Intelligent control and management method for pulse power supply, and related device
Patent Information
- Application Number
- PCT/CN2025/142085
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2025-02-20
- Filing Date
- 2025-12-12
- Publication Date
- 2026-08-27
Smart Images
Figure CN2025142085_27082026_PF_FP_ABST
Abstract
Description
Intelligent control and management methods and related equipment for pulse power supplies Technical Field
[0001] This invention relates to the field of pulse power supply technology, and in particular to an intelligent control and management method and related equipment for pulse power supplies. Background Technology
[0002] Pulse power supplies are common power conversion devices widely used in power electronics, power transmission, and battery discharge. However, traditional pulse power supplies have some problems during the discharge process, such as the inability to automatically identify the load's specifications, the inability to implement personalized discharge control strategies for different devices, and the inability to handle resonant frequency shifts that occur during load discharge. These problems reduce discharge efficiency, increase energy waste, and may damage the load.
[0003] To address the aforementioned issues, researchers have proposed an intelligent control and management method for pulsed power supplies. This method improves the discharge efficiency and intelligence level of the pulsed power supply by automatically identifying load information, formulating personalized discharge control strategies, and monitoring and adjusting the resonant frequency of the discharge circuit in real time. This approach provides a new solution for the application of pulsed power supplies in power electronics, power transmission, and battery discharge, and is of great significance for promoting the advancement of related technologies.
[0004] However, the specific implementation process of this method and its effectiveness in practical applications still require further research and verification. For example, how to establish a database to quickly identify load specifications, how to design discharge control strategies to meet the discharge needs of different devices, and how to accurately detect and adjust the resonant frequency of the discharge circuit all require in-depth discussion. Only by solving these key technical problems can the reliability and stability of the intelligent control management method be ensured in practical applications. Summary of the Invention
[0005] The main objective of this invention is to provide an intelligent control and management method and related equipment for pulse power supplies, so as to accurately detect and adjust the resonant frequency of the discharge circuit.
[0006] To achieve the above objectives, the present invention provides an intelligent control and management method for a pulse power supply. One end of the pulse power supply is connected to a load. The pulse power supply includes a monitoring module and a reading module. The pulse power supply discharges and controls the load. The intelligent control and management method includes the following steps:
[0007] The communication identification code of the load is obtained from the preset database, and the communication identification code is parsed to obtain the discharge specification parameters;
[0008] The real-time status data of the load is obtained, and the discharge specification parameters and the real-time status data are sent to the pulse power supply through a two-way negotiation mechanism to perform strategy discharge planning and obtain a discharge control strategy.
[0009] Based on the discharge control strategy, the pulse power supply is controlled to discharge the load, and during the discharge process, the monitoring module monitors whether the load has a resonant frequency shift.
[0010] If present, the resonant frequency offset data of the load is read through the pulse power supply reading module, and 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.
[0011] The load is discharged based on the adjusted discharge circuit.
[0012] Furthermore, the discharge control strategy is obtained by sending the discharge specification parameters and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning, including:
[0013] By using a multi-dimensional parameter parsing engine, the discharge specification parameters are subjected to deep semantic mapping and structural deconstruction to obtain a load specification semantic network, which includes a first over-discharge voltage threshold, a second over-discharge voltage threshold, a maximum discharge current threshold, and a temperature protection threshold.
[0014] By using a multi-objective constraint optimization algorithm and a two-way negotiation mechanism, correlation analysis and extreme value constraints are performed on the discharge specification parameters based on the load specification semantic network to obtain discharge compatibility data.
[0015] The discharge compatibility data and the real-time status data are sent to the pulse power supply for probabilistic path deduction to obtain the dynamic evolution sequence of the discharge scenario.
[0016] Determine whether the dynamic evolution sequence has discharge safety risks and / or performance degradation hazards. If so, decouple the dynamic evolution sequence into segments through the adaptive segmented control module in the pulse power supply to obtain a segmented discharge control scheme.
[0017] The extreme value stability of the segmented discharge control scheme was verified, and the verification results were obtained.
[0018] If the verification result is a discharge safety boundary condition and the performance loss is minimized, then a discharge control strategy is obtained by performing a strategy discharge planning on the load based on the segmented discharge control scheme.
[0019] The present invention also provides an intelligent control and management device for a pulse power supply, wherein one end of the pulse power supply is connected to a load, and the pulse power supply is provided with a monitoring module and a reading module. The intelligent control and management device includes:
[0020] The parsing module is used to obtain the communication identification code of the load from a preset database, and to perform identification parsing on the communication identification code to obtain the discharge specification parameters;
[0021] The planning module is used to acquire the real-time status data of the load, and send the discharge specification parameters and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategy discharge planning and obtain a discharge control strategy.
[0022] The first monitoring module is used to control the pulse power supply to discharge the load based on the discharge control strategy, and to monitor whether the load has a resonant frequency shift during the discharge process.
[0023] The second monitoring module, if the load has a resonant frequency offset, reads the resonant frequency offset data of the load through the pulse power supply reading module, and adjusts 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.
[0024] A discharge module is used to discharge the load based on the adjusted discharge circuit.
[0025] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the steps of any of the methods described above.
[0026] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of any of the methods described above.
[0027] The method of this invention solves the technical problem of accurately detecting and adjusting the resonant frequency of a discharge circuit. During the discharge process, this method can monitor in real time whether the load experiences a resonant frequency shift. Once a shift is detected, the shift data can be acquired through a reading module, and the discharge circuit of the pulse power supply can be automatically adjusted to maintain the stability of the resonant frequency. This avoids instability during the discharge process and improves the overall stability of the discharge. Attached Figure Description
[0028] Figure 1 is a schematic diagram of the steps of the intelligent control and management method for pulse power supply of the present invention;
[0029] Figure 2 is a structural block diagram of the intelligent control and management device for pulse power supply of the present invention;
[0030] Figure 3 is a schematic block diagram of the structure of the computer device of the present invention. Detailed Implementation
[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0032] Figure 1 is a schematic diagram of the intelligent control and management method for a pulse power supply according to the present invention. One end of the pulse power supply is connected to a load, and the pulse power supply is equipped with a monitoring module and a reading module. The intelligent control and management method includes the following steps:
[0033] S1. Obtain the communication identification code of the load from the preset database, and perform identification and parsing on the communication identification code to obtain the discharge specification parameters.
