Research on coal volume detection and energy-saving optimization intelligent control method of belt conveyor based on laser and binocular visual fusion

Real-time coal flow monitoring is crucial for efficient mine transportation. Traditional vision acquisition devices generate two-dimensional images that are not accurately identified in the complex underground environment, such as dust, water mist, and low light. In this paper, we propose a conveyor belt detection method integrates laser scanning binocular to address problem. Our proposed has several advantages over traditional approaches. Firstly, calibrate camera using Zhang’s calibration enhance accuracy of system. Secondly, extract centerline stripe grayscale center gravity method, which improves system’s performance environments. Thirdly, calculate cross-sectional area material trapezoidal accumulation visualize it two dimensions based on single frame, while point cloud data from multiple consecutive frames visualized 3D model at realistic scale. Finally, use continuous multi-frame current flow, apply BP neural network establish an energy-saving optimization conveyor. We also design PLC fuzzy controller control algorithms adjust belt’s operating speed intelligently according size, achieving operation intelligent experiments show our can obtain volume real-time, making innovative practical solution mines.