2022 Vol. 48, No. 6

Academic Column of Editorial Board Member
Current situation and development trend of mine wireless communication system
HUO Zhenlong
2022, 48(6): 1-5. doi: 10.13272/j.issn.1671-251x.17942
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Abstract:
The mine wireless communication system has the advantages of wide communication coverage, convenient connection, good mobility, easy to establish, simple setup and maintenance, etc. It plays an irreplaceable role in in the construction of mine intelligence. The whole mine wireless communication system mainly include WiFi(IEEE 802.11b/g/n/ac), 3G, 4G, 5G and WiFi6(IEEE 802.11ax) system. 3G system is gradually replaced. 4G and WiFi system are gradually reduced. 5G and WiFi6 system are at the initial stage, and will gradually become the mainstream. The 4G, 5G and WiFi/WiFi6 systems are analyzed from the aspects of technical points, main functions and applications. And the advantages and disadvantages of each system are pointed out in this paper. ① The main disadvantage of the mine 4G wireless communication system is that the bandwidth and real-time indicators cannot meet the needs of application scenarios such as high-definition intelligent video and remote control. ② The problems of 5G in practical use are listed as following points. In the case of multiple systems and multiple antennas, there is a security problem of wireless transmission signals. 5G base stations are mainly explosion-proof and intrinsically safe types, which are large in quality and volume. There are problems of inconvenient to use and limited in use. 5G terminal ecology is lacked. The communication modules are expensive and power consumption is high. It does not have the conditions for large-scale industrial applications. There are few practical application cases, and the application scenarios need to be further explored. ③ WiFi6 wireless communication system has high latency, relatively low mobility and reliability. The mobile phone terminal is not mature enough, which affects the quality of voice calls. Based on the above analysis, it is pointed out that the demand of intelligent mine for wireless communication system is the demand of large bandwidth, low latency, high reliability, multi-access, multi-system, multi-interface, and position information. Finally, this study points out the development trend of mine wireless communication system. The trend includes system fusion (wireless communication and wired communication fusion, wireless communication internal fusion, wireless communication system and other system fusion). It also includes communication and positioning integration, equipment intrinsic safety, terminal module low power consumption and protocol interface standardization.
Special of Intelligent Transportation Technology and Application in Coal Mine
Current status and development trend of intelligent technology of underground coal mine transportation system
CHEN Xiaojing
2022, 48(6): 6-14, 35. doi: 10.13272/j.issn.1671-251x.17933
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Abstract:
The underground coal mine transportation system can be divided into the main transportation system and the auxiliary transportation system according to the different transportation objects. This paper expounds the status of the bottom equipment and system intelligent technology of the main and auxiliary transportation systems in the underground coal mine in China. This study also analyzes the problems existing in the intelligent technology of the main and auxiliary transportation systems from the aspect of the top planning, standard system and single machine intelligent technology. The three key technologies of intelligent main transportation system are introduced, including distributed communication control technology of belt conveyor based on full digital FCS (fieldbus control system), enhanced protection technology of belt conveyor based on multi-sensor fusion of machine audio and vision, and coordinate economic operation control technology of coal flow. The two key technologies of intelligent auxiliary transportation system are also introduced, including management and control integration technology of underground coal mine auxiliary transportation based on industrial Internet architecture, vehicle-to-everything and unmanned driving technology in underground coal mine. Combined with the requirements of Coal mine intelligent construction guide (2021 edition) for intelligent main coal flow transportation system and intelligent auxiliary transportation system, this paper expounds the intelligent development trend and goal of main transportation system and auxiliary transportation system of underground coal mine from the short term and medium and long term. At present, the research of the main transportation system of underground coal mine in China should focus on the enhanced protection and detection technology of belt conveyor based on multi-sensor fusion of machine audio and vision, intelligent inspection robot technology, etc. And the research of the auxiliary transportation system should focus on the fine closed-loop control and advanced auxiliary driving technologies.
Current status and development trend of intelligent transportation technology in China's open-pit mines
WANG Zhongxin, XIN Fengyang, CHEN Hongliang, SONG Bo, TIAN Fengliang, ZENG Xiangyu, BAI Yiming
2022, 48(6): 15-26. doi: 10.13272/j.issn.1671-251x.17921
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Abstract:
As one of the most important factors in the production process of open-pit mine, the intelligence of transportation is an important research content of the whole intelligence technology of mine. This paper introduces the composition and classification of the transportation system in open-pit mine. It is clarified that the intelligent transportation system in open-pit mine takes the efficient transportation of coal and rock in open-pit mine as the application scenario and the intelligent transportation equipment as the core carrier. The digital technologies such as the Internet of things, cloud computing, big data, artificial intelligence, and mobile Internet are integrated with the operating principles and technological requirements of the open-pit mine transportation system. The autonomous collaborative and efficient operation system of equipment, environment and materials in the transportation system is established. And the real-time, accurate and efficient transportation integrated management system that plays a role in a wide range is further established. The components of the intelligent transportation system in open-pit mine mainly include infrastructure, transportation tools and computing technology. It is pointed out that the difference between the traditional transportation system and the intelligent transportation system in open-pit mine lies in that the intelligent transportation system takes improving the safety and efficiency of field production operation as the goal. The service object is changed from the original production management personnel to the production operating personnel. The research and application status of intelligent transportation system in open-pit mine in China are summarized from five aspects, including intelligent infrastructure, intelligent equipment, intelligent management and control, intelligent maintenance and intelligent design. The key technologies of the truck transportation system and the belt conveyor transportation system in the open-pit mine to realize the intelligence are analyzed in this paper. The key technologies of intelligent truck transportation include environment perception technology for complex road conditions in mines, line control transformation technology for unmanned driving truck, multi-objective intelligent scheduling technology, and intelligent collaboration technology for manned-unmanned mixed equipment group. The key technologies of intelligent belt conveyor transportation include autonomous traverse technology of belt conveyor in working face, transportation technology of self-moving large-angle belt conveyor, operation control technology of belt conveyor, on-line status detection technology of belt conveyor, intelligent inspection technology of belt conveyor, unmanned maintenance technology of belt conveyor, intelligent management and control platform of belt conveyor transportation system. It is pointed out that the development trend of intelligent transportation in open-pit mine is continuous, unmanned, low-carbon, efficient coordination and intrinsic safety.