[0034] In the intelligent control and management method, step S1 is a crucial step in implementing the entire method. Specifically, the pulse power supply pre-establishes a database containing communication identification codes and corresponding discharge specifications for various common discharge devices, such as voltage, current, and capacity. When a load is connected to the pulse power supply, its monitoring module automatically reads the load's communication identification code and matches and parses it against the information in the database. In this way, the pulse power supply can quickly obtain the specific discharge specifications of the load, laying the foundation for subsequent discharge control strategy formulation. For example, when a smartphone is connected to the pulse power supply, it first searches for the phone model's communication identification code in its internal database and parses out parameters such as a discharge voltage of 5V and a discharge current of 2A. With this information, the pulse power supply can formulate the optimal discharge control strategy based on the actual needs of the 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, enabling it to be compatible with various types of loads without complex manual settings and configurations.
[0035] S2, acquire the real-time status data of the load, and send the discharge specification parameters and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategy discharge planning and obtain a discharge control strategy.
[0036] In this step, the pulse power supply plays a crucial role. First, the pulse power supply's monitoring module collects various load indicators in real time, such as voltage, current, and charge level, to understand its current operating status. Simultaneously, the pulse power supply sends the previously acquired load specifications along with this real-time status data to its control module via bidirectional negotiation. This bidirectional negotiation mechanism means that the pulse power supply not only receives information from the load unidirectionally but also interacts and negotiates its own discharge capabilities and strategies with the device. Therefore, the pulse power supply can formulate the optimal discharge control strategy based on the actual needs and status of the device. For example, if the load's battery charge is low, the pulse power supply can formulate a rapid discharge strategy; conversely, if the device temperature is high, the pulse power supply can appropriately adjust the discharge rate to prevent overheating. Through this bidirectional interaction and intelligent negotiation, the pulse power supply ultimately arrives at a highly targeted discharge control strategy. This strategy not only considers the device's discharge specifications but also integrates real-time operating conditions, significantly improving discharge efficiency and safety. Taking smartphones as an example, during the discharge process, the pulse power supply monitors the phone's battery voltage, current, temperature, and other indicators in real time. Based on factors such as the phone's usage pattern and remaining battery power, it dynamically adjusts the discharge speed and power to maximize discharge efficiency while avoiding safety hazards such as overheating. This intelligent discharge control strategy is another important feature of this pulse power supply management method.
[0037] S3, based on the discharge control strategy, control the pulse power supply to discharge the load, and monitor whether the load has a resonant frequency shift during the discharge process through the monitoring module.
[0038] Specifically, based on the discharge control strategy derived from S2, the pulse power supply begins the actual discharge process on the load. During this period, the pulse power supply's monitoring module continuously monitors whether the load experiences a resonant frequency shift. A resonant frequency shift refers to a change in the operating frequency of the load's internal circuitry during discharge due to various reasons, causing a desynchronization with the pulse power supply's discharge circuit and affecting discharge efficiency and stability. For example, when a smartphone is discharging, if its battery or discharge circuit is aging or malfunctioning, the phone's internal resonant frequency will shift. This prevents the pulse power supply's discharge circuit from maintaining optimal resonance with the phone, thus reducing discharge efficiency and even causing fluctuations and interruptions during the discharge process. To address this, the pulse power supply's monitoring module detects changes in the load's resonant frequency in real time. Once a shift is detected, the pulse power supply immediately takes remedial measures. Specifically, the pulse power supply's reading module reads and records the load's resonant frequency shift data and then feeds this data back to the pulse power supply's discharge control module. The control module then adjusts the discharge circuit inside the pulse power supply in real time based on this offset data to ensure that the resonant frequency of the discharge circuit remains synchronized with the load, thereby ensuring the stability and efficiency of the discharge process. Through this real-time monitoring and automatic adjustment, this intelligent control management method can minimize various discharge problems caused by resonant frequency offset, significantly improving the performance and reliability of the pulse power supply in practical applications.
[0039] S4, if present, the resonant frequency offset data of the load is read through the pulse power supply reading module, and 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.
[0040] Specifically, during the aforementioned monitoring process, if the pulse power supply's monitoring module detects a resonant frequency shift in the load, corresponding measures need to be taken to adjust and correct the discharge circuit to ensure the stability of the discharge process. Specifically, the pulse power supply's internal reading module automatically reads and records the load's resonant frequency shift data. Taking a smartphone as an example, suppose the monitoring module detects a resonant frequency shift in the phone's internal discharge circuit, causing it to lose resonance with the pulse power supply's discharge circuit. In this case, the pulse power supply's reading module will immediately record the phone's discharge circuit's resonant frequency shift value, for example, a shift of 200Hz. With this specific shift data, the pulse power supply's control module can specifically adjust and compensate for its own discharge circuit. It dynamically adjusts key components such as inductors and capacitors within the pulse power supply based on the phone's discharge circuit's shift, restoring the discharge circuit's resonant frequency to a state matching the phone's discharge circuit. Through this real-time adjustment, the pulse power supply can ensure optimal resonance with the load during subsequent discharge processes, thereby ensuring discharge efficiency and stability. In short, this step is a crucial guarantee mechanism in intelligent control management methods. It can promptly detect and compensate for resonant frequency shifts caused by load-related issues, ensuring a smooth discharge process and further improving discharge efficiency and quality. This plays a crucial role in enhancing the reliability and user experience of pulse power supplies in practical applications.
[0041] S5, Discharge the load based on the adjusted discharge circuit.
