In order to increase transportation efficiency，improve the safety of workers，and enhance cleaning efficiency，the discrete element modeling-multi-body dynamic simulation (EDEM-RecurDyn) method was proposed to analyze the cleaning ability of the cleaning mechanism. Firstly，EDEM-RecurDyn was used to analyze the stress changes of the hob，and the three factors that affected the stress of the hob were obtained: the rotating speed of the drum，the dragging speed，and the depth of cleaning. The reference range was determined. Then，the response surface was used to carry out the three-factor three-level test，and the cleaning energy consumption ratio was obtained by analyzing rolling. The quadratic fitting polynomial was carried out. Finally，based on Matlab，a particle swarm optimization algorithm was used to solve the parameters. The results show that when the drum speed of the cleaning mechanism is 78 r/min，the traction speed is 0.05 m/s; when the cleaning depth is 100 mm，the cleaning mechanism is subjected to the least resistance and the lowest energy consumption.

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 improve the safety risk management level of coal chemical enterprises and eliminate safety management shortcomings，the production characteristics and safety risks of the coal chemical industry were analyzed. Based on the actual situation of the enterprises，the operation practice of the safety management system in coal chemical enterprises was discussed，covering the operation mode of the system，system operation planning，and implementation and operation effect of key elements of the system. From the aspects of safety leadership，system construction，safety risk management，unsafe behavior management，and system operation effectiveness，the ideas and methods for the operation of the safety management system in coal chemical enterprises were systematically elucidated. The results show that the operation ideas and modes of the safety management system in coal chemical enterprises combine multiple safety systems in the chemical industry，with comprehensive safety management elements and effective system operation. It can improve the safety risk management level of coal chemical enterprises and has practical and promotional value.

In order to play the important role of safety culture in preventing coal mine accidents and avoid the disconnection between safety culture and safety management，the current safety culture status of Shengli Energy was analyzed. On this basis，the 4-level safety culture model was applied to analyze the construction ideas and contents of the concept culture covering the two supreme principles of ″people-oriented″ and ″safety first″，the institutional culture of ″compliance with laws and regulations，clear responsibilities，and integration and innovation″，the behavioral culture of ″compliance with regulations，self-discipline，and responsibility″，and the material culture of ″advanced equipment，complete protection，visual standardization，and beautiful environment″. A safety culture system was established for Shengli Energy，and a standardized and replicable safety culture construction model was formed. The results show that the on-site application effect is good，and the mechanical and electrical injury risk rate in the enterprise has been lowered to 5% or less. The fault rate of flammable and explosive devices has been lowered to 3% or less，which has a certain reference value for the construction of safety culture systems for national coal enterprises.

In order to solve the stability problem caused by low strength and significant creep characteristics of soft rock in slope engineering of open-pit coal mines，the creep deformation characteristics and constitutive model of soft rock were studied. By taking the mudstone of an open-pit coal mine in eastern Inner Mongolia as the research object，based on the results of the triaxial creep test and the analysis of the traditional Burgers creep model，a new non-linear five-element creep damage model was established by introducing a strain-triggered non-linear dashpot. According to the principle of least squares，the parameters of the improved creep damage model were identified. The results show that the improved Burgers creep damage model has a fitting coefficient with the experimental data，which can fully describe the creep deformation characteristics of mudstone in the whole stage，especially in the accelerated creep damage stage. The fitting degree of the improved creep damage model is obviously better than that of the traditional Burgers creep model.

In order to improve the current situation of single disaster monitoring methods，weak early warning analysis capabilities，and untimely disaster disposal in mines，a single disaster classification and early warning model for gas，water，fire，roof，and dust was established based on the analysis method of formation mechanism. Through mathematical and statistical methods，the changing trends of data characteristic graphs of disaster monitoring data such as sudden changes，gradual increases，fluctuations，periodic changes，and constant changes were analyzed. Accordingly，a disaster fusion and early warning analysis plan was proposed. A disaster monitoring and early warning platform construction plan was designed. The on-site application of the platform in the Huangbaici Coal Mine of Wuhai Energy Company was analyzed from the perspectives of hardware and software deployment. The results show that the multi-disaster fusion and early warning analysis scheme based on the disaster formation mechanism and characteristic graph analysis technology can realize the disaster source tracing，correlation，and transmission analysis and improve the accuracy of early warning. The method of real-time dynamic planning of disaster avoidance routes based on tunnel parameter calculation and Dijkstra’s algorithm can improve the escape efficiency of personnel in mine disasters.

In order to realize mining in high gas mines in low gas states，a set of comprehensive gas treatment technology based on over-pre-pumping of coal seam gas，pumping while mining，and pumping and mining in the goaf area was proposed. The comprehensive gas treatment technology and measures for 81305 comprehensively drained working face in Baode Coal Mine were introduced，and the comprehensive gas treatment measures were recorded in detail，such as pre-pumping before mining of the present coal seam，switching between downdraft and updraft，pumping and drainage by buried pipeline in goaf area，pressure equalizing，air guide curtain and negative pressure spray setting，rationally distributed air flow，and closing at the end of mining. The results show that the volume of the gas pumping and drainage in the 81305 working face has reached a total of 18.549 1 million m³，and the comprehensive pumping and drainage rate has reached 81.07%，indicating excellent application effects. The comprehensive gas treatment technology combining various pumping and drainage methods can make the gas concentration in the comprehensively drained working face in Baode Mine safe and controllable during mining.

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 ensure the safety of miners，protect equipment，and improve production efficiency，the identification and intrusion early warning technology of large-scale engineering vehicles in opencast coal mines based on multi-sensor information fusion was studied. Firstly，based on the overall technical framework and implementation method，the identification and detection method of engineering vehicles based on YOLOv8 was proposed. The software and hardware platform for detection was built in the opencast coal mine，and the identification accuracy of the engineering vehicle identification and detection method based on YOLOv8 was tested through a total of 6 300 sample datasets from on-site shooting and networks. The results show that the engineering vehicle detection model based on YOLOv8 can quickly and accurately identify multiple vehicle targets in the opencast coal mine，and the detection accuracy is more than 0.85，with a low missed detection rate. In addition，the incomplete vehicle image can be recognized. The engineering vehicle identification and intrusion early warning system studied in this paper provides vehicle identification and early warning hints for the working equipment to avoid safety accidents.

The roller bearings of open pit belt conveyors face problems of low fault identification accuracy. To improve the accuracy and efficiency of fault diagnosis，a fault signal detection method of roller bearings with CPSO algorithm based on OVMD was proposed. Firstly，the excellent global optimization characteristics of CPSO were utilized，and the optimal parameter setting of the variational mode decomposition (VMD) algorithm was precisely locked to achieve effective parameter tuning of VMD. Then，VMD technology after parameter tuning was used to process the vibration data，and specific frequency band signal components were accurately extracted from the vibration data. Finally，the sparse maximum harmonic noise ratio deconvolution (SMHD) technology was used to purify the above frequency band signals，which significantly enhanced the identification accuracy of the fault characteristics of roller bearings of belt conveyors. The results show that CPSO has better performance for VMD improvement than other VMD optimization algorithms. The VMD algorithm after CPSO optimization combined with SMHD can successfully identify the specific fault points of the inner and outer rings of the rolling bearings under complex working conditions and determine the specific damage forms of the bearings.