Analysis of the status and framework design of intelligentcoal mine auxiliary transportation system
CHANG Kai, LIU Zhigeng, YUAN Xiaoming, LI Yuan
2022, 48(6): 27-35. doi: 10.13272/j.issn.1671-251x.2022010052
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Abstract:
This paper introduces the development and application status of intelligent auxiliary transportation technology in open-pit coal mine and underground coal mines at home and abroad. The intelligent auxiliary transportation system of open-pit coal mine has realized the functions of unmanned driving, automatic loading, automatic unloading, active obstacle avoidance and intelligent dispatching of mining trucks in fixed sections. And the system has achieved good application results in engineering practice. At present, the auxiliary transportation intelligence of underground coal mine is still in the development stage of single machine intelligence of equipment. The intelligent auxiliary transportation system integrating vehicle scheduling, operation status monitoring, traffic command, material control and other functions has not yet been formed. The main problems of intelligent auxiliary transportation system in underground coal mine are analyzed. The underground positioning system has low precision and poor real-time performance. The dispatching system function lacks effective integration. The driving assistance system module is not perfect. The unmanned driving technology lags behind and the test conditions are lacking. Based on the relevant requirements of intelligent auxiliary transportation in Coal Mine Intelligent Construction Guide (2021 edition), this paper puts forward the overall goal of the construction of intelligent auxiliary transportation. According to the overall goal, the intelligent coal mine auxiliary transportation system framework is designed. ① Coding and centralized loading transportation of materials realizes the whole process information management and control of materials from storage, coding, loading, transportation, unloading and recycling. ② Automatic loading and unloading and automatic connection realizes the automatic transfer and connection of materials among rail locomotives, monorail cranes, trackless and other different auxiliary transportation modes. ③ Accurate positioning and intelligent navigation achieves accurate real-time positioning, route planning and real-time navigation of personnel and transportation equipment. ④ Intelligent vehicle dispatching realizes the functions of auxiliary transportation comprehensive information display, data transmission, status monitoring, dispatching command and health management. ⑤ Driving assistance system builds several intelligent subsystems, such as anti fatigue driving warning, 360° panoramic look around monitoring, collision prevention, traffic sign identification, auxiliary braking for downhill driving, adaptive lighting, etc. Driving assistance system improves the safety of locomotive operation. ⑥ The auxiliary operation robot realizes the robot automatic operation of underground auxiliary operation scenes. The auxiliary operation robot reduces the number of personnel and improves the overall automation level of auxiliary operation. ⑦ Unmanned driving realizes the normal unmanned driving operation of locomotives in underground coal mine. The research can provide reference for the construction and development of intelligent auxiliary transportation system.
Research on unmanned driving system of underground trackless rubber-tyred vehicle in coal mine
ZHOU Libing
2022, 48(6): 36-48. doi: 10.13272/j.issn.1671-251x.17946
<Abstract>(471) <HTML> (58) <PDF>(90)
Abstract:
The unmanned driving of underground trackless rubber-tyred vehicles in coal mine can significantly reduce the number of underground auxiliary transportation operating personnel, and reduce labor intensity. It is one of the leading development directions of intelligent auxiliary transportation. Compared with the unmanned driving of the ground vehicles, there are a series of new challenges for unmanned driving of underground trackless rubber-tyred vehicles. There is the interference of 'corridor effect' and 'multipath effect' in the underground roadway. There are high requirements for precise vehicle control under complex road conditions such as mixed traffic in narrow scenes. The underground satellite refusal environment causes positioning problems. Machine vision application is affected by the changeable illumination underground and the blocking of the roadway wall. The equipment shall meet MA certification. Multiple redundancy design is required for safety measures. In order to solve the above challenges, the architecture of the unmanned driving system for underground trackless rubber-tyred vehicle in coal mine based on the vehicle-to-everything is proposed. And the critical technologies of system implementation are analyzed. The integrated positioning method based on simultaneous localization and mapping (SLAM) and ultra wide band (UWB)/inertial navigation system (INS) is used to realize the precise positioning of the vehicle in the state of high-speed movement. By relying on the multi-sensor (millimeter-wave radar, laser radar, ultrasonic radar, camera) of the vehicle body and mining intelligent roadside unit, the road condition information around the vehicle body is identified. Through the vehicle-to-everything, the relevant information is shared. The multi-source data acquisition technology is used to obtain environmental perception data, vehicle operation data, roadside monitoring data, and mobile target data. The massive data is exchanged through 5G and other wireless communication networks to the distributed computing unit based on edge computing for fusion analysis. The vehicle driving path is reasonably planned in combination with global and local path planning algorithms to realize the systematic vehicle intelligent scheduling of warehouse management. Considering the safety access requirements of underground electromechanical equipment, the perception, wire control and decision-making control equipment shall be designed for mining. The mining intrinsically safe products shall be used as far as possible to meet the design requirements of low cost, small volume and high efficiency. Underground unmanned driving vehicles need to realize the redundant design of perception, decision-making and control links to realize the safe and reliable control of vehicles under abnormal conditions. The field test results show that the vehicle positioning precision can reach 0.3 m. The communication bandwidth is more than or equal to 50 Mbit/s. The data communication delay is less than or equal to 50 ms. Therefore the positioning precision and data exchange can meet the basic requirements of underground unmanned driving vehicles. The obstacle avoidance and continuous path planning can be realized for typical environments such as T-shaped roadway and U-shaped curve. Based on the multi-sensor fusion strategy, the perception capability of multiple targets can be improved. The vehicle dynamic following error is less than 0.54 m/s, and the average control error perpendicular to the roadway wall is less than 0.2 m. These results meet the control requirements of unmanned driving vehicles.
