In order to explore the negativity and universality of security incidents，a reverse thinking paradigm based on Negative Systems Theory in safety science was first applied in conjunction with the Ecosystem Theory from systems science. This approach identified the characteristics of intelligent systems—openness，inclusivity，interactive coupling，and dynamic balance—and clarified the logical framework for intelligent system security analysis. Subsequently，the sources of security incident hazards were identified from the perspective of negative systems theory，and the mechanisms behind security incidents were explained. The concept of cohesive coupling was introduced，and an s-shaped framework model was proposed to analyze the stability of subsystems within intelligent systems and investigate their underlying technological support. Finally，using the concepts of producers，transmitters，consumers，and decomposers，the dynamic balance of information flow throughout the entire process in intelligent systems was analyzed，collectively shaping the security analysis framework under the negative systems theory perspective. The results indicated that the security analysis framework established under the negative systems theory perspective could effectively analyze the secure and stable operation as well as the dynamic balance of information flow throughout intelligent systems，ensuring the comprehensiveness and reliability of intelligent system security analysis.

Inattentional blindness is a human error related to selective attention or inattention. In order to reduce safety accidents caused by inattentional blindness in high-altitude work among construction workers，30 participants were selected for an experiment to analyze the importance of inattentional blindness in the failure of safety risk perception，as well as the personality trait-related factors that affect inattentional blindness and the failure of safety risk perception，and to establish practical safety management strategies. The results show that inattentional blindness accounts for 50% of the failure of safety risk perception. In addition，personality traits have a significant relationship with inattentional blindness and failure of safety risk perception，with extraversion，conscientiousness，and openness significantly correlated with inattentional blindness and failure of safety risk perception. Workers with low extraversion，high conscientiousness，and low openness have lower proportions of safety risk perception failure and inattentional blindness.

In order to monitor the risk during the navigation of MASS，the safety control structure of MASS was constructed based on System-theoretic Accident Model and Process (STAMP). STPA was used to define the losses/accidents and system-level hazards，identify unsafe control actions，analyze loss scenarios，and construct an accident model for system state transition. FTPN was used to model the process model，and a given MASS navigation situation was used to obtain the relevant fuzzy time functions and to project the situational evolution of FTPN. A new risk level expression was introduced，and a two-dimensional path diagram of system loss/accident was used to visualize the real-time system risk level and system unsafe states transition paths. The results show that at the current moment of the set navigation situation，no safe water depth input，no updated collision avoidance path，unsafe heading and speed，and grounding are the highest risk system unsafe states and correspond to the four highest risk transition paths. The study shows that the FTPN process model driven by STPA can comprehensively assess the real-time risk level of MASS navigation. Visualize real-time risk with a two-dimensional path diagram of real-time losses/accidents of the system，which can monitor the unsafe system states during MASS navigation and describe their transition paths.

The properties and risk characteristics of grey rhinos have been deeply investigated and used，which are new emerging concepts and metaphor theories in risk management areas. To prevent and mitigate risks and challenges nowadays，it is urgent to systematically investigate the grey rhino-related contexts. The risk classification system，evolutionary mechanism，and management strategies of grey rhinos were explored by integrating existing theoretical models and risk classification approaches. The results indicated that the grey rhino risk was attributed to objective environmental factors and subjective cognitive factors. Moreover，the grey rhino risk can be divided into hidden，stationary，and collisional gray rhinos based on properties，cognitive，state，time，spatial，and subject perspectives. The risk development trend of each grey rhino species was evaluated based on the mechanism of risk perception，risk distance，and risk framework influence factors. Furthermore，an evolutionary framework of "accumulation-fluctuation-mutation" within a complicated system was proposed to develop risk management strategies and improve cognitive abilities by identifying hidden gray rhinoceroses that were not easily perceived. The innovative idea is strengthened to resolve static grey rhinos of normalized coexistence，and agile mode is created to deal with sudden bursts of collision gray rhinos.

In order to achieve a quantitative evaluation of individual pilot's exceedance risk，a refined evaluation model for pilot's individual exceedance risk was established based on QAR data and the flight operations quality assurance(FOQA) monitoring items. Firstly，according to accident statistics，International Civil Aviation Organization (ICAO) and the core risks divided by the FOQA station，the FOQA monitoring items associated with the three types of core risks were selected as the evaluation indexes，and the risk value for each core risk of the individual pilots was calculated. In the next step，the weight of each core risk value was calculated by entropy weighted TOPSIS. Then，the refined evaluation model for the pilot's individual exceedance risk was established. Finally，the model was applied to the quantitative evaluation of actual flight risk. By collecting 9 317 pieces of multi-source fusion data from the FOQA station of Civil Aviation Administration of China (CAAC)，the individual exceedance risk of the pilots were quantified，the ranking of individual pilots' exceedance risk was obtained，and the pilot's individual exceedance risk levels were also divided with the use of K-means clustering algorithm. The results show that the model can quantify and rank 1 693 individual pilots' exceedance risk，and divide the pilot's exceedance risk into three types，including high risk，medium risk and low risk.

