基于机器学习的煤矿开采沉陷自动化监测系统

Automatic monitoring system of coal mining subsidence based on machine learning

煤矿开采存在沉陷风险,为提高系统自主监测性能,提出基于机器学习的煤矿开采沉陷自动化监测系统。系统由基准站、监控站子系统、实时数据采集子系统、数据处理子系统、网络通讯子系统以及综合数据库组成。以GNSS定位技术、数据库技术、无线网络通讯技术等为支撑,构建系统框架,采用机器学习中的支持向量机法完成数据的自动学习,实现数据的自动化检测管理。实验结果表明,监测所得的沉陷数据与实际数据之间的误差较低,符合实际操作要求,监测精度接近于100%,具有一定的可行性。

In order to improve the self-monitoring performance of the system,since the risk of the coal mining subsidence,an automatic monitoring system of coal mining subsidence based on machine learning is proposed.The system consists of reference station,monitoring station sub-system,real-time data acquisition sub-system,data processing sub-system,network communication sub-system and comprehensive database.Supported by GNSS positioning technology,database technology and wireless network communication technology,the system framework is constructed,and the support vector machine method in machine learning is used to complete the automatic learning of data,and realize the automatic detection and management of data.The experiment results show that the error between the monitoring data and the actual data is low,which is in line with the actual operation requirements,and the monitoring accuracy is close to 100%,which has a certain feasibility.