To enhance the accuracy and reliability of geological earthquake disaster events predictions，a predictive model combining knowledge graph with GCN was proposed. Initially，the knowledge graph for geological earthquake disaster events was constructed，and the multi-source disaster-related information was consolidated into structured data. Then，the KGCN model was employed for deep learning of entities and relationships within the knowledge graph，uncovering potential association rules to forecast the evolution of disasters. Finally，the effectiveness of the model was validated through a set of geological earthquake disaster cases. The results show that the predictive model combing knowledge graphs with GCN exhibits excellent effectiveness in forecasting the evolution of geological earthquake disaster events，especially in dealing with complex multi-source data. The information can be efficiently integrated，and potential relationships can be accurately uncovered by the model. Excellent prediction accuracy is achieved in various aspects，including disaster levels，casualty levels，and disaster victim categories. Notably，the accuracy in predicting the disaster emergency response levels reaches 89.92%.

In order to solve the problems of imperfect construction of risk classification control mechanism，low informatization level，and insufficient employee participation in the construction of double-prevention mechanism in coal chemical enterprises and build a long-term double-prevention mechanism for enterprises，firstly，the design idea，system architecture，and function module of the digital platform were described in detail. Then，with risk classification control and hidden danger investigation and management as the core，data collection，intelligent analysis，early warning，task distribution，progress tracking，and other functions were integrated to realize the digital management of the whole process of safety production of coal chemical enterprises. Finally，combined with specific cases，the application effect of the double-prevention digital platform in coal chemical enterprises was evaluated. The results show that the platform can provide enterprises with a standardized and intelligent closed-loop management system from the aspects of risk identification and assessment，classification control，and hidden danger investigation and management，and it achieves good application effects in risk management and control，hidden danger investigation，and main responsibility implementation. The platform can grasp the risk dynamics in real time，discover and eliminate hidden dangers in time，improve the emergency response speed，and reduce the accident rate. It can provide a new idea for the informatization and intelligent construction of the double-prevention mechanism of coal-to-oil coal chemical enterprises and provide a reference for the construction of the double-prevention digital platform of other industrial and mining enterprises.

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.

The causality causal graph of hazardous chemical accidents was developed to improve the safety management level of hazardous chemical enterprises. Firstly，based on the accident investigation report，an entity-relationship joint extraction model was proposed through an improved CasRel technique. Furthermore，the proposed model aimed to improve the extraction accuracy of textual information by incorporating the relationship-aware bidirectional encoder representation method (R-Bert) and Span pointer network. Subsequently，similarity calculation methods were used to generalize the events to enhance the graph's comprehensiveness and accuracy. Then，the refined data was stored in the Neo4j graph database visualizing the associations between events. Finally，the corresponding guestion-answering system was proposed based on the developed causal graph，and then an intelligent question-answering system for the causality of hazardous chemical accidents was proposed. The results indicated that the F₁ value calculated by the improved CasRel model was 90.5%，and the prediction accuracy of the proposed model was 2% higher than that simulated by the original model. The hazardous chemical accidents causal graph and intelligent question-answering system performed well in terms of multiple evaluation indexes，clearly revealing the logical relationship between events. Therefore，the proposed model in this study can meet question-answering needs of hazardous chemical accidents，facilitating the exploration of accident patterns and potential risk factors，and enabling accident trend prediction.

There is a risk of collision in the batch operation of logistics UAVs. To evaluate the collision probability between UAVs in the air，a model was proposed to analyze the collision risk of logistics UAVs in the aerial operation stage. A UAV collision probability model with the double errors of positioning and velocity was proposed based on the conflict zone theory，and the logistics UAV collision probability variations are analyzed from different angles. The key risk factors in small civil UAV accident/incident data were analyzed and identified based on the data from the Federal Aviation Administration of the United States. The UAV safety flight interval model was proposed based on the logistics UAV failure data of 20 thousand flight hours to determine the shortest flight interval under the acceptable safety level. Furthermore，the effects of track angles and wind directions on the collision probability between UAVs under constant flight intervals were investigated. The results showed that the risk for all flight scenarios was acceptable when the shortest safety interval between logistics UAVs exceeded 90.71 m. When the track angle was 30，60，and 90°，the collision probability varied little with the wind direction. Specifically，the collision probability was maintained constantly when the track angle was 90°. The collision probability varied a lot when the angle was 140°，thereby the safe flight of the UAV was sensitive to environmental factors.

