Intelligent control management method of pulse power supply and related equipment

Abstract

The invention relates to an intelligent control management method for a pulse power supply and related equipment, and the method comprises the following steps: obtaining the real-time state data of a load, and transmitting a to-be-discharged specification parameter and the real-time state data to the pulse power supply for strategy discharge planning through a bidirectional negotiation mechanism, thereby obtaining a discharge control strategy; controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether the load has resonant frequency deviation or not; if the load exists, reading resonant frequency deviation data of the load through a reading module of the pulse power supply, and performing resonant frequency adjustment on a discharge loop in the pulse power supply based on the resonant frequency deviation data to obtain an adjusted discharge loop; and discharging the load based on the adjusted discharge loop. The technical problem of how to accurately detect and adjust the resonant frequency of the discharge loop is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of pulse power supply, and in particular to an intelligent control management method of a pulse power supply and related equipment. Background Art

[0002] Pulse power supply is a common power conversion device, which is widely used in power electronics, power transmission, battery discharge and other fields. However, there are some problems with traditional pulse power supply during the discharge process, such as the inability to automatically identify the specification parameters of the load, the inability to personalize the discharge control strategy for different devices, and the inability to deal with the resonant frequency offset of the load during the discharge process. These problems will reduce the discharge efficiency, increase energy waste, and may cause damage to the load.

[0003] In response to the above problems, researchers proposed an intelligent control and management method for pulse power supplies. This method improves the discharge efficiency and intelligence level of pulse power supplies by automatically identifying load information, formulating personalized discharge control strategies, and real-time monitoring and adjusting the resonant frequency of the discharge circuit. This method provides a new solution for the application of pulse power supplies in power electronics, power transmission, battery discharge and other fields, and is of great significance to promoting the advancement of related technologies.

[0004] However, the specific implementation process of this method and its effect in practical applications still need further research and verification. For example, how to establish a database to quickly identify the specification parameters of the load, how to design a discharge control strategy to meet the discharge requirements of different devices, and how to accurately detect and adjust the resonant frequency of the discharge circuit, etc., all need in-depth discussion. Only by solving these key technical problems can the reliability and stability of this intelligent control management method in practical applications be ensured. Summary of the invention

[0005] The main purpose of the present invention is to provide an intelligent control management method of a pulse power supply and related equipment, which solves the technical problem of how to accurately detect and adjust the resonant frequency of a discharge circuit.

[0006] To achieve the above object, the present invention provides an intelligent control management method for a pulse power supply, wherein one end of the pulse power supply is connected to a load, a monitoring module and a reading module are provided in the pulse power supply, and the pulse power supply discharges and controls the load. The intelligent control management method comprises the following steps: Obtaining the communication identification code of the load from a preset database, and performing identification resolution on the communication identification code to obtain specification parameters to be discharged; Acquire the real-time status data of the load, and send the specification parameters to be discharged and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning to obtain a discharge control strategy; Controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether there is a resonant frequency shift of the load through a monitoring module during the discharge process; If it exists, the resonant frequency offset data of the load is read through a reading module of the pulse power supply, and the resonant frequency of the discharge circuit in the pulse power supply is adjusted based on the resonant frequency offset data to obtain an adjusted discharge circuit; The load is discharged based on the adjusted discharge loop.

[0007] Furthermore, the communication identification code is subjected to identification resolution to obtain the specification parameters to be discharged, including: Preprocessing the communication identification code using digital signal processing technology to obtain clean communication identification data; Using syntax analysis technology, extracting and parsing specific fields in the clean communication identification data to obtain the model of the load; The model is mapped and transformed through a parameter mapping table to obtain standard discharge specification data, and the standard discharge specification data is used as the specification parameters to be discharged; wherein the parameter mapping table contains standard discharge specification data corresponding to the identifier, and the standard discharge specification data includes a standard discharge voltage and a standard discharge current.

[0008] Furthermore, the specification parameters to be discharged and the real-time status data are sent to the pulse power supply through a two-way negotiation mechanism for strategic discharge planning to obtain a discharge control strategy, including: Through a multi-dimensional parameter parsing engine, the specification parameters to be discharged are deeply semantically mapped and structurally deconstructed to obtain a load specification semantic network; wherein the load specification semantic network includes an over-discharge voltage threshold, an over-discharge voltage threshold, a maximum discharge current threshold, and a temperature protection threshold; Through a multi-objective constraint optimization algorithm and a two-way negotiation mechanism, correlation analysis and extreme value constraints are performed on the specification parameters to be discharged based on the load specification semantic network to obtain the compatibility data to be discharged; Sending the to-be-discharged compatibility data and the real-time status data to the pulse power supply for probabilistic path deduction to obtain a dynamic evolution sequence of the discharge scene; Determine whether the dynamic evolution sequence has discharge safety risks and / or performance degradation risks. If so, perform segmented decoupling on the dynamic evolution sequence through an adaptive segmented control module in the pulse power supply to obtain a segmented discharge control scheme; Performing extreme value stability verification on the segmented discharge control scheme to obtain verification results; If the verification result is a discharge safety boundary condition and the performance loss is minimal, a strategic discharge planning is performed on the load based on the segmented discharge control scheme to obtain a discharge control strategy.

[0009] Furthermore, controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether the load has a resonant frequency shift through a monitoring module during the discharge process, includes: The discharge control strategy is analyzed by segmentation instructions through a multi-objective partitioning algorithm to obtain a discharge control instruction set; discharging the load according to the discharge control instruction set by using a differential sampling algorithm; During the discharge process, the load is monitored for electrical parameters by the monitoring module to obtain electrical monitoring data; wherein the electrical monitoring data includes voltage monitoring data and current monitoring data; By using the Hilbert transform algorithm, the phase of the electrical monitoring data is extracted to obtain a phase characteristic sequence; wherein the phase characteristic sequence includes an instantaneous phase angle and a phase change rate; Calculating the resonant state of the load based on the phase characteristic sequence to obtain resonant characteristic data; wherein the resonant characteristic data includes a resonant frequency and a resonant bandwidth; It is determined whether the load has a resonant frequency shift based on the resonance characteristic data.

[0010] Furthermore, the resonant frequency of the discharge circuit in the pulse power supply is adjusted based on the resonant frequency offset data to obtain the adjusted discharge circuit, including: Using a preset orthogonal frequency division multiplexing coding technology, based on the resonant frequency offset data, the discharge channel of the load is allocated and optimized to obtain a distributed decoupled discharge channel; The energy transmission efficiency of the distributed decoupled discharge channel is evaluated to obtain an energy transmission gain matrix; wherein the energy transmission gain matrix includes rows, columns and elements, the rows represent different discharge channels, the columns represent different discharge stages, and the elements represent the energy transmission efficiency of each discharge channel in each discharge stage; Performing dynamic prediction of the discharge path of the load based on the energy transfer gain matrix to obtain multiple discharge path sequences; Reconstructing the power spectrum density of each discharge path sequence to obtain a corresponding power density curve; Performing nonlinear power correction on each of the power density curves using a preset nonlinear compensation function to obtain an adaptive power modulation signal; Based on the adaptive power modulation signal, the resonant frequency of the discharge circuit in the pulse power supply is adjusted to obtain an adjusted discharge circuit.

