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Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to meet the different requirements of coal mine monitoring, positioning, video, audio, remote control, 5G and other services for latency, reliability, bandwidth and other indicators, the coal mine information comprehensive bearer network should have the following functions. ① The network slicing function supports FlexE interface technology or channelized sub interface technology, and divides port bandwidth resources into different network slices. The services between different network slices are isolated and carried by each other without affecting each other. ② The online bandwidth expansion function of network slicing ensures that there is no packet loss during the bandwidth adjustment process. ③ Network slices set the power-off protection function. ④ The network complies with IEEE 802.3 and TCP/IP protocols, supports IPv6 protocol and IPv6 service bearer, and supports both IPv4 and IPv6 services simultaneously. ⑤ The network supports 10GE optical interface, 1GE optical interface, 10/100/1000 Mbit/s adaptive interface. The core and aggregation nodes should support optical interfaces of 50GE or above. ⑥ It is advisable to use a circular or double ring structure. ⑦ The real time monitoring function for business quality, monitors the delay, jitter, and packet loss rate of specified services in real time. ⑧ 1588v2 clock synchronization function supports 5G base station service access. The main technical indicators of the coal mine information comprehensive bearer network should meet the following requirements. ① The optical port transmission distance should be ≥ 20 km. The transmission distance of the electrical port should be ≥ 100 meters. ② The transmission rate of the backbone network should be ≥ 10 Gbit/s. The transmission rate of the access network should be ≥ 1 Gbit/s. ③ The packet loss rate for different frame lengths is ≤ 0.01% (under 70% network traffic load conditions). ④ The single node transmission delay should be ≤ 1 ms (when the Ethernet frame length is 1518 bytes). ⑤ Node forwarding jitter should be ≤100 μs. ⑥ The number of slices supported by a single interface should be ≥ 5. ⑦ The minimum bandwidth of FlexE interface technology should be ≤1 Gbit/s. The minimum bandwidth of channelized sub interface technology should be ≤ 2 Mbit/s. ⑧ The self-healing time of network reconstruction should be ≤ 50 ms. ⑨ After a power outage in the power grid, the continuous working time of the bearer network equipment under standby power supply should be ≥ 4 hours.

With the development of coal mine intelligence and the application of coal mine robots, an efficient collaboration between coal mine operators and coal mine robots plays a crucial role in the execution of complex underground tasks. To optimize the complex operational relationship of multiple coal mine operators and multiple robots, based on the concept of digital twin and extensive experience in the XR field, the research is conducted on the design and key technologies of XR intelligent operation and maintenance system for complex collaborative tasks involving multiple humans and multiple robots in coal mines. Firstly, for a typical scenario of collaboration between two types of coal mine operators (i.e central control operators and field control operators) and two types of coal mine robots (i.e. detection robots and operating robots) in complex tasks, the overall system architecture is designed. The system is divided into three parts: the physical subsystem, VR operation and maintenance subsystem, and AR operation and maintenance subsystem. The content, functions, and collaborative operation relationships among these three parts are introduced. Then, an analysis of key technologies related to the VR operation and maintenance subsystem, AR operation and maintenance subsystem, and communication networking is carried out. The solutions corresponding to each key technology are discussed, and the integration and operation of the two types of coal mine operators, two types of coal mine robots, and VR/AR operation and maintenance subsystem are implemented. Finally, a laboratory environment simulating complex underground conditions is set up to create a test site, where the task points and specific tasks are defined. The feasibility and effectiveness of the system are tested and verified. The experimental results show that the coal mine XR intelligent operation and maintenance system is able to function in collaborative situations between multiple humans and multiple robots corresponding to different complex tasks. Through the collaborative operation of the VR operation and maintenance subsystem and the AR operation and maintenance subsystem, the collaborative perception, decision-making, and control between virtual space and physical space can be achieved. This allows for the iterative optimization and verification of complex tasks in a physical space from a virtual space, forming an intelligent operational mode of human-human, human-robot, and robot-robot interactive collaboration.

