To investigate the constituent elements and improve the performance of campus safety management work, the theoretical connotation and practical paths of the campus safety management evaluation system from the perspective of risk metaphor were explored. Firstly, the constituent elements of campus safety management were sorted out based on the safety management correlation model, and the dimensions of campus safety management were analyzed from the metaphorical characteristics of black swan risks, gray rhino risks, golden monkey risks, and white rabbit risks. Secondly, through sorting out campus safety policy texts and accident cases, the evaluation indicators for campus "safety management system-safety management behavior-safety management status" were systematically designed. Then, the weights of evaluation indicators were calculated using the fuzzy analytic hierarchy process(FAHP) and the entropy weight method(EWM). The game theory was introduced to determine the subjective-objective combined weights, and the technique for order preference by similarity to ideal solution was used to compare the levels of safety management performance. The safety management data of typical cases were entered into the information platform, and the campus safety management model was proposed for the evaluation results based on risk metaphor theory. The results show that the risk metaphor graph is an important tool for constructing campus safety management models by integrating multi-dimensional perspectives. The campus safety management evaluation system can present the scores of indicators at all levels and the overall safety management level, and the risk metaphor theory provides theoretical support and practical guidance for campus safety management.

In order to enhance maritime emergency rescue efficiency and reduce casualties, a systematic safety design strategy for maritime casualty disposal cabins was proposed. CTA was first used to identify rescue personnel's core needs during critical tasks. The Kawakita Jiro Method method and Analytic Hierarchy Process (AHP) were then used to prioritize these needs. Quality Function Deployment (QFD) was used to translate them into design elements, and Axiomatic Design (AD) was used to map functional requirements to design parameters. Results show that the integrated CTA-QFD-AD approach effectively identifies functional requirements and safety design elements. Optimizing key factors, such as ergonomic dimensions, spatial layout, Color-Material-Finish (CMF), environmental interfaces (lighting, noise, vibration), and tool/information interfaces, can significantly improve human-machine efficiency and ensures rescue safety.

To enhance the personalized thermal protection effectiveness for workers exposed to hot environments and clarify the individualized protection strategies with different body types, this study investigated the physiological and perceptual responses with different BMI in hot environments to explore individual thermoregulation capacity during heat exposure. First, two hot environments were set in a climate chamber, including neutral ((25.1±0.4)℃, (52.8%±1.9%) Relative Humidity(RH), 0.1 m/s) and high temperature ((35.0±0.5)℃, (50.0%±2.9%) RH, 0.1 m/s). Twenty male participants were recruited and divided into two groups: overweight (BMI ≥ 24) and underweight (BMI < 18.5). Second, participants were initially seated in neutral environment for 10 minutes, then performed alternating cycles of exercise and recovery in the hot environment. Physiological and perceptual responses parameters were recorded during the human trials. Finally, the regulatory mechanism of BMI on thermal responses and the extent of its impact were evaluated by analyzing the differences in subjective and objective indicators between the two groups of subjects. The results indicated that the two groups significantly (p<0.001) differed in core temperature, with the increase of 0.55 ℃ for the overweight group and 0.46 ℃ for the underweight group. The mean skin temperature of overweight and underweight participants increased by 4.2 and 2.9 ℃, respectively, indicating that overweight individuals were more sensitive to high temperatures. The total sweat rate and heart rate of overweight participants were significantly higher than those of underweight participants, while no significant difference was observed for heart rate between the two groups. Differences in BMI led to statistically significant differences in perceptual responses parameters (p<0.05), not only affecting the intensity of thermal responses but also inducing differences in the patterns of regulatory mechanisms. Therefore, individualized thermal protection strategies should be formulated based on body type characteristics.

In order to address the challenges of inaccurate region segmentation and insufficient detail restoration in flood disaster recognition models within complex urban environments, AttResU-Net, an enhanced U-Net semantic segmentation model integrating residual networks and a self-attention mechanism was proposed. Building upon the classical U-Net architecture, the model employed a deep residual network as the encoder to strengthen feature representation. Simultaneously, self-attention mechanisms were incorporated into the decoder to enhance response capability on key flood-related regions. A comprehensive training and testing pipeline was established. The improved AttResU-Net was trained and evaluated on the FloodNet dataset, which contains diverse and complex urban environmental categories. Quantitative metrics and qualitative visual results demonstrate the model's superior performance, achieving a mean pixel accuracy (mPA) of 79.75%, pixel accuracy (PA) of 90.01%, and mean precision (mPrecision) of 81.78%. Comparative experiments against state-of-the-art models reveal that AttResU-Net attains significantly higher segmentation accuracy and global recognition capability, particularly for urban features such as trees, water bodies, roads, and buildings.

To enhance the immersion and effectiveness of fire training and improve the interactive experience of acquiring fire-extinguishing skills, a VR particle fire-extinguishing algorithm based on a heat transfer was proposed and developed on a VR platform. First, a thermodynamic interaction model at the particles level was constructed by introducing a heat transfer mechanism to simulate the energy transfer process between extinguishing agent particles and flame particles. Then, the random walk algorithm was combined with the vortex dynamics model to enhance the natural behavior of the particle system during spraying, diffusion, and turbulence, thereby improving the dynamic realism of flame propagation and fire suppression. Finally, a VR-based fire training system featuring multiple typical fire scenarios was developed based on the Unity 3D engine, and simulation experiments were conducted to validate the matching relationship between different types of fire extinguishers and fire categories. The results show that the collision frequency and energy attenuation curves between extinguishing particles and flame particles vary significantly depending on the extinguisher type and its applicable scenario. Furthermore, the flame decay rate governed by the heat transfer model is closely related to the type of fire extinguisher, effectively reflecting the physical patterns of fire suppression processes.

