A method and system for selecting parameters of a pulse power supply system

Abstract

The present invention discloses a method and system for selecting parameters of a pulse power supply system, belonging to the technical field of pulse power supply systems. The method includes the following steps: S1: Collect historical data on the state of the pulse power supply system; S2: Process the historical data on the state of the pulse power supply system; S3: Train an optimal numerical model for selecting parameters of the pulse power supply system; S4: Select the optimal parameters of the pulse power supply system according to the user's energy output requirements for the pulse power supply system; S5: Interface display: Visually display the optimal parameters of the pulse power supply system to the user. The present invention solves the problem that the existing pulse power supply system cannot flexibly select parameters according to user needs, resulting in poor use effect of the pulse power supply system and reducing the performance and reliability of the pulse power supply system. The present invention can flexibly select the parameters of the pulse power supply system according to user needs, improve the use effect of the pulse power supply system, and enhance the performance and reliability of the pulse power supply system.

Description

Technical Field

[0001] The present invention relates to the technical field of pulse power supply systems, and specifically to a method and system for selecting parameters of a pulse power supply system. Background Art

[0002] Pulse power supply systems have been widely used in various application scenarios; however, different application scenarios have different parameter requirements for pulse power supply systems. Therefore, the selection of parameters for pulse power supply systems is particularly important.

[0003] Chinese Patent with publication number CN118861604A discloses a method for predicting the life of a pulse power supply system, including: obtaining multiple life prediction values of a pulse power supply to be inspected in the current charge-discharge cycle according to multiple life prediction models, weighted summing the life prediction values sorted according to a target arrangement method to obtain a life estimation value under the target arrangement method, calculating the prediction accuracy of each life prediction value according to the magnitude relationship between each life prediction value and the life of the pulse power supply to be inspected in the previous charge-discharge cycle and the difference between each life prediction value and the life estimation value, sorting all life prediction values according to the prediction accuracy to obtain a sequence, calculating the consistency between the sequence and the target arrangement method, and taking the life estimation value under the arrangement method with the maximum consistency as the life of the pulse power supply to be inspected in the current charge-discharge cycle; enhancing the accuracy of life prediction of the pulse power supply; however, this patent has the following defects:

[0004] The existing technology cannot flexibly select the parameters of the pulse power supply system according to user needs, resulting in poor use effect of the pulse power supply system and reducing the performance and reliability of the pulse power supply system. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for selecting parameters of a pulse power supply system, which can flexibly select the parameters of the pulse power supply system according to user needs, improve the use effect of the pulse power supply system, and enhance the performance and reliability of the pulse power supply system, solving the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for selecting parameters of a pulse power supply system includes the following steps:

[0008] S1. Data acquisition: Collect the historical data of energy output, pulse frequency, duty cycle, pulse width, and voltage and current of the pulse power supply system, and determine the historical data of the state of the pulse power supply system based on big data.

[0009] S2. Data processing: Clean, normalize, and integrate the historical data of the state of the pulse power supply system based on big data, and securely store and back up the integrated historical data of the state of the pulse power supply system.

[0010] S3. Model Training: Based on deep learning technology, actively learn the internal relationship between the parameter selection and energy output of the pulse power supply system, and train the optimal numerical model for parameter selection of the pulse power supply system;

[0011] S4. Parameter Selection: According to the user's energy output requirements for the pulse power supply system, select the optimal parameters of the pulse power supply system based on the optimal numerical model for parameter selection of the pulse power supply system;

[0012] S5. Interface Display: Visually display the optimal parameters of the pulse power supply system to the user.

[0013] Preferably, in the above S1, collect the historical data of the pulse power supply system state based on big data, including:

[0014] Collect the historical energy output situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical energy output data of the pulse power supply system;

[0015] Collect the historical pulse frequency situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical pulse frequency data of the pulse power supply system;

[0016] Collect the historical duty cycle situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical duty cycle data of the pulse power supply system;

[0017] Collect the historical pulse width situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical pulse width data of the pulse power supply system;

[0018] Collect the historical voltage and current situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical voltage and current data of the pulse power supply system;

[0019] Among them, based on the historical energy output data of the pulse power supply system, the historical pulse frequency data of the pulse power supply system, the historical duty cycle data of the pulse power supply system, the historical pulse width data of the pulse power supply system, and the historical voltage and current data of the pulse power supply system, determine the historical data of the pulse power supply system state based on big data.