[0011] Furthermore, the power spectrum density of each discharge path sequence is reconstructed to obtain a corresponding power density curve, including: Extracting time-frequency domain features of the discharge path sequence by discrete cosine transform to obtain a time-frequency feature sequence; Performing biorthogonal wavelet decomposition on the time-frequency feature sequence to obtain multidimensional feature components; wherein the multidimensional feature components include approximate components, horizontal detail components, vertical detail components and diagonal detail components; Recombining and merging the multi-dimensional feature components by Lagrange interpolation method to obtain a reconstructed feature matrix; The Nyquist criterion is used to optimize the sampling frequency of the reconstructed characteristic matrix to obtain a sampling sequence; wherein the sampling sequence includes power sampling points, frequency sampling points and phase sampling points; Using Parseval's theorem, power spectrum density estimation and reconstruction are performed on the sampling sequence to obtain a power spectrum density function; The power spectrum density function is subjected to curve fitting by using Chebyshev polynomial interpolation technology to obtain a power density curve.

[0012] Furthermore, the discharging channel allocation and optimization of the load based on the resonant frequency shift data to obtain the allocated decoupled discharging channel includes: Based on the resonant frequency offset data, dividing the available frequency spectrum of the load to obtain a plurality of subcarrier frequency bands; Using a multipath propagation model, path loss analysis is performed on each of the subcarrier frequency bands to obtain a path loss matrix of each subcarrier frequency band; wherein the path loss matrix includes path rows, path columns, and path elements, the path rows represent different subcarrier frequency bands, the path columns represent different transmission paths, and the path elements represent the path loss of each subcarrier frequency band on each transmission path; By using a genetic algorithm, based on the path loss matrix, each of the subcarrier frequency bands is optimally allocated to obtain an optimal discharge channel combination; Performing mutual coupling effect analysis on the optimal discharge channel combination to calculate the coupling coefficient between the discharge channels; If the coupling coefficient is within a preset coupling coefficient range, the optimal discharge channel combination is used as a distribution decoupling discharge channel; If not, the allocation strategy of the subcarrier frequency band is dynamically adjusted through a preset channel spectrum reallocation algorithm to ensure that the coupling coefficient is within a preset range.

[0013] The present invention also provides an intelligent control management device for a pulse power supply, wherein one end of the pulse power supply is connected to a load, and a monitoring module and a reading module are provided in the pulse power supply. The intelligent control management device comprises: A parsing module, used to obtain the communication identification code of the load from a preset database, and perform identification parsing on the communication identification code to obtain specification parameters to be discharged; A planning module, used to obtain the real-time status data of the load, and send the specification parameters to be discharged and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning to obtain a discharge control strategy; A first monitoring module, used for controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether there is a resonant frequency shift of the load through the monitoring module during the discharge process; A second monitoring module is used for, if present, reading the resonant frequency offset data of the load through the reading module of the pulse power supply, and adjusting the resonant frequency of the discharge circuit in the pulse power supply based on the resonant frequency offset data to obtain an adjusted discharge circuit; A discharge module is used to discharge the load based on the adjusted discharge loop.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The intelligent control management method of the pulse power supply provided by the present invention comprises the following steps: obtaining the communication identification code of the load from a preset database, and performing identification analysis on the communication identification code to obtain the specification parameters to be discharged; obtaining the real-time status data of the load, and sending the specification parameters to be discharged and the real-time status data to the pulse power supply through a two-way negotiation mechanism for strategic discharge planning to obtain a discharge control strategy; controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether the load has a resonant frequency offset through a monitoring module during the discharge process; if so, reading the resonant frequency offset data of the load through a reading module of the pulse power supply, and adjusting the resonant frequency of the discharge circuit in the pulse power supply based on the resonant frequency offset data to obtain an adjusted discharge circuit; discharging the load based on the adjusted discharge circuit, and solving the technical problem of how to accurately detect and adjust the resonant frequency of the discharge circuit through the above-mentioned technical means, and realizing that during the discharge process, the method can monitor in real time whether the load has a resonant frequency offset. Once an offset is found, the offset data can be obtained through the reading module, and the discharge circuit of the pulse power supply can be automatically adjusted to maintain the stability of the resonant frequency. This can avoid instability during the discharge process and has the beneficial effect of improving the overall stability of the discharge. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of the intelligent control management method of the pulse power supply in one embodiment of the present invention; Figure 2 It is a structural block diagram of an intelligent control and management device for a pulse power supply in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of an intelligent control management method of a pulse power supply in one embodiment of the present invention; In one embodiment of the present invention, a method for intelligent control and management of a pulse power supply is provided. One end of the pulse power supply is connected to a load. A monitoring module and a reading module are provided in the pulse power supply. The method for intelligent control and management comprises the following steps: In step S1, the communication identification code of the load is obtained from a preset database, and the communication identification code is analyzed to obtain the specification parameters to be discharged.

[0021] Specifically, in the intelligent control management method, obtaining the communication identification code of the load from the preset database, and performing identification analysis on the communication identification code to obtain the specification parameters of the load is a key step in realizing the entire method. Specifically, a database will be pre-established inside the pulse power supply, which contains the communication identification codes of various common discharge devices and the corresponding discharge specification parameters, such as voltage, current, capacity, etc. When the load is connected to the pulse power supply, the monitoring module of the pulse power supply will automatically read the communication identification code of the device and match and analyze it with the information in the database. In this way, the pulse power supply can quickly obtain the specific discharge specification parameters of the load, laying the foundation for the subsequent discharge control strategy formulation. For example, when a smart phone is connected to the pulse power supply, the pulse power supply will first search the communication identification code of the mobile phone model in the internal database, and parse out its discharge voltage of 5V, discharge current of 2A and other parameters. With this information, the pulse power supply can formulate the optimal discharge control strategy according to the actual needs of the mobile phone, such as adopting a constant voltage and constant current discharge mode, thereby achieving an efficient and safe discharge process. This method of automatically identifying and parsing communication identification codes greatly improves the versatility and adaptability of the pulse power supply, making it compatible with a variety of different types of loads without the need for complex manual settings and configurations.

[0022] In step S2, the real-time status data of the load is obtained, and the specification parameters to be discharged and the real-time status data are sent to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning to obtain a discharge control strategy.

[0023] Specifically, the pulse power supply plays a key role in the step of obtaining the real-time status data of the load and planning the strategic discharge through the two-way negotiation mechanism. First, the monitoring module of the pulse power supply will collect various indicators of the load in real time, such as voltage, current, power, etc., to understand its current operating status. At the same time, the pulse power supply will send the previously acquired load specification parameters together with these real-time status data to its own control module through two-way negotiation. The two-way negotiation mechanism means that the pulse power supply not only receives the information of the load unidirectionally, but also interacts and negotiates with the device about its own discharge capability and strategy. The purpose of this is to enable the pulse power supply to formulate the optimal discharge control strategy according to the actual needs and status of the device. For example, if it is found that the battery power of the load is low, the pulse power supply can formulate a fast discharge strategy; and if it is found that the temperature of the device is high, the pulse power supply can appropriately adjust the discharge speed to prevent the device from overheating. Through this two-way interaction and intelligent negotiation method, the pulse power supply can finally come up with a highly targeted discharge control strategy. This strategy not only takes into account the discharge specifications of the device, but also integrates the real-time operating conditions, greatly improving the efficiency and safety of discharge. Taking a smartphone as an example, during the discharge process, the pulse power supply will monitor the battery voltage, current, temperature and other indicators of the mobile phone in real time, and dynamically adjust the discharge speed and power according to the usage mode of the mobile phone, the remaining power and other factors, so as to maximize the discharge efficiency and avoid safety hazards such as overheating of the mobile phone. This intelligent discharge control strategy is another important feature of the pulse power management method.

