Coal flow volume measurement based on linear model partitioning

The precision and computational efficiency of coal quantity measurement for belt conveyors based on linear laser stripes are low. There is a trailing phenomenon during belt operation, as well as data misalignment caused by deviation and drifting. In order to solve the above problems, a coal flow volume measurement method based on linear model partitioning is proposed. Firstly, the method uses a high-speed line laser camera to collect coal flow data. Secondly, a point cloud registration algorithm based on linear model partitioning is used to fuse the point cloud data at the bottom of the belt with the surface data of the coal flow, forming a complete three-dimensional coal flow data. Finally, the coal flow volume measurement is achieved through a coal flow volume measurement model. The experimental results show that the coal flow volume measurement method based on linear model partitioning has a precision of over 95% when measuring rough surface iron blocks, smooth surface iron blocks, and physical objects (coal and gangue) in high dust environment, coal flow surface watering environment, dim environment, and normal lighting environment. Moreover, the measurement precision of smooth-surface iron blocks is higher than that of rough-surface iron blocks in four simulated environments. It indicates that the better the flatness of the object surface, the higher the measurement precision. The environment has little impact on measurement precision. The actual test results show that the coal flow volume measurement method based on linear model partitioning has a measurement precision of over 97%. The corresponding average time is within 80 ms. Compared with the measurement method based on the KD tree, the overall precision has been improved by more than 6% and the processing timeliness has been doubled.