Abstract
Achieving high production in the top coal caving process from thick coal seams is crucial. Thus, the timely decision of when to stop caving poses an urgent challenge to impact the mining loss rate and cost recovery. To address this issue, an innovative recognition system has been developed using Near-Infrared Spectroscopy (NIRS) technology. It stands out for its on-site usability, it enables rapid data collection and local recognition at the longwall face. Furthermore, to overcome the limitations of existing methods in adapting to variations in spectral data quality during on-site collection and the lack of integration of spectral data across different feature processing stages, a coal-rock recognition method has been developed which can ignore the influence of acquisition factors(granularity, light source angle, and detection sensor angle). This method incorporates the features of convolution and multi-view into the BLS model, the designed model structure exhibits a remarkable recognition accuracy of 99.78 %. The model was deployed into the recognition system, and experimental tests were conducted on the working face. The results showed that the recognition system can effectively identify the entire coal-caving process and achieve a recognition accuracy of 92.3 %. This capability is crucial for determining the optimal point to stop roof caving.