With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors.

For the laser tracking and positioning system of a moving target using a four-quadrant detector, the accuracy of laser spot position detection has a serious impact on the tracking performance of the system. For moving target tracking, the traditional spot position detection method of a four-quadrant detector cannot give better consideration to both detection accuracy and operation speed. In view of this, an improved method based on piecewise low-order polynomial least squares fitting and a Kalman filter is proposed. Firstly, the tracking and positioning mathematical model of the system is created, and the experimental device is established. Then, the shortcomings of traditional methods are analyzed, and the improved method and the real-time tracking and positioning algorithm of the system are studied. Finally, through the experiment, the system operation effects are compared and analyzed before and after the improvement. The experimental results of system dynamic tracking show that, the least squares fitting of the experimental data using a 5-segment and quadratic polynomial can achieve better results. By using the improved method, the maximum tracking distance of a moving object is increased from 12 m to more than 30 m. At a distance of 7.5 m, the maximum tracking speed can reach 2.11 m/s, and the root mean square error (RMSE) of the position is less than 4.59 mm. At 15.5 m, the maximum tracking speed is 2.04 m/s and the RMSE is less than 5.42 mm. Additionally, at 23.5 m, it is 1.13 m/s and 5.71 mm.

Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

Aiming at the problem of serious shutdowns of conveyors caused by abnormal coal flow of scraper conveyors, a coal flow monitoring method based on speckle structured light is proposed. The point cloud data of the coal body on the scraper conveyor is collected through the speckle structured light acquisition system. Then, the proposed PDS-Algorithm (Planar Density Simplification Algorithm) is used to complete the simplification and differentiation of the collected point cloud data, which provides a basis for constructing geometric characteristics of coal flow lineament. This paper uses the processed point cloud data to calculate the volume of the coal mass and monitor the coal flow of the scraper conveyor. Finally, this method is used in the detection of abnormal coal flow of a coal mine scraper conveyor, and the results show that the proposed abnormal flow monitoring method can meet the accuracy and real-time requirements of coal mine abnormal alarms.

In the process of coal mining, a certain amount of gas will be produced. Environmental perception is very important to realize intelligent and unmanned coal mine production and operation and to reduce the accident rate of gas explosion and other disasters. The identification of geometric features of the coal mine working face is the main part of the environmental perception of the working face. In this study, we identify geometric features in a large-scale coal mine working face point cloud (we take the ball as an example) so as to provide a method for the environmental perception of the coal mine working face. On the basis of previous research, we upgrade the dynamic graph convolution neural network (DGCNN) for directly processing point clouds from two aspects: extracting local features and global features of point clouds. First, a multiscale dynamic graph convolution neural network (MS-DGCNN) is proposed, and the combination of max-pooling and average-pooling is used as the symmetry function. Second, we use MS-DGCNN to learn the features of a variety of geometric point clouds in the point cloud data set we make and then look for the ball in the large-scale point cloud of the coal mining working face. Finally, we compare the performance of MS-DGCNN with that of other deep neural networks directly processing point clouds. This study enables MS-DGCNN to obtain more powerful feature expression ability and enhance the generalization of the model. In addition, this study provides a solid foundation for the geometric feature identification of MS-DGCNN in the environmental perception of the coal mine working face and creates a precedent for the application of MS-DGCNN in the field of energy. At the same time, this study makes a beneficial exploration for the development of a transparent coal mine working face.

The application of a Gleason Coniflex cutter (plane-cutter) to a modern Phoenix bevel gear machine tool in face gear manufacturing has an advantage of involving a universal cutter or grinder and an available existing machine. It is valuable to research this method for face gear manufacturing. First, the principle of the application of the plane-cutter in face gear manufacturing is presented. Then, the geometry of the cutter is defined, and the model of the face gear generated by this method in abstract is established. Third, a method that uses a predesigned contact path for the synthesis with the motion parameters of the plane-cutter is proposed; controllable transmission errors are considered in this process. Fourth, based on the equivalence principle of the position and direction, the computer numerical control (CNC) motion rules of all spindles of the machine are determined, and the surface generated by the machine is presented. Finally, numerical simulation of an example demonstrates that although the surface generated by the plane-cutter, to a certain extent, deviates from the theoretical surface generated by the traditional method, the surface, in meshing with the standard involute surface of the pinion, presents a good geometric meshing performance based on tooth contact analysis (TCA), except for a shortened contact ellipse.