A control method of a switching power supply and a switching power supply

Abstract

The application relates to the technical field of power supply control, and discloses a control method of a switching power supply and the switching power supply. The application carries out duty cycle modulation analysis on real-time sampling of a feedback signal of the switching power supply, collects temperature data of a bus capacitor to generate temperature characteristic information, records the working environment characteristics and safety limit requirements of the bus capacitor based on the duty cycle modulation, carries out load voltage optimization analysis in combination with the temperature characteristics and the working environment information, evaluates the influence of adjustment optimization on the overall efficiency and stability of the power supply, proposes optimization adjustment measures, and drives a PFC boost circuit to adjust the load voltage of the bus capacitor. The method can accurately adjust the load voltage of the switching power supply, prolongs the service life of the bus capacitor, improves the power supply efficiency and stability, avoids system failure caused by over-temperature or aging, ensures reliable long-time operation of the power supply, and solves the problem of poor optimization effect of adjustment of the load voltage of the bus capacitor in the prior art.

Description

Technical Field

[0001] This invention relates to the technical field of power supply control, and more particularly to a control method for a switching power supply and a switching power supply. Background Technology

[0002] Switching power supplies (SPS) are common power supply devices in modern electronic devices. Their main function is to convert the input voltage into the required output voltage and provide stable power output. Bus capacitors in SPS are usually used for filtering to smooth the output voltage. The temperature characteristics of bus capacitors have a significant impact on their performance. High-temperature environments accelerate capacitor aging and reduce their lifespan. In addition, temperature changes may also affect the electrical performance of capacitors, such as changes in capacitance. Current technologies generally obtain the temperature data of bus capacitors through temperature sensors and capacitor monitoring systems, and use some empirical formulas or simplified models to estimate the aging rate of capacitors. However, these technologies rely heavily on empirical formulas and simplified models, lacking accurate evaluation of the capacitor regulation effect and judgment of the impact of voltage load voltage regulation. Summary of the Invention

[0003] The purpose of this invention is to provide a control method and a switching power supply for a switching power supply, which aims to solve the problem of poor optimization effect in adjusting the load voltage of the bus capacitor in the prior art.

[0004] The present invention is implemented as follows: In a first aspect, the present invention provides a control method for a switching power supply, comprising:

[0005] The feedback signal of the switching power supply is sampled in real time, and the sampling signal is analyzed and adjusted based on preset standards in order to modulate the duty cycle of the power switching transistor of the switching power supply.

[0006] Continuous acquisition and feature analysis of temperature data of bus capacitors in switching power supply are performed to obtain temperature feature information composed of original monitoring data sequence and key temperature monitoring features;

[0007] Based on the operation record of the duty cycle modulation of the switching power supply, the working environment of the bus capacitor is simulated to generate the working environment characteristics and safety limitation requirements of the bus capacitor.

[0008] Based on the temperature characteristics and the working environment characteristics, the thermal stress and aging rate of the bus capacitor are predicted. Combining the prediction results with the safety limit requirements, the load voltage of the bus capacitor is adjusted and optimized. At the same time, based on the working environment characteristics, the overall efficiency and stability of the switching power supply are evaluated and monitored to obtain optimized adjustment measures.

[0009] The optimized adjustment measures drive the PFC boost circuit of the switching power supply to adjust the load voltage of the bus capacitor.

[0010] Secondly, the present invention provides a control power supply for a switching power supply, which employs a control method for a switching power supply as described in any one of the first aspects.

[0011] This invention provides a control method for a switching power supply, which has the following beneficial effects:

[0012] This invention uses real-time sampling of the feedback signal from a switching power supply for duty cycle modulation analysis. It collects temperature data from the bus capacitor to generate temperature characteristic information. Based on the duty cycle modulation records, it simulates the operating environment characteristics and safety limitations of the bus capacitor. Combining temperature characteristics and operating environment information, it performs load voltage optimization analysis, evaluates the impact of adjustment optimization on the overall efficiency and stability of the power supply, proposes optimization measures, and drives the PFC boost circuit to adjust the load voltage of the bus capacitor. This method can accurately adjust the load voltage of the switching power supply, extend the service life of the bus capacitor, improve power supply efficiency and stability, avoid system failures caused by overheating or aging, and ensure long-term reliable operation of the power supply. It solves the problem of poor optimization effect in adjusting the load voltage of the bus capacitor in existing technologies. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the steps of a control method for a switching power supply provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further 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.

[0015] The implementation of the present invention will be described in detail below with reference to specific embodiments.

[0016] Reference Figure 1 The diagram shows a preferred embodiment of the present invention.

[0017] In a first aspect, the present invention provides a control method for a switching power supply, comprising:

[0018] S1: Real-time sampling of feedback signals from the switching power supply, and analysis of adjustment measures for the sampled signals based on preset standards, in order to modulate the duty cycle of the power switching transistors of the switching power supply.

[0019] S2: Continuously collect and analyze the temperature data of the bus capacitor in the switching power supply to obtain temperature feature information composed of the original monitoring data sequence and key temperature monitoring features;

[0020] S3: Based on the operation record of the duty cycle modulation of the switching power supply, simulate the working environment of the bus capacitor to generate the working environment characteristics and safety limitation requirements of the bus capacitor;

[0021] S4: Based on the temperature characteristic information and the working environment characteristics, predict the thermal stress and aging rate of the bus capacitor. Combine the prediction results with the safety limit requirements to perform load voltage adjustment and optimization analysis on the bus capacitor. At the same time, based on the working environment characteristics, evaluate and monitor the overall efficiency and stability of the switching power supply to obtain optimized adjustment measures.

[0022] S5: Drive the PFC boost circuit of the switching power supply to adjust the load voltage of the bus capacitor according to the optimization adjustment measures.

