To enhance the evacuation efficiency of deeply buried subway stations，a standard subway station was selected to establish an elevator-assisted evacuation model for deeply buried subway stations. The average evacuation time of passengers was selected as the primary evaluation metric. Variation characteristics in evacuation efficiency were calculated and analyzed under the combined influence of various factors，including buried depth of the subway，passenger flow intensity，the proportion of passengers opting for elevator evacuation，elevator operating parameters，the number of elevators，and acceptable queue size through simulation. The results indicate that the advantages of elevator-assisted evacuation are more pronounced when the subway depth exceeds 30 m. When passengers maintain their original evacuation paths，the evacuation time is inversely related to the proportion of passengers choosing to use the elevator during off-peak periods，but positively related during peak hours. Furthermore，when passengers alter their evacuation paths due to queue size，evacuation efficiency improves under different buried depth scenarios. In a subway with a burial depth of 90 m and an acceptable queue size of 30，the overall evacuation efficiency reaches its peak. When planning subway exits，reasonably increasing the number of elevators and their rated load，as well as operating speed，can effectively balance evacuation efficiency with cost control.

In order to enhance emergency management in the field of gas pipeline networks，Gas-kBERT model was proposed. The model incorporated data from the gas pipeline network field expanded by Chat Generative Pre-Trained Transformer，(ChatGPT)and Chinese Gas Language Understanding Subject-Predicate-Object(CGLU-Spo) and related corpora were constructed in this field. By altering the model's masking (MASK) mechanism，domain knowledge was successfully injected into the model. Considering the professionalism and specificity of the gas pipeline network field，Gas-kBERT was pre-trained on various scales and contents of corpora and fine-tuned on named entity recognition and classification tasks within this field. Experimental results demonstrated that，compared to the general BERT model，Gas-kBERT exhibited significant performance improvements in F₁-score in text mining tasks in the gas pipeline network field. Specifically，in the named entity recognition task，the F₁-score was increased by 29.55%，and in the text classification task，the F₁-score improvement reached up to 83.33%. This study proves that the Gas-kBERT model performs exceptionally well in text mining tasks in the gas pipeline network field.

In order to enhance the reliability and safety of gas network operations and improve the fault diagnosis capabilities for gas network leaks，while addressing issues such as the scarcity of real gas network leak data samples and variations in operating conditions，a gas network leak localization method based on transfer learning was proposed. Firstly，the Random Forest feature importance ranking method was used to select five pressure monitoring points in the TGNET simulation network. Subsequently，pressure monitoring point data under three different pressure conditions were respectively used as the source domain and target domain input features. The traditional JDA method of transfer learning was improved to reduce the feature distance between the source domain and the target domain. Furthermore，the CS algorithm was employed to optimize the dimensionality after mapping d' and the learning rate λ parameters of the improved transfer learning algorithm，ultimately achieving the diagnosis of unlabeled target domain leak segments. The results indicated that the proposed leak localization method for complex gas networks can effectively improve the localization accuracy of unlabeled gas network leaks，achieving higher accuracy compared to traditional.

To enhance the ability to cope with reservoir landslide hazard risks under extreme climate，a framework for multi-dimensional scientific observation and hydrometeorological early warning was constructed using multi-source monitoring data and machine learning algorithms. The spatiotemporal pattern and main controlling factors of landslide deformation were identified by analyzing the multi-annual observations of the two landslide cases，involving Sentinel-1，global navigation satellite system (GNSS) surface displacement and fiber optic (FO) strain. Leveraging the boosting decision tree (BDT) algorithm，a hydrometeorological early warning method based on slip zone real-time strain (RTS) was proposed，and the generalized framework of monitoring，early warning and emergency management strategies for reservoir landslides was systematically discussed. The results indicate that landslides with different deformation mechanisms show different spatiotemporal deformation characteristics，and landslide activities are closely related to localized anti-sliding treatment measures. Landslide kinematics are characterized by subzone-independent displacements and their drivers，which are highly correlated with hydrometeorological extremes. The RTS-based early warning model provides specific hydrometeorological thresholds，emphasizing the emergency response-oriented landslide monitoring and early warning concept.

To investigate the research dynamics，hotspots，and frontier trends in the field of emergency supplies scheduling，the data sources of China National Knowledge Infrastructure (CNKI) and Web of Science(WoS) were used to search and filter 321 Chinese and 497 English articles. The bibliometric and knowledge mapping software were used to conduct basic feature analysis and development trend analysis. The results show that the number of articles in the field of emergency supplies scheduling both domestically and internationally has a wavy growth. The overall research is in the rapid development stage. In domestic core author teams，there is relatively low collaboration density. While in international contexts，cross-national and cross-regional academic exchanges are frequent，with China，Canada，and Singapore serving as the core. The research focus of both domestic and international studies is basically the same，mainly revolving around model design，optimization algorithms，path location issues，etc. However，international research has been earlier and more in-depth in studying the psychological perception of disaster victims.

During the attack of public health emergencies，the surge in material demand in isolated communities leads to a series of problems，i.e.，serious shortfalls in supply，fairness of distribution and special distributional needs. To address this issue，a multi-period，multi-type，cross-regional emergency material dynamic allocation problem was studied based on a hierarchical distribution network of "material supply point-distribution center-isolated community". Especially，the demand time windows were applied to characterize the realistic requirements of isolated communities for the delivery time，and the special needs of some vulnerable groups，e.g.，aging populations，were considered. Thus，a multi-objective non-linear dynamic program was developed by integrating the three dimensions of considerations：economy，material distribution satisfaction rate and distribution equity. In view of the multi-objective structure，the Epsilon constraint algorithm was used to solve the proposed model，and the material allocation during the attack of public health emergencies in Shanghai was taken as a case study to analyze the Pareto front of the optimal solutions and conduct the sensitivity analysis on some key parameters in the model. The results show that，under the constraint of limited emergency material supply，the material allocation strategy considering the aging populations helps to prioritize the material supply of vulnerable groups. However，it also contributes to an increase in the unfairness rate of the isolated communities as a whole. For the material allocation strategy considering the delivery time windows of isolated communities，the total operation cost of the cross-regional distribution network and the unmet rate of the material demand in the isolated communities are high，but the overall distribution of supplies in the system is relatively fair，and it could better meet the needs of isolated communities in terms of delivery of supplies，which provides a useful reference for the decision makers to balance the individual demand and the overall interests.