[0042] Specifically, after the resonant frequency adjustment steps described above, the discharge circuit of the pulse power supply has achieved optimal resonance with the load. Based on this adjusted discharge circuit, the pulse power supply can then begin the actual discharge process on the load. At this point, the pulse power supply will, according to the previously established discharge control strategy, utilize the adjusted discharge circuit to deliver appropriate discharge current and voltage to the load, thereby achieving efficient and safe discharge. Taking a smartphone as an example, assuming the pulse power supply has detected the resonant frequency deviation of the phone's discharge circuit in the aforementioned steps and successfully adjusted its own discharge circuit, the pulse power supply will then, based on real-time status data such as the phone's battery capacity and remaining power, deliver optimal discharge parameters to the phone through the adjusted discharge circuit, such as a 5V / 2A constant voltage / constant current discharge mode. This not only enables rapid discharge of the phone but also ensures the stability of the discharge process, avoiding discharge interruptions or abnormal situations caused by resonant frequency mismatch. Through this intelligent discharge process control, this pulse power supply management method can significantly improve the overall discharge performance and user experience.
[0043] In a specific embodiment, the step of parsing the communication identifier code to obtain the discharge specification parameters includes:
[0044] The communication identification code is preprocessed using digital signal processing technology to obtain clean communication identification data;
[0045] Using syntax analysis techniques, specific fields in the clean communication identification data are extracted and parsed to obtain the payload model;
[0046] The model is mapped and converted using a parameter mapping table to obtain standard discharge specification data, which is then used as the discharge specification parameters. The parameter mapping table contains standard discharge specification data corresponding to the identifier, including standard discharge voltage and standard discharge current.
[0047] Specifically, in the intelligent control and management method of pulse power supplies, parsing the communication identification code to obtain the discharge specification parameters is one of the key steps in realizing the entire process. This step includes three main sub-steps: preprocessing 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 a parameter mapping table. First, the pulse power supply uses digital signal processing technology to preprocess the read communication identification code. Since the communication identification code usually comes from the load itself, it may have various noise interferences and data gaps. Therefore, a series of filtering and calibration 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 smartphone is connected to the pulse power supply, the pulse power supply's reading module will first obtain the phone's communication identification code. This identification code may have some slight fluctuations and distortions due to electromagnetic interference, signal attenuation, and other factors. The pulse power supply's digital signal processing technology can effectively eliminate these interferences and extract clear and stable identification digital signals. Next, the pulse power supply uses syntax analysis technology to parse these clean communication identification data. Specifically, it identifies specific fields in the identification data and extracts and parses the load's model information. These specific fields typically refer to the part of the identification code that represents the device model. Through syntax analysis, the pulse power supply can accurately extract this crucial information from complex identification data. Taking the aforementioned smartphone as an example, its communication identification code may consist of a complex string of numbers and letters, where the 3rd to 6th digits represent the phone's specific model. The pulse power supply's syntax analysis technology can accurately identify and extract these four digits, thus obtaining the phone's model information. Finally, the pulse power supply uses 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 the device model should use. For example, in the mapping table, the pulse power supply can find the phone model number "XYZ-2022," whose corresponding standard discharge voltage is 5V and standard discharge current is 2A. With this information, the pulse power supply can determine the discharge specifications of the mobile phone, laying the foundation for subsequent discharge strategy formulation. Through the implementation of the above three steps, the pulse power supply can accurately extract the required discharge specifications from the load's communication identification code. This process involves several complex technical means such as digital signal processing, syntax analysis, and parameter mapping, but it is a crucial link in the intelligent control and management method of the pulse power supply. With this accurate discharge specification information, the pulse power supply can formulate targeted discharge control strategies according to the actual needs of different devices.For example, for mobile phones requiring 5V and 2A, a pulse power supply can employ a constant voltage and constant current discharge mode to provide optimal discharge parameters. For laptops requiring 12V and 4A, the pulse power supply can switch to a different discharge strategy to meet their higher power demands. In short, this step ensures that the pulse power supply can quickly and accurately acquire key load parameter information, laying a solid foundation for subsequent intelligent discharge management. This not only improves discharge compatibility but also creates conditions for personalized discharge control, dynamic frequency adjustment, and other functions, ultimately promoting the application and widespread adoption of pulse power supplies in various electronic devices.
[0048] In a specific embodiment, the step of sending the discharge specification parameters and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategy discharge planning and obtain a discharge control strategy includes:
[0049] Through a multi-dimensional parameter parsing engine, the discharge specification parameters are subjected to deep semantic mapping and structural deconstruction to obtain a load specification semantic network; wherein, the load specification semantic network includes a first over-discharge voltage threshold, a second over-discharge voltage threshold, a maximum discharge current threshold, and a temperature protection threshold.
[0050] By using a multi-objective constraint optimization algorithm and a two-way negotiation mechanism, correlation analysis and extreme value constraints are performed on the discharge specification parameters based on the load specification semantic network to obtain discharge compatibility data.
[0051] The discharge compatibility data and the real-time status data are sent to the pulse power supply for probabilistic path deduction to obtain the dynamic evolution sequence of the discharge scenario.
[0052] Determine whether the dynamic evolution sequence has discharge safety risks and / or performance degradation hazards. If so, the dynamic evolution sequence is decoupled into segments by the adaptive segmented control module in the pulse power supply to obtain a segmented discharge control scheme.
[0053] The extreme value stability of the segmented discharge control scheme was verified, and the verification results were obtained.
[0054] If the verification result is a discharge safety boundary condition and the performance loss is minimized, then a discharge control strategy is obtained by performing a strategy discharge planning on the load based on the segmented discharge control scheme.