[0020] Preferably, in the above S2, process the historical data of the pulse power supply system state based on big data, including:

[0021] Clean the historical data of the pulse power supply system state based on big data using a data cleaning tool;

[0022] Among them, check and identify duplicate values, missing values, and outliers in the historical data of the pulse power supply system based on big data, and process the duplicate values, missing values, and outliers in the historical data of the pulse power supply system based on big data;

[0023] For duplicate values, directly delete the duplicate values; for missing values, delete the data records containing missing values or use the average value to replace the missing values; for outliers, delete the data records containing outliers or use the median to replace the outliers.

[0024] Preferably, in step S2, when processing the historical data of the pulse power supply system based on big data, it further includes:

[0025] Perform normalization processing on the historical data of the pulse power supply system based on big data based on the Z-score normalization method to unify the historical data of the pulse power supply system based on big data, remove the dimensional differences between the historical data of the pulse power supply system based on big data, and determine the normalized historical data of the pulse power supply system;

[0026] Perform integration processing on the normalized historical data of the pulse power supply system, integrate the normalized historical data of the pulse power supply system into a unified data view, and check the integrated historical data of the pulse power supply system to check whether there are any omissions in the integrated historical data of the pulse power supply system. After passing the inspection, perform secure storage backup on the integrated historical data of the pulse power supply system.

[0027] Preferably, in step S3, training the optimal numerical model for selecting pulse power supply system parameters includes:

[0028] Divide the historical data of the pulse power supply system into a training set and a test set;

[0029] Based on deep learning technology, use the training set to train the deep learning model, so that the deep learning model actively learns the internal relationship between the selection of pulse power supply system parameters and the energy output, and then train the numerical model for selecting pulse power supply system parameters;

[0030] Perform performance testing on the numerical model for selecting pulse power supply system parameters based on the test set to determine whether the numerical model for selecting pulse power supply system parameters can achieve the expected effect;

[0031] When the numerical model for selecting pulse power supply system parameters cannot achieve the expected effect, adjust the parameters and structure of the numerical model for selecting pulse power supply system parameters, and continuously iterate and optimize the adjusted numerical model for selecting pulse power supply system parameters to determine the optimal numerical model for selecting pulse power supply system parameters.

[0032] Preferably, in S4, the optimal pulse power supply system parameters are selected, including:

[0033] Obtain the numerical model for selecting the optimal pulse power supply system parameters, and deploy the numerical model for selecting the optimal pulse power supply system parameters in the actual pulse power supply system parameter selection environment;

[0034] Based on the user's energy output requirements for the pulse power supply system, predict and analyze the pulse power supply system parameters based on the numerical model for selecting the optimal pulse power supply system parameters, and select the optimal pulse power supply system parameters by online adjustment and adaptive regulation of the pulse power supply system parameters.

[0035] Preferably, in S5, the optimal pulse power supply system parameters are displayed to the user, including:

[0036] Provide a user interface to display the optimal pulse power supply system parameters to the user in a visual form, and track the usage of the user's pulse power supply system in real time, obtain user feedback and parameter selection test results, and continuously adjust and optimize the pulse power supply system parameters according to the user feedback and parameter selection test results to better adapt to different scenarios and user requirements.

[0037] Preferably, the method for selecting the pulse power supply system parameters further includes:

[0038] Fault detection: Set up a fault detection circuit with a memristor, and utilize the resistance change characteristic of the memristor to sense the power supply system fault. When the resistance change of the memristor exceeds the set resistance change threshold, trigger the protection mechanism;

[0039] Adaptive protection: Set up an adaptive control algorithm based on the memristor, and dynamically adjust the power supply system parameters according to the resistance change value of the memristor and in combination with the real-time state of the power supply system to achieve adaptive protection of the power supply system;

[0040] Intelligent control: Integrate an intelligent control unit with a microcontroller or a programmable logic chip (FPGA) into the power supply system, combine the fault detection circuit with a memristor with the microcontroller or the programmable logic chip (FPGA), and utilize the resistance change characteristic of the memristor. The intelligent control unit sets the control logic through software to achieve intelligent control of the power supply system;

[0041] Parameter optimization: Set up a high-frequency circuit connected to the fault detection circuit, and according to the load change of the power supply system, utilize the high-frequency characteristic of the memristor in the fault detection circuit to optimize the high-frequency performance of the power supply by dynamically adjusting the resistance value of the memristor.

