Improving the accuracy and detection speed of bolt recognition under the complex background of the train underframe is crucial for the safety of train operation. To achieve efficient detection, a lightweight detection method based on SFCA-YOLOv8s is proposed. The underframe bolt images are captured by a self-designed track-based inspection robot, and a dataset is constructed by mixing simulated platform images with real train underframe bolt images. By combining the C2f module with ScConv lightweight convolution and replacing the Bottleneck structure with the Faster_Block structure, the SFC2f module is designed for feature extraction to improve detection accuracy and speed. It is compared with FasterNet, GhostNet, and MobileNetV3. Additionally, the CA attention mechanism is introduced, and MPDIoU is used as the loss function of YOLOv8s. LAMP scores are used to rank the model weight parameters, and unimportant weight parameters are pruned to achieve model compression. The compressed SFCA-YOLOv8s model is compared with models such as YOLOv5s, YOLOv7, and YOLOX-s in comparative experiments. The results indicate that the final model achieves an average detection accuracy of 93.3% on the mixed dataset, with a detection speed of 261 FPS. Compared with other classical deep learning models, the improved model demonstrates superior performance in detection effectiveness, robustness, and generalization. Even in the absence of sufficient real underframe bolt images, the algorithm enables the trained network to better adapt to real environments, improving bolt recognition accuracy and detection speed, thus providing technical references and theoretical support for subsequent related research.

For the rail head wear detection, the feasibility of collecting rail images by CCD camera is analyzed. MATLAB image processing technology is used to establish the calculation model of rail wear measurement. By using the methods of edge detection and image filling, the key problems of rail image positioning are studied. The results show that the rail abrasion image detection system can get the rail abrasion accurately in real time under the condition of non-contact. The method can calculate the rail wear more accurately and provide a reference for the loss prevention of the rail and the development of new detection technology.

Liquid metal current limiter (LMCL) has the advantages of simple structure and automatic current limiting. It has become a new technology to limit short-circuit current in power system. In order to improve the performance of traditional insulation partition LMCL (ILMCL), a resistive partition LMCL (RLMCL) is designed. Specifically, the force on the liquid metal liquid column model is calculated, and the motion mechanism of the liquid metal self-shrink is clarified; furthermore, a mathematical model for the liquid metal self-shrink effect is established. Using the simulation software Fluent, the dynamic diagrams of liquid metal self-shrinking gas-liquid two-phase are obtained. Finally, the current limiting characteristics and the arc plasma dynamic process of the RLMCL with different liquid metal level heights are compared and analyzed by experiments. The results indicate that the liquid metal level height is a key factor affecting the liquid metal self-shrinking speed. Whether the liquid metal fills through-hole will change the way the liquid metal shrinks. Under the same current action, as the liquid metal level height increases, the starting current limiting time, liquid metal self-shrinking time, and current half-wave time of the RLMCL increase while the peak arc voltage and arcing time gradually decrease.

The phenomenon of vacuum arc plasma reignition has a significant impact on the safe and stable operation of the power system. In order to reveal the mechanism of the reignition phenomenon, a vacuum arc plasma experimental platform based on a removable interrupter chamber is constructed. The evolution of the vacuum arc plasma morphology in the interrupter chamber is captured by a high-speed camera during the opening and closing process. The evolutionary behavior of plasma morphology under multiphysics fields in the diffusion–extinction phase of vacuum arc plasma is experimentally investigated, and the effects of electromagnetic field, temperature field, and metal vapor flow on vacuum arc plasma reignition are studied. Furthermore, the copper vapor diffusion–extinction multiphysics (CVDEM) model is developed. Specifically, the distributions of key parameters, such as temperature (electrons and ions), density (electrons and ions), pressure, velocity, current density, and magnetic flux density, during the plasma combustion process are calculated. Finally, the multiphysics field coupling mechanism is investigated. The results of the simulation are in good agreement with the experimental data. The results indicate that the evolution of the vacuum arc plasma morphology is effectively captured by the detachable interrupter experimental platform designed in this article. The maximum values of the plasma temperature field, current density field, and pressure field are concentrated at the cathode position. The maximum electron and ion density is located above the cathode. The maximum value of the magnetic induction is at the edge of the plasma column. As the current decreases, the interpolar temperature field and the current density field contract, leading to the dissipation of the interpolar plasma pressure. The temperature field decays at an accelerated rate during the diffusion phase, and the temperature field has the highest sensitivity to external influences. During the extinction phase, the overall temperature of the plasma is characterized by the ion temperature, and the rapid decay of the ion temperature field and current density field is the main cause of plasma extinction.

Smart charging stations require mobile charging robots to respond to dynamically arriving charging requests with heterogeneous priorities, varying travel costs, and uneven workloads while maintaining online scheduling feasibility. Conventional single-layer approaches often optimize task assignment or route ordering separately, which limits their ability to coordinate allocation quality, route efficiency, and workload regulation under real-time constraints. This study proposes a hierarchical improved consensus-based bundle algorithm–ant colony optimization (ICBBA-ACO) framework for dynamic multi-robot task allocation. The upper ICBBA layer combines deterministic task clustering, intra-cluster greedy bundling, conflict resolution, and feedback-guided workload-aware reassignment, while the lower ACO layer refines the visiting order of unstarted tasks under fixed ownership using the same normalized four-objective scheduling cost. Complete decision time is evaluated separately against a $200\mathrm{ms}$ online requirement, and estimated motion energy is retained only as a distance-derived auxiliary indicator. In a five-method comparison over 100 paired scenarios, ICBBA-ACO achieves a mean composite objective of $J=0.663052$, a mean decision time of $33.07\mathrm{ms}$, and 100% deadline compliance. GA-MRTA obtains a lower unconstrained mean objective of $J=0.615790$, but requires approximately $2199.30\mathrm{ms}$ on average and satisfies the $200\mathrm{ms}$ requirement in only $8.89\%$ of the evaluated updates. Thus, ICBBA-ACO provides the lowest mean objective among the compared methods that maintain full deadline compliance, demonstrating a favorable quality–runtime trade-off within the tested operating range. ROS-based engineering verification further completes all 15 repeated trials and all 48 verification tasks with no recorded invariant violations.