放煤量智能感知是智能化综放开采的关键技术，通过综放工作面后部刮板输送机煤流量的实时监测可动态调控放煤口动作，结合煤矸识别和顶煤厚度监测信息可有效避免工作面过放和欠放，提高煤炭资源开采回收效益，防止刮板输送机过载等安全生产事故发生。提出基于激光扫描的综放工作面放煤量智能监测方法,引入三角微元法构建放煤量回归实时计算模型，通过测量临近放煤口位置的后部刮板输送机上部运煤量来表征工作面实时放煤量，采用高性能多次回波信号反射激光雷达扫描，快速捕获、存储高精度三维激光点云数据，提出基于最小二乘法的激光回波数据回归处理算法，与传统三次样条插值算法的对照实验表明，数据回归算法的计算精度和计算效率具有明显优势，同时在实验室环境下验证了该算法的鲁棒性，研制了放煤量激光扫描测量装置防爆样机，在晋能集团塔山煤矿8222工作面开展了放煤量智能监测系统的工业性试验，验证了监测技术和装置的可靠性。

Intelligent perception of coal caving volume is the key technology for intelligent fully-mechanized caving mining. Through the real-time monitoring of coal flow of the scraper conveyor at the rear of the fully-mechanized caving face, the action of the coal caving port can be dynamically regulated. Combined with coal gangue identification and top coal thickness monitoring information, over-discharge and under-discharge of the working face could be avoided effectively. By using the proposed method, it is easy to improve the efficiency of coal resource mining and recovery, and prevent the occurrence of safety production accidents such as overloading of scraper conveyors. In this paper, an intelligent monitoring method of coal caving volume in fully-mechanized caving face based on laser scanning was proposed, and the triangular micro-element method was introduced to construct a real-time calculation model of coal caving volume regression. To characterize the real-time coal discharge at the working face, high-performance multiple echo signal reflection lidar scanning was used to quickly capture and store high-precision 3D laser point cloud data, and a regression processing algorithm based on the least squares method for laser echo data was proposed. The control experiment of the spline interpolation algorithm shows that the calculation accuracy and efficiency of the data regression algorithm have obvious advantages. At the same time, the robustness of the algorithm was verified in the laboratory environment. The 8222 working face of Tashan Coal Mine of Jinneng Group has carried out an industrial test of the intelligent monitoring system of coal discharge, which has verified the reliability of the monitoring technology and device.