In order to quickly and effectively extinguish the lithium-ion battery fire，solve the problems concerned in the fire protection field，such as long fire extinguishing time and high water consumption，and explore the inhibition effect of hydrogel extinguishing agent on the thermal runaway of large capacity lithium-ion batteries. Firstly，the microstructure of hydrogel at high temperatures was analyzed by an environmental scanning electron microscope. Then，by building a lithium battery combustion test platform，the hydrogel fire extinguishing test was carried out. Taking the 135 Ah square aluminium case ternary lithium-ion battery pack for vehicles as the test object，the electric heating method was used to induce its thermal runaway and explore the cooling inhibition effect of hydrogel on lithium batteries. The results show that the pore structure of the hydrogel is destroyed after being heated，which is conducive to its adhesion to the surface of the object and continuous cooling. When using hydrogel for fire extinguishing and cooling，the maximum cooling rate of the battery surface is twice that of water. After the hydrogel is sprayed，the temperature of the lithium battery rises slowly，and the heating rate is only half that of water. Compared with water，the hydrogel can delay the thermal runaway of adjacent batteries for a longer time，which can bring longer safety time for rescue and escape.

In order to improve the safety management effect of subway construction and reduce the occurrence of subway collapse accidents，the risk factors of subway construction accidents were analyzed. Firstly，the multi-factor coupling mode of 'human' and 'object' in accident causation were analyzed systematically. On this basis，with the help of accident causation 24Model and GSM，the management model of safety system was constructed. Finally，the multi-factor coupling failure mode of human-object system was applied to 16 existing subway construction collapse accidents. The multi-factor coupling mode and action path of 14 "human" reasons and 6 "object" reasons were given respectively，and their risk levels were calculated. The results show that there are 6 modes and 14 action paths for the multi-factors coupling effect of "human" and "object" in subway construction. The management model is divided into the target layer and the matching layer between the cause of the accident and the safety defense line. The matching layer is constructed from three levels: micro matching，medium matching and macro matching. Based on the established safety system management model of subway construction，the "three defense lines" of subway collapse accident safety construction management were given.

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.

To reduce the influences of background interference factors in natural environments such as clouds，mist，dust，lights，sunrise，and sunset on the smoke and flame target detection accuracy，a smoke and fire detection algorithm based on an improved YOLO-V5 algorithm was proposed. Smoke，flame target images，and interference image data sets were obtained from the on-site collection and web crawling approaches to solve sample imbalance and improve model generalization ability. A bidirectional feature pyramid network (BiFPN) was used to replace the original feature pyramid network (FPN) + path aggregation network (PAN) structure，and then multi-scale feature fusion on the target was performed to strengthen the model feature fusion ability. At the same time，distance intersection-over-union(DIoU) non-maximum suppression(NMS) is used to replace the original non-maximum suppression (NMS) to speed up the convergence of the detection box loss function and enhance the model reasoning ability. The results showed that the improved algorithm's accuracy，recall rate，mean average precision(mAP) and FPR were 79.2%，68.6%，74.2%，and 12.8%，respectively. Compared with the original YOLO-V5 algorithm，the proposed algorithm improved accuracy rate，recall rate，and mAP by 1.9%，0.9%，and 2.7%，respectively. Furthermore，the FPR was decreased by 3.7%.

In order to effectively reduce the accident rate of mountain highways，the traffic accident data of mountain highways in Yunnan province from 2016 to 2021 was taken as the research object，based on the DEMATEL-AISM. This paper analyzes the causality of risk factors and draws the UP and DOWN directed topological hierarchical diagrams，and finally determines 19 risk factors，constructs an N-K-coupling degree model to quantify the risk factors，couples the risk factors of mountain highway traffic accidents in all dimensions，explores the relationship between risk factors，and proposes a full-dimensional coupling model of traffic accidents in mountainous areas. The results show that in the single dimension，the coupling value of human factors being too close to the vehicle and fatigue driving is 0.741，and the coupling value of road factors is 0.816，which are the two effects that have a greater impact on the system in the single dimension，and the coupling values of human-vehicle and human-road are 0.157 and 0.124 in the two-dimensional. The maximum effect of human factors is human-road-ring in multi-dimensional，with a coupling value of 0.891，in which the driver's bad driving behavior，the sharp bend of the road and the long downhill，and the rain，fog，and ice and snow days of the environment are easy to be coupled with other factors more than 70%，which constitutes a strong coupling relationship and the probability of traffic accidents is large.