[0023] Specifically, in step S1 of the embodiment provided by the present invention, the feedback signal of the switching power supply is collected in real time by a sensor. Common signals include voltage signals or current signals. These signals directly reflect the operating status of the system. Real-time sampling of the feedback signal is the key to ensuring that the system can adaptively adjust according to the actual working status. The sampling signal can reflect important information such as the current load of the system, output voltage, and current fluctuations. As the basis for subsequent adjustments, real-time sampling can ensure that the system responds to changes in working conditions in a timely manner, thereby avoiding instability or failure caused by delays in changes and ensuring the stability and reliability of the power supply system.

[0024] More specifically, the collected feedback signals are compared and analyzed with preset standards. Preset standards usually refer to the ideal range of values ​​that feedback signals such as voltage and current should meet under normal working conditions. Preset standards provide the expected behavior of the system and serve as a reference state. By comparing the actual sampled signals with the standard signals, the deviation of the current system can be identified, ensuring that the system always maintains the predetermined working state. This step can monitor the operating status of the system in real time, detect deviations from the standard state in a timely manner, and provide a basis for the next adjustment, avoiding instability of the system due to excessive errors.

[0025] More specifically, based on the difference between the sampled signal and the preset standard, duty cycle adjustment measures are calculated and formulated through adjustment algorithms. Duty cycle adjustment directly controls the on and off times of the switching transistor, thereby affecting the power supply output. Duty cycle is a core parameter for adjusting the output power and stability of the switching power supply. By comparing the difference characteristics, calculating and adjusting the duty cycle can effectively regulate the output voltage or current of the power supply, ensuring its stable operation. Optimizing duty cycle adjustment can effectively reduce fluctuations and improve the power supply efficiency and output stability of the system. By dynamically adjusting the duty cycle, the system can adapt to load changes and maintain stable and efficient operation of the power supply.

[0026] More specifically, based on the duty cycle adjustment calculated by the algorithm, the power switching transistor is driven to perform duty cycle modulation, thereby actually controlling the waveform and power output of the power supply. The duty cycle of the power switching transistor directly affects the output characteristics of the power supply. By precisely controlling the duty cycle, effective regulation of the switching power supply can be achieved, thereby meeting load requirements and avoiding overload or underload conditions. This step can optimize the power supply output voltage and current by precisely controlling the operating state of the power switching transistor, effectively improving the response speed, control accuracy, and energy efficiency of the power supply system.

[0027] Understandably, through real-time sampling and feedback signal analysis, the system can dynamically adjust its operating state, respond to instantaneous load fluctuations, ensure the stability of the power supply system, and the duty cycle adjustment enables the power supply to precisely control the output power, avoid energy waste, improve overall efficiency, and timely signal feedback and duty cycle adjustment reduce system failures or delays caused by load changes, thus extending the lifespan of the power supply. Through the combination of preset standards and algorithms, the switching power supply can flexibly adjust under different loads and operating environments to ensure optimal performance.

[0028] Specifically, in step S2 of the embodiment provided by the present invention, a temperature sensor is installed around the bus capacitor of the switching power supply to monitor the temperature change of the capacitor in real time. The temperature sensor should have the characteristics of high accuracy and fast response to ensure that it can continuously and stably record the temperature data of the bus capacitor. The temperature of the bus capacitor is directly related to the working state of the power supply. Temperature changes may affect the performance of the capacitor and the stability of the system. Continuous temperature acquisition can provide timely and accurate data support for subsequent analysis. By continuously monitoring the temperature data, the working state of the capacitor can be reflected in real time, overheating or abnormal fluctuations can be detected in time, and capacitor damage or system failure can be avoided. This helps to improve the safety and reliability of the power supply system.

[0029] More specifically, the collected temperature data is denoised to remove the influence of factors such as sensor error and environmental interference. Common denoising methods include moving average filtering and Kalman filtering. Temperature data is often affected by external noise, such as changes in ambient temperature and electromagnetic interference, which may lead to data instability. Denoising can produce a more accurate and stable temperature signal. The denoised data is smoother and more reliable, which facilitates subsequent feature extraction and analysis and avoids erroneous judgments and system responses caused by noise interference.

[0030] More specifically, feature extraction is performed on the preprocessed data to identify key characteristics of temperature fluctuations. For example, Fourier transform, wavelet transform, and other methods can be used to extract information such as frequency characteristics, fluctuation amplitude, and trend changes of temperature data. Temperature change patterns and fluctuation characteristics can reflect the working status of capacitors and potential failure risks. Through feature extraction, the raw temperature data can be transformed into more informative feature information, which is convenient for subsequent analysis and decision-making. By extracting key features, valuable information can be mined from complex temperature data, helping to determine whether the bus capacitor is within the normal operating range and providing a basis for further optimization and control.

[0031] More specifically, the extracted temperature features are combined with raw monitoring data (such as real-time temperature sequences) to form a temperature feature information sequence. This data can be stored and managed using technologies such as databases or cloud storage. Temperature feature information is a crucial basis for subsequent analysis, modeling, and optimization. By storing and organizing this information, data support can be provided for system optimization, fault prediction, and real-time control. The constructed and stored temperature feature information can be easily used for historical data analysis, trend prediction, and performance evaluation. The system can then adaptively adjust based on this data during actual operation, improving the power system's self-monitoring and adjustment capabilities.

[0032] More specifically, by utilizing real-time temperature characteristic information and combining it with intelligent algorithms for analysis, the operating status of the bus capacitors can be determined, and alarms or corresponding protective measures can be taken for abnormal states. Real-time analysis of temperature characteristic information can promptly detect abnormal phenomena such as overheating of capacitors, provide early warnings, and take protective measures to avoid faults. Real-time analysis and feedback can effectively ensure the safe operation of the power system, respond promptly to potential temperature problems, and improve the system's fault tolerance and fault prevention capabilities.

[0033] Understandably, real-time temperature data acquisition and analysis can accurately monitor the operating temperature of bus capacitors, preventing capacitor aging or damage caused by excessive temperature. Through feature extraction and real-time analysis, potential temperature anomalies can be quickly identified, allowing for early warning and intervention to prevent accidents. Continuous accumulation and analysis of temperature characteristic information can optimize the temperature control of the power system, improving its stability and efficiency. Building a temperature characteristic information database facilitates subsequent data analysis, modeling, and prediction, providing intelligent decision support for the system and reducing manual intervention.