Research on the network connected automatic driving technology in underground coal mine
LI Chenxin, ZHANG Liya
2022, 48(6): 49-55. doi: 10.13272/j.issn.1671-251x.17930
<Abstract>(242) <HTML> (140) <PDF>(60)
Abstract:
This paper analyses the development status and technical characteristics of intelligent and networked technologies for conventional ground automatic driving. This paper also analyses the coal mine environmental characteristics, such as no GNSS (global navigation satellite system) signal coverage, low roadway illumination, lots of obstructions and obstacles and ubiquitous coal dust. In the context of the above technical and environmental characteristics, this study puts forward the key technologies of automatic driving research in underground coal mine. The technologies include mobile high-precision positioning technology without GNSS, laser radar technology, underground obstacle detection technology based on millimeter wave radar, underground low illumination video real-time enhancement and characteristic matching technology, underground environment high-precision map technology, underground autonomous vehicle decision planning technology, underground autonomous vehicle control execution technology, underground 5G communication technology and C-V2X direct connection communication technology. It is pointed out that the application of automatic driving in underground coal mine has the advantages of significant demand for fewer people or no people, clear operation management subject, closed scene, fixed route, slow speed, controllable permeability, good 5G construction foundation, easy open interface, etc. The reference architecture of 'human-vehicle-roadway-cloud' coal mine underground network connected automatic driving system is constructed. The system includes underground automatic driving vehicle, roadway infrastructure, personnel, coal mine cloud/edge computing platform and coal mine automatic driving application service platform. This paper designs the coal mine automatic driving vehicle architecture, which includes perception positioning system, network connected collaborative system, vehicle-mounted operating system and vehicle basic components. This paper puts forward three stages of the evolution of the network connected automatic driving in underground coal mines. The first stage is remote control and automatic driving, which realizes the transfer of vehicle drivers from underground to ground. The second stage is the automatic driving of vehicles with emergency takeover boundary. The vehicles are mainly driven by automatic driving, and remote emergency takeover is used as a safety guarantee method. The third stage is 'human-vehicle-roadway-cloud' collaborative control, the underground autonomous vehicles operate safely, efficiently and autonomously to realize highly unmanned intelligent transportation.
Research on multi-object detection in driving scene of underground unmanned electric locomotive
GUO Yongcun, TONG Jiale, WANG Shuang
2022, 48(6): 56-63. doi: 10.13272/j.issn.1671-251x.2022030001
<Abstract>(264) <HTML> (40) <PDF>(51)
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At present, there are some problems in the identification of stones and other small obstacles in the track during the driving of unmanned underground electric locomotive in coal mines, such as slow detection speed, low detection precision, and easy to cause missing detection and wrong detection for overlapping objects. In order to solve the above problems, a multi-target detection model (SE-HDC-Mask R-CNN) for underground electric locomotive is proposed. The model is improved on the basis of Mask R-CNN. By embedding a squeeze-and-excitation (SE) module in the residual block of the backbone feature extraction network ResNet, the importance and interrelation of each channel are learned. The capability of feature selection and capture of the network is enhanced. The standard convolution with a kernel size of 3×3 in the residual block is replaced with hybrid dilated convolution (HDC). On the premise of not changing the size of the feature image and not increasing the amount of parameter calculation, the receptive field can be increased by increasing the distance between the values when the convolution kernel processes the data. The experimental result show that the SE-HDC-Mask R-CNN model can effectively extract track, electric locomotive, signal light, pedestrian and stone objects. The average precision rate on the multi scene operation data set of underground electric locomotive is 95.4%, the average mask segmentation precision is 88.1%, the average bound box intersection ratio is 91.7%, the three indicators are all improved by 0.5% compared with the Mask R-CNN model. The detection precision of signal light and stone (small objects) is improved by 0.7% and 4.1% respectively. The comprehensive performance of SE-HDC-Mask R-CNN model is better than that of YOLOV2, YOLOV3-Tiny, SSD and Faster R-CNN model. The SE-HDC-Mask R-CNN model can effectively solve the problem of missing detection of small objects. The SE-HDC-Mask R-CNN model can effectively realize object detection in coal roadway straight track, curved track, dark environment, multi-object overlapping and other scenarios. It has certain generalization capability and high robustness, and basically meets the requirements of unmanned electric locomotive obstacle detection.