[0024] In step S3, based on the discharge control strategy, the pulse power supply is controlled to discharge the load, and during the discharge process, a monitoring module is used to monitor whether there is a resonant frequency shift in the load.

[0025] Specifically, based on the discharge control strategy obtained in the above steps, the pulse power supply starts the actual discharge process of the load. During this period, the monitoring module of the pulse power supply will continue to monitor whether the load has a resonant frequency shift. The so-called resonant frequency shift refers to the change in the operating frequency of the internal circuit of the load due to various reasons during the discharge process, which is out of sync with the discharge circuit of the pulse power supply, affecting the discharge efficiency and stability. For example, when a smartphone is discharging, if its battery or discharge circuit is aging or faulty, it may cause the resonant frequency inside the mobile phone to shift. This will make the discharge circuit of the pulse power supply unable to maintain the best resonant state with the mobile phone, thereby reducing the discharge efficiency and even causing fluctuations and interruptions during the discharge process. In order to cope with this situation, the monitoring module of the pulse power supply will detect the resonant frequency changes of the load in real time. Once an offset is found, the pulse power supply will immediately take measures to remedy it. Specifically, the reading module of the pulse power supply will read and record the resonant frequency offset data of the load, and then feed this data back to the discharge control module of the pulse power supply. The control module will then adjust the discharge circuit inside the pulse power supply in real time based on these offset data to ensure that the resonant frequency of the discharge circuit is synchronized with the load, thereby ensuring the stability and efficiency of the discharge process. Through this real-time monitoring and automatic adjustment method, the intelligent control management method can minimize various discharge problems caused by resonant frequency offset and greatly improve the performance and reliability of the pulse power supply in practical applications.

[0026] In step S4, if present, the resonant frequency offset data of the load is read through the reading module of the pulse power supply, and the resonant frequency of the discharge circuit in the pulse power supply is adjusted based on the resonant frequency offset data to obtain an adjusted discharge circuit.

[0027] Specifically, in the aforementioned monitoring process, if the monitoring module of the pulse power supply finds that the load has a resonant frequency offset, then corresponding measures need to be taken to adjust and correct the discharge circuit to ensure the stability of the discharge process. Specifically, the reading module inside the pulse power supply will automatically read and record the resonant frequency offset data of the load. Taking a smart phone as an example, suppose the monitoring module detects that the internal discharge circuit of the mobile phone has a resonant frequency offset, resulting in the inability to maintain a good resonant state with the discharge circuit of the pulse power supply. At this time, the reading module of the pulse power supply will immediately record the resonant frequency offset value of the discharge circuit of the mobile phone, such as an offset of 200Hz. With these specific offset data, the control module of the pulse power supply can adjust and compensate its own discharge circuit in a targeted manner. It will dynamically adjust the key components such as the inductance and capacitance inside the pulse power supply according to the offset of the discharge circuit of the mobile phone, so that the resonant frequency of the discharge circuit is restored to a state matching the discharge circuit of the mobile phone. Through this real-time adjustment, the pulse power supply can ensure that it maintains the best resonant state with the load during the subsequent discharge process, thereby ensuring discharge efficiency and stability. In short, this step is an important guarantee mechanism in the intelligent control management method. It can timely detect and compensate for the resonant frequency deviation caused by the load's own problems, so that the entire discharge process can proceed smoothly, and the discharge efficiency and quality are further improved. This plays a vital role in improving the reliability and user experience of pulse power supply in practical applications.

[0028] In step S5, the load is discharged based on the adjusted discharge circuit.

[0029] Specifically, after the resonant frequency adjustment in the aforementioned steps, the discharge circuit of the pulse power supply has maintained the best resonant state with the load. Based on this adjusted discharge circuit, the pulse power supply can start the actual discharge process of the load. At this time, the pulse power supply will use the adjusted discharge circuit to deliver appropriate discharge current and voltage to the load according to the previously formulated discharge control strategy, thereby achieving efficient and safe discharge. Taking a smart phone as an example, it is assumed that the pulse power supply has detected the resonant frequency offset of the mobile phone discharge circuit in the aforementioned steps and successfully adjusted its own discharge circuit. Next, the pulse power supply will deliver the best discharge parameters to the mobile phone through the adjusted discharge circuit according to the real-time status data of the mobile phone such as the battery capacity and remaining power, such as the 5V / 2A constant voltage and constant current discharge mode. This not only can quickly discharge the mobile phone, but also ensure the stability of the discharge process, avoiding discharge interruptions or abnormalities caused by resonant frequency mismatch. Through this intelligent discharge process control, the pulse power supply management method can greatly improve the overall performance and user experience of discharge.

[0030] In a specific embodiment, performing identification resolution on the communication identification code to obtain the specification parameters to be discharged includes: Preprocessing the communication identification code using digital signal processing technology to obtain clean communication identification data; Using syntax analysis technology, extracting and parsing specific fields in the clean communication identification data to obtain the model of the load; The model is mapped and transformed through a parameter mapping table to obtain standard discharge specification data, and the standard discharge specification data is used as the specification parameters to be discharged; wherein the parameter mapping table contains standard discharge specification data corresponding to the identifier, and the standard discharge specification data includes a standard discharge voltage and a standard discharge current.