[0042] Preferably, the intelligent control includes:

[0043] Construct a memristor state model and use the following state formula for the memristor:

[0044]

[0045] In the above formula, represents the state function of the memristor; represents the state of the memristor at time represents the current at time and are the characteristic parameters of the memristor;

[0046] Use the state of the memristor as the feedback signal. By comparing it with the set target output value, an error signal is obtained;

[0047] According to the state of the memristor and the error signal , the following calculation formula is used to obtain the control signal:

[0048]

[0049] In the above formula, represents the control signal; represents time; and respectively represent the gain factors of proportional, integral, differential, and memristor state feedback;

[0050] The intelligent control unit obtains the control signal and realizes the intelligent control of the power supply system according to the control signal.

[0051] According to another aspect of the present invention, a pulse power supply system parameter selection system is provided for implementing a pulse power supply system parameter selection method as described above, including:

[0052] A data acquisition module for collecting historical data on the state of the pulse power supply system based on big data;

[0053] A data processing module for cleaning, normalizing, and integrating the historical data on the state of the pulse power supply system based on big data;

[0054] A model training module for training an optimal numerical model for pulse power supply system parameter selection based on deep learning technology;

[0055] A parameter selection module for selecting optimal pulse power supply system parameters based on the optimal numerical model for pulse power supply system parameter selection;

[0056] An interface display module for providing a user interface to visually display the optimal pulse power supply system parameters to the user.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] The present invention collects the energy output, pulse frequency, duty cycle, pulse width, and voltage and current historical data of the pulse power supply system, determines the historical data of the pulse power supply system state based on big data, cleans, normalizes, and integrates the historical data of the pulse power supply system state based on big data, and securely stores and backs up the integrated historical data of the pulse power supply system state. Based on deep learning technology, actively learns the internal relationship between the selection of pulse power supply system parameters and energy output, trains the optimal numerical model for the selection of pulse power supply system parameters, predicts and analyzes the pulse power supply system parameters based on the optimal numerical model for the selection of pulse power supply system parameters according to the user's energy output requirements for the pulse power supply system, and selects the optimal pulse power supply system parameters by online adjustment and adaptive regulation of the pulse power supply system parameters. The pulse power supply system parameters can be flexibly selected according to user needs, the use effect of the pulse power supply system is improved, and the performance and reliability of the pulse power supply system are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flowchart of the method for selecting pulse power supply system parameters of the present invention;

[0060] Figure 2 It is a module diagram of the system for selecting pulse power supply system parameters of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] To solve the problem that the existing pulse power supply system parameters cannot be flexibly selected according to user needs, resulting in poor use effect of the pulse power supply system and reduced performance and reliability of the pulse power supply system, please refer to Figure 1 - Figure 2 This embodiment provides the following technical solutions:

[0063] A method for selecting pulse power supply system parameters includes the following steps:

[0064] S1. Data collection: Collect the energy output, pulse frequency, duty cycle, pulse width, and voltage and current historical data of the pulse power supply system to determine the historical data of the pulse power supply system status based on big data;

[0065] In this embodiment, collecting the historical data of the pulse power supply system status based on big data includes:

[0066] Collect the historical energy output of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical energy output data of the pulse power supply system;

[0067] Collect the historical pulse frequency of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical pulse frequency data of the pulse power supply system;

[0068] Collect the historical duty cycle of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical duty cycle data of the pulse power supply system;

[0069] Collect the historical pulse width of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical pulse width data of the pulse power supply system;

[0070] Collect the historical voltage and current of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical voltage and current data of the pulse power supply system;

[0071] Among them, based on the historical energy output data of the pulse power supply system, the historical pulse frequency data of the pulse power supply system, the historical duty cycle data of the pulse power supply system, the historical pulse width data of the pulse power supply system, and the historical voltage and current data of the pulse power supply system, determine the historical data of the pulse power supply system status based on big data.