The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient management, which restricts the development of coal mine intelligence. For the above-mentioned problems, a large language model for the maintenance of coal mine equipment based on multi-source text (XCoalChat) was proposed to better manage and utilize the existing massive knowledge of coal mine equipment maintenance. The dataset of coal mine equipment maintenance based on ReliableCEMK-Self-Instruction was constructed to obtain a wide and diverse amount of knowledge through sample generation. Aiming at the illusory problem of the large language model, a knowledge graph enhancement method based on the "Coal Mine Equipment Maintenance System—Full Life Cycle—Specification" was proposed to improve the knowledge density. A triple-LoRA fine-tuning mechanism and DPO direct preference optimization method were introduced into the top of the baseline model, which guarantees that XCoalChat can handle multiple Q&A and maintenance decision analysis tasks with limited computing power. Compared with ChatGLM, Bloom, and LLama, the comprehensive assessment of XCoalChat was performed by experiments including coal mine dialog consulting, coal mine professional consulting, and maintenance decision analysis. The results showed that XCoalChat achieved the best response accuracy in professional consulting and maintenance decision analysis; XCoalChat also took the least reasoning time on average. XCoalChat outperformed other mainstream large language models, which verify that XCoalChat is an effective large language model in the field of coal mine equipment maintenance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, the engineering thematic maps used in coal mines are mostly CAD drawings. Extracting data efficiently from CAD drawings and quickly organizing them into geographic information system (GIS) services, thereby supporting the creation of mine spatial objects and business attribute expansion, integrating real-time safety production data are the key to building a coal mine GIS one-map. The implementation process of converting CAD drawings into GIS services based on the ArcGIS platform is cumbersome. The ArcGIS platform has high costs and cannot run well across platforms. In order to solve the above problems, a coal mine GIS one-map rapid construction platform has been designed. The platform includes three major functional modules: CAD drawing management, map service publishing, and thematic map management. The CAD drawing management module is used for basic information management and status tracking of drawings. The map service publishing module is used for map packaging and online preview. The thematic map management module is used for map service management, mine object creation, and attribute extension. Based on the open design alliance (ODA), the Teigha for Java SDK is used to achieve precise recognition and fast and accurate extraction of all elements in CAD drawings. By constructing a hierarchical description model for coal mine CAD drawing data based on GIS data features, rapid storage of all element data in CAD drawings has been achieved. Following the object-oriented design approach, a web-based coal mine CAD drawing dataset storage interface and thematic map service publishing platform are developed under the Spring Cloud framework, achieving rapid construction of coal mine GIS one-map. Taking the mining engineering plan of a certain coal mine as an example, traditional methods and fast construction platforms are used to construct coal mine GIS one-map. The comparison results show that the fast construction platform can significantly improve the efficiency of constructing coal mine GIS one-map, providing a spatiotemporal digital base for the intelligent construction of coal mines.

