2021 Vol. 47, No. 6

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Analysis and countermeasures of ten 'pain points' of intelligent coal mine
WANG Guofa, REN Huaiwei, ZHAO Guorui, DU Yibo, PANG Yihui, XU Yajun, ZHANG Desheng
2021, 47(6): 1-11. doi: 10.13272/j.issn.1671-251x.17808
<Abstract>(567) <HTML> (38) <PDF>(100)
Abstract:
Based on the current situation of the development of intelligent coal mines in China, the overall technical architecture of intelligent coal mine construction of 'one cloud + one converged network + three-level platform + N application modules' is proposed. The study analyzes the idea of building an intelligent integrated control platform based on micro-service architecture. The idea is to construct the intelligent coal mine platform of unified procurement, unified construction and integrated operation and maintenance model with unified technical architecture, functional modules increasing or decreasing and varied hardware parameters. This paper systematically analyzes ten pain points in the development of intelligent coal mines in China, including unified understanding and concepts, unbalanced development of intelligent mines, immature 5G application scenarios, insufficient technical support for 'transparent geology', unbalanced development and extraction and unbalanced extraction and support, poor adaptability to complex conditions, difficulties in compatibility and coordination of intelligent giant systems, immature development of intelligent robots above and below mines, insufficient management and talent reserves and insufficient investment guarantee. From the aspects of theoretical innovation, technological innovation, equipment innovation, management model innovation and talent system innovation, this study points out the direction of scientific and technological research that needs to be carried out for the intelligent construction of coal mines in China, and proposes the development direction and measures for building a new system of intelligent and green coal industry.
Binocular vision-based perception and positioning method of mine external fire
SUN Jiping, LI Yue
2021, 47(6): 12-16. doi: 10.13272/j.issn.1671-251x.17766
<Abstract>(168) <HTML> (20) <PDF>(21)
Abstract:
In order to solve the problem that most of the current mine external fire monitoring methods do not have fire source positioning function, a binocular vision-based perception and positioning method of mine external fire is proposed. Firstly, the method sets up mine visible binocular cameras or near-infrared binocular cameras at multiple points in roadways, chambers and working faces with cables, tapes and electromechanical equipment to capture images of the monitoring area. Then, the adopted images are preprocessed and binarized images are obtained by threshold segmentation. The roundness, rectangularity and number of sharp corners in the image are calculated, and the images are identified by flames according to the roundness, rectangularity and number of sharp corners. If there is flame in the image detection area, a fire alarm signal will be issued and the temperature, smoke, carbon dioxide, carbon monoxide, oxygen and infrared sensor information are taken into consideration to improve the accuracy of alarms. Finally, the method uses mine visible binocular cameras or near-infrared binocular cameras to measure the distance of the fire source, locates the fire source based on the camera position, and outputs the fire source location information to control the fire extinguishing device near the fire source. Far-infrared binocular cameras can also be used for fire perception and fire source positioning, but the cost is high. The binocular vision-based perception and positioning method of mine external fire can sense and locate the fire source. This method has the advantages of wide monitoring range, low cost, fast response and visualization, and solves the problem of locating the fire source of mine external fire.
Key technology and platform of intelligent mine basic information acquisition based on industrial Internet of things
HE Yaoyi, LIU Lijing, ZHAO Lichang, ZHOU Libing
2021, 47(6): 17-24. doi: 10.13272/j.issn.1671-251x.17798
<Abstract>(186) <HTML> (15) <PDF>(38)
Abstract:
The data acquisition of intelligent mine is based on ubiquitous perception and cannot be separated from the basic technical support of industrial Internet of things and big data. This paper analyzes the current situation of coal mine automation and monitoring data acquisition technology and platform, that is, the lack of identity marks of the perception node, data not being shared, system maintenance difficulties, system software smokestack construction and data fusion difficulties. This paper proposes the process and key technologies of intelligent mine basic information acquisition based on industrial Internet of things, including low-power ubiquitous perception technology, perception node identification and data sharing technology, long-term maintenance-free technology, data hierarchical interaction and fusion technology, etc. On this basis, the structure and design concept of the intelligent mine basic information platform based on private cloud deployment is proposed. Only one set of distributed software platform based on microservice technology can solve the problem of the collection, classification and storage, interaction, fusion and analysis of all kinds of automation data in the entire mine, and realizes the linkage control with the control execution devices. Through the establishment of a unified technology and service system, including a unified technology architecture and technology stack, unified master data, unified data storage mechanism, unified data model, unified authority and user interface mode, it is ensured that multiple businesses integration can be realized under the same software platform. By establishing a unified data acquisition mode based on the Internet of things, that is, loading and adapting different protocol drivers, data acquisition by professional systems of different manufacturers is realized. Through the establishment of a unified data processing and storage mechanism, data fusion and release mechanism, it is able to provide consistent data sources for intelligent mines.