[0055] Specifically, in this intelligent control and management method, the core of the entire process involves sending the load's specifications and real-time status data to the pulse power supply via a two-way negotiation mechanism, followed by strategic discharge planning to ultimately obtain the optimal discharge control strategy. This process involves a series of complex technical means, including multi-dimensional parameter analysis, multi-objective constraint optimization, and dynamic evolution sequence deduction, with the aim of ensuring that the pulse power supply can formulate the safest and most efficient discharge scheme according to the actual needs of different devices. First, the pulse power supply uses a multi-dimensional parameter analysis engine to perform deep semantic mapping and structural deconstruction on the discharge specifications obtained from the device. The purpose of this process is to transform the originally complex parameter data into a specification semantic network rich in semantic information. This network includes key indicators such as the first over-discharge voltage threshold, the second over-discharge voltage threshold, the maximum discharge current threshold, and the temperature protection threshold. For example, for a smartphone, its discharge specifications might include information such as a discharge voltage of 5V, a discharge current of 2A, and a battery capacity of 3000mAh. The pulse power supply's multi-dimensional parameter analysis engine performs semantic analysis and structuring on this raw data, transforming it into a semantic network containing indicators such as the first over-discharge voltage of 5.5V, the second over-discharge voltage of 3.5V, the maximum discharge current of 3A, and the upper temperature limit of 45℃. This semantic network lays the foundation for subsequent discharge strategy formulation. Next, the pulse power supply utilizes a multi-objective constraint optimization algorithm and a bidirectional negotiation mechanism to perform correlation analysis and extreme value constraints based on the aforementioned load specification semantic network, in order to find the interrelationships and constraints between these parameters, thereby obtaining the device's discharge compatibility data. For example, in the case of a smartphone, the optimization algorithm can find a negative correlation between its over-discharge voltage threshold and maximum discharge current, meaning that the higher the current, the lower the voltage should be. Simultaneously, there is a positive correlation between the upper temperature limit and the discharge current, meaning that the higher the temperature, the lower the current needs to be. With these correlation analysis results, the pulse power supply can obtain detailed discharge compatibility data for the phone, providing a basis for subsequent discharge control strategy formulation. With the load's specification semantic network and discharge compatibility data, the pulse power supply can send this information, along with real-time status data acquired from the device, such as battery level and temperature, to its control module for strategy planning. This process involves probabilistic path deduction, aiming to simulate the dynamic evolution sequence under different discharge scenarios. For example, the pulse power supply can deduce that if the phone is discharged with 5V / 2A parameters, under conditions of low battery level and high ambient temperature, its discharge process will experience a dynamic sequence of initial rapid discharge followed by slower discharge, eventually reaching 100% battery capacity. By analyzing this sequence, the pulse power supply can promptly identify potential safety risks and performance degradation hazards, providing a basis for subsequent strategy formulation. Once a potential safety or performance problem is detected, the pulse power supply will adopt adaptive segmented control measures.It decouples the originally continuous dynamic evolution sequence into segments, formulating a segmented discharge control scheme. In this scheme, each segment adopts a corresponding control strategy for different discharge states to ensure safety and performance stability. Finally, the pulse power supply verifies the extreme value stability of this segmented discharge control scheme. If the verification results show that the scheme can meet the discharge safety boundary conditions while minimizing performance loss, then the pulse power supply will use it as the final discharge control strategy and apply it to the actual discharge process. In summary, this intelligent control management method, through a two-way negotiation mechanism, fully utilizes advanced multi-dimensional parameter analysis, multi-objective optimization, and dynamic simulation technologies to ensure that the pulse power supply can formulate the optimal discharge control strategy according to the actual needs of different devices. This not only improves discharge efficiency and safety but also enhances the versatility and adaptability of the pulse power supply in various electronic devices, laying an important technical foundation for its widespread application in the future.
[0056] In a specific embodiment, 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 shift during the discharge process via a monitoring module, includes:
[0057] The discharge control strategy is segmented and parsed using a multi-objective partitioning algorithm to obtain a discharge control instruction set;
[0058] The load is discharged according to the discharge control command set using a differential sampling algorithm.
[0059] During the discharge process, the electrical parameters of the load are monitored by the monitoring module to obtain electrical monitoring data, which includes voltage monitoring data and current monitoring data.
[0060] The electrical monitoring data is phase-extracted using the Hilbert transform algorithm to obtain a phase feature sequence, which includes the instantaneous phase angle and the rate of phase change.
[0061] Based on the phase feature sequence, the resonance state of the load is calculated to obtain resonance feature data, which includes resonance frequency and resonance bandwidth.
[0062] Based on the resonant characteristic data, it is determined whether the load has a resonant frequency offset.
[0063] Specifically, in the aforementioned steps, the pulse power supply has already formulated the optimal discharge control strategy based on the load's specifications and real-time status data. Next, the pulse power supply needs to perform the actual discharge process on the load according to this discharge control strategy. Simultaneously, the pulse power supply also needs to monitor in real-time whether there is a resonant frequency shift in the load. First, the pulse power supply uses a multi-objective partitioning algorithm to segment the discharge control strategy into instructions, obtaining a detailed discharge control instruction set. This instruction set contains the specific control commands required for each stage of the discharge process, such as voltage, current, and time parameters. By segmenting the strategy, the pulse power supply can more precisely control the discharge process and improve discharge efficiency. Taking a smartphone as an example, the discharge control strategy may include three segments: the first segment uses a constant voltage 5V 0.5A discharge mode, the middle segment switches to a constant voltage 5V 1A, and the last segment switches to a constant voltage 5V 0.2A supplementary discharge. The pulse power supply will execute these three discharge modes sequentially according to this segmented instruction set to achieve the entire discharge process. With the segmented instructions, the pulse power supply can discharge the load using a differential sampling algorithm. Differential sampling refers to the pulse power supply sampling the discharge voltage and current at extremely short time intervals. By analyzing these voltage and current changes on tiny time scales, the pulse power supply can precisely control the discharge process, avoiding problems such as instantaneous impacts or oscillations. Simultaneously, the pulse power supply's monitoring module closely monitors various electrical parameters of the load during the discharge process, such as voltage and current. This real-time monitoring data is recorded as the basis for subsequent resonant frequency analysis. Specifically, the pulse power supply uses a Hilbert transform algorithm to extract the phase from this voltage and current data, obtaining a time series reflecting phase characteristics. This phase characteristic sequence contains information such as the instantaneous phase angle and phase change rate, laying the foundation for subsequent resonant state calculations. Next, the pulse power supply calculates and analyzes the load's resonant state based on this phase characteristic data. It uses a specialized algorithm to extract key parameters such as the resonant frequency and resonant bandwidth as the basis for judging the resonant frequency deviation. For example, for the aforementioned smartphone, the pulse power supply's monitoring module continuously collects voltage and current data during the discharge process and obtains the phase characteristic sequence through a Hilbert transform. Analyzing this sequence, the pulse power supply reveals that the resonant frequency of the phone's discharge circuit is 1MHz at the start of discharge, but gradually decreases to 900kHz over time. This indicates a resonant frequency shift within the phone. Upon detecting this shift, the pulse power supply immediately takes remedial measures. Based on the monitored resonant characteristic data, it adjusts its discharge circuit in real time to ensure the resonant frequency keeps pace with load changes and maintains optimal resonance. This not only prevents a decrease in discharge efficiency but also ensures the stability and safety of the entire discharge process.Through the aforementioned multi-step implementation, the pulse power supply can monitor the load's operating status in real time during discharge, promptly detect and compensate for resonant frequency deviations, 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 to various devices. Whether it's a smartphone, laptop, or other electronic device, as long as it's connected to this pulse power supply, it can enjoy its intelligent and efficient discharge experience.