[0034] Specifically, in step S3 of the embodiment provided by the present invention, the duty cycle modulation operation records of the switching power supply are collected and analyzed. Through the system log or control module, the duty cycle modulation operation records of the switching power supply under different loads and operating conditions are collected. These records should include information such as changes in duty cycle, working load, current, and voltage. The duty cycle modulation operation records reflect the changes in the power supply's operating state and record how the switching power supply responds to load changes and external conditions. By analyzing these operation records, the operating mode and possible changes in the operating environment of the switching power supply can be inferred. These data provide an important basis for simulating the operating environment of the bus capacitor, helping to identify possible fluctuations or instabilities in the power supply system and ensuring the accuracy of subsequent environmental simulation.

[0035] More specifically, based on the duty cycle modulation operation records of the switching power supply, combined with the voltage and current waveforms of the power supply, simulation software (such as MATLAB / Simulink) or a custom model is used to simulate the working environment of the bus capacitor. During the simulation, the effects of factors such as temperature, load changes, and current fluctuations on the bus capacitor need to be considered. The bus capacitor will exhibit different characteristics under different working environments, such as voltage fluctuations and temperature rises. Through simulation, the performance of the capacitor under different working conditions can be accurately simulated, and potential risks and instability factors can be identified. This simulation process can provide detailed working environment characteristics, including capacitor temperature changes and load voltage, to help understand the performance of the bus capacitor in actual work and provide data support for further optimization and adjustment.

[0036] More specifically, based on the simulation results, the environmental characteristics of the bus capacitor under different operating conditions are analyzed, such as temperature, voltage drop, and duty cycle. Characteristic curves of the bus capacitor under various operating environments are generated, including the relationship between temperature and voltage, and temperature changes when the load changes. Generating the operating environment characteristics of the bus capacitor helps to understand the stability and performance changes of the capacitor under different conditions. Through these characteristics, the lifespan, operating efficiency, and failure risk of the capacitor can be better predicted. Through characteristic analysis, the performance of the bus capacitor in practical applications can be revealed, the stability of the power supply system can be optimized, and damage caused by overheating or overload can be avoided.

[0037] More specifically, based on the operating environment characteristics of the bus capacitors, safety limits are set for the capacitors. These safety limits include maximum operating temperature, maximum load voltage, and minimum operating current, ensuring that the bus capacitors can operate stably under these limiting conditions. To guarantee system safety, the safe operating range of the bus capacitors must be clearly defined. By setting safety limits, operating conditions exceeding the safe range can be avoided in practical applications, preventing capacitor failures due to overload, overheating, or other reasons. Clearly defined safety limits can effectively prevent overload and overheating problems in the power system, improving the safety and reliability of the entire power system. This provides a clear basis for the optimization and protection of the power system.

[0038] More specifically, based on the safety limits and operating environment characteristics of the bus capacitors, an adaptive adjustment strategy is formulated. When the power system approaches or exceeds safety limits, the duty cycle, load, or other system parameters are automatically adjusted to ensure that the operating environment of the bus capacitors is always maintained within a safe range. In actual operation, load changes and environmental factors can cause fluctuations in the operating state of the bus capacitors. Dynamic adjustment can help the system respond to these fluctuations in a timely manner, ensuring that the capacitors operate within a safe range. Through dynamic adjustment, the safety of the power system can be significantly improved, and the occurrence of capacitor overload, overheating, and other faults can be reduced. This strategy helps to extend the service life of the bus capacitors and ensure the efficient and stable operation of the power system.

[0039] Understandably, by simulating the operating environment of the bus capacitor, its performance under different loads, temperatures, and other factors can be fully understood, thus providing a basis for optimizing power supply design. By setting safety limits and dynamic adjustment strategies for the bus capacitor, the power supply system can be effectively prevented from operating under extreme conditions, avoiding failures caused by overload operation. Through the analysis of operating environment characteristics and safety requirements, potential problems can be identified and prevented early, improving the stability and reliability of the entire power supply system. Through the system's dynamic adjustment and feedback mechanism, the switching power supply can intelligently respond to environmental changes, ensuring that the bus capacitor is always within a safe operating range, thereby improving the overall performance of the system.

[0040] Specifically, in step S4 of the embodiment provided by the present invention, the thermal stress and aging rate of the bus capacitor are predicted based on temperature characteristic information and working environment characteristics. Based on the previously extracted temperature characteristic information and the working environment characteristics of the bus capacitor (such as voltage, temperature fluctuations, load changes, etc.), a thermal stress and aging rate prediction model (such as the Arrhenius model or temperature-lifetime model) is used to predict the thermal stress and aging rate of the bus capacitor. Based on the temperature characteristic data of the capacitor, its thermal stress change at different temperatures is evaluated, and the aging rate of the capacitor is predicted using a temperature-lifetime relationship model. Considering the temperature fluctuations in the working environment, the lifespan of the bus capacitor is closely related to its working temperature. High temperatures will accelerate the aging of the capacitor. By predicting the thermal stress and aging rate, the durability of the capacitor can be understood in advance, providing a basis for subsequent optimization. This step provides a predictive model for the lifespan of the capacitor in the power system, which helps to discover possible aging problems of the capacitor in advance and can provide early warning of performance degradation of the capacitor during use, thus optimizing the system maintenance plan.

[0041] More specifically, based on the predicted results and safety constraints, load voltage regulation optimization analysis is performed on the bus capacitors. Combining the predicted results of thermal stress and aging rate of the bus capacitors, and considering their safety constraints (such as maximum operating voltage, temperature, etc.), load voltage regulation optimization analysis is conducted. By controlling the duty cycle or adjusting the output voltage of the power supply, the rate of temperature rise of the capacitors is reduced, thereby slowing down the thermal stress and aging process. The temperature and aging of the bus capacitors under different voltage regulation strategies are simulated, and their impact on capacitor lifespan is evaluated. By reasonably adjusting the load voltage, the capacitors can avoid operating in a high-temperature, overloaded state, thus extending their service life. The optimization analysis can reduce the thermal load and aging rate of the capacitors while ensuring power supply performance. This step can significantly extend the service life of the bus capacitors without sacrificing power supply performance, thereby optimizing the stability and efficiency of the power supply system.

