The generalized finite difference method for seismic wavefields numerical simulation is capable of adapting to undulating stratigraphic interfaces, eliminating the staircase scattering effect caused by such interfaces, and enhancing the accuracy of forward modeling. However, when the second-order generalized finite difference method is used to solve the wave equation, low temporal accuracy can lead to temporal dispersion at larger time intervals, affecting the precision of forward simulation. A fourth-order generalized finite-difference forward modeling algorithm for the scalar wave equation was studied, along with its stability conditions and dispersion characteristics. By transferring the fourth-order time derivative to the spatial derivative term, fourth-order accuracy in time was achieved, effectively suppressing temporal dispersion. Compared to the second-order generalized finite-difference method, the fourth-order approach allows for larger time intervals, thereby reducing computational costs to some extent. Experimental results demonstrate that the proposed algorithm effectively mitigates both staircase scattering and temporal dispersion, yielding higher computational accuracy. When applied to reverse time migration, it produces high-quality imaging profiles.

Although the multi-task convolutional neural networks (MTCNN) face detection algorithm has achieved good results in some face recognition tasks, the accuracy of face detection needs to be improved in the face of some complex small-scale and multi-person face detection tasks. An improved MTCNN algorithm was proposed. Firstly, the intersection over union (IoU) threshold parameter was fine-tuned when creating the data set to classify face samples more accurately. Secondly, replacing the max pooling layer of the network with convolutional layers can improve network performance. Finally, the squeeze-excitation(SE) attention mechanism was introduced into the O-Net network to improve the feature expression ability of the network. The test results show that compared with the original MTCNN algorithm, the detection accuracy of the P-Net network and R-Net network of the improved algorithm has increased by 1%, and the detection accuracy of the O-Net network has increased by 0.5%. Moreover, the improved algorithm performs better in the actual face detection task.

With the implementation of river dredging projects in China, a large amount of dredged silt has been generated. The treatment and disposal of silt have gradually attracted great attention. Using solidification technology is one of the effective ways to solve the problems caused by dredged silt. Taking the dredged silt from Beibaidang in Zhejiang as the research object, the solidified products were analyzed through X-ray diffraction (XRD), and the porosity and pore structure of the solidified silt soil were quantitatively analyzed by means of X-ray computed tomography (X-CT) and mercury intrusion porosimetry (MIP) tests. Meanwhile, it explores the mechanical variation laws and solidification mechanism of the soil under soaking and dry-wet cycling conditions. The research shows that the calcite content in the solidified soil increases with the increase of the solidifying agent dosage. The increase of ordinary sand dosage improves the small and medium-sized porosity inside the soil, but the overall porosity shows a decreasing trend. The results of water stability tests indicate that the strength and stability of the solidified silt soil are significantly improved with the increase of the solidifying agent content, and they first increase and then decrease with the increase of sand dosage.

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.

To investigate the impact of temperature on axial stress in large-span concrete variable-section continuous beam bridges under various wind speed fields, a method was proposed to calculate vertical temperature gradients separately based on inconsistent deck slab thickness and simulate lateral fluctuating wind speed fields using the spectral method. Firstly, vertical temperature gradient variations and their depths were calculated by employing the concrete heat-conduction equation, daily maximum and minimum temperatures, and deck slab thickness of the variable-section continuous beam bridge. Secondly, bridge modeling was performed using MIDAS Civil, and ZKH standard static live loads were simulated to represent moving train loads. Finally, static array wind loads and pulsating wind loads were applied to the bridge. The results indicate that the axial stresses in the left and right lanes obtained from the proposed method, which uses vertical temperature gradients and their depths derived from the concrete heat-conduction equation, daily temperature extremes, and bridge deck slab thickness, are larger compared to existing studies. Under the same wind speed field model, the harm to the bridge is greatest under gradient heating, followed by gradient cooling, while no temperature change results in the least impact. When the bridge is subject to the same temperature model, both the axial stress values and amplitudes of the bridge under pulsating wind loads are larger and more severe than those under no wind or static wind conditions, posing greater hazards to the bridge. The research findings can provide references for the structural design and safe operation of large-span concrete variable-section continuous beam bridges.

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.

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.

The offshore heavy oil thermal recovery platform has the characteristics of small space, high steam injection temperature, and high steam injection pressure, with temperatures up to 300 ℃. Once high-temperature and high-pressure steam leaks, it will cause serious consequences and pose a huge threat to equipment and inspection personnel. An effective steam leakage monitoring method was urgently needed. In order to solve these problems, the influence of thermodynamics, fluid mechanics and other factors were considered comprehensively to study the mechanism of steam leakage monitoring in offshore heavy oil thermal recovery. A virtual sensing monitoring method based on mechanism and inference was proposed, and for the first time, the indirect measurement method of steam leakage was applied to steam leakage monitoring in offshore heavy oil thermal recovery. A steam leakage monitoring model was built, and a hybrid sensing technology suitable for steam leakage monitoring in offshore heavy oil thermal recovery was formed for real-time online monitoring of steam leakage. The results show that this method can achieve leak discrimination and leak estimation based on operational data, and directly characterize the failure state of steam leaks online. The minimum detectable leak rate can reach 0.5%, and the accuracy of leak discrimination is above 96.49%. Compared with traditional methods, the minimum detectable leakage rate has increased by 90%, and the leakage discrimination rate has increased by at least 1.6%. This method solves the problems of limited installation of physical sensors on site, difficulty in obtaining effective monitoring data, and limited accuracy due to personnel experience, making up for the shortcomings of on-site monitoring methods for thermal recovery platforms and providing safety guarantees for offshore heavy oil development.

In order to study the influence of floating offshore wind power suction anchor size on the horizontal bearing capacity, based on the ABAQUS finite element software, a three-dimensional finite element model of suction anchor foundation was established by using elastic-plastic constitutive model. The results show that the increase of diameter and height can improve the horizontal bearing capacity of suction anchor, and the increase of height is more obvious. With the change of diameter and height, the position of the mooring point also needs to be adjusted accordingly. When the ratio of suction anchor diameter to height D/H>1, the position of the mooring point needs to be increased correspondingly to make it move in translation. The change of diameter will affect the pressure change of the anchor wall, while the height has little effect on the pressure change of anchor wall. The research results are used in global first offshore floating wind power + aquaculture platform “Guoneng Gongxiang Hao” and can provide a reference for relevant project design.