[0031] Specifically, in the intelligent control and management method of the pulse power supply, the identification parsing of the communication identification code to obtain the specification parameters to be discharged is one of the key links to realize the whole process. Specifically, this step includes three main sub-steps: pre-processing the communication identification code using digital signal processing technology, extracting model information using syntax analysis technology, and converting it into standard discharge specification data through the parameter mapping table. First, the pulse power supply will use digital signal processing technology to pre-process the communication identification code read. Since the communication identification code usually comes from the load itself, there may be various noise interference and data missing problems, so a series of filtering, calibration and other operations are required to convert it into clean and complete digital signal data. The purpose of this step is to ensure that the subsequent syntax analysis can proceed smoothly and obtain accurate identification information. For example, when a smart phone is connected to a pulse power supply, the reading module of the pulse power supply will first obtain the communication identification code of the mobile phone. This identification code may have some subtle fluctuations and distortions due to factors such as electromagnetic interference and signal attenuation. The digital signal processing technology of the pulse power supply can effectively eliminate these interferences and extract clear and stable identification digital signals. Next, the pulse power supply will use syntax analysis technology to parse these clean communication identification data. Specifically, it will identify specific fields in the identification data, extract and parse the model information of the load. The specific fields mentioned here usually refer to the part of the code used to indicate the device model in the identification code. Through syntax analysis, the pulse power supply can accurately extract this key information from the complex identification data. Taking the aforementioned smartphone as an example, its communication identification code may be composed of a complex string of digits and letters, in which the 3rd to 6th digits represent the specific model of the mobile phone. The syntax analysis technology of the pulse power supply can accurately identify and extract these 4 digits to obtain the model information of the mobile phone. Finally, the pulse power supply will use a pre-established parameter mapping table to convert the extracted device model information into standard discharge specification parameters. This parameter mapping table contains the models of various common discharge devices and their corresponding standard discharge voltage, current and other data. By looking up the mapping table, the pulse power supply can quickly obtain the standard discharge specifications that should be adopted by the model device. For example, in the mapping table, the pulse power supply can find the mobile phone model numbered "XYZ-2022", and its corresponding standard discharge voltage is 5V and the standard discharge current is 2A. With this information, the pulse power supply can determine the discharge specification parameters of the mobile phone, laying the foundation for the subsequent discharge strategy formulation. Through the implementation of the above three steps, the pulse power supply can accurately extract the required discharge specification parameters from the communication identification code of the load. This process involves many complex technical means such as digital signal processing, syntax analysis, parameter mapping, etc., but it is a crucial link for the intelligent control and management method of the pulse power supply.With this accurate discharge specification information, the pulse power supply can develop targeted discharge control strategies based on the actual needs of different devices. For example, for mobile phones with voltage requirements of 5V and current requirements of 2A, the pulse power supply can adopt a constant voltage and constant current discharge mode to provide them with the optimal discharge parameters. For laptops with voltage requirements of 12V and current requirements of 4A, the pulse power supply can switch to another set of discharge strategies to meet their higher power requirements. In short, the implementation of this step ensures that the pulse power supply can quickly and accurately obtain the key parameter information of the load, laying a solid foundation for subsequent intelligent discharge management. This not only improves the compatibility of discharge, but also creates conditions for realizing personalized discharge control, dynamic frequency adjustment and other functions, and ultimately promotes the application and popularization of pulse power supplies in various electronic devices.

[0032] In a specific embodiment, the specification parameters to be discharged and the real-time status data are sent to the pulse power supply through a two-way negotiation mechanism for strategic discharge planning to obtain a discharge control strategy, including: Through a multi-dimensional parameter parsing engine, the specification parameters to be discharged are deeply semantically mapped and structurally deconstructed to obtain a load specification semantic network; wherein the load specification semantic network includes an over-discharge voltage threshold, an over-discharge voltage threshold, a maximum discharge current threshold, and a temperature protection threshold; Through a multi-objective constraint optimization algorithm and a two-way negotiation mechanism, correlation analysis and extreme value constraints are performed on the specification parameters to be discharged based on the load specification semantic network to obtain the compatibility data to be discharged; Sending the to-be-discharged compatibility data and the real-time status data to the pulse power supply for probabilistic path deduction to obtain a dynamic evolution sequence of the discharge scene; Determine whether the dynamic evolution sequence has discharge safety risks and / or performance degradation risks. If so, perform segmented decoupling on the dynamic evolution sequence through an adaptive segmented control module in the pulse power supply to obtain a segmented discharge control scheme; Performing extreme value stability verification on the segmented discharge control scheme to obtain verification results; If the verification result is a discharge safety boundary condition and the performance loss is minimal, a strategic discharge planning is performed on the load based on the segmented discharge control scheme to obtain a discharge control strategy.

[0033] Specifically, in this intelligent control management method, the specification parameters and real-time status data of the load are sent to the pulse power supply through a two-way negotiation mechanism, and then strategic discharge planning is carried out to finally obtain the optimal discharge control strategy, which is the core content of the entire process. This process involves a series of complex technical means such as multi-dimensional parameter analysis, multi-objective constraint optimization, and dynamic evolution sequence deduction. Its purpose is to ensure that the pulse power supply can formulate the safest and most efficient discharge plan according to the actual needs of different devices. First, the pulse power supply will perform deep semantic mapping and structural deconstruction on the discharge specification parameters obtained from the device through a multi-dimensional parameter analysis engine. The purpose of this process is to transform the originally complex parameter data into a specification semantic network rich in semantic information. In this network, key indicators such as over-discharge voltage threshold, over-discharge voltage threshold, maximum discharge current threshold, and temperature protection threshold will be included. For example, for a smart phone, its discharge specification parameters may include information such as discharge voltage of 5V, discharge current of 2A, and battery capacity of 3000mAh. The multi-dimensional parameter parsing engine of the pulse power supply will perform semantic analysis and structuring on these raw data, and convert them into a semantic network containing indicators such as over-discharge voltage 5.5V, over-discharge voltage 3.5V, maximum discharge current 3A, and temperature upper limit 45℃. This semantic network lays the foundation for the subsequent discharge strategy formulation. Next, the pulse power supply will use the multi-objective constraint optimization algorithm and the two-way negotiation mechanism to perform correlation analysis and extreme value constraints based on the above load specification semantic network. The purpose is to find out the relationship and constraints between these parameters, and then obtain the discharge compatibility data of the device. For example, for the example of a smartphone, the optimization algorithm can find that there is a negative correlation between its over-discharge voltage threshold and the maximum discharge current, that is, the larger the current, the lower the voltage cannot be. At the same time, there is also a positive correlation between the temperature upper limit and the discharge current, that is, the higher the temperature, the smaller the current needs to be. With these correlation analysis results, the pulse power supply can obtain detailed discharge compatibility data of the mobile phone, providing a basis for the subsequent discharge control strategy formulation. With the load's specification semantic network and discharge compatibility data, the pulse power supply can send this information together with the status data obtained from the device in real time, such as battery power, temperature, etc., to its own control module for strategy planning. This process involves probabilistic path deduction, the purpose of which is to simulate the dynamic evolution sequence under different discharge scenarios. For example, the pulse power supply can deduce that if the mobile phone is discharged with a discharge parameter of 5V / 2A, when the battery power is low and the ambient temperature is high, its discharge process will experience a dynamic change sequence of first fast and then slow, and finally reach 100% power. By analyzing this sequence, the pulse power supply can promptly discover the possible safety risks and performance degradation hazards, and provide a basis for subsequent strategy formulation. Once potential safety or performance issues are discovered, the pulse power supply will take adaptive segmented control measures.It will decouple the originally continuous dynamic evolution sequence into segments and develop a segmented discharge control scheme. In this scheme, each segment adopts corresponding control strategies for different discharge states to ensure safety and performance stability. Finally, the pulse power supply will verify the extreme stability of this segmented discharge control scheme. If the verification results show that the scheme can meet the discharge safety boundary conditions and the performance loss is minimal, then the pulse power supply will use it as the final discharge control strategy and apply it to the actual discharge process. In short, this intelligent control management method makes full use of advanced multi-dimensional parameter analysis, multi-objective optimization, dynamic simulation and other technical means through a two-way negotiation mechanism to ensure that the pulse power supply can formulate the optimal discharge control strategy according to the actual needs of different equipment. This not only improves the efficiency and safety of discharge, but also enhances the versatility and adaptability of pulse power supplies in various electronic devices, laying an important technical foundation for its wide application in the future.