Vanishing point detection method in complex environment of mine roadway
CHENG Jian, WANG Ruibin, YU Huasen, YAN Pengpeng, WANG Kai
2021, 47(6): 25-31. doi: 10.13272/j.issn.1671-251x.2021040097
<Abstract>(108) <HTML> (18) <PDF>(11)
Abstract:
By detecting and identifying the vanishing point position in the image, it is able to assist mobile robots in mines roadways for autonomous navigation. The existing vanishing point detection methods have large errors in the mine roadway with poor lighting conditions and insufficient structured information. In order to solve the above problems, a vanishing point detection method in complex environment of mine roadways is proposed. Firstly, the image is pre-processed by reducing, filtering, graying, etc. This method can reduce the calculation amount significantly and the straight line characteristics can be better preserved. Then, the straight line detection algorithm is used to detect the straight line of the image. The straight line length threshold and the average gradient constraint are introduced to eliminate the interference line with small length and the interference line generated by shadows in the image respectively. Moreover, the block matching algorithm is used to generate the block motion trajectory straight line of the image. Finally, the straight lines after removing the interference and the block motion trajectory straight lines are converted into sample points in the parameter space. The outlier factor value of each sample point is calculated by the local anomaly factor algorithm, and the outlier factor value of the sample point and the length of the corresponding straight line are used as the criteria to measure the importance of the sample points. On this basis, the weight function of the weighted regression algorithm is designed to obtain the best estimate of the vanishing point. The experimental results on the mine roadway data set and public data set show that compared with the edge-based vanishing point detection method and the deep learning-based vanishing point detection method, the method in this paper has stronger robustness to light changes. It has higher accuracy in complex environment with poor lighting conditions and lack of straight line information, and has better real-time performance than the vanishing point detection method based on deep learning. This method can better meet the needs of mine roadway robot navigation.
Research on task allocation of edge computing in intelligent coal mine
ZHU Xiaojuan, ZHANG Hao
2021, 47(6): 32-39. doi: 10.13272/j.issn.1671-251x.2021050011
<Abstract>(163) <HTML> (22) <PDF>(23)
Abstract:
Most of the current task allocation of edge computing in intelligent coal mine uses centralized allocation algorithms, which takes a single factor into account when prioritizing tasks and does not consider the narrow and long characteristics of the coal mine network topology. In order to solve this problem, combined with the characteristics of tasks in coal mine scenarios, an edge computing task allocation strategy based on dynamic priority and real-time bidding strategy is proposed. The tasks are classified into different levels. On the one hand, tasks that exceed the computing capacity of edge nodes are directly uploaded to the cloud for processing. On the other hand, the tasks that can be processed at the edge computing layer are classified into three levels according to their importance. Level 1 is for tasks related to environmental monitoring and staff safety operation protocol detection. Level 2 is for tasks related to production process equipment status monitoring. And level 3 is for other routine tasks. However, allocating tasks according to these 3 levels alone can cause low priority tasks to be blocked by high priority tasks. The urgency of the task must be considered as well so that the tasks approaching the deadline are given higher priority. The priority is dynamically generated and the task queue is updated according to the fixed priority, urgency and calculation amount of the task. According to the characteristics of narrow and long underground coal mine roadways and restricted transmission, a real-time bidding model for task allocation is established. The quotation of the edge node for tasks is determined by four factors, including computing capacity, processing time, energy consumption and waiting time of the edge node. The requesting node transmits the task to the edge node that has the lowest processing cost within 2 hops and satisfies the task demand for execution, thereby completing task allocation. The simulation results show that the proposed task allocation strategy can allocate tasks to edge nodes with matching computing power for processing, so that edge nodes can process urgent and important tasks first. The method achieves better results in reducing delay and energy consumption, and optimizing resource allocation.