[0042] More specifically, this study assesses and monitors the impact of load voltage regulation optimization on the overall efficiency and stability of the switching power supply based on the characteristics of the operating environment. According to the characteristics of the operating environment (such as temperature, load fluctuations, voltage fluctuations, etc.), the impact of load voltage regulation optimization on the overall efficiency and stability of the switching power supply is evaluated. Simulation models are used to test the power supply efficiency and stability under different optimization strategies. By simulating the changes in system energy efficiency under different load voltage adjustments, the degree of efficiency improvement after optimization is evaluated. The fluctuations and stability of the system under different operating conditions are monitored, and it is analyzed whether voltage regulation can maintain the long-term stable operation of the power supply system. Load voltage regulation not only affects the lifespan of the bus capacitors but also the efficiency and stability of the switching power supply. By evaluating the efficiency and stability after optimization, it is ensured that while improving capacitor lifespan, the overall performance of the power supply system is not affected. Through comprehensive evaluation, it can be ensured that load voltage regulation optimization measures will not cause system efficiency loss, while improving power supply stability and avoiding system instability or performance degradation caused by over-regulation.

[0043] More specifically, this paper proposes optimized adjustment measures and implements dynamic adjustment. Based on the above analysis results, an optimized scheme for load voltage regulation is proposed, and dynamic adjustment is achieved through a control system. A dynamic adjustment strategy is set to adjust the output voltage according to real-time temperature and load changes to avoid capacitor overheating or overload. A feedback mechanism is designed to monitor the capacitor temperature and operating status in real time and dynamically adjust the power supply load voltage based on real-time data. In actual operation, load changes and environmental fluctuations are unavoidable. The dynamic adjustment strategy can ensure that the power supply system maintains optimal performance under different operating conditions. Dynamic adjustment can adaptively adjust the power supply output voltage according to the real-time status of the bus capacitor, maintain the efficient operation of the power supply system, and extend the service life of the bus capacitor.

[0044] Understandably, by optimizing the load voltage, the thermal stress and aging process of the bus capacitors can be reduced, thereby extending the capacitors' lifespan and preventing failures caused by overheating. Optimizing the load voltage not only effectively protects the capacitors but also improves the overall efficiency of the power supply. By dynamically adjusting the voltage, energy efficiency can be maximized, and by precisely adjusting the voltage, the stability of the power supply system can be maintained, reducing system fluctuations and instability. This ensures that the power supply can work reliably under various loads and environments. By introducing real-time temperature and load monitoring and combining it with an intelligent feedback mechanism, the power supply output voltage can be dynamically adjusted, improving the system's intelligence and adaptability.

[0045] Specifically, in step S5 of the embodiment provided by the present invention, based on the previously analyzed load voltage regulation optimization measures, the control system transmits regulation commands to the PFC (Power Factor Correction) boost circuit. Specifically, based on the load voltage requirements and optimization goals of the bus capacitor, a target voltage is set and used as input to issue regulation commands to the PFC circuit. The PFC boost circuit is mainly responsible for increasing the input voltage and adjusting it to an appropriate load voltage. Through precise regulation commands, it can ensure that the bus capacitor operates within a safe and efficient voltage range. Accurate regulation commands also ensure that the PFC boost circuit precisely controls the output voltage, avoiding excessively high or low voltages that could cause capacitor overheating or failure, thereby ensuring the safe operation of the bus capacitor.

[0046] More specifically, based on optimized adjustment measures, the switching control mechanism of the PFC boost circuit is adjusted. By controlling parameters such as the switching frequency and duty cycle of the switching transistor, the circuit output voltage is adjusted to meet the load requirements of the bus capacitor. The operating frequency of the PFC boost circuit is adjusted to adapt to the load voltage regulation requirements. Higher frequencies result in faster voltage response, while lower frequencies help improve efficiency. The output voltage is finely adjusted by controlling the duty cycle of the switching transistor. The output voltage of the PFC boost circuit is determined by the adjustment of the switching frequency and duty cycle. Precise control of the switching frequency and duty cycle according to the voltage requirements of the bus capacitor enables precise voltage regulation. Through optimized switching control, more stable and efficient voltage regulation can be achieved, improving the dynamic response capability of the PFC circuit and the overall efficiency of the system, while avoiding capacitor overload and overheating.

[0047] More specifically, the operating voltage of the bus capacitor is monitored in real time, and the measurement results are fed back to the PFC boost circuit. Through the system feedback mechanism, the output of the PFC circuit is adjusted in real time to ensure that the bus capacitor operates within the set safe voltage range. Voltage sensors or other monitoring devices are used to continuously acquire the voltage information of the bus capacitor. When the voltage deviates from the set value, the feedback mechanism will dynamically adjust the output of the PFC circuit to correct voltage fluctuations. The voltage of the bus capacitor needs to remain stable within the set range. Real-time voltage monitoring and adjustment based on feedback information ensure that the capacitor operates within a safe range and prevents damage caused by excessively high or low voltage. Through real-time feedback adjustment, the system can quickly respond to load changes or environmental fluctuations, ensuring that the capacitor is always in a safe and stable operating state, thereby improving the reliability and lifespan of the system.

[0048] More specifically, based on the load voltage regulation requirements of the bus capacitor, the efficiency and stability of the PFC boost circuit are optimized. By adjusting the switching frequency, duty cycle, and filter configuration, the circuit's conversion efficiency and stability are optimized, reducing power loss and improving system response speed. Adjusting the circuit's operating point ensures high-efficiency operation. Optimizing conversion efficiency reduces system heat loss, improves overall performance, and ensures stable operation of the PFC boost circuit during load voltage regulation, avoiding output voltage fluctuations or excessive transient responses. Optimizing the efficiency and stability of the PFC circuit reduces system energy loss and improves system operational stability. Effective stability management prevents capacitor damage due to voltage fluctuations during operation. Optimizing the PFC boost circuit not only improves the overall efficiency of the power supply system but also ensures stable operation under different load conditions, enhancing the overall performance of the power supply system.

