To comprehensively and deeply analyze the overall benefits of provincial-level construction of new type of power load management system, a study on the comprehensive evaluation system for such systems was conducted. Firstly, an evaluation index system that considers technical, social, environmental, and economic benefits was established. Secondly, the subjective weights of the comprehensive benefits were determined through an improved analytic hierarchy process. Given that the index system encompasses both qualitative and quantitative indicators, the objective weights were determined based on the concept of intuitionistic fuzzy numbers, and the comprehensive weights were determined through cooperative game theory. Finally, the concept of perpendicular distance was introduced to enhance the traditional technique for order preference by similarity to ideal solution (TOPSIS) method, thereby improving the accuracy of the evaluation results. By applying the proposed evaluation system to evaluate the construction of new power load management systems in different provinces, the results show that the studied system exhibits strong systematicity, scientificity, accuracy, and feasibility in the comprehensive and multidimensional benefit evaluation of the construction of new power load management systems.

The operation of the Three Gorges Reservoir(TGR) generated a high amplitude of hydro-fluctuation belt(HFB). The preservation and restoration of the HFB had become a major scientific issue after water storage. The classification of bank slopes is the basis for carrying out the protection and restoration of HFB. Taking four typical drinking water sources of the TGR as the research objects, firstly, based on GF-2 remote sensing images covering the study area, and on the basis of radiometric calibration, orthoscopic correction, atmospheric correction, etc., combined with the samples of different bank slope types in the HFB obtained by UAV shooting and visual interpretation, and an object-oriented method for identifying bank slope types in the HFBa was constructed. Secondly, combined with random forest, support vector machine and neural network methods, the classification of bank slope types of typical water sources was carried out, and the classification effect of different machine learning methods was compared to realize the accurate identification of bank slope types in the HFB of typical water sources. Finally, the influence of pixel oriented and object oriented strategies on the classification accuracy of the bank slope in the fall zone was analyzed. The results show that the classification of bank slopes based on multiresolution segmentation-object-oriented classification is a convenient, cost-effective method, and has high accuracy. It can be used for classification of bank slope types in the large-scale HFB of the TGR. This method can solve the problems of internal spectral heterogeneity and increased homogeneity between objects in high-resolution remote sensing images, effectively improving the accuracy of slope classification.The study was of great significance in promoting ecological protection, restoration, and management of the HFB in the TGR, and maintaining important ecological security barriers in the Yangtze River Basin.

According to the fault data of a certain type EMU traction system in China in 2022, the location and frequency distribution of key faulty equipment were analyzed, and the impact duration caused by various failure problems was counted. These two are combined as the spatio-temporal characteristics of EMU traction system faults. The distribution model was selected to compare the duration distribution characteristics of various faults, and the K-S test method was used to analyze the fitting effects. The results show that the fault influence time of pantograph, traction converter and roof high-voltage cable is the most suitable for the logistic distribution model fitting, while the lognormal distribution is most suitable for the traction transformer, main circuit breaker and traction motor. It is of great significance to predict the failure time of EMU traction system, point out the optimization direction of system equipment maintenance, and improve the efficiency of train operation and scheduling.

Logging data constitutes the basis for oil and gas field development and evaluation. However, in actual mining, factors like poor wellbore stability and equipment failure give rise to the distortion or loss of logging data. A prediction model based on variational mode decomposition (VMD) was proposed to address the issues of unstable and inaccurate results in existing prediction models. The model combines convolutional neural networks (CNN), bidirectional long short term memory (Bi-LSTM), and attention mechanism to predict missing sections in well logging curves. With logging sequence data as input, the VMD algorithm was employed to decompose the sequence into a series of amplitude-modulated and frequency-modulated signal subsequences. The features were extracted by the CNN network and trained by the Bi-LSTM network. During training, the Attention mechanism was utilized to learn the importance weight of each time step dynamically. Finally, the predicted value of the logging curve was outputted. The method was applied to predict logging curves in the Biyang Block of Henan Province and compared with other common machine learning prediction models. The results show that the application effect of the CNN-BiLSTM-Att model improved based on VMD is remarkable, with an error of only the order of 10^-3 and a prediction accuracy of 92.02%. The research results provide new ideas for accurate prediction of logging curves.

A skeleton-based architectural point cloud fusion method was explored to address the challenges of model incompleteness in real-world 3D architectural models. The process begins with reverse modeling of the architectural scene using 3D point clouds. The acquired reverse point cloud data was combined with the original architectural design data to generate a forward point cloud model. A method based on the rotational symmetry axis (ROSA) was then applied to extract skeleton lines from both the reverse and forward point cloud models. The fusion of the forward and reverse point cloud models was achieved by skeleton matching, resulting in a reconstructed 3D model with improved completeness. Experimental validation shows that this method significantly reduces areas of model incompleteness, providing new insights and methods for 3D modeling and reverse engineering in architectural reality capture.