Application of 5G technology in coal mine heading face transportation system
GU Yidong
2022, 48(6): 64-68. doi: 10.13272/j.issn.1671-251x.17919
<Abstract>(264) <HTML> (27) <PDF>(73)
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In the transportation system communication network of the heading face, there are problems such as insufficient access capacity, low transmission reliability and insufficient transmission bandwidth. In order to solve the above problems, the necessity and feasibility of the application of 5G technology in the transportation system of the coal mine heading face are discussed. The performance index of the optimized 5G system in coal mine has reached the expectation. The performance can fully meet the requirements of the current intelligent construction of coal mine for wireless communication system. 5G communication module and 5G customer premise equipment (CPE) in coal mine can provide equipment support for various equipment and sensors of heading face to access 5G network. This paper introduces the key problems to be solved in the mining transformation of 5G equipment. The 5G network architecture for mining is established to meet the demand of underground 5G signal coverage. In order to solve the problem of high power consumption of 5G equipment, the structure of 5G equipment is optimized and the heat dissipation device is designed. The problem that the RF power exceeds the standard after the superposition of multiple RF outputs and antenna gain of 5G base station is solved. The 5G network architecture and function of the heading face transportation system are proposed. With the characteristics of large bandwidth, low delay, high reliability and wide connection of 5G network, the unified access of various sensing equipment, HD video monitoring and remote centralized control of each link of the heading face transportation system can be realized.
Research on key technologies of intelligent gangue sorting robot
ZHANG Yuanhao, PAN Xiangsheng, CHEN Xiaojing, HUO Zhenlong, REN Shuwen, JI Liang
2022, 48(6): 69-76, 111. doi: 10.13272/j.issn.1671-251x.17931
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This paper introduces the application and research status of the intelligent gangue sorting robot. This paper points out that the intelligent gangue sorting robot is mainly based on the principle of X-ray and image identification. And the high-pressure pneumatic sorting and truss robot grasping sorting are used to separate coal and gangue. The sorting actuators are mainly truss type, parallel type and series type of intelligent gangue sorting robot. The sorting actuators have fast response speed and often separate the gangue in the form of 'pulling' and 'grasping'. In the process of belt transportation, the compatibility of different gangue sizes and the optimization of movement path need to be considered in the 'pulling' of the intelligent gangue sorting robot. And the working space of the manipulator and the bearing capacity of the robot need to be considered in the 'grasping'. This paper analyzes the key technologies such as deep learning-based coal and gangue identification, unstructured multi-constraint environment-oriented motion planning of gangue sorting manipulator, force feedback-based active compliance control of manipulator and multi-arm cooperative sorting task allocation strategy and control. These technologies are used for intelligent gangue sorting robot to effectively realize gangue sorting in complex on-site environment. This paper points out that coal and gangue identification technology based on deep learning is one of the key technologies of gangue sorting robot. It still needs further research on the efficient construction method of coal gangue data set, improving the generalization of coal gangue identification algorithm, and the real-time optimization of coal gangue identification algorithm. Combined with the demand of field application and intelligent robot development, the future research directions of intelligent gangue sorting robot are pointed out. In the complex environment on site, it is suggested to improve the robustness and adaptability of the coal gangue identification algorithm. It is suggested to develop intelligent sensing and control technology for complex environment and high-precision three-dimensional pose estimation technology for gangue. It is suggested to develop intelligent gangue picking technology of gangue picking robot based on force position hybrid control. It is suggested to research intelligent gangue sorting robot underground gangue sorting technology.
Coal block abnormal behavior identification based on improved YOLOv5s + DeepSORT
ZHANG Xuhui, YAN Jianxing, ZHANG Chao, WAN Jicheng, WANG Lixin, HU Chengjun, WANG Li, WANG Dong
2022, 48(6): 77-86, 117. doi: 10.13272/j.issn.1671-251x.17915
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Coal block detection methods mainly include traditional image detection methods and deep learning target detection methods. The traditional image detection method has low detection precision and poor real-time performance, and can not accurately determine the coal pile. Although the deep learning target detection method can achieve real-time detection, it does not identify the number, retention, and blockage of coal blocks. And there are many identification model parameters. To solve the above problems, a coal block abnormal behavior identification method based on improved YOLOv5s + DeepSORT is proposed. Firstly, video images of coal blocks on a belt conveyor in a fully mechanized coal mining face are collected by the camera and inspection robot, and data sets are made. Secondly, the MobileNetV3_YOLOv5s_AF-FPN model is used for detecting the coal image target. The original YOLOv5s backbone feature extraction network is replaced by MobileNetV3 to reduce the number of parameters and improve the reasoning speed. The original feature pyramid network in YOLOv5s is improved to AF-FPN to improve the detection performance of the YOLOv5s network for multi-scale coal targets. DeepSORT is used for multi-target tracking of coal blocks. The coal block image detected by the improved YOLOv5s is taken as the input of DeepSORT for multi-target tracking. DeepSORT is used to estimate the state of coal blocks, perform data association and matching, and update the tracker parameters to determine the tracking results. The continuously tracked coals are ID-coded, and the number of coals in the current frame is counted. Finally, the continuously tracked target is taken out from the target tracker, and a distance threshold is set. Whether the target is detained or not is determined. The quantity threshold is set to determine whether it is blocked. The identification of abnormal behavior of coal block retention and blocking state is finally realized. The reliability of the coal abnormal behavior identification method based on the improved YOLOv5s + DeepSORT is experimentally verified by using the self-built dkm_data2021 data set. The results show that compared with the YOLOv5s model, the average detection precision of the improved YOLOv5s model is improved by 1.45%, the parameter quantity is reduced by 35.3%, the reasoning is accelerated by 12.7%, the average missed detection rate is reduced by 11.08%, and the average false detection rate is reduced by 11.54%. The detection precision of coal block abnormal behavior identification method based on the improved YOLOv5s+DeepSORT is 80.1%, which can accurately identify the status of coal block retention and blockage. The result verifies the reliability of the method.
Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm
LI Zaiyou, SUN Yanbin, WANG Xiaoguang, CHEN Yong, LIU Guangwei, GUO Zhiqing
2022, 48(6): 87-94, 127. doi: 10.13272/j.issn.1671-251x.17929
<Abstract>(273) <HTML> (127) <PDF>(37)
Abstract:
In order to solve the problem of unmanned truck transportation scheduling in open-pit mines, the minimum sum of fuel cost, fixed start-up cost, breakdown maintenance cost, and network base station construction and maintenance cost are taken as the objective functions. The mining amount of mining station, crushing amount of crushing station, truck number and truck transportation workload are taken as the constraint conditions. The optimization model of unmanned truck transportation scheduling in open-pit mines is established. To solve the problem of imbalance between global exploration and local mining ability in the tunicate swarm algorithm, an improved tunicate swarm algorithm (ITSA) based on Singer mapping and adaptive updating mechanism of parameter position is proposed. And it is applied to solve the optimization model of unmanned truck transportation scheduling in open-pit mines. Singer mapping is introduced to enhance the distribution of the initial tunicate swarm in the solution space and accelerate the compression of the solution space, thus improving the convergence speed of the algorithm. Through the adaptive updating mechanism of parameter position, the positions of the tunicate and the optimal tunicate are adjusted to increase the search range of the solution space. Therefore, the algorithm jumps out of the local optimization. The simulation results show that ITSA has better convergence precision, convergence speed and stability compared with the four population intelligent optimization algorithms of grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA), atom search optimization algorithm (ASO) and tunicate swarm algorithm (TSA). In the unimodal benchmark function, the evaluation indexes of ITSA are far better than those of the other four algorithms, which shows that ITSA has better local mining capacity. In the multi-peak benchmark function, the evaluation indexes of ITSA show better optimization performance, which indicates that ITSA has better global exploration performance. The practical application scenario verification shows that ITSA has faster convergence speed and higher convergence precision when used for solving the unmanned truck transportation scheduling optimization model. And ITSA reduces the truck transportation cost and transportation distance.
Combined prediction model of truck multi-section travel time in open-pit mine based on velocity field
TIAN Fengliang, WANG Zhongxin, SUN Xiaoyu, XIN Fengyang, SONG Bo, WANG Jinjin, ZENG Xiangyu, ZHOU Hao, ZHAO Ming
2022, 48(6): 95-99, 146. doi: 10.13272/j.issn.1671-251x.17916
<Abstract>(178) <HTML> (26) <PDF>(25)
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Due to the complexity of road in open-pit mine, the existing truck travel time prediction methods are difficult in the actual deployment. This leads to the truck optimal scheduling system only realizing scheduling instead of optimization. A combined prediction model of truck multi-section travel time in open pit mine based on velocity field is proposed. The open-pit mine road is divided into multiple road sections. the random forest algorithm is used to construct the unit prediction model to predict the travel time of the truck in each section. Then the predicted values of the unit prediction models are accumulated to obtain the travel time predicted value of the truck on in the composite road section. In order to improve the prediction precision, the average velocity of the truck is taken as an influence factor of the travel time. The velocity field is constructed according to the collected velocity information of the truck. The average value of the truck velocity at all points on a road section is calculated, which is approximate to the average velocity of the truck on the road section, and the average velocity is input into the unit prediction model. Based on the data of truck schedule information in truck dispatching system of Yimin Open-pit Mine, the combined prediction model is trained, and the prediction precision and real-time performance of the model are tested. The results show that the combined prediction model of truck travel time in multiple sections of open-pit mine based on velocity field has high prediction precision for truck travel time in composite road sections. The average absolute error percentage is 4.81%, which is more than 2% lower than the single prediction model based on random forest algorithm. The operation time of the combined prediction model is less than 1 s, which can realize the real-time prediction of truck travel time.
Automatic control system of auxiliary transportation traffic light based on UWB precise positioning
BAO Xiangyu, SHAN Chengwei, WU Yanming
2022, 48(6): 100-111. doi: 10.13272/j.issn.1671-251x.2022030051
<Abstract>(238) <HTML> (62) <PDF>(53)
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The current auxiliary transportation system lacks effective control strategy. The vehicles waiting in the blind area at the intersection are disorderly. And it is difficult for one vehicle to give another the right of way. It is prone to collision accidents, resulting in low transport efficiency. This paper proposes an automatic control system of auxiliary transportation traffic light based on UWB precise positioning. The system determines the distribution requirements of UWB positioning base stations at typical intersections. The system sets two control parameters of position information and driving state, various release mechanisms and interval management and control strategies. The system specifies the sequencing principle and priority of forks. And the system has three control modes of automatic control, manual control and timing switching. Firstly, the UWB positioning base station scans the data of the vehicle positioning card. The logic controller reads the vehicle data information of the positioning base station in real time and solves the position information and the driving state of the vehicle. The logic controller controls the traffic light to execute the control command and directs the transportation vehicles to pass in an orderly manner. The logic controller is connected with an upper computer through a ring network. The upper computer can issue a control instruction to remotely change the light. And the logic controller uploads various information such as driving data, abnormal driving behaviors, traffic light states of underground vehicles to a mine vehicle dispatching system of the upper computer in real time. Therefore, the combination of local control and remote auxiliary control is realized. The system is tested in a simulated roadway. The result shows that the logic controller code operates normally. The logic response time of the system is<200 ms. The response time of the traffic light state switching is<1 s. And the curve alarm can correctly execute the alarm command. Dahaize Coal Mine uses trackless vehicles to carry out underground transportation tasks. There are about 140 recorded vehicles. The transportation lines are not fixed and the transportation tasks are intensive. The traffic at important intersections is large. The application results of the system in the complex environment of Dahaize Coal Mine show that the intersection information configuration is flexible. The system can highly adapt to various forms of intersections on-site, and meet the specific needs of intersection management and control. By adjusting the control threshold, the system can adjust the size of the intersection control area to adapt to the change of on-site transportation flow. The positioning base station adopts different data acquisition strategies, which reduces the laying quantity and construction cost of the positioning sub station. The upper computer can monitor the traffic scheduling status of underground vehicles in real time, monitor the driving behavior of vehicles, and realize the remote control of traffic lights.
