In the actual production of coal mines, the transmission needs of existing underground applications cannot be met due to a lack of strategies and customized equipment for underground 5G application scenarios, which causes increased underground data processing delay and low transmission efficiency. To solve the problem above, the mobile edge computing (MEC) technology based on the 5G wireless base station is studied, and underground 5G communication capabilities is improved by edge caching and dynamic resource allocation according to the actual situation of coal mines. The experimental result shows that under the premise of maintaining the rated power and transmit power of the existing base station, the average delay of executing tasks is 15ms, which is 50% lower than the average delay of all local execution methods. The average delay is reduced by 37.5% than all MEC execution methods. At the same time, the uplink rate of a single base station can reach 1Gbps and the downlink rate can reach 1.5 Gbps. Our method can significantly improve the reliability of mining 5G communication systems and the rational allocation of resources.
Real-time coal flow monitoring is crucial for efficient coal mine transportation. Traditional vision acquisition devices generate two-dimensional images that are not accurately identified in the complex underground coal mine environment, such as dust, water mist, and low light. In this paper, we propose a conveyor belt coal flow detection method that integrates laser scanning and binocular vision to address this problem. Our proposed method has several advantages over traditional approaches. Firstly, we calibrate the binocular camera using Zhang's calibration method to enhance the accuracy of the system. Secondly, we extract the centerline of the laser stripe using the grayscale center of gravity method, which improves the system's performance in complex environments. Thirdly, we calculate the cross-sectional area of the material accurately using the trapezoidal area accumulation method, and visualize it in two dimensions based on a single frame, while the point cloud data from multiple consecutive frames are visualized in a 3D model at a realistic scale. Finally, we use the continuous multi-frame cross-sectional area to calculate the current conveyor flow, and apply the BP neural network to establish an energy-saving optimization model for the belt conveyor. We also design a PLC fuzzy controller based on fuzzy control algorithms to adjust the belt's operating speed intelligently according to the coal flow size, achieving energy-saving operation and intelligent control of the belt conveyor. Our experiments show that our method can accurately obtain coal volume and control the belt speed of the conveyor in real-time, making it an innovative and practical solution for coal flow detection and energy-saving operation in underground coal mines.INDEX TERMS machine vision; stereo matching; energy-saving optimization; coal volume detection; belt conveyor
Coal mine safety has always been the most important prerequisite for underground coal mine work. Mine personnel inspection is an effective means to ensure underground safety production. Therefore, the quality of inspection will play a decisive role in safety production. At present, due to the influence of the complex environment in coal mines, ghost images are prone to appear in the process of personnel detection, which has a certain impact on the accuracy of detection. Aiming at this phenomenon, a Vibe method for secondary detection based on ghost images is proposed. In the process of underground coal mine personnel detection, the minimum bounding rectangle of the personnel area is delineated, and each pixel of the personnel area and all the areas outside the rectangle are calculated separately. The process of judging whether it is a ghost image and eliminating the ghost image by the number of pixels whose similarity reaches the threshold. Through subjective and objective verification, the proposed improved algorithm has been effectively improved compared to the traditional Vibe algorithm and the Vibe+ algorithm, which is prone to ghosting problems. In terms of the accuracy, recall rate, F1 value and other objective evaluation indicators of the algorithm model, it is proposed Compared with the two algorithms, the improved Vibe algorithm improves by 2.71%, 4.79%, and 3.73% respectively. Experimental data shows that the improved Vibe algorithm effectively suppresses the appearance of ghosts in the process of underground coal mine personnel detection, improves the accuracy of foreground and background separation, enhances the ability to detect moving targets in coal mines, and provides technical support for safe production in coal mines.
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