Lightning is the main cause of active distribution line fault. It is of great significance to study the lightning risk assessment of active distribution network. The distribution line with distributed photovoltaic system in Nanjing area was taken as the research object. The calculation model of lightning overvoltage on distribution lines with distributed photovoltaic system was established, and the interaction between photovoltaic side and distribution line side during lightning strike was analyzed. The electrical geometric model on both sides was constructed. The trip rates of the photovoltaic side and the line side were calculated, and the risk was evaluated according to the calculation results. The results show that when lightning strikes the nearest tower on the photovoltaic side, the lightning trip-out rate on the photovoltaic side increases from 27.52 times/(100 km·a) to 29.63 times/(100 km·a), and the lightning risk is higher. When the photovoltaic side is struck by lightning, the tripping rate of the adjacent three towers affected by the lightning intrusion wave is doubled, and the risk level is also higher.

Helium is recognized as an extremely important yet highly scarce resource. In China, helium is primarily extracted from natural gas, where its low concentration presents significant challenges for extraction. Membrane separation technology for helium extraction from natural gas has been increasingly studied in recent years. However, the technology is still considered immature, and substantial experimental difficulties are encountered. Molecular dynamics (MD) simulations were employed as an effective approach to address these challenges. Recent advancements in membrane materials for MD simulations in helium extraction from natural gas were reviewed. Emphasis was placed on the methods used for constructing membrane models, the selection of simulation force fields, and the techniques applied to evaluate the separation performance of membrane materials. Two dimensional graphene like thin films and hybrid membrane materials were currently popular membrane materials. COMPASS and UFF force fields have a wide range of applications. The energy barrier for helium to pass through most membrane materials is low, and most membrane materials have high selectivity and permeability for helium and methane. The research results have good guiding significance for the practical production of membrane separation and helium extraction from natural gas.

The double semi-trailer truck train has strong transportation capacity and relatively low transportation costs. However, compared to semi-trailers, double semi-trailer truck train has more vehicle units, resulting in higher driving difficulty and lower lateral stability at high speeds. To address this issue, a control strategy combining model predictive control (MPC) with differential braking was proposed. Based on the principles of MPC, an MPC lateral stability controller for double semi-trailer truck trains has been designed. MPC regulates the yaw moments of three vehicle units, with differential braking technology dynamically allocating braking forces to individual wheels. The double semi-trailer truck train model has been built in Trucksim, and a simplified vehicle model has been established in MATLAB/Simulink. Through joint simulation of Trucksim and MATLAB/Simulink, the effectiveness of the designed system was verified under different vehicle speeds, loads, and friction coefficients. The research results indicate that the designed controller effectively reduces the centroid yaw angle, lateral acceleration, and yaw rate of each vehicle unit, thereby enhancing the stability of double semi-trailer truck train during high-speed lane changes.

To address the cumbersome calibration process of fisheye cameras and its inapplicability to everyday scene images, a novel convolutional neural network(CNN)-based method was proposed that simultaneously calibrates the intrinsic parameters of fisheye lenses and corrects image distortion. The accuracy of fisheye camera calibration and image distortion correction was improved by predicting the displacement of pixel points under different distortion parameters. A coordinate attention module was introduced in the encoding part to enhance the model's accuracy and generalization ability to increase attention to image position information. Additionally, a cross-scale fusion module was designed in the skip connections to enhance image detail features. To address the issues of dataset scarcity and incomplete distortion parameter distribution, a new large-scale dataset labeled with corresponding distortion parameters and images after distortion correction was created. Experimental results show that compared to other fisheye camera calibration methods, this method achieves a reprojection error of 0.312 pixel, indicating the highest calibration accuracy. Additionally, compared to other image distortion correction methods, a peak signal to noise ratio(PSNR) of 38.055 dB and an structural similarity(SSIM) of 0.874 are achieved, indicating the best quality of image distortion correction.

As the service life of China's oil and gas pipelines increase, the pipelines are inevitably subjected to defects such as corrosion and denting due to external factors. It becomes imperative that the impact of these defects, including denting and corrosion, on the ultimate bearing capacity of the pipelines be analyzed. A simulation was conducted on the safety of an X80 pipeline with dents and corrosion defects. By changing factors such as dent length, width, depth, and material type, the influence of a single dent area under the action of spherical and ellipsoidal pressure heads on the ultimate bearing capacity of the pipeline was analyzed. At the same time, the influence of various factors such as the size and spacing of dents and dents on pipeline safety was studied when composite defects such as dents and corrosion coexist. The results show that the length of dents, corrosion length, and corrosion depth are important factors affecting the ultimate bearing capacity of composite defects, and the damage to pipelines is greater when dents act in the vicinity of the corrosion center point.