High-voltage switch mechanical characteristic parameter optimization platform

Abstract

The invention discloses a high-voltage switch mechanical characteristic parameter optimization platform, and relates to the technical field of high-voltage switch mechanical characteristic parameter optimization, and the platform comprises a parameter collection module which is used for collecting mechanical characteristic parameters of a high-voltage switch in real time, and a data analysis module which is used for analyzing the mechanical characteristic parameters of the high-voltage switch based on the collected mechanical characteristic parameters. And analyzing the relevance between the high-voltage switch performance and the mechanical characteristic parameters through an adaptive optimization algorithm, and generating optimization reference data. According to the mechanical characteristic parameter optimization platform for the high-voltage switch, the influence degree and the mutual relation of each parameter on the performance can be accurately determined; based on the generated optimization reference data and adjustment reference data, parameters such as the on-off time, the switching-on time and the switching-off time of the high-voltage switch can reach better values, so that the comprehensive performance index is improved, parameter combinations can be comprehensively evaluated under various working conditions, potential performance problems can be found and adjusted in time, and the service life of the high-voltage switch is prolonged. And the high-voltage switch always keeps good performance in a complex actual operation environment.

Description

Technical Field

[0001] The present invention relates to the technical field of high-voltage switch mechanical characteristic parameter optimization, and in particular to a high-voltage switch mechanical characteristic parameter optimization platform. Background Art

[0002] In the power system, high-voltage switches are one of the key devices to ensure the safe transmission and distribution of electricity; the mechanical characteristic parameters of high-voltage switches, such as breaking time, closing time, opening time, travel, etc., play a vital role in its performance and reliability; accurately adjusting and optimizing these mechanical characteristic parameters can improve the operating accuracy of high-voltage switches, extend their service life, and enhance the stability of power systems. However, at present, the determination and adjustment of the mechanical characteristic parameters of high-voltage switches mainly rely on traditional empirical methods and on-site debugging; technicians usually preliminarily set parameters based on past experience and some basic calculation formulas, but this method has great limitations.

[0003] On the one hand, high-voltage switches of different models and specifications differ in structure, material and working environment, and it is difficult to accurately determine the optimal parameter combination suitable for a specific switch by relying solely on experience; moreover, empirical methods are often more conservative and may not be able to fully tap the performance potential of high-voltage switches; on the other hand, although on-site debugging can optimize parameters to a certain extent, this method is time-consuming and labor-intensive, requiring a large number of tests and adjustments, which not only increases costs and time investment, but may also cause unnecessary losses to equipment; it is difficult to determine the best parameters at one time, and often requires repeated attempts, which is inefficient; that is, with the continuous development of the power system and the continuous improvement of the performance requirements for high-voltage switches, the existing parameter determination methods have been difficult to meet actual needs; there is an urgent need for a technical means that can accurately predict the relationship between the mechanical characteristic parameters and performance of high-voltage switches, so as to quickly determine the optimal parameter combination during the design, debugging and operation process, and improve the performance and reliability of high-voltage switches. Summary of the invention

[0004] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: a high-voltage switch mechanical characteristic parameter optimization platform, including a parameter acquisition module, which is used to collect the mechanical characteristic parameters of the high-voltage switch in real time, the mechanical characteristic parameters including breaking time, closing time, opening time, stroke, contact stroke, opening and closing current, speed and pressure, and these parameters fully reflect the working state and performance of the high-voltage switch; A data analysis module is used to analyze the correlation between the high-voltage switch performance and the mechanical characteristic parameters based on the collected mechanical characteristic parameters through an adaptive optimization algorithm to generate optimization reference data, wherein the adaptive optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm to improve the prediction accuracy and optimization efficiency between the mechanical characteristic parameters and the high-voltage switch performance; The parameter optimization module adopts a feedback mechanism to adjust the mechanical characteristic parameters by real-time monitoring of the operating status of the high-voltage switch in combination with the optimization reference data to optimize the performance of the high-voltage switch and obtain the adjustment reference data. The parameter optimization process realizes the automation of parameter adjustment through an iterative algorithm; wherein the feedback mechanism adjusts the parameter weights of the optimization model in combination with the actual operating data of the high-voltage switch under different environmental conditions to ensure that the optimization results are adapted to different working environments; A simulation test module is used to simulate the operation of the high-voltage switch based on the adjusted reference data, obtain simulation results, and evaluate the performance under the parameter combination; The feedback adjustment module has a closed-loop control mechanism, which is used to further optimize and adjust parameters to form optimal parameters based on real-time simulation results combined with preset performance goals.

[0005] Preferably, the simulation test module can simulate the working state of the high-voltage switch under various working conditions, including voltage fluctuations, mechanical wear, temperature changes, evaluate the stability and performance of mechanical characteristic parameters under different environmental conditions, and provide comprehensive parameter optimization feedback. The simulation test module integrates finite element analysis and multi-body dynamics simulation technology to accurately simulate the dynamic behavior of the high-voltage switch under different working conditions and provide simulation results for quantitative performance evaluation.

[0006] Preferably, the process of obtaining the optimized reference data by the data analysis module is as follows: Perform algorithm selection and model construction, using adaptive optimization algorithms including genetic algorithm and particle swarm optimization algorithm; these algorithms can generate adaptive optimization solutions for high-voltage switches of different models and specifications to improve the prediction accuracy and optimization efficiency between mechanical characteristic parameters and high-voltage switch performance; Constructing an optimization model: Constructing a multi-objective optimization model based on the collected mechanical characteristic parameters and the selected optimization algorithm; the model takes the high-voltage switch performance as the target and the mechanical characteristic parameters as the variables, and establishes a mathematical relationship model between the two; First, use historical data to train the model: Use the continuously updated historical data to train the constructed model; by letting the model learn a large amount of historical data, it can understand the intrinsic relationship between different combinations of mechanical characteristic parameters and high-voltage switch performance, thereby improving the model's prediction ability; During the training process, the performance of the model under different parameter settings is evaluated through the cross-validation method, the model parameters are continuously adjusted, and the optimal parameter combination is selected to obtain a trained multi-objective optimization model to improve the accuracy and stability of the model; so that the model can better adapt to high-voltage switches of different models and specifications; Based on the trained multi-objective optimization model, the correlation between the high-voltage switch performance and the mechanical characteristic parameters is analyzed; the influence degree and mutual relationship of each mechanical characteristic parameter on the high-voltage switch performance are determined to obtain analysis results, such as which parameters have a greater impact on the breaking performance, and which parameters have a strong correlation; based on the analysis results, optimization reference data is generated; the optimization reference data includes information on the direction and approximate amplitude of each mechanical characteristic parameter to be adjusted in order to achieve better performance under the current high-voltage switch state, which can provide a reference basis for the parameter optimization module to achieve the optimization of the high-voltage switch performance.

