Abstract: Due to the limitations of conventional coal-level monitoring principles and the complex operating environment inside coal bunkers, existing coal-level monitoring systems are subject to limitations and delays. Their monitoring results are often unable to accurately characterize the actual material distribution inside the bunker, resulting in increased labor intensity, reduced operational safety, and lower production efficiency.To comprehensively improve the accuracy, real-time performance, and intelligence of coal-level monitoring systems, a panoramic radar level scanner was developed. The system employs Frequency-Modulated Continuous-Wave (FMCW) radar technology to perform high-resolution scanning of the coal surface inside the bunker through multidimensional rotation, thereby generating point-cloud data. After the point-cloud data are transformed into an appropriate coordinate system, a global coordinate system is established. The data are then rasterized to improve computational efficiency and reduce the computational workload, facilitating the calculation of key coal-level parameters.Based on the Poisson surface reconstruction algorithm, a geometric model of the coal bunker is defined, and the acquired data are fitted to generate a closed surface. A web-based management platform was also developed to display the processed coal-level parameters and three-dimensional information of the material, thereby enabling three-dimensional visualization of the coal bunker. Field tests and comparative analyses demonstrated the superiority of the proposed system. The results indicate that the panoramic radar level scanner based on three-dimensional visualization technology can accurately and dynamically display key coal-level parameters and three-dimensional material information in real time. It effectively improves the accuracy and real-time performance of coal-level measurement and provides a reliable data foundation for the development of intelligent coal preparation plants.