In view of the booming of flight flow of urban logistics UAVs and low airspace utilization due to isolation mode of operation, safe and efficient cross-operation needs to be implemented. The crossing separation model was constructed by studying the crossing operation collision risk at the same altitude, and the core parameters such as lateral error, vertical error and longitudinal proximity rate were determined. By introducing the Event model method and constructing separation model, the failure probability of airborne CDR system was further considered. By using event tree analysis method, a comprehensive model of urban logistics UAV crossing separation was proposed and constructed. Results show that the required crossing separation is 158 m for the target safety of level 1.5×10^-8 and 155 m for the target safety of level 1×10^-6 when the route angle is 60 degrees. With the increase of crossing angle, the required separation generally shows an upward trend. When it approaches 180°, that is, opposite direction operation, the required separation increases sharply, which is consistent with the actual cognition.

In order to enhance the capacity of urban flood prevention and disaster reduction, taking Zhenba County as an example, this paper explores the coupled disaster formation process and risk assessment methods of flood disasters. By analyzing the rainfall data and flood disaster statistics in recent years, the frequent occurrence and severity of flood disasters in this area as well as the huge losses they have caused to the economy and society were revealed. High-precision terrain data was obtained through unmanned aerial vehicle technology and combined with the SWAT for flood simulation, providing accurate disaster data support. A multi-factor risk assessment matrix was constructed, and the comprehensive flood risk index was obtained through normalization processing. The risk assessment results show that 14 areas with grade IV risks are identified, and the simulation results of the model are highly consistent with the actual situation. The research results indicate that the constructed coupled disaster formation simulation and risk assessment method can accurately identify high-risk areas. Effective countermeasures are proposed for high-risk areas, including improving flood control capacity, strengthening monitoring and early warning, conducting disaster prevention and education, and strengthening land planning and management.

In order to address the floor heave issue during the mining of fully mechanized top-coal caving faces in ultra-thick coal seams, the floor heave mechanism was investigated through field investigations, theoretical analysis, and numerical simulations. Corresponding control technologies were proposed. The ZF1409 working face in a coal mine in Shaanxi province was selected as the engineering case. Key findings include: Severe floor heave predominantly occurs during periodic weighting periods. Higher shield support resistance is observed, particularly in areas with mudstone-dominated floor strata or thin residual coal beneath the floor. The sliding surface of the floor stratum primarily consists of weak mudstone with low ultimate bearing capacity. Floor heave initiates when the applied load exceeds the ultimate bearing capacity. The frictional resistance and cohesion generated by the self-weight of the rock mass along the sliding surface are overcome. Numerical modeling of the ZF1409 face reveals that shield support resistance plays a critical role in floor heave evolution. Under fully mechanized top-coal caving conditions in ultra-thick coal seams, the sudden rupture of the near-field key stratum forms a cantilever beam structure, causing rapid escalation of shield resistance and subsequent floor heave. Implementing underground regional hydraulic fracturing technology to precondition the near-field key stratum effectively mitigates floor heave by optimizing stress redistribution.

In order to accurately locate the hidden spontaneous combustion area inside the broken coal pillar using elastic wave detection, the response characteristics and mechanism of the elastic wave velocity of coal to temperature were experimentally studied. Firstly, coal samples were pretreated by temperature programmed furnace to obtain coal samples with different oxidation degrees. Then, elastic wave and low-field NMR tests were conducted to analyze the characteristics of elastic wave and pore cracks of coal with different oxidation degrees. Finally, combined with thermogravimetric(TG) analysis and single variable briquette experiments, the elastic wave response characteristics and mechanism of coal oxidation spontaneous combustion were revealed. The results show that the P-wave and S-wave velocities of raw coal gradually decrease with increasing temperature, and the P-wave velocity decreases 2.4 times that of the S-wave velocity after the maximum mass loss temperature of TG curve. The decreasing trend of P-wave velocity shows an obvious segmental characteristic. Before the maximum mass loss temperature of the TG curve in stage Ⅰ, the P-wave velocity decreases at a rate of approximately 1.27 (m·s^-1)/℃. from the maximum mass loss temperature to the final temperature, the decline rate of P-wave velocity increases to 4.51 (m·s^-1)/℃. The porosity of coal increases from 1.57% at 30 ℃ to 9.6% at 360 ℃. In particular, after the temperature exceeds the maximum mass loss temperature, the number and aperture of cracks increase significantly, which not only lengthens the propagation path of elastic waves, but also decreases the propagation speed of elastic waves. As the propagation medium of elastic waves, the coal matrix does not decompose obviously before the maximum mass loss temperature, and the corresponding P-wave velocity of briquette remains stable at about 900 m/s. After exceeding the maximum mass loss temperature, the coal matrix decomposes rapidly, and the corresponding P-wave velocity of briquette drops to 798 m/s at 360 ℃, which proves that the coal matrix gradually transitions from a "high wave velocity" medium to a"low wave velocity" medium.

In order to solve the problem of mixed heavy metal substances in coal hindering the infiltration of dust suppression solution into coal pores and cracks, resulting in a weakened wetting effect of coal, experiments were conducted to explore the synergistic effect of chelating agents and surfactants on coal wettability. Firstly, based on the water injection test platform and the dust cutting simulation test platform, six injection methods were designed to experimentally determine the antagonistic effect between surfactants and chelating agents. Then, the effects of six injection methods on the dust production characteristics and wetting effect of coal samples were studied through cutting tests and surface wettability analysis. Finally, spray dust suppression was conducted to test the settling efficiency of pulverized coal enhanced by different injection methods. The results show that the coal sample treated by first injecting the chelating agent followed by the surfactant exhibits the lowest dust production concentration, at only 170.93 mg/m³, which is 64.49% lower than the 481.33 mg/m³ in the pure water group. Furthermore, the sequential injection protocol employing prior a chelating agent infusion followed by a surfactant delivery exhibits the most significant improvement in coal wettability, with the fastest rate of wetting-induced sedimentation of coal dust. In addition, the coal samples treated by the above method achieve a dust suppression efficiency of 91.2% in in spray dust suppression tests. Through molecular simulation and theoretical analysis, injecting a chelating agent solution to decompose heavy metals in coal fractures can improve the fluidity of the solution in coal. Subsequently, injecting a surfactant solution further enhances the wettability of coal.

