A novel multi-objective optimization design method for permanent magnet roller based on improved particle swarm optimization algorithm and RMxprt co-simulation was developed to address the issue of low optimization efficiency when relying solely on expert experience. Firstly, an improved particle swarm optimization algorithm was proposed to enhance the convergence speed of the optimization process. Secondly, based on the analysis of the relationship between the structural and performance parameters of the permanent magnet roller, the variable parameters, constraint parameters, and optimization parameters for the improved particle swarm optimization algorithm were determined. Lastly, a MATLAB program for the improved particle swarm optimization algorithm was developed to achieve closed-loop iteration and comparative optimization of the input and output parameters in RMxprt, thereby improving the efficiency and effectiveness of the optimization design for the permanent magnet roller.

With the acceleration of the transition to new energy systems, it is urgent to carry out in-depth research on the complex energy characteristics of multi-load users. A technology of constructing user energy characteristic label library and a user portrait method were proposed, which comprehensively considered the coupling characteristics of electric, cold and thermal multiple loads. Firstly, the high redundancy and low correlation features were eliminated by the fast correlation filtering algorithm, and the features with strong distinguishing ability were selected by the random forest and recursive feature elimination algorithm. In the clustering stage, the improved three-way adaptive density peak clustering (3W-ADPC) algorithm improved the load clustering effect by combining the adaptive neighbor search and the three-branch clustering algorithm. The empirical results show that the proposed method has dual advantages in computational efficiency and clustering accuracy, and can accurately reveal the comprehensive energy use characteristics and deep information of multi-load users, which confirms the practical value of the proposed method in the study of multi-load users’ behavior.

With the rapid development of infrastructure projects in China, the use of anchor bolts in mining, geology, and tunnel engineering continues to increase. The non-destructive testing of anchor bolt quality is crucial for enhancing the stability and safety of engineering projects. Based on the stress wave detection method, wavelet threshold functions and STA/LTA algorithms were employed to comprehensively evaluate anchor bolt engineering. A software for non-destructive testing and intelligent analysis of anchor bolt anchorage was developed, integrating signal filtering and acquisition of anchorage parameters. Through numerical simulation analysis of anchor bolt anchorage, the lengths of anchor bolts were calculated using both manual picking and software-based arrival time extraction. The results show that the software-calculated anchor bolt lengths have an overall error controlled within 5%, offering higher precision than manual picking, which is significant for improving the safety and stability of project engineering.

Restricted by topographic conditions and surrounding environment, the design spacing of double-hole tunnels in western China is often close. Therefore, the special structural form of multi-arch or small close tunnels is often used in the tunnel design. However, in the western region of China, high-intensity seismic zones are widely distributed, and the middle rock wall between the close tunnels is more susceptible under the strong earthquakes, especially the near-field ground motions, which results in large plastic deformation of local surrounding rock, and the safety operation of the tunnel was affected. Therefore, in order to study the distribution of plastic zone of surrounding rock of small close-distance tunnel under near-fault ground motion, reasonable near-fault ground motions were selected, and 3D finite element numerical models considering different tunnel close-distance and surrounding rock conditions were established. The dynamic response characteristics of small close-distance tunnel and the distribution law of plastic zone of surrounding rock were revealed. Thus, through the grouting reinforcement of the middle rock wall, the seismic effectiveness of the reinforcement measures on the plastic zone was verified. The results show that the acceleration response of the arch foot near the middle rock wall tunnel is more significant under the influence of near-field ground motion. Under transverse seismic excitation, the maximum principal stress value of the left spandrel and the right arch foot of the right line tunnel are more significant, while the minimum principal stress of the side walls and the inverted arch of the tunnel is larger. The plastic zone of surrounding rock of small-close-distance tunnel under strong earthquake is greatly affected by rock mass grade. When the tunnel spacing is 0.50 times of the tunnel span, the development of plastic zone in grade Ⅳ and grade Ⅴ surrounding rock is the most serious, and the phenomenon of plastic zone penetration of surrounding rock at the haunch of tunnel near the middle rock wall appears. Taking the plastic zone of surrounding rock as the discriminant index, the grouting reinforcement of middle rock wall is beneficial to the seismic performance of small close distance tunnel. The research results can provide a reference for the seismic safety of small close-distance tunnels in high-intensity earthquake areas.

To enhance the grid connection stability of virtual synchronous machines, a global optimization design method for virtual synchronous machine control parameters was proposed. Firstly, a small-signal model of the virtual synchronous generator with virtual exciter and governor was established, and the system eigenvalues were obtained by solving the state matrix. Secondly, the sensitivity of the controller parameters to the position of eigenvalues was studied, and a wide range of parameter optimization was conducted using genetic algorithms based on the main eigenvalue positions. Finally, analytical solutions of the model and MATLAB/Simulink simulation data were compared. The results show that significant improvements in frequency stability can be achieved by optimizing a wide range of virtual synchronous machine parameters. After optimization, the system response transient stability time is 0.25 s, only 5% of the transient stability time with general parameters, and the frequency stability improves noticeably with load changes.