[0007] Preferably, the process of generating optimization reference data based on the multi-objective optimization model of machine learning is as follows: Assume that the performance index of the high-voltage switch is y, which includes comprehensive performance indicators such as breaking reliability and closing stability. The mechanical characteristic parameter vector is , where x1 is the breaking time, x2 is the closing time, and so on, and n is the number of mechanical characteristic parameters; The multi-objective optimization model is expressed as: Where f is the prediction function built based on the machine learning algorithm, Optimize the parameter vector of the model for multiple objectives. The parameter vector is continuously optimized and adjusted during the training process. The optimal individual is obtained based on the genetic algorithm formula in the adaptive optimization algorithm, and the optimal particle position is obtained based on the particle swarm optimization algorithm formula; After the iteration is terminated, the optimal solution is selected based on the final optimal particle position and the optimal individual in the genetic algorithm, and the mechanical characteristic parameters corresponding to the optimal solution are combined. Combined with the analysis of the multi-objective optimization model, such as the sensitivity analysis of the impact of parameters on performance, optimization reference data is generated; for example, the adjustment direction and adjustment range recommendations for each mechanical characteristic parameter are obtained so that the parameter optimization module can perform subsequent parameter adjustment operations.

[0008] Preferably, the genetic algorithm formula in the adaptive optimization algorithm is: Encoding and initialization population: Through binary encoding, for each mechanical characteristic parameter x i , perform encoding operation; assume that each parameter is encoded as a binary string of length l, then the encoding of the individual is represented as , where p1 to p l is the encoding of x1, p l+1 to p 2l is the code of x2, and so on; where each individual represents a set of mechanical characteristic parameter combinations; Initialize the population P(0) and randomly generate m individuals, that is, ; Fitness evaluation: Calculate the fitness value F(pi) of each individual. The fitness function is defined according to the high-voltage switch performance index y. , where y i is an individual p i The corresponding prediction performance index value, y target is the expected performance target value, k is a constant. By this definition, the larger the fitness value, the closer the individual is to the optimal solution; Selection operation: Use roulette wheel selection method to select parent individuals; the probability of individual pi being selected ; Crossover and mutation operations: Use single-point crossover to perform crossover operations on the selected parent individuals. Suppose the two parent individuals are p a and p b , randomly select the intersection point , then the offspring individual p´ after crossover a and p´ b for: , ; The offspring individuals after crossover are mutated with a certain mutation probability p m Change the value of some gene bits in the individual; for individual p i A gene position p in i,j , if the random number , where r is a random number between 0 and 1, then ; Update the population and iteration termination conditions: After selection, crossover and mutation operations, a new generation of population P(t+1) is obtained. Repeat the fitness evaluation, selection, crossover and mutation operations until the global optimal solution no longer changes significantly. The iteration termination conditions are met and the optimal individual is obtained.

[0009] Preferably, the formula of the particle swarm optimization algorithm is: Each particle i is represented by a mechanical characteristic parameter vector , and each particle has a velocity vector Initialize the position and velocity of the particle. The position is randomly initialized within the reasonable value range of the mechanical characteristic parameters, and the velocity is randomly initialized within a certain range. Individual optimal and global optimal update: Calculate the fitness value F(x i ), where the fitness function is defined in the same way as in the genetic algorithm, and is defined based on the high-voltage switch performance index; for each particle i, if ,in is the optimal position of particle i in history, then =x iAt the same time, find the particle with the smallest fitness value among all particles, that is, the particle with the best performance, and its position is recorded as ; Speed ​​and position update: The speed and position update formula of the particle is: , ; Where w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between 0 and 1, t is the number of iterations, j = 1, 2, ..., n; Repeat the operations of updating individual optimal and global optimal solutions and updating speed and position until the iteration is terminated, that is, the global optimal solution no longer changes significantly, and the optimal particle position is obtained.

[0010] Preferably, the process of obtaining the adjustment reference data by the parameter optimization module is as follows: First, the optimization reference data is obtained from the data analysis module. By real-time monitoring of the operating status of the high-voltage switch, the actual operating data of the current high-voltage switch is obtained. The actual operating data includes the real-time values ​​of the breaking time, closing time, opening time, stroke, contact stroke, opening and closing current, speed and pressure. The feedback mechanism combines the actual operating data of the high-voltage switch under different environmental conditions to adjust the parameter weights of the optimization model. The different environmental conditions include voltage fluctuations, mechanical wear and temperature changes. When it is found that the mechanical characteristic parameters such as the opening time have a more significant impact on the switch performance in a high temperature environment among the environmental conditions, the weight of this parameter is increased in the current optimization process; Let the original weight vector be , n is the number of mechanical characteristic parameters, and the weight vector adjusted according to environmental factors is ; Calculate parameter adjustment: Calculate the adjustment amount of each mechanical characteristic parameter based on the optimized reference data and the adjusted weights ; The calculation process combines the gap between the current parameter value and the target value, the parameter weight, and the operating status of the high-voltage switch. The target value is obtained based on the optimized reference data; The target value is the ideal value or value range pre-set for each mechanical characteristic parameter during the optimization of the mechanical characteristic parameters of the high-voltage switch. These values ​​are intended to achieve the best performance of the high-voltage switch. For example, for the mechanical characteristic parameter of breaking time, the target value may be set to 5ms, which means that during the optimization process, the breaking time is expected to be adjusted to about 5ms to achieve the best breaking performance, such as minimizing arc energy and ensuring fast and reliable circuit disconnection. For a certain mechanical characteristic parameter x i , the adjustment amount The calculation formula , where k is the adjustment coefficient, is the target value of the parameter obtained based on the optimized reference data, is the current actual value; Apply the calculated adjustment amount to the current mechanical characteristic parameters to obtain the adjusted parameter value ,in, ; The adjusted parameter values ​​and related adjustment information are combined to generate adjustment reference data, such as adjustment amount, adjustment basis, etc. The adjustment reference data will be used as input to the subsequent simulation test module to evaluate the performance of the high-voltage switch under the parameter combination, and also provide basic data for the feedback adjustment module to further optimize the parameters.