Intelligence is the direction of coal mine development, and intelligent video analysis is an effective way to promote coal mine intelligence. The mine-wide intelligent video analysis technology has real-time monitoring, early warning, and decision support capabilities. It helps to improve the safety, production efficiency, resource utilization efficiency, and environmental sustainability of mining enterprises. The key technologies of mine-wide intelligent video analysis are introduced in detail, including video acquisition and processing technologies such as video acquisition equipment, video pre-processing, video compression and coding, basic video analysis technologies such as object detection and tracking, motion detection and analysis, object recognition and classification, and advanced video analysis technologies such as behaviour recognition and analysis, event detection and alarm, video monitoring and arming. A mining intelligent AI visual intelligence service platform that integrates video recognition analysis and industrial linkage control functions is developed. The paper introduces the application of intelligent video analysis technology in mining production scenarios such as intelligent water and gas exploration and discharge systems, coal rock recognition and cutting systems, heading working faces, fully mechanized working faces, coal flow transportation systems, mine hoist systems, auxiliary transportation systems, coal preparation plants, and intelligent loading and coal blending systems. The analysis points out that the current mine-wide intelligent video analysis technology still faces challenges in terms of video quality, complex backgrounds, real-time requirements, data privacy and security, system reliability and stability, etc. It is suggested to strengthen the research on algorithm improvement and optimization, multimodal data fusion, real-time analysis and edge computing, enhanced learning and independent decision-making, data privacy and security protection, hardware equipment and sensor technology in the future. Therefore, the development of mine-wide intelligent video analysis technology is comprehensively promoted and promote the process of mine intelligence is promoted.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, the intelligent construction of coal mines is in a transitional exploration period from primary to intermediate and advanced intelligence. The independent innovation capability of existing intelligent equipment still needs to be strengthened. The core technologies are bottleneck problems. The reliability problem under complex underground storage conditions is prominent. It is of great significance to use the new generation of information technology to promote the digital transformation and upgrading of coal mine equipment, and to promote safe, efficient, and green coal mining through digitization and intelligence. This paper reviews the evolution process of intelligent development of coal mining technology and equipment in China. This paper analyzes the production characteristics of each stage of coal mining. It is considered that the coal mine intelligence has completely changed the coal production mode. The production factors are mainly intelligent equipment, intelligent systems, and compound technical talents. They have essentially improved the level of safe and efficient production of coal mines, and helped the coal mines to reduce personnel, increase safety and improve efficiency. Among them, intelligent coal mine equipment is the core element of coal mine intelligence. Considering the demand for the digital and intelligent transformation and development of future coal mine equipment, we have explored the path of integrating the new generation of information technology and coal mine equipment. It uses equipment as an intelligent carrier. It endows the equipment with humanized intelligent capabilities such as intelligent perception and analysis, intelligent judgment and reasoning, intelligent decision-making and control, intelligent diagnosis and learning through the new generation of information technology. The system architecture of the Internet of things, 5G, big data, cloud computing, and artificial intelligence technology enabling intelligent perception, intelligent transmission, intelligent analysis, intelligent computing and intelligent decision-making of coal mine equipment has been constructed. The paper analyzes the theoretical, technical, and talent challenges in the development of digital intelligence in coal mining equipment. The paper proposes suggestions for the development of digital intelligence in coal mining equipment. Consolidating the foundation is the priority. It is necessary to build a top-level design theoretical system for intelligent coal mining equipment, strengthen key core technology research, accelerate the construction of new infrastructure, and consolidate the foundation of intelligent coal mining development. The intelligent technology for coal mining equipment and special application scenarios in coal mines develop in collaboration and coupling. It is suggested to meet the on-site needs of coal mines, form the intelligent technology for coal mine specific scenarios, and explore innovative models of equipment digital intelligence integration. The intelligent technology and green mining technology integrate and develop. It is suggested to create digital, intelligent, and green equipment. It is suggested to develop new models of intelligent, efficient, and low-carbon coal mining, and promote energy conservation and consumption reduction in coal production. It is suggested to establish a sound system for cultivating intelligent talents in coal mines, cultivate interdisciplinary and composite talents, and enhance talent reserves for the construction of intelligent coal mines.

Machine vision technology has positively improved coal mine safety monitoring methods and enhanced equipment automation levels. This article elaborates in detail on the principles of equipment information state perception based on machine vision in different scenarios and systems during the current intelligent construction process of coal mines. It summarizes the practical applications of machine vision perception technology in coal mine safety monitoring, picking recognition, coal rock recognition, positioning navigation, transportation detection, pose detection, and information measurement. The analysis points out that in the future, coal mine machine vision perception technology should deeply explore the understanding needs of mining face machine vision scenes. It is suggested to build a production full field of view monitoring and detection system, and improve the integrated monitoring effect of multiple spatiotemporal, multi-dimensional, and multivariate. It is suggested to improve the video autonomous monitoring and alarm capability, enhance visual guidance capability, and form a unified visual data management method for ground production management and operation systems. The key research should focus on technologies such as simultaneous spatiotemporal measurement of the pose of fully mechanized mining equipment (groups), perception of dynamic changes in the mining environment, full field of view monitoring and autonomous warning for production, and visual guidance and control of coal mining robots. It is pointed out that the coal mine machine vision perception technology still has challenges in explosion-proof or intrinsically safe intelligent vision sensors, efficient methods of visual measurement and analysis, the measurement precision of detection and recognition, and high-quality image annotation. Through the development of visual sensors with edge computing capabilities, a distributed vision measurement scheme is constructed to achieve accurate recognition and measurement of mining information in various complex environments. It can effectively improve the deeper integration and application of machine vision perception technology in the coal industry.

