The integrated monitoring system for rail transit is a highly complex control and supervision system used for the regulation and control of various facilities and services in the rail transit network. Due to its high requirement for reliability, it is necessary to establish both a primary and a backup system. When the primary system encounters issues, the backup system takes over the line control authority. When the primary system recovers, the backup system returns the line control authority. However, existing switching methods suffer from issues such as poor reliability and low stability, seriously affecting the safe operation of rail transit. We propose a switching method for the integrated monitoring system and the main and backup control centers of rail transit, aiming to improve the reliability and stability of the integrated monitoring system. Firstly, we establish primary and backup monitoring systems with consistent software and hardware, which operate independently and simultaneously, providing the basic conditions for reliable and stable switching. Secondly, we construct a line control authority switching method that effectively enhances the timeliness of line monitoring during primary system failures through automatic switching of line control authority. Finally, we demonstrate the advantages of our proposed method in terms of stability and reliability through a case study based on real scenarios.
In the wind power industry,the algorithm library plays an important role in the development of control systems as a carrier for the application and accumulation of industry knowledge.In order to solve the problem that the wind power algorithm library of foreign brand controllers is not open source and realize the independent development of wind power al-gorithm library,this paper analyzes the specific needs of the wind power industry for the algorithm library,combines the commonly used control system program design standards in the industrial control industry,proposes a design method of wind power algorithm library from the perspective of functional design and interface design,and describes the process of realizing it through IEC61131-3 language and C language.The algorithm library developed by this method has been ap-plied to large-scale wind turbines.
In order to solve the problem of partial loss of data information and structure of 3D point cloud model due to subjective and objective factors such as occlusion and noise, a partial deletion reconstruction method of 3D point cloud model based on PF-Net was proposed, and a model deletion reconstruction system was developed. Based on the deep learning PF-Net network architecture, the Batch Normal layer and the Dropout layer are introduced to normalize the original datasets in batches, which further improves the reconstruction efficiency and accuracy of some missing point cloud models. In this paper, 11 point cloud models are selected to carry out reconstruction experiments with some missing data information and structural features, and the experiments show that the proposed method has higher reconstruction efficiency than the L-Gan and PCN methods when the same dataset is used for training and testing. In the eleven test categories, the average improvement of the refactoring method in this paper is 12%-27%. The proposed method has a significant effect in dealing with the partial deletion reconstruction of small-scale models, and at the same time improves the efficiency and accuracy of reconstruction, which has good application value.
For the modeling of the single coil structure commonly used in wireless charging, Maxwell static magnetic field was used to analyze the influence of changing the longitudinal distance of the coil on the transmission characteristics of the three single coil structures when other parameters were unchanged. By comparing the performance of the three single-coil structures, a new transmitting coil structure was proposed on this basis. The magnetic field simulation showed that the new transmitting coil could generate greater magnetic field intensity and more significant energy transmission effect than the traditional coil.
Aiming at the classical knapsack problem in combinatorial optimization, in order to improve the local search ability and global search ability of the basic fireworks algorithm, an improved fireworks algorithm is proposed by combining the basic fireworks algorithm, greedy optimization strategy and simulated annealing algorithm. In order to ensure the diversity of the initial population, the Tent mapping is proposed to initialize the population ; greedy repair operator and greedy optimization operator are introduced to correct the intermediate solution ; at the same time, the simulated annealing mechanism is introduced to make the poor solution have a certain probability to be accepted to improve the ability of the algorithm to jump out of the local optimum. By solving the typical test function, it is found that the improved fireworks algorithm can accurately solve the theoretical optimal solution of the Griewank function. Compared with the basic fireworks algorithm, simulated annealing algorithm and particle swarm optimization algorithm, the improved fireworks algorithm can find the optimal value of Sphere function with higher accuracy. By solving six groups of knapsack problems with different dimensions, it is found that the improved fireworks algorithm can hit the optimal solution with 100 % probability for most test data, and hit the optimal solution with more than 70 % probability for its test data, and the standard deviation of hit times is less than 13. The experimental results show that the improved fireworks algorithm has higher solving accuracy and faster solving speed, and can effectively solve the 0-1 knapsack problem.
