In order to study the relationship between rainfall and regional geological disaster risk, construct the meteorological warning model of geological disasters in Huangshui Basin, and provide reference for the geological disaster warning work in the basin. Huangshui Basin in Qinghai Province was taken as an example, where the susceptibility to geological hazards was evaluated using the information value method. The independence of each factor was tested using the Pearson correlation coefficient, and the accuracy of the evaluation results was assessed through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Subsequently, a geological-rainfall coupling model was also established using the logistic regression method, incorporating rainfall data, to carry out geological disaster meteorological risk warning and verification. The results show that Huangshui Basin geological disasters of very high susceptibility area and high susceptibility area is mainly distributed in the river valley plain zone and its tributaries at all levels in the middle and lower reaches of the two sides, the region is a geological disaster area. The results of the susceptibility evaluation are assessed for precision, and an AUC value of 0.785 is found. Geological-rainfall coupled warning model in the susceptibility value, the day rainfall, the first 1 day rainfall, the first 2 days day rainfall, the first 2 days of rainfall and the first 3 days of rainfall on the disaster are all positive, the model's prediction accuracy for the occurrence of geological disasters is 84.2%, and the AUC value is 0.864. Based on the five typical geological disasters that occurred in Huangshui Basin on 29th August 2020, the established geological-rainfall coupling model was verified, and it is found that with the geological-rainfall coupling model warning, four geological disasters reach the first warning level, and the other one reaches the second warning level. Combined with the warning rainfall threshold, the model was used to achieve the early warning of the landslide in Hongyagou Village on 5 September 2024. It can be seen that the constructed meteorological early warning model for geological disasters in the Huangshui Basin has a good early warning effect and a relatively high prediction accuracy rate, which can provide references for disaster prevention and mitigation for relevant departments.

In order to help enterprises better adapt to the dynamic environment in the real business, a multiperiod intermodal routing and storage co-optimization model with transport price uncertainty was investigated. Firstly, an integer programming mathematical model was established in the environment of transport price certainty. Secondly, a robust optimization model was established in the environment of uncertain transport prices, and the robust optimization model was transformed into an equivalent linear robust peer-to-peer problem. Subsequently, on the basis of the traditional k-shortest algorithm, iterative greedy algorithm (IG) and adaptive large neighbourhood search algorithm (ALNS), a hybrid heuristic algorithm of MKIGALNS was proposed to solve the problems. Finally, the correctness of the proposed model as well as the effectiveness of the algorithm were verified by different sizes of arithmetic case experiments. The experimental results indicate that in 10 sets of arithmetic cases, the average total operating cost is CNY 439 191 when storage is not allowed and CNY 391 378 when storage is allowed, so the storage decision should be made, which is conducive to the reduction of operating cost. And through the related robust experiments, the total operating cost as well as the multiperiod intermodal operation strategy changes with the change of the uncertain budget value, which reveals the intrinsic connection.

With the rapid development of the power system, the large-scale integration of new energy into the grid and the coordinated optimization of source-grid-load-storage have increased the proportion of power electronic equipment, making the stability of the power grid, especially the assessment of transient stability, particularly important. Aiming at the problem of insufficient consideration of topological structure in traditional methods, a deep learning method based on Transformer-graph attention network(GAT) parallel feature fusion was proposed for the transient stability evaluation of power systems. The busbar voltage amplitude, phase angle and topological adjacentation matrix were taken as input features. Batch data were generated using the Siemens simulation software PSS/E, and features were extracted in parallel through Transformer and GAT. Weighted fusion was carried out using the attention mechanism. The comparison results with other methods show that this method simulates different load conditions and fault conditions in the IEEE 39-node system. The results indicate that the evaluation accuracy and robustness are relatively high, and it can effectively improve the safety and stability of the power system.

The weak fault characteristics and the presence of numerous harmonic signals in distribution networks with renewable energy sources reduce the effectiveness of traditional fault diagnosis methods. A fault diagnosis method based on an improved graph neural network was proposed. Wavelet transform was applied to extract the detail coefficients of current and voltage before and after faults. Weighted projection correlation analysis was performed to calculate the correlation between electrical quantities. Highly correlated quantities were selected as inputs to construct the fault diagnosis model using a graph neural network. Fault simulation models for different voltage levels were developed in MATLAB/Simulink. The results indicate that fault signals are effectively enhanced, and faults are accurately located and classified in distribution networks with renewable energy sources at different voltage levels. Good diagnostic performance is maintained in the presence of data loss and noise, demonstrating strong robustness and generalization.

Traditional robot localization and navigation methods in complex building environments are characterized by low accuracy, heavy reliance on sensors, and an inability to effectively address dynamic obstacles, making it challenging to achieve satisfactory results in practical applications. To address these issues, building information modeling (BIM) technology was introduced. BIM, with its geometric and semantic information, was utilized to assist robot localization and navigation in complex environments. More accurate environmental perception and optimal path planning were provided to robots, reducing the risk of collisions with environmental components and improving the accuracy and efficiency of task execution. The current status of BIM technology in robot localization, mapping, and path planning was compared, the advantages and challenges of its application in architectural environments were analyzed, and future prospects for its application in intelligent buildings and robotic intelligence were explored.