Research on the architecture and key technologies of intelligent coal mine data middle platform
SHU Lichu
2021, 47(6): 40-44. doi: 10.13272/j.issn.1671-251x.2020120052
<Abstract>(148) <HTML> (18) <PDF>(33)
Abstract:
The data middle platform is the data base of intelligent coal mine construction and the foundation of coal mine big data application. This paper proposes the idea of intelligent coal mine data middle platform construction, including data aggregation, data development, data storage, data asset management and data service. This study designs the intelligent coal mine data middle platform architecture, analyzes the data standard specification, big data basic support, data aggregation, data development, data resource pool, data asset management, data service, operation and maintenance guarantee and other function modules of the data middle platform. This paper discusses the solutions of key technologies such as large concurrency and low latency data processing, data classification and storage, data governance, and the construction of big data-based coal mine disaster-risk model in the process of building an intelligent coal mine data middle platform. The application shows that the intelligent coal mine data middle platform realizes data aggregation, data development, as well as the data classification and storage of various perception data, basic data and management data, data asset management, data modeling, model training and data services, etc. The platform makes the multi-source heterogeneous data of coal mines change from data resources to data assets, provides applications such as scheduling decision, disaster risk analysis, equipment health diagnosis and preventive maintenance based on big data, and solves the problems of serious information silos, difficult data integration and low level of intelligent analysis in coal mines.
Coal stacking identification method of belt conveyor based on surface reconstructio
YOU Lei, ZHU Xinglin, QIN Wei, LUO Minghua
2021, 47(6): 45-50. doi: 10.13272/j.issn.1671-251x.2021050007
<Abstract>(127) <HTML> (17) <PDF>(9)
Abstract:
The existing coal stacking identification method of belt conveyor has problems of false trigger alarm and high cost. In order to solve the above problems, a coal stacking identification method of belt conveyor is proposed. The method uses infrared structured light technology to quickly reconstruct the coal flow surface of belt conveyor.Firstly, the depth map of the coal stacking is obtained by using infrared structured light technology. Secondly, the depth map is mapped to a point cloud map, and the point cloud data is used to construct a convex quadrilateral network. And the convex quadrilateral network is triangulated by the approximate Delaunay subdivision method to complete the reconstruction of the coal stacking surface.Finally, according to the distance from the triangle vertex to the camera and the proportion of the triangle area whose vertex distance is less than the threshold to the total area, it is determined whether there is a coal stacking accident.The approximate Delaunay subdivision method replaces the insertion sorting process with the traversal process. There is a small probability of not satisfying the Delaunay property, but the algorithm complexity is low. Therefore it can improve the real-time performance of coal stacking identification.The experimental results show that the infrared structured light technology improves the algorithm's robustness to illumination effectively. The success rate of the approximate Delaunay subdivision method is 99.466 1%, and the surface reconstruction time of the approximate Delaunay subdivision method and the classic Delaunay subdivision method under the same conditions is 1.28 ms and 134.93 ms respectively. The approximate Delaunay subdivision method improves the calculation speed greatly when the accuracy meets the application requirements. By setting an appropriate threshold, the number of missed detection and the number of false detection are both 0. The statistics of the processing time of a large number of images show that the processing time of each frame is less than 20 ms, which meets the real-time requirements.
Damage detection method for mine conveyor belt based on deep learning
ZHANG Mengchao, ZHOU Manshan, ZHANG Yuan, YU Yan, LI Hu
2021, 47(6): 51-56. doi: 10.13272/j.issn.1671-251x.2021040010
<Abstract>(184) <HTML> (21) <PDF>(48)
Abstract:
In order to solve the problem that the current conveyor belt damage detection methods lack research on damage types other than conveyor belt tear, a damage detection method for mine conveyor belt based on deep learning is proposed. And the conveyor belt damage types are classified by the Yolov4-tiny target detection network. The Yolov4-tiny target detection network uses CSPDarknet53-tiny as the backbone feature extraction network, draws on the Resnet residual idea, uses residual blocks to prevent the loss of high-level semantic features in the deep network. At the same time, the method uses feature pyramid network to obtain the fusion of high-level and low-level semantic information to achieve the purpose of improving detection precision. The two effective feature layers in CSPDarknet53-tiny are input into the prediction network Yolo Head, and the prediction frames are filtered by the score ranking and non-maximum suppression algorithm to predict the types of conveyor belt damage. The experimental results show that the average precision of the Yolov4-tiny target detection network on the conveyor belt damage data set for the four damage types of surface scratches, tears, surface damage and breakdown is 99.36%, 94.85%, 89.30%, and 86.76% respectively, and the mean average precision is 92.57%. Compared with Faster-RCNN, RFBnet, M2det, SSD, Yolov3, EfficientDet and Yolov4 target detection networks, the Yolov4-tiny target detection network achieves the fastest detection speed on the data set with a frame rate of 101 frames/s. The network achieves better balance between speed and precision, and occupies relatively less computing resources. The detection of fresh samples outside the data set verifies that the method in this paper has good generalization ability.
