Construction of intelligent early warning platform for mining geology driven by multi-source data

Aiming at the problems of multi-source coupling and early warning lag of mining geological disasters in coal mines, this paper takes Yushuwan Coal Mine as the engineering background, and constructs an intelligent early warning platform for mining geological disasters based on multi-source data. The platform adopts the "cloud-edge-end" collaborative architecture, accesses microseism, stress, hydrology and other sensor data, and integrates Internet of Things sensing, distributed computing and artificial intelligence algorithms to realize real-time acquisition, fusion processing and dynamic early warning of multi-source heterogeneous data. The research introduces the LSTM semi-supervised dynamic early warning model and the D-S evidence theory multi-source information fusion algorithm, and constructs a multi-disaster coupling early warning model. The ablation experiment results show that the F1-score of the model reaches 91.1%. It significantly enhances the ability of disaster correlation analysis and the accuracy and timeliness of identification. The engineering application shows that the platform improves the monitoring coverage rate by 35%, the effective data rate by 22%, and the early warning accuracy rate by 88.2%. It realizes the early identification and accurate early warning of disaster risk, and significantly improves the intelligent prevention and control level of mine geological disasters.