[0072] S2. Data processing: Clean, normalize, and integrate the historical data of the pulse power supply system status based on big data, and securely store and back up the integrated historical data of the pulse power supply system status;

[0073] In this embodiment, processing the historical data of the pulse power supply system status based on big data includes:

[0074] Clean the historical data of the pulse power supply system status based on big data using a data cleaning tool;

[0075] Among them, check and identify duplicate values, missing values, and outliers in the historical data of the pulse power supply system status based on big data, and process the duplicate values, missing values, and outliers in the historical data of the pulse power supply system status based on big data;

[0076] For duplicate values, directly delete the duplicate values; for missing values, delete the data records containing missing values or use the average value to replace the missing values; for outliers, delete the data records containing outliers or use the median to replace the outliers;

[0077] Based on the Z-score normalization method, normalize the historical data of the pulse power supply system state based on big data to unify the historical data of the pulse power supply system state based on big data, remove the dimensional differences between the historical data of the pulse power supply system state based on big data, and determine the normalized historical data of the pulse power supply system state;

[0078] Integrate and process the normalized historical data of the pulse power supply system state, integrate the normalized historical data of the pulse power supply system state into a unified data view, and check the integrated historical data of the pulse power supply system state to check whether there are any omissions in the integrated historical data of the pulse power supply system state. After passing the inspection, perform a secure storage backup on the integrated historical data of the pulse power supply system state.

[0079] S3. Model training: Based on deep learning technology, actively learn the internal relationship between the parameter selection and energy output of the pulse power supply system, and train the optimal numerical model for parameter selection of the pulse power supply system;

[0080] In this embodiment, training the optimal numerical model for parameter selection of the pulse power supply system includes:

[0081] Divide the historical data of the pulse power supply system state, and divide the historical data of the pulse power supply system state into a training set and a test set;

[0082] Based on deep learning technology, use the training set to train the deep learning model, so that the deep learning model actively learns the internal relationship between the parameter selection and energy output of the pulse power supply system, and then trains the numerical model for parameter selection of the pulse power supply system;

[0083] Perform a performance test on the numerical model for parameter selection of the pulse power supply system based on the test set to determine whether the numerical model for parameter selection of the pulse power supply system can achieve the expected effect;

[0084] When the numerical model for parameter selection of the pulse power supply system cannot achieve the expected effect, adjust the parameters and structure of the numerical model for parameter selection of the pulse power supply system, and continuously iterate and optimize the adjusted numerical model for parameter selection of the pulse power supply system to determine the optimal numerical model for parameter selection of the pulse power supply system.

[0085] S4. Parameter selection: According to the user's energy output requirements for the pulse power supply system, select the optimal parameters of the pulse power supply system based on the optimal numerical model for parameter selection of the pulse power supply system;

[0086] In this embodiment, the optimal parameters of the pulse power supply system are selected, including:

[0087] Obtain the numerical model for selecting the optimal parameters of the pulse power supply system, and deploy the numerical model for selecting the optimal parameters of the pulse power supply system in the actual environment for selecting the parameters of the pulse power supply system;

[0088] According to the user's energy output requirements for the pulse power supply system, based on the numerical model for selecting the optimal parameters of the pulse power supply system, predict and analyze the parameters of the pulse power supply system, and through online adjustment and adaptive regulation of the parameters of the pulse power supply system, the optimal parameters of the pulse power supply system are selected.

[0089] It should be noted that based on the model predictive control method, the parameters of the pulse power supply system can be adaptively regulated. By predicting the state and input signal of the pulse power supply system and online adjusting its parameters, precise control of the behavior of the pulse power supply system can be achieved.

[0090] S5. Interface display: Display the optimal parameters of the pulse power supply system to the user in a visual form.

[0091] In this embodiment, displaying the optimal parameters of the pulse power supply system to the user includes:

[0092] Provide a user interface to display the optimal parameters of the pulse power supply system to the user in a visual form, and track the usage of the user's pulse power supply system in real time, obtain user feedback and the test results of parameter selection, and continuously adjust and optimize the parameters of the pulse power supply system according to the user feedback and the test results of parameter selection to better adapt to different scenarios and user requirements.

