Abstract
With the rapid advancement of intelligent mining in the coal industry, traditional water hazard prevention technologies have proven inadequate due to low efficiency and insufficient precision, failing to meet the precise control requirements under complex geological conditions. To address these challenges, an intelligent water hazard prevention system for intelligent mining was proposed. The proposed framework establishes a closed-loop management system covering the entire process from data perception, model construction, dynamic evaluation, integrated governance to emergency response, significantly enhancing both proactive measures and reliability in water hazard prevention. Firstly, through intelligent geophysical exploration (including “long-digging and long-probing”technology), dynamic seismic detection during drilling, and a multi-source monitoring network integrating electrical, microseismic, and hydrological parameters across the “well-ground-hole” triad, the accuracy of anomaly boundary identification was significantly improved. Combined with real-time data processing and dynamic imaging, this enables precise detection of water-conducting structures ahead of working faces. Secondly, leveraging intelligent exploration and monitoring achievements, multi-attribute high-precision modeling technology was developed. By integrating borehole data, seismic information, and real-time drilling data, combined with regional stratigraphic analysis, segmented LiDAR scanning, global calibration algorithms, implicit iterative interpolation algorithms, and TIN-GTP grid modeling techniques, critical hazards like aquifers and collapse columns through parametric modeling was mapped. This resulted in centimeter-level 3D roadway models and comprehensive multi-attribute hydrogeological models, supporting spatial correlation analysis of water hazard risks and enabling dynamic updates to static geological models with transparency. Thirdly, utilizing attribute information such as water-bearing/intermediate layers thickness, elevation, and hydrological monitoring data from the integrated mine hydrogeological model, we employed LSTM-GCN hybrid networks and Bayesian models. Through spatiotemporal information coupling algorithms, we evaluated flood risk assessment, achieved real-time prediction of mine water inflow sources, and conducted dynamic risk analysis via thermal maps of mine roof and floor water hazards.A dynamic evaluation system for mine water hazard risks was established . Subsequently, three innovative algorithms: a 3D geological model-based intelligent drilling trajectory design algorithm, an AI-powered video-driven intelligent identification algorithm for water probing and drainage drill rods, and a natural language report template generation algorithm were developed. A digital hydrogeological information management system and a smart control system for disaster-causing water discharge and grouting were also established. Through spatial overlay analysis of 3D trajectories with coal seam structures, multi-objective optimization models, and adaptive control algorithms, integrated autonomous drilling trajectory generation, remote monitoring and trajectory optimization was achieved, as well as automated dynamic updates of design reports. The efficiency of water hazard prevention and control engineering was significantly improved. Finally, by implementing a dual real-time analysis feedback mechanism for geological and hydrogeological data in water hazard management projects, information updates drived by synchronizeing mining engineering geology and hydrogeological data. Combining personnel positioning with dynamic tunnel model simulations of water surge propagation, a multi-constraint path dynamic optimization algorithm to rapidly generate disaster scenario simulations and rescue plans was utilized . Future research will focus on intelligent control platform for Internet of Things and comprehensive smart systems, advancing water hazard prevention toward autonomous “sensing-analysis-decision” processes to provide core support for coal mine safety production and intelligent construction.
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