The application of technologies such as 5G, UWB, ZigBee and WiFi6 in coal mine mobile communication, personnel and vehicle positioning, and wireless transmission has promoted coal mine safety production and intelligent construction. However, due to the limitations of electrical explosion-proof measures, the wireless transmission power underground in coal mines is not greater than 6 W, which restricts the wireless transmission distance in the mine, and increases the usage of base stations and system costs. It is not convenient for system use and maintenance. Under the condition that the wireless transmission power is limited by electrical explosion-proof measures, selecting a wireless operating frequency band with smaller transmission attenuation can effectively increase the wireless transmission distance, reduce the usage of base stations, and reduce system costs. In order to meet the needs of selecting and optimizing the working frequency band of wireless transmission in mines, wireless transmission tests in the 700 MHz to 6 GHz frequency band are conducted in the auxiliary transportation roadway and fully mechanized working face of the Sandaogou Coal Mine of the National Energy Group. The test results are analyzed and the optimal frequency band for wireless transmission in mines is proposed. ① The optimal operating frequency band for wireless transmission in auxiliary transportation roadways is 700 to 910 MHz. ② The optimal working frequency band for wireless transmission in fully mechanized working faces is 700 to 1 710 MHz. ③ The wireless transmission attenuation of the auxiliary transportation roadway is smaller than that of the fully mechanized working face. As the frequency increases, the difference in wireless transmission attenuation between the auxiliary transportation roadway and the fully mechanized working face decreases. ④ The optimal working frequency band for wireless transmission in mines is 700 to 1 710 MHz.

The intelligence of fully mechanized working face is a key link in the intelligence of coal mines. The perception and analysis of hydraulic support state is the premise and foundation of intelligent control of fully mechanized working face. The pressure and posture of hydraulic supports are key state data that can be directly monitored. The fusion analysis of the two is the basis for intelligent control and execution of hydraulic supports. Taking the pressure and posture of hydraulic supports as the research object, an state perception architecture for intelligent fully mechanized working face is introduced. The current status of hydraulic support pressure perception and analysis technology is summarized from two perspectives: hydraulic support stress and overlying rock pressure. It is pointed out that the current big data based mine pressure analysis has not been applied, the mine pressure and roadway pressure data at the working face have not been synchronously measured, the full face data analysis of the entire mining area has not been achieved, and there is insufficient attention paid to advanced hydraulic support. The paper elaborates on the principles and methods of hydraulic support posture perception, and summarizes the existing hydraulic support posture analysis methods. The necessity of pressure-posture fusion analysis for hydraulic support is analyzed. The existing methods for pressure-posture fusion analysis of hydraulic supports are summarized. The paper explores the development trend of hydraulic support state perception and analysis technology in fully mechanized working face. It is suggested to do in-depth research on big data technology, multidimensional data fusion perception and analysis technology, and digital twin technology in the application of hydraulic support state perception and analysis, so as to achieve the precise analysis of hydraulic support state data, intelligent control and decision-making.

This paper analyzes the outstanding technical weaknesses in the intelligent construction of coal mines in China. This paper puts forward 12 innovative research and development directions, including intelligent system design, information infrastructure, intelligent geological support, intelligent mining and transportation, intelligent safety prediction and early warning, intelligent power supply and intelligent water affairs, intelligent washing, intelligent parks, intelligent open-pit coal mines, intelligent well construction, intelligent equipment and robots, and emergency rescue and supervision. 46 coal mine intelligent innovation teams have been established, forming the strongest team of coal mine intelligent innovation. The target collaboration, organization collaboration, knowledge collaboration and business collaboration models of the innovation chain and industry chain are established. Relying on 136 provincial experimental platforms, this study takes the lead in obtaining a number of innovative achievements in the important technical direction of the intelligent coal mine. This paper provides technical support for the construction of intelligent coal mine in our country. This study puts forward 9 key breakthrough directions to continuously promote the intelligent construction of coal mines and improve the technical equipment support capacity. It is suggested to continuously build the intelligent support technology system of coal mine, accelerate the construction of the intelligent technology standard system of coal mine and the construction benefit evaluation system, and accelerate the research and application of the intelligent 'MineCloud' of coal mine. It is suggested to develop complete sets of intelligent mining process equipment for large mining height caving coal, strengthen the research and development of intelligent fast heading technology and equipment, and develop highly reliable and stable intelligent mine sensors. It is suggested to accelerate the research and development of key technologies of coal mine robot cluster, accelerate the construction of intelligent washing complete equipment and process package development, and strengthen the advanced and mature intelligent process and the stability and high reliability of core equipment in open-pit coal mines.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The efficient management and utilization of coal mine equipment maintenance knowledge is an indispensable foundation for advancing the establishment of intelligent mines. This knowledge has problems such as scattered, low sharing, and insufficient management, which restricts the development of coal mine intelligence. For the above-mentioned problems, a large language model for the maintenance of coal mine equipment based on multi-source text (XCoalChat) was proposed to better manage and utilize the existing massive knowledge of coal mine equipment maintenance. The dataset of coal mine equipment maintenance based on ReliableCEMK-Self-Instruction was constructed to obtain a wide and diverse amount of knowledge through sample generation. Aiming at the illusory problem of the large language model, a knowledge graph enhancement method based on the “Coal Mine Equipment Maintenance System—Full Life Cycle—Specification” was proposed to improve the knowledge density. A triple-LoRA fine-tuning mechanism and DPO direct preference optimization method were introduced into the top of the baseline model, which guarantees that XCoalChat can handle multiple Q&A and maintenance decision analysis tasks with limited computing power. Compared with ChatGLM, Bloom, and LLama, the comprehensive assessment of XCoalChat was performed by experiments including coal mine dialog consulting, coal mine professional consulting, and maintenance decision analysis. The results showed that XCoalChat achieved the best response accuracy in professional consulting and maintenance decision analysis; XCoalChat also took the least reasoning time on average. XCoalChat outperformed other mainstream large language models, which verify that XCoalChat is an effective large language model in the field of coal mine equipment maintenance.

