In order to solve the problem of false alarms and missed alarms in pipeline gas leakage detection using a single sensor，timely warning and feedback of leakage status，a multi-source data fusion pipeline leakage detection method based on cross-attention was proposed. Firstly，the pre-trained ShuffleNetV2 model was used to extract spatial features from thermal imaging data. Then，a 1DCNN BiGRU model was constructed by combining a one-dimensional CNN (1DCNN) and a bidirectional gated recurrent unit (BiGRU) to extract temporal features from gas sensors. Finally，cross-attention was used to capture the spatiotemporal correlation of the data and obtain the feature representations of the two data sources. The residual method was used to connect the features and input them into the classification layer to obtain the recognition results. The results show that the constructed SCGA model has a gas recognition accuracy of 99.22%，and the loss value fluctuates between 0-0.04. Compared with support vector machines (SVM)，1DCNN，and BiGRU models that only use gas sensor data，the accuracy is improved by at least 4.12%. Compared with MobileNetV3，ShuffleNetV2，and ResNet18 models that only use thermal image sensor data，the accuracy is improved by at least 1.14%. Compared with the multi-source data fusion model SCG，which simply connects temporal and spatial features，the accuracy is improved by 1%. It was verified that the SCGA model has high accuracy.

To ensure the safe，high-quality，efficient，and environmentally friendly construction of future infrastructure projects，it is crucial to investigate the prevalent issues and characteristics of intelligent safety management and control practices in infrastructure projects. Firstly，the evolutionary process of intelligent safety management and control was comprehensively reviewed. It includes four distinct stages: intelligent identification and control of risk sources，data-driven identification and rectification of safety hazards，enhancement of intelligent safety management and control system，and comprehensive integration of intelligent management and control. On this basis，the successful experiences of different stages were summarized. Secondly，the challenges encountered in intelligent safety management and control were analyzed from the perspective of overall collaborative management，and its characteristics were analyzed from the perspective of "people，objects，environment，and management". Subsequently，starting from the three dimensions of safety management process，the whole life cycle of engineering construction，and the development level，the thoughts on intelligent management and control for infrastructure engineering safety were proposed，focusing on "reverence for safety，intelligent safety，inherent safety，digital intelligence，digital capability，and digital value". Finally，the future development path of intelligent safety management and control was envisioned. The results show that the safety management and control of infrastructure projects are transitioning from experience-based to data-driven approaches，while rapidly integrating new technologies，equipment，and processes. Intelligent safety management and control represents a significant extension of intelligent construction and management in infrastructure engineering. This is mainly evidenced by the transformation of safety management concepts，methods，and systems，as well as the value extraction from data assets. In the future，the intelligent management and control for infrastructure engineering safety will develop to comprehensive data fusion，knowledge-driven，virtual and real integration，human-machine collaboration，and full life-cycle control. At the same time，the importance of data privacy protection and standardised governance will become increasingly prominent.

In order to accelerate the speed and efficiency of autonomous systems testing，the method of generating a scene database for unmanned driving in campus environments was proposed. Firstly，the simulation test scenarios in complex campus environment were analyzed，and the campus scenes were simplified as a combination of road network structure，ground properties，interacting members and environmental factors. Secondly，the method of generating the scene database based on importance indicators was proposed to solve the boundedness of the campus scenario database. Then，the complexity indicators and interest probability indicators were used to describe the importance indicators of scenarios. The fuzzy analytic hierarchy process(FAHP) was used to evaluate the complexity of the scenario. The interest probability of the scenario was calculated by combining the kernel density estimation method and the interested weight calculation method. Next，the parameter space was segmented to obtain the set of similar scenarios，and the scenario sets were sorted according to test priority and importance indicators. The filtered scenarios were gradually added to the test scenario database，and the scenario database with test sequences was generated. Finally，the test evaluations based on the real-world campus scenario database were conducted to verify the effectiveness of the scenario database generation method proposed in this paper. The results show that the campus test scenes can be effectively described using four scene elements and the tree structure. The method proposed in this paper can generate a campus test scene library with high test efficiency，high coverage，conformity to natural probability，and interest interval，which is helpful to improve the efficiency of unmanned simulation test in complex campus environment.

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.

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 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.

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 verify the scientific rationality of the risk assessment database of safety management and early warning mechanism for thermal power enterprises and achieve the controllability of safety risks in thermal power enterprises，an indicator system of safety management risk assessment was constructed，and an evaluation model for safety management risks in thermal power enterprises was established. The AHP modeling was used to analyze and evaluate the indicator system and determine the safety management risk level and weights for thermal power enterprises. Early warning strategies for safety management in thermal power enterprises were proposed，and the reliability of the results was validated through examples. The results show that the risk assessment database of safety management can assess and warn of potential accident risks in advance and automatically provide control and corrective measures and plans，thereby eliminating accidents in their early stages，reducing the frequency of accidents，and improving the level of safety production management.

In order to further improve the safety production management level of heavy-haul railway enterprises and consolidate the foundation of safety production management，the safety production standardization construction and evaluation system was introduced into heavy-haul railway enterprises to promote safety production standardization construction in heavy-haul railway enterprises. Through research and analysis，the basic principles，system composition，element framework，operational model，work procedures，and evaluation methods of safety production standardization construction were clarified，and research on the safety production standardization evaluation system for heavy-haul railway enterprises was carried out. The safety production standardization evaluation system was mainly composed of three parts: a list of veto items，a list of bonus points，and a list of evaluation elements and weights. The evaluation elements included three core elements，40 first-level elements，and 190 second-level elements. At the same time，weight conversion solved the problem of how to evaluate the safety production standardization construction in some heavy-haul railway enterprises，which do not involve one or more first-level elements in the core elements within their business scope. The results show that the safety production standardization evaluation system effectively promotes the implementation of the main responsibilities of heavy-haul railway enterprises，as well as the systematization of safety management，standardization of job operation behaviors，intrinsic safety of equipment and facilities，and allocation of equipment in working environments，which strengthens the safety production management level of Xinzhun Railway Company.

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.