[0093] On the basis of the foregoing embodiment, the method for selecting the parameters of the pulse power supply system further includes:

[0094] Fault detection: Set a fault detection circuit with a memristor, utilize the resistance change characteristic of the memristor to sense the faults of the power supply system, and trigger the protection mechanism when the resistance change of the memristor exceeds the set resistance change threshold;

[0095] Adaptive protection: Set an adaptive control algorithm based on the memristor, and dynamically adjust the parameters of the power supply system according to the resistance change value of the memristor and in combination with the real-time state of the power supply system to achieve the adaptive protection of the power supply system;

[0096] Intelligent control: Integrate an intelligent control unit with a microcontroller or a programmable logic chip (FPGA) into the power supply system, combine the fault detection circuit with a memristor with the microcontroller or the programmable logic chip (FPGA), utilize the resistance change characteristic of the memristor, and the intelligent control unit sets the control logic through software to achieve the intelligent control of the power supply system;

[0097] Parameter optimization: Set up a high-frequency circuit connected to the fault detection circuit. According to the load changes of the power supply system, utilize the high-frequency characteristics of the memristor in the fault detection circuit, and optimize the high-frequency performance of the power supply by dynamically adjusting the resistance value of the memristor.

[0098] Specifically, by adopting the memristor, a non-linear element with memory characteristics, its resistance value can be dynamically changed according to the amount of charge passing through the memristor. This characteristic endows the memristor with great potential in the intelligent control of power supply systems. Traditional power supply control systems usually rely on fixed PID controllers or digital signal processors (DSPs), which are difficult to cope with complex and variable load conditions and environmental interferences. The introduction of the memristor can achieve more efficient and flexible adaptive control through its dynamic resistance characteristics and memory ability. This solution dynamically adjusts the output of the power supply system through the real-time state feedback and adaptive algorithm of the memristor, improving the stability, efficiency, and response speed of the power supply system; this solution can be applied to various scenarios such as DC-DC converters, AC-DC power supplies, and battery management systems.

[0099] Based on the foregoing embodiments, the intelligent control includes:

[0100] Construct a memristor state model, and adopt the following state formula of the memristor:

[0101]

[0102] In the above formula, represents the state function of the memristor; represents the state of the memristor at time represents the current at time and are the characteristic parameters of the memristor;

[0103] Adopt the state of the memristor as the feedback signal. By comparing it with the set target output value, obtain the error signal ;

[0104] According to the state of the memristor and the error signal , use the following calculation formula to obtain the control signal:

[0105]

[0106] In the above formula, represents the control signal; represents time; and represent the gain factors of proportional, integral, derivative, and memristor state feedback respectively;

[0107] The intelligent control unit obtains the control signal and realizes the intelligent control of the power supply system according to the control signal.

[0108] Specifically, by introducing memristor state feedback, dynamic and adaptive control is achieved; through the design of the memristor array, the regulation accuracy and response speed of the system are significantly improved; by combining the control signal algorithm considering the error signal, the dynamic optimization of control parameters is realized, enhancing the adaptability and robustness of the system; the global stability of the system is also ensured, avoiding the common oscillation and divergence problems in traditional control systems; this solution utilizes the dynamic resistance characteristic and memory ability of the memristor to construct a closed-loop feedback control system, and the state of the memristor is used as a feedback signal, compared with the target output value, and the resistance value of the memristor is dynamically adjusted through an adaptive algorithm, so as to achieve precise control of the power supply output.

[0109] To better demonstrate the parameter selection principle of the pulse power supply system, this embodiment provides a pulse power supply system parameter selection system for implementing a pulse power supply system parameter selection method as described above, including:

[0110] A data acquisition module for acquiring historical data of the state of the pulse power supply system based on big data;

[0111] A data processing module for cleaning, normalizing, and integrating the historical data of the state of the pulse power supply system based on big data;

[0112] A model training module for training an optimal numerical model for selecting parameters of the pulse power supply system based on deep learning technology;

[0113] A parameter selection module for selecting the optimal parameters of the pulse power supply system based on the optimal numerical model for selecting parameters of the pulse power supply system;

[0114] An interface display module for providing a user interface to visually display the optimal parameters of the pulse power supply system to the user.