Coal spontaneous combustion disaster is one of the main disasters in the process of coal mining. The acquisition of multi-source three-dimensional parameters of spontaneous coal combustion, the in-depth analysis of data, the establishment of mathematical models and accurate prediction are the basic guarantee for the production of coal mining enterprises. This paper summarizes the basic status of research and application of coal spontaneous combustion monitoring and early warning from three aspects: monitoring methods, mathematical analysis and prediction methods of coal spontaneous combustion multi-source three-dimensional parameters, and further prospects the future development direction of coal spontaneous combustion intelligent monitoring and accurate prediction.

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.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The intelligent maintenance of coal mine electromechanical equipment is an important part of the construction of intelligent mines. The research status of intelligent fault diagnosis and predictive maintenance of coal mine electromechanical equipment is summarized from four aspects, coal mine electromechanical equipment fault mechanism, equipment condition monitoring, signal analysis and processing, and fault diagnosis and prediction algorithm. ① Equipment fault mechanism research is mainly for different equipment, which uses different methods to establish equipment fault analysis models and applies incentives to the models to obtain dynamic response to equipment fault so as to provide a basis for subsequent fault diagnosis. ② Equipment condition monitoring research has established a relatively complete condition monitoring system for coal mine electromechanical equipment, which can obtain equipment parameters accurately and timely and provide data support for equipment fault diagnosis. ③ Signal analysis and processing research not only uses traditional time domain, frequency domain and time-frequency domain analysis methods, but also combines multiple methods for signal processing and characteristic extraction, which improves the efficiency of signal processing and the reliability of processing results. ④ Fault diagnosis and prediction algorithms mainly use artificial neural networks, including machine learning, deep learning and other intelligent algorithms to establish equipment fault diagnosis and prediction models so as to achieve intelligent diagnosis and prediction of faults. It is pointed out the problems of intelligent maintenance research of coal mine electromechanical equipment are as follows. ① The study of equipment fault mechanism lacks the study of fault mechanism under multiple fault compound state. And more research is needed on the chain reaction caused by the fault of a certain part of the equipment. ② The fault data obtained in the simulated environment cannot fully reflect the actual operating conditions of the equipment, and it is necessary to continuously collect on-site monitoring data for a long time, preferably the whole life cycle data of the equipment. ③ At present, there is few research using the combined algorithm, and the research object is more limited to a certain part or component of the equipment. Finally, the development trend of intelligent maintenance of coal mine electromechanical equipment is proposed as follows. ① It is suggested to study the application of intelligent sensors with higher sensitivity to monitor equipment, combine the methods such as random resonance and blind source separation to extract weak characteristic signals from strong noise to identify early equipment faults in a timely manner, thus realizing predictive maintenance. ② The use of independent diagnosis method can no longer meet the diagnosis needs of actual equipment. The diagnosis and prediction based on multiple sensor information fusion technology can identify all faults in the equipment accurately and effectively. ③ It is proposed to use the migration learning algorithm as a 'bridge' to establish the correlation between simulation, test data and field data, and provide data support and guarantee for solving the problem of the difference between simulation and test conditions and field conditions.