Analysis Research
On demand dynamic linkage control system for air volume of multiple coal working faces
YANG Xu, ZHANG Lang, MA Qiang, LIU Yanqing, ZHANG Hongjie, ZHAO Kaikai, LI Wei, DUAN Sigong, GENG Feng
2022, 48(6): 112-117. doi: 10.13272/j.issn.1671-251x.2022010022
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When multiple coal working faces are mined at the same time in the coal mine, the change of branch air volume of any roadway will cause the change of branch air volume of other roadway. The air volume control of any coal working face in the mine will affect the air volume change of other coal working faces in the mine. Therefore, it is necessary to intelligently control the air volume of multiple coal working faces. However, the current research on intelligent control of air volume in coal working face is mainly based on automatic control algorithm of mine air volume or mine air volume control equipment. It lacks the research on the on demand dynamic linkage control system for air volume of multiple coal working faces. In order to solve the above problems, this paper puts forward a design scheme of on demand dynamic linkage control system for air volume of multiple coal working faces. Taking the louvered remote automatic regulating air window deployed in the return air crossheading of 3308 working face in Shanxi Tiandi Wangpo Coal Industry as the research object, the computational fluid dynamics (CFD) method is used to simulate the flow field distribution of the regulating air window. The Origin software is applied to fit the non-linear relationship between the wind area of the regulating air window and the wind resistance, and obtain the functional relationship between them. The relative error between the wind resistance of the regulating air window measured on site and that calculated by fitting is less than 6%. Based on the functional relationship between the wind area and the air resistance of the regulating air window and a joint calculation method of wind resistance regulation of multiple coal working faces, the upper computer calculation software is developed. And based on the upper computer calculation software, the underground explosion-proof and intrinsically safe control substation and the louvered remote automatic regulating air window, the on demand dynamic linkage control system for air volume of multiple coal working faces is constructed. The on demand dynamic linkage control of air volume on demand is carried out on 3308 and 3203 coal working faces in Wangpo Coal Industry. The field application shows that the relative error between the target air volume and the actual air volume after control is less than 7%. This result indicates that the on-demand dynamic linkage control system for air volume of multiple coal working faces has certain use effect.
Analysis of electromagnetic wave energy safety of underground metal structure near-field coupled large loop transmitting antenna
FAN Sihan, YANG Wei, LIU Junbo
2022, 48(6): 118-127. doi: 10.13272/j.issn.1671-251x.2022030093
<Abstract>(182) <HTML> (28) <PDF>(14)
Abstract:
When the metal structures distributed in the underground roadway are in the near-field of the large loop transmitting antenna, they will couple the electromagnetic wave energy of the large loop transmitting antenna. Once the metal structure has a breakpoint and friction occurs, it may produce friction discharge spark and ignite gas. This poses a threat to the safety of coal mine. In order to solve this problem, the safety of electromagnetic wave energy of underground metal structure near-field coupled large loop transmitting antenna is analyzed from two aspects of near-field coupling risk coefficient and safe distance. By establishing the equivalent circuit of electromagnetic wave energy of metal structure near-field coupled large loop transmitting antenna, the expressions of near-field coupling risk coefficient and safe distance between metal structure and large loop transmitting antenna are derived. The influence of the radius of the large loop transmitting antenna, the radius of the equivalent receiving coil of the metal structure, the friction discharge spark load and the distance between the metal structure and the large loop transmitting antenna on the near-field coupling risk coefficient and the safe distance are analyzed. The simulation results show that the near-field coupling risk coefficient increases slightly at first and then decreases slightly or increases all the time with the increase of the radius of the large loop transmitting antenna. Under certain conditions, the friction discharge spark load can make the near-field coupling risk coefficient reach the peak value. When the radius of the large loop transmitting antenna is greater than or equal to the radius of the equivalent receiving coil of the metal structure, the near-field coupling risk coefficient at the peak value may exceed the critical value 0.46 of the near-field coupling risk coefficient. This may cause danger. When the radius of the large loop transmitting antenna is smaller than the radius of equivalent receiving coil of the metal structure, the near-field coupling risk coefficient at the peak value is less than the critical value 0.46 in most cases. This will not cause danger in most cases. Under certain conditions, the radius of the large loop transmitting antenna can make the near-field coupling risk coefficient reach the peak value. The near-field coupling risk coefficient at the peak value first increases and then decreases with the increase of the radius of equivalent receiving coil of the metal structure. It is more likely to exceed the critical value 0.46 of the near-field coupling risk coefficient, which is likely to cause danger in the gas environment. The safe distance increases with the increase of the radius of the large loop transmitting antenna. The safety of the electromagnetic wave energy on the friction discharge spark load decreases with the increase of the radius of the large loop transmitting antenna. When the radius of the large loop transmitting antenna is greater than or equal to the radius of the equivalent receiving coil of the metal structure, the safe distance increases with the increase of the radius of the equivalent receiving coil of the metal structure. The safety of the electromagnetic wave energy on the friction discharge spark load decreases with the increase of the radius of the equivalent receiving coil of the metal structure. When the radius of the large loop transmitting antenna is smaller than the radius of the equivalent receiving coil of the metal structure, the safe distance first increases slowly and then decreases with the increase of the radius of the equivalent receiving coil of the metal structure. The safety of the electromagnetic wave energy on the friction discharge spark load first decreases and then increases with the increase of the radius of the equivalent receiving coil of the metal structure.
