Human-machine-environment temperature field monitoring and analysis in deep high-temperature mining areas

Mineral resources exploitation moving deeper into the earth is an inevitable trend with economic and social development. However, the deep high temperature poses a significant challenge to the safety and efficiency of human and machine. The prevention of potential thermal risks in deep mining is critical. Here, the key and difficult issues of human-machine-environment temperature monitoring are discussed according to the characteristics of deep high-temperature environment. Then, a monitoring and analysis method of human-machine-environment temperature field suitable for deep high-temperature mining areas is proposed. This method covers human-machine-environment temperature monitoring, data storage and transmission, data processing, results visualization, and thermal risks warning. The monitoring sensor networks are constructed to collect real-time data of miners, machines, and environments. The data is transmitted to the central processing system for storage and analysis using both wired and wireless transmission technologies. Moreover, digital filtering and Kriging interpolation algorithms are applied to denoise and handle outliers in the monitored data, as well as to calculate the temperature field. The temperature prediction model is constructed using Long Short-Term Memory (LSTM) method. Finally, potential thermal risks are identified by combining real-time monitoring and prediction results, thereby guiding management personnels and miners to take appropriate measures. The proposed monitoring and analysis method can be applied to deep mines that affected by high temperature. It not only provides data and methodological support for assessing thermal risks in mines, but also offers scientific basis for optimizing mining operations and implementing safety measures.