To reduce traffic accident risks，on-road experiments were conducted to investigate the differences in potential risk perception ability between skilled and unskilled drivers under two typical risk scenarios：dynamic motorcycle-following and parallel overtaking. A wearable eye tracker was employed to collect drivers' dynamic visual parameters，with their visual characteristics analyzed across different scenarios. The results demonstrate that skilled drivers exhibit significantly stronger risk perception abilities than unskilled drivers in both scenarios. Specifically，in the dynamic motorcycle-following scenario，skilled drivers show a higher probability of fixating on distant areas ahead，enabling better prediction of upcoming traffic conditions. In the parallel overtaking scenario，skilled drivers display shorter fixation durations，along with greater horizontal search breadth and vertical search depth. Moreover，the proportion of fixation time on rearview mirrors is significantly higher for skilled drivers compared to unskilled drivers，indicating superior visual search efficiency and enhanced rear traffic monitoring capability. Evaluations using the grey near-optimal comprehensive evaluation method reveal that skilled drivers achieved significantly higher scores in hazard perception ability.

To address the issue of natural coal ignition in goaf under normal fault geological structures，the oxygen consumption rate and heat release intensity of coal samples were measured using a temperature-programmed oxidation device. Based on a porous media model of the goaf and the gas component transport equation，a numerical model for CO₂ injection via side pressure into the goaf influenced by normal faults was established. The numerical model was used to simulate the mechanism by which the variation in the distance between the working face and the fault affects the width of spontaneous combustion oxidation band in the goaf，and analyze gas migration characteristics under different CO₂ injection locations and flow rates. The results indicate that as the distance between the working face and the fault increases，the width of the oxidation band initially increases and then decreases，reaching a maximum width at 70 m from the fault. With the increase of CO₂ injection depth，the oxidation band width initially decreases and then increases again，reaching a minimum width when the CO₂ injection position is 40 m from the working face. Furthermore，with the increase of CO₂ injection volume，the width of the oxidation band width decreases following a negative exponential trend. When the CO₂ injection rate is 1000m³/h and CO₂ volume fraction at the working face is below 0.4% for safety，width of the oxidation band reaches its minimum.

In order to reduce the various risks associated with storm and flood hazards，an ontology model of storm and flood hazard risk is proposed. Storm and flood disaster risk elements were identified from five aspects：environmental factors，equipment and facility factors，management factors，human factors，and information factors. The concepts of storm flood class and state space were given respectively，and data attributes and object attributes of each hazard element are defined. And visualization of the storm flood ontology model based on Protégé application. The results show that the storm and flood ontology model can express the risk elements and attributes clearly and accurately，and retrieve the links between the risk elements scientifically and quickly，so as to help the decision-makers respond effectively. Providing a data storage and retrieval platform for heavy rainfall and flood disaster risk assessment，it can realize user knowledge reuse and sharing，and provide a reference for emergency response and scientific decision-making by the government and emergency management authorities at the same time.

To gain a deep understanding and evaluate the command capability and current status of counties in response to flood disasters，this paper identified 12 major influencing factors through literature analysis. Social network analysis was conducted using Ucinet software，and Netdraw software was utilized for visualization to depict the social network relationship diagram among these influencing factors. By calculating the in-degree and out-degree，as well as centrality of the influencing factors，indicator classification was performed. Drawing on the maturity model of command and control capability，an assessment model based on social networks for the maturity of county-level flood disaster emergency command capability was developed. Puyang county was selected as the empirical research object. Through questionnaires and interviews，a total of 342 data entries from 24 townships in Puyang County were collected to evaluate the county's flood disaster emergency command capability. The results indicate that it is feasible to classify indicators hierarchically through social network analysis. The established indicator system is scientifically and accurately reflected in the comprehensive operational level of county-level flood disaster emergency command. The assessment model can effectively evaluate the maturity levels of emergency command capability.

In order to solve the problems that machine learning exists in the hazard intelligent prediction field of tunnel water inrush，such as relatively simple models and imperfect prediction accuracy，a prediction model based on the stacking ensemble learning was proposed. Firstly，the tunnel water inrush disaster dataset was established by collecting 232 groups of water inrush disaster data from 95 tunnels，and the data was preprocessed. Then，3 base learners and 2 meta learners were selected to train 8 sets of stacking ensemble models in different combinations，and 6 sets of optimal ensemble models were selected. Finally，the optimal stacking ensemble model was selected by comparing and analyzing the prediction results of 6 groups of parameters optimized and stacking ensemble model with the grid search parameters and the 5-fold cross-validation hyperparameter optimization model. The results show that SVM(Support Vector Machine )+NB (Naive Bayes) + LR (Linear Regression) ensemble model is obtained after the optimal single model SVM is improved with the stacking ensemble learning method. Its accuracy，recall，and F₁ score are 0.94，0.91，and 0.92，respectively. The overall prediction effect is better than that of other compative models，and it can accurately predict the hazard level of tunnel water inrush.