[0049] More specifically, dynamic voltage regulation and real-time monitoring are implemented under load variations. By monitoring load changes in the bus capacitors, the operating mode of the PFC circuit is automatically adjusted to maintain the capacitor voltage within the ideal range. The output voltage of the PFC boost circuit is automatically adjusted according to load changes to prevent capacitor voltage instability caused by load fluctuations. Real-time monitoring tools ensure that the adjustment process does not negatively impact the system and can trigger protection mechanisms when necessary. Load changes can cause fluctuations in bus capacitor voltage. Through dynamic regulation, the capacitors are always kept in optimal operating condition, avoiding faults caused by overload or undervoltage. Dynamic regulation improves the system's adaptability to load changes, ensures capacitor voltage stability, and reduces the risk of failure.

[0050] Understandably, by optimizing parameters such as the switching frequency and duty cycle of the PFC boost circuit, the output voltage can be precisely controlled, ensuring that the bus capacitor operates within a safe range. Optimizing the efficiency of the PFC circuit improves overall energy efficiency, reduces system power loss, thereby saving energy and reducing system heat load. Through real-time feedback mechanisms and dynamic adjustments, it can quickly respond to system load changes, maintain stable operation of the power supply system, and prevent voltage fluctuations from affecting the normal operation of the bus capacitor. By precisely adjusting the load voltage, capacitor overload and overheating are avoided, thereby extending the service life of the bus capacitor and reducing the occurrence of failures. By globally optimizing the efficiency and stability of the PFC boost circuit, not only is the working efficiency of the bus capacitor improved, but the performance and reliability of the entire power supply system are also enhanced.

[0051] This invention provides a control method for a switching power supply, which has the following beneficial effects:

[0052] This invention uses real-time sampling of the feedback signal from a switching power supply for duty cycle modulation analysis. It collects temperature data from the bus capacitor to generate temperature characteristic information. Based on the duty cycle modulation records, it simulates the operating environment characteristics and safety limitations of the bus capacitor. Combining temperature characteristics and operating environment information, it performs load voltage optimization analysis, evaluates the impact of adjustment optimization on the overall efficiency and stability of the power supply, proposes optimization measures, and drives the PFC boost circuit to adjust the load voltage of the bus capacitor. This method can accurately adjust the load voltage of the switching power supply, extend the service life of the bus capacitor, improve power supply efficiency and stability, avoid system failures caused by overheating or aging, and ensure long-term reliable operation of the power supply. It solves the problem of poor optimization effect in adjusting the load voltage of the bus capacitor in existing technologies.

[0053] Preferably, the step of real-time sampling of the feedback signal of the switching power supply and analyzing the adjustment measures of the sampled signal based on a preset standard to modulate the duty cycle of the power switching transistor of the switching power supply includes:

[0054] S11: Select a voltage signal or a current signal as a feedback signal, and collect the selected feedback signal of the switching power supply in real time through a pre-installed sensing module, and generate a timestamp for each collected feedback signal to form a feedback monitoring sequence.

[0055] S12: Perform periodic and trend pattern mining on the feedback monitoring sequence to generate the expected pattern of the feedback signal of the switching power supply, and perform adaptive parameter deployment on the preset standard duty cycle modulation algorithm according to the expected pattern of the feedback signal.

[0056] S13: Perform difference analysis on the feedback signal at the current moment according to the standard reference signal of the preset standard to obtain signal difference characteristics. At the same time, calculate the modulation measures on the signal difference characteristics according to the duty cycle modulation algorithm after adaptive parameter deployment to obtain the duty cycle modulation measures. Then, perform duty cycle modulation on the power switch tube of the switching power supply according to the duty cycle measures.

[0057] Specifically, based on the control objectives of the switching power supply, either a voltage signal or a current signal is selected as the feedback signal. The voltage signal is typically used to detect the stability of the bus voltage, while the current signal is used to assess the power consumption during load changes. Selecting the appropriate signal type ensures that the feedback signal accurately reflects the operating state of the switching power supply. The voltage and current signals reflect the output voltage and load current of the switching power supply, respectively, and they play different roles in the control system. Selecting the appropriate signal type can more accurately reflect the actual operating state of the system, providing a valid basis for subsequent adjustment measures. Selecting an appropriate signal as the feedback input ensures that the subsequent control algorithm can accurately acquire the dynamic characteristics of the system, laying the foundation for the next step of signal acquisition and analysis.

[0058] More specifically, a sensing module is pre-installed in the switching power supply to collect selected voltage or current signals in real time. Each collected feedback signal is timestamped, and these timestamps accurately record the acquisition time of the feedback signal, forming a feedback monitoring sequence containing a time series. Real-time acquisition and timestamping are used to accurately record the operating status of the switching power supply at different points in time. This data is an important basis for subsequent analysis and adjustment, and can help the system understand the changing patterns of the feedback signal. Through the timestamped sampling data, the historical state of the system can be clearly traced, and trend and periodic changes can be identified in subsequent analysis, providing effective data support for adjustment measures.

[0059] More specifically, the periodicity and trend of the collected feedback monitoring sequences are mined to analyze the fluctuation patterns, periodic characteristics, and long-term trends of the feedback signals. By using time series analysis techniques, the expected patterns of the feedback signals can be identified. Feedback signals usually have certain periodicity and trends, and these patterns can help predict the future state of the switching power supply. By mining these patterns, basic data can be provided for the adaptive adjustment of the duty cycle modulation algorithm. Mining periodicity and trend patterns can help the system anticipate the changing trends of the feedback signals, thereby achieving more effective regulation, avoiding over-adjustment or lag response, and improving the control performance and stability of the switching power supply.