[0064] In a specific embodiment, 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:
[0065] Using a preset orthogonal frequency division multiplexing coding technique, based on the resonant frequency offset data, the load is allocated and optimized for discharge channels to obtain allocated decoupled discharge channels;
[0066] The energy transfer efficiency of the allocated decoupled discharge channels is evaluated to obtain an energy transfer gain matrix; wherein, the energy transfer 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 transfer efficiency of each discharge channel in each discharge stage.
[0067] Based on the energy transfer gain matrix, the discharge path of the load is dynamically predicted to obtain multiple discharge path sequences.
[0068] Power spectral density is reconstructed for each of the discharge path sequences to obtain the corresponding power density curves;
[0069] An adaptive power modulation signal is obtained by performing nonlinear power correction on each power density curve using a preset nonlinear compensation function.
[0070] Based on the adaptive power modulation signal, the resonant frequency of the discharge circuit in the pulse power supply is adjusted to obtain the adjusted discharge circuit.
[0071] 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, a preset Orthogonal Frequency-Division Multiplexing (OFDM) coding technique is introduced. OFDM is a highly efficient modulation technique that can divide a communication channel into multiple sub-channels, transmitting data at a lower data rate 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 technique is to allocate and optimize the discharge channel according to the load's resonant frequency offset data to obtain a 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 resonant state with the load, which may reduce energy transmission efficiency or even cause system instability. To solve this problem, the specific situation of the resonant frequency offset is first analyzed using OFDM coding technique, and the discharge channel is adjusted accordingly. For example, in a smartphone charging scenario, if the phone's internal circuitry ages, causing a change in its resonant frequency, the pulse power supply records the specific offset data through a reading module. Then, OFDM technology is applied to reallocate the discharge channels, ensuring that each sub-channel achieves optimal energy transfer efficiency. This process considers not only the current frequency offset but also how to avoid other potential interference factors, achieving more refined and intelligent discharge channel optimization. Next, to evaluate the energy transfer efficiency of these allocated and decoupled discharge channels, the system constructs an energy transfer gain matrix. This 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 provides an intuitive and quantitative tool to help us understand the performance of different discharge channels at different points in time. Continuing with the smartphone example, during the discharge process, the system monitors the performance of each discharge channel in real time and summarizes this information into a gain matrix. Analyzing this matrix clearly shows which channels perform best at which stage and which need improvement, thus providing a basis for further optimization. Subsequently, dynamic discharge path prediction is performed on the load based on the energy transfer gain matrix, aiming to find multiple discharge path sequences. This means not only considering the current optimal path but also anticipating potential future changes and planning multiple alternative paths in advance. This is because the load state is not fixed, especially during long-term discharge, factors such as temperature changes and battery power consumption can affect the initially set discharge strategy. Therefore, dynamic discharge path prediction helps the pulse power supply adjust its behavior in real time, ensuring that it can always supply power to the load with maximum efficiency.For example, when charging a smartphone, the optimal discharge path changes as the battery gradually fills. Dynamic prediction allows the pulse power supply to flexibly switch to the most suitable discharge path for the current conditions without affecting the user experience. For each discharge path sequence, the next step is power spectral density reconstruction to obtain the corresponding power density curve. Power spectral density describes the signal distribution in the frequency domain, and the reconstruction process aims to better understand the performance of these paths in practical applications. Specifically, this ensures that all available discharge paths are fully utilized while avoiding unnecessary energy loss. In the smartphone charging case, the reconstructed power density curve helps identify which frequency bands have the most effective power output and which may generate excessive heat or other side effects, thus guiding subsequent nonlinear power correction. Finally, a preset nonlinear compensation function is used to perform nonlinear power correction on each power density curve to generate an adaptive power modulation signal. Nonlinear compensation addresses unavoidable non-ideal factors in the real world, such as component aging and changes in ambient temperature, which can cause deviations in predictions under linear models. By introducing nonlinear compensation, the system response can be made closer to reality, improving overall performance. For example, during smartphone charging, as the device temperature rises, the characteristics of some electronic components change. Nonlinear compensation ensures that the pulse power supply can still charge the phone stably and efficiently, maintaining a good charging experience even under extreme conditions. Finally, based on the adaptive power modulation signal obtained from the above series of processes, the resonant frequency of the discharge circuit in the pulse power supply is adjusted to achieve 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 smartphone applications, this means that no matter what changes occur inside the phone, the pulse power supply can react promptly and automatically adjust its 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 pulse power supply's adaptability to various complex environments.
[0072] In a specific embodiment, the step of reconstructing the power spectral density of each of the discharge path sequences to obtain the corresponding power density curve includes:
[0073] The time-frequency feature sequence is obtained by extracting time-frequency features from the discharge path sequence using discrete cosine transform.