[0034] In a specific embodiment, controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether the load has a resonant frequency shift through a monitoring module during the discharge process, includes: The discharge control strategy is analyzed by segmentation instructions through a multi-objective partitioning algorithm to obtain a discharge control instruction set; discharging the load according to the discharge control instruction set by using a differential sampling algorithm; During the discharge process, the load is monitored for electrical parameters by the monitoring module to obtain electrical monitoring data; wherein the electrical monitoring data includes voltage monitoring data and current monitoring data; By using the Hilbert transform algorithm, the phase of the electrical monitoring data is extracted to obtain a phase characteristic sequence; wherein the phase characteristic sequence includes an instantaneous phase angle and a phase change rate; Calculating the resonant state of the load based on the phase characteristic sequence to obtain resonant characteristic data; wherein the resonant characteristic data includes a resonant frequency and a resonant bandwidth; It is determined whether the load has a resonant frequency shift based on the resonance characteristic data.

[0035] Specifically, in the above steps, the pulse power supply has formulated the optimal discharge control strategy based on the specification parameters and real-time status data of the load. Next, the pulse power supply needs to perform the actual discharge process on the load according to this discharge control strategy. At the same time, the pulse power supply also needs to monitor in real time whether the load has a resonant frequency offset problem. First, the pulse power supply will use the multi-objective partitioning algorithm to perform segmented instruction parsing on the discharge control strategy to obtain a detailed discharge control instruction set. This instruction set contains specific control commands that need to be executed at each stage of the discharge process, such as voltage, current, time and other parameters. By segmenting the strategy, the pulse power supply can control the discharge process more finely and improve the discharge efficiency. Taking a smartphone as an example, the discharge control strategy may include 3 segments: the front segment adopts a constant voltage 5V 0.5A discharge mode, the middle segment switches to a constant voltage 5V 1A, and the back segment switches to a constant voltage 5V 0.2A supplementary discharge. The pulse power supply will execute these 3 discharge modes in sequence according to this segmented instruction set to realize the entire discharge process. With segmented instructions, the pulse power supply can discharge the load through a differential sampling algorithm. The so-called differential sampling means that the pulse power supply will perform high-frequency sampling of the discharge voltage and current at extremely short time intervals. By analyzing the voltage and current changes on these tiny time scales, the pulse power supply can accurately control the discharge process and avoid problems such as instantaneous impact or oscillation. At the same time, the monitoring module of the pulse power supply will also pay close attention to the various electrical parameters of the load during the discharge process, such as voltage and current. These real-time monitoring data will be recorded as the basis for subsequent resonant frequency analysis. Specifically, the pulse power supply will use the Hilbert transform algorithm to extract the phase of these voltage and current data to obtain a time series reflecting the phase characteristics. This phase characteristic sequence contains information such as the instantaneous phase angle and the phase change rate, which lays the foundation for the subsequent resonant state calculation. Next, the pulse power supply will calculate and analyze the resonant state of the load based on these phase characteristic data. It will use a special algorithm to extract key parameters such as resonant frequency and resonant bandwidth from it as a basis for judging the resonant frequency offset. For example, for the smartphone mentioned earlier, the monitoring module of the pulse power supply will continuously collect voltage and current data during the discharge process and obtain the phase characteristic sequence through Hilbert transform. By analyzing this sequence, the pulse power supply can find that the resonant frequency of the mobile phone discharge circuit is 1MHz at the beginning of discharge, but gradually decreases to 900kHz over time. This indicates that the resonant frequency has shifted inside the mobile phone. Once the resonant frequency is found to be shifted, the pulse power supply will immediately take measures to remedy it. It will adjust its own discharge circuit in real time based on the monitored resonance characteristic data to ensure that the resonant frequency of the discharge circuit can keep up with the changes in the load and maintain the optimal resonance state. This not only avoids the decrease in discharge efficiency, but also ensures the stability and safety of the entire discharge process.Through the implementation of the above multiple steps, the pulse power supply can monitor the working status of the load in real time during the discharge process, promptly detect and compensate for the resonant frequency offset problem, thereby ensuring efficient, safe and stable discharge. This not only improves the performance of the pulse power supply in practical applications, but also further enhances its versatility and applicability for different devices. Whether it is a smartphone, laptop or other electronic device, as long as it is connected to the pulse power supply, it can enjoy its intelligent and efficient discharge experience.

[0036] In a specific embodiment, the resonant frequency of the discharge circuit in the pulse power supply is adjusted based on the resonant frequency offset data to obtain the adjusted discharge circuit, including: Using a preset orthogonal frequency division multiplexing coding technology, based on the resonant frequency offset data, the discharge channel of the load is allocated and optimized to obtain a distributed decoupled discharge channel; The energy transmission efficiency of the distributed decoupled discharge channel is evaluated to obtain an energy transmission gain matrix; wherein the energy transmission gain matrix includes rows, columns and elements, the rows represent different discharge channels, the columns represent different discharge stages, and the elements represent the energy transmission efficiency of each discharge channel in each discharge stage; Performing dynamic prediction of the discharge path of the load based on the energy transfer gain matrix to obtain multiple discharge path sequences; Reconstructing the power spectrum density of each discharge path sequence to obtain a corresponding power density curve; Performing nonlinear power correction on each of the power density curves using a preset nonlinear compensation function to obtain an adaptive power modulation signal; Based on the adaptive power modulation signal, the resonant frequency of the discharge circuit in the pulse power supply is adjusted to obtain an adjusted discharge circuit.

