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- Online First 2024, Volume 50, Issue 3 Cover Catalogue Previous Issue
Online First have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
2024, 50(3): 1-5.
doi: 10.13272/j.issn.1671-251x.18184
Abstract: High power radio waves emitted by mobile communication systems such as 5G, 5.5G, WiFi6, WiFi7, UWB, ZigBee, as well as personnel and vehicle positioning systems in mines, pose a risk of igniting gas and coal dust. Therefore, it is necessary to set a reasonable threshold for the explosion-proof safe power of radio waves emitted by explosion-proof radio equipment, and limit the power of radio waves emitted by explosion-proof radio equipment. The European standard CLC/TR 50427:2004 Assessment of inadvertent ignition of flammable atmospheres by radio-frequency radiation-Guide specifies a threshold for the safe reception and ignition power of radio waves in explosive gas environments. But it lacks content on the threshold for the safe transmission power of radio waves. Although the national standard GB/T 3836.1-2021 Explosive atmospheres-Part 1:Equipment-General requirements and the international standard IEC 60079-0:2017 Explosive atmospheres-Part 0:Equipment-General requirements have relevant provisions on the safe transmission power threshold for radio wave explosion protection, they mistakenly modify the safe reception ignition power threshold for radio wave explosion protection in the European standard CLC/TR 50427:2004 to the safe transmission power threshold for radio wave explosion protection. It greatly reduces the maximum transmission power allowed by radio equipment in explosive atmospheres. There’s a lack of slender structural objects such as cranes that can serve as receiving antennas in coal mines. The existing radio communication and positioning systems in mines operate at frequencies far greater than 30 MHz. Therefore, the threshold for the safe reception and ignition power of radio waves should be 8 W, instead of the radio wave explosion-proof safe transmission power threshold of 6 W specified in the national standard GB/T 3836.1-2021 and the international standard IEC 60079-0:2017. When the energy of the radio waves emitted by the transmitting antenna is fully absorbed by the equivalent antenna, which is the most unfavorable for wireless explosion-proof transmission and coupling, and the operating frequency of the radio equipment is the equivalent antenna resonance frequency, the reception and ignition power reaches its maximum. It is half of the total power received by the equivalent antenna, that is, half of the transmission power. In practical engineering, both radio transmission efficiency and coupling efficiency are not equal to 1. Therefore, the threshold for safe transmission power of radio waves should be more than twice the threshold for safe reception and ignition power of radio waves. The threshold for the safe reception and ignition power of underground radio waves in coal mines is 8 W. Therefore, the threshold for the safe transmission power of underground radio waves in coal mines should be greater than 16 W.
2024, 50(3): 6-13.
doi: 10.13272/j.issn.1671-251x.2023060078
Abstract: The analysis points out that the current hydraulic pressure technology in coal mines faces challenges such as the inability to quickly and accurately adjust the output pressure and flow rate of fracturing pumps, the need to improve the remote safety monitoring effect, and the level of automation. An underground hydraulic fracturing automatic control system in coal mine has been designed. Based on the hydraulic fracturing technology and system composition in coal mines, it is clear that the key technologies of the control system include rapid and precise adjustment of the output flow and pressure of high-pressure and high flow fracturing pumps, remote high reliability and safety, high-speed real-time monitoring, one click start stop, and graphical analysis. Based on the KXH12 intrinsic safety controller as the core, combined with frequency converters, combination switches, monitoring hosts, electric valves and other equipment, as well as a dual line redundant communication scheme with fiber optic and CAN bus, the control system hardware has been developed. The fracturing pump controller, water tank controller, and central controller software have been developed. It achieves functions such as fast variable flow water injection, stable pressure maintenance, remote high-speed real-time monitoring and alarm of the fracturing system. Industrial tests are conducted on the hydraulic fracturing automatic control system at the coal mine site, and the results show that the pressure control precision of the system is 0.1 MPa, and the flow control precision is 0.1 m3/h. During 14 consecutive fracturing processes, good pressure retention effects are achieved after each coal layer crack. The operation is simple with high safety, meeting the requirements of hydraulic fracturing technology in coal mines underground.
2024, 50(3): 14-21.
doi: 10.13272/j.issn.1671-251x.2023100008
Abstract: With the continuous construction and promotion of intelligent mining information infrastructure, the switching of mine terminal equipment between private and public networks has introduced information security risks to the mine network. It is necessary to study the isolation boundaries of the mine network and build system protection measures. The study analyzes the main risks faced by the mine network, and points out that the key to dealing with risks is to define isolation boundaries, strengthen system protection measures, and develop specific underground equipments. In response to the needs of mine network security protection, three major isolation boundaries have been defined: business management network and industrial control network, transmission network and server area, and underground industrial control network and industrial control network on the ground. A mine network security system protection architecture based on boundary isolation and system protection is proposed. A mine network security system based on network, host, application, and data subsystems protection is designed, along with corresponding security transmission processes and protection ideas. In response to the current situation where mine network security protection mainly focuses on networks on the ground and lacks underground network security protection measures, a mine explosion-proof and intrinsically safety network interface has been developed as underground network security protection equipment. Corresponding protection rules have been formulated for industrial protocols commonly used in underground terminals such as Modbus, Profibus, IEC 61850, RTSP, etc. The test results show that the average recognition rate of the interface device against network attacks is 98.8%, the average protection rate is 98.0%, and the throughput of the gigabit interface is not less than 95% of the line speed. It achieves underground information security protection function and ensures data transmission performance.