[0011] Preferably, the process of obtaining the simulation result by the simulation test module is as follows: Based on finite element analysis and multi-body dynamics simulation technology, finite element analysis and multi-body dynamics simulation models are constructed: the finite element analysis method is used to model and analyze the physical fields of the electric field, magnetic field, and stress and strain of the high-voltage switch to determine the changes in the physical state inside the high-voltage switch under different working conditions; at the same time, the multi-body dynamics simulation technology is used to construct a mechanical motion model of the high-voltage switch to simulate its dynamic behavior during operation, including the motion trajectory of the contacts, speed changes, and the interaction between mechanical components; for example, for the contact system of the high-voltage switch, the finite element analysis is used to calculate the electric field distribution and the force between the contacts under different voltage and current conditions, and the multi-body dynamics simulation is used to simulate the distribution of the contacts. The closing action process, including the impact of collision and bounce during closing and the arc generation and extinction process during opening on the movement of contacts, etc.; according to actual needs, set the working state parameters of the high-voltage switch under various working conditions, the working state parameters include voltage fluctuation: such as setting different voltage amplitudes, frequency change ranges, mechanical wear: simulating the switch performance at different wear stages by changing the friction coefficient and wear degree parameters of mechanical parts, temperature changes: setting the ambient temperature range and the temperature rise caused by the self-heating of the switch when it is working; using the adjustment reference data generated by the parameter optimization module as the input parameters of the simulation, and setting the initial state and operating conditions of the high-voltage switch in the simulation model according to these parameters; Start the simulation program, and simulate the operation process of the high-voltage switch based on the constructed finite element analysis and multi-body dynamics simulation model, combined with the input adjustment reference data and the set working conditions. During the simulation process, the model will calculate various state variables of the high-voltage switch at different times according to physical laws and mathematical algorithms, such as electric field strength, current distribution, displacement, velocity, acceleration and interaction force of mechanical parts; During the simulation calculation process, data related to the performance of the high-voltage switch is collected in real time to obtain simulation data, which includes electrical performance data such as breaking capacity, insulation performance, on-resistance, mechanical performance data such as contact wear, mechanical life, operating force changes, and dynamic characteristic data such as opening and closing speed curves, contact bounce time, and vibration frequency; the collected simulation data is used for subsequent evaluation of the performance of the high-voltage switch; based on the collected simulation data, indicators for evaluating the performance of the high-voltage switch are calculated; the calculated performance indicators are integrated and analyzed to generate simulation results; the simulation results include specific values ​​and performance curves of various performance indicators of the high-voltage switch under given adjustment reference data and operating conditions, such as speed-time curves and stress-strain curves; and performance evaluation conclusions, such as whether the design requirements are met and in which aspects there are performance advantages or deficiencies.

[0012] Preferably, the process of obtaining the optimal parameters by the feedback adjustment module mainly includes the following steps: Receive the simulation results provided by the simulation test module, and at the same time, obtain the performance goals preset by the user through the customization mechanism; wherein, the user sets specific performance indicator priorities according to their own needs: including setting the breaking reliability as the highest priority, followed by the closing stability, and the parameter adjustment range: including, for example, stipulating the upper and lower limits of the allowable adjustment of a certain mechanical characteristic parameter. These preset information reflects the user's expected performance of the high-voltage switch in different application scenarios; compare and analyze the various performance indicators in the simulation results with the preset performance goals, and calculate the deviation value of each performance indicator. For example, for the performance indicator of breaking time, if the preset goal is that the breaking time should be less than or equal to 10ms under certain conditions, and the simulation results show that the actual breaking time is 12ms, then the deviation value is 2ms; in this way, determine the current parameter The gap between the performance of the high-voltage switch and the expected performance under the numerical setting; determine the parameter adjustment strategy according to the deviation value and the priority of the performance indicator; if the deviation of a high-priority performance indicator is large, for example, the breaking reliability does not meet the requirements and the deviation is significant, then the mechanical characteristic parameters that affect the breaking reliability, such as breaking speed, contact pressure, etc., will be adjusted; at the same time, the limitation of the parameter adjustment range will be considered to avoid other problems caused by adjustments beyond the reasonable range; for example, if the breaking speed adjustment range is set to [5m / s, 10m / s], when the current speed is 7m / s and the breaking reliability needs to be improved, it can only be adjusted within this range, such as increasing to 8m / s; according to the determined adjustment strategy, the mechanical characteristic parameters are adjusted; the amplitude and direction of the adjustment are based on the analysis of the simulation results and the guidance of the optimization algorithm; The adjusted parameters are input into the simulation test module again for simulation testing to obtain new simulation results; then the above-mentioned deviation analysis, adjustment strategy determination and parameter adjustment process are repeated, and the optimization is continuously iterated until the termination condition is met; the termination condition may include that the deviations of all performance indicators are within an acceptable range, the number of iterations reaches a preset upper limit, or the performance improvement is not obvious after several consecutive iterations; after the termination condition is met, the final parameter combination is verified; by performing additional simulation tests under different working conditions or referring to actual operating experience, it is ensured that the parameter combination can stably achieve good performance under various conditions; at the same time, check whether the parameters are within the preset adjustment range and whether they meet the actual constraints of the physical characteristics and operating principles of the high-voltage switch; after verification, determine that the mechanical characteristic parameters at this time are the optimal parameters.

[0013] Preferably, the parameter acquisition module is also used for adjusting the data, including archiving and version management of historical mechanical characteristic parameters, which ensures that during multiple iterative optimization processes of the platform, historical data can be traced and further analysis can be performed based on these data.

[0014] The present invention provides a high-voltage switch mechanical characteristic parameter optimization platform, which has the following beneficial effects: 1. The high-voltage switch mechanical characteristic parameter optimization platform can accurately determine the influence of each parameter on the performance and the relationship between them by collecting various mechanical characteristic parameters of the high-voltage switch in real time and using an adaptive optimization algorithm to analyze their correlation with performance. Based on the generated optimization reference data and adjustment reference data, the parameters such as the breaking time, closing time, and opening time of the high-voltage switch can reach better values, thereby improving the comprehensive performance indicators such as breaking reliability and closing stability.

[0015] 2. The high-voltage switch mechanical characteristic parameter optimization platform comprehensively evaluates parameter combinations under various working conditions through simulation test modules, which can identify potential performance problems and make timely adjustments, so that the high-voltage switch can always maintain good performance in complex actual operating environments.

[0016] 3. The high-voltage switch mechanical characteristic parameter optimization platform adopts a multi-objective optimization algorithm based on machine learning through the data analysis module, and uses the continuously updated historical data to train the model, which improves the prediction accuracy and reduces the blindness in the optimization process; at the same time, the parameter optimization module adjusts the weights through the feedback mechanism combined with real-time monitoring data and environmental factors, realizes the automation and intelligence of parameter adjustment, accelerates the optimization process, quickly finds a better parameter combination, and improves the efficiency of high-voltage switch performance optimization.