In order to better protect the safety of bridge piers and ships, a step-by-step progressive high energy consumption anti-collision magnetorheological damper for bridge piers was designed for the problems of passive energy dissipation, poor dynamic response and anti-collision energy dissipation, and limited adaptability of the device. The structural parameters of the damper were established through the establishment of a mechanical model. The structural strength and magnetic circuit were analyzed using finite elements. The results show that the strength of the structural components meets the requirements, the magnetic induction strength at the gap under 2 A current can reach about 1.2 T, three pistons are set up in the cylinder barrel, which can work together under different crash depth displacements to divide the energy dissipation of the damper into three stages, the minimum damping force under no current is 15 kN, the maximum damping force is 496 kN, and the damping force improves with displacement by 481 kN. The damping force under 2.5 A current increases with displacement from 78 kN to 1 204 kN, which is an improvement of about 15 times. It effectively improve the force of the damper and achieve the excellent effect of graded progressive impact energy dissipation. The application in the bridge pier collision avoidance device can achieve semi-active collision avoidance energy dissipation. The theoretical and finite element simulation results basically coincide with each other, proving the rationality of the damper design.

Multi-label learning is a common problem in real application scenarios. The construction of large-scale multi-label datasets often means high cost, so semi-supervised learning technology appears. At present, most semi-supervised learning is mainly used in the field of single label classification. Although semi-supervised learning in the field of multiple labels classification has made some progress, there is still much room for improvement in training time consumption, training effects and the use of potential relationships between labels. A multi-label semi-supervised curriculum learning model was proposed under the dual structure semi supervised course learning under dual structure(SSCD) to solve the above problems. Firstly, a curriculum learning scheme based on dual difference was designed, which greatly reduces the training time and improves the robustness of the model. Secondly, a single attention mechanism was designed to explore the potential relevance between labels. The performance of SSCD in the prediction task was evaluated on three open test datasets, and the results compared with four benchmark models show that the comprehensive indicators of SSCD are optimal in all aspects. Finally, the structure ablation experiment was carried out to prove the effectiveness of the proposed single attention mechanism.

Process production safety monitoring is the main technical method for safety risk control and accident prevention, and data is a significant basis for safety control and decision-making. In the existing security monitoring network, there are many sensor nodes and large amounts of data, which cause heavy channel load in the wireless sensor network. This often leads to issues such as data latency and loss, which affects the timeliness and accuracy of safety control decisions. Therefore, the security risk factors of typical process production scenarios were focused on. Based on this, the sensor deployment plan and wireless sensor network data transmission architecture were clarified, a security monitoring data flow scheduling method based on superior-subordinate server was proposed, and the data stream congestion index and abnormal data packet frequency index were used as the main indicators of data flow performance evaluation. Subsequently, the chemical polymerization reactor were taken as an engineering scenario, the performance improvement was examined after the subordinate server was initiated to share the data flow for the superior server. Through the comprehensive study of channel load balancing, the method of superior-subordinate server is benefit to ensure the effectiveness of orderly transmission of safety monitoring data and risk control.

Particle profile control and blockage is recognized as an important method for enhancing oil recovery. The migration and deposition characteristics of particles in porous media are understood to facilitate the optimization of particle preparation, thereby improving compatibility with reservoir pore throats and blocking efficiency. Factors such as particle concentration, particle size, porous medium structure, particle size ratio, and fluid parameters within the medium were reviewed for their effects on migration and deposition. Research results from various simulation methods, including simplified geometry, mesoscopic simulation, lattice Boltzmann method-discrete element method (LB-DEM) and computational fluid dynamics-discrete element method(CFD-DEM) were summarized. It is indicated that the critical value of the particle size ratio influences the deposition location and blockage degree in porous media. Different particle sizes are subjected to significant differences in forces, with larger particles being notably affected by hydrodynamics, gravity, and fluid flow rates. The fluid flow model within porous media is not yet fully unified. However, the Brinkman-Forchheimer-Darcy model is noted for its strong applicability. The CFD-DEM method, approached from a microscopic perspective, has validated the flow-solid coupling of migration and deposition within the medium, providing a basis for profile control schemes in heterogeneous reservoirs.

Landslide disasters pose a serious threat to residents’ lives and socio-economic development. Taking Xinyuan County as the study area, 17 landslide influencing factors were selected as the initial factor set. Through multiple collinearity analysis, 10 landslide factors were screened and an evaluation index system for landslide susceptibility in the study area was constructed. The landslide susceptibility was then evaluated based on three typical models: logistic regression (LR), support vector machine (SVM), and random forest (RF). The evaluation results of the model were compared and validated using the area under curve (AUC), landslide ratio, and field investigation under the receiver operating characteristics(ROC) curve. The results show that low-susceptibility areas are mainly concentrated in the valley plain of the Kongnais River, where the terrain is flat and the landslide susceptibility is relatively low. High-susceptibility areas are mainly located in the northern part of the Kongnais River valley, the Awulale hilly area, and the watersheds on both sides of the southern Yishikelike Mountains and Nalati Mountains, as well as the area south of the Qiafu River, where the terrain is complex and varied, leading to a higher susceptibility to landslides. Among the three evaluation models, the SVM model performs the best, with an AUC value of up to 0.985, indicating its high accuracy in landslide susceptibility assessment. Furthermore, the high-susceptibility areas identified by the SVM model have a high density of landslide points, accounting for 86% of the total, further validating its effectiveness in landslide susceptibility assessment. Based on the above results, the SVM model is more reasonable than the other two alrorithms in assessing landslide susceptibility in Xinyuan County, providing a scientific theoretical basis and reference for landslide prevention and control in the region.