The basic architecture of 5G wireless communication system in coal mine is proposed: 5G core network, base band unit (BBU), remote radio unit hub (RHUB) and 5G base station are connected by 5G bearer network to realize signaling transmission among network elements. Underground 5G base stations and antenna are used to realize underground wireless signal coverage. After the base stations are converged through a RHUB, they are connected to BBU and connected to the 5G core network. The advantages and disadvantages of two kinds of 5G core network construction schemes are compared, namely user plane function/mobile edge computing (UPF/MEC) sinking and independent private network. This paper studies the bearer networks between 5G core network and BBU, between BBU and RHUB, between RHUB and 5G base station. This study focuses on the characteristics and application scenes of the three bearing methods commonly used in the bearer network between the 5G core network and the BBU, namely the optical fiber direct connection, the slicing packet network (SPN) and the 5th generation fixed network (F5G). The design schemes of coal mine 5G base stations, 5G terminals and 5G communication modules are proposed. Combining the characteristics of 5G communication technology and the requirements of coal mine intelligence development, the typical applications of 5G wireless communication systems in coal mines are discussed, such as integrated communication, high-bandwidth service applications, intelligent working face service application, remote control of mining vehicles or unmanned driving, etc. It is pointed out the further optimization directions of the coal mine 5G wireless communication system, including the design of miniaturization industry 5G core network, the low-power and intrinsically safe design of coal mine 5G base stations and other equipment, the underground application of 5G equipment in the 700 MHz frequency band to improve the coverage of 5G base stations, the customization of coal mine 5G mobile terminals and the development of industry APPs, the development of low-power 5G communication modules, industry application scenes in combination with on-site needs, etc.

The existing research results of intelligent mining in coal mines have not been combined with the production practice to specify the application of key technologies of intelligent mining in coal mines. Taking the Madiliang Coal Mine as the engineering background, the key technologies and application effects of the construction achievements of intelligent mining in the coal mine named ten intelligent systems are described. The systems include intelligent dispatching remote centralized control system, equipment intelligent early warning and remote consultation system, intelligent coal mining system, intelligent heading system, intelligent belt transportation system, unattended integrated coal transportation and sale control system, intelligent traffic safety control system, Internet + remote office system, Internet of things + intelligent storage express service system and intelligent ventilation system. The key problems in intelligent mining in coal mines at this stage are pointed out in this paper, which include the urgent need to change the ideology and concept, large initial investment, unbalanced input-output ratio, poor adaptability of mining mode, unsound personnel training system and insufficient key technology innovation. In order to solve the above problems, it is proposed that the mutual collaboration between intelligent subsystems should be further enhanced, the research of intelligent robots should be promoted, the independent perception, analysis and decision-making capabilities of equipment should be improved, and the top-level architecture of intelligent coal mines and big data application centers should be built so as to realize intelligent coal mining, transportation and sales, improve mine production efficiency, ensure the safety of personnel, and achieve the goal of unmanned (fewer people) underground mining.

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Based on cloud computing and big data technology, a design concept of operation and maintenance service platform for Guizhou coal mines intelligent system is proposed. The B/S structure is adopted to design the overall platform architecture including the acquisition layer, the support layer, the application layer, the user layer and the standard specification system and the safety operation and maintenance guarantee system. The platform subsystem, including monitoring and fault analysis, coal mine enterprise service, operation and maintenance dispatching order, operation and maintenance personnel material management, operation and maintenance organization service, information release, training service, operation and maintenance scheduling and command, operation and maintenance service supervision, operation and maintenance background management and handheld terminal APP, is built; the key technology and business process of platform construction are described in detail.

In order to reduce the number of operators in the working face of coal mines, an unmanned 5G ground remote control method for working face is proposed. The industrial cameras and sensors are set up in the working face, which transmit video, audio and sensor signals to the ground through the 5G network. The ground operator operates the equipment remotely, which transmits control commands to the working face through the 5G network, and controls the actions of the mine equipment. The wireless transmission distance and transmission bandwidth calculation method are proposed for unmanned ground remote control of the fully mechanized working face. The wireless transmission distance between the base stations at the two ends of the fully mechanized working face should not be less than 1/2 of the length of the fully mechanized working face. The total uplink transmission bandwidth required for unmanned ground remote control of the fully mechanized working face is proportional to the length of the fully mechanized working face, inversely proportional to the center distance of the hydraulic support, inversely proportional to the number of supports and cameras, and proportional to the transmission bandwidth required for the compressed video of a single camera. In order to reduce the demand for uplink transmission bandwidth for unmanned ground remote control of fully mechanized working face, it is possible to only transmit the camera videos of the adjacent shearers. The minimum bandwidth of uplink transmission required for unmanned ground remote control the fully mechanized working face is proportional to the number of cameras of the adjacent shearers, and proportional to the required transmission bandwidth of a single camera after video compression. The wireless transmission distance test method for the unmanned ground remote control system of the fully mechanized working face is proposed. The method is applied for the wireless transmission system such as 5G for the unmanned ground remote control system of the fully mechanized working face. Under the premise of ensuring transmission bandwidth, time delay and reliability, the wireless transmission distance is tested. The test shows that the uplink wireless transmission bandwidth of the base station shall not be less than 20 Mbit/s, and the wireless transmission distance shall not be less than 150 m. This study proposes the all-mine integrated information transmission network based on network hard slicing. Through network hard slicing, different channels are allocated for coal mine safety monitoring, mine monitoring, personnel and vehicle and equipment positioning, video monitoring, voice communication, 5G communication, etc. This method guarantees the high reliability and low time delay requirements of coal mine safety monitoring and mine monitoring, and unifies the underground information transmission network of coal mines. Moreover, it integrates multiple networks such as coal mine safety monitoring network, mine industrial Ethernet and mine 5G communication network.