Research on the identification method of non-coal foreign object ofbelt conveyor based on deep learning
HU Jinghao, GAO Yan, ZHANG Hongjuan, JIN Baoquan
2021, 47(6): 57-62. doi: 10.13272/j.issn.1671-251x.2021020041
<Abstract>(149) <HTML> (28) <PDF>(43)
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In order to solve the problems of single identification target and lack of positioning ability of the existing image identification methods of foreign objects, an identification method of non-coal foreign object of belt conveyor based on deep learning is proposed.This method uses the target detection algorithm YOLOv3 as the basic framework, and uses the Focal Loss function to replace the cross entropy loss function in the original model to improve the YOLOv3 model. By adjusting the optimal hyperparameters (weight parameter α and focus parameter γ) to balance the ratio between samples, the method solves the non-coal foreign object sample imbalance problem. Therefore, the model focuses more on learning complex target sample characteristics during training and improves the model forecast performance. A foreign object dataset is built and the classification performance and speed are tested by the foreign object dataset.The results show that the Focal Loss function performs better than the cross entropy loss function in the foreign object dataset, and the accuracy is increased by 5% when γ=2 and α=075. Therefore, the optimal hyperparameter is γ=2 and α=075.The improved YOLOv3 model's identification accuracy of the three non-coal foreign objects of bolts, angle ironsand nuts increases by about 47%, 35% and 68% respectively, and the recall rate increases by about 66%, 35% and 60% respectively. Under the 2080Ti platform, the predicted type of each image is consistent with the actual type, and the confidence level is above 94%.
Research on personnel re-identification in complex underground environment
WEI Li, YUN Xiao, CHENG Xiaozhou, SUN Yanjing
2021, 47(6): 63-70. doi: 10.13272/j.issn.1671-251x.17701
<Abstract>(146) <HTML> (12) <PDF>(16)
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Intelligent identification of personnel in underground video monitoring in coal mines is of great significance for improving the efficiency of personnel supervision and reducing the occurrence of safety accidents. Affected by the complex underground environment and the performance limitations of monitoring video equipment, the underground video monitoring images have problems such as low resolution, occlusion and background interference, resulting in small differences among underground personnel and low accuracy of personnel re-identification. In order to solve the above problems, a network structure based on distance metric and channel attention is proposed. The structure is used for personnel re-identification in complex underground environments. In order to solve the problem that it is not easy to distinguish personnel from background in monitoring images, a channel attention module is introduced into the backbone network to make it pay more attention to the foreground characteristics of personnel and suppress the background information. Moreover, the size of the characteristic map output from the last layer of the backbone network is doubled so as to obtain more fine-grained characteristics, enrich the characteristic information of personnel and enhance the network's ability to learn characteristics. On the basis of realizing the classification of personnel with different identities, using the absolute distance information between the images of personnel, the distance metric module is used to sample and weight the personnel images who are difficult to identify, increase the weight of the difficult samples in the back propagation, and make the network pay more attention to the discriminative personnel characteristics. The identity loss and distance metric loss are jointly used to optimize the characteristic layer, so that the network can extract more discriminative personnel characteristics to improve the re-identification accuracy. The Miner-CUMT data set is used to verify the proposed method for personnel re-identification in complex underground environments. The results show that the method can make full use of the key information of personnel with different identities in the underground, so that the identification network has stronger discrimination ability and improves the accuracy of personnel identification in the underground.