[0017] 4. The high-voltage switch mechanical characteristic parameter optimization platform can adapt to high-voltage switches of different models and specifications, and generate adaptive optimization solutions based on their respective characteristics. There is no need to develop optimization methods separately for each model, which saves R&D costs and time and enhances the versatility and practicality of the platform. For example, whether it is a small distribution switch or a large transmission switch, the platform can effectively optimize according to its specific parameters and performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flowchart of a high-voltage switch mechanical characteristic parameter optimization platform. DETAILED DESCRIPTION

[0019] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses. like Figure 1 As shown, the present invention provides a technical solution: a high-voltage switch mechanical characteristic parameter optimization platform, including a parameter acquisition module, which is used to collect the mechanical characteristic parameters of the high-voltage switch in real time, and the mechanical characteristic parameters include breaking time, closing time, opening time, stroke, contact stroke, opening and closing current, speed and pressure, and these parameters fully reflect the working state and performance of the high-voltage switch; A data analysis module is used to analyze the correlation between the high-voltage switch performance and the mechanical characteristic parameters based on the collected mechanical characteristic parameters through an adaptive optimization algorithm to generate optimization reference data, wherein the adaptive optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm to improve the prediction accuracy and optimization efficiency between the mechanical characteristic parameters and the high-voltage switch performance; The parameter optimization module uses a feedback mechanism to monitor the operating status of the high-voltage switch in real time and adjust the mechanical characteristic parameters in combination with the optimization reference data to optimize the performance of the high-voltage switch and obtain the adjustment reference data. The parameter optimization process realizes the automation of parameter adjustment through an iterative algorithm. Among them, the feedback mechanism combines the actual operating data of the high-voltage switch under different environmental conditions to adjust the parameter weights of the optimization model to ensure that the optimization results are suitable for different working environments. A simulation test module is used to simulate the operation of the high-voltage switch based on the adjusted reference data, obtain simulation results, and evaluate the performance under the parameter combination; The feedback adjustment module has a closed-loop control mechanism, which is used to further optimize and adjust parameters to form optimal parameters based on real-time simulation results combined with preset performance goals.

[0020] The simulation test module can simulate the working state of high-voltage switches under various working conditions, including voltage fluctuations, mechanical wear, and temperature changes, evaluate the stability and performance of mechanical characteristic parameters under different environmental conditions, and provide comprehensive parameter optimization feedback. The simulation test module integrates finite element analysis and multi-body dynamics simulation technology to accurately simulate the dynamic behavior of high-voltage switches under different working conditions and provide simulation results for quantitative performance evaluation.

[0021] The process of obtaining optimized reference data by the data analysis module is as follows: Perform algorithm selection and model construction, using adaptive optimization algorithms including genetic algorithm and particle swarm optimization algorithm; these algorithms can generate adaptive optimization solutions for high-voltage switches of different models and specifications to improve the prediction accuracy and optimization efficiency between mechanical characteristic parameters and high-voltage switch performance; Constructing an optimization model: Constructing a multi-objective optimization model based on the collected mechanical characteristic parameters and the selected optimization algorithm; the model takes the high-voltage switch performance as the target and the mechanical characteristic parameters as the variables, and establishes a mathematical relationship model between the two; Model training and optimization: First, use historical data to train the model: Use the continuously updated historical data to train the constructed model; by letting the model learn a large amount of historical data, it can understand the intrinsic relationship between different combinations of mechanical characteristic parameters and high-voltage switch performance, thereby improving the model's prediction ability; Then optimize the model parameters: During the training process, the performance of the model under different parameter settings is evaluated through the cross-validation method, the model parameters are continuously adjusted, and the optimal parameter combination is selected to obtain a trained multi-objective optimization model to improve the accuracy and stability of the model; so that the model can better adapt to high-voltage switches of different models and specifications; Analyze the correlation: Based on the trained multi-objective optimization model, analyze the correlation between the high-voltage switch performance and the mechanical characteristic parameters; determine the degree of influence and mutual relationship of each mechanical characteristic parameter on the high-voltage switch performance, and obtain analysis results, such as which parameters have a greater impact on the breaking performance, and which parameters have a strong correlation; generate optimization reference data based on the analysis results; the optimization reference data includes information on the direction and approximate amplitude of each mechanical characteristic parameter to be adjusted in order to achieve better performance under the current high-voltage switch state, which can provide a reference basis for the parameter optimization module to achieve the optimization of high-voltage switch performance.

[0022] The process of generating optimization reference data based on the multi-objective optimization model of machine learning is as follows: Assume that the performance index of the high-voltage switch is y, which includes comprehensive performance indicators such as breaking reliability and closing stability. The mechanical characteristic parameter vector is , where x1 is the breaking time, x2 is the closing time, and so on, and n is the number of mechanical characteristic parameters; The multi-objective optimization model is expressed as: Where f is the prediction function built based on the machine learning algorithm, Optimize the parameter vector of the model for multiple objectives. The parameter vector is continuously optimized and adjusted during the training process. The optimal individual is obtained based on the genetic algorithm formula in the adaptive optimization algorithm, and the optimal particle position is obtained based on the particle swarm optimization algorithm formula; After the iteration is terminated, the optimal solution is selected based on the final optimal particle position and the optimal individual in the genetic algorithm, and the mechanical characteristic parameters corresponding to the optimal solution are combined. Combined with the analysis of the multi-objective optimization model, such as the sensitivity analysis of the impact of parameters on performance, optimization reference data is generated; for example, the adjustment direction and adjustment range recommendations for each mechanical characteristic parameter are obtained so that the parameter optimization module can perform subsequent parameter adjustment operations.

[0023] Genetic algorithm formula in adaptive optimization algorithm: Encoding and initialization population: Through binary encoding, for each mechanical characteristic parameter x i , perform encoding operation; assume that each parameter is encoded as a binary string of length l, then the encoding of the individual is represented as , where p1 to p l is the encoding of x1, p l+1 to p 2l is the code of x2, and so on; where each individual represents a set of mechanical characteristic parameter combinations; Initialize the population P(0) and randomly generate m individuals, that is, ; Fitness evaluation: Calculate the fitness value F(pi) of each individual. The fitness function is defined according to the high-voltage switch performance index y. , where y i is an individual p i The corresponding prediction performance index value, is the expected performance target value, k is a constant. By this definition, the larger the fitness value, the closer the individual is to the optimal solution; Selection operation: Use roulette wheel selection method to select parent individuals; the probability of individual pi being selected ; Crossover and mutation operation: Use single-point crossover to perform crossover operation on the selected parent individuals. Suppose the two parent individuals are and , randomly select the intersection point , then the offspring individuals after crossover and for: , ; The offspring individuals after crossover are mutated with a certain mutation probability p m Change the value of some gene bits in the individual; for individual p i A gene position p in i,j , if the random number , where r is a random number between 0 and 1, then ; Update the population and iteration termination conditions: After selection, crossover and mutation operations, a new generation of population P(t+1) is obtained. Repeat the fitness evaluation, selection, crossover and mutation operations until the global optimal solution no longer changes significantly. The iteration termination conditions are met and the optimal individual is obtained.