[0074] The time-frequency feature sequence is subjected to bioorthogonal wavelet decomposition to obtain multidimensional feature components; wherein, the multidimensional feature components include approximate components, horizontal detail components, vertical detail components and diagonal detail components;
[0075] The multidimensional feature components are recombined and merged using Lagrange interpolation to obtain a reconstructed feature matrix.
[0076] The sampling frequency of the reconstructed feature matrix is optimized using the Nyquist criterion to obtain a sampling sequence; wherein the sampling sequence includes power sampling points, frequency sampling points, and phase sampling points.
[0077] Using Passevar's theorem, the power spectral density of the sampled sequence is estimated and reconstructed to obtain the power spectral density function;
[0078] The power spectral density function is curve-fitted using Chebyshev polynomial interpolation to obtain the power density curve.
[0079] Specifically, in the aforementioned steps, the pulsed power supply obtained multiple discharge channels using OFDM technology and evaluated their energy transfer efficiency at different discharge stages. Next, the pulsed power supply needs to conduct in-depth analysis of these discharge path sequences to ultimately obtain the ideal power density curve, providing a basis for subsequent resonant frequency adjustment. First, the pulsed power supply uses Discrete Cosine Transform (DCT) to extract time-frequency domain features from 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 aforementioned smartphone discharge as an example, the pulsed power supply can perform DCT transformation on the power change sequences of the four sub-channels throughout the entire discharge process to obtain the corresponding time-frequency feature sequences. These time-frequency feature sequences can intuitively show the spectral characteristics of each sub-channel at different time periods. Next, the pulsed power supply performs biorthogonal wavelet decomposition on these time-frequency feature sequences. Wavelet decomposition is a multi-resolution signal analysis technique that can decompose the original signal into approximate and detail components at different scales. Here, the pulse power supply employs biorthogonal wavelets, which not only obtains detailed components in multiple directions such as horizontal, vertical, and diagonal, but also an approximate component representing the overall trend. In the aforementioned smartphone discharge example, after wavelet decomposition, the pulse power supply obtains multidimensional feature components including approximate components, horizontal details, vertical details, and diagonal details. These components collectively reflect the complex characteristics of each sub-channel in the time-frequency domain. With these multidimensional feature components, the pulse power supply then uses Lagrange interpolation to recombine and merge them, obtaining a complete reconstructed feature matrix. This matrix not only contains all the information of the original time-frequency features but also has higher time-frequency resolution. To further optimize this reconstructed feature matrix, the pulse power supply uses the Nyquist criterion to optimize its sampling frequency. The Nyquist sampling theorem states that distortion-free signal reconstruction can only be achieved when the sampling frequency is greater than or equal to twice the signal bandwidth. Therefore, the pulse power supply optimizes the sampling of parameters such as power, frequency, and phase of the reconstructed feature matrix according to this criterion, obtaining a high-quality sampling sequence. Finally, the pulsed power supply uses Passevar's theorem to estimate the power spectral density of this optimized sampling sequence, thus obtaining a power spectral density function. This function can intuitively reflect the power distribution of each frequency band. To further improve the smoothness and accuracy of the curve, the pulsed power supply uses Chebyshev polynomial interpolation to curve-fit the power spectral density function, ultimately obtaining an ideal power density curve. For the aforementioned smartphone discharge example, after the above series of complex signal processing steps, the pulsed power supply can obtain the power density curves of each sub-channel throughout the entire discharge process. These curves not only reflect the power characteristics of the sub-channel in the frequency domain but also possess high time-frequency resolution and curve smoothness.With this power density curve data, the pulsed power supply can further analyze the resonant characteristics of the load and dynamically adjust its discharge circuit accordingly. It can accurately determine the load's resonant frequency based on the peak frequency and bandwidth of the curve, and adjust its discharge circuit to synchronize with it, thus ensuring optimal resonant performance throughout the discharge process. In summary, this step involves several advanced signal processing techniques, including discrete cosine transform, wavelet decomposition, Lagrange interpolation, Nyquist sampling, and Passevar spectral density estimation. Through the comprehensive application of these techniques, the pulsed power supply can deeply analyze the load's power characteristics during discharge and provide reliable data support for subsequent resonant frequency adjustment, further improving discharge efficiency and stability. This has significant implications for the widespread application of pulsed power supplies in power electronics, battery discharge, and other fields.
[0080] In a specific embodiment, the step of allocating and optimizing the discharge channel of the load based on the resonant frequency offset data to obtain an allocated decoupled discharge channel includes:
[0081] Based on the resonant frequency offset data, the load is divided into available spectrum to obtain multiple subcarrier frequency bands;
[0082] Using a multipath propagation model, path loss analysis is performed on each of the subcarrier frequency bands to obtain the path loss matrix of each subcarrier frequency band. 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.
[0083] The optimal discharge channel combination is obtained by optimizing the allocation of each subcarrier frequency band based on the path loss matrix using a genetic algorithm.
[0084] The optimal discharge channel combination is subjected to mutual coupling effect analysis to calculate the coupling coefficient between each discharge channel;
[0085] If the coupling coefficient is within the preset coupling coefficient range, then the optimal discharge channel combination will be used as the allocated decoupling discharge channel.
[0086] If not, the allocation strategy of the subcarrier frequency band is dynamically adjusted through a preset channel spectrum reallocation algorithm so that the coupling coefficient is within a preset range.