Real-time detection algorithm of underground human body based on lightweight parameters
DONG Xinyu, SHI Jie, ZHANG Guoying
2021, 47(6): 71-78. doi: 10.13272/j.issn.1671-251x.2021010035
<Abstract>(149) <HTML> (30) <PDF>(9)
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The existing underground personnel target detection methods cannot achieve the real-time detection results due to the deep network and huge calculation amount, a real-time detection algorithm of underground human body based on lightweight parameters is proposed. The method uses the depthwise separable convolution module and the inverted residual module to construct a lightweight characteristic extraction network. Through the depth separable convolution compressing parameter amount and calculation, the operation speed of the characteristic extraction network is improved. The inverted residual structure extracts enough information through a higher dimensional tensor to ensure the accuracy of the characteristic extraction network. Combining the lightweight characteristic extraction network and the SSD multi-scale detection method, an underground human body real-time detection model is established. The model adds traditional convolutional layers to 27 layers to perform convolution operations on the basic structure of the lightweight inverted residual characteristic extraction network. 6-layer characteristic maps are extracted for multi-scale prediction. The test results show that the size of the model is 18 Mbyte, the frame rate is about 35 frames/s, and the performance is better than the commonly used VGG16+Faster R-CNN model and VGG16+ multi-scale detection model. In order to meet the needs of target detection of specific underground environments, a semi-automatic annotation method for human body data based on Faster R-CNN is designed, which can reduce manual workload significantly and improve the accuracy of underground human body detection. The color information of miners' clothing is used for secondary screening of the detection result frame to eliminate the false detection frames that detecting the background as human bodies. The test results show that the algorithm realizes real-time positioning detection and frame selection of mine working face personnel with an accuracy of 92.86% and a recall rate of 98.11%. The algorithm solves the problem of missing and false detection of underground personnel effectively.
Top coal thickness detection method for intelligent fully-mechanized working face
YANG Xiuyu, LIU Shuai, LIU Qing, YANG Qingxiang
2021, 47(6): 79-83. doi: 10.13272/j.issn.1671-251x.2020080059
<Abstract>(122) <HTML> (12) <PDF>(9)
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Detecting the thickness of the top coal in a fully mechanized working face in advance can provide a basis for precise control of the top coal caving, which is beneficial to achieving a balance between the coal caving recovery rate and coal quality. By analyzing the principle of ground-penetrating radar detecting coal-rock interface and coal seam thickness, a ground-penetrating radar device for detecting top coal thickness is designed, and an intelligent method for detecting the thickness of top coal in fully mechanized working face based on ground penetrating radar is further proposed. The radar pulse wave is transmitted and received by the ground-penetrating radar device, and the received signal is amplified, sampled and integrated to form a radar frame, which is transmitted to the control unit of the unmanned coal mining machine in real time by WiFi and finally to the console of the central control room. The coal-rock interface extraction software processes and analyzes the reflected signal waveform and gray-scale image, and determines the position of the coal-rock interface according to the maximum and minimum amplitude of the reflected signal. The method calculates the coal seam thickness by the time difference between the position of maximum or minimum amplitude and the starting point of radar pulse emission. The method is tested in the 12309 fully mechanized working face of Wangjialing Coal Mine. And the results show that the top coal thickness detection result interpreted by radar reflection wave gray-scale image at a certain place is 3.383 m, and the error of the actual value (3.16 m) detected manually is 7%. The maximum thickness of the top coal that can be detected is 5 m, and the maximum detection error does not exceed 10%. The performance meets the actual detection requirements.