Coal spontaneous combustion (CSC) is one of the main disasters in the process of coal mining. The rapid and precise monitoring of the characteristic parameters of CSC and the timely warning of the danger degree are important guarantees for achieving safe and efficient production in coal mines. The principle and research and application status of CSC monitoring and early warning technology are summarized from two aspects: CSC precursor information monitoring technology and prediction methods. And the main problems of the existing CSC monitoring and early warning technology are analyzed as follows. ① The integrated monitoring of CSC precursor information is greatly affected by underground environment. ② The construction of early warning index system and model is based on experiments, and is a bit tricky to correlate the actual scene. ③ There are few effective samples of CSC, and the prediction timeliness lacks advancement. Based on the development trend of coal mine intelligence, the research prospects of intelligent monitoring and early warning technology for CSC are proposed. ① It is suggested to establish a joint prediction model of CSC early warning and real-time forecast. ② It is proposed to develop a multi-source information fusion analysis method based on mechanism modeling and machine learning. ③ It is recommended to build a one-stop, visual and intelligent CSC monitoring and early warning platform for mines. It is expected to be beneficial to improve the ability of monitoring and early warning for CSC and enhance the intelligent development level of coal mines.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current research on intelligent coal mines focuses on top-level design, theoretical architecture, core technologies, control platforms, construction routes, standard development and evaluation systems, etc. There is no research on the technical research before and after the release of The guiding opinions on accelerating the development of intelligent coal mines. Intelligent coal mines are technology-intensive and capital-intensive, and need to be guided by policies to form a joint force which brings the government, academia, industry, finance and users together. Using CNKI (China National Knowledge Infrastructure) as a tool, this paper analyzes the current status of intelligent coal technology research, hot spots of concern and major research institutions. This paper summarizes the current status of intelligent coal mine policy development at the national level and in major coal-producing provinces such as Guizhou, Shandong and Shanxi. Although there are slight differences in the starting point and implementation progress of intelligent coal mines, all provinces have issued guiding documents in terms of implementation methods and acceptance and grading, and have made attempts to develop standards and establish innovative R&D centers to accelerate the pace of intelligent coal mine research and construction.

At present, there are more researches on the concept and development plan of intelligent mines, and less researches on the intelligent construction results and key technologies. Based on the 5-layer technical structure of intelligent mine construction, taking the intelligent construction of Daliuta Coal Mine as an example, the paper discusses the intelligent construction and effectiveness in terms of fully mechanized mining, excavation, transportation, power supply, water supply and drainage systems. A total of about 100 fixed mine positions are canceled, and more than 200 operators are reduced. The construction helps to reduce the labor costs by 40 million yuan/a, save the industrial water and electricity consumption by 30%, improve the equipment utilization by 5%, and increase the total workforce efficiency by 10%. It is pointed out that the key points of intelligent mine construction are broadening the network channel, unifying the interfaces of various subsystems, ensuring the associated equipment safe and reliable, and hiring professional personnel teams. This paper summarizes the current difficulties in the intelligent mine construction and discusses the key technologies to be tackled. The technologies include automatic coal cutting, personnel detection of mobile equipment, wireless sensing, centralized refueling, intelligent security inspection at the mine entrance and unmanned driving.