[0115] It should be noted that the energy output, pulse frequency, duty cycle, pulse width, and voltage and current historical data of the pulse power supply system are collected through the data acquisition module to determine the state historical data of the pulse power supply system based on big data. The state historical data of the pulse power supply system based on big data is cleaned, normalized, and integrated through the data processing module, and the integrated state historical data of the pulse power supply system is securely stored and backed up. The model training module actively learns the internal relationship between the parameter selection and energy output of the pulse power supply system to train the optimal numerical model for parameter selection of the pulse power supply system. The parameter selection module predicts and analyzes the parameters of the pulse power supply system based on the optimal numerical model for parameter selection of the pulse power supply system according to the user's energy output requirement for the pulse power supply system. By online adjusting and adaptively regulating the parameters of the pulse power supply system, the optimal parameters of the pulse power supply system can be selected, and the parameters of the pulse power supply system can be flexibly selected according to the user's needs, improving the usage effect of the pulse power supply system, and enhancing the performance and reliability of the pulse power supply system.

[0116] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0117] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for selecting parameters of a pulse power supply system, characterized in that, It includes the following steps: S1. Data acquisition: Collect the energy output, pulse frequency, duty cycle, pulse width, and voltage and current historical data of the pulse power supply system to determine the historical data of the pulse power supply system state based on big data; S2. Data processing: Clean, normalize, and integrate the historical data of the pulse power supply system state based on big data, and securely store and back up the integrated historical data of the pulse power supply system state; S3. Model training: Based on deep learning technology, actively learn the internal relationship between the parameter selection of the pulse power supply system and the energy output, and train the optimal numerical model for the parameter selection of the pulse power supply system; S4. Parameter selection: According to the user's energy output requirements for the pulse power supply system, select the optimal parameters of the pulse power supply system based on the optimal numerical model for the parameter selection of the pulse power supply system; S5. Interface display: Visually display the optimal parameters of the pulse power supply system to the user; It also includes: Fault detection: Set up a fault detection circuit with a memristor, utilize the resistance change characteristic of the memristor to sense the faults of the power supply system, and trigger the protection mechanism when the resistance change of the memristor exceeds the set resistance change threshold; Adaptive protection: Set up an adaptive control algorithm based on the memristor, and dynamically adjust the parameters of the power supply system according to the resistance change value of the memristor and in combination with the real-time state of the power supply system to achieve the adaptive protection of the power supply system; Intelligent control: Integrate an intelligent control unit with a microcontroller or programmable logic chip into the power supply system, combine the fault detection circuit with the memristor with the microcontroller or programmable logic chip, utilize the resistance change characteristic of the memristor, and the intelligent control unit sets the control logic through software to achieve the intelligent control of the power supply system; Parameter optimization: Set up a high-frequency circuit connected to the fault detection circuit, and utilize the high-frequency characteristics of the memristor in the fault detection circuit to optimize the high-frequency performance of the power supply by dynamically adjusting the resistance value of the memristor according to the load change of the power supply system; The said intelligent control includes: Construct a memristor state model, and adopt the following state formula of the memristor: In the above formula, represents the state function of the memristor; represents the state of the memristor at time represents the current at time and are the characteristic parameters of the memristor; Using the state of the memristor as a feedback signal, and obtaining an error signal by comparing it with a set target output value ; According to the state of the memristor and the error signal , the control signal is obtained by using the following calculation formula: In the above formula, represents a control signal; represents time; , , and respectively represent the gain factors of proportional, integral, derivative, and memristor state feedback; The intelligent control unit obtains the control signal and realizes the intelligent control of the power supply system according to the control signal.

2. The method for selecting parameters of a pulse power supply system according to claim 1, characterized in that, In the said S1, collecting the historical data of the pulse power supply system state based on big data includes: Collect the historical energy output situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical energy output data of the pulse power supply system; Collect the historical pulse frequency situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical pulse frequency data of the pulse power supply system; Collect the historical duty cycle situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical duty cycle data of the pulse power supply system; Collect the historical pulse width situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical pulse width data of the pulse power supply system; Collect the historical voltage and current situation of the pulse power supply system based on the historical database of the pulse power supply system to obtain the historical voltage and current data of the pulse power supply system; Among them, based on the historical data of the energy output of the pulse power supply system, the historical data of the pulse frequency of the pulse power supply system, the historical data of the duty cycle of the pulse power supply system, the historical data of the pulse width of the pulse power supply system, and the historical data of the voltage and current of the pulse power supply system, the historical data of the state of the pulse power supply system based on big data is determined.