Design of rotary borehole data processing and 3D display software
WANG Xiaolong, ZHANG Ju
2021, 47(6): 84-90. doi: 10.13272/j.issn.1671-251x.2021020002
<Abstract>(134) <HTML> (24) <PDF>(12)
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The drilling tracker and hand held tracker measurement software for underground rotary borehole in coal mines has problems such as single function, counterintuitive display, inability to analyze and display the blind area of gas extraction and inability to guide the construction of boreholes. In order to solve the above problems, a borehole data processing and 3D display software is designed. The software includes four functional modules, including borehole data pre-processing, borehole depth and track calculation, borehole track 3D modeling and display and borehole track design guidance. The borehole data pre-processing module focuses on the calculation of geomagnetic declination and the elimination of data outliers. The borehole depth and track calculation module uses the borehole water pressure monitoring data combined with the measured point inclination data to realize the extraction of borehole hydrostatic pressure and the calculation of borehole depth. Moreover, the module obtains the calculation of borehole track and the extraction of borehole measuring point coordinates so as to complete the deep-level data mining of the borehole track. Borehole track 3D modeling and display module presents borehole information such as borehole track and coal seam direction through 3D entities effectively, which facilitates the determination of the blind area for gas extraction from borehole. The module includes the 3D display of borehole group track and coal seam distribution, the coverage area of designed borehole in the coal face, and the coverage area of actual borehole in the coal face. The borehole track design guidance module provides the blind area of the borehole extraction, calculate the offset characteristics of the existing borehole track, and provides the guidance track of the subsequent supplementary borehole construction. The software is used to perform 3D display and offset law analysis on the actual borehole of a mine, and the drilling site is supplemented by drilling with precision drilling technology and track measurement technology. The extraction coverage area of the boreholes after supplementary drilling reaches more than 95% of the designed coverage area. The module controls or eliminates the blind area of gas extraction in boreholes effectively. The software solves the problem of 3D display of the track of the drilling site or roadway borehole group, reproduces the relationship between the borehole trajectory and the coal seam visually, and provides a new method for quantitative evaluation of the blind area of gas extraction in boreholes.
Intelligent electric control system design for mine horizontal directional drilling rig
LIU Zhu, LI Linbo, DU Jianrong, ZHANG Yuxiang, SONG Jiancheng
2021, 47(6): 91-95. doi: 10.13272/j.issn.1671-251x.2021010064
<Abstract>(151) <HTML> (18) <PDF>(10)
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In order to solve the problems of low degree of intelligence, single function and poor safety of electric control system of domestic horizontal directional drilling rigs, an intelligent electric control system for mine horizontal directional drilling rig is designed. The system is mainly composed of a microprocessor, a voltage and current detection module, a safety barrier acquisition module, a leakage blocking module, a leakage current detection module and a pilot module. The safety barrier acquisition module uses switching safety barriers, analog safety barriers, and thermal resistance safety barriers to limit voltage and current and obtain fault isolation so as to improve the accuracy of signal acquisition and the safety factor in the signal acquisition process. The leakage blocking module adopts the method of applying direct current to detect the equivalent insulation resistance of the motor to ground, and compares the calculated resistance value with the setting value of the leakage blocking action resistance to realize the removal of the motor leakage blocking fault. The leakage current detection module adopts the method of applying a zero-sequence current transformer to detect the leakage current in the circuit online. After the analysis and judgment by the processor, the module achieves the purpose of removing the leakage fault quickly. The pilot module not only realizes the remote control function, but also collects the fault signal on the basis of meeting the requirements of intrinsic safety performance so as to reduce the misoperation effectively. The test results show that the system realizes the basic functions of remote and on-site start-stop control of the motor and remote pilot protection. The current acquisition accuracy is high and the error is less than 2%. The fault identification is accurate and the leakage blocking protection and overload protection actions are accurate and reliable.
Design of multifunctional miner lamp based on LoRa and RT-Thread
ZHANG Di, QUAN Yue, GUO Hai, ZHOU Xiaojie, DONG Fei, ZHAO Duan
2021, 47(6): 96-102. doi: 10.13272/j.issn.1671-251x.2021030102
<Abstract>(78) <HTML> (18) <PDF>(16)
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In order to solve the problems of single function, short wireless communication distance and high power consumption of current miner lamp, a multifunctional miner lamp based on LoRa wireless communication technology and the Internet of things operating system RT-Thread is designed. The lamp integrates the functions of mine lighting, environmental status sensing, personnel status monitoring and positioning, and wireless communication and safety alarm. The miner lamp uses STM32 microcontroller as the core, uses a variety of sensors to realize real-time perception of environmental temperature and humidity, gas concentration and personnel movement status information. The miner lamp uses long-distance low-power LoRa wireless communication technology, combined with LoRa intelligent gateway, to realize information interaction with the ground remote monitoring platform. The multifunctional miner lamp can also carry out local abnormal status early warning and remote reporting to the monitoring platform. At the same time, the monitoring platform can send safety early warning information and release the safety status of the working face to miners timely. The test results show that the miner lamp has high data acquisition accuracy, good wireless communication performance and low power consumption. Under the condition of multifunctional implementation, the miner lamp can measure environmental parameters effectively through a variety of sensors, and the measurement error is less than 1%. The maximum packet loss rate is less than 4% and the maximum time delay is less than 100 ms through the data interaction between the intelligent gateway and the monitoring platform. When the miner lamp is in standby mode, the lowest average current consumption is less than 10 μA, which can extend the service time of the miner lamp effectively.