[0024] The formula of particle swarm optimization algorithm is: Particle representation and initialization: Each particle i is represented as a mechanical characteristic parameter vector , and each particle has a velocity vector Initialize the position and velocity of the particle. The position is randomly initialized within the reasonable value range of the mechanical characteristic parameters, and the velocity is randomly initialized within a certain range. Individual optimal and global optimal update: Calculate the fitness value F(x i ), where the fitness function is defined in the same way as in the genetic algorithm, and is defined based on the high-voltage switch performance index; for each particle i, if ,in is the optimal position of particle i in history, then =x i At the same time, find the particle with the smallest fitness value among all particles, that is, the particle with the best performance, and its position is recorded as ; Speed ​​and position update: The speed and position update formula of the particle is: , ; Where w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between 0 and 1, t is the number of iterations, j = 1, 2, ..., n; Iteration termination condition: Repeat the individual optimal and global optimal update and speed and position update operations until the iteration is terminated, that is, the global optimal solution no longer changes significantly, and the optimal particle position is obtained.

[0025] The process of obtaining adjustment reference data in the parameter optimization module is as follows: First, the optimization reference data is obtained from the data analysis module. By real-time monitoring of the operating status of the high-voltage switch, the actual operating data of the current high-voltage switch is obtained. The actual operating data includes the real-time values ​​of the breaking time, closing time, opening time, stroke, contact stroke, opening and closing current, speed and pressure. The feedback mechanism combines the actual operating data of the high-voltage switch under different environmental conditions to adjust the parameter weights of the optimization model. The different environmental conditions include voltage fluctuations, mechanical wear and temperature changes. When it is found that the mechanical characteristic parameters such as the opening time have a more significant impact on the switch performance in a high temperature environment among the environmental conditions, the weight of this parameter is increased in the current optimization process; Let the original weight vector be , n is the number of mechanical characteristic parameters, and the weight vector adjusted according to environmental factors is ; Calculate parameter adjustment: Calculate the adjustment amount of each mechanical characteristic parameter based on the optimized reference data and the adjusted weights ; The calculation process combines the gap between the current parameter value and the target value, the parameter weight, and the operating status of the high-voltage switch. The target value is obtained based on the optimized reference data; It should be further explained that, in the specific implementation process, the target value is the ideal value or value range pre-set for each mechanical characteristic parameter during the optimization of the mechanical characteristic parameters of the high-voltage switch, and these values ​​are intended to achieve the best performance of the high-voltage switch; for example, for the mechanical characteristic parameter of breaking time, its target value may be set to 5ms, which means that in the optimization process, it is expected that the breaking time will be adjusted to about 5ms to achieve the best breaking performance, such as minimizing arc energy and ensuring fast and reliable circuit disconnection, etc.; The target value is set based on the design requirements, operating standards and actual application scenarios of the high-voltage switch. Different application scenarios may have different emphases on the performance of the high-voltage switch, resulting in differences in the target values. For example, in scenarios with extremely high requirements for power supply continuity, the target value of the closing time may be set shorter to reduce the power outage time. In some scenarios with high requirements for equipment life, the target value of the contact wear-related parameters may be set to a lower level to extend the service life of the equipment. The optimization reference data is generated by the data analysis module after analyzing the correlation between the performance of the high-voltage switch and the mechanical characteristic parameters through adaptive optimization algorithms, including genetic algorithms and particle swarm optimization algorithms; it contains information on the direction and approximate amplitude of the adjustment of each mechanical characteristic parameter to achieve better performance under the current high-voltage switch state; this information provides a reference for determining the target value; For example, the optimization reference data shows that the current breaking time is too long and needs to be shortened, and a reasonable shortening range is given based on historical data and algorithm analysis. Based on this, when setting the target value of the breaking time, the recommended adjustment direction and range will be referred to, combined with the actual operating limitations and performance requirements of the high-voltage switch, to determine a specific target value, such as shortening from the current 8ms to 5ms. In other words, the target value is to convert the general adjustment suggestions in the optimization reference data into specific and actionable numerical targets; the optimization reference data is a relatively abstract adjustment trend and range, while the target value clarifies the precise state that each mechanical characteristic parameter is expected to achieve during the optimization process; in the parameter optimization module, when calculating the parameter adjustment amount, it is based on these target values, combined with the current parameter value and other factors, such as the weight coefficient, to determine the specific adjustment range; For a certain mechanical characteristic parameter x i , the adjustment amount The calculation formula , where k is the adjustment coefficient, is the target value of the parameter obtained based on the optimized reference data, is the current actual value; Apply the calculated adjustment amount to the current mechanical characteristic parameters to obtain the adjusted parameter value ,in, ; The adjusted parameter values ​​and related adjustment information are combined to generate adjustment reference data, such as adjustment amount, adjustment basis, etc. The adjustment reference data will be used as the input of the subsequent simulation test module to evaluate the performance of the high-voltage switch under the parameter combination, and also provide basic data for the feedback adjustment module to further optimize the parameters. When generating the adjustment reference data, the data will also be converted into a format and timestamp will be added to facilitate subsequent processing and data management of the system. For example, adjust the format of the reference data to , where t is the timestamp and reason is the reason for adjustment, such as: calculated after adjusting the weights based on the optimization reference data and environmental factors; It should be further explained that, in the specific implementation process, through the above process, the parameter optimization module can generate reasonable adjustment reference data based on the information provided by the data analysis module and the actual operation of the high-voltage switch, providing strong support for the optimization of the high-voltage switch performance.