The development of hydrogen-powered aircraft is a key strategy for the aviation industry to achieve carbon neutrality. Compared to high-pressure gaseous hydrogen, cryogenic liquid hydrogen will be the main fuel for future hydrogen-powered commercial aviation. However, the occurrence of cavitation in liquid hydrogen during transport has the potential to result in an unstable or even interrupted fuel supply to the engine, which could ultimately lead to catastrophic risks to flight safety. Using numerical simulation method, based on homogeneous mixed flow model, Navier-Stokes (RANS) method and Zwart cavitation model, the cavitation flow characteristics and development law of liquid hydrogen in aircraft transport pipelines were deeply studied, and partially compared with normal temperature water. The results show that the cavitation number, the outlet/inlet pressure ratio, and the length/diameter ratio have a significant influence on the occurrence and development of cavitation. The condensation process of liquid hydrogen is considerably slower than the evaporation process. The effect of the cavitation number on the evaporation process is minimal, but it has a significant effect on the maximum condensation rate. The critical pressure ratio for the disappearance of cavitation in liquid hydrogen is lower than in water. At the same pressure ratio, water cavitates more easily than liquid hydrogen, with a greater number of cavitation bubbles and a thicker cavitation region. Reducing the length/diameter ratio can inhibit the occurrence and development of cavitation in liquid hydrogen. It is recommended that the diameter of the contraction section be increased to achieve a higher outlet flow, rather than shortening the length of the pipeline.

In deep heavy oil reservoirs, substantial heat losses during steam injection are often associated with suboptimal steam chamber development, significantly reducing the efficiency of steam flooding. A novel steam chamber expansion model was introduced, incorporating a wellbore heat loss coefficient derived from vapor-liquid interface theory and heat transfer principles. Compared to existing models, the modified model was shown to predict a more pronounced steam override and a larger steam-swept area at the reservoir top. Validation against field monitoring data reveals a deviation of only 7.61%, demonstrating strong agreement with actual development conditions. Further analysis of the wellbore heat loss rate and steam chamber morphology shows that the heat loss rate peaks early in the injection process and subsequently decreases over time. It is observed that the wellbore heat loss rate increases with greater reservoir depth. Additionally, the mobility ratio is found to be negatively correlated with steam chamber development, while the shape factor is positively correlated, with larger shape factors resulting in a wider steam-swept area and a reduced impact of steam override. The research is closely integrated with theoretical concepts and practical applications, enabling rapid and accurate predictions of steam chamber front positions, optimizing steam injection parameters, and informing the design of development strategies for medium to deep heavy oil reservoirs.

Occluded pedestrian re-identification is a challenging task in the field of computer vision. A method was proposed using the FGMS-Net network, which significantly enhances pedestrian re-identification in occluded environments through several improvements. Firstly, an improved foreground segmentation technique was employed to effectively remove background and other clutter information, resulting in more accurate feature extraction. Secondly, to address the occlusion issue, a multi-scale feature discrimination method was introduced, enabling the model to better capture local features and thereby enhancing identification capability. Finally, an attention mechanism was added to the backbone network, allowing the network to focus more on critical information and improve overall recognition performance. The experimental results show that method proposed has achieved significant performance improvement in the task of pedestrian re recognition with occlusion. On the Occluded-DukeMTMC dataset, the cumulative matching feature Rank-1 and mean average precision (mAP) reach 71.7% and 61.6%, respectively.

Under the guidance of the full life cycle design concept, a scheme of moving the existing downspout pipe scheme to the reinforced concrete peripheral column was proposed. The main feature of this scheme is that the downspouts are pre-embedded into the surrounding columns and used for roof drainage during the building's service life. At the end of the building's lifespan, the downspouts were used to remove blast holes. To demonstrate the feasibility of this scheme, first of all, it was verified that the material and diameter of the embedded downspout could simultaneously meet the drainage function and the function of removing the blast hole. Secondly, by drawing on the axial embedded hole blasting technology of reinforced concrete beams and using the new type of blasting equipment, the long bag for loading explosives, the feasibility of using downspouts for blasting demolition was demonstrated from both the charge structure and the blasting operation aspects. Finally, the finite element analysis software ABAQUS was used to analyze the stress distribution and stress-strain laws when two types of cross-sectional peripheral columns were placed in four downspouts with different diameters. The analysis results show that when the hollow rate is small, the embedded pipe has little influence on the specimens. In order to avoid significant impact on the peripheral columns, the hollow rate of the specimens should not be greater than 2%. In combination with the requirements of blasting demolition, the hollow ratio should not be less than 0.18% either. Subsequently, it was pointed out that the relay service duration of pipes and concrete holes is sufficient to reach the building's life cycle. Finally, it is clarified that after the downspout is built in, it can also enhance the aesthetic appeal of the building and avoid the safety risk of thieves climbing along the downspout. Therefore, it is feasible to insert reinforced concrete peripheral columns into the downspout pipe.

In order to solve the problem of uneven data distribution or confusion of characteristics in material supply chain under multi-source data, a data balance processing method of material supply chain based on multi-source data was proposed. The data distribution space of material supply chain was established, non-boundary and boundary areas were set, the center of the area was calibrated, the distances between different characteristic data and the center point were calculated respectively, the unbalanced data was searched in advance by using the edge mixed sampling algorithm, and the boundary area to which it belongs was determined according to the data distance characteristics. The unbalanced data was regarded as the working nodes in the supply chain cluster. When the number of working nodes in the cluster changes, the load generated by each cycle execution node was obtained by flow calculation, and the load value was converted into the execution thread score. The unbalanced data was obtained by comparing the scores, the threshold of data memory and CPU resource consumption in the material supply chain was calculated, and the parallel task thread was established. The scheduler was used to transfer the execution thread in the working node to the next node to achieve the purpose of load balancing. The experimental results show that the proposed method has the advantages of short response time, large data throughput, good processing effect and strong stability, and has good practical application value.