[0060] More specifically, based on the expected pattern of the generated feedback signal, the parameters of the preset standard duty cycle modulation algorithm are adjusted. These parameters are adaptively optimized according to the trend and pattern of the feedback signal, making the duty cycle adjustment more precise. The preset standard duty cycle modulation algorithm needs to be dynamically adjusted according to the changes in the real-time feedback signal. The deployment of adaptive parameters can optimize the control effect according to the actual operating conditions, thereby improving the adjustment efficiency and response speed. Through the deployment of adaptive parameters, the system can flexibly adjust the duty cycle adjustment strategy according to the changes in the actual feedback signal, achieve fine control, and improve the system's responsiveness to changes.

[0061] More specifically, based on a preset standard reference signal, a difference analysis of the current feedback signal is performed. By comparing the difference between the reference signal and the currently acquired signal, signal difference features are extracted. Then, these difference features are input into an adaptive duty cycle modulation algorithm to calculate the corresponding duty cycle adjustment measures. The difference analysis is to assess the deviation between the current signal and the ideal reference signal. Based on this deviation, the duty cycle can be adjusted in a targeted manner to ensure that the switching power supply is always in the optimal operating state. Through difference analysis, system deviations can be effectively identified and corrected, ensuring that the duty cycle adjustment meets the preset standard, avoiding performance degradation caused by voltage or current fluctuations, and improving the stability and reliability of the system.

[0062] More specifically, based on the calculated duty cycle adjustment measures, the duty cycle of the power switching transistor in the switching power supply is adjusted to precisely control the output voltage or current. Duty cycle adjustment is a key step in adjusting the operating state of the switching power supply, directly affecting the stability and efficiency of the power supply. Duty cycle modulation is the core means of achieving voltage and current regulation. Through precise duty cycle control, the power supply output can be adjusted in a timely manner according to the differences in feedback signals, avoiding excessive or insufficient voltage output and ensuring the stable operation of the power supply system. Through precise duty cycle adjustment, changes in feedback signals can be responded to in real time, maintaining the stability of the system output and improving the efficiency and performance of the switching power supply.

[0063] Understandably, real-time sampling and timestamp marking enable accurate capture of feedback signal changes, providing precise control basis. Periodic and trend pattern mining allows the system to anticipate feedback signal changes, avoiding over-adjustment and delayed response. The combination of adaptive parameter deployment and signal difference analysis effectively optimizes the duty cycle adjustment algorithm, improving the accuracy and efficiency of adjustment. Through precise duty cycle adjustment, the system can be guaranteed to operate stably under different loads and environmental conditions, reducing the impact of fluctuations and instability. Optimized duty cycle adjustment measures can reduce energy waste, improve the overall efficiency of the power system, and extend equipment life.

[0064] Preferably, the step of continuously acquiring and analyzing temperature data of the bus capacitor in the switching power supply to obtain temperature feature information composed of the original monitoring data sequence and key temperature monitoring features includes:

[0065] S21: Real-time temperature monitoring of the bus capacitor is performed by a temperature sensor pre-set in the switching power supply, and the temperature signals monitored at each time are converted from analog to digital form. At the same time, a corresponding timestamp is assigned to the digital temperature signal so that the temperature signals at each time are arranged into the original monitoring data sequence according to the time sequence relationship.

[0066] S22: Perform multi-dimensional feature mining on the original monitoring data sequence to retrieve the corresponding key temperature monitoring features from the pre-built bus capacitor temperature feature knowledge base;

[0067] S23: Combine the original monitoring data sequence with the key temperature monitoring features to form temperature feature information.

[0068] Specifically, by using a temperature sensor pre-installed in the switching power supply, the temperature change of the bus capacitor is monitored in real time. The temperature signal is converted from analog form (such as voltage or current signal) to digital form. The converted signal will include a timestamp to record the temperature data at each sampling moment. Real-time temperature monitoring can provide the temperature status of the bus capacitor when the power system is operating. This data is crucial for analyzing the thermal performance of the power supply during operation. The analog signal is converted to a digital signal to facilitate subsequent processing and analysis. At the same time, assigning a timestamp to each sampling point helps to accurately record and track the trend of data change. Through real-time data acquisition by the temperature sensor, continuous monitoring of the bus capacitor temperature can be achieved. After being converted to digital form, it is convenient for subsequent data storage, analysis and processing, providing a data foundation for subsequent feature analysis.

[0069] More specifically, the temperature signal collected at each moment is stored together with the corresponding timestamp and arranged in chronological order to form the original monitoring data sequence. The original monitoring data sequence is the basis for subsequent analysis. The correspondence between each data point and the timestamp can provide continuity in the time dimension for further analysis. Arranging the data in chronological order can ensure the accuracy and continuity of the analysis and avoid data disorder. Time series data can ensure that the temporal relationship of temperature data is accurately preserved, which is convenient for subsequent trend analysis, anomaly detection and temperature feature mining.

[0070] More specifically, multi-dimensional feature mining is performed on the original monitoring data sequence. This includes, but is not limited to, time-domain features (such as maximum, minimum, average, and standard deviation), frequency-domain features (such as FFT analysis), and behavioral features such as the periodicity and abrupt changes in temperature. By extracting these features, key characteristics of the temperature signal can be identified, and the main factors affecting the bus capacitor temperature can be found. Through multi-dimensional feature mining of the original data sequence, hidden patterns and trends can be extracted from the data, helping to analyze the reasons behind temperature changes. These features provide valuable information for subsequent analysis, judgment, and decision-making. Feature mining can extract key features of temperature data, helping the system identify the changing trends, abnormal situations, and potential risks of power supply temperature, thereby providing more accurate temperature monitoring and management support.