[0037] Specifically, in the process of adjusting the resonant frequency of the discharge circuit in the pulse power supply based on the resonant frequency offset data, the method introduces a preset orthogonal frequency division multiplexing (OFDM) coding technology. OFDM is an efficient modulation technology that can divide a communication channel into multiple sub-channels and use a lower data rate for transmission on each sub-channel, thereby effectively combating frequency selective fading or narrowband interference and improving transmission efficiency and reliability. In this step, the purpose of using OFDM coding technology is to allocate and optimize the discharge channel according to the resonant frequency offset data of the load to obtain a distributed decoupled discharge channel. When a resonant frequency offset is detected in the load, it means that the original discharge circuit no longer maintains the optimal resonance state with the load, which may reduce the energy transmission efficiency and even cause system instability. To solve this problem, OFDM coding technology is first used to analyze the specific situation of the resonant frequency offset, and the discharge channel is adjusted accordingly. For example, in a smartphone charging scenario, if the internal circuit of the phone ages and causes its resonant frequency to change, the pulse power supply will record the specific offset data through the reading module, and then apply OFDM technology to reallocate the discharge channel to ensure that each sub-channel can achieve the optimal energy transfer efficiency. This process not only takes into account the current frequency offset, but also takes into account how to avoid other potential interference factors, achieving a more refined and intelligent discharge channel optimization. Next, in order to evaluate the energy transfer efficiency of these allocated decoupled discharge channels, the system constructs an energy transfer gain matrix. The matrix contains rows, columns, and elements, where rows represent different discharge channels, columns represent different discharge stages, and elements represent the energy transfer efficiency of each discharge channel at each discharge stage. The significance of constructing such a matrix is ​​that it can provide an intuitive and quantitative tool to help us understand the performance of different discharge channels at different time points. Continuing with the example of smartphones, during the discharge process, the system monitors the performance of each discharge channel in real time and summarizes this information into a gain matrix. By analyzing this matrix, we can clearly see which channels perform best at which stage and which need to be improved, thus providing a basis for further optimization. Subsequently, the discharge path of the load is dynamically predicted based on the energy transfer gain matrix, aiming to find multiple discharge path sequences. This means that not only the current optimal path should be considered, but also possible changes in the future should be foreseen and multiple alternative paths should be planned in advance. This is because the state of the load is not fixed, especially during long-term discharge, factors such as temperature changes and battery power consumption will affect the discharge strategy set at the beginning. Therefore, dynamically predicting the discharge path helps the pulse power supply to adjust its behavior at any time to ensure that it can always power the load with the highest efficiency.For example, when charging a smartphone, as the battery gradually fills up, the optimal discharge path will change. The dynamic prediction function allows the pulse power supply to flexibly switch to the discharge path that best suits the current conditions without affecting the user experience. For each discharge path sequence, the next step is to reconstruct the power spectrum density to obtain the corresponding power density curve. The power spectrum density describes the distribution of the signal in the frequency domain, and the reconstruction process is to better understand the performance of these paths in practical applications. Specifically, this is to ensure that all available discharge paths can be fully utilized while avoiding unnecessary energy loss. In the case of smartphone charging, the reconstructed power density curve can help identify which frequency bands have the most effective power output and which may generate excessive heat or other side effects, thereby guiding the subsequent nonlinear power correction work. Finally, the nonlinear power correction of each power density curve is performed through a preset nonlinear compensation function, with the aim of generating an adaptive power modulation signal. Nonlinear compensation is to deal with the inevitable non-ideal factors in the real world, such as component aging, changes in ambient temperature, etc., which may cause deviations in the prediction under the linear model. By introducing nonlinear compensation, the response of the system can be closer to the actual situation and the overall performance can be improved. For example, during the charging of a smartphone, as the temperature of the device rises, the characteristics of some electronic components will change. At this time, nonlinear compensation can ensure that the pulse power supply can still charge the mobile phone stably and efficiently, and maintain a good charging experience even under extreme conditions. Finally, based on the adaptive power modulation signal obtained by the above series of processing, the resonant frequency of the discharge circuit in the pulse power supply is adjusted to realize the adjusted discharge circuit. This step is the core of the entire process because it directly determines whether the pulse power supply can reach the ideal resonance state with the load again. In the application scenario of smartphones, this means that no matter what changes occur inside the mobile phone, the pulse power supply can respond in time and automatically adjust its own discharge parameters to ensure that the charging process is both fast and safe. This intelligent management method not only improves user satisfaction, but also enhances the adaptability of the pulse power supply in the face of various complex environments.

[0038] In a specific embodiment, the power spectrum density of each discharge path sequence is reconstructed to obtain a corresponding power density curve, including: Extracting time-frequency domain features of the discharge path sequence by discrete cosine transform to obtain a time-frequency feature sequence; Performing biorthogonal wavelet decomposition on the time-frequency feature sequence to obtain multidimensional feature components; wherein the multidimensional feature components include approximate components, horizontal detail components, vertical detail components and diagonal detail components; Recombining and merging the multi-dimensional feature components by Lagrange interpolation method to obtain a reconstructed feature matrix; The Nyquist criterion is used to optimize the sampling frequency of the reconstructed characteristic matrix to obtain a sampling sequence; wherein the sampling sequence includes power sampling points, frequency sampling points and phase sampling points; Using Parseval's theorem, power spectrum density estimation and reconstruction are performed on the sampling sequence to obtain a power spectrum density function; The power spectrum density function is subjected to curve fitting by using Chebyshev polynomial interpolation technology to obtain a power density curve.

[0039] Specifically, in the above steps, the pulse power supply obtains multiple discharge sub-channels through OFDM technology and evaluates their energy transfer efficiency at different discharge stages. Next, the pulse power supply needs to conduct in-depth analysis of these discharge path sequences, and finally obtain an ideal power density curve to provide a basis for subsequent resonant frequency adjustment. First, the pulse power supply uses discrete cosine transform (DCT) to extract time-frequency domain features of these discharge path sequences. DCT is an efficient frequency domain transformation algorithm that can convert the original time domain sequence into a time-frequency feature sequence, highlighting the spectral information contained in the sequence. Taking the above-mentioned smartphone discharge as an example, the pulse power supply can perform DCT transformation on the power change sequence of the four sub-channels during the entire discharge process to obtain the corresponding time-frequency feature sequence. These time-frequency feature sequences can intuitively show the spectral characteristics of each sub-channel in different time periods. Next, the pulse power supply will perform biorthogonal wavelet decomposition on these time-frequency feature sequences. Wavelet decomposition is a multi-resolution signal analysis technology that can decompose the original signal into approximate components and detail components of different scales. Here, the pulse power supply uses biorthogonal wavelets, which can not only obtain detail components in multiple directions such as horizontal, vertical, and diagonal, but also obtain an approximate component representing the overall trend. For the aforementioned smartphone discharge example, after wavelet decomposition, the pulse power supply can obtain multidimensional feature components including approximate components, horizontal details, vertical details, and diagonal details. These components reflect the complex characteristics of each subchannel in the time and frequency domain. With these multidimensional feature components, the pulse power supply will then use the Lagrange interpolation method to reorganize and merge them to obtain a complete reconstructed feature matrix. This matrix not only contains all the information of the original time-frequency features, but also has a higher time-frequency resolution. In order to further optimize this reconstructed feature matrix, the pulse power supply will use the Nyquist criterion to optimize its sampling frequency. The Nyquist sampling theorem points out that distortion-free signal reconstruction can only be achieved when the sampling frequency is greater than or equal to twice the signal frequency band. Therefore, the pulse power supply will optimize the sampling of the power, frequency, phase and other parameters of the reconstructed feature matrix according to this criterion to obtain a high-quality sampling sequence. Finally, the pulse power supply will use Parseval's theorem to estimate the power spectral density of this optimized sampling sequence, thereby obtaining a power spectral density function. This function can intuitively reflect the power distribution of each frequency band. In order to further improve the smoothness and accuracy of the curve, the pulse power supply will use Chebyshev polynomial interpolation technology to curve fit the power spectral density function, and finally obtain an ideal power density curve. For the aforementioned smartphone discharge example, after the above series of complex signal processing steps, the pulse power supply can obtain the power density curves of each sub-channel during the entire discharge process. These curves can not only reflect the power characteristics of the sub-channel in the frequency domain, but also have high time-frequency resolution and curve smoothness.With these power density curve data, the pulse power supply can further analyze the resonant characteristics of the load and dynamically adjust its own discharge circuit accordingly. It can accurately determine the resonant frequency of the load based on the peak frequency and bandwidth of the curve, and adjust its own discharge circuit to synchronize with it, thereby ensuring that the optimal resonant state is maintained throughout the discharge process. In short, this step involves a number of advanced signal processing technologies such as discrete cosine transform, wavelet decomposition, Lagrange interpolation, Nyquist sampling, and Parseval spectral density estimation. Through the comprehensive use of these technical means, the pulse power supply can deeply analyze the power characteristics of the load during the discharge process, and provide reliable data support for the subsequent resonant frequency adjustment, further improving the discharge efficiency and stability. This is of great significance for the widespread application of pulse power supplies in power electronics, battery discharge and other fields.