[0026] The process of obtaining simulation results by the simulation test module is as follows: Based on finite element analysis and multi-body dynamics simulation technology, finite element analysis and multi-body dynamics simulation models are constructed: the finite element analysis method is used to model and analyze the physical fields of the electric field, magnetic field, stress and strain of the high-voltage switch, and the physical state changes inside the high-voltage switch under different working conditions are determined; at the same time, the multi-body dynamics simulation technology is used to construct a mechanical motion model of the high-voltage switch to simulate its dynamic behavior during operation, including the motion trajectory of the contacts, speed changes, and the interaction between mechanical components; for example, for the contact system of the high-voltage switch, the finite element analysis is used to calculate the electric field distribution and the force conditions between the contacts under different voltage and current conditions, and the multi-body dynamics simulation is used to simulate the contact opening and closing action process, including the collision and bounce during closing, and the arc generation and extinction process during opening. The impact on contact movement, etc.; according to actual needs, set the working state parameters of the high-voltage switch under various working conditions. The working state parameters include voltage fluctuation: such as setting different voltage amplitudes, frequency variation ranges, mechanical wear: simulating the switch performance at different wear stages by changing the friction coefficient and wear degree parameters of mechanical parts, temperature changes: setting the ambient temperature range and the temperature rise caused by the self-heating of the switch when it is working; the setting of these working conditions is intended to comprehensively evaluate the various complex situations that the high-voltage switch may encounter in actual operation, so as to ensure that the optimized parameters can make the high-voltage switch operate stably and reliably under different environmental conditions; the adjustment reference data generated by the parameter optimization module is used as the input parameter of the simulation, and the initial state and operating conditions of the high-voltage switch in the simulation model are set according to these parameters; Start the simulation program, and simulate the operation process of the high-voltage switch based on the constructed finite element analysis and multi-body dynamics simulation model, combined with the input adjustment reference data and the set working conditions. During the simulation process, the model will calculate various state variables of the high-voltage switch at different times according to physical laws and mathematical algorithms, such as electric field strength, current distribution, displacement, velocity, acceleration and interaction force of mechanical parts; During the simulation calculation process, data related to the performance of the high-voltage switch is collected in real time to obtain simulation data. The simulation data includes electrical performance data, such as breaking capacity, insulation performance, on-resistance, mechanical performance data, such as contact wear, mechanical life, operating force changes, and dynamic characteristics data, such as opening and closing speed curves, contact bounce time, and vibration frequency. The collected simulation data is used for subsequent evaluation of the performance of the high-voltage switch. Based on the collected simulation data, the indicators used to evaluate the performance of the high-voltage switch are calculated. For example, for breaking performance, the accuracy of the breaking time, the arc energy during the breaking process, and the insulation recovery voltage between the contacts after breaking can be calculated. For closing performance, the stability of the closing speed, the collision energy of the contacts during closing, and the stability of the contact resistance after closing can be calculated. For mechanical performance, the stress level of mechanical components, fatigue life prediction values, and indicators of the movement smoothness of the operating mechanism are calculated. The calculated performance indicators are integrated and analyzed to generate simulation results. It should be further explained that, in the specific implementation process, the simulation results include the specific values ​​of various performance indicators of the high-voltage switch under given adjustment reference data and operating conditions, performance curves, such as speed-time curve, stress-strain curve; and performance evaluation conclusions, such as whether the design requirements are met, in which aspects there are performance advantages or deficiencies, etc.; these simulation results will provide a basis for the feedback adjustment module to further optimize the mechanical characteristic parameters of the high-voltage switch, and also provide an important reference for the design, manufacture, operation and maintenance of the high-voltage switch.

[0027] The process of obtaining the optimal parameters of the feedback adjustment module mainly includes the following steps: Receive the simulation results provided by the simulation test module, and at the same time, obtain the performance targets preset by the user through the customization mechanism; the user sets specific performance indicator priorities according to their own needs: including setting the breaking reliability as the highest priority, followed by the closing stability, and the parameter adjustment range: including, for example, specifying the upper and lower limits of the allowable adjustment of a certain mechanical characteristic parameter. These preset information reflects the user's expected performance of the high-voltage switch in different application scenarios; Compare and analyze each performance indicator in the simulation results with the preset performance target, and calculate the deviation value of each performance indicator. For example, for the performance indicator of breaking time, if the preset target is that the breaking time should be less than or equal to 10ms under specific conditions, and the simulation results show that the actual breaking time is 12ms, then the deviation value is 2ms. In this way, determine the gap between the high-voltage switch performance and the expected performance under the current parameter settings; determine the parameter adjustment strategy based on the deviation value and the performance indicator priority; If the deviation of a certain high-priority performance indicator is large, for example, the breaking reliability does not meet the requirements and the deviation is significant, then the focus will be on adjusting the mechanical characteristic parameters that affect the breaking reliability, such as breaking speed, contact pressure, etc. At the same time, the limitation of the parameter adjustment range will be considered to avoid other problems caused by adjustments beyond the reasonable range. For example, if the adjustment range of the breaking speed is set to [5m / s, 10m / s], when the current speed is 7m / s and the breaking reliability needs to be improved, it can only be adjusted within this range, such as increasing to 8m / s. According to the determined adjustment strategy, the mechanical characteristic parameters are adjusted. The amplitude and direction of the adjustment are based on the analysis of the simulation results and the guidance of the optimization algorithm. For example, if it is found that the closing time is too long, the value of the closing speed mechanical characteristic parameter may be appropriately increased, but the adjustment amplitude will take into account other relevant factors, such as mechanical impact, wear, etc. In the adjustment process, a method similar to that in the parameter optimization module will be used, combined with weight coefficients (these weight coefficients may be dynamically adjusted according to the performance indicator deviation and priority) to calculate the specific adjustment amount of each parameter. The adjusted parameters are input into the simulation test module again for simulation testing to obtain new simulation results; then the above deviation analysis, adjustment strategy determination and parameter adjustment process are repeated, and the optimization is continuously iterated until the termination condition is met; the termination condition may include that the deviations of all performance indicators are within an acceptable range, the number of iterations reaches a preset upper limit, or the performance improvement is not obvious after several consecutive iterations; after the termination condition is met, the final parameter combination is verified; by performing additional simulation tests under different working conditions or referring to actual operating experience, it is ensured that the parameter combination can stably achieve good performance under various conditions; at the same time, check whether the parameters are within the preset adjustment range and whether they meet the actual constraints of the physical characteristics and operating principles of the high-voltage switch; after verification, determine that the mechanical characteristic parameters at this time are the optimal parameters; these optimal parameters will be provided to the high-voltage switch manufacturing system or operation and maintenance personnel as the final results to guide the design improvement of the high-voltage switch, parameter setting during the manufacturing process, or parameter adjustment during operation, so as to optimize the performance of the high-voltage switch, meet user needs and ensure reliable operation in different application scenarios; It should be further explained that, in the specific implementation process, through the above series of steps, the feedback adjustment module can continuously adjust and optimize the mechanical characteristic parameters according to the simulation results and the user's preset performance goals, and finally obtain the optimal parameters that meet the requirements, thereby achieving continuous improvement in the performance of the high-voltage switch.