[0071] More specifically, based on a pre-built knowledge base of bus capacitor temperature characteristics, key temperature monitoring features that match the current monitoring data are retrieved. These features include abnormal patterns in historical data, critical values ​​of temperature fluctuations, and normal operating ranges. The knowledge base of bus capacitor temperature characteristics contains a large amount of historical temperature monitoring data and its related analysis results. By comparing with these historical features, it is possible to identify whether the current monitoring data is within the normal range or whether there are any abnormal phenomena. With the support of the knowledge base, more efficient and accurate temperature feature identification can be achieved. This will help the system judge the current temperature status of the power supply based on the lessons learned from historical data and enhance its ability to warn of future anomalies.

[0072] More specifically, the original monitoring data sequence is combined with key temperature monitoring features extracted from the feature knowledge base to form complete temperature feature information. This information not only includes the original temperature data but also the key features obtained from the knowledge base analysis, thus constituting a comprehensive analysis of the temperature status of the bus capacitors. By combining the original data with the extracted key features, the temperature status of the bus capacitors can be comprehensively described. The temperature feature information can provide strong support for subsequent system optimization, fault detection, and maintenance decisions. Through the combination of original data and key features, the generated temperature feature information can comprehensively reflect the current working status of the system and provide high-quality data support for subsequent decisions, helping to prevent possible faults in advance.

[0073] Understandably, real-time acquisition and digital conversion of temperature data ensures data accuracy and reliability, providing a solid foundation for subsequent analysis. The use of time-series data allows for accurate tracking of temperature change trends, facilitating the identification of abnormal fluctuations and periodic changes, and providing a basis for fault early warning. Multi-dimensional feature mining comprehensively captures the complexity of temperature signals, providing more in-depth analysis of system operation status. Integration with a temperature feature knowledge base enhances the understanding of temperature change patterns and the ability to predict abnormal situations, enabling the system to react more accurately based on historical experience. Complete temperature feature information provides strong data support for subsequent temperature control, fault diagnosis, and system optimization, improving system reliability and safety.

[0074] Preferably, the step of performing multi-dimensional feature mining on the original monitoring data sequence to retrieve corresponding key temperature monitoring features from a pre-built bus capacitor temperature feature knowledge base includes:

[0075] S221: Perform multi-dimensional analysis of the original monitoring data sequence, including statistical characteristics, time-domain characteristics, rate of change, and outliers, to obtain the data time-series characteristics of the bus capacitor;

[0076] S222: Perform Fourier transform, wavelet transform and dynamic time warping sequence depth mining on the original monitoring data sequence to obtain the temperature fluctuation pattern of the bus capacitor.

[0077] S223: Based on the pre-built knowledge base of bus capacitor temperature characteristics, the data time series characteristics are matched with the temperature fluctuation pattern, and the results of the information matching are vectorized to generate an information matching feature matrix.

[0078] S224: The information matching feature matrix is ​​reduced in dimensionality by principal component analysis, and an adjacency matrix is ​​constructed for the reduced information matching feature matrix.

[0079] S225: By performing cluster analysis on the adjacency matrix, the results of the cluster analysis are used as a basis to extract key matching information by performing vector clustering on the information matching feature matrix, and the corresponding temperature monitoring key features are retrieved from the bus capacitor temperature feature knowledge base based on the key matching information.

[0080] Specifically, the original monitoring data sequence is analyzed for statistical characteristics, time-domain characteristics, rate of change, and outliers to obtain the time-series characteristics of the bus capacitor. Statistical characteristics, such as maximum, minimum, mean, standard deviation, and variance, are used to describe the distribution of the data. Time-domain characteristics, such as trends and periodicity, are used to analyze the temporal variation patterns of the data. The rate of change of temperature data over time is analyzed to identify rapid or gradual changes in temperature fluctuations. Outlier analysis includes detecting abnormal changes in temperature data and identifying outliers (such as sudden changes or drastic fluctuations) in temperature fluctuations. Through these multi-dimensional feature analyses, the temperature fluctuation of the bus capacitor can be understood from multiple perspectives, helping the system identify temperature change trends, periodicity, and potential abnormal behaviors, providing comprehensive data support for subsequent analysis and processing. These features provide strong support for subsequent temperature pattern recognition, fault detection, and prediction. Through the analysis of statistical and time-domain characteristics, the changing patterns of temperature signals can be clearly grasped, thereby discovering early signals of temperature anomalies and providing accurate decision support.

[0081] More specifically, Fourier transform, wavelet transform, and dynamic time warping (DTW) are applied to the original monitoring data sequence to extract the temperature fluctuation pattern of the bus capacitor. Fourier transform converts the time series signal from the time domain to the frequency domain, identifying periodic features in temperature fluctuations. Wavelet transform can extract multi-scale information of the temperature signal, adapting to changes in the signal at different frequency bands. Dynamic time warping (DTW) is used to analyze the similarity between time series, and is particularly suitable for nonlinear temperature data with asynchronous time axes. Fourier transform, wavelet transform, and DTW can help identify temperature fluctuation patterns from different frequencies and time scales. These methods can identify periodic, abrupt, and nonlinear changes in the signal, providing more accurate features for subsequent temperature pattern recognition. These transformations can delve deeper into the underlying patterns in the temperature data, revealing more potential patterns. Especially for nonlinear and non-stationary temperature signals, using these methods can significantly improve the accuracy and robustness of identification.

[0082] More specifically, based on a pre-built knowledge base of bus capacitor temperature characteristics, information matching is performed on time-series features and temperature fluctuation patterns. The matching results are then vectorized to form an information matching feature matrix. Features extracted from the original data are matched with existing features in the knowledge base to find similar patterns and behaviors. Through mathematical methods (such as word embedding and feature encoding), the matched features are represented in vector form, facilitating subsequent processing and analysis. Information matching helps compare actual monitoring data with historical features, identifying potential similarities and anomalies. This step enables more accurate judgments based on empirical data in the knowledge base and improves the efficiency and accuracy of data processing through vectorization. Vectorization makes temperature feature information more operable and computable, facilitating subsequent operations such as dimensionality reduction and cluster analysis. In this way, the system can quickly find historical patterns similar to the current temperature state, thereby improving the accuracy and speed of judgment.

