基于多传感器融合的矿井环境监测系统研究

Research on monitoring system of mine environments based on multi-sensor fusion

针对矿井的环境状态进行监测、诊断及预报井下环境状态,并采取相应的控制措施,这是提高矿井安全、高效生产的重要手段。由于煤矿井下的环境较为恶劣,传感监测方法受到限制,监测信息不够全面准确,依靠单一的监测手段和传统的数据处理方法难以完成,建立基于多传感器融合和BP神经网络的矿井环境监测系统是解决问题的有效途径。试验结果表明,笔者设计的基于多传感融合的矿井环境监测系统可有效评估矿井环境的安全状态。

To monitor, diagnose and forecast the mine environments according to specific mine environments and take corresponding control measures is an important means to improve mine safety and efficient production. Due to severe environments of underground collieries, traditional sensing method was limited due to incomplete and inaccurate information, and single detection means and traditional data processing methods were hard to be competent. To establish the monitoring system of mine environments based on multi-sensor fusion and BP neural network was the effective approach to solve the problems. Test results showed that the designed monitoring system based on multi-sensor fusion could effectively assess the safety status of the mine environments.