基于粗糙集融合最小二乘支持向量机的煤矿安全预警模型

Coal mine safety early warning model based on rough set fusion least squares support vector machine

针对煤矿安全影响因素多、各影响因素之间相互关联、样本信息采集困难等问题,分析了煤矿安全生产系统风险,从人员、设备、环境、管理四个方面构建了煤矿安全影响因素指标体系,基于粗糙集理论融合最小二乘支持向量机方法的提出了煤矿安全机预警模型,并以实测数据为例对该预警模型的计算结果进行了训练和检验。结果表明,粗糙集融合最小二乘支持向量机能够有效提高预警效率,反映各控制因素对煤矿安全的影响,计算结果与样本值拟合精度较高,对保障煤矿安全生产具有重要而现实的意义。

Aiming at the problems of many factors affecting coal mine safety,the correlation among various factors and the difficulty in collecting sample information,the risk of coal mine safety production system is analyzed.The index system of coal mine safety influence factors is constructed from four aspects.Based on rough set theory and least squares support vector machine(LS-SVM),an early warning model of coal mine safety machine is proposed.The calculation results of the model are trained and tested by taking the measured data.the results of the early warning model are trained and tested.The results show that the least squares support vector machine based on rough set fusion can effectively improve the efficiency of early warning and reflect the influence of various control factors on coal mine safety.The fitting accuracy between the calculated results and sample values is high,which has important and practical significance to ensure the safety production of coal mines.