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Collect screen paperboard of mine power transformer and ordinary power transformer in practical lifetime cycle to make insulation test samples, and according to the standard of power, use test means such as viscosity tester, scanning electron microscopy to compare and analyze microstructures of insulation paper such as polymerization degree, surface morphology of mine power transformer and ordinary power transformer, as well as electrical properties such as partial discharge and electrical strength; at the same time, effect of microstructure changes on electrical performances during aging was discussed. The analysis results show that the insulation defects gradually form and expand in the insulation paper during the aging process; compared with the insulation paper of ordinary power transformers, the surface morphology changes of the insulation paper of mine power transformers are more obvious, and the formed defects are more significant.In the aging process, the polymerization degree of insulation paper of ordinary power transformer and mine power transformer presents a gradual decline trend, but the decline speed of polymerization degree of mine power transformer insulation paper is faster.The operating environment of mining area is the main reason that the aging of insulation paper of mine power transformer is more serious than that of ordinary power transformer.In the process of operation, the insulation paper of mine power transformer will produce more discharge quantity, more discharge times and wider discharge phase than ordinary power transformer.In the aging process, the electrical strength of insulation paper of both ordinary power transformer and mine power transformer decreases gradually, but the electrical strength of insulation paper of mine power transformer is lower than that of ordinary power transformer.The microstructural defects of insulation paper of mine power transformers are the direct causes of the more significant changes in their electrical properties.In the process of state monitoring and maintenance of power equipment, it is suggested that the insulation state monitoring of mine power transformers should be carried out strictly according to the power standards,electrical property parameters are more suitable for insulation status monitoring and fault diagnosis of mine power transformer.

Application principle of polarization spectrum method based on recovery voltage method was introduced which was used in state detection of transformer oil-paper insulation, and a viewpoint was pointed out that main time constant of polarization spectrum curve was an important factor for judging state of transformer oil-paper insulation. Influence of charging voltage, residual charge and temperature on measurement result of polarization spectrum curve was analyzed separately with conclusions that charging voltage only influenced the maximal value of recovery voltage but did not influence main time constant of polarization spectrum curve, and residual charge and temperature would influence the main time constant. Matters needed attention were proposed that transformer should be fully discharged firstly, charging voltage should be as high as possible in range of 0-2 000 V and environmental temperature should be stable during actual application of polarization spectrum method based on recovery voltage method in state detection of transformer oil-paper insulation.

The paper introduced structure and key technologies of mine-used intrinsically safe wireless through-the-earth communication system，and analyzed working principle of the system. The system uses low-frequency electromagnetic radiation technology, digital communication technology and intrinsically safe power supply technology, which has characteristics of smaller volume, lighter weight and easy operation. The test results of prototype show that the system has realized two-way through-the-earth communication for 50 m with communication rate of about 150 bit/s and bit error rate of no larger than 0.2 %, and has a certain practical value

An online intelligent examination system of coal mining enterprises was proposed based on analysis of actual examination requirements of enterprise employees. A Web examination system is built firstly according to professional characteristics of the employees, and the system is optimized functionality. Secondly, GAAA algorithm integrated with genetic algorithm and ant algorithm is improved. The improved GAAA algorithm firstly updates ant colony pheromone, then searches test database by a comprehensive search of genetic algorithm and parallel distribution of ant algorithm for optimizing group volume of the database. At last, the improved GAAA algorithm is applied in the system to achieve examination of employees at anytime they like and anywhere they are and auto-generating examination paper. The practical application shows that speed of auto-generating examination paper of the system is high, repetition rate is low, the algorithm is feasible, and the system can meet practical requirements of online examination effectively and improve online examination efficiency of coal mine enterprise.

This paper introduces the current situation of intelligent development of shearer at home and abroad. The shearer is currently developing towards intelligent robotics and is in the primary stage of intelligence. This paper elaborates on the development process and trends of intelligent shearer. There are six scientific research projects that have played important roles in the intelligent development of shearer, including the UK remotely operated longwall face (ROLF) program, mine operating system (MINOS), NASA long wall automation cooperation project, Australian Australian coal industry’s research program (ACARP), Australian commonwealth scientific and industrial research organization (CSIRO) project, and EU new mechanisation and automation of longwall and drivage equipment (NEMAEQ). The development trend of shearer is to use continuously developing monitoring, sensing, and remote control technologies to replace manual intervention and operation, continuously improve the production efficiency and reliability of working face equipment, and develop towards informationization, integration, unmanned, and intelligent direction. This paper introduces the key technologies of intelligent perception, intelligent control, and intelligent communication of shearer. It points out that intelligent cutting technology based on coal rock recognition, intelligent drum speed regulation technology, precise positioning technology of shearer, and planning and mining technology based on three-dimensional geological models are key technologies that urgently need to be broken through. The human-machine relationship that cannot be ignored in the intelligent development process of shearer is discussed from two aspects: dust reduction and noise reduction.

Ultra-wide band (UWB) radar can penetrate non-magnetic media such as coal and rock to detect life information of personnel after collapse. Due to the complex mining environment, UWB radar loaded with vital sign signals is prone to interference from environmental noise and clutter signals. It is difficult to recognize human subject information. This paper introduces the principle of UWB radar life detection system and its application in mine rescue. This paper summarizes the current research status of UWB radar life information recognition from three aspects: UWB radar life information extraction, dynamic and static human object recognition, and life quantification. This paper points out the current issues with the application of UWB radar life detection technology in the field of mine rescue. ① There is limited research on filtering methods for non-stationary signals and environmental noise in underground collapse environments. ② The extraction and representation methods for posture, behavior, life status, and other information of moving (or micro moving) objects need to be improved. The human life information recognition model is not yet perfect and the feature correlation between models is low. ③ There is a lack of solutions to the 'overlapping' problem caused by multiple objects. This paper proposes the prospects for the research direction of UWB radar life information recognition for mine rescue. ① It is suggested to continuously optimize noise and clutter adaptive filtering methods for multiple types of mine disaster environments. ② It is suggested to construct a human life information recognition model suitable for the field of mine rescue. ③ It is suggested to further improve the quantification capability of multi-object after mine shelter. ④ It is suggested to conduct depth exploration of the method for determining the optimal detection frequency band for UWB radar.

In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of 'sky', 'air', and 'ground' , as well as integrated collaborative monitoring method of 'sky-air-ground'. The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the 'sky-air-ground' is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.

Currently, the construction of intelligent mines is facing problems such as incomplete data standards, difficulty in integrating heterogeneous data from multiple sources, and the need to improve sharing mechanisms. Establishing a unified intelligent mine metadata standard is of great significance for forming a multi-source heterogeneous data fusion and sharing mechanism. Based on the intelligent mine data classification and coding standards, the construction method of intelligent mine metadata standards is studied. It is clarified that metadata standard construction is an extended research and value mining of data classification and coding. This paper defines the connotations of metadata entities and metadata attributes. This paper designs an intelligent mine metadata description framework that includes a basic description framework and an extended description framework. This paper provides basic metadata attribute description items that reflect the features of intelligent mine business scenarios, as well as extended metadata attribute description items for the four major thematic domain groups of basic, production, safety, and management in the intelligent mine data classification and coding standards, thus forming a metadata standard. Taking the inertial sensor of the shearer inertial navigation system in the production theme domain group as an example, the construction process of metadata standards is introduced. Building metadata standards based on intelligent mine data classification and coding standards can solve the problem of fusion and sharing of multi-source heterogeneous data, promote mine enterprises to manage, analyze, and apply data more efficiently, and improve the intelligence level of mine data governance.

The goaf roadways have problems of high surrounding rock stress and large deformation under the conditions of hard roof and wide coal pillars in thick coal seams. In order to solve the above problems, this paper takes the 16403 fully mechanized mining face of Laoshidan Coal Mine as the engineering research background. The paper theoretically analyzes the influencing factors of large deformation of goaf roadways from the perspective of lateral fracture structure of wide coal pillar roof. The numerical simulation method is used to study the implementation of different roof cutting and pressure relief schemes for 16402 transportation roadway. The stress transmission law of the lateral roof of the 16403 return airway near the goaf during mining is studied. The hydraulic fracturing drilling is carried out on site for roof cutting and pressure relief to achieve deformation control of the surrounding rock of the goaf roadway. The research results indicate that the fracture structure of 'cantilever beam in low hard rock layer+masonry beam in high hard rock layer' is the main reason for the large deformation of the wide coal pillar in the goaf roadway of the extra thick coal seam. The roof cutting and pressure relief technology can be used to destroy the lateral fracture structure of the wide coal pillar roof to control the large deformation of the surrounding rock in the goaf roadway. The change in roof cutting angle can cause a change in the length of key block B. The larger the roof cutting angle, the smaller the length of key block B. The degree of load transfer from the lateral roof of the goaf side to the coal pillar is weaker, and the mining stress borne by the surrounding rock of the goaf roadway is smaller. When the roof cutting angle is 100°, the vertical stress and deformation of the surrounding rock of the goaf roadway are the smallest. After hydraulic fracturing drilling is carried out at a roof cutting angle of 100° in the 16402 transportation roadway, the deformation of the roof and floor of the 16403 return air roadway is reduced by 86.5% compared to the 16402 return air roadway without roof cutting pressure relief. The deformation of the two sides is reduced by 87.1%. The stability of the surrounding rock of the goaf roadway is greatly improved.

In response to the difficulties in practical application, susceptibility to signal interference, high cost, and complex implementation of existing coal rock recognition technologies, this paper theoretically analyzes the relationship between coal rock cutting heat production and coal rock hardness. The paper proves the rationality of using infrared thermal imaging to obtain cutting temperature changes for coal rock recognition. A coal rock cutting test bench is built for roadheader. Long-term cutting tests are conducted on ordinary coal seams, coal rock interfaces, and sandstone layers with different hardness. The cutting temperature and vibration signals of the cutting head are obtained through infrared thermal imaging and vibration sensors, and their change patterns are analyzed. The research results indicate the following points. ① As the cutting time increases, the cutting temperature gradually increases. The higher the hardness of coal rock, the higher the cutting temperature, and the faster the rate of increase in cutting temperature. At the initial stage of cutting, coal and rock cannot be recognized by cutting temperature, but during stable cutting, coal and rock can be recognized based on cutting temperature features. ② The vibration intensity of the cutting head increases with the increase of coal rock hardness, but does not show a significant change with the increase of cutting time. Therefore, it can compensate for the lack of recognition of coal rock through cutting temperature at the beginning stage of cutting.③ Accurate recognition of coal and rock cannot be achieved through a single cutting temperature or vibration intensity. Therefore, coal and rock can be recognized through vibration intensity during the initial cutting stage and frequent flash temperatures. In the stable cutting stage, coal and rock can be recognized through temperature obtained from infrared thermal imaging.

Currently, algorithms such as support vector machine (SVM) and random forest (RF) are less applied in the field of coal mine roadway support. The paper studies the applicability of different machine learning models for support parameter design.Thus a higher performance model would be established to achieve reasonable and scientific design of anchor bolt support. Firstly, it is suggested to establish an intelligent prediction database for coal mine roadway support. Through on-site research, questionnaire survey, and literature search, the coal mine roadway samples are collected. The data is processed using methods such as filling in missing values, modifying outliers in box charts, and removing local abnormal factors to establish a coal roadway support database. The paper proposes a coal roadway support parameter prediction model based on synthetic minority oversampling technique (SMOTE) - genetic algorithm (GA) - SVM. The data in the database is divided into training and testing sets. The SMOTE technology is used to balance training samples, and improve the model's fitting capability for minority class samples. The training process uses GA to globally optimize the hyperparameters of SVM, further improving the overall performance of the model. The test results show that the classificaton precision of the SMOTE-GA-SVM model reaches 83.8%, which is 21.8% higher than the traditional SVM model. The machine learning methods such as SVM, artificial neural network (ANN), RF, AdaBoost (ADA), and naive Bayesian classifier (NBC) are introduced into the prediction of coal roadway anchor support parameters. The corresponding support parameter prediction models are established. The comparison results showed that the best to worst prediction models are ranked as SMOTE-GA-SVM, RF, GA-ANN, SVM, NBC, and ADA, with an average classificaton precision of 69.9%. The result verifies the feasibility of machine learning methods in predicting the parameters of coal roadway bolt support. The SMOTE-GA-SVM model is applied in Shanxi Huobaoganhe Coal Mine Co., Ltd., with a precision of 87.5% and strong applicability and reliability.

Ultra wideband (UWB) radar life detection technology has the advantages of low power consumption, good penetration, and high confidentiality. It is beneficial for improving the survival rate of trapped personnel after disasters. This paper systematically summarizes the research progress and current status of UWB radar life detection technology both domestically and internationally. According to the different forms of transmitted signals, UWB radar life detection technology is divided into continuous wave radar life detection technology and pulse wave radar life detection technology. The principles and application advantages of the two detection technologies are introduced respectively. Based on the respective features of continuous wave radar life detection technology and pulse wave radar life detection technology, this paper analyzes the key technologies of UWB radar life detection from three perspectives: detection signal transmission, echo signal preprocessing, and life signal extraction and analysis, and summarizes the research status of the three key technologies. The paper proposes prospects for the research on UWB radar life detection technology. The technology breaks through the hardware performance of life detector transceivers, improves the transmission signal bandwidth, and optimizes RF power amplification technology to increase the detection distance through walls. The technology comprehensively utilizes multiple feature extraction methods and intelligent pattern classification methods, as well as new generation information technologies such as artificial intelligence, big data, and cloud computing, to improve the precision of target recognition. The technology develops a human target recognition and positioning equipment based on multi input multi output radar and a high-precision distributed networked fully polarized UWB radar life detector to enhance the dimension of detection results.

Insulation is considered the weakest link in electrical equipment. The combined effects of special working conditions in coal mines and aging factors such as electrical, thermal, and mechanical stresses make it difficult to determine the aging mechanism and evaluate the condition of EPDM insulation for high-voltage cables used in mines. This paper introduces the basic performance and aging factor types of EPDM insulation for high-voltage mobile flexible cables used in coal mines. Based on the physical, chemical, mechanical, and electrical properties of EPDM under the influence of multiple aging factors, the aging mechanism of EPDM is proposed. This paper summarizes the basic principles and existing problems of online monitoring methods for insulation resistance, partial discharge, dielectric loss factor, and temperature of mining high-voltage cables. The paper summarizes the current research status of insulation status evaluation methods for mining high-voltage cables. The paper introduces the evaluation methods for insulation status of multi parameter based on improved radar map and single parameter based on dielectric loss mining high-voltage cables. To cope with the development of coal mine intelligence, on the one hand, it is suggested to do research on intelligent perception and control of mining electrical equipment to compensate for the lack of state perception and state evaluation feature quantities. On the other hand, it is necessary to study lightweight models or algorithms to reduce the computational complexity, parameter quantity, and analysis time of intelligent terminals near devices. It improves the feasibility of state evaluation technology, and lays the foundation for achieving intelligent analysis and decision-making.

The in-situ monitoring technology for overburden movement in mining has the features of adapting to complex geological conditions in deep shaft high pressure water and thick coal seam mining, multi layer dynamic monitoring, and high-precision remote real-time online transmission. It provides effective data support for mining enterprises to carry out roof disaster prevention and control. Starting from the practical background of coal mining application, this paper systematically reviews the development process, technological progress, and application effects of in-situ monitoring technology for overburden movement in mining. Based on the development history of mining pressure theory and overburden movement monitoring technology in China, this paper comprehensively introduces the important stages of in-situ monitoring technology for overburden movement in mining. It elaborates on the theoretical innovation and technological breakthroughs achieved by this technology in three aspects: multidimensional real-time collaborative monitoring, unmanned online monitoring, and deep rock movement monitoring. Based on the monitoring engineering examples of coal mines such as Bulianta Coal Mine, Tongxin Coal Mine, and Gaojiabao Coal Mine, the effectiveness of in-situ monitoring technology for overburden movement in mining is demonstrated in practical engineering applications. The application prospects of this technology in different types of mining areas and research fields are discussed. It is pointed out that the development trend of in-situ monitoring technology for overburden movement in mining is precision, intelligence, and integration, namely optimizing sensor performance and layout plans to improve monitoring precision and accuracy, using artificial intelligence, big data, and Internet of things technology to achieve automatic analysis and prediction, combining the in-situ monitoring technology with other technologies to form a complete monitoring system.

5G technology is the foundation for achieving intelligent mining construction. In response to the problem of difficult interoperability between mining 5G intelligent terminals and control platforms among different manufacturers, it is necessary to unify and standardize the data exchange and sharing process in the 5G system. The deployment architecture of '5G+intelligent mining' is proposed. The mining 5G intelligent terminals can interact and share data with servers containing control platforms through 5G base stations, switches, and 5G core networks in coal mines. This paper introduces the message types, message transmission formats, message composition, and message connection methods transmitted in the mining 5G system. For specific application scenarios in the mining field, the data in the mining 5G system is divided into voice data, video data, sensor data, and control data. The data types transmitted by the mining 5G intelligent terminal in different application scenarios are provided. This paper analyzes the basic transmission capabilities that mining 5G intelligent terminals and control platforms should have to meet data interaction. It is pointed out that the process of data interaction between mining 5G intelligent terminals and control platforms includes device information query and reporting, device upgrade, log collection, alarm issuance and processing, and platform configuration issuance and processing. It proposes the participating entities, security levels, and processes for data sharing of mining 5G intelligent terminals (including data aggregation, application, authorization, provision, and feedback). By implementing standardized processes, while ensuring communication reliability, the data exchange and sharing process under the '5G+intelligent mine' system architecture has been simplified to accelerate the intelligent construction of mines.

The existing mine pressure prediction models are mostly single prediction models that rely on fixed length time series features. It is difficult to accurately capture the composite features of mine pressure time series data, which affects the accuracy of mine pressure prediction. To solve this problem, an ensemble learning mine pressure prediction method based on variable time series shift Transformer-long short-time memory (LSTM) is proposed. Based on the Laida criterion and Lagrange polynomial method, the outlier values in the mine pressure monitoring data are eliminated, and the missing values are inserted. Then normalized preprocessing is performed. The paper proposes a variable time series shift strategy to divide mine pressure time series data at different scales. It avoids potential data shift issues that may exist in fixed length time series. On this basis, the ensemble learning mine pressure prediction model based on Transformer-LSTM is constructed. By combining the attention mechanism and the accurate time series feature representation capability, the dynamic features of the mine pressure change law are captured at multiple levels. The voting algorithm of ensemble learning is used to jointly predict the mine pressure data to overcome the limitations of a single prediction model. The experimental results show that the voting algorithm of ensemble learning can reduce the volatility of mean absolute error (MAE) and root mean square error (RMSE) of mine pressure prediction. It effectively reduces the sensitivity impact of different scale feature series to the mine pressure prediction results. Compared with the Transformer model, the MAE of the Transformer-LSTM model's prediction results on two roof mine pressure datasets of fully mechanized working faces improves by 8.9% and 9.5% respectively, and the RMSE has increased by 12.7% and 16.5% respectively. The above indexes are also higher than those of back propagation (BP) neural network model and LSTM model. The method proposed in the paper effectively improves the accuracy of mine pressure prediction.

Mine communication is an indispensable part of the intelligent development of coal mines. This paper analyzes the advantages and disadvantages of different communication systems and technologies in different development stages of wireless communication in underground coal mines. It is concluded that 5G and WiFi6 currently have significant technological advantages and good applicability in terms of transmission rate, delay, reliability, and user capacity. They are the mainstream solutions for mine wireless communication technology in recent years. This paper discusses the problems in underground wireless communication system construction and the corresponding development directions. ① Technological convergence issues. Different scenarios have different requirements for bandwidth, delay, power consumption and reliability. A single communication technology can not meet the requirements of all application scenarios in coal mines. It is necessary to select appropriate communication technology according to the actual application situation to meet the application requirements. Different technologies are integrated through protocol converters, gateways and other devices to form an integrated communication network platform for technological convergence above the ground and underground. ② System integration issues. Since each system is provided by different manufacturers, and the communication protocol and hardware interface are inconsistent, it is difficult to truly realize the data sharing of each system in the mine. It is necessary to provide standard interfaces, open network architecture, and unified user data to provide interconnection and inter-working functions for monitoring, emergency, production, and other systems. It is further required to provide unified data messages to enable data transmission with low delay, high concurrency, real-time, and synchronization within each system. ③ Device power consumption issues. The existing mine 5G base station and other equipment can only be made into a single flameproof product due to their high power consumption. The equipment is bulky, large, and difficult to install and maintain, which restricts the popularization of 5G technology in coal mine. Low-power design can be started from the main chip, RF module, and power amplifier module.

As a device for timely rescue after disasters, life detectors play an irreplaceable role in locating, exploring, and searching for living organisms. This paper analyzes the working principles, technical features, usage scenarios, advantages and disadvantages of the currently used life detectors. The detectors include radar life detectors, audio life detectors, infrared life detectors, and other types of life detectors. Combined with practical applications, this paper points out the problems of the above life detectors. ① The detection technology is single and the comprehensive integration is low. ② The detection methods are traditional and equipment intelligence is insufficient. ③ The parts of the detector are not standardized, making equipment maintenance difficult. ④ Some life detectors have a relatively large structure and are inconvenient to carry, which affects rescue efficiency. In response to the existing shortcomings and problems, the development trends of life detectors are proposed. ① It is suggested to improve the integration level of life detectors. It is suggested to design life detection devices that use multi-source fusion to collect information, and use multiple information sources to obtain and detect life information. ② It is suggested to improve the intelligence level of life detectors, give them partial autonomy in decision-making, and reduce errors in life detection caused by improper operation by operators. ③ It is suggested to design modular life detectors and develop relevant standard procedures to standardize the components of the same type of life detector. It will reduce the difficulty of repair and maintenance of life detectors. ④ It is suggested to improve the circuit board design precision of the life detector, fully utilize spatial structure, reduce volume and improve portability without reducing the functionality of the life detector.

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.

The conveyor belt and driving device are the main components of the belt conveyor and are the high-risk areas for faults. Taking conveyor belt faults and driving device faults as the starting point, this paper analyzes the mechanisms of conveyor belt deviation, slipping, damage, stacking and scattering, as well as the faults of the driving device roller, idler and reducer. It focuses on the research progress of knowledge-driven and data-driven fault diagnosis methods for belt conveyors. Based on the knowledge processing technology, the knowledge-driven method realizes the unification of symbol processing and numerical processing, the unification of reasoning process and algorithm process. It mainly includes expert system and fault tree analysis. The data-driven method uses machine learning and data mining techniques to analyze and process historical data. It establishes diagnostic models, and achieves fault diagnosis purposes. It mainly includes support vector machines (SVM), comparison method, and diagnosis methods based on sound and vision. This paper analyzes the current challenges and future development trend of belt conveyor fault diagnosis methods. ① The historical fault data and real-time data should be combined to infer equipment health. The early minor faults should be predicted so as to remind the staff to carry out predictive maintenance. ② The correlation between coupling faults of belt conveyors should be revealed. The emerging technologies such as artificial intelligence should be used to study joint diagnosis methods for coupling faults. ③ The multimodal machine learning technology should be utilized to study the mechanism of multimodal information fusion and utilization of belt conveyors. Fault diagnosis methods for multimodal information fusion of belt conveyors needs to develop. ④ The fault knowledge graph and the belt conveyor domain knowledge should be combined to realize the belt conveyor equipment fault tracking and fault early warning. Through the knowledge query, knowledge reasoning and auxiliary decision-making functions, the capability of fault handling and precise mining of potential fault risks of equipment can be improved.

Mine air stopping is a key facility separating production systems from abandoned ones. Ensuring full lifecycle risk management is of great significance for ensuring safe production. Based on the current status of comprehensive control technology for mine air stopping, the shortcomings of the existing air stopping management mode are analyzed from two aspects: static attribute information management and dynamic information monitoring. The paper puts forward the concept of intelligent management and control in the full lifecycle of air stopping. It indicates the overall construction path of air stopping risk monitoring and early warning based on big data strategy of the Internet of Things (IoT)+Artificial Intelligence (AI)+Cloud Platform (CP), and integrated with cloud-edge architecture. The IoT platform is applied to access digital monitoring and linkage control equipment for air stopping units, achieving transparency in the full lifecycle control of air stopping. The method focuses on the evolution signs of air stopping information and efficient edge computing. It deploys mobile terminals, edge computers and other field terminals to adapt to online prediction and advance warning of air stopping service status. In the smart cloud, the virtual reality scenario of a distributed mine air stopping and its full lifecycle evolution characteristics are reconstructed iteratively in real time with multi-source fused information and digital twin models at the core. The paper analyzes key theories and and model construction methods, including the physical-digital driving mode of evolution information in air stopping environments, the multi-source heterogeneous data fusion model of air stopping perception information, the edge detection and hierarchical intelligent warning of air stopping anomalies, and the emergency intelligent decision-making and collaborative prevention and control throughout the entire lifecycle of air stopping. The physical and functional architecture of an air stopping full lifecycle safety risk monitoring and warning system based on the IoT is designed. The on-site embedded air stopping multi-parameter integrated monitoring sensors and supporting intelligent hosts are developed. The early sensing and monitoring and warning methods for air stopping risks are proposed to achieve digital empowerment and effectively promote the construction of smart mines.

This paper reviews the general situation of the development and innovation practice of China's coal industry in the past 50 years promoted and witnessed by the Journal of Mine Automation from coal mine mechanization to automation and intelligence. This paper expounds on the innovation process of coal mine automation and intelligence. It focuses on the analysis of the research and development and practice of coal mine information infrastructure, automation and intelligent equipment and systems. The integration and unified hosting of heterogeneous networks is the information foundation for coal mine automation and intelligence. On the basis of the continuous maturity of the heterogeneous network of the backbone network and access network, the wired communication transmission rate has reached over 10 Gibit/s. The wireless communication technologies such as 5G and WiFi6 have been widely applied, providing a reliable communication platform for the intelligent construction of coal mines. Wired communication, wireless communication, IP broadcasting, personnel locaiton monitoring system, and video monitoring system are integrated to build a multi-network integrated communication system, achieving integrated carrier network and unified location service. The development of intelligent automation equipment in coal mines is rapid. It explores adaptive cutting technology based on transparent geology, automatic coal release control technology on the basis of technologies such as memory cutting of coal mining machines and automatic following and moving of hydraulic brackets at coal mining working face. On the basis of achieving profiling cutting, positioning navigation, and remote control, the excavation working face has made breakthroughs in technologies such as drilling anchor integration, multi-machine collaboration, and cutting template planning of roadheader based on geological model, achieving good results. The development of intelligent equipment for coal flow transportation systems is supported by intelligent variable frequency energy-saving transmission. The integrated research and application of coal mine robots are achieved. The mature application of inspection robots is obtained. The breakthroughs in intelligent auxiliary operation robots are achieved. The development of a multi-scene robot cluster scheduling and command system forms a robot cluster. The automation and intelligent construction of coal mines have officially entered the stage of intelligent mine comprehensive control from single system automation, comprehensive mine automation, and the Internet of Things. The realization of intelligent perception, intelligent self-control, high-speed transmission, collaborative control, and intelligent decision-making is an inevitable requirement for the development of intelligence. The paper proposes key directions for the iterative development of coal mine intelligent technology, including the establishment of high-precision transparent geological models, the normal and reliable operation of unmanned operating systems, the construction of intelligent compatibility and collaboration mechanisms for coal mine giant systems, the intelligent collaborative control of the entire process of 'excavation, mining, transportation, washing, and sales', and the optimization of a flexible coal production and supply system.

This paper introduces the evolution and development process of coal fire and its features. This paper expounds on the detection mechanism and research progress of four layers of space exploration technology divided according to the different spatial positions of detectors. The technologies include underground, surface, aerial, and aerospace detection methods. The paper analyzes the advantages and disadvantages of the above four detection technologies. The existing research problems are pointed out. ① The detection precision is insufficient and it is difficult to accurately delineate the acquisition range. ② The detection technology is outdated, and it is difficult to accurately detect high-temperature points in fire areas. ③ The detection method is single and cannot accurately determine the specific information of underground coal fire combustion. In view of the new features of underground coal fire, such as concealment, coupling and complexity, the development directions of underground coal fire detection technology are proposed. ① The detection instrument data needs to be finely processed, so as to improve the sensitivity of magnetic difference to temperature, the detection precision of the resistivity method, the feature extraction rate and the result precision of the infrared detection method. ② It is suggested to utilize high-tech equipment to serve coal fire detection technology. ③ It is suggested to improve the collaborative utilization of multi-level detection technology. Firstly, large-scale fire detection is carried out using the four th level aerospace remote sensing technology to obtain the basic spatial distribution features of underground coal fire anomalies. Secondly, further exploration is carried out using the third level aerial remote sensing detection technology to obtain the specific spatial distribution of underground coal fires. Finally, a more detailed distribution of high-temperature points in the coal field fire area is obtained by using the surface detection technology and underground detection technology at the second level and the first level to supply and verify. ④ It is suggested to develop 3D visual and dynamic monitoring and early warning of coal field fire areas. 3D inversion and joint inversion imaging are performed by use of data from four different levels of detection technology. Based on the imaging results, visual monitoring and early warning of coal fire development and spread are carried out, so as to achieve active prevention and control of underground coal fine disasters.

The existing wireless communication technologies in wireless blind spot coverage have challenges in non-line-of-sight scenarios in coal mines. In order to solve the above problems, a solution approach is proposed to introduce intelligent reflecting surface (IRS) into the wireless communication system in coal mines to achieve blind compensation of wireless signal coverage. This paper analyzes the causes of wireless coverage blind spots in coal mines, including closed mine environments characteristics, common non-line-of-sight scenarios, and safety constraints on transmission power and antenna settings. However, traditional wireless coverage blind spot solutions for underground coal mines have limitations in hardware deployment, maintenance costs, and technical implementation. They cannot truly meet safe and efficient communication needs of specific mining scenarios. IRS has significant performance advantages in achieving coverage blind compensation due to its low cost, low power consumption, easy deployment, and scalability. This paper introduces IRS technology from three aspects: hardware structure, auxiliary channel model and typical application scenarios (precise positioning, signal and energy simultaneous transmission, UAV communication, edge computing and physical layer security). This paper proposes the use of IRS technology to optimize underground wireless blind spot coverage. By deploying IRS reasonably in coal mines, it reduces the absorption and scattering of main signal components by walls, and utilizes the adjustable reflection characteristics of IRS to optimize signal propagation. It significantly improves signal strength and coverage range. The future research directions of IRS technology in underground wireless communication in coal mines are pointed out. The directions include energy management of IRS-aided coverage and blind compensation systems, IRS-aided underground communication based on artificial intelligence, application of new IRS technology, and flexible deployment of IRS.

This paper analyzes the technologies of mine ranging and positioning, such as ultrasound, laser, video, inertial navigation, pedometer, and radio. Among them, radio ranging and positioning have the advantages of long positioning distance, small positioning error, no impact from light, little impact from dust, easy identification, and low system cos. They are suitable for positioning mine personnel and precise positioning of mine personnel. The paper analyzes radio ranging and positioning technologies such as UWB, ZigBee, RFID, 5G and WiFi6. Mining UWB has the advantages of long positioning distance, small positioning error, and low cost. It is suitable for precise positioning systems for mine personnel. Mining ZigBee has the advantages of long positioning distance, small positioning error, and low cost. It is suitable for mine personnel positioning systems. Mining RFID has the advantage of having the lowest system cost. It is suitable for coal mine underground personnel management systems. The positioning precision of mine 5G and WiFi6 is lower than UWB, and the system cost is higher than ZigBee. It is unsuitable for mine personnel positioning and precise positioning systems. The received signal strength indication (RSSI), time of arrival (TOA), time differences of arrival (TDOA), two-way ranging (TWR), symmetrical double-sided two way ranging (SDS-TWR), angle of arrival (AOA) and other mine personnel positioning algorithms are analyzed. The RSSI positioning method has a large positioning error, making it difficult to meet the needs of mine personnel positioning and precise positioning. The TOA positioning method requires accurate and synchronized timing between the positioning card and the substation clock. The positioning card and substation have high costs. It is unsuitable for mine personnel positioning and precise positioning. The TDOA positioning method does not require synchronization between the positioning card and the positioning sub-station clock. It has low requirements for the precision of the positioning card clock and low cost of the positioning card. However, it requires accurate timing of the positioning sub-station clock and synchronization between the positioning sub-stations. The positioning substation cost is high. It can be used for mine personnel positioning and precise positioning. The TWR positioning method and SDS-TWR positioning method do not require clock synchronization between the positioning substation and the positioning card, nor do they require clock synchronization between the positioning substations, nor do they require clock synchronization between the positioning cards. This reduces the complexity and cost of the positioning card and the positioning substation. It is suitable for mine personnel positioning and precise positioning. The AOA positioning method system is complex, costly, with large long-distance positioning errors and dead angles. It can only be used for straight-line obstacle-free positioning. The positioning antenna and positioning precision are greatly affected by mechanical vibration. It is not suitable for the independent positioning of mine personnel and precise positioning. However, it can be used in conjunction with other mine positioning methods.

Efficient analysis and utilization of coal mine safety big data is of great significance for improving the safety management level and production efficiency of coal mines. At present, there are some problems in coal mine safety big data governance, such as unclear data features and governance methods. In order to solve the above problems, this paper emphatically analyzes the features of coal mine safety big data. It is concluded that coal mine safety big data has 5V features, namely, large data volume (Volume), multiple data varieties (Variety), fast processing velocity (Velocity), low value density (Value), veracity (Veracity), and also has the features of inconsistent structural degree. This paper introduces the main data governance methods and models that can be applied to coal mine safety management. The methods are divided into five categories: single variable method, multivariate statistical analysis method, intelligent pattern recognition method, system dynamics model and comprehensive integration model. From the perspective of the subject and object, this paper puts forward a big data governance method system for coal mine safety. It is believed that the selection of data governance methods must be consistent with the data governance goals of the subject and object of intelligent mines. Selection of subject-based governance methods: the specific content of data governance is determined according to the needs, levels, tasks and security management objectives of data subjects. Selection of object-based governance methods: the specific content of data governance is determined according to the timeliness of object objects, throughput requirements and security management objectives. Finally, it is concluded that the determination of coal mine safety big data governance method needs to meet the common and individual needs according to the different scopes and objects under the unified goal and standard.

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.

The mine 5G is the foundation of intelligent mine construction. In order to meet the diversified application requirements of intelligent mine, it is necessary to promote the realization of mine 5G-advanced communication evolution system through 5G-advanced communication evolution technology. The current status of mine 5G technology research and system deployment is reviewed. The mine 5G has preliminarily realized audio and video call, HD video analysis, equipment remote control and other applications. However, there are some problems such as single terminal form, heavy uplink load, separate construction of 5G-advanced communication system and positioning system, etc. This paper analyzes the applicability of 5G-advanced communication evolution technology of 3GPP Release 17 in mine 5G-advanced communication. It is pointed out that RedCap lightweight terminal technology, NR direct connection communication technology and NR positioning technology will become the key technologies of mine 5G communication evolution. The key technologies of mine 5G-advanced communication evolution are studied. RedCap lightweight terminal technology can meet the research and development requirements of low-power consumption and low-cost multimode terminals of mine wireless sensors, single-channel wireless transmission mine video monitoring equipment, intelligent miner lamps and intelligent wearable devices. NR direct connection communication technology is applied to mine automatic driving vehicle-mounted terminal, roadway side equipment and emergency communication relay equipment. It can meet the requirements of low delay and high reliable transmission of mine automatic driving and emergency communication. NR positioning technology is applied to mine communication and positioning integration to meet the technical requirements of mine communication and positioning system fusion. This paper puts forward the architecture of mine 5G-advanced communication evolution system. The study provides the direction for building 5G fully connected mine, supporting multi-link wireless access and realizing the integration of communication and perception.

The rock bolting construction equipment is an important part of the complete set of bolt support technology in coal roadway, which directly affects the speed and quality of rock bolting. After a long period of development, China's coal mines have developed various forms of rock bolting equipment, including single bolter, mobile bolter, roadheader bolter, bolter miner, bolter conveyor and straddle bolter. This paper systematically analyzes the development history, technical performance, technical parameters, applicable conditions and latest development of six kinds of rock bolting construction equipment. The single bolter is light, flexible and mature in technology, forming a series of products, which are widely used. The onboard bolter can be integrated on different platforms to form a variety of integrated construction equipment. The mobile bolter has formed a variety of models from two arms to nine arms. It can closely follow the heading equipment, work alternately with the heading equipment, and complete the rock bolting construction with multi-arm cooperation and multi-arm parallel operation. The onboard bolter is equipped on the cantilever roadheader, and various types of roadheader bolters are developed, realizing two functions of heading and bolt construction. The bolter miner has realized localization. The bolter miner with forward moveable bolting platform, shield type bolter miner and bolter miner with the integration of drilling and bolting are developed. The bolter conveyor from two arms to seven arms and the ten-arm straddle bolter are developed. The rock bolting, heading and transportation have been carried out in different zones. This paper introduces the technology and equipment of rock bolting with the integration of drilling and bolting. They include bolt with the integration of drilling and bolting, pump-injected anchoring agent, onboard bolter with the integration of drilling and bolting, drill box, rock bolt bin, anchoring agent pump-injection system and intelligent control system. The traditional six-step rock bolt construction process is simplified into one continuous process. It realizes the automatic operation of 'one key rock bolting', significantly improving the speed and automation degree of rock bolting. The paper points out the development trend of rock bolting construction equipment in coal roadway. The trend includes following aspects: the digital intelligent bolt onboard bolter, onboard bolter positioning, pose self-adjustment and multi-onboard bolter collaborative control technology; the optimization of bolt (cable) structure and materials, upgrading of anchor grouting materials and improvement of construction equipment; the environmental dynamic sensing and stability discrimination of surrounding rock with heading; the fault diagnosis technology of rock bolting construction equipment and the online monitoring of roadway surrounding rock deformation and bolt load; establishing a big data platform to realize automation and intelligenitization of rock bolting.

Based on the basic concepts and connotations of digital twin, this paper analyzes the application advantages of digital twin technology in coal mine power grid. The operation management of the coal mine power grid is assisted by two means of synchronous entity operation status and digital simulation operation. The technology has the characteristics of data-driven, real-time update and synchronous feedback. The basic framework of the digital twin system of the coal mine power grid is proposed. The framework is composed of physical entity layer, digital twin model layer, user service management layer and data exchange layer. The operation mode of the digital twin system of the coal mine power grid is explored from two aspects of the physical entity and the digital twin model. This paper introduces the key technologies for establishing the digital twin system of the coal mine power grid. The technologies include digital twin model construction of coal mine power grid, intelligent data acquisition, intelligent communication based on 5G, digital twin intelligent database, and digital twin equipment intelligent management platform. The application scenarios of digital twin technology in the coal mine power grid are proposed. The scenarios include the condition evaluation of underground electrical equipment, fault location and protection of the coal mine power grid, intelligent monitoring of the coal mine power grid and intelligent inspection of underground lines. The digital twin technology is applied to the coal mine power grid to carry out dynamic simulation modeling on the state and operation of the coal mine power grid. On the one hand, the higher operation requirement of the current large-scale coal mine power grid relative to the ground power grid can be met. The safety of coal mine production can be guaranteed. On the other hand, the intelligent process of the coal mine power grid can be promoted. The efficient and reasonable utilization of data resources can be realized.

Fully mechanized mining equipment is gradually becoming larger, more complex and more intelligent. The traditional equipment management methods of regular maintenance and post maintenance are no longer able to meet the high reliability requirements of equipment operation in coal mine intelligent construction. Therefore, studying the relevant theories and technologies of fully mechanized equipment health status assessment has great practical significance for coal mine intelligent mining. This paper proposes the scope definition of fully mechanized mining equipment health status assessment and the fully mechanized mining equipment health status assessment process. This paper summarizes the research status and development trends of comprehensive mining equipment health status assessment methods from four aspects: signal acquisition, feature extraction and fusion, health status level classification, and health status assessment model establishment. The current challenges faced by fully mechanized mining equipment health status assessment related technologies are analyzed from aspects such as signal acquisition and sensor optimization layout, data processing and feature extraction, establishment of health status assessment models, and application of fully mechanized mining equipment status assessment. In response to the current research status and challenges mentioned above, the development trend of fully mechanized mining equipment health status assessment technology is discussed from the aspects of improving data collection schemes and fault mechanism research methods, building high-performance big data computing platforms, establishing deep learning assessment models, researching dynamic evaluation models for fully mechanized mining equipment health status, and developing fully mechanized mining equipment health status assessment systems. It is pointed out that in the process of coal mine intelligence, it is necessary to ensure that the theoretical research, algorithm development, and engineering application of fully mechanized mining equipment health status assessment go hand in hand.

The deep integration of the coal industry and artificial intelligence (AI) is an important path for modern mines to achieve intelligent personnel reduction, cost reduction, and efficiency improvement. AI empowerment in the entire process and business application scenarios of the coal industry is a specific technical measure to achieve coal mine intelligence. In the context of the current development of intelligent coal mines, a basic paradigm for the evolution of primary intelligent coal mines to new generation intelligent coal mines has been proposed. The composition, functions, and technical connotations of primary intelligent coal mines and new generation intelligent coal mines have been compared and analyzed. It is pointed out the importance of AI empowerment technology for new generation intelligent coal mine and its two key applications and implementation: the coal mine industry mechanism AI model and the coal mine Industry internet platform. The paper summarizes the current research status of industrial mechanism AI models for complex operations such as coal mine geology, mining, excavation, and safety monitoring. The paper clarifies the rapid development trend of industrial mechanism AI analysis in intelligent coal mine construction. A new generation of intelligent coal mine multi-level cloud edge collaborative industrial Internet platform architecture is designed. Using industrial information software and hardware facilities such as group data center, mine data center, production system centralized control center, and combining the features of massive data cloud computing and small amount of data edge computing, a multi-level cloud edge collaborative mechanism of group cloud, mine cloud and link edge, scene edge is proposed. It is pointed out that further research directions in the future should continue to strengthen the development and software research of AI models for coal mining industry mechanisms. Gradually a knowledge software system empowered by AI throughout the entire process of coal mining will be formed. It is suggested to fully utilize the digital resources and information facilities of the coal mining industry Internet platform to gradually realize the AI technology support of the coal mining industry Internet platform.

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.

The problem of low detection accuracy of driving displacement exists in the driving process of anchor digging robots. In order to solve the above problem, taking the supporting bolt as the positioning benchmark, by analyzing the distance relationship between the anchor digging robot and the supporting bolt, the positioning model of 'anchor digging robot-supported anchor' is established. This paper proposes a binocular vision-based displacement detection method for anchor digging robots. Due to the complexity of the underground coal mine environment, the disparity map obtained by using the traditional Census transform algorithm has limitations. By analyzing the binocular vision ranging principle, an improved Census transform algorithm is proposed to obtain the disparity map of the anchor and the depth information of the anchor image. This paper presents a method of anchor feature recognition and positioning, and uses edge detection algorithm to extract the anchor contour in disparity map. The minimum circumscribed rectangle and the maximum circumscribed rectangle algorithm are used to frame the anchor outline and extract the pixel coordinates of anchor feature points. By analyzing coordinate conversion relationships, the pixel coordinates of feature points are converted to world coordinates. By using the least square method, the spatial coordinates of feature points are fitted into a straight line. The plane parallel to the roadway section is established through the straight line. The distance between the binocular camera and the plane is calculated, and then the distance between the anchor digging robot and the plane is obtained. A mobile robot platform is set up to carry out the displacement detection experiment of the anchor digging robot. The results show the following points. The improved Census transform algorithm reduces the mismatch rate from 19.85% to 11.52%, which is 41.96% lower than the traditional Census transform algorithm. The method of anchor feature point recognition and positioning can effectively extract the spatial coordinates of anchor feature points. The distance between the camera and the three parallel sections is 3 010.428, 2 215.910, 1 415.127 mm respectively through straight line fitting. In the robot positioning experiment, the real calculated displacement is compared with the theoretical displacement. The results show that the real calculated displacement curve coincides with the theoretical displacement curve basically. The error between the theoretical displacement and the calculated displacement is less than 20 mm. The autonomous, accurate and real-time displacement detection of the anchor digging robot can be realized.

Roadway dust cleaning robot can effectively solve the problem of coal mine roadway dust accumulation. However, there is no relatively mature product to achieve 'automatic dust accumulation monitoring-autonomous/semi-autonomous movement-adaptive dust cleaning operation'. This paper analyzes the research status of three kinds of dust cleaning equipment developed at home and abroad, which are wheel-rail type roadway dust cleaning device, explosion-proof sprinkler and tunnel dust cleaning vehicle with chassis and hydraulic mechanical arm. It is pointed out that the wheel-rail type roadway dust cleaning device does not contain power system. The dust-cleaning effect of roadway wall dust is limited. The explosion-proof sprinkler has its own power, which can realize full section dust reduction of the trackless long roadway. However, the jet water surface is wide, and it is unable to deal with local areas with serious dust accumulation such as roadway walls and pipelines. Tunnel dust cleaning vehicle can solve the problem of dust accumulation in long-distance tunnels. However, manual driving and operation are still required, and it is impossible to realize tunnel dust accumulation monitoring and adaptive dust cleaning. Based on the above analysis, it is pointed out that in order to realize 'automatic dust accumulation monitoring-autonomous/semi-autonomous movement-adaptive dust cleaning operation', the research should be carried out from the aspects of dust accumulation monitoring, structure design and control of dust cleaning device, and optimization strategy of dust cleaning mode. It is pointed out that the main technical problems faced by the above research include the common problems of coal mine robots such as explosion-proof safety design, underground precise positioning, and long-distance wireless communication. The problems also include the characteristic problems of roadway dust cleaning robot such as dust monitoring, adaptive dust cleaning, and vehicle arm coordinative operation. In view of the characteristic problem of the roadway dust cleaning robot, this paper puts forward the corresponding key technologies. ① It is suggested to research and develop a multi-sensor fusion monitoring technology based on the weighing method, laser method and image method to realize long-term monitoring of dust cleaning in coal mine roadway and dynamic evaluation of dust cleaning effect. ② It is suggested to develop dust cleaning structure based on explosion-proof mechanical arm and 'wind-water-brush' linkage dust cleaning device to realize adaptive dust cleaning. ③ It is suggested to establish a unified workspace for vehicle chassis and mechanical arm. It is suggested to develop small deviation automatic compensation and flexible obstacle avoidance technology for roadway dust cleaning robot based on torque control. The vehicle arm coordination of the roadway dust cleaning robot in the dynamic scene is realized.

The paper analyzes the image characteristics of the thrown coal and rock when rock burst and coal and gas outburst occur. ① The coal and rock thrown out during rock burst and coal and gas outburst are mainly black, but the underground equipment of the coal mine is generally not black. Therefore, nonblack mining equipment can be used as the background and color cameras can be used to identify coal and rock. ② The normal coal falling speed, the moving speed of shearer, roadheader, and the moving speed of underground personnel and vehicles are far less than the speed of coal and rock thrown out in the event of rock burst and coal and gas outburst. Therefore, according to the speed characteristics, the interference from normal coal falling, movement of equipment such as shearer and roadheader, and movement of underground personnel and vehicles can be eliminated. ③ The explosion of gas and coal dust will also cause the objects in the roadway to have a high speed in a short time, accompanied by high brightness. But the rock burst and coal and gas outburst will not produce high brightness. Therefore, according to the average image brightness, the interference of gas and coal dust explosion can be eliminated. The paper proposes a color camera set method. The camera of the heading face should be set at the roof of the heading roadway or near the roof on both sides of the heading roadway. The camera of the working face should be set on the top of the hydraulic support. The paper puts forward a coal mine rock burst and coal and gas outburst perception alarm method based on color image. ① The color camera with fill light shall be set at the roof of the heading roadway or near the roof on both sides of the heading roadway, and at the top of the hydraulic support of the working face. The nonblack mining equipment is used as the background. ② The method monitors and identifies whether the color of the color image has changed greatly. ③ If the image color changes significantly, the average brightness of the image is identified, otherwise the monitoring of the identified image color change continues. ④ If the average brightness of the image is less than the set brightness threshold, the movement speed of the object causing a large change in the image color is identified, otherwise the monitoring of the identified image color change continues. ⑤ If the movement speed of the object is greater than the set speed threshold value, the methane concentration in the monitoring area is identified, otherwise the monitoring of the identified image color change continues. ⑥ If the methane concentration rises rapidly or reaches the alarm value, the coal and gas outburst alarm will be given. Otherwise, the rock burst alarm will be given. The method has the advantages of non-contact, wide monitoring range, low cost, convenient use and maintenance.

In the process of intelligent coal mine construction, there are problems such as the 'data island' phenomenon, low data quality, lack of data governance system, and insufficient data empowerment. In order to solve the above problems, this paper analyzes the basic requirements of intelligent coal mine big data governance. This paper studies the key technologies of intelligent coal mines such as data acquisition and storage, data cleaning and standardization, data asset planning, data sharing and exchange. Combined with the field practice of data governance in Xiaobaodang Coal Mine, the overall technical architecture of intelligent coal mine big data governance based on the Industrial Internet system is proposed. The architecture functions correspond to the basic requirements of intelligent coal mine big data governance. It realizes the access, integration and fusion of multi-source heterogeneous perception data downward, provides data services for the development of various coal mine intelligent applications upward, and sediment various business indicators and model algorithms of coal mines in the middle, forming important data assets for coal mines. Unified access to to data form various coal mine systems is achieved through data access storage services based on different data access protocols. The necessary protocol conversion and data preprocessing are realized during the access process. The data processing is achieved through data cleaning and standardization services to improve data quality. The data is transformed into systematic data assets by adopting a hierarchical governance architecture. Finally, data assets are provided to other systems through standard interfaces through data sharing services, achieving the implementation of data value. The practical application of intelligent coal mine data governance achievements in different business scenarios is demonstrated from the perspective of coal mine single system application, mine-level application and company-level system application. After unified data governance, intelligent coal mine big data can achieve data fusion applications. It can break data islands, improve data quality, form coal mine unique data assets, and provide important value for coal mine production and operation.

This paper analyses the development status and technical characteristics of intelligent and networked technologies for conventional ground automatic driving. This paper also analyses the coal mine environmental characteristics, such as no GNSS (global navigation satellite system) signal coverage, low roadway illumination, lots of obstructions and obstacles and ubiquitous coal dust. In the context of the above technical and environmental characteristics, this study puts forward the key technologies of automatic driving research in underground coal mine. The technologies include mobile high-precision positioning technology without GNSS, laser radar technology, underground obstacle detection technology based on millimeter wave radar, underground low illumination video real-time enhancement and characteristic matching technology, underground environment high-precision map technology, underground autonomous vehicle decision planning technology, underground autonomous vehicle control execution technology, underground 5G communication technology and C-V2X direct connection communication technology. It is pointed out that the application of automatic driving in underground coal mine has the advantages of significant demand for fewer people or no people, clear operation management subject, closed scene, fixed route, slow speed, controllable permeability, good 5G construction foundation, easy open interface, etc. The reference architecture of 'human-vehicle-roadway-cloud' coal mine underground network connected automatic driving system is constructed. The system includes underground automatic driving vehicle, roadway infrastructure, personnel, coal mine cloud/edge computing platform and coal mine automatic driving application service platform. This paper designs the coal mine automatic driving vehicle architecture, which includes perception positioning system, network connected collaborative system, vehicle-mounted operating system and vehicle basic components. This paper puts forward three stages of the evolution of the network connected automatic driving in underground coal mines. The first stage is remote control and automatic driving, which realizes the transfer of vehicle drivers from underground to ground. The second stage is the automatic driving of vehicles with emergency takeover boundary. The vehicles are mainly driven by automatic driving, and remote emergency takeover is used as a safety guarantee method. The third stage is 'human-vehicle-roadway-cloud' collaborative control, the underground autonomous vehicles operate safely, efficiently and autonomously to realize highly unmanned intelligent transportation.

This paper introduces the development and application status of intelligent auxiliary transportation technology in open-pit coal mine and underground coal mines at home and abroad. The intelligent auxiliary transportation system of open-pit coal mine has realized the functions of unmanned driving, automatic loading, automatic unloading, active obstacle avoidance and intelligent dispatching of mining trucks in fixed sections. And the system has achieved good application results in engineering practice. At present, the auxiliary transportation intelligence of underground coal mine is still in the development stage of single machine intelligence of equipment. The intelligent auxiliary transportation system integrating vehicle scheduling, operation status monitoring, traffic command, material control and other functions has not yet been formed. The main problems of intelligent auxiliary transportation system in underground coal mine are analyzed. The underground positioning system has low precision and poor real-time performance. The dispatching system function lacks effective integration. The driving assistance system module is not perfect. The unmanned driving technology lags behind and the test conditions are lacking. Based on the relevant requirements of intelligent auxiliary transportation in Coal Mine Intelligent Construction Guide (2021 edition), this paper puts forward the overall goal of the construction of intelligent auxiliary transportation. According to the overall goal, the intelligent coal mine auxiliary transportation system framework is designed. ① Coding and centralized loading transportation of materials realizes the whole process information management and control of materials from storage, coding, loading, transportation, unloading and recycling. ② Automatic loading and unloading and automatic connection realizes the automatic transfer and connection of materials among rail locomotives, monorail cranes, trackless and other different auxiliary transportation modes. ③ Accurate positioning and intelligent navigation achieves accurate real-time positioning, route planning and real-time navigation of personnel and transportation equipment. ④ Intelligent vehicle dispatching realizes the functions of auxiliary transportation comprehensive information display, data transmission, status monitoring, dispatching command and health management. ⑤ Driving assistance system builds several intelligent subsystems, such as anti fatigue driving warning, 360° panoramic look around monitoring, collision prevention, traffic sign identification, auxiliary braking for downhill driving, adaptive lighting, etc. Driving assistance system improves the safety of locomotive operation. ⑥ The auxiliary operation robot realizes the robot automatic operation of underground auxiliary operation scenes. The auxiliary operation robot reduces the number of personnel and improves the overall automation level of auxiliary operation. ⑦ Unmanned driving realizes the normal unmanned driving operation of locomotives in underground coal mine. The research can provide reference for the construction and development of intelligent auxiliary transportation system.