Experimental Research
Coal gangue detection based on CBA-YOLO model
GUI Fangjun, LI Yao
2022, 48(6): 128-133. doi: 10.13272/j.issn.1671-251x.2022020033
<Abstract>(383) <HTML> (155) <PDF>(68)
Abstract:
There are some problems in coal gangue detection, such as small differences of characteristics between samples and dense targets. This leads to low precision and poor real-time performance of the existing coal gangue detection methods. In order to solve this problem, a method of coal gangue detection based on CBA-YOLO model is proposed. The CBA-YOLO model is based on YOLOv5m, which has faster speed and higher precision. The convolutional block attention module (CBAM) is added to the Backbone of YOLOv5m. The spatial attention module and the channel attention module are connected in series to focus on the difference of characteristics and reduce the data dimension. And the detection performance of coal gangue is improved. In the Neck part, the bi-directional feature pyramid network (BiFPN) structure is adopted to improve the calculation efficiency of the model by integrating the features of different scales. Therefore, the detection speed of coal gangue is improved. In the Prediction part, the Alpha-IoU function is used as the loss function. And the weight coefficient is set to accelerate the learning of high confidence targets, so as to further improve the detection precision of coal gangue. The experimental results show that the average detection precision of CBA-YOLO model for coal gangue is 98.2%, which is 3.4% higher than that of YOLOv5 model. The detection speed is increased by 10%. CBA-YOLO model is more robust and can effectively avoid missed detection, false detection and overlap.
Research on positioning algorithm of underground personnel based on UWB
HE Lei, WEI Mingsheng, QIU Xinyu, TANG Shoufeng, LI Wenshuai, ZHANG Xu
2022, 48(6): 134-138. doi: 10.13272/j.issn.1671-251x.2022020035
<Abstract>(164) <HTML> (19) <PDF>(43)
Abstract:
Aiming at the requirement of high real-time and high precision personnel positioning in underground mine, the positioning algorithm of underground personnel based on ultra wide band (UWB) is studied. The double-sided two-way ranging (DS-TWR) mode is adopted to measure the distance between the positioning base station and the positioning tag. This mode does not need the clock synchronization of the positioning base station and the positioning tag system. Therefore, the positioning precision is improved from the source. According to the ranging information, the weighted least squares (WLS) algorithm and CHAN algorithm are used to estimate the coordinates of the positioning tag. The performance of the two algorithms is compared and analyzed through static and dynamic experiments. The positioning precision is comprehensively evaluated through the root mean square error and the cumulative distribution function (CDF) of the error. The experimental results show that in static experiment, the root mean square errors of CHAN algorithm and WLS algorithm are 5.878 6 cm and 8.007 4 cm respectively. The root mean square error of CHAN algorithm is 26.59% lower than that of WLS algorithm. In dynamic experiment, the root mean square errors of CHAN algorithm and WLS algorithm are 12.2923 cm and 21.1809 cm respectively. The root mean square error of CHAN algorithm is 41.97% lower than that of WLS algorithm. The positioning precision of CHAN algorithm is higher than that of WLS algorithm. And CHAN algorithm is more suitable for underground personnel positioning in coal mines.
