Research On Load Monitoring Method Of Coal Flow Of Belt Conveyor Based On Laser Speckle

Belt conveyor is the main transport machinery in coal industry.The monitoring of the coal flow load on the conveyor belt is related to the production capacity and economic benefits of the coal industry,and it is the key technology of production statistics.It is also an effective strategy to achieve intelligent speed regulation of the belt conveyor so as to save energy and reduce consumption.However,the current common methods of coal flow load detection on belt conveyor,such as electronic belt scale,ultrasonic distance meter and so on,are not accurate enough,and the nuclear scale has poor detection safety.In view of the existing problems and development status at this stage,this paper studies the coal flow load monitoring method of belt conveyor based on laser speckle,and the main research contents are as follows:(1)The design scheme of coal flow load monitoring of belt conveyor is formulated.The principle of laser speckle depth imaging is applied to the point cloud imaging of the load-bearing surface of the loaded conveyor belt.The mask RCNN example segmentation algorithm is applied to the point cloud segmentation of coal flow load.The camera monitoring range is tested by 3D modeling in SolidWorks software,and the key parameters of the monitoring device are adjusted by using the visualization results.(2)This paper analyzes the principle of laser speckle imaging,verifies the relationship between the measurement depth of the load-bearing surface of the belt conveyor and the speckle pixel offset,and uses the improved speckle image binary processing algorithm to obtain a wealth of speckle information.According to the central pixel extraction,exclusive or matching,the point cloud imaging of the bearing surface of the loaded conveyor belt is completed.(3)Processing the input image sample data of the mask RCNN,using the improved mine image enhancement algorithm to restore the disturbed image,according to the actual production of the coal mine belt conveyor,improving the length width ratio and scale of the detection anchor frame in the mask RCNN,so as to enrich the semantic information in the field and improve the accuracy of the case segmentation.Using the highlighted mask file to map to the depth map to complete the segmentation of coal flow semantic categories and extract the coal flow load point cloud data(4)According to the test requirements,build a physical experiment platform,analyze the volume measurement principle based on volume element calculation,verify the coal flow load monitoring method of belt conveyor,design the experimental model and method,carry out dynamic measurement experiment,and analyze the experimental resultsIn this paper,based on the study of the laser speckle monitoring method of the coal flow load of the belt conveyor,the experimental results show that the monitoring method has high measurement accuracy,reliability and robustness,to a certain extent,It provides the basis for the monitoring of intelligent transportation system in coal mine.