Coal flow monitoring in the main coal transportation system of coal mines is an important part of the intelligent transportation of coal mines, which can realize energy saving and consumption reduction in the transportation system, and improve the efficiency and safety of coal transportation in mines. This paper proposes a coal flow measurement method for conveyor belts that combines laser scanning and machine vision. Compared with traditional contact measurement, it can quickly and accurately obtain the full cross-sectional profile information of coal flow. The geometrical mapping relationship between pixel points in the image plane and the corresponding points in the physical space is studied. The conversion coefficient of the image system is obtained through camera calibration experiments, and the definition of the captured image is improved by means of digital image filtering and enhancement. The image segmentation algorithm is used to adjust the laser spot The effective area of the fringe is segmented, and the breakpoint in the laser spot image is repaired by the image expansion operation algorithm, and then the edge skeleton contour of the spot area is fitted, and the center line of the laser spot fringe is calculated and extracted. The Riemann sum method is used to calculate the deformed contour area of the laser spot on the surface of the coal flow in each image, and the cross-sectional area of the coal flow above the belt conveyor is reversed according to the projection angle of the laser in the experimental system, and finally combined with the belt conveyor speed and the image acquisition frame rate Calculation of other parameters to obtain coal flow information.