Design of Data-Driven Mine Disaster Risk Perception and Intelligent Early Warning System

This paper designs a data-driven mine disaster risk perception and intelligent early warning system. The core of the system is the adaptive spatiotemporal correlation disaster risk prediction algorithm (ASTRA). The algorithm integrates the time series characteristics and spatial location characteristics in the mine monitoring data, and uses an adaptive weight mechanism to dynamically adjust the weight according to the correlation between different data points and the occurrence of disasters, so as to accurately capture the deep data association and achieve efficient prediction of mine disaster risks. In order to verify the performance of the system and algorithm, the research team built a professional simulation environment and conducted experiments using massive historical monitoring data collected from multiple actual mines. The simulation results show that compared with the traditional algorithm, the system warning accuracy of the ASTRA algorithm has been greatly improved from 75% to 90%, the false alarm rate has been significantly reduced from 20% to 10%, and the missed alarm rate has also been reduced from 15% to 5%. In actual mine application cases, the system successfully warned of multiple potential disasters and effectively ensured the safe production of mines. This series of results shows that the system and algorithm designed in this study have significant advantages in the field of mine disaster warning, providing solid technical support for mine safety management.