In order to detect the spontaneous combustion of coal gangue in open-pit mines in time and avoid greater losses caused by the spread of fire，the technology of spontaneous combustion and fire detection in open-pit mines based on infrared thermal imaging and deep learning was studied. As a result，the problem that a single detection method cannot achieve all-round spontaneous combustion and fire detection of coal gangue in large open-pit mines was solved. First，it was proposed to realize spontaneous combustion and fire detection in different areas through infrared thermal imaging and fire image target recognition，and a spontaneous combustion recognition of coal seam and early warning system architecture of open-pit mines was built. Then，thermal imaging equipment fixed on the ground of the open-pit mine was used to capture infrared images of the coal gangue area where no fire has occurred in real time and monitor the temperature changes of the coal gangue. Finally，the drone with an airborne camera was used to take photos of coal seams，waste piles，and surrounding environments，and then a fire detection model was built through the YOLOv8 target detection algorithm based on deep learning，so as to realize the detection of image targets of flames and smoke，thereby completing fire detection and early warning. The results show that the joint detection technology based on thermal infrared images and flame and smoke image recognition has an average detection accuracy of 70.5% for flames and an average recognition accuracy of 74% for smoke，which can meet the detection needs of spontaneous combustion and fire in open-pit mines.

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.

To solve the problems of complex production technology knowledge expression，decentralized information storage，and insufficient data sharing faced by coal preparation，a training platform for regulations and standards of coal preparation was constructed by introducing knowledge graph technology. The platform was designed using a service-oriented architecture (SOA). The system was developed by applying Python language and a large language model. The results show that through the standardized data processing process，the platform can build a complete coal preparation knowledge base and realize the functions of knowledge search，intelligent question and answering，and intelligent training. The platform covers regulations and standards of coal preparation and specialized technical documents and can provide convenient training services on regulations and standards of coal beneficiation. It can also be used as a reference for knowledge sharing and training application services in other areas of production technology.

In order to prevent the occurrence of coal mine production site accidents，the application of information technology in coal mine mining was discussed through literature data，summary，and induction，and the meaning and main types of coal mine enterprise informatization were clarified in detail against the background of production informatization. The research shows that the key technologies of ″Internet +″ coal mine safety informatization include the Internet of Things，cloud computing，and big data，which provide efficient and scientific decision-making basis and technical means for coal mine safety. In order to support big data generated by the Internet of Things for coal mines，the ″Internet +″ coal mine safety information application framework composed of an information perception layer，data transmission layer，cloud computing resource layer，application service layer，and user interface layer is proposed. In addition，a construction model based on a proprietary cloud was proposed to make the system elastic and scalable.

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 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 promote the improvement of the new mode of contactless delivery，improve the quality of cold chain food，and develop new quality productive forces，it is necessary to study the internal influence mechanism of risk factors of contactless delivery of food cold chain. Firstly，the characteristics of the contactless delivery mode were systematically analyzed based on the national standard documents and related literature，and the risk index system of the contactless delivery of food cold chain was constructed，including six first-level indicators and 17 second-level indicators. Then，the FDANP was used to evaluate the risks at all levels，determine the causal relationship between the factors，and quantify the weight of the risk factors. The results show that information risk，personnel risk，cold chain technology，and equipment risk can affect other risk factors in the first-level indicators. Among the second-level indicators，factors such as natural disasters，delivery timeliness，and optimal transportation planning are the key risk factors. In the future，the new mode of contactless delivery of food cold chain can be improved from three aspects: improving the level of informatization，attaching importance to emergency response capabilities，and enhancing supervision.

To study the impact of marine environmental corrosion on the seismic performance of wind power towers，a 3 MW offshore wind power tower was used as an example. Based on a universal finite element analysis platform，wind power tower analysis models before and after corrosion were established，and time history analysis was used to study the collapse mechanism of the wind power tower during earthquakes. Through modal analysis，the impact of marine corrosion on the frequency and mode of vibration of structures was studied. Three natural ground motions (two far-field motions and one near-field motion) were selected，and time history analysis was used to study the deformation and damage evolution of the wind power tower. The collapse mechanism of the wind power tower was revealed based on the calculation results. The research results show that marine corrosion has a relatively small impact on the frequency and mode of vibration of the wind power tower. Under the action of near-field or short-term earthquakes，marine corrosion will not significantly change the collapse mechanism of the wind power tower. Under the action of long-term earthquakes，the corroded parts of the wind power tower will be damaged first，which will trigger higher-order modes of vibration and cause significant changes in the seismic damage and collapse mechanism of the wind power tower.

To improve the emergency management level of coal-fired power generation and ensure the safety and stability of the coal-fired power industry，an index system for the evaluation of the emergency management ability of coal-fired power plants was established in this paper from the aspects of emergency preparedness ability，emergency prevention and early warning ability，emergency response ability，emergency support ability，and emergency recovery ability. An improved fuzzy comprehensive evaluation method based on entropy weight was constructed，and the safety emergency management ability of a typical coal-fired power plant was evaluated systematically. Finally，measurements and suggestions for optimizing safety emergency management were proposed. The results show that the weight of the evaluation indexes for the emergency management ability of coal-fired power plants taper off as emergency recovery ability (0.269)，emergency support ability (0.227)，emergency preparedness ability (0.197)，emergency prevention and early warning ability (0.172)，and emergency response ability (0.135). The fuzzy comprehensive evaluation model can effectively solve the difficulty of evaluating complex problems with multiple factors and levels and improve the objectivity and scientificity of emergency management evaluation in coal-fired power plants.

In order to prevent the occurrence of fire accidents caused by external factors in coal mines，achieve efficient and rapid response after the risk is out of control，and minimize casualties and property losses，the Daliuta coal mine of Shendong Coal Group was used as an example. The key external factors causing fire accidents in large and super-large coal mines were analyzed，and pre-disaster prevention and control measures were summarized. In addition，emergency response technologies were explored. The results show that long-distance tape machine friction ignition，overheating of high-voltage cables and equipment，and spontaneous combustion of auxiliary transportation vehicles are key external factors causing fires in large coal enterprises. Emergency response technologies such as airflow short circuits，local backflow，new air door control，fire zone closure，and compressed air self-rescue can be adopted to deal with fire accidents caused by external factors in coal mines. Fire accidents caused by external factors in coal mines feature common occurrences，serious losses，and difficult responses. Therefore，a comprehensive management technology combining pre-disaster prevention and emergency response is needed to ensure the safety of mine production.