Design of low-power distributed gas concentration monitoring system based on LoRa
PAN Xiaobo
2021, 47(6): 103-108. doi: 10.13272/j.issn.1671-251x.2021030052
<Abstract>(173) <HTML> (29) <PDF>(14)
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Catalytic combustion methane sensors are high power consumption. The wired gas concentration monitoring system has high installation cost, poor scalability and flexibility and heavy maintenance workload. In order to solve the above problems, a low-power distributed gas concentration monitoring system based on LoRa is designed. Moreover, the software and hardware design of gas concentration collection nodes and LoRa intelligent gateway are discussed in details. The gas concentration collection nodes use the STM32L151 ultra-low-power series processor. The system power supply is divided into three controllable parts through the power management module, and the power consumption is reduced through the power control strategy. ① The microcontroller core system adopts a low-power mode. ② MJC4/2.8J methane detection carrier catalytic element adopts dynamic energization to reduce the average current. ③ The modules in the collection nodes other than the microcontroller core system are powered on demand. The LoRa intelligent gateway uses the embedded real-time operating system μC/OS-II for task scheduling to optimize the performance of the gateway and improve CPU utilization. The test results show that the gas concentration collection nodes have good data transmission performance and the power consumption control strategy can reduce the average current of the collection nodes effectively, thus extending the battery life and reducing the system maintenance workload.
Q-learning algorithm based mine adaptive OFDM modulation
ZHU Jingru, ZHANG Yuzhi, WANG Anyi, LI Ping
2021, 47(6): 109-115. doi: 10.13272/j.issn.1671-251x.2021040053
<Abstract>(98) <HTML> (11) <PDF>(7)
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When the traditional adaptive OFDM (Orthogonal Frequency Division Multiplexing) modulation technology based on fixed signal-to-noise ratio threshold is applied to complex mine channels, the feedback channel state cannot completely match the actual channel state, resulting in high bit error rate and low throughput. In order to solve the above problem, a Q-learning algorithm based mine adaptive OFDM modulation method is proposed and applied to the mine adaptive OFDM modulation system. The system is composed of a transmitter, a mine wireless channel and a receiver. The transmitter is a sensor-equipped mine cart, which can move freely in a narrow roadway. The transmitter uses Q-learning algorithm to update the state-action value function continuously in the dynamic interaction with the mine wireless channel. And the transmitter uses a greedy strategy to select the modulation method according to the updated state-action value function to approximate the optimal adaptive modulation strategy so as to reduce the system BER and improve the communication throughput. The performance of two mine adaptive OFDM modulation systems based on SARSA algorithm and fixed signal-to-noise ratio threshold is compared. The result shows that the average BER of the adaptive OFDM modulation system based on Q-learning algorithm are 1.1×10-3，2.1×10-3, and the total throughput are 3,115 bit, 2,719 bit respectively in the uniform and non-uniform movement states of mine cart. These results are better than the adaptive OFDM modulation system based on SARSA algorithm and fixed signal-to-noise ratio threshold. And the convergence speed of Q-learning algorithm in the system is better than that of SARSA algorithm.