[0087] Specifically, in the aforementioned steps, the pulse power supply acquires the resonant frequency offset data of the load. Based on this data, the pulse power supply then needs to optimize and decouple the discharge channels to ensure effective compensation for the resonant frequency offset and maintain optimal discharge efficiency during subsequent discharge processes. First, the pulse power supply divides the available spectrum resources of the load according to the acquired resonant frequency offset data, obtaining multiple subcarrier frequency bands. The purpose of this step is to fully utilize 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 phone's discharge circuit drops from 1MHz to 900kHz, it can divide the original single 1MHz frequency band into four subcarrier frequency bands: 900kHz, 950kHz, 1MHz, and 1.05MHz. In this way, the pulse power supply can optimize and control each sub-channel separately for the offset of different frequency bands. Next, the pulse power supply uses a multipath propagation model to perform path loss analysis on these subcarrier frequency bands, obtaining 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, multipath analysis by a pulsed power supply might reveal that the 900kHz band experiences less loss on a 2-meter straight transmission path, while the 1.05MHz band suffers relatively greater loss on a 5-meter winding transmission path. With this specific path loss data, the pulsed power supply can select the optimal transmission path to deploy each subcarrier frequency band based on the actual situation. Based on the aforementioned path loss matrix, the pulsed power supply uses a genetic algorithm to optimize the allocation of subcarrier frequency bands, obtaining an optimal discharge channel combination. A genetic algorithm is a heuristic search algorithm that simulates the process of biological evolution, finding the optimal solution to a problem through continuous selection, crossover, and mutation. Here, the pulsed power supply uses the genetic algorithm to repeatedly try various combinations of subcarrier frequency bands and calculates the overall performance index of each combination based on the path loss matrix. After multiple rounds of evolution, a discharge channel combination with optimal overall performance is finally obtained as the allocated decoupling discharge channel. However, before determining the final discharge channel, the pulsed power supply needs to further analyze the mutual coupling effect between the various subchannels. Since there is a certain degree of spectral overlap and electromagnetic coupling between subchannels, it is necessary to calculate their actual coupling coefficients to ensure that interference between subchannels is minimized. If the calculation results show that the coupling coefficients of each sub-channel are within the preset safety range, then 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 subcarrier frequency band allocation strategy until the coupling coefficients meet the requirements.Taking smartphone discharge as an example, if the pulse power supply detects that the coupling coefficient between the 900kHz and 950kHz sub-channels is too high, it needs to use a preset channel spectrum redistribution algorithm to dynamically adjust the center frequencies of these two sub-channels, minimizing their spectral overlap. In this way, the pulse power supply can ultimately obtain a set of optimally decoupled discharge channels. In summary, this step involves multiple techniques, including spectrum partitioning, path loss analysis, genetic algorithm optimization, and coupling effect calculation. Through the comprehensive application of these methods, the pulse power supply can dynamically adjust and optimize the discharge channels according to the actual resonant frequency shift of the load, ensuring effective compensation for frequency shifts during discharge and maintaining optimal discharge efficiency and stability. This is of great significance for enhancing the application value of pulse power supplies in fields such as battery discharge.
[0088] The intelligent control and management system for the pulse power supply of the present invention is described below. Please refer to Figure 2, which includes:
[0089] The parsing module 21 is used to obtain the communication identification code of the load from a preset database, and to perform identification parsing on the communication identification code to obtain the discharge specification parameters;
[0090] The planning module 22 is used to acquire the real-time status data of the load, and send the discharge specification parameters and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategy discharge planning and obtain a discharge control strategy.
[0091] The first monitoring module 23 is used to control the pulse power supply to discharge the load based on the discharge control strategy, and to monitor whether the load has a resonant frequency shift during the discharge process.
[0092] The second monitoring module 24, if the load has a resonant frequency offset, is used to read the resonant frequency offset data of the load through the pulse power supply's reading module, and adjust 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; and
[0093] The discharge module 25 is used to discharge the load based on the adjusted discharge circuit.
[0094] The specific implementation of each unit in this embodiment can be referred to the method embodiment described above, and will not be repeated here.
[0095] Referring to Figure 3, this embodiment of the invention also provides a computer device. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database, while the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.
[0096] Those skilled in the art will understand that the structure shown in Figure 3 is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied.
[0097] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.
[0098] Those skilled in the art will understand that implementing all or part of the processes in the above methods can be accomplished by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.
[0099] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.
[0100] 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 structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A method for intelligent control management of a pulsed power supply, characterized in that, One end of the pulse power supply is connected to a load. The pulse power supply is equipped with a monitoring module and a reading module. The pulse power supply discharges and controls the load. The intelligent control management method includes the following steps: The communication identification code of the load is obtained from the preset database, and the communication identification code is parsed to obtain the discharge specification parameters; The real-time status data of the load is obtained, and the discharge specification parameters and the real-time status data are sent to the pulse power supply through a two-way negotiation mechanism to perform strategy discharge planning and obtain a discharge control strategy. Based on the discharge control strategy, the pulse power supply is controlled to discharge the load, and during the discharge process, the monitoring module monitors whether the load has a resonant frequency shift. If present, the resonant frequency offset data of the load is read through the pulse power supply reading module, and 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. The load is discharged based on the adjusted discharge circuit; The discharge control strategy is obtained by sending the discharge specifications and real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning, including: The load specification semantic network is obtained by performing deep semantic mapping and structural deconstruction on the discharge specification parameters through a multi-dimensional parameter parsing engine. The load specification semantic network includes a first over-discharge voltage threshold, a second over-discharge voltage threshold, a maximum discharge current threshold, and a temperature protection threshold. By using a multi-objective constraint optimization algorithm and a two-way negotiation mechanism, correlation analysis and extreme value constraints are performed on the discharge specification parameters based on the load specification semantic network to obtain discharge compatibility data. The discharge compatibility data and the real-time status data are sent to the pulse power supply for probabilistic path deduction to obtain the dynamic evolution sequence of the discharge scenario. Determine whether the dynamic evolution sequence has discharge safety risks and / or performance degradation hazards. If so, decouple the dynamic evolution sequence into segments through the adaptive segmented control module in the pulse power supply to obtain a segmented discharge control scheme. The extreme value stability of the segmented discharge control scheme was verified, and the verification results were obtained. If the verification result is a discharge safety boundary condition and the performance loss is minimized, then a discharge control strategy is obtained by performing a strategy discharge planning on the load based on the segmented discharge control scheme.