To achieve dynamic risk management and control during the CO₂ injection process in CCUS oilfields and prevent CO₂ leakage acidents, a dynamic assessment method for the probability of high-risk scenarios was proposed. Firstly, a novel improved FPN model was developed by integrating a three-parameter Weibull distribution model for equipment failure probability based on Grey Model (GM) and Support Vector Machine (SVM) with FPN model. Subsequently, the risk analysis was conducted on the above ground CO₂ injection system of CCUS process. The CO₂ leakage accident chain was established to quantitatively predict the accident occurrence probability. The dynamic characteristic of equipment failure probability changing with time (t) and the impact of protection layers on risk were taken into account by the improved FPN. The results indicate that when t=500 h, the risk exhibits an exponential increase. The safety valve is identified as the protection layer with the highest importance. When t=303 days, the system's residual risk reaches the PetroChina risk acceptance criterion of 1×10^-5, indicating that the system risk becomes unacceptable beyond this point. It is recommended to add a high-high level interlock protection layer, which extends the time until system risk becomes unacceptable to 5 832 days, thereby significantly reducing maintenance frequency and costs.

To characterize the performance curve of urban bus systems under different rainfall intensities, a resilience measurement model for urban bus system operations was constructed. Taking the bus system in the main urban area of Yangzhou as a case study, the operational resilience of transit networks across different regions and individual routes were analyzed. Building upon aforementioned resilience measurement results, the operational status of bus stops under different resilience levels was further quantified. The spatio-temporal patterns of resilience evolution within the integrated "network-route-stop" hierarchy were elucidated. The results show that during rainfall events, urban bus systems exhibit varying performance disturbances across different regions. The variations in the absorption, recovery, and adaptation capacities of bus systems across different regions, significant spatial heterogeneity in resilience is observed, resulting in pronounced spatial heterogeneity in operational resilience. The combined effect of morning peak hours and extreme rain events has a notable impact on bus operational resilience. It is also found that the operational resilience of bus routes varies across different regions under rainfall events of varying intensity, and different levels of resilience lead to significant differences in the performance losses of bus stops. Notably, under low resilience conditions, the reliability of stops decreases (an increase in arrival intervals and a decrease in stop punctuality).

In order to improve the efficiency of emergency rescue and reduce accident losses in chemical industrial parks, this study comprehensively considered the risk of chemical parks and the overall risk of emergency supplies, divides the cost of emergency rescue into the cost of construction of emergency supplies reserve, storage cost and transportation cost, and established a model for selecting the site of emergency material reserve in chemical parks. The model aims to minimize the overall risk, minimize the cost of emergency rescue and optimize the coverage of the reserve to the demand point. Taking a chemical industrial park in Tianjin as an example for validation analysis, the model was solved using the NSGA-II algorithm. When constructing different numbers of emergency material storage within the chemical industrial park, the optimal solutions for the location, cost, risk, and coverage of these storage facilities were obtained. The research results show that the model converges stably within 200 generations. As the amount of emergency material storage increases, investment costs continue to rise, coverage gradually improves, risks are significantly reduced in the early stages, and stabilize in the later stages. The model can generate multiple site selection schemes, effectively addressing the challenge of balancing the "risk-cost-coverage ratio" in the site selection process for emergency material storage within chemical industrial parks. This helps decision-makers make more scientific and reasonable decisions in complex emergency material storage site selection issues based on different preferences.

To remedy the fragmented command tiers, a weak data-to-decision link and poor dynamic adaptability, which have long limited the effectiveness of China's emergency-response system, an integrated framework based on digital-twin technology was investigated. A generic four-tier, five-step model—comprising an emergency-command center, on-site command post, rescue teams and individual rescuers, together with the steps Sense, Simulate, Strategize, Decide and Act—was established and extended to the S3DA2 framework. A cost-minimization formula for optimal decision making was derived. Building on this framework, an emergency digital-twin battlefield technology system was established, encompassing multi-source data fusion, twin modelling, fluid-solid-coupled/artificial intelligence hybrid simulation and multi-objective decision optimization. A fluid-solid coupling, multi-agent rescue simulator was developed and validated with the 2024 Tuanzhou Dyke breach on Dongting Lake, enabling a real-time closed loop between the physical scene and its virtual replica. Results show that breach-width predictions deviate by less than 10%, total sealing time is reduced from 82 h to 52 h, and the estimated rock-fill demand of 5.9 × 10⁴ m³ matches field measurements.

Aiming at the problems of uncertainty and imprecision of public opinion information in emergency response decision-making of emergency events network public opinion, a decision-making method for emergency response to online public opinion on emergency events based on PLTSs was proposed. Firstly, methods such as Python programming and machine learning were used to crawl, preprocess and analyze the sentiment tendency of online public opinion information to obtain the decision matrix characterized by PLTSs. Secondly, the CRITIC assignment method was used to objectively determine the weights of each attribute. Then, the comprehensive relevance of each case was obtained and ranked based on grey correlation analysis(GRA) model. Finally, the effectiveness and practicability of the proposed method were verified through typhoon disaster cases. The results of the study show that the proposed method is able to monitor the online public opinion of emergencies in real time, obtain decision-making data objectively and intelligently so as to realize the quantitative assessment of typhoon disasters. It provides good decision-making support for the relevant emergency departments to effectively respond to the online public opinion of emergencies.