[0028] The parameter acquisition module is also used to adjust the data, including archiving and version management of historical mechanical characteristic parameters. This ensures that during the platform's multiple iterative optimization processes, historical data can be traced and further analysis can be performed based on these data. At the same time, it has the ability to conduct in-depth analysis of multi-dimensional data, and through methods such as cluster analysis and correlation analysis, it can discover hidden parameter associations, providing more accurate input for subsequent optimization algorithms.

[0029] It should be further explained that, in the specific implementation process, by real-time collection of various mechanical characteristic parameters of the high-voltage switch and using an adaptive optimization algorithm to analyze their correlation with performance, the degree of influence of each parameter on the performance and the relationship between them can be accurately determined; based on the generated optimization reference data and adjustment reference data, the breaking time, closing time, opening time and other parameters of the high-voltage switch can reach better values, thereby improving the breaking reliability, closing stability and other comprehensive performance indicators; for example, the optimized breaking time is shorter and more stable, which can effectively reduce arc energy, reduce the erosion of switch contacts, and extend the service life of the equipment, while ensuring that the circuit is quickly and reliably cut off when a fault occurs, thereby ensuring the safe and stable operation of the power system.

[0030] It should be further explained that, in the specific implementation process, the simulation test module is used to comprehensively evaluate the parameter combination under various working conditions, so that potential performance problems can be discovered and adjusted in time, so that the high-voltage switch can always maintain good performance in a complex actual operating environment; for example, in the case of large voltage fluctuations, the insulation performance of the switch is ensured to be unaffected by optimizing the parameters, thereby avoiding insulation breakdown and other faults caused by voltage fluctuations; and the platform can adapt to high-voltage switches of different models and specifications, and generate adaptive optimization solutions based on their respective characteristics, without the need to develop optimization methods separately for each model, thus saving R&D costs and time and enhancing the versatility and practicality of the platform; for example, whether it is a small distribution switch or a large transmission switch, the platform can effectively optimize according to its specific parameters and performance requirements.

[0031] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A high-voltage switch mechanical characteristic parameter optimization platform, characterized in that: include: A parameter acquisition module is used to acquire the mechanical characteristic parameters of the high-voltage switch in real time, wherein the mechanical characteristic parameters include breaking time, closing time, opening time, stroke, contact stroke, opening and closing current, speed and pressure; A data analysis module is used to analyze the correlation between the performance of the high-voltage switch and the mechanical characteristic parameters based on the collected mechanical characteristic parameters through an adaptive optimization algorithm to generate optimization reference data, wherein the adaptive optimization algorithm includes a genetic algorithm and a particle swarm optimization algorithm; The parameter optimization module adopts a feedback mechanism to adjust the mechanical characteristic parameters by real-time monitoring the operating status of the high-voltage switch in combination with the optimization reference data to obtain the adjustment reference data; wherein the feedback mechanism adjusts the parameter weights of the optimization model in combination with the actual operating data of the high-voltage switch under different environmental conditions; A simulation test module, used to simulate the operation of the high-voltage switch based on the adjusted reference data to obtain a simulation result; The feedback adjustment module has a closed-loop control mechanism, which is used to further optimize and adjust parameters to form optimal parameters based on real-time simulation results combined with preset performance goals.

2. A high-voltage switch mechanical characteristic parameter optimization platform according to claim 1, characterized in that: The simulation test module can simulate the working state of the high-voltage switch under various working conditions, including voltage fluctuations, mechanical wear, and temperature changes. The simulation test module integrates finite element analysis and multi-body dynamics simulation technology to accurately simulate the dynamic behavior of the high-voltage switch under different working conditions and provide simulation results for quantitative performance evaluation.

3. A high-voltage switch mechanical characteristic parameter optimization platform according to claim 2, characterized in that: The process of obtaining optimized reference data by the data analysis module is as follows: Perform algorithm selection and model building, using adaptive optimization algorithms including genetic algorithm and particle swarm optimization algorithm; Constructing an optimization model: Constructing a multi-objective optimization model based on the collected mechanical characteristic parameters and the selected optimization algorithm; the model takes the high-voltage switch performance as the target and the mechanical characteristic parameters as the variables, and establishes a mathematical relationship model between the two; Use the continuously updated historical data to train the constructed model; During the training process, the performance of the model under different parameter settings is evaluated through the cross-validation method, the parameters of the model are continuously adjusted, and the optimal parameter combination is selected to obtain a trained multi-objective optimization model; Based on the trained multi-objective optimization model, the correlation between high-voltage switch performance and mechanical characteristic parameters is analyzed; Determine the influence degree and mutual relationship of each mechanical characteristic parameter on the performance of the high-voltage switch, obtain the analysis results, and generate optimization reference data based on the analysis results.

4. A high-voltage switch mechanical characteristic parameter optimization platform according to claim 3, characterized in that: The process of generating optimization reference data based on the multi-objective optimization model of machine learning is as follows: Assume that the performance index of the high-voltage switch is y, which includes breaking reliability and closing stability. The mechanical characteristic parameter vector is , where x1 is the breaking time, x2 is the closing time, and so on, and n is the number of mechanical characteristic parameters; The multi-objective optimization model is expressed as: Where f is the prediction function built based on the machine learning algorithm, Parameter vector for the multi-objective optimization model; The optimal individual is obtained based on the genetic algorithm formula in the adaptive optimization algorithm, and the optimal particle position is obtained based on the particle swarm optimization algorithm formula; After the iteration is terminated, the optimal solution is selected based on the final optimal particle position and the optimal individual in the genetic algorithm. The mechanical characteristic parameters corresponding to the optimal solution are combined and combined with the analysis of the multi-objective optimization model to generate optimization reference data.

5. A high-voltage switch mechanical characteristic parameter optimization platform according to claim 4, characterized in that: Genetic algorithm formula in adaptive optimization algorithm: Encoding and initialization population: Through binary encoding, for each mechanical characteristic parameter x i , perform encoding operation; assume that each parameter is encoded as a binary string of length l, then the encoding of the individual is represented as , where p1 to p l is the encoding of x1, p l+1 to p 2l is the code of x2, and so on; where each individual represents a set of mechanical characteristic parameter combinations; Initialize the population P(0) and randomly generate m individuals, that is, ; Fitness evaluation: Calculate the fitness value F(pi) of each individual. The fitness function is defined according to the high-voltage switch performance index y. , where y i is an individual p i The corresponding prediction performance index value, is the expected performance target value, k is a constant; Selection operation: Use roulette wheel selection method to select parent individuals; the probability of individual pi being selected ; Crossover and mutation operations: Use single-point crossover to perform crossover operations on the selected parent individuals. Suppose the two parent individuals are p a and p b , randomly select the intersection point , then the offspring individual p´ after crossover a and p´ b for: ， ； The offspring individuals after crossover are mutated with a certain mutation probability p m Change the value of some gene positions in individuals; for individual p i A gene position p in i,j , if the random number , where r is a random number between 0 and 1, then ; Update population and iteration termination conditions: After selection, crossover and mutation operations, a new generation of population P(t+1) is obtained. Repeat fitness evaluation, selection, crossover and mutation operations until the global optimal solution no longer changes significantly and the optimal individual is obtained.