[0083] More specifically, principal component analysis (PCA) is used to reduce the dimensionality of the information matching feature matrix, enabling more efficient analysis. PCA can transform high-dimensional data into low-dimensional data, retaining the main information in the data and removing redundant parts. Dimensionality reduction can reduce computational complexity, improve analysis efficiency, and avoid overfitting. PCA dimensionality reduction can effectively simplify the data structure, improve the computational efficiency and processing speed of the model, and the dimensionality-reduced features can help the system maintain high-efficiency analysis performance when facing high-dimensional data and reduce noise interference.

[0084] More specifically, based on the dimensionality-reduced information matching feature matrix, an adjacency matrix is ​​constructed, and cluster analysis is performed. Cluster analysis can group similar features together to form several clusters. The adjacency matrix represents the relationship between different features and is usually used to describe the similarity between features. Algorithms such as K-means and hierarchical clustering are used to group the feature matrix and extract the main feature clusters. Through adjacency matrix and cluster analysis, similar temperature features can be grouped together, which helps to identify patterns, discover potential anomalies, and reduce computational complexity. Cluster analysis can help the system find the features most relevant to the temperature change of the bus capacitor and provide accurate predictions based on these features. Through cluster analysis, the system can group large-scale feature information into a few important clusters, simplifying the analysis process, reducing interference factors, and improving the system's processing efficiency and accuracy.

[0085] More specifically, based on the results of cluster analysis, key matching information is extracted, and relevant key temperature monitoring features are retrieved from the bus capacitor temperature feature knowledge base. By retrieving relevant features from the knowledge base, the system's understanding and analysis of bus capacitor temperature can be further improved. These features can help the system accurately assess the current temperature status and potential risks. Finally, the extracted key matching information can provide accurate temperature monitoring features, providing strong support for the system's anomaly detection, fault warning, and optimization decision-making.

[0086] Understandably, the ability to extract key information from temperature signals from different angles and dimensions lays the foundation for subsequent pattern recognition and anomaly detection. Through Fourier transform, wavelet transform, and dynamic time warping, deep-seated patterns in temperature fluctuations can be captured, revealing complex temperature change patterns. Information matching and PCA dimensionality reduction effectively reduce computational complexity and improve the efficiency and accuracy of data processing. Through adjacency matrix and cluster analysis, the features most relevant to the temperature changes of the bus capacitor can be accurately identified, providing efficient and reliable data support. The final generated matching feature matrix can provide decision support for temperature monitoring, fault diagnosis, and system optimization, improving the reliability and intelligence level of the system.

[0087] Preferably, the step of simulating the operating environment of the bus capacitor based on the operation record of the duty cycle modulation performed by the switching power supply to generate the operating environment characteristics and safety limitation requirements of the bus capacitor includes:

[0088] S31: Continuously record the operation behavior of the power switching transistor of the switching power supply to perform duty cycle modulation based on the sampled signal, so as to obtain the operation record of the duty cycle modulation performed by the switching power supply.

[0089] S32: Draw the duty cycle modulation curve according to the operation record, and perform preliminary feature extraction on the duty cycle modulation curve for adjustment time, change amplitude and fluctuation frequency. Then, based on the preliminary feature extraction, perform in-depth analysis of the correlation of each preliminary feature to generate the electrical parameter display spectrum of the switching power supply.

[0090] S33: Substitute the electrical parameter display map into a pre-trained lightweight CNN model, and let the lightweight CNN model simulate the working environment of the electrical parameter display map to generate the working environment characteristics of the switching power supply;

[0091] S34: Retrieve the specification and performance information of the bus capacitor, and perform risk prediction on the specification and performance information based on the characteristics of the working environment. Using the result of the risk prediction as a constraint, analyze the safe working range of the bus capacitor to obtain a safety limit requirement composed of several working intervals and the corresponding safety weights of each working interval.

[0092] Specifically, based on the sampled signal, the duty cycle modulation operation behavior of the power switching transistors in the switching power supply is continuously recorded to obtain a duty cycle modulation operation record. The sampled signal continuously monitors the duty cycle modulation process of the switching power supply, recording the duty cycle change in each cycle, and collecting complete historical operation data to ensure that the recorded operation behavior covers the modulation modes under different operating conditions. Accurate operation records provide a data foundation for subsequent analysis. These records contain the duty cycle modulation behavior of the power supply, reflecting the power output mode of the switching power supply under different operating conditions. This data can be further used to simulate the operating environment of the bus capacitor. By accurately and continuously recording the operation behavior, high-quality data can be provided for analyzing the modulation characteristics of the switching power supply. This provides a reliable basis for subsequent feature extraction and environmental simulation, ensuring the accuracy of the simulation results.

[0093] More specifically, based on the operation records, a duty cycle modulation curve is plotted, and preliminary features such as adjustment time, amplitude of change, and fluctuation frequency are extracted. Adjustment time analysis examines the time characteristics within the duty cycle adjustment period to identify the response time of the adjustment behavior. Amplitude of change extracts the maximum and minimum values ​​of the duty cycle fluctuation during modulation to understand the fluctuation range. Fluctuation frequency analysis examines the frequency characteristics of the duty cycle change to identify the characteristics of high-frequency and low-frequency fluctuations. Preliminary feature extraction helps to filter out key information from a large number of operation records. In particular, under the operating conditions of the switching power supply, the time, amplitude, and frequency changes of the duty cycle modulation behavior can reveal the stability and reliability characteristics of the power supply. These features lay the foundation for subsequent in-depth pattern analysis. By extracting these basic features, a comprehensive understanding of the behavior patterns of the switching power supply under different operating conditions can be achieved. These features will help to more accurately simulate the working environment and provide data support for risk assessment and safe operating range analysis.

[0094] More specifically, based on the preliminary feature extraction results, in-depth correlation analysis of various features is conducted to generate an electrical parameter display map of the switching power supply. Through multi-dimensional correlation analysis, the intrinsic relationships between different features are explored, identifying how key factors jointly affect the changes in electrical parameters. The analysis results are visualized as a map, showