In order to reduce safety accidents and economic losses caused by roller failures of underground belt conveyors in coal mines and improve the safety and transportation efficiency of workers and unit equipment，the N-BEATS prediction model with deep structure and residual network was applied to predict the life of rolling bearings for abnormal vibration of roller bearings at different positions under different working conditions. Firstly，the principle and structure of the N-BEATS prediction model were analyzed，and a life prediction model suitable for roller bearings was established based on the N-BEATS principle. Then，a vibration signal monitoring platform for roller bearings based on DVS technology was built against the actual roller operating conditions of a conveyor belt. The vibration signals of roller bearings under different working conditions were collected. Finally，the collected vibration data of roller bearings were input into the N-BEATS model，convolutional neural network (RCNN)，and similarity prediction model，and they were compared with the actual values. The remaining life prediction quality of the three types of roller bearings was evaluated. The results show that the N-BEATS prediction model has an average absolute error increase of 5.3% and 4.1%，respectively，compared to RCNN and similarity prediction models. The relative root mean square error of the N-BEATS prediction model is increased by 6.3% and 5.2%.

In order to solve the problems of imperfect construction of risk classification control mechanism，low informatization level，and insufficient employee participation in the construction of double-prevention mechanism in coal chemical enterprises and build a long-term double-prevention mechanism for enterprises，firstly，the design idea，system architecture，and function module of the digital platform were described in detail. Then，with risk classification control and hidden danger investigation and management as the core，data collection，intelligent analysis，early warning，task distribution，progress tracking，and other functions were integrated to realize the digital management of the whole process of safety production of coal chemical enterprises. Finally，combined with specific cases，the application effect of the double-prevention digital platform in coal chemical enterprises was evaluated. The results show that the platform can provide enterprises with a standardized and intelligent closed-loop management system from the aspects of risk identification and assessment，classification control，and hidden danger investigation and management，and it achieves good application effects in risk management and control，hidden danger investigation，and main responsibility implementation. The platform can grasp the risk dynamics in real time，discover and eliminate hidden dangers in time，improve the emergency response speed，and reduce the accident rate. It can provide a new idea for the informatization and intelligent construction of the double-prevention mechanism of coal-to-oil coal chemical enterprises and provide a reference for the construction of the double-prevention digital platform of other industrial and mining enterprises.

To ensure the effective contact area between the carbon brush and the rotor and reduce the occurrence of electric sparks，a new type of carbon brush grinding and polishing device was proposed. Based on the dynamic modeling principle of the clearance mechanism，a method for characterizing the internal clearance between the rotating pairs by using the massless virtual rod method was adopted to establish the continuous contact dynamic equation of the connecting rod with clearance. MATLAB was used to solve the equation，and the influence of the clearance between the rotating pairs and the speed of the crankshaft on the dynamic stability of the connecting rod mechanism with clearance was studied. In order to reduce the influence of fitting clearance and speed on its dynamic performance，a low speed of 10 r/min was used for attitude adjustment，and the fitting clearance between the rotating pairs should be controlled within ± 0.2 mm. By adjusting the posture of the carbon brush，the dynamic stability of the carbon brush mechanism was improved. The results show that under the same speed of the crankshaft and structural parameter conditions，smaller clearance between the rotating pairs indicates better dynamic stability of the attitude adjustment mechanism. Under the condition of constant clearance，a slower speed of the crankshaft indicates better dynamic stability of the attitude adjustment mechanism.

In order to effectively improve the quality and effect of safety production education and training，blockchain technology was utilized to design a safety production training platform and construct a new model for training supervision. By analyzing the current situation of the safety production training industry and the challenges it faces，the problems of production and operation units and training institutions，as well as the limitations of supervision means were discussed. The platform was divided into a data storage layer，a blockchain platform layer，a business logic layer，and a data display layer. The new model for supervision of safety production and training was analyzed. The results show that the use of blockchain technology in safety production training to build a new comprehensive supervision model can prevent data tampering and fraud，achieve trustworthy data storage and secure sharing，and effectively improve the efficiency and convenience of safety training and supervision work.

To solve the problem of coal dust leakage from the coal handling system of coal-fired power plants，by comparing different solutions，dust prevention and control equipment and technologies were adopted including curved coal fallout pipe，fully enclosed dust-removing guide chute，pulsed bag dust collector，and wet dust collector. The optimized solution for dust reduction and prevention of the coal handling system was put forward. The dust prevention and control system covering the whole operation process of coal fuel ″turning，stacking，picking，and transporting″ was formed and was applied in Suizhou Power Generation Project. The results of the study show that the method effectively reduces the spillage of coal dust in all aspects of coal fuel transfer and basically realizes a dust-free operating environment and clean fuel transport. It provides a feasible dust prevention technology for controlling the coal dust pollution produced during the operation of the coal handling system.

In order to ensure the safe and efficient operation of the emergency command system of hydropower stations and improve the level of digitalization and intelligentization of emergency responses，the difficulties of traditional emergency responses of hydropower stations were analyzed. The knowledge graph technology was used to standardize the massive heterogeneous information of hydropower stations，and a standardized information base was formed. The research and application of multi-source heterogeneous information fusion technology in the process of accident responses were carried out to realize the real-time acquisition，processing，and analysis of multi-source heterogeneous information. By means of data mining，analysis and processing，fusion and application，the fault early warning model was built to accurately study the nature of the accident and fault location and automatically generate the optimal response guidance scheme，so as to guide emergency response personnel to carry out fault control，equipment isolation，and risk identification safely and efficiently. In addition，it helped deduce the effect of emergency response measures and fault development situations intelligently in the response process and dynamically optimize response measures. At the same time，by building a visual information sharing platform，the key points of accident responses and the progress of measures were displayed dynamically，and the key guidance information was pushed intelligently according to the division of duties of the personnel in the emergency command system，so as to realize the complementary interaction and cooperative command of each level of emergency response and establish an efficient，intelligent，and visual emergency command system.

There are many and complex factors influencing the safety status of electromechanical equipment in coal mines，and it is difficult to identify the potential safety hazards. To address these issues，a comprehensive safety evaluation model of electromechanical equipment in coal mines based on AHP-TOPSIS method was proposed. According to the cause theory of electromechanical equipment accidents in coal mines，20 evaluation indexes were selected from four aspects of human factors，electromechanical equipment maintenance，management organization，and working environment，and the safety status evaluation index system of electromechanical equipment in coal mines was constructed. Then，the AHP method was used to calculate the weight of each index，and the TOPSIS method was used to calculate the relative nearness degree between the sample and the ideal solution. The index weight was coupled with the relative nearness degree，and the safety level of electromechanical equipment in coal mines was predicted. Finally，the model was applied to a mine of Shanxi Coal Group. The results show that the safety level of electromechanical equipment in the coal mines mine is Level Ⅱ. According to the prediction results，the potential safety hazards are identified by reverse order analysis，and the analysis results are in line with the actual situation.

In order to improve the level of reservoir and dam safety monitoring and build an intelligent monitoring system for reservoirs and dams in Dadu River Basin，the problems and difficulties of traditional reservoir and dam monitoring operations were analyzed，and the innovative monitoring mode of reservoir and dam management center in Dadu River Basin was discussed. The results show that the reservoir and dam management center in Dadu River Basin has introduced intelligent three-dimensional (3D) deformation monitoring，Beidou satellite high-precision deformation monitoring，unmanned aerial vehicle (UAV) monitoring，underwater unmanned vehicle monitoring，intelligent patrol，intelligent deep inclination measurement monitoring，and other technologies and has built an intelligent safety management and control system for the reservoir and dam through technology innovation. The system integrates a number of monitoring technologies，integrates and analyzes multi-source data，and can realize intelligent management and control of operational safety risks and rapid emergency response.

In order to effectively leverage the role of information technology in the safety management of power generation enterprises，the current situation of information management systems and power enterprise safety management was analyzed from the perspectives of group companies and grassroots enterprises. In response to key and difficult issues such as safety education，outsourcing management，and risk control，the construction of a safety credit management information system was proposed，which used technologies such as photography，recording，and positioning to automatically record the behavior of personnel at all levels in fulfilling safety responsibilities，providing decision-making basis for improving the safety management of power enterprises. The results indicate that the construction and use of a safety management information system can significantly reduce safety management costs，effectively conduct performance evaluations，identify weaknesses in safety management，and predict potential safety risks.

In order to prevent safety accidents caused by fatigue failure of mining drilling rigs due to overload，a safety condition monitoring technology of drilling rigs based on digital twins was studied. First，the overall framework of the digital twins-based monitoring system for safe operation conditions of drilling rigs was proposed，and then a real-time analysis method of drilling rig stress conditions based on a proxy model was studied. The finite element method was used to obtain stress distribution and amplitude data sets of the rig overturning frame，which were used for training and testing the proxy model. The results show that the maximum difference between the large-scale stress value calculated by the finite element method and the predicted value by the proxy model does not exceed 5×10^-6 MPa. The absolute variance of the difference between the predicted values and the calculated values at all nodes is about 2×10^-6 MPa. This shows that the proposed proxy model can be used instead of the finite element method to analyze the real-time stress of the rig overturning frame.