To investigate the effects of different incidental emotions on low sensation seekers' unsafe behavior decision-making process from a neural level，ERPs technique was used. The Chinese version of the Sensation Seeking Questionnaire for College Students was used to select low and medium sensation seekers (control group). Then，video clips were used as emotional stimulus materials to induce positive and negative emotions before the experiment，and a risk scenario task was set to perform risk decision-making experiments. The results indicated that when the emotional stimulus variable was not considered，there was no significant difference in safe behavior decision-making between the low sensation-seeking and the control groups. Under positive emotion，low sensation seekers maintained risk aversion. However，low sensation seekers were more likely to make unsafe behavior decisions regarding behavioral performance under negative emotion. Under negative emotion，the latency of LPP induced by low sensation seekers was delayed，indicating that the interference of negative emotion made it more difficult for individuals to classify and evaluate risks. Therefore，the intrinsic reasons for their external behavioral performance can be explained from a neural level. Therefore，emotions play a moderating role in the process of sensation seeking affecting safe behavior decision-making.

To solve the current safety risk issues of UAV operations in low-altitude urban environments，a FBN was used to identify and analyze the key risk factors of low-altitude UAV operations. Firstly，risk factors were analyzed from the perspective of human-machine-environment-management based on operation process of low-altitude UAVs. GeNIe software was used to develop a Bayesian network(BN) for risk assessment of low-altitude UAV operations，and the prior probabilities of the underlying events were analyzed using expert prior knowledge and fuzzy sets. Finally，univariate，bivariate，and sensitivity analyses were performed，and the network feasibility was validated. The results indicated that the key risk factors for low-altitude UAV operation were UAV battery failure，environmental obstacles on the operation route，and UAV operation supervision technology. FBN reverse inference showed that environment-related risk (79%) and UAV equipment risk (60%) were the main risk factors in the UAV operation process.

To effectively evaluate the safety risk status and optimize safety management strategies in university laboratories，a dynamic safety assessment method was proposed based on a multi-layered safety control structure model. Firstly，a multi-layer safety control structure model was developed to identify the causal factors and control deficiencies of fire and explosion accidents in the university laboratory. Then，a safety dynamic feedback evaluation method was developed integrating an accident model based on system theory and Bayesian networks. Finally，the proposed evaluation model was used to determine the risk probability，analyze risk factors affecting safety incidents，and identify key risk factors in university laboratory safety. The results indicated that the proposed evaluation method based on the safety control structure model covered all risks identified by traditional methods and identified potential risks. Furthermore，the incorrect operation of hazardous chemicals，violation of operation procedures during the experiment，and the aging，short circuits，or defects in electrical lines were key risk factors affecting laboratory safety risks of universities.

To solve the problems such as forward tilt deformation，serpentine shape，axis deviation and correction during shield tunneling，which affected the safety and efficiency of shield construction，a multi-objective optimization method of shield attitude combining CatBoost and NSGA-Ⅲ was proposed. Taking Guiyang Metro as the background，22 influencing factors were selected as input parameters，and the nonlinear mapping function relationship between input parameters and shield attitude was established by using CatBoost algorithm. The importance of input parameters was evaluated by random forest (RF) algorithm. A CatBoost-NSGA-Ⅲ multi-objective optimization model was established to minimize the absolute value of the shield attitude，and the applicability and effectiveness of the proposed method were verified by a case study. The results show that the prediction model obtained by using CatBoost algorithm to train engineering measured data has high accuracy，and the R² range of 5 shield attitude targets is 0.916-0.943. By using the CatBoost-NSGA-Ⅲ multi-objective optimization method，the attitude of the shield can be optimized significantly，and the average value of the overall improvement is 53.34%.

In order to investigate the effect of EA on the process of coal oxidation spontaneous combustion，EA was uniformly mixed with long-flame coal in Xiashijie at the mass ratios of 2%，4%，6%，8% and 10% to experiment. The contents of active functional groups and the variation characteristics of thermal transport characteristics of each sample were studied using the microscopic infrared spectroscopy test and laser thermal conductivity test. The optimal mass ratio was determined to be 8% through the evaluation of the inhibitory effect of coal oxidation spontaneous combustion. The coal spontaneous combustion temperature-programmed test was used to compare the influence of four inhibitors on the gas release of coal oxidation spontaneous combustion，and the degree of inhibition of EA on coal oxidation spontaneous combustion was further determined. The results showed that the peak areas of -CH₃，-CH₂，-OH and -C=O- in EA-tc are reduced，but the peak area of C-O is increased compared with RC. At the same coal temperature，the thermal diffusivity and thermal conductivity of the RC are higher than those of EA-tc，and its specific heat is lower than those of EA-tc. The average inhibition rates of thermal diffusivity，specific heat capacity and thermal conductivity of 8% EA on low-temperature oxidation process of coal samples are 20.8%，9.8% and 13.1%，respectively. Compared with RC，CO release amount of 8% EA-tc is reduced by 52.3% at 170 ℃，and the resistance rate of EA on coal oxidation spontaneous combustion is maintained at 50.5% to 72.5% at 30 to 170 ℃. The inhibitory effect of EA on coal oxidation spontaneous combustion is better than that of the other three inhibitors.