2. The intelligent control management method of a pulse power supply according to claim 1, characterized in that, The step of parsing the communication identifier code to obtain the discharge specification parameters includes: The communication identification code is preprocessed using digital signal processing technology to obtain clean communication identification data; The specific fields in the clean communication identification data are extracted and parsed using syntax analysis techniques to obtain the payload model; The model is mapped and converted using a parameter mapping table to obtain standard discharge specification data, which is then used as the discharge specification parameter. The parameter mapping table contains standard discharge specification data corresponding to the identifier, including standard discharge voltage and standard discharge current.
3. The intelligent control management method of a pulse power supply according to claim 1, wherein, 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 shift during the discharge process via a monitoring module, includes: The discharge control strategy is segmented and parsed using a multi-objective partitioning algorithm to obtain a discharge control instruction set. The load is discharged according to the discharge control command set using a differential sampling algorithm. During the discharge process, the electrical parameters of the load are monitored by the monitoring module to obtain electrical monitoring data, which includes voltage monitoring data and current monitoring data. The electrical monitoring data is phase-extracted using the Hilbert transform algorithm to obtain a phase feature sequence, which includes the instantaneous phase angle and the rate of phase change. Based on the phase feature sequence, the resonance state of the load is calculated to obtain resonance feature data, which includes resonance frequency and resonance bandwidth. Based on the resonant characteristic data, it is determined whether the load has a resonant frequency offset.
4. The intelligent control and management method for pulse power supply according to claim 1, characterized in that, The step 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 technique, the load is allocated and optimized for discharge channels based on the resonant frequency offset data to obtain allocated decoupled discharge channels; The energy transfer efficiency of the allocated decoupled discharge channels is evaluated to obtain an energy transfer gain matrix. The energy transfer 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 transfer efficiency of each discharge channel in each discharge stage. Based on the energy transfer gain matrix, the discharge path of the load is dynamically predicted to obtain multiple discharge path sequences. Power spectral density is reconstructed for each of the discharge path sequences to obtain the corresponding power density curves; An adaptive power modulation signal is obtained by performing nonlinear power correction on each of the power density curves using a preset nonlinear compensation function. Based on the adaptive power modulation signal, the resonant frequency of the discharge circuit in the pulse power supply is adjusted to obtain the adjusted discharge circuit.
5. The intelligent control and management method for pulse power supply according to claim 4, characterized in that, The step of reconstructing the power spectral density of each of the discharge path sequences to obtain the corresponding power density curves includes: The time-frequency feature sequence is obtained by extracting time-frequency features from the discharge path sequence using discrete cosine transform. The time-frequency feature sequence is subjected to bioorthogonal wavelet decomposition to obtain multidimensional feature components, which include approximate components, horizontal detail components, vertical detail components and diagonal detail components. The multidimensional feature components are recombined and merged using Lagrange interpolation to obtain a reconstructed feature matrix. The sampling frequency of the reconstructed feature matrix is optimized using the Nyquist criterion to obtain a sampling sequence, which includes power sampling points, frequency sampling points, and phase sampling points. The power spectral density of the sampled sequence is estimated and reconstructed using Passevar's theorem to obtain the power spectral density function. The power spectral density function is curve-fitted using Chebyshev polynomial interpolation to obtain the power density curve.
6. The intelligent control and management method for pulse power supply according to claim 4, characterized in that, The process of allocating and optimizing the discharge channel for the load based on the resonant frequency offset data to obtain an allocated decoupled discharge channel includes: Based on the resonant frequency offset data, the available spectrum of the load is divided to obtain multiple subcarrier frequency bands; The path loss of each subcarrier frequency band is analyzed using a multipath propagation model to obtain the path loss matrix of each subcarrier frequency band. 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. The optimal discharge channel combination is obtained by optimizing the allocation of each subcarrier frequency band based on the path loss matrix using a genetic algorithm. The optimal discharge channel combination is subjected to mutual coupling effect analysis to calculate the coupling coefficient between each discharge channel; If the coupling coefficient is within the preset coupling coefficient range, then the optimal discharge channel combination will be used as the allocated decoupling discharge channel. If the coupling coefficient is not within the preset coupling coefficient range, 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 the preset range.
7. 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, and the pulse power supply is equipped with a monitoring module and a reading module. The intelligent control and management device includes: The parsing module is used to obtain the communication identification code of the load from a preset database, and to perform identification parsing on the communication identification code to obtain the discharge specification parameters; The planning module is used to acquire the real-time status data of the load, and send the discharge specification parameters and the real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategy discharge planning and obtain a discharge control strategy. The first monitoring module is used to control the pulse power supply to discharge the load based on the discharge control strategy, and to monitor whether the load has a resonant frequency shift during the discharge process. The second monitoring module is used to read the resonant frequency offset data of the load through the reading module of the pulse power supply, and adjust 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. A discharge module is used to discharge the load based on the adjusted discharge circuit; The discharge control strategy is obtained by sending the discharge specifications and real-time status data to the pulse power supply through a two-way negotiation mechanism to perform strategic discharge planning, including: The load specification semantic network is obtained by performing deep semantic mapping and structural deconstruction on the discharge specification parameters through a multi-dimensional parameter parsing engine. The load specification semantic network includes a first over-discharge voltage threshold, a second over-discharge voltage threshold, a maximum discharge current threshold, and a temperature protection threshold. By using a multi-objective constraint optimization algorithm and a two-way negotiation mechanism, correlation analysis and extreme value constraints are performed on the discharge specification parameters based on the load specification semantic network to obtain discharge compatibility data. The discharge compatibility data and the real-time status data are sent to the pulse power supply for probabilistic path deduction to obtain the dynamic evolution sequence of the discharge scenario. Determine whether the dynamic evolution sequence has discharge safety risks and / or performance degradation hazards. If so, the dynamic evolution sequence is decoupled into segments by the adaptive segmented control module in the pulse power supply to obtain a segmented discharge control scheme. The extreme value stability of the segmented discharge control scheme was verified, and the verification results were obtained. If the verification result is a discharge safety boundary condition and the performance loss is minimized, then a discharge control strategy is obtained by performing a strategy discharge planning on the load based on the segmented discharge control scheme.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it performs the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.