In order to ensure the navigation safety in the process of ship driving and operation and accurately evaluate the practical operation skills of the crew，aiming at the problem of the lack of a complete evaluation index system for the evaluation of ship mooring operation，combining the safety evaluation of mooring operation conducted by the expert evaluation method and fuzzy comprehensive evaluation method，the membership function of each evaluation index was constructed. The analytic hierarchy process(AHP) was used to determine the suggested weight value and the standard value of each evaluation index to ensure the accuracy and reliability of the evaluation results. The three-dimensional virtual ship technology was used to develop a set of mooring maneuvering automatic evaluation systems according to the influence degree of each factor on mooring maneuvering，so as to realize the comprehensive evaluation of the ship's mooring operation. The study shows that the evaluation model has better accuracy，strong systematization，and easy to operate. The results of the system's assessment，when compared with the results of the expert assessment，showed a high degree of consistency，demonstrating the validity and reliability of the model in assessing the mooring skills of crew members.

To solve the information fusion and situation awareness issues of terminal decision-making groups in emergency rescue of major chemical fires，a fire situation awareness model for large-scale storage tanks was proposed based on the evolution characteristics of the storage tank fire situation and the multi-layer network theory. A network edge weight calculation method based on Bayesian parameter estimation was proposed by developing a chemical storage tank fire situation information set. Node importance and inter-layer correlation coefficient were used to present the connection strength of nodes within the network layer and the correlation strength between network layers，respectively. Moreover，the evolution trend of key situations within and between storage tanks was identified. Then，the evolution process of the fire emergency rescue situation in a tank was analyzed. The results indicated that the situation awareness model based on multi-layer networks can better analyze the fire situation evolution characteristics of large storage tanks. The greater the importance of the node，the faster the fire evolution of the corresponding storage tank. The stronger the correlation between layers，the more significant the evolution trend between adjacent storage tanks.

In order to solve the problems of difficulty in quantifying the indicators and difficulty in taking into account randomness and fuzziness in the evaluation process of the risk of the coal mine intelligentization project，the cloud model theory was adopted to carry out a quantitative and comprehensive evaluation of the system. First of all，based on the coal mine informationization system construction project，a multi-dimensional analysis was carried out to establish a multi-indicator and multi-dimensional evaluation system of the project risk. Then，the combination of hierarchical analysis (AHP) method and criteria importance though intercrieria correlation (CRITIC) method was used to assign weights，determine the weight matrix of the indicators，and the cloud model was used to realize the conversion between the quantitative and qualitative indicators，to complete the evaluation of the risk of the coal mine intelligence project，and to put forward the targeted policy according to the evaluation results. The cloud model was used to realize the quantitative and qualitative conversion of indicators，complete the risk evaluation of the coal mine intelligentization project，and put forward targeted policies based on the evaluation results to minimize the existing risks of the project. Finally，taking a coal mine of National Energy Group as an example，the risk evaluation of the construction and implementation process of coal mine intelligentization project was carried out. The results show that the cloud model can realize the quantitative evaluation of project risks，and the results of risk evaluation coincide with the actual situation on the site; the results of risk evaluation can help to solve the hidden risks on the site and improve the ability of risk control.

To solve the issues of wind turbine blades in terms of classification difficulty and blurry segmentation of small defects in surface defect detection，an improved U-Net semantic segmentation network was proposed based on dilated convolution and convolutional attention modules. Based on the encoding-decoding structure of the network model，a transferable VGG16 feature extraction layer was used to replace the encoding part of the U-Net network. Then，a convolutional attention module was added to the skip module between encoding and decoding. The global weight was enhanced by selecting small defect information. Dilated convolution was used in the decoding section to enhance the network's feature extraction ability，and the pre-trained VGG16 model was used to realize transfer learning. The hybrid loss function of Focal and Dice was validated against the models of DeeplabV3+，Pyramid Scene Parsing Network(PSPnet)，High-Resolution Network(HRNet)，and U-Net. The results showed that the improved U-Net network had higher prediction accuracy in blade defect classification and segmentation tasks，mean intersection over union，mean pixel accuray，and recall values were 83.60%，92.84%，and 88.50%，respectively. The mean intersection over union simulated by the improved U-Net model was 13.98% and 9.38% higher than that by the DeeplabV3+ and standard U-Net model，respectively. Therefore，the proposed model can improve the sensitivity of blade defect detection，effectively reduce false positives of detection results，and provide guidance to wind turbine blade defect detection.

In order to clarify the relationship between the exterior insulation facade structure and fire spread in high-rise buildings，Pyrosim fire simulation software was used to study the impact of different exterior insulation facade structures on fire spread in high-rise buildings. The results show that during the process of fire spread on the exterior facade of high-rise buildings，the insulation systems of different exterior facade structures reduce the air entrainment capacity and heat release rate as the degree of structural space limitation increases. However，their chimney effect is significantly enhanced，and the smoke flow rate is faster，leading to an accelerated vertical fire spread speed. As the thickness of the air layer increases，the temperature and smoke flow rate of the aluminum curtain wall structure insulation system first increase and then show significant fluctuations. The aging of the performance of the external wall insulation system will increase the risk of fire on the exterior facade of high-rise buildings.