3. The method for selecting parameters of a pulse power supply system according to claim 2, characterized in that, In step S2, the processing of the historical data of the state of the pulse power supply system based on big data includes: Cleaning the historical data of the state of the pulse power supply system based on big data by using a data cleaning tool; Among them, checking and identifying duplicate values, missing values, and outliers in the historical data of the state of the pulse power supply system based on big data, and processing the duplicate values, missing values, and outliers in the historical data of the state of the pulse power supply system based on big data; For duplicate values, directly delete the duplicate values; for missing values, delete the data records containing missing values or use the average value to replace the missing values; for outliers, delete the data records containing outliers or use the median to replace the outliers.

4. The method for selecting parameters of a pulse power supply system according to claim 3, characterized in that, In step S2, the processing of the historical data of the state of the pulse power supply system based on big data further includes: Normalizing the historical data of the state of the pulse power supply system based on big data by using the Z-score normalization method to unify the historical data of the state of the pulse power supply system based on big data, removing the dimensional differences between the historical data of the state of the pulse power supply system based on big data, and determining the normalized historical data of the state of the pulse power supply system; Performing integration processing on the normalized historical data of the state of the pulse power supply system, integrating the normalized historical data of the state of the pulse power supply system into a unified data view, and checking the integrated historical data of the state of the pulse power supply system to check whether there are any omissions in the integrated historical data of the state of the pulse power supply system. After passing the inspection, perform secure storage backup on the integrated historical data of the state of the pulse power supply system.

5. The method for selecting parameters of a pulse power supply system according to claim 4, wherein In step S3, training the optimal numerical model for selecting the parameters of the pulse power supply system includes: Dividing the historical data of the state of the pulse power supply system into a training set and a test set; Based on deep learning technology, using the training set to train the deep learning model, enabling the deep learning model to actively learn the internal relationship between the selection of pulse power supply system parameters and energy output, and then training the numerical model for selecting the parameters of the pulse power supply system; Performing performance testing on the numerical model for selecting the parameters of the pulse power supply system based on the test set to determine whether the numerical model for selecting the parameters of the pulse power supply system can achieve the expected effect; When the numerical model for selecting the parameters of the pulse power supply system cannot achieve the expected effect, adjusting the parameters and structure of the numerical model for selecting the parameters of the pulse power supply system, and continuously iteratively optimizing the adjusted numerical model for selecting the parameters of the pulse power supply system to determine the optimal numerical model for selecting the parameters of the pulse power supply system.

6. A method for selecting parameters of a pulse power supply system according to claim 5, characterized in that, In step S4, selecting the optimal parameters of the pulse power supply system includes: Obtaining the optimal numerical model for selecting the parameters of the pulse power supply system and deploying the optimal numerical model for selecting the parameters of the pulse power supply system in the actual environment for selecting the parameters of the pulse power supply system; According to the user's energy output demand for the pulse power supply system, the parameters of the pulse power supply system are predicted and analyzed based on the optimal numerical model for selecting the parameters of the pulse power supply system. By online adjusting and adaptively regulating the parameters of the pulse power supply system, the optimal parameters of the pulse power supply system are then selected.

7. The method for selecting parameters of a pulse power supply system according to claim 6, wherein In step S5, the optimal parameters of the pulse power supply system are displayed to the user, including: A user interface is provided to visually display the optimal parameters of the pulse power supply system to the user, and the usage situation of the user's pulse power supply system is tracked in real time. The user feedback and the test results of parameter selection are obtained. According to the user feedback and the test results of parameter selection, the parameters of the pulse power supply system are continuously adjusted and optimized to better adapt to different scenarios and user requirements.

8. A pulse power supply system parameter selection system for implementing a pulse power supply system parameter selection method according to any one of claims 1-7, characterized in that, Including: A data acquisition module for collecting historical data on the state of the pulse power supply system based on big data; A data processing module for cleaning, normalizing, and integrating the historical data on the state of the pulse power supply system based on big data; A model training module for training the optimal numerical model for selecting the parameters of the pulse power supply system based on deep learning technology; A parameter selection module for selecting the optimal parameters of the pulse power supply system based on the optimal numerical model for selecting the parameters of the pulse power supply system; An interface display module for providing a user interface to visually display the optimal parameters of the pulse power supply system to the user.

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