To improve the accuracy of human thermal injury assessment and protect rescuers' safety in thermal radiation environments，a coupled heat and mass transfer model of skin-microenvironment-firefighting clothing system was proposed to predict skin burn injuries under dynamic conditions. Based on mechanism of heat and moisture transfer in porous media，the periodic movement of fabric caused by human activities and its impact on heat and mass transfer in the skin-microenvironment-firefighting clothing system were considered. Furthermore，the proposed model was used to simulate skin temperature，time of skin burn，and the distribution of temperature and humidity in the fabric layers for both dry and wet cases in real time. The results show that the relative error between simulated values predicted by the model and the experimental measurements presented in the literature is only 3.79%. When exposed to 8.5 kW/m² thermal radiation environments，the time to second-degree burn for the dry case is 33.7 s earlier than that for the wet case. When firefighters approach a 20 kW/m² radiant heat source at a speed of 1 m/s，the heat transferred is impeded by the increase in thermal layer thickness. This extends the time for second-degree burns to occur by 10.9 s and reduces the heat absorbed by the skin surface by 20%. When the air gap thickness in the microenvironment is the same as the amplitude of the periodic motion of fabric，the skin temperature increases rapidly and fluctuates significantly，and the time to second-degree burn occurs 43.7 s earlier. Human body movement and moisture in fabric layers affect heat transfer process between the human body and thermal environment，thereby their impact on the accuracy of rescue assessments cannot be ignored.

In order to systematically review the research and development status of safety resilience in the field of civil aviation at home and abroad，and deepen the research on safety resilience in the field of civil aviation，firstly，the concept of aviation safety resilience was explored by reviewing policy documents，standards and research literature related to air transport systems in recent years. Then the research and application of safety resilience in civil aviation airport，air traffic control，flight operation and other fields were discussed. Finally，the existing problems were analyzed and corresponding suggestions and prospects are put forward. The results show that the safety management of civil aviation has formed a relatively perfect system，and the safety resilience covers all stages of safety management before，during and after，but it still cannot meet the needs of perfect connection and integration with the existing safety management system. Current studies focus more on the resilience of airports and route networks. In terms flight operation，more measures are taken to improve safety resilience based on the actual flight operation. The basic research on aviation resilience assessment is relatively lacking，and the research on aviation personnel resilience at operational level is far from enough. In the future，relevant research should be carried out around the individual operation resilience，enrich the basic research of resilience assessment，further deepen the research and form a relatively stable discipline system，pay attention to the connection between safety resilience and safety management system，and assess individual operation resilience from the perspective of safety.

To address the flight safety risks posed by faults in aircraft control systems，a composite framework for fault risk assessment based on IRPN was proposed. This framework comprehensively considered four key risk factors: fault probability，severity，detectability，and risk damping. First，system fault modes were deduced bidirectionally using FMECA-FTA method. Second，human and environmental factors were incorporated，and a Bayesian network approach was employed to construct a hybrid probability model for calculating fault probabilities. Third，fault severity was categorized into three evaluation parameters，which were comprehensively assessed using the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation methods. Next，utilizing resources such as pilot quick reference manuals and aircraft type design manuals，a criterion-based reasoning method was applied to establish detectability scoring criteria，allowing for a more scientific evaluation of fault mode detectability levels. Finally，the FRAM was introduced to define risk damping coefficients，characterizing the propagation of risk during the evolution of fault risks. The computational validation was carried out with the case of jamming failure mode of aircraft flap seam wing actuation system. The research results show that its IRPN assessment result is 158，which is in perfect agreement with the actual operation. The validity and accuracy of the failure composite risk index calculated by the IRPN composite risk assessment framework are confirmed by the failure mode example simulation and the real verification of unsafe events.

To explore human error in complex emergency rescue scenarios for hazardous chemical accidents in chemical industry parks and improve human reliability in emergency rescue actions，a comprehensive analysis method was established to quantitatively evaluate the human reliability of emergency rescue for hazardous chemical accidents. Firstly，based on the laws，regulations and standards related to the emergency rescue of hazardous chemical accidents，20 emergency behaviors in chemical industry parks were summarized and extracted. Secondly，cognitive reliability and error analysis method(CREAM) was introduced to determine the probability of human error. Analytic hierarchy process(AHP) and entropy weight method were combined to quantify the severity of errors in emergency behaviors. Finally，from the perspectives of possibility and severity，the weak aspects of emergency behaviors in chemical industry parks were explored，and the strategies for enhancing emergency rescue capabilities in chemical industry parks were discussed. The practical application of the method was verified with the example of N chemical industry park. The results show that 20 emergency behaviors were divided into 3 clusters. There are 4 emergency behaviors identified that needed to be prioritized for improvement in the petrochemical zone: risk assessment，fire-fighting，initial disposal of enterprises and rescue of people in distress. For these emergency behaviors，it was proposed that N chemical industry park should focus on optimizing the accident information transmission mechanism，improving decision-making and command effectiveness，and strengthening rapid response and disaster identification capabilities，so as to provide countermeasures and suggestions for improving its emergency rescue capability.