[0040] In a specific embodiment, the discharging channel allocation and optimization of the load based on the resonant frequency shift data to obtain the allocated decoupled discharging channel includes: Based on the resonant frequency offset data, dividing the available frequency spectrum of the load to obtain a plurality of subcarrier frequency bands; Using a multipath propagation model, path loss analysis is performed on each of the subcarrier frequency bands to obtain a path loss matrix of each subcarrier frequency band; wherein the path loss matrix includes path rows, path columns, and path elements, the path rows represent different subcarrier frequency bands, the path columns represent different transmission paths, and the path elements represent the path loss of each subcarrier frequency band on each transmission path; By using a genetic algorithm, based on the path loss matrix, each of the subcarrier frequency bands is optimally allocated to obtain an optimal discharge channel combination; Performing mutual coupling effect analysis on the optimal discharge channel combination to calculate the coupling coefficient between the discharge channels; If the coupling coefficient is within a preset coupling coefficient range, the optimal discharge channel combination is used as a distribution decoupling discharge channel; If not, the allocation strategy of the subcarrier frequency band is dynamically adjusted through a preset channel spectrum reallocation algorithm to ensure that the coupling coefficient is within a preset range.

[0041] Specifically, in the aforementioned steps, the pulse power supply obtains the resonant frequency offset data of the load. Based on these data, the pulse power supply then needs to optimize the allocation and decoupling of the discharge channel to ensure that the resonant frequency offset can be effectively compensated in the subsequent discharge process and maintain the best discharge efficiency. First, the pulse power supply will divide the available spectrum resources of the load according to the obtained resonant frequency offset data to obtain multiple subcarrier frequency bands. The purpose of this step is to make full use of frequency domain resources to meet the needs of different sub-channels and improve the overall spectrum utilization. Taking the aforementioned smartphone discharge as an example, if the pulse power supply detects that the resonant frequency of the mobile phone discharge circuit drops from 1MHz to 900kHz, it can divide the original single 1MHz frequency band into 4 subcarrier frequency bands such as 900kHz, 950kHz, 1MHz, and 1.05MHz. In this way, the pulse power supply can optimize and control each subchannel according to the offset of different frequency bands. Next, the pulse power supply will use the multipath propagation model to perform path loss analysis on these subcarrier frequency bands to obtain a detailed path loss matrix. This matrix contains the specific loss data of each subcarrier frequency band on different transmission paths, providing a basis for subsequent optimization allocation. For smartphone discharge, the multipath analysis of the pulse power supply may find that the loss of the 900kHz frequency band on a 2-meter straight transmission path is small, while the loss of the 1.05MHz frequency band on a 5-meter winding transmission path is relatively large. With these specific path loss data, the pulse power supply can select the optimal transmission path to deploy each subcarrier frequency band according to the actual situation. Based on the above path loss matrix, the pulse power supply will use a genetic algorithm to optimize the allocation of subcarrier frequency bands and obtain an optimal discharge channel combination. Genetic algorithm is a heuristic search algorithm that can simulate the process of biological evolution and find the optimal solution to the problem through continuous selection, crossover, mutation and other operations. Here, the pulse power supply will use genetic algorithms to repeatedly try various combinations of subcarrier frequency bands and calculate the overall performance indicators of each combination based on the path loss matrix. After multiple rounds of evolution, a discharge channel combination with the best comprehensive performance is finally obtained as the distribution decoupling discharge channel. However, before determining the final discharge channel, the pulse power supply needs to further analyze the mutual coupling effect between each subchannel. Since there will be a certain amount of spectrum overlap and electromagnetic coupling between sub-channels, it is necessary to calculate the actual coupling coefficient between them to ensure that the interference between the sub-channels is minimized. If the calculation results show that the coupling coefficients of each sub-channel are within the preset safety range, the pulse power supply can directly use the optimal channel combination obtained by the genetic algorithm. However, if the range is exceeded, the pulse power supply needs to further adjust the sub-carrier frequency band allocation strategy until the coupling coefficient meets the requirements.Taking the discharge of a smartphone as an example, if the pulse power supply finds that the coupling coefficient between the two sub-channels of 900kHz and 950kHz is too high, it needs to use a preset channel spectrum reallocation algorithm to dynamically adjust the center frequencies of the two sub-channels to minimize the spectrum overlap between them. In this way, the pulse power supply can eventually obtain a set of optimal discharge channel combinations that are decoupled from each other. In summary, this step involves multiple technologies such as spectrum division, path loss analysis, genetic algorithm optimization, and coupling effect calculation. Through the comprehensive application of these means, the pulse power supply can dynamically adjust and optimize the discharge channel according to the actual resonant frequency offset of the load, ensuring that the frequency offset can be effectively compensated during the discharge process and maintaining the best discharge efficiency and stability. This is of great significance for enhancing the application value of pulse power supplies in fields such as battery discharge.

[0042] The above describes the intelligent control management method of the pulse power supply in the embodiment of the present invention. The following describes the intelligent control management system of the pulse power supply in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, an intelligent control management system for a pulse power supply includes: The parsing module 21 is used to obtain the communication identification code of the load from a preset database, and perform identification parsing on the communication identification code to obtain the specification parameters to be discharged; The planning module 22 is used to obtain the real-time status data of the load, and send the specification parameters to be discharged and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning to obtain a discharge control strategy; A first monitoring module 23, used for controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether there is a resonant frequency shift of the load through the monitoring module during the discharge process; A second monitoring module 24 is used for, if present, reading the resonant frequency offset data of the load through a reading module of the pulse power supply, and adjusting the resonant frequency of the discharge circuit in the pulse power supply based on the resonant frequency offset data to obtain an adjusted discharge circuit; The discharge module 25 is used to discharge the load based on the adjusted discharge loop.

[0043] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0044] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0045] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0046] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0048] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0049] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent control and management method for a pulse power supply, characterized in that: One end of the pulse power supply is connected to a load, a monitoring module and a reading module are provided in the pulse power supply, and the pulse power supply discharges and controls the load. The intelligent control management method comprises the following steps: Obtaining the communication identification code of the load from a preset database, and performing identification resolution on the communication identification code to obtain specification parameters to be discharged; Acquire the real-time status data of the load, and send the specification parameters to be discharged and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning to obtain a discharge control strategy; Controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether there is a resonant frequency shift of the load through a monitoring module during the discharge process; If it exists, the resonant frequency offset data of the load is read through a reading module of the pulse power supply, and the resonant frequency of the discharge circuit in the pulse power supply is adjusted based on the resonant frequency offset data to obtain an adjusted discharge circuit; The load is discharged based on the adjusted discharge loop.