To reveal the characteristics of coal mine accidents in China in recent years，and put forward targeted countermeasures and suggestions for accident prevention，firstly，the major and catastrophic coal mine accidents in China from 2013 to 2023 were collected and analyzed from the aspects of the year，type，month，province and cause of the accident. Secondly，taking "2·22" particularly serious collapse accident in Inner Mongolia Xinjing coal mine as an example，the accident was analyzed based on 24Model. Finally，combined with the above analysis and research results，the accident prevention countermeasures and suggestions in line with the current situation of coal mine safety production in China were put forward. The analysis and research results show that the number of major accidents and deaths in coal mines has shown an overall downward trend，and the level of coal mine safety production in China has significantly improved since the 12^th Five Year Plan. Gas accidents are still the main accidents in coal mines in China，accounting for 51%. The fourth quarter of each year is a period of high incidence of coal mine accidents，accounting for 30.43% of the total number of accidents. Affected by geological conditions and occurrence，accidents often occur in the main coal producing areas. The proportion of accidents caused by unsafe human behavior is as high as 74%. When there are hidden dangers at both the individual and organizational levels and the dual prevention measures are not in place，accidents are likely to occur. Countermeasures and suggestions for reducing coal mine safety accidents are put forward from four dimensions，including safety supervision，strengthening safety through science and technology，team development and safety culture.

To clarify the essential characteristics and differences between inherent safety, behavior-based safety, process safety, and functional safety and to promote a virtuous cycle of high-quality development and high-level safety, this study employed literature review and comparative analysis methods to explore their basic connotations and evolution processes, interrelationships, realistic challenges, and development paths based on the safety management paradigm shift. The results indicate that inherent safety is an idealized form of safety. Behavior-based safety is an interdisciplinary field that involves the theories and methods of safety science and behavioral science. Process safety protects humans, machines, and the environment through systematic approaches from a full life cycle perspective. Functional safety aims at preventing unacceptable risks caused by functional failures of systems. The four types of safety, led by inherent safety, involve a gradual progression from concepts to practice. These types of safety share a unified internal structure encompassing the elements of humans, machines, environment and management. The current representative standards cover various industry sectors and focus on accident prevention. In the future, the synergistic effect of the four in safety governance should be fully utilized. By using artificial intelligence technology to empower the new engine of safety production, the four should be continuously improved in specific practices tailored to local conditions.

In order to clarify the correlation of eVTOL risk factors，and explore their impact on risk prevention and control in the UAM ecosystem，complex network theory was used to establish a risk evolution model. Based on the UAV accident database at home and abroad and the statistics of general aviation accidents，combined with the operation characteristics of eVTOL in urban low-altitude scenes，35 types of risk factors and 10 types of dangerous events were identified from the perspective of human-machine-environment. Gephi software was used to construct the network model，and the key nodes were evaluated comprehensively by the node degree，proximity centrality，internode centrality and PageRank(PR) algorithm. The key edges were evaluated by the internode number，so as to determine the key risk propagation path. In order to reduce the system risk，the measures to reduce the chain breaking disaster were proposed，and the system safety after chain breaking control was measured by network efficiency index. The results show that there are strong correlations among the eVTOL risk factors in the UAM ecosystem，and there are eight key risk transmission chains. The system safety is improved by 4.74%，16.21% and 18.10% by blocking key human factors，key system technical failure factors and key intermediate dangerous events，respectively.

In order to reveal the complex causality between EWSV and their multiple antecedent conditions，and to improve the efficiency of safety governance，a comprehensive model integrating contemporary deterrence theory，protection motivation theory，and social learning theory was constructed from a perspective of complexity theory. Based on this，six antecedent conditions affecting EWSV were identified from three perspectives: leader，coworker，and employee. Then，the fsQCA was used to reveal what configuration of antecedent conditions would lead to high level of EWSV. The results show that a single antecedent condition is insufficient to explain high level of EWSV but safety-specific leader punishment omission and coworker work safety violations(CWSV) play universal roles in forming high level of EWSV. Three types of driving modes composed of five condition configurations can lead to high level of EWSV. Three types of condition configurations lead to non-high level of EWSV. Reducing CWSV and improving employees' perception for formal sanctions are crucial for achieving non-high level of EWSV. Different combinations of multiple antecedent conditions can lead to high level of EWSV，and there is a complex causality (concurrency，equivalence，and asymmetry) between high level of EWSV and their antecedent conditions.

To promote the construction of emergency technology and management majors and strengthen the university talent cultivation of emergency rescue, the majors' characteristics were determined based on the national needs in emergency. Then, a talent development mode for emergency technology and management majors was proposed from aspects of professional ethics, theoretical knowledge systems, and practical technical ability. Firstly, the majors' professional ethics were clarified based on the professional ethics requirements from Teaching Standards for Emergency Rescue Technology and National Vocational Skill Standards for Emergency Rescuers. Secondly, a core curriculum system covering the entire emergency lifecycle was proposed using the PPRR(Prevention, Preparation, Response, Recovery) model including prevention, preparation, response, and recovery. Next, a combination of learning and training, integration of government-industries-research organizations was proposed to advance the cultivation of emergency skills and the enhancement of practical innovation capabilities. Finally, a talent cultivation system was developed from the perspectives of textbooks, faculties, teaching process management, a talent assessment system, and a continuous improvement mechanism. The results indicated that the talent development system integrated with public service and social responsibility attributes, full-cycle work content, disasters, and multi-subjects practical applications. Moreover, the system developed a close connection between talent cultivation and practical work needs and guided for cultivating of high-level applied talents in the emergency field who had moral and professional qualifications and theory and practice capabilities.