2024, 50(3): 22-34, 41.
doi: 10.13272/j.issn.1671-251x.2024010079
Abstract: The application of digital twin technology in the field of intelligent mining needs to face many complex and special technological breakthroughs. This article elaborates on the applicability of digital twins in the field of intelligent mining, and summarizes the research and application status of digital twin technology in coal mine safety, production, and operation management. In terms of coal mine safety management, digital twin technology is mainly applied in disaster warning, risk control, disaster rescue, etc. In terms of coal mine production, digital twin technology is mainly applied in the overall mining working face area, monitoring and control of single machine mechanical equipment status, and predictive maintenance of mechanical equipment. Starting from five dimensions: physical entity, virtual entity, connection interaction, digital twin data, and functional services, this paper explores the key common problems that urgently need to be solved in the field of intelligent mining. The physical entity dimension needs to focus on breaking through the research and development of comprehensive perception and control equipment. The virtual entity dimension needs to conduct in-depth research on physical, behavioral, and rule models. The connection interaction dimension needs to tackle key technologies for underground 5G network transmission in coal mines. The twin data dimension needs to solve problems such as high-performance computing. The functional service dimension needs to develop simulation software and artificial intelligence algorithms to better adapt to the on-site environment. This article looks forward to the development trend of digital twin technology in the field of intelligent mining from the aspects of disaster prevention design, production system design, geological environment prediction in the planning, development, and construction stages of mines, disaster warning and prevention, optimization of production scheduling decisions, and full life cycle management of production equipment in the production and operation stage of mines. It is believed that fine twinning should be carried out for key components or equipment, core links, important or dangerous places, areas, etc.
2024, 50(3): 35-41.
doi: 10.13272/j.issn.1671-251x.2023090088
Abstract: Although current small object detection methods have improved the detection performance, they are mostly objected at conventional scenarios. In harsh underground environments in coal mines, there are difficulties in extracting small object feature information during the underground small object detection process. In order to solve the problem. a small object detection method for coal mine underground scenes based on YOLOv7-SE has been proposed. Firstly, the simulated annealing (SA) algorithm is integrated with the k-means++clustering algorithm to accurately capture small underground objects by optimizing the estimation of initial anchor box values in the YOLOv7 model. Secondly, a new detection layer is added to the YOLOv7 backbone network to obtain high-resolution feature maps of underground small objects, reducing the interference of a large amount of coal dust on the feature representation of underground small objects. Finally, a dual layer attention mechanism is introduced after the aggregation network module in the backbone network to enhance the feature representation of small underground objects. The experimental results show the following points. ① The loss function of the YOLOv7-SE network model after training is stable around 0.05, indicating that the parameter settings of the YOLOv7-SE network model are reasonable. ② The average precision (AP) of helmet detection based on the YOLOv7-SE network model has improved by 13.86%, 25.3%, 16.13%, 12.71%, 15.53%, 11.59% and 12.20% compared to Faster R-CNN, RetinaNet, CenterNet, FCOS, SSD, YOLOv5 and YOLOv7, respectively. The self rescue device detection AP based on the YOLOv7-SE network model has improved by 12.37%, 20.16%, 15.22%, 8.35%, 19.42%, 9.64% and 7.38% compared to Faster R-CNN, RetinaNet, CenterNet, FCOS, SSD, YOLOv5 and YOLOv7, respectively.The frames per second (FPS) of the YOLOv7-SE network model has increased by 42.56, 44.43, 31.74, 39.84, 22.74 and 23.34 frames/s compared to Faster R-CNN, RetinaNe, CenterNet, FCOS, SSD and YOLOv5, respectively, and decreased by 9.36 frames/s compared to YOLOv7. The YOLOv7-SE network model effectively enhances the feature extraction capability of the YOLOv7-SE network model for small underground objects while ensuring detection speed. ③ In the detection of safety helmets and self rescue devices, the YOLOv7-SE network model effectively improves missed and false detection, and improves detection precision.