2. The intelligent control management method of pulse power supply according to claim 1 is characterized in that: The performing identification resolution on the communication identification code to obtain the specification parameters to be discharged includes: Preprocessing the communication identification code using digital signal processing technology to obtain clean communication identification data; Using syntax analysis technology, extracting and parsing specific fields in the clean communication identification data to obtain the model of the load; The model is mapped and transformed through a parameter mapping table to obtain standard discharge specification data, and the standard discharge specification data is used as the specification parameters to be discharged; wherein the parameter mapping table contains standard discharge specification data corresponding to the identifier, and the standard discharge specification data includes a standard discharge voltage and a standard discharge current.

3. The intelligent control management method of pulse power supply according to claim 1 is characterized in that: The two-way negotiation mechanism is used to send the specification parameters to be discharged and the real-time status data to the pulse power supply for strategic discharge planning to obtain a discharge control strategy, including: Through a multi-dimensional parameter parsing engine, the specification parameters to be discharged are deeply semantically mapped and structurally deconstructed to obtain a load specification semantic network; wherein the load specification semantic network includes an over-discharge voltage threshold, an over-discharge voltage threshold, a maximum discharge current threshold, and a temperature protection threshold; Through a multi-objective constraint optimization algorithm and a two-way negotiation mechanism, correlation analysis and extreme value constraints are performed on the specification parameters to be discharged based on the load specification semantic network to obtain the compatibility data to be discharged; Sending the to-be-discharged compatibility data and the real-time status data to the pulse power supply for probabilistic path deduction to obtain a dynamic evolution sequence of the discharge scene; Determine whether the dynamic evolution sequence has discharge safety risks and / or performance degradation risks. If so, perform segmented decoupling on the dynamic evolution sequence through an adaptive segmented control module in the pulse power supply to obtain a segmented discharge control scheme; Performing extreme value stability verification on the segmented discharge control scheme to obtain verification results; If the verification result is a discharge safety boundary condition and the performance loss is minimal, a strategic discharge planning is performed on the load based on the segmented discharge control scheme to obtain a discharge control strategy.

4. The intelligent control management method of pulse power supply according to claim 1 is characterized in that: The step of controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether the load has a resonant frequency deviation through a monitoring module during the discharge process, includes: The discharge control strategy is analyzed by segmentation instructions through a multi-objective partitioning algorithm to obtain a discharge control instruction set; discharging the load according to the discharge control instruction set by using a differential sampling algorithm; During the discharge process, the load is monitored for electrical parameters by the monitoring module to obtain electrical monitoring data; wherein the electrical monitoring data includes voltage monitoring data and current monitoring data; By using the Hilbert transform algorithm, the phase of the electrical monitoring data is extracted to obtain a phase characteristic sequence; wherein the phase characteristic sequence includes an instantaneous phase angle and a phase change rate; Calculating the resonant state of the load based on the phase characteristic sequence to obtain resonant characteristic data; wherein the resonant characteristic data includes a resonant frequency and a resonant bandwidth; It is determined whether the load has a resonant frequency shift based on the resonance characteristic data.

5. The intelligent control management method of pulse power supply according to claim 1, characterized in that: The method of adjusting the resonant frequency of the discharge circuit in the pulse power supply based on the resonant frequency offset data to obtain the adjusted discharge circuit includes: Using a preset orthogonal frequency division multiplexing coding technology, based on the resonant frequency offset data, the discharge channel of the load is allocated and optimized to obtain a distributed decoupled discharge channel; The energy transmission efficiency of the distributed decoupled discharge channel is evaluated to obtain an energy transmission gain matrix; wherein the energy transmission gain matrix includes rows, columns and elements, the rows represent different discharge channels, the columns represent different discharge stages, and the elements represent the energy transmission efficiency of each discharge channel in each discharge stage; Performing dynamic prediction of the discharge path of the load based on the energy transfer gain matrix to obtain multiple discharge path sequences; Reconstructing the power spectrum density of each discharge path sequence to obtain a corresponding power density curve; Performing nonlinear power correction on each of the power density curves using a preset nonlinear compensation function to obtain an adaptive power modulation signal; Based on the adaptive power modulation signal, the resonant frequency of the discharge circuit in the pulse power supply is adjusted to obtain an adjusted discharge circuit.

6. The intelligent control management method of pulse power supply according to claim 5, characterized in that: The reconstructing the power spectrum density of each discharge path sequence to obtain a corresponding power density curve includes: Extracting time-frequency domain features of the discharge path sequence by discrete cosine transform to obtain a time-frequency feature sequence; Performing biorthogonal wavelet decomposition on the time-frequency feature sequence to obtain multidimensional feature components; wherein the multidimensional feature components include approximate components, horizontal detail components, vertical detail components and diagonal detail components; Recombining and merging the multi-dimensional feature components by Lagrange interpolation method to obtain a reconstructed feature matrix; The Nyquist criterion is used to optimize the sampling frequency of the reconstructed characteristic matrix to obtain a sampling sequence; wherein the sampling sequence includes power sampling points, frequency sampling points and phase sampling points; Using Parseval's theorem, power spectrum density estimation and reconstruction are performed on the sampling sequence to obtain a power spectrum density function; The power spectrum density function is subjected to curve fitting by using Chebyshev polynomial interpolation technology to obtain a power density curve.

7. The intelligent control management method of pulse power supply according to claim 5, characterized in that: The discharging channel allocation and optimization of the load based on the resonant frequency shift data to obtain the allocated decoupled discharging channel comprises: Based on the resonant frequency offset data, dividing the available frequency spectrum of the load to obtain a plurality of subcarrier frequency bands; Using a multipath propagation model, path loss analysis is performed on each of the subcarrier frequency bands to obtain a path loss matrix of each subcarrier frequency band; wherein the path loss matrix includes path rows, path columns, and path elements, the path rows represent different subcarrier frequency bands, the path columns represent different transmission paths, and the path elements represent the path loss of each subcarrier frequency band on each transmission path; By using a genetic algorithm, based on the path loss matrix, each of the subcarrier frequency bands is optimally allocated to obtain an optimal discharge channel combination; Performing mutual coupling effect analysis on the optimal discharge channel combination to calculate the coupling coefficient between the discharge channels; If the coupling coefficient is within a preset coupling coefficient range, the optimal discharge channel combination is used as a distribution decoupling discharge channel; If not, the allocation strategy of the subcarrier frequency band is dynamically adjusted through a preset channel spectrum reallocation algorithm to ensure that the coupling coefficient is within a preset range.

8. An intelligent control and management device for a pulse power supply, characterized in that: One end of the pulse power supply is connected to a load, a monitoring module and a reading module are provided in the pulse power supply, and the intelligent control management device includes: A parsing module, used to obtain the communication identification code of the load from a preset database, and perform identification parsing on the communication identification code to obtain specification parameters to be discharged; A planning module, used to obtain the real-time status data of the load, and send the specification parameters to be discharged and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning to obtain a discharge control strategy; A first monitoring module, used for controlling the pulse power supply to discharge the load based on the discharge control strategy, and monitoring whether there is a resonant frequency shift of the load through the monitoring module during the discharge process; A second monitoring module is used for, if present, reading the resonant frequency offset data of the load through the reading module of the pulse power supply, and adjusting the resonant frequency of the discharge circuit in the pulse power supply based on the resonant frequency offset data to obtain an adjusted discharge circuit; A discharge module is used to discharge the load based on the adjusted discharge loop.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images