Taking improving the safety and efficiency of tower crane operation as an example，a method of integrated identification of fatigue state and unsafe behavior was proposed in order to detect the potential safety hazards of drivers in real time. A live video stream was captured via a camera，and the video was analyzed and pre-processed to extract critical information for identifying subsequent fatigue and unsafe behavior. In terms of fatigue state recognition，the analysis method based on the state of eyes and mouth was used to monitor the physiological indicators such as the state of eyes opening and closing，the blink frequency and yawn frequency. In terms of unsafe behavior identification，computer vision and deep learning technology were combined to detect the potential dangerous operations of drivers in real time，thus ensuring timely detection of safety risks. The results show that the performance of the optimized YOLOv5-ECA(Efficient Channel Attention) model is significantly improved in fatigue state and unsafe behavior recognition. The accuracy rate and recall rate of the model on the test set are more than 90%，showing good recognition ability.

To ensure the safety of personnel and property within the storage environment，the traditional YOLOv11 object detection algorithm was improved，and a method and model to identify unsafe behaviors of personnel in the complex environment of tobacco warehouses were proposed. First，a statistical analysis of common unsafe behavior types in tobacco storage was conducted，and the classification of unsafe behaviors of warehouse personnel was explored，including item-related，action-related，and area-related unsafe behaviors. Second，based on the characteristics of unsafe behaviors of warehouse personnel，a dataset augmentation and denoising preprocessing approach was proposed to enhance fine-grained feature extraction，and introduced to improve the saliency mapping of personnel behaviors. Then，the YOLOv11 algorithm was improved through functional enhancement modules and K-means++ anchor box optimization，and a fast detection method for unsafe behaviors of tobacco warehouse personnel was proposed. Finally，the proposed method's effectiveness was validated by comparing with self-built datasets and the open Microsoft COCO dataset. The results show that the method can quickly and effectively identify unsafe behaviors of warehouse personnel，with a significant improvement in recognition accuracy compared to traditional methods(accuracy rate is 94.91% and 88.69% respectively).

In order to enrich the safety & security management theories and innovate the safety & security management paradigm，the research work of parallel safety & security management was carried out based on the parallel system theory and safety & security management theory. Firstly，starting from the parallel system theory and combining with the knowledge of modern safety & security management，the concept of parallel safety & security management was explored and the parallel safety & security management conceptual model was proposed. On this basis，the parallel safety & security management model was constructed and explained. Finally，taking urban safety & security as an example，this paper analyzed the application of the parallel safety & security management model. The research shows that parallel safety & security management is a new paradigm for safety & security management through realizing the virtual-real interaction between artificial safety & security systems and real safety & security systems to achieve multiple objectives such as solving complex safety & security problems，effective implementation of safety & security solutions and efficient training of personnel. Its model has the characteristics of breaking the constraints of real-world conditions，flexibility and scalability，high performance distributed computing，redundancy and fault tolerance，simulation prediction and continuous improvement，task scheduling and load balancing，which can provide a model reference for safety & security management of complex systems，such as urban safety & security management.

In order to improve the efficiency and accuracy of safety risk management in machinery manufacturing enterprises，the Bayesian network and machine vision technology were combined. Based on improved YOLOv5，Intersection over Union(IoU) values of safety hazard events occurring at the operation site were calculated. By leveraging the audit risk assessment in conjunction with AHP to derive the danger weights，the prior probabilities of the root nodes of Bayesian network were determined. Bayesian network model and design management system were established to realize closed-loop control. A safety risk management model of machinery manufacturing enterprises was constructed and verified by examples. The results show that the model has a more accurate identification and evaluation ability，and can find some potential safety hazards，so as to optimize the current management process. At the same time，the model also successfully realizes the effective combination of qualitative and quantitative analysis，integrates the expert experience and data quantification results，and confirms each other，so that the risk assessment results have a certain improvement in scientificity and reliability，which can provide a practical new idea for safety risk management.

To meet the requirements of the future Trajectory-Based Operation (TBO) mode，improve air traffic safety and efficiency，and obtain executable four-dimensional (4D) trajectory results with multi-point control，this study first established a fundamental 4D trajectory dynamics model by integrating atmospheric environment modeling，aircraft point-mass motion modeling，and performance modeling. Subsequently，a 4D trajectory simulation framework was developed using Simulink to validate the executability of the fundamental dynamics model. Following this，an optimal control-based 4D trajectory waypoint control model was constructed with reference trajectories as optimization targets，where the waypoint control problem was transformed into a nonlinear programming problem through the Radau pseudospectral method. Finally，comparative analyses were conducted using historical flight data from QAR. Results demonstrate that the simulated velocity and mass parameters exhibit zero deviation from QAR records，while median errors in longitude，latitude，and altitude are 0.000 11°，0.001 2°，and 19.24 m，respectively，all satisfying safety separation requirements. Critical parameters including position，time，and heading angle at selected waypoints showed zero deviation. Notably，strict control of 13 waypoints is achieved within a 65-minute flight segment.

