In order to effectively reduce the casualties and property losses caused by cabin turbulence，a decision analysis method based on a dynamic Bayesian network is proposed for cabin turbulence emergency response. Firstly，according to the relevant laws and regulations at domestic and international，combined with the emergency duties of key personnel on the ground and in the air，the turbulence emergency disposal process is analysed from pre-flight，in-flight and post-flight，and 24 key events are selected to construct a structured BT model. Secondly，the mapping conditions and transformation rules are established to form a DBN model. Then，the objective direct node a priori probability and the supplementary node fuzzy probability obtained by the triangular fuzzy probabilities of supplementary nodes obtained by the fuzzy number expert judgement method to obtain the a priori probabilities of all nodes. Finally，the time slice intervals of 1 and 2 min are selected to focus on the simulation inference of moderate and heavy turbulence，and to study the characteristics of the influence of each dynamic element on the failure of cabin turbulence event disposal. The results show that: the emergency response nodes are significantly affected by the degree of turbulence and time changes，and the optimal time for emergency response is within 5 min. Among them，the probability of failure for the failure of the crew fixation measures in place increases with the increase of the degree of turbulence，human factors such as the failure of the crew to fasten the seat belts and the over-servicing by the cabin crew are the key reasons for the failure of the response.

In order to accurately and quickly achieve relation extraction from few-shot emergency plan texts，KMKP based on knowledge prompts was proposed. First，a prompt template was constructed，utilizing learnable typed entity markers that incorporate relation semantics. The effectiveness of input guidance on the pre-trained language model (PLM) was thereby enhanced by these markers. Second，the boundary loss function was utilized to optimize model training，enabling the PLM to learn specific dependency relationships in the emergency domain and apply structured constraints to [MASK] predictions. Third，a gradient-free emergency knowledge storage database was created using the training data，and a knowledge retrieval mechanism was constructed by integrating KNN algorithm. The feature connections between training and prediction data can be captured through this mechanism and the gradient-free normation was used to correct the predictions of PLM. Finally，the experimental validation and analysis were performed using four public datasets under few-shot settings (1-，8-，and 16-shot). The results show that compared to the state-of-the-art model，KnowPrompt，F1 score is boosted by an average of 2.1%，2.8%，and 1.9% by KMKP. In a 16-shot emergency plan instance test，a relation extraction accuracy of 91.02% is achieved by KMKP. Catastrophic forgetting and overfitting issues in few-shot scenarios are effectively mitigated.

To optimize the professional talent training objectives，curriculum structure，and teaching syllabus of hazardous chemicals safety supervision，the state-of-the-art knowledge system and professional capabilities of hazardous chemicals safety supervision personnel were analyzed. Questionnaires and interviews were used to investigate the recruitment needs，on-the-job training，requirements，and school-government cooperative model for hazardous chemicals safety supervision positions. The results showed shortcomings such as insufficient talents trained in hazardous chemicals safety supervision，imperfect professional knowledge system，and mismatched professional capability structure. Hazardous chemicals safety supervision personnel should master professional knowledge such as laws and regulations，safety management skills (e.g.，integrity and process safety management)，chemical processes，and chemical reactions. Furthermore，professional capabilities in risk assessment (e.g.，equipment，processes，and instrument systems)，chemical reaction analysis，and chemical process simulation analysis should be enhanced.

To explore the effects of vibration and time pressure on monitoring tasks in DCS and reduce human error，a monitoring experiment was designed to measure the monitoring performance and workload under vibration conditions (static，low and high) and time pressure conditions (no time pressure and time pressure). Statistical methods were used to explore the influence of vibration and time pressure on the monitoring performance and workload. The results show that the vibration (monitoring time and accuracy) has no significant effect on monitoring performance. Both the monitoring time and the workload show rising trends with the increase of the vibrating level. The time pressure has a significant impact on monitoring time and workload，but has no significant impact on accuracy. The monitoring time in the state confirmation task is significantly longer than that of data comparison，but the accuracy difference is not significant. The monitoring performance and workload of DCS operators in the vibrating condition are basically the same as that in the static condition. In the time-pressure condition，the DCS operators' workload is heavy，but the monitoring time is short.

To drive multi-stakeholder work safety governance in enterprise-intensive areas such as industrial parks，the dynamic mechanism for forming a multi-stakeholder work safety governance model was explored. Firstly，the key stakeholders involved in the industrial park work safety alliance were identified through text-based data mining. Subsequently，a comprehensive investigation was conducted on the development process and governance model of the work Safety Alliance in Suzhou Industrial Park. Finally，an alliance network was proposed to analyze the translation process based on ANT. Then，the dynamic mechanisms of the Industrial Parks work Safety Alliance were investigated. The results indicated that the benefits of resource exchange could be balanced by developing a work safety resource-sharing platform and proposing a multi-agent governance mechanism in a regional work safety alliance，which can promote multiple stakeholders' collaboration in work safety governance. Furthermore，the active mobilization of core actors，coupled with concerted efforts from both internal and external parties，contributes to the efficient operation of the alliance.

