Abstract: With the continuous increase in mining depth, the risks associated with roof instability have become more prominent, where the diagnosis, monitoring, early warning and prevention of roof-related hazards are critically important. A theoretical framework has been established for the fully-automated recognition of multiple seismic sources induced by mining activities. A novel source localization method, applicable to multi-level and multi-stope environments, has been proposed, which eliminates the requirement for pre-measured wave velocities. Additionally, a rapid wave velocity imaging technique has been developed, which transforms seismic noise into usable signals through velocity field inversion. A series of proprietary technologies have been developed, including intelligent acoustic emission sensors, data acquisition instruments, and a real-time data processing system. These components have been integrated into a comprehensive technical solution for intelligent acoustic perception and microseismic monitoring, incorporating collaborative sensing, information processing, and intelligent early warning. Furthermore, a multi-index joint early warning methodology for rock mass instability in roof strata has been proposed, along with a partitioned support and prevention strategy based on rock mass damage and dynamic responses of surrounding rock. These technologies have been successfully applied in over 20 domestic mining enterprises, providing early warnings of roof collapses and other hazards on multiple occasions, and enabling timely and safe evacuation of personnel and equipment. The research outcomes have significantly enhanced the overall effectiveness of roof stability monitoring and disaster prevention.