As one of the most important factors in the production process of open-pit mine, the intelligence of transportation is an important research content of the whole intelligence technology of mine. This paper introduces the composition and classification of the transportation system in open-pit mine. It is clarified that the intelligent transportation system in open-pit mine takes the efficient transportation of coal and rock in open-pit mine as the application scenario and the intelligent transportation equipment as the core carrier. The digital technologies such as the Internet of things, cloud computing, big data, artificial intelligence, and mobile Internet are integrated with the operating principles and technological requirements of the open-pit mine transportation system. The autonomous collaborative and efficient operation system of equipment, environment and materials in the transportation system is established. And the real-time, accurate and efficient transportation integrated management system that plays a role in a wide range is further established. The components of the intelligent transportation system in open-pit mine mainly include infrastructure, transportation tools and computing technology. It is pointed out that the difference between the traditional transportation system and the intelligent transportation system in open-pit mine lies in that the intelligent transportation system takes improving the safety and efficiency of field production operation as the goal. The service object is changed from the original production management personnel to the production operating personnel. The research and application status of intelligent transportation system in open-pit mine in China are summarized from five aspects, including intelligent infrastructure, intelligent equipment, intelligent management and control, intelligent maintenance and intelligent design. The key technologies of the truck transportation system and the belt conveyor transportation system in the open-pit mine to realize the intelligence are analyzed in this paper. The key technologies of intelligent truck transportation include environment perception technology for complex road conditions in mines, line control transformation technology for unmanned driving truck, multi-objective intelligent scheduling technology, and intelligent collaboration technology for manned-unmanned mixed equipment group. The key technologies of intelligent belt conveyor transportation include autonomous traverse technology of belt conveyor in working face, transportation technology of self-moving large-angle belt conveyor, operation control technology of belt conveyor, on-line status detection technology of belt conveyor, intelligent inspection technology of belt conveyor, unmanned maintenance technology of belt conveyor, intelligent management and control platform of belt conveyor transportation system. It is pointed out that the development trend of intelligent transportation in open-pit mine is continuous, unmanned, low-carbon, efficient coordination and intrinsic safety.

Data is the core resource to promote the development of intelligent coal mine technology, innovation of application mode and improvement of market competitiveness. Data governance is the key bottleneck for the development of smart mine from primary stage to advanced stage. However, at present, there is no consensus on the concept connotation, development goal and key technologies of data governance for smart mine. In order to solve the above problems, this paper analyzes the important position of data governance in the construction of smart mines, and defines the concept and connotation of data governance for smart mine. Data governance for smart mine is guided by the governance concept of 'precipitation, reuse, sharing and collaboration'. All departments within the coal enterprise work together with industry and government regulatory departments to formulate data standards and management specifications. The data structure and data transmission link of coal enterprises are reshaped to break through the data barriers of smart mines and ensure data quality and data security. The smart mine technology linkage and business collaboration mechanism are built to realize the process of digital-driven decision-making and flexible production. This paper discusses the development goal of data governance for smart mine from three aspects, including overall goal, development strategy and phased tasks. It is pointed out that the key point of data governance for smart mine is to solve the problem of information isolated island in the process of smart mine construction. Therefore, the data sharing and business collaboration are realized so as to make smart mine become an organic whole to promote the value enhancement of coal enterprises. Smart mine data governance needs to properly handle the five aspects relationships, including local coordination and overall coordination, physical integration and logical integration, intelligent business system and data governance platform, group-level data governance platform and mine-level data governance platform, and local processing and cloud processing. It is proposed that smart mine data governance needs to break through six key technologies, including data standard, global data modeling, data aggregation, data development, data management and data center architecture.

Through the analysis and processing of monitoring data, the prognostics health management (PHM) technology for mine inverter power devices can extract signal characteristics, locate the open circuit fault position of power devices, predict the life of power devices and improve the safety and reliability of mine inverter. This paper introduces the principle and research status of signal characteristics extraction method in PHM technology, including coordinate transformation method, spectrum analysis method, wavelet analysis method, empirical mode decomposition method. This paper introduces the principle and research status of power device open circuit fault diagnosis method in PHM technology, including state estimation method, neural network method, support vector machine method. This paper introduces the principle and research status of power device life prediction method in PHM technology, including analytical model method, physical model method, data-driven method. The above methods are compared from five aspects, including implementation difficulty, timeliness, immunity, accuracy and data demand. The signal characteristic extraction method is single. There is open-circuit fault of multiple power devices of mine inverter. The life prediction of power devices based on data-driven method fails to consider the variable working conditions of inverter. In order to solve the above problems, the research directions of PHM technology for mine inverter power devices are proposed. The directions include signal characteristic extraction based on multi-method fusion, open-circuit fault diagnosis of multiple power devices based on intelligent algorithm, fault-tolerant control and health management, and power device life prediction under variable working conditions.

Some key targets will be missed when using coal-gangue image recognition technology to recognize coal-gangue. Compared with the image target recognition model, the video target recognition model is closer to the requirements of the coal-gangue recognition and separation scene. The coal-gangue features in the video data can be extracted more widely and deeply. However, the influence of frame repetition, frame similarity and contingency of key frame on the model performance is not considered in the current coal-gangue video target recognition technology. In order to solve the above problems, this paper proposes an aggregation enhanced coal-gangue video recognition model based on long and short-term storage (LSS) model. Firstly, the key frames and non-key frames are used to screen the massive information. Multi-frame aggregation is carried out on the video frame sequence of the coal-gangue. The feature information of the key frame and the adjacent frame is aggregated through temporal relation networks (TRN), and a long-term video frame and a short-term video frame are established. The calculation amount of the model is reduced while the key feature information is not lost. Secondly, the feature weights among the long-term video frames, the short-term video frames and the keyframes are reallocated by using an attention mechanism that integrates semantic similarity weights, learnable weights and region of interest (ROI) similarity weights. Finally, the LSS module is designed to store the effective features of long-term video frames and short-term video frames. The module fuses them in the key frame recognition to enhance the characterization capability of the key frame features, so as to realize coal-gangue recognition. The model is tested based on the coal-gangue video data set in Zaoquan Coal Preparation Plant. The results show that in comparison with the memory enhanced global-local aggregation (MEGA) network, the flow-guided feature aggregation for video object detection (FGFA), the relation distillation networks (RDN) and deep feature flow for video recognition (DFF) model for video recognition, the mean average precision of the aggregation enhanced coal-gangue video recognition model based on LSS is 77.12 % and better than that of other models. The recognition precision of the modes is negatively correlated with the moving speed of the target in the video. The recognition precision of the model in this paper is 83.82% for the slow-moving target detection, and the performance is the best.

Intelligent coal mine data classification and coding is a key part of intelligent coal mine data governance and fusion sharing standard system. However, there is no perfect methodology system. It is urgent to deeply study the technical objectives, technical principles and key technical processes of intelligent coal mine data classification and coding. Based on the analysis of the core problems to be solved in the process of data classification and coding in the advanced stage goal realization of the intelligent coal mine, the essence of intelligent coal mine data classification and coding is expounded. Intelligent coal mine data classification and coding is the recognition and construction of the intelligent coal mine information world, the optimization and mapping of the intelligent coal mine consciousness world, and the transformation and description of the intelligent coal mine physical world. This paper analyzes the goal requirements of intelligent coal mine data classification and coding from three aspects of overall strategic objectives, business strategic objectives and functional strategic objectives. Intelligent coal mine data classification and coding should promote the reshaping of the business mode of coal enterprises, change the value creation driving mode, focus on key value activities, and realize the scientific organization of intelligent coal mine data. The paper proposes that the intelligent coal mine data classification and coding should follow the basic principles of scientificity, standardization, integrity, uniqueness and effectiveness. The paper also proposes the basic idea of intelligent coal mine data classification and coding. Intelligent coal mine should adopt the data classification idea of 'benchmark- expansion' two stages. In the first stage, the logical sequence of 'business domain - data domain - object - attribute - data element' is adopted to determine the classification benchmark of intelligent coal mine data. In the second stage, the logical sequence of 'intelligent application system - intelligent business system - system functions - data resources' is adopted for data classification, verification and supplement. The hierarchical coding method of line classification should be adopted for intelligent coal mine data coding. Based on the above basic ideas, it is pointed out that in general, intelligent coal mine data classification and coding needs to focus on five key steps: determining business domain, determining data domain, identifying object classes, extracting object class attributes and defining data elements.

The comprehensive control of coal mine disasters is a key problem to be solved in the process of intelligent mine construction. It is an inevitable requirement for the construction of intelligent mines to realize the normalization control of coal mine disasters with the help of digital twin technology. Based on the connotation and essence of the digital twin, this paper analyzes the recognition rule of the digital twin and puts forward the application mode of digital twin technology in coal mine disaster research combining with data interactive process of mine disaster digital twin. Real-time monitoring is carried out by coal mine underground disaster monitoring sensor. The monitoring data is uploaded to the cloud through the edge communication module and cloud communication module. The digital twin numerical simulation software is deployed in the cloud. The monitoring data uploaded by the sensor is used as the initial condition parameter, boundary condition parameter and effect verification parameter. The best optimization parameters and solutions are sought through real-time simulation analysis and continuous trial and error. When the technical means are mature in the twin world, they can be used to analyze, judge and make decisions on the best parameters and solutions for virtual entities. The decision instructions can be sent to underground actuators to control the action of disaster prevention equipment. This paper discusses the practical application of digital twin enabling disaster predictive management and control from three aspects: disaster monitoring scheme optimization, disaster rehearsal and precise planning of disaster avoidance route, and post-disaster rescue scheme formulation and accident investigation. Based on the 'cloud-pipe-edge-end' architecture of the industrial Internet, the digital twin service system for coal mine disasters is constructed. The key technologies of digital twin for mine disasters are analyzed. The technologies include intelligent sensing and execution equipment for coal mine disasters, simulation software for coal mine disasters and common support technologies. It is expected to provide reference for the construction of digital twin-enabling intelligent mines.