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At present, research and application of automation of fully-mechanized roadheader and intelligence of fully-mechanized process in China have achieved initial results, but there are still some problems in intelligent perception and remote control technologies. In terms of actual conditions of fully-mechanized heading face in Tangkou Coal Mine, automatic location, cutting, remote control technologies of fully-mechanized roadheader are used to develop an intelligent remote control system for fully-mechanized roadheader. Full-automatic explosive proof type total station is adopted to locate the roadheader combining with many sensors. Operation system, electrical control system and hydraulic pressure system of the roadheader are upgraded to realize location data analysis, automatic planning of cutting actions and other functions. Remote control of automatic location and one-key cutting of the roadheader are realized by use of long distance transmission protocol. The application show that after implementation of the intelligent remote control system for fully-mechanized roadheader, unmanned operation in range of 30-50 m away from fully-mechanized heading face in Tangkou Coal Mine is realized, which improvs safety production level, and only one operator is needed in a team to achieve goal of downsizing staffs and improving efficiency.

Aiming at characteristics of electrical explosion-proof and large wireless transmission attenuation in underground coal mine, 5G technology and its application scope were analyzed.Mine-used 5G should adopt intrinsically safe explosion-proof. Mine-used 5G for control should have strong anti-interference ability. Mine-used 5G should be used for remote control of coal mining face and tunneling face. Mine-used 5G should be used for ground remote control of unmanned vehicle in underground coal mine. Mine-used 5G that has not been developed for characteristics of mine mobile communication is less cost-effective than mine-used WiFi mobile communication system. It is strictly forbidden to replace mine-used wired dispatching communication system with mine-used 5G mobile communication system. Mine-used 5G that is not developed for coal mine safety monitoring characteristics cannot replace coal mine safety monitoring system. Positioning accuracy of mine-used 5G that has not been developed for precise positioning characteristics of mine moving target is lower than that of mine-used UWB precision positioning system. Transmission rate of mine-used 5G in frequency range of 450-6 000 MHz is lower than that of mine-used WiFi6. Reliability of mine-used 5G that is not developed for monitoring characteristics of fixed equipment in underground coal mine is lower than that of mine-used wired monitoring system. It is urgent to research and develop mine-used 5G based on characteristics of safe production in coal mine, not just for explosion-proof transformation of existing ground 5G products.

Mine wireless communication and mine-used 5G mobile communication technology is one of the key technologies of coal mine intelligence.In order to improve wireless transmission distance and diffraction ability as well as stability and reliability of wireless communication system in underground coal mine, reduce base station consumption, networking cost and maintenance workload, the effect of mine-used 5G working frequency band and antenna position of base stations on wireless transmission loss and transmission distance is studied.The major conclusions are as follows: ① Wireless transmitting power in underground coal mine is limited by intrinsically safe explosion-proof, receiving sensitivity is limited by electromagnetic noise, and antenna gain is limited by intrinsically safe explosion-proof and tunnel space.Therefore, under the conditions of limited wireless transmitting power, receiving sensitivity and antenna gain in coal mine, it is necessary to increase wireless transmission distance and diffraction ability, improve stability and reliability of wireless communication system, and reduce base station consumption, networking cost and maintenance workload by optimizing wireless working frequency band and antenna location setting.② 700 MHz is recommended as mine-used 5G working frequency band. Compared with other 5G frequency bands such as 2.6 GHz, 3.5 GHz and 4.9 GHz, 700 MHz frequency band in underground coal mine has the advantages of low wireless transmission loss, long wireless transmission distance, strong diffraction ability, less base station consumption, low networking cost and maintenance workload and so on.③ Analysis method of transmission loss/position change rate is put forward, which is convenient to analyze the change of transmission loss caused by position change in different transversal areas of tunnel.④ The antennas of wireless base stations should be set close to tunnel side, not less than 0.01 m away from tunnel side, and the height of antennas should be about 2/5 of tunnel height.This is convenient for installation and maintenance, not only does not affect pedestrians and driving but also can satisfy the requirements of low transmission loss and long transmission distance. ⑤ In order to improve wireless transmission distance, wireless terminals used in underground coal mine, such as mobile phones, personnel positioning cards, portable wireless methane detection alarms, multi-functional wireless lamps, portable wireless cameras, portable wireless instruments and equipments, wearable wireless devices, vehicle positioning cards, vehicle wireless devices, wireless cameras, wireless sensors, Internet of things devices and so on, should be closer to the tunnel center as much as possible under the condition of not affecting normal use.

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Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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