Research on large deformation mechanism and repair support technology of high stress soft rock roadway
YAN Xiaowei
2021, 47(6): 116-123. doi: 10.13272/j.issn.1671-251x.2021010007
<Abstract>(175) <HTML> (14) <PDF>(12)
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In the context of the destruction and continuous deformation of the surrounding rock of the high stress soft rock roadway, how to scientifically and reasonably repair and support the roadway that has undergone large deformation, and how to achieve effective control of the surrounding rock of the roadway. In order to solve the above problems, taking the No.4 crosscut transport roadway of Baijiao Coal Mine +300 level as an example, this paper comprehensively analyzes the deformation and destruction characteristics of the roadway surrounding rock based on the roadway surrounding rock observation boreholes, the surrounding rock mechanical condition and the support technology. It is pointed out that the prominent tectonic stress, weak lithology of the roadway surrounding rock, concentrated layout of the roadway, low strength of the roadway surrounding rock support, mismatch of support materials and substandard construction quality are the causes of the continuous deformation of the roadway. It is pointed out that for the broken surrounding rock of the roadway that has been separated and destructed, when only anchor bolts and cables are used to reinforce the support, the existence of discontinuous deformation in the surrounding rock will lead to insufficient overall stability of the surrounding rock structure of the roadway. Unable to effectively resist the continuous extrusion of stress, the roadway is prone to continuous deformation. Therefore, grouting reinforcement is required to fill the cracks caused by discontinuous deformation in the surrounding rock, and then anchor bolts and cables are used to further support the roadway. On this basis, a roadway repair support program of 'high-pressure grouting + high-strength and high-prestressed bolt and cable combined support + shotcreting' is proposed. Firstly, the surrounding rock cracks and discontinuous structural surface are reinforced in time by grouting reinforcement. Then the surrounding rock are supported by prestressed strong anchor bolts and cables support to form a bearing structure in the surrounding rock. The surface of the surrounding rock is sealed by surface shotcreting to stop the weathering of the surrounding rock and improve the stability of the surface surrounding rock. Numerical simulation and field test results show that after adopting 'high-pressure grouting + high-strength and high-prestressed bolt and cable combined support + shotcreting', the maximum displacement of the top and bottom plates is 109 mm, and the maximum displacement of the two sides is 212 mm. This method can effectively control the deformation of the roadway surrounding rock.
Research on automatic transport system in auxiliary shaft pithead
LI Hui
2021, 47(6): 124-127. doi: 10.13272/j.issn.1671-251x.17696
<Abstract>(73) <HTML> (13) <PDF>(9)
Abstract:
The transport environment of the auxiliary cage shaft pithead of the coal mine is complicated. At present, most of the mines are taking manual operation method. This method consumes a lot of manpower and material resources and has low transport efficiency. Moreover, it is difficult to ensure safety. Taking the auxiliary cage shaft pithead transport system of Huating Coal Mining Group Co., Ltd. as the research object, using the technologies of inertial measurement, PLC and electromagnet connection, this paper designs an automatic transport system in auxiliary shaft pithead. The composition of the system is introduced, and the solutions of the key technologies of the system are described, including electric locomotive positioning based on inertial measurement and RFID beacon, automatic coupling of electric locomotive and carriage based on electromagnet, automatic switching, and automatic rollover based on absolute encoder. The application results show that the system realizes the automatic control of the transport equipment of the auxiliary cage shaft pithead, improves the transport efficiency, reduces labor input, and realizes the transformation from human resource type to economic and technical type in old mines.
Development of mine-used infrared carbon monoxide sensor based on mini pump suctio
DONG Kangning, YANG Jinfang
2021, 47(6): 128-132. doi: 10.13272/j.issn.1671-251x.2021010082
<Abstract>(152) <HTML> (38) <PDF>(9)
Abstract:
The existing mine-used carbon monoxide sensor based on electrochemical principle is susceptible to the influence of alkane gases, hydrogen benzene and other gases and the environmental pressure of the mine. Therefore, the measurement results have large errors and need to be adjusted regularly. In order to solve this problem, a mine-used infrared carbon monoxide sensor based on mini pump suction is proposed. The sensor is based on the principle of non-dispersive infrared absorption. By using the fact that CO gas has strong absorption of 4.5 μm infrared radiation, the sensor detects CO gas concentration through measuring the initial energy of infrared radiation and the energy of infrared radiation after it is absorbed by the gas. The detection sampling speed of diffusion carbon monoxide sensor is slow, and the detection results are easily interfered by external factors such as wind speed and temperature in the detection environment. In order to solve the above problem, mini pump suction method is adopted. The gas flows into the infrared sensitive element gas chamber through the flow of the mini pump to ensure the stability of the sensor airflow. The 6-month industrial test results show that, compared with traditional electrochemical sensor, the sensor has the advantages of fixed infrared wavelength and the measurement data not being affected by other gases. During operation, the maintenance period of the mine-used infrared carbon monoxide sensor is longer than 6 months. The main maintenance operation is cleaning without the replacing sensitive elements and calibration.

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