Coal is the guarantee energy in China, and its dominant position in energy will not change for a period of time in the future. Coal intelligent mining will show a rapid development trend and enter into a new stage of development. Through induction and analysis, the great significance, development law, contradiction, construction method and future development direction of intelligent construction of coal mine are summarized in this paper. This paper systematically expounds the reform process of coal mining technology and management mode in China, and summarizes the five stages of manual mining, ordinary mining, comprehensive mechanized mining, automatic mining, primary intelligent mining, as well as the corresponding technical modes. Breaking through the intelligent research ideas such as traditional coal roadheader mining, this paper puts forward 10 characteristics of medium and high-level intelligent mining technology of coal, namely, complete transparency of coal mine, comprehensive intelligence of perception, high-end intelligence of equipment, real-time reliability of network, integrated platform control, intelligent analysis of data, group collaboration intelligence, professional team employees, dynamic decision-making intelligence, disaster prevention and control matching, and gives the definition, characteristics, content and function of each characteristic. Based on the premise of medium and high-level intelligent technology, the corresponding ‘1+1’ management and control mode of coal mine mining is conceived, that is, one level is set in one coal mine, and a new flat platform management and control mode of ‘one command and control center+one professional team’ is constructed. Therefore, the mode can promote the reform of production mode, match the requirements of corresponding mining mode in the future intelligent rapid development and realize the real meaning of reducing personnel, increasing safety and improving efficiency. Finally, the further development of intelligent mining technology and mode in coal mine is prospected.

The existing research only analyzes the rock burst appearance characteristics under specific conditions, lacks research on the rock burst appearance induced by coal pillar under the condition of thick and hard roof. In order to solve this problem, taking I010203 working face of a mine in Xinjiang as the research object, the paper analyzes the characteristics and causes of working face rock burst under the condition of thick and hard roof multi-seam mining by means of field monitoring and numerical simulation. By analyzing the spatial distribution law of microseismic and large energy events monitored on site, the distribution characteristics of support pressure, and the distribution characteristics of surrounding rock stress during the mining of the working face simulated by numerical simulation, this paper obtained the following results. The stress concentration and the microseismic event density in the transport roadway area of the working face are higher than those in the return air roadway area. And the stress concentration and microseismic event density in the roof and coal seam area are higher than those in the floor area. Based on the above results, the causes of coal pillar rock burst appearance in the mining process of I010203 working face are analyzed by using rock burst caused by dynamic and static combined load theory. ① It is easy to produce lateral overhanging roof above the coal pillar in the transport roadway section. The sudden breaking of the long overhanging roof generates a large amount of dynamic loads. At the same time, a large amount of static loads accumulate on the coal pillars and the roof during the mining process. When the superposition of the two exceeds the critical load, it may cause the rock burst appearance of the working face or a large energy event. The mining process of the I010203 working face mainly includes the main controlling factors such as coal pillar concentrated stress, overlying roof and surrounding rock supporting stress. Therefore, it is easy to produce coal pillar type and thick hard roof caving type rock burst appearance. ② When the mining position of the working face gradually approaches the boundary of the ‘knife shape’ goaf, the boundary stress is transferred downward to the I010203 working face, which leads to the further increase of the rock burst risk of the I010203 working face.

The paper puts forward a perception and alarm method of rock burst and coal and gas outburst based on temperature. The infrared thermal imager is used to monitor the temperature of objects, and the methane sensor is used to monitor the concentration of ambient methane. When the temperature of the objects is higher than the ambient temperature of the coal mine and the temperature of the exposed coal rock, and the number, volume and area of objects that are higher than the ambient temperature and the temperature of the exposed coal rock are large, it is determined that rock burst, coal and gas outburst, mine fire or gas and coal dust explosion accidents have occurred. The temperature of the high temperature object is further determine. If it is greater than the set threshold, it is determined that a mine fire or a gas and coal dust explosion accident has occurred. Otherwise, it is determined that a rock burst or a coal and gas outburst accident has occurred. The change of methane concentration is further analyzed. If the methane concentration rises rapidly, it is determined that a coal and gas outburst accident has occurred. Otherwise, it is determined that a rock burst accident has occurred. The paper puts forward a perception and alarm method of rock burst and coal and gas outburst based on velocity. The lidar, millimeter-wave radar, ultrasonic radar, binocular vision camera are used to monitor the moving speed of objects. The methane sensors are applied to monitor the concentration of ambient methane. When the moving speed of the object is not less than the set threshold, it is determined that rock burst, coal and gas outburst or gas and coal dust explosion accident have occurred. The number, volume and area of objects with abnormal velocity is further determined. If the number of objects with abnormal velocity is small, the volume and area are small, it is determined that a gas and coal dust explosion accident has occurred. If the number of objects with abnormal velocity is large, the volume and area are large, it is determined that a rock burst or a coal and gas outburst accident has occurred. The changes of methane concentration are further analyzed. If the methane concentration increases rapidly, it is determined that a coal and gas outburst accident has occurred. Otherwise, it is determined that a rock burst accident has occurred. A multi-information fusion method for perception, alarming and judging disaster source of rock burst and coal and gas outburst is proposed. The method monitors and integrates various information such as temperature, speed, acceleration, burial depth, sound, air pressure, wind speed, wind direction, dust, methane concentration, equipment status, micro-seismic, geosound, stress, infrared radiation, electromagnetic radiation and images so as to monitor the pressure and coal and gas outbursts. The source of the disaster is determined through the magnitude of parameter changes at different locations, the sequence relationship and sensor damage.

The pore structure has a significant impact on the capability of coal seams to adsorb methane. But there is currently limited research on the features of adsorption pore structure in high-rank coal and its influence on methane adsorption capability. Taking the high-rank coal samples from Nuodong Coal Mine of Guizhou Xing'an Coal Industry Co., Ltd. as the research object, low-temperature N2 adsorption and low-temperature CO2 adsorption experiments are conducted. Combined with fractal theory, this paper studies the pore structure features of high-rank coal adsorption pores. Through high-pressure isothermal methane adsorption experiments, the influence of coal reservoir properties, pore structure features, and fractal dimension on methane adsorption capability is analyzed. The results show the following points. ① The pore morphology of high-rank coal reservoirs is relatively simple, mostly consisting of parallel plate pores and narrow slit pores with open ends. Micro pores dominate the pore structure of coal, with pore volume and pore specific surface area accounting for more than 98%, providing space for gas enrichment. ② The method calculates the comprehensive fractal dimension of high-rank coal pores based on the proportion of pore volume in different aperture segments, with micropore fractal dimension dominating the comprehensive fractal dimension. The pore structure of coal samples has obvious fractal features and strong heterogeneity of pores. ③ The Langmuir model can describe the adsorption behavior of high-rank coal. The physical properties, pore structure, and fractal dimension of coal reservoirs have a significant impact on methane adsorption capability. Langmuir volume is linearly positively correlated with maximum vitrinite reflectance, vitrinite content, ash content, and moisture content. It is linearly negatively correlated with inertinite content. The Langmuir volume is linearly positively correlated with the pore specific surface area and pore volume of the adsorption pores. The Langmuir volume is weakly linearly correlated with the fractal dimension. The research results can provide theoretical guidance for the exploration and development of high-rank coalbed methane and the prevention and control of coal mine methane disasters in southwestern Guizhou.

Based on the current research results and practical experience of 5G in coal mines, this paper analyzes the research status and development direction of key technologies for 5G in coal mines from three aspects:5G private network architecture, coverage optimization, and intelligent application scenario construction. In terms of 5G private network networking for coal mines, two types of models, namely large-scale networking at the coal group level and independent private networks for mines, can meet the needs of 5G system construction and deployment for coal mines. In terms of enhancing 5G wireless coverage for coal mines: the wireless transmission performance of 5G for coal mines needs continuous research and improvement. At present, it is advisable to use low-frequency (below 1 GHz) multi carrier SUL (supplementary uplink) for optimizing the coverage of 5G for coal mines. To achieve breakthroughs and improvements in the explosion-proof safety power threshold of underground radio waves, it is necessary to synchronously increase the wireless transmission power on both sides of the 5G base station and terminal used in coal mines. It can optimize the overall coverage capability of the 5G system used in coal mines. Pre research on 5G coverage enhancement technology for coal mines based on 6G RIS (reconfigurable intelligent surface) is needed to provide further support for improving wireless coverage capabilities. In terms of building intelligent 5G application scenarios for coal mines, it is necessary to carry out joint technical research and development in the fields of mining equipment and mining communication. It is suggested to conduct research on the information physical mapping of equipment remote control based on 5G for coal mines and future autonomous group collaborative control of equipment. It is suggested to conduct research the expected functional safety mechanism of intelligent applications. It is suggested to develop 5G modules for coal mines that are small, lightweight, and fully compatible with industrial control protocols for on-site equipment.