Defogging algorithm of underground coal mine dust and fog image based on boundary constraint
CAO Huchen, YAO Shanhua, WANG Zhonggen
2022, 48(6): 139-146. doi: 10.13272/j.issn.1671-251x.2022010010
<Abstract>(221) <HTML> (35) <PDF>(80)
Abstract:
The existing defogging algorithms of underground coal mine images mainly include defogging algorithm based on image enhancement, defogging algorithm based on CNN and defogging algorithm based on physical model. The former two have poor defogging effect and are prone to over-exposure. The physical model-based defogging algorithm processes the dust and fog according to the atmospheric scattering model. However, the dark channel-based atmospheric light value estimation method is applied to the underground coal mine environment, and the selected atmospheric light value will be small. The problems of image overexposure, incapability of inhibiting point light source irradiation are easily caused. In order to solve the above problems, the image defogging algorithm based on dark primary color prior (He algorithm) and the defogging algorithm based on boundary constraint and context regularization (Meng algorithm) are fused. The defogging algorithm of underground coal mine dust and fog image based on boundary constraint is proposed. The method comprises the following steps. Firstly, Gamma correction is performed on the input image. The color channel opening operation processing is performed on the corrected image to obtain the low-resolution pixel block. The maximum brightness value is selected from the low-resolution pixel block as the underground atmospheric light value of the coal mine. Secondly, the Gamma-corrected image is processed by He algorithm and Meng algorithm respectively. The boundary constraint map obtained by Meng algorithm is filtered to obtain a clearer boundary constraint map. And the rough transmittance difference of Meng algorithm and He algorithm is compared and then fused. Finally, the contextual regularization is performed on fused rough transmittance to obtain the refined transmittance. The obtained atmospheric light value and the refined transmittance are used to obtain the defogged image through the atmospheric scattering model. The simulation results show that the proposed defogging algorithm of underground coal mine dust and fog image based on boundary constraint has no problems such as overexposure. The defogging effect is better, the defogged image is brighter and the color is closer to the original image. The peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM) are used to objectively evaluate the defogging effect of the proposed algorithm. The results show that the proposed algorithm has an average improvement of 61.52%, 36.51% and 24.57% in PSNR, SSIM and FSIM compared with the He algorithm. Compared with the algorithm in literature [9], the proposed algorithm has increased by 15.51%, 19.27% and −0.30% on average. Compared with Meng algorithm, the proposed algorithm has increased by 18.93%, 7.19% and 1.21% on average. Compared with the algorithm in literature [11], the proposed algorithm has increased by 18.29%, 10.54% and 1.19% on average. It shows that the proposed algorithm has better defogging effect, brighter image and more details in the underground environment of coal mine.
Study on acoustic emission characteristics of sandy mudstone under uniaxial loading and unloading of Buertai Coal Mine
CAO Jinzhong, YANG Zhiliang, WANG Wenjie
2022, 48(6): 147-153, 158. doi: 10.13272/j.issn.1671-251x.2021110046
<Abstract>(155) <HTML> (19) <PDF>(13)
Abstract:
The loading-unloading effect induced by repeated mining disturbance is an important inducement to cause the rock mass failure in roadway engineering. However, there is a lack of research on the mechanical response of the widely distributed weakly cemented sandy mudstone in west China under loading and unloading conditions. In order to solve this problem, the sandy mudstone in borehole BK209 of Shendong Buertai Coal Mine of National Energy Group is taken as the research object, and the uniaxial graded loading and unloading test scheme is adopted (I, II and III loading and unloading paths are set, wherein the loading and unloading path I adopts displacement control, and the loading and unloading paths II and III adopt load control). This paper studies the mechanical and acoustic emission (AE) characteristics of sandy mudstone under different loading and unloading paths. The result shows the following four points. ① Under the loading and unloading path I, the stress of the sample drops rapidly after the stress peak, showing the characteristics of brittleness. Under the loading and unloading path II, the stress drop after the peak stress of the sample is limited and the stress has a certain residual strength. Under loading and unloading path III, the stress of the sample falls after the peak stress, showing certain ductility characteristics. ② The shorter the loading and unloading path, the greater the increase of elastic modulus in the unloading section. The main reason is that the detritus is produced by shear slip in the interface of the new fracture during loading process. When the unloading path is shorts, the detritus falling off under tensile stress fully fills the nearby gap. Therefore, the friction capacity between the fracture surfaces becomes stronger. ③ In the first loading stage, the AE ringing count under the path I is in a symmetrical change of first increasing and then decreasing. The AE ringing count under the path II is in a left-biased peak change. And the AE ringing count under the path III is in a fluctuation change of increasing and decreasing, and the fluctuation in the middle is large. In the second loading stage, under the load paths I and III, when the stress increases to near the peak value and reaches the yield limit of sandy mudstone, the AE ringing count increases sharply and Kaiser point appears. During the last loading to the peak value, the AE ringing count under path I first occurs uniformly and continuously, and then increases gradually. The AE ringing counts under paths II and III occur uniformly and continuously, and the stress near the peak increases in a leaping manner. ④ In the first loading stage, the AE ringing count is less because of the low stress, and the AE is in relatively quiet period. In the unloading stage, there is basically no ring count, and the AE is in intermittent period. With the increase of stress and loading and unloading times, the AE ringing count increases relatively. The AE is in a fluctuating period. During the last loading to the peak value, the sandy mudstone is in the destruction stage, and the AE ringing count increases sharply. The AE enters the active period.
Design of coal mine safety monitoring system logical control automatic detection device
CHEN Xiangfei
2022, 48(6): 154-158. doi: 10.13272/j.issn.1671-251x.2022020007
<Abstract>(169) <HTML> (24) <PDF>(31)
Abstract:
The logical control detection of coal mine safety monitoring system includes whether the control (power off, interlocking) is executed correctly and whether the control execution time reaches the standard. Because the communication mechanism and communication protocol of monitoring system from different manufacturers are different, it is difficult to realize the standardized detection of system logical control function. And the manual detection efficiency is low and the error is large. In order to solve the above problems, based on the analysis of industry standards and safety standard detection requirements, a coal mine safety monitoring system logical control automatic detection device is designed. The device is not limited by a communication protocol and a bus form. It controls multiple sensors to send out interlocking signals in a serial port communication mode. At the same time, I/O interface is used to collect the execution results of system logical control (whether the circuit breaker is powered off). The device records the generation time of the interlocking signals and the generation time of the logical control execution result so as to determine the logical control execution time of the system. The device restores each sensor and the breaker to an initial state after each logical control is executed. The test results show that the device can detect the control execution time reliably and accurately. When testing nearly 300 logical controls, the automatic detection time is about 2 hours, which improves the detection efficiency.