2024, 50(3): 42-47, 141.
doi: 10.13272/j.issn.1671-251x.2024030009
Abstract: The objects in the fully mechanized working face have the features of high-speed motion, multi-scale, occlusion, etc. The existing object detection algorithms have problems such as low precision, large memory of models, and strong hardware dependence. In order to solve the above problems, a large block coal detection algorithm based on MES-YOLOv5s is proposed in fully mechanized working face. The method adopts a lightweight design, uses MobileNetV3 as the backbone network to reduce the memory occupied by the model and improve the detection speed on the CPU side. The method adds an efficient multi-scale attention (EMA) module to the neck network, fuses contextual information of different scales, and further reduces computational overhead. The method uses SIoU loss function instead of CIoU loss function to improve training speed and inference accuracy. The ablation experiment results show that MobileNetV3 significantly reduces the memory and detection time occupied by the model, but the mAP loss is severe. The EMA module and SIoU loss function can restore the precision of the loss to a certain extent, while ensuring that the model has a high detection speed on the CPU, meeting the real-time detection needs of coal mine underground objects. The comparative experimental results show that compared with DETR, YOLOv5n, YOLOv5s, and YOLOv7 models, the MES-YOLOv5s model has the best overall performance, with an mAP of 84.6%. The model occupies 11.2 MiB of memory and has a detection time of 31.8 ms on the CPU side. It can maintain high recall and precision in high-speed motion, multi-scale, occlusion, and multi-object working environments.
2024, 50(3): 48-55.
doi: 10.13272/j.issn.1671-251x.2024010037
Abstract: Currently, research on point cloud denoising in underground roadways has not fully met the special denoising needs of roadway point clouds. Especially in narrow, enclosed, and complex underground roadway environments, the research has not fully addressed the challenges caused by pipe wall attachments, dust, and human noise. By analyzing the unstructured scenes and sensor errors underground, considering the noise caused by personnel, mobile devices, and pipeline networks, a point cloud denoising method for unstructured roadways based on region growth is proposed. The method uses 3D laser scanning technology to obtain 3D point cloud information of underground roadway scenes, and analyzes the abnormal points caused by unstructured underground scenes and sensor errors, as well as the noise features formed by personnel, mobile devices, and air and water pipelines. The method uses k-dimensional trees (kd-tree) to construct the topological relationship of point clouds, selects appropriate seed nodes and growth criteria, and sets appropriate curvature and angle thresholds. The method implements effective segmentation of roadway point clouds through region growth algorithms, and removes outlier point clouds that have not been added to the segmentation area. Based on the features of noise, further denoising optimization is carried out based on the segmentation results of the roadway point cloud region. The experimental results indicate that for situations where there are features such as personnel and equipment in the roadway, it is recommended to set the angle threshold of the region growth algorithm to around 10° and the curvature threshold to around 3. In practical applications, it is necessary to balance the reduction of data volume with the denoising effect to ensure the effectiveness of data processing and improve data quality. When using a region growth based unstructured roadway point cloud denoising method for denoising, the reduction in point cloud quantity is between SOR filter and low-pass filter, which can effectively remove noise such as personnel and equipment.
2024, 50(3): 56-64.
doi: 10.13272/j.issn.1671-251x.2023100005
Abstract: There is a phenomenon of low lighting and excessive dust in underground mines, which leads to uneven lighting, blurriness, and loss of details in the images captured by monitoring videos. It affects subsequent intelligent image recognition. Existing mine image enhancement methods generally suffer from unclear texture details and poor visual effects. A method for image enhancement based on structural texture decomposition is proposed. Firstly, the maxRGB algorithm is used to extract the initial lighting component from the original image. Then, an optimization objective function is constructed to sequentially optimize and solve the structural component, texture component, and noise component in the initial lighting component. The weighted guided filtering is applied to the initial lighting component as a prior constraint, and the structural component with clear edges is obtained iteratively. Combined with the maximum neighborhood difference method and weighted average local variation, a local variation deviation function is constructed as constraint weights. The texture component with rich details is obtained iteratively. Secondly, the original image is transformed into the HSV color space, and the lighting component of the original image is extracted. Combined with the structural component, texture component, and noise component, Retinex theory is used for reconstruction to obtain the enhanced initial lighting component. To avoid excessive lighting enhancement, adaptive Gamma correction with weight distribution (AGCWD) is introduced to process the initial lighting information of the image and obtain the final lighting component. Finally, the image is converted to RGB color space to obtain an enhanced image. The experimental results show the following points. ① The image enhancement algorithm based on structural texture decomposition can ensure clearer texture details at the edges of the image, reduce halo artifacts during the image enhancement process, and achieve a more balanced grayscale histogram of the enhanced image. ② Compared with five image enhancement algorithms, including the Retinex algorithm based on structure and texture aware Retinex(STAR), the joint intrinsic-extrinsic prior model (JieP), the weighted variational mode (WVM), the semi-decoupled decomposition (SDD), and multi-scale Retinex with color restoration (MSRCR), the natural image quality evaluator (NIQE) of the image enhancement algorithm based on structural texture decomposition is reduced by 8.69%, 29.05%, 11.2%, 29.53%, and 33.54%, respectively. The visual information fidelity (VIF) increases by 91.17%, 117.86%, 59.38%, 48.78%, 183.12%, and the entropy index (Entropy) increases by 3.20%, 8.02%, 4.07%, 3.49%, and 22.68%, respectively. ③ The image enhancement algorithm based on structural texture decomposition has a running time only longer than the MSRCR algorithm. But the enhancement effect is better, which can meet the needs of image enhancement in underground mines.