To quantitatively analyze the coupling relationship between risk factors of personal injury and death accidents in Chinese electricity production and identify key risk factors，these risk factors were categorized into four dimensions: human，machine，environment and management based on the 4M accident causation theory. Utilizing grounded theory，32 secondary risk factors were identified，and the coupling methods were categorized into three types: multi-factor，double-factor and single-factor risk coupling. Drawing from investigation reports of 196 personal injury and death accidents in Chinese electricity production from 2016 to 2022，the N-K model was used to measure the coupling level of risk factors，while DEMATEL method was used to pinpoint key risk factors. Finally，the countermeasures and suggestions for risk prevention and control were put forward. This study shows that the frequency of accidents is closely related to the coupling level of risk factors，and the coupling level of risk factors is proportional to the number of participating factors. The risk coupling value of the four types of risk factors is 0.162 8，indicating the highest coupling level. The risk value of human factors participating in the risk coupling is relatively high，followed by the risk values of management and environmental factors. Among the three-factor risk couplings，the human-machine-environment risk coupling has the highest risk value at 0.104 7. For the double-factor risk couplings，the human-environment risk coupling has the highest risk value at 0.05. Both risk couplings involve human risk factors. The failure to implement safety production investment and main responsibility，illegal operations and illegal command and decision-making are the key factors in the coupling effect of risk factors of personal injury accidents in electricity production，and priority should be given to prevention and control.

In order to explore the effects of organizational ethical climate (specifically，caring，rule-based and self-interested climates) on coal miners' safety performance，a chained mediation model containing positive emotions and role-width self-efficacy was constructed. Firstly，a questionnaires survey method was used to gather data from 432 front-line male coal miners across three large-scale coal mining enterprises located in Liaoning，Henan，and Shandong Provinces. Secondly，with positive emotions and role-width self-efficacy as joint mediating variables，and safety performance as the dependent variable，a structural equation model was constructed. And the mediating effect was tested using the Bootstrap method. Finally，the survey data were empirically analyzed using path analysis and mediation effects testing methods. The results show that the mediating effects influence safety performance. Specifically，the caring climate indirectly enhances safety performance through enhancing positive emotions and role-width self-efficacy. the rule-based climate mainly affects safety performance through role-width self-efficacy，whereas the self-interested climate exerts a negative effect. Notably，fostering a caring and rule-based ethical climate，along with enhancing employees' positive emotions and role-width self-efficacy，can significantly improve coal miners' safety performance.

In order to reduce the number of accidents，casualties and enhance the ability to control the level of accident hazards，and to study the general characteristics and hidden patterns of heavy and large safety accidents in China's chemical production industry，this paper used statistical analysis to comprehensively analyze 41 heavy and large accidents that occurred from 2000 to 2023 in terms of time，region，production stage，type of accidents，causes of accidents and other elements. The results show that the number of accidents shows a fluctuating upward trend in 2007-2019，and July and August are the high incidence period of accident every year. The number of heavy chemical accidents in East China accounts for 52.2% of the whole country，of which 12 accidents occurred in Shandong Province，accounting for 57.1% of the total. The highest proportion of accidents occurred in the formal production stage of enterprises，accounting for 53.6% of the total. The main types of accidents is container explosion. The domino effect exists in accidents，with heavy domino accidents and large domino accidents accounting for 14.63% and 39.02% of the total accidents respectively. There are more accidents with domino effect in heavy and large accidents than those without domino effect，accounting for 53.65%，of which the casualties of accidents with large dominoes account for 40.1% of the total accidents and casualties. The most frequent cause of accident statistics is the illegal execution of production，accounting for 49.3% of the total，and the resulting accidents with domino effect account for 66.6% of the accidents. In response to the problems analyzed in the above accidents，this paper proposes some measures to improve the management system from three perspectives: the company，the equipment and the employees.

In order to improve the theoretical guidance of safety training and promote the effective promotion of safety training in enterprises，a safety training curriculum system framework based on "principle，law，technique，instrument，power，and goal" was created by applying the related theories such as the ACT training method，safety-Ⅰ and safety-Ⅱ. Then，important training points and the level of mastery of knowledge for various types of employees were clarified，and the application study was conducted. The results show that the training covers six dimensions: safety management objective，safety ideology principles，safety laws and regulations，safety management requirements，safety management tools and safety production situation. The 77 sets of video courses made according to the framework of the developed safety training course system have achieved good feedback in application. It can offer enterprises some direction and a point of reference when creating training materials，making training schedules and setting up safety training.

In order to quantify the psychological changes of crowds and the impact of competitive factors on the behaviors of evacuees in emergency evacuation situations，a dynamic model was constructed that closely links the state of personnel psychological state with competitive influencing factors. In this model，the crowd was divided into three categories based on individual behavioral characteristics: independent evacuees，cooperative evacuees，and competitive evacuees. Three major factors influencing individual decision-making during the evacuation process were analyzed in depth: personal characteristics，social guidance mechanisms，and environmental factors. Specific parameter values were set，and SD were employed to simulate and study the behavioral characteristics of pedestrians during emergency evacuation. The research results indicate that as environmental visibility decreases and panic spreads，the proportion of competitive evacuees significantly increases. Conversely，when there are positive guiding behaviors and acts of helping others in the environment，these positive factors can effectively alleviate the crowd's anxiety and reduce competitive behaviors. Particularly noteworthy is that when multiple factors are coupled，the greater the competitive intensity，the more significant the positive effect of helping behaviors becomes. This shows that in highly stressful evacuation environments，positive social guidance and mutual assistance behaviors play a non-negligible role in relieving competitive pressure and improving evacuation efficiency.