To address the complex scenarios of identifying danger zones in tower crane operations during construction，an early warning method of tower crane danger zone was proposed using computer vision technology. This method combined dynamic determination of tower crane danger zones with computer vision to detect personnel wearing situations of safety helmets and safety belt at the construction site and the inadvertent intrusion beneath the tower crane. Additionally，the YOLOv5 algorithm was adapted with attention models，and interactive window detection software was developed. Results indicate that the recognition accuracy of this model for human intrusion behavior and personal protective equipment exceeds 85%，demonstrating high precision. This method can be effectively applied in tower crane construction scenarios，optimizing fixed danger zone delineation to dynamic tower crane danger zones，and providing real-time monitoring of inadvertent personnel intrusion with warnings.

In order to mitigate the impact of traffic accidents on the operation efficiency of freeways，and improve the throughput capacity of the accident area，based on real-time information via vehicle-to-vehicle and vehicle-to-road，a cooperative lane change guidance strategy in freeway traffic accident areas was proposed with the safety potential field theory. Firstly，in view of various traffic accidents in different lanes in two-lane or multi-lane traffic of one-way，the guidance area of collaborative lane change was divided into four areas: accident protection area，guidance transition area，collaborative lane change guidance area and free lane change area. The guidance threshold of lane change was determined to update vehicle status. Furthermore，the safety potential field of traffic accidents was established，and the corresponding guidance strategies of vehicle cooperative lane change were proposed according to different scenarios. The calculation methods of the safe distance for vehicle lane change and the latest lane change position were given. Finally，based on simulation of urban mobility (SUMO) software，the simulation results were verified in various scenarios. The results show that in the two-lane of one-way scenario，the optimization effect is most obvious when the vehicle cooperative guidance rate is at 75%. In the multi-lane of one-way scenario，the optimization effect is most obvious when the guidance rate is at 50%. Meanwhile，through comparative analysis，it is found that the average speed of vehicles passing through the accident section is increased by up to 6.3% and the maximum vehicle delay is reduced by 14.6% after adopting the lane change guidance strategy.

To mitigate the risks of leakage，fires and explosions in petrochemical equipment，focusing on a typical catalytic cracking unit，a novel early warning method for detecting abnormal states using probability distribution functions was introduced. Spline fitting principles were used to uncover the trends in operating parameters such as pressure，temperature and flow rate over time，and to extract characteristic parameters such as deviation rate and deviation amount. By employing the Weibull distribution，the failure probability distribution function of the equipment was determined. The extracted characteristic parameters were integrated with the failure function to construct a probabilistic distribution mathematical model incorporating these features. Based on this model，a comprehensive early warning process was developed，facilitating real-time risk assessment and anomaly detection during the catalytic cracking process. The findings demonstrate that this method can effectively predict anomalies under conditions of oscillation，step changes，and gradual trends in operating parameters. Compared to traditional instrument systems，this early warning method advances the warning time by 87 to 621 seconds，addressing the limitation of limited response time following single-threshold alarms in the conventional systems. Furthermore，a comparison of various data processing methods reveals that the early warning model based on spline fitting exhibits superior performance.

In order to clarify the research progress of intelligent risk management in coal mines，the research status of data-driven coal mine safety risk management models was comprehensively analyzed. The prediction methods and analysis models for coal mine safety risk assessment were also reviewed. Firstly，the intelligent risk management was defined，and the scope of analysis was determined by searching relevant literature. Then，the research status，existing problems and development trend of accident big data were reviewed from three aspects: data-driven analysis method，coal mine safety risk assessment model and coal mine big data prediction and early warning platform. The results show that the theory and application framework of data-driven risk analysis in the field of coal mine safety has been basically formed，but it still cannot meet the needs of risk assessment and emergency management. In the application of early warning platform，a unified and general basic framework of big data analysis platform for coal mine safety production has been formed，but its application and promotion in production practice are far from enough. In the future，it is necessary to construct the comprehensive risk assessment model to study the risk of coal mining，starting from improving data quality and integrating dynamic and static multi-source data. Besides，the application of data-driven analysis in production practice should also be strengthened. These works can promote the transformation of coal mine safety risk management mode from empiricism to data-driven，and realize the informatization and intelligence of coal mine safety risk management.

