基于数字孪生技术的大型煤矿远程智能监控研究

Research on Remote Intelligent Monitoring of Large Coal Mines Based on Digital Twin Technology

摘要:

为了保证大型煤矿开采工作的安全性与质量，利用数字孪生技术优化设计大型煤矿远程智能监控方法。利用数字孪生技术构建大型煤矿虚拟模型，在该模型下确定测点位置，远程采集大型煤矿实时运行数据。通过对数据特征的提取与匹配，从煤矿开挖设备和施工环境两个方面，监测大型煤矿运行状态。改装大型煤矿远程智能控制器，以运行状态的监测结果作为控制程序的启动条件，实现对大型煤矿的远程智能监控任务。通过与传统监控方法的对比得出结论:优化设计方法对煤矿开挖设备的监控性能明显升高，对环境中瓦斯浓度和温度的监测误差分别降低了0.34%和0.19℃，控制误差分别降低0.09%和0.145℃，同时监控范围扩大27.4%。

Abstract:

In order to ensure the safety and quality of large-scale coal mining work, digital twin technology is used to optimize the design of remote intelligent monitoring methods for large-scale coal mines. Using digital twin technology to construct a virtual model of large-scale coal mines, determine the location of measurement points under this model, and remotely collect real-time operational data of large-scale coal mines. By extracting and matching data features, monitoring the operation status of large coal mines from two aspects: mining equipment and construction environment. Retrofitting a remote intelligent controller for large coal mines, using the monitoring results of operating status as the starting condition for the control program, to achieve remote intelligent monitoring tasks for large coal mines. By comparing with traditional monitoring methods, it can be concluded that the optimized design method significantly improves the monitoring performance of coal mining excavation equipment, reduces the monitoring errors of gas concentration and temperature in the environment by 0.34% and 0.19 ℃, and reduces the control errors by 0.09% and 0.145 ℃, respectively. At the same time, the monitoring range is expanded by 27.4%.