To accurately and efficiently detect fires in complex tunnel environments，an enhanced YOLOv8s-based tunnel fire detection algorithm was proposed. Firstly，the Cross-Stage Partial Transformer Block (CSP-PTB) module was introduced to reconstruct the backbone network structure，thereby reducing computational complexity while preserving feature extraction capabilities. Secondly，CBAM was integrated to enhance the perception of the model of key areas and improve the discriminative power of feature representation. Finally，the Normalized Wasserstein Distance (NWD) loss function was employed to optimize the training process，effectively addressing the issue of insufficient detection accuracy for small targets. Experimental results demonstrate that the improved YOLOv8s model achieves a mean average precision (mAP) of 0.848，representing a 2% improvement over the original YOLOv8s model. The recall rate reachs 0.812，marking a significant increase of 9.3% compared to the original model. Additionally，the computational cost (GFLOPS) of the improved model is reduced by 6.7%，achieving dual objectives of performance enhancement and efficiency optimization. Compared with mainstream object detection models such as Faster R-CNN(Faster Region-based Convolutional Neural Network)，SSD(Single Shot MultiBox Detector)，and YOLOv5s，the improved model exhibits superior performance，with mAP improvements of 7.3%，10.1%，and 4.2%，respectively，thus meeting the stringent requirements for tunnel fire detection.

To comprehensively and efficiently analyze and evaluate chemical process risks，a risk analysis and evaluation auxiliary system for chemical processes was designed and developed based on SDG and CBR. The system was integrated with foundational verification functions for layer of protection analysis (LOPA) and safety integrity level (SIL)，enabling semi-quantitative risk analysis and evaluation. Additionally，CBR technology was utilized to effectively preserve and reuse historical case experiences in chemical processes，thereby enhancing the efficacy of HAZOP and the auxiliary evaluation system. The system was applied to conduct a risk evaluation of the propylene oxidation to acrylic acid process. The results demonstrate that the system aligns closely with the independent risk evaluation outcomes of the enterprise，exhibiting strong practicality and reliability. The SDG-CBR coupling reasoning mechanism is verified to overcome the reliance of traditional HAZOP on manual experience，achieving a qualitative-to-semi-quantitative transformation in hazard identification while enhancing evaluation efficiency and accuracy.

In order to improve and advance the nuclear radiation detection and monitoring integrated technology based on pixel sensors，a radiation noise suppression and nuclear detection method leveraging the parallel advantages of FPGA was proposed，with corresponding programs developed. By analyzing the characteristics of radiation noise signals，radiation noise suppression and two-dimensional wavelet transform programs based on FPGA were developed to output clear radiation field images. The images were decomposed into horizontal，vertical，and diagonal components，and the results of linear fitting statistics for each component were investigated to identify the component with the best linear fit. The research results demonstrate that the FPGA program modules effectively execute radiation noise suppression and nuclear detection functions in images. After noise reduction，the peak signal-to-noise ratio(PSNR) of the images is improved by approximately 11 dB. The diagonal component is shown to best characterize the radiation response information of the images，achieving a linearity of 0.99624 in linear fitting for different dose rates.

In order to predict the slope state more accurately and effectively prevent the slope instability accident，an improved ISSA-KELM slope stability prediction model was proposed. Firstly，six main factors such as bulk density and cohesion in slope instability characteristics were used as prediction indexes to establish a data set for slope stability evaluation. Secondly，SSA was enhanced by incorporating Sine chaotic mapping，Levy flight strategy，dynamic adaptive weights，and fusion of optimal explosion strategy and reverse learning. These improvements aimed at enhancing the global search capability and stability of SSA. Subsequently，ISSA was employed to optimize the kernel parameter ψ and regularization coefficient C in KELM for improved prediction accuracy while avoiding overfitting issues associated with KELM. The results show that the accuracy rate，precision，recall rate and F₁ score of ISSA-KELM model reached 0.945 9，1，0.866 7 and 0.929，respectively，which are superior to SSA-KELM，PSO-KELM and PSO-SVM models，and the predicted results of the model are the closest to the actual values. It shows that the established ISSA-KELM model has strong generalization ability.

To address the safety risks caused by rapid fire spread and thermal smoke accumulation in confined spaces such as basements and underground warehouses due to lithium-ion electric bicycle fires，this study combines experimental and numerical simulation methods to investigate the temperature field evolution，flame characteristics，and smoke diffusion behavior of lithium-ion electric bicycle combustion under different ambient temperatures，clarifying the effect of temperature on combustion characteristics. The results show that under experimental conditions，the average temperature at the combustion center in the confined space is approximately 600 ℃，with a peak temperature exceeding 920 ℃. Ambient temperature significantly affects the initial stage of lithium-ion electric bicycle combustion. In a 40 ℃ environment，the time for combustion to enter the rapid development phase is shortened by 20 seconds compared to 20 and 0 ℃ environments. The time required for nearby temperatures to reach the ignition point of the lithium-ion electric bicycle is reduced by approximately 25 seconds compared to 20 and 0 ℃ environments. After about 80 seconds，the temperature rise rates converge. At 20 seconds，the flame morphology of lithium-ion electric bicycle combustion differs noticeably across environments：the flame height at 40 ℃ is approximately 1.15 times that at 20 ℃ and 1.32 times that at 0 ℃. Flame morphology converges after about 80 seconds. Within the first 30 seconds，the smoke diffusion velocity and production rate in a 40 ℃ environment are significantly higher than those at 20 and 0 ℃，but smoke concentrations stabilize to similar levels after approximately 50 seconds.