6. A high-voltage switch mechanical characteristic parameter optimization platform according to claim 5, characterized in that: The formula of particle swarm optimization algorithm is: Each particle i is represented by a mechanical characteristic parameter vector , and each particle has a velocity vector Initialize the position and velocity of the particle. The position is randomly initialized within the reasonable value range of the mechanical characteristic parameters, and the velocity is randomly initialized within a certain range. Individual optimal and global optimal update: Calculate the fitness value F(x i ), where the fitness function is defined in the same way as in the genetic algorithm, and is defined based on the high-voltage switch performance index; for each particle i, if ,in is the optimal position of particle i in history, then = At the same time, find the particle with the smallest fitness value among all particles, that is, the particle with the best performance, and its position is recorded as ; Speed ​​and position update: The speed and position update formula of the particle is: ， ； Where w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers between 0 and 1, t is the number of iterations, j = 1, 2, ..., n; Repeat the operations of updating individual optimal and global optimal solutions and updating speed and position until the iteration is terminated, that is, the global optimal solution no longer changes significantly, and the optimal particle position is obtained.

7. A high voltage switch mechanical characteristic parameter optimization platform according to claim 6, characterized in that: The process of obtaining the adjustment reference data by the parameter optimization module is as follows: First, the optimization reference data is obtained from the data analysis module. By real-time monitoring of the operating status of the high-voltage switch, the actual operating data of the current high-voltage switch is obtained. The actual operating data includes the real-time values ​​of the breaking time, closing time, opening time, stroke, contact stroke, opening and closing current, speed and pressure. The feedback mechanism combines the actual operating data of the high-voltage switch under different environmental conditions to adjust the parameter weights of the optimization model. When it is found that the mechanical characteristic parameter has a significant impact on the switch performance under environmental conditions, the weight of this parameter is increased in the current optimization process; Let the original weight vector be , n is the number of mechanical characteristic parameters, and the weight vector adjusted according to environmental factors is ; Calculate parameter adjustment: Calculate the adjustment amount of each mechanical characteristic parameter based on the optimized reference data and the adjusted weights ; The calculation process combines the gap between the current parameter value and the target value, the parameter weight, and the operating status of the high-voltage switch. The target value is obtained based on the optimized reference data; For the mechanical characteristic parameter x i , the adjustment amount The calculation formula ; where k is the adjustment coefficient, is the target value of the parameter obtained based on the optimized reference data, is the current actual value; Apply the calculated adjustment amount to the current mechanical characteristic parameters to obtain the adjusted parameter value ,in, ; Combine the adjusted parameter values ​​and related adjustment information to generate adjustment reference data.

8. A high voltage switch mechanical characteristic parameter optimization platform according to claim 7, characterized in that: The process of obtaining the simulation results by the simulation test module is as follows: Based on finite element analysis and multi-body dynamics simulation technology, finite element analysis and multi-body dynamics simulation models are constructed: the finite element analysis method is used to model and analyze the physical fields of the electric field, magnetic field, and stress and strain of the high-voltage switch to determine the changes in the physical state inside the high-voltage switch under different working conditions; at the same time, the multi-body dynamics simulation technology is used to construct a mechanical motion model of the high-voltage switch to simulate its dynamic behavior during operation, including the motion trajectory of the contacts, speed changes, and the interaction between mechanical components; according to actual needs, the working state parameters of the high-voltage switch under various working conditions are set, and the working state parameters include voltage fluctuations, mechanical wear, and temperature changes; The adjustment reference data generated by the parameter optimization module are used as input parameters for the simulation, and the initial state and operating conditions of the high-voltage switch in the simulation model are set according to these parameters; Start the simulation program, and simulate the operation process of the high-voltage switch based on the constructed finite element analysis and multi-body dynamics simulation model, combined with the input adjustment reference data and the set working conditions; During the simulation, the model calculates various state variables of the high-voltage switch at different times based on physical laws and mathematical algorithms; During the simulation calculation process, data related to the high-voltage switch performance is collected in real time to obtain simulation data, which includes electrical performance data, mechanical performance data and dynamic characteristic data; the collected simulation data is used for subsequent evaluation of the performance of the high-voltage switch; based on the collected simulation data, indicators for evaluating the performance of the high-voltage switch are calculated; the calculated performance indicators are integrated and analyzed to generate simulation results.

9. A high voltage switch mechanical characteristic parameter optimization platform according to claim 8, characterized in that: The process of obtaining the optimal parameters by the feedback adjustment module mainly includes the following steps: Receive the simulation results provided by the simulation test module, and at the same time, obtain the performance targets preset by the user through the customization mechanism; wherein the user sets specific performance indicator priorities according to their own needs: including setting the breaking reliability as the highest priority, followed by the closing stability, and the parameter adjustment range: including, for example, specifying the upper and lower limits of the allowable adjustment of a certain mechanical characteristic parameter; Compare and analyze each performance indicator in the simulation results with the preset performance target, and calculate the deviation value of each performance indicator; Determine the parameter adjustment strategy based on the deviation value and performance indicator priority; if the deviation of a high-priority performance indicator is large, then focus on adjusting the mechanical characteristic parameters that affect the breaking reliability; adjust the mechanical characteristic parameters based on the determined adjustment strategy; The adjusted parameters are input into the simulation test module again for simulation testing to obtain new simulation results; then the above-mentioned deviation analysis, adjustment strategy determination and parameter adjustment process are repeated, and the optimization is continuously iterated until the termination condition is met.

10. A high voltage switch mechanical characteristic parameter optimization platform according to claim 9, characterized in that: The termination conditions include that the deviations of the performance indicators are within an acceptable range, the number of iterations reaches a preset upper limit, or the performance improvement is not obvious after several consecutive iterations; After the termination condition is met, the final parameter combination is verified; By conducting additional simulation tests under different operating conditions, we can ensure that the parameter combination can stably achieve good performance in various situations. At the same time, we can check whether the parameters are within the preset adjustment range and whether they meet the actual constraints of the physical characteristics and operating principles of the high-voltage switch. After verification, it is determined that the mechanical characteristic parameters at this time are the optimal parameters.

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