将SVM、PSO 2种算法结合构建了PSO-SVM突出预警模型，界定了突出预警等级标签的划分原则；在此基础上融合Spark大数据平台开发了掘进工作面突出预警系统，系统包括模型管理、风险识别及Spark配置等8个模块。以贵州某矿掘进工作面监测监控系统为数据源，筛选其中1 059组预警指标及对应预警等级标签导入数据挖掘模型进行智能化学习及训练，并将系统应用于该掘进工作面突出风险预警。运行结果表明突出预警模型测试集的预测精度为92%，系统能在工作面突出动力现象发生前22 min准确预警。Abstract: In order to realize rapid and accurate warning of tunneling faces gas outburst risk, based on the relationship between the characteristics of gas emission in working face and the “three elements” of outburst, a real-time early warning index system including in-situ stress coefficient, gas concentration and gas emission coefficient was established. We combine the SVM and PSO algorithms to build the PSO-SVM outburst warning model, defines the classification principles of outburst warning labels. On this basis,?an outburst warning system of heading face is developed by integrating Spark big data platform. The system includes 8 modules, such as model management, risk identification and Spark configuration. Taking the monitoring and control system of heading face in a mine in Guizhou as the data source, 1059 groups of early-warning indicators and corresponding early-warning grade labels were selected and imported into the data mining model for intelligent learning and training, and the system was applied to the outburst risk early-warning of the heading face. The operation results show that the prediction accuracy of the outburst warning model test set is 92%, and the system can accurately predict the outburst dynamic phenomenon 22 min before the occurrence of the working face.
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