To ensure the SOTIF of eVTOL vehicles in UAM and reduce the verification difficulty of artificial intelligence algorithms，an obstacle avoidance model was proposed based on RTA method. Firstly，SAC (soft actor-critic) algorithm improved by the artificial potential field method was used as the complex function of the eVTOL intelligent obstacle avoidance system. Then，dynamic response planning (DRP) was used as a backup function of the intelligent avionics system to mitigate SOTIF hazards. Moreover，monitoring and decision-making modules were adopted to obtain environmental conditions and develop an RTA architecture. Finally，the simulated obstacle avoidance performance was compared between the two systems using complex function and RTA. The results showed that both methods can achieve obstacle avoidance，but the traditional obstacle avoidance system using complex functions may impose SOTIF risk. The RAT architecture design increased safe flight time from 78.4% to 98.15%，with the total route length only increasing by 0.95%，reducing risks in operational scenarios while ensuring efficiency.

In order to improve the high-precision detection and early warning of crane safety operation and enhance the safety management ability of enterprises，focusing on the needs of unmanned industrial safety incident analysis and monitoring and early warning，an inspection robot that combines ground and air flight in hoisting scene was customized to intelligentize hoisting safety monitoring，pop-up image recording and safety alarm.A lifting dataset Cranes-Dataset (CRN-Dataset) containing 3 120 images was made，and DPIM algorithm was proposed to enhance the rapid detection ability of multi-scale objects.Based on corner detection and density-based spatial clustering of applications with noise and considering the safety attributes of the space distance between cranes and workers，the process of triggering alarms based on safety rules was developed to record real-time illegal operation image and popup alarm.The results show that，after actual deployment and verification，the DPIM algorithm significantly improves target identification ability compared with other traditional algorithms，and it is suitable for real-time calculation and data transmission of embedded edge intelligent analysis nodes to complete field deployment.

To improve the efficiency of disaster response，the "Hebei rainstorm" and "Heilongjiang rainstorm" were adopted as illustrative cross-regional research cases，and text-image-audio multimodal data were collected from short videos. In the face of massive unstructured data，deep learning technology was employed to realize the extraction of multimodal emotional features，cross-modal integration and intelligent sentiment classification in short videos. By comprehensively using spatial and temporal big data，the multimodal emotional characteristics of short video of rainstorm disaster were deeply mined and analyzed in the spatial and temporal dimension. The results indicate that the model's accuracy exceeds 85%，efficiently fulfilling the objectives set for short video analysis. From the temporal perspective，the emotional fluctuations of netizens broadly align with the cycle of rainstorm disasters，providing a basis for assessing disaster severity and public opinion trends. Furthermore，the intervention of media and government entities plays a significant role in shaping the emotional evolution surrounding rainstorm disasters. In terms of spatial dimensions，negative emotions exhibit a "low-high-low" trend as disasters shift locations，and the resonance and diffusion of these emotions display distinct regional characteristics. Therefore，it is imperative to prioritize public opinion guidance in disaster-stricken areas，as well as in some eastern regions of China and non-disaster areas experiencing similar phenomena.

In order to monitor the health state of construction formwork support systems and prevent the risk of safety accidents caused by formwork collapses，a new intelligent monitoring method combining EMI and CNN for joints of formwork support systems was proposed. Firstly，based on the electromechanical coupling and sensing-driving characteristics of PZT，PZT-joint coupling model was built based on the electromechanical impedance sensing mechanism. Secondly，the original conductivity of PZT patch，coupled with the monitored structure，was used as a monitoring signature for identifying joint looseness based on the EMI technique. Thirdly，EMI-CNN model was built with the 801 original conductance signals of PZT over the sensitive frequency range as the inputs，and the nine degrees of joint looseness as the outputs. In total，the dataset consisted of 189 samples，162 for training and 27 for testing. At last，taking an actual formwork support system joint from building site as an example，EMI-CNN model was verified and compared with EMI-BP model by the experiment. The research results show that EMI-CNN model reached convergence after 85 iterations. The prediction accuracy of the EMI-CNN model reached 100%，which is 29.63% better than EMI-BP model. This proposed method is distinguished by its real-time，accurate and non-destructive monitoring capabilities，providing an effective solution for health monitoring of joints in construction formwork support systems.

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.

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.