In order to further study the detonating effect of UAV，and improve the fire extinguishing efficiency of fire bombs，ANSYS Workbench software was used to establish a finite element model of fire bombs，and the explosion and dispersion process of fire extinguishing agent was simulated to clarify the impact of different detonation heights on the dispersion characteristics of fire extinguishing agent. Meanwhile，full-scale experiments on the vertical dropping of fire extinguishing bombs by unmanned aerial vehicles at different detonation heights were conducted. The results show that the simulation data such as the explosion process of fire extinguishing bomb，the throwing process of fire extinguishing agent and the spreading radius of fire extinguishing agent are in good agreement with the full-scale test results. After the fire extinguishing bomb explodes，the extinguishing agent disperses in the air in a cone shape and spread evenly in the horizontal direction，and the spreading shape is roughly circular. With the increase of detonation height，the landing time of fire extinguishing agent increases，and the horizontal velocity of fire extinguishing agent decreases. The increase of landing time plays a dominant role relative to the decrease of horizontal velocity，resulting in the increase of the spreading radius of fire extinguishing agent with the increase of detonation height. When the detonation height of the fire extinguishing bomb increases from 5 to 12 m，the numerical simulation data of the fire extinguishing agent spread radius increases from 2.04 to 3.56 m，and the error between the numerical simulation data and the experimental data is within 5%.

To prevent miners from mistakenly entering dangerous areas，a lightweight underground miner object detection model based on YOLOv5s-MPD was proposed，which combined with depth camera to locate miner targets and detect whether miners had entered dangerous areas in real time. Specifically，the MobileNetv3 lightweight neural network was used as the backbone feature extraction network to significantly reduce the model size. Secondly，Polarized Self-Attention (PSA) module was introduced to enhance the perception of targets. Finally，Deformable Convolution Network v2 (DCNv2) was used to replace the standard convolution in the C3 module of the feature fusion layer，solving the problem of partial feature information loss in conventional convolution. The improved model was used in combination with the color images obtained by the depth camera to detect miner targets and obtain the spatial three-dimensional coordinates of the target center points. The results show that compared with YOLOv5s，the improved model reduces the number of parameters and computation by 83.54% and 77.03%，respectively. The model size is only 3.4 MB，and a detection speed of 70.2 f/s，which is increased by 54.97%. The mean average precision is 0.825. Compared with mainstream object detection models，the improved model has a more balanced number of parameters，computation，model size，detection speed，and mean average precision. In the actual positioning accuracy test，within a range of 1-8 meters，the average absolute error and average relative error of the distance between the camera and the miner target were 0.11 meters and 1.74%，respectively. The maximum absolute error and maximum relative error were 0.25 meters and 2.96%，respectively. In the dynamic detection，the miner target could be detected and its location information output，with a detection success rate of 97.5%.

To optimize the mixed inert gas fire suppression technique for goafs，the effects of CO₂/N₂ ratio and O₂ volume fraction on the low-temperature oxidation characteristics of coal were investigated. Taking the long flame coal from Dongxia mine in Gansu as the research subject，a temperature-programmed experimental system combined with gas chromatography analysis was employed to carry out low-temperature oxidation experiments under various CO₂/N₂ ratios (ranging from 0∶10 to 10∶0) and O₂ volume fractions (6%，10%，14%，and 18%). The results indicate that at the same O₂ volume fraction，as the CO₂/N₂ ratio increases，the rates of oxygen consumption and CO production of the long-flame coal gradually slow down，with the decline rates increasing as the temperature rises. The apparent activation energy of the long-flame coal increases gradually with the increase in the CO₂/N₂ ratio during the slow oxidation stage，while it initially increases and then decreases during the accelerated and rapid oxidation stages，reaching the maximum value at a CO₂/N₂ ratio of 4∶6.

To reduce building risks in landslide-prone areas and guide disaster mitigation measures，a comprehensive vulnerability analysis method was proposed based on Entropy Weight Method(EWM)-Analytic Hierarchy Process(AHP) and trapezoidal-semi-trapezoidal membership functions. Using the Sifangbei landslide in Wanzhou District，Chongqing，as a case study. The landslide failure probability was calculated using the Morgenstern-Price and Monte Carlo methods. Field surveys of building attributes were used to analyze landslide hazards and validate the analysis method. The results indicate that the composite model using crack indicators provides reliable vulnerability analysis for buildings in the Sifangbei landslide zone. The proposed method is feasible. The landslide is most dangerous when the reservoir water level drops from 175 m to 145 m. Combined with a 50-year rainfall event，leading to over 7.5 million yuan in economic losses. Vulnerable buildings are mainly located in the middle and rear parts of the landslide and along its right boundary.

In order to effectively prevent cross operation accidents in the construction process，firstly，130 cross operation accident cases nationwide from 2010 to 2022 were collected as research objects，and an evaluation system for cross operation in construction was established based on the grounded theory. Then，according to the relationship between the indicators，a spectral clustering algorithm was used to analyze the combination of accident causes. The weight of each cause factor was allocated，and different levels of risk were divided by principal component analysis and analytic hierarchy process. Finally，the Apriori algorithm was used to obtain the correlation chain of each factor in the causative combination. The results show that the evaluation system includes 5 core categories，13 main categories and 40 categories. The core category that has the greatest influence on the risk of cross operation in the construction process is technical management. The 40 categories are grouped into 4 causa l combinations，among which 1 is high risk，1 is medium risk，and 2 are low risk. In addition，for the complex cluster of factors，the key combination of causes is identified and targeted preventive measures are proposed to reduce the probability of cross-operation accidents.

In order to systematically explore the root causes of industrial hot work accidents through a large amount of text data，a text mining method based on 24Model was proposed. Firstly，220 hot work accident reports were collected and sorted as datasets，and a 24Model classifier based on Bidirectional Encoder Representations from Transformers (BERT) was constructed. The pre-trained model was used to train and evaluate the accident report dataset to construct a classification model. Then，through the combination weight of the Keyword extraction algorithm based on BERT (KeyBERT) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms，combined with the 24Model framework，a keyword index system for hot work accident text was established. Finally，the interrelationships between accident causes were obtained through the analysis of the network co-occurrence relationship between text-mining keywords. The results show that the BERT-based 24Model classifier model can systematically and accurately determine the causative categories of hot work accidents. The weight of the safety management system was the largest among the 4-level keyword index systems obtained through the combination of weights. Furthermore，7 key causative factors of hot work accidents were obtained by combining them with the co-occurrence network analysis. This shows that 24Model can strengthen the interpretability of text mining results，which provides an important reference for the prevention and management of hot work accidents.

In order to solve the problem that the traditional spray technology in coal mine dust pollution control is not ideal and the spray dust reduction performance is low，Sodium dodecyl sulfate (SDS)，Coconut Diethanol Amide (CDEA) and Cocoamidopropyl Betaine(CAB-35) were selected in this paper. The wettability of the three surfactants was first analyzed macroscopically by contact angle test. Then，combined with molecular dynamics simulation and quantum chemical analysis，the action mechanism of single/compound surfactants on bituminous coal was studied from a microscopic perspective. The results show that among the single surfactants，SDS has the smallest contact angle and the largest molecular orbital energy difference，and is more likely to form high-strength hydrogen bonds with water molecules； the mixed surfactants all show better wettability of bituminous coal than single surfactants，and the contact angle is smaller when non-ionic surfactants are mixed with anionic or zwitterionic surfactants，and the contact angle reduction rate is also much greater than that of single solutions，and the synergistic effect is more prominent; In the CDEA+CAB-35 (4∶2) system，water and surfactants form more and higher strength hydrogen bonds，and the surfactant molecules pull each other to form a tight adsorption layer，which attracts water molecules to infiltrate the surface of coal dust and improves the wettability of coal dust to the best extent.

To improve the application of small-angle inclined piles in collapsible loess areas，finite element models of 2×2 pile groups with three different inclination angles were established. The modulus reduction method was employed to simulate the collapsibility effect of loess，and the influence of loading and soaking sequences on the bearing characteristics of pile groups with different inclination angles was analyzed. The results indicate that，based on the conducted tests of 0，10 and 15° pile groups，under both loading-before-soaking and soaking-before-loading conditions，the displacement of the pile cap and the settlement of the surrounding foundation soil are smaller for inclined pile groups compared to vertical pile groups. Moreover，the inclined pile group is less affected by the water-induced collapsibility of loess. However，the bending moment and shear force of the inclined piles are higher than those of the vertical piles. Compared to the loading-before-soaking condition，the soaking-before-loading condition results in smaller pile cap displacements and soil settlements but larger internal forces in the inclined pile shafts. Additionally，the shaft friction of inclined piles is smaller under the soaking-before-loading condition. Inclined pile groups with larger inclination angles demonstrate superior load-bearing capacity and resistance to loess collapsibility induced by soaking. Pre-soaking treatment of loess foundations effectively enhances the ultimate bearing capacity of pile group foundations.

In order to improve urban governance and promote sustainable development，the resilience measurement of core cities in Chinese mainland was analyzed based on panel data from 25 core cities (municipalities directly under the central government，provincial capitals，and regional capitals) between 2011 and 2020. Technique for order preference by similarity to an ideal solution(TOPSIS)-Entropy Weight Method was applied. The resilience situation for 2026 and 2029 was predicted using a BP neural network model. This research aimed to explore the dynamic spatial differentiation of regional resilience. The results show that the standard deviation of the resilience index across cities fluctuates around 0.180，with the resilience disparity between cities remaining relatively stable. However，some cities show a downward trend in their resilience index year by year. The standard deviation of the predicted resilience index for 2026 decreases to 0.173，indicating a reduction in the resilience disparity between cities and a narrowing of the resilience gap. In the four time points of 2014，2020，2026，and 2029，the spatial heterogeneity of urban resilience evolves relatively stably. The urban resilience rankings are as follows: Eastern region > Central region > Western region > Northeastern region. Among them，the economic and infrastructure resilience in the Eastern region is the highest，while the social and ecological resilience in the Central region is the highest.

In order to improve the evacuation efficiency of public buildings and reduce casualties caused by emergencies，in view of the optimization problems of emergency exits and evacuation plans，a mathematical model and algorithm for the single-source multi-sink evacuation problem were proposed. Firstly，all evacuation paths were identified using path algorithm and sorted based on evacuation time. Then，the recursive formula for calculating evacuation time was derived based on the User Equilibrium (UE) principle and k-shortest path. The capacity allocation issues of each evacuation path at intersections were analyzed. After that then the optimization strategy for emergency exits is obtained. Finally，taking a building as an example，the evacuation network was established and calculated to validate the effectiveness of the algorithm and optimization strategy. The results show that the evacuation efficiency of the building network increases gradually with the increase in the number of evacuees，and the rate of change stabilizes after reaching a certain threshold value，Evacuation efficiency is closely associated with the total traffic capacity of specific emergency exits. Through the optimization of specific emergency exits，the evacuation efficiency of the network is enhanced，evacuation time can be reduced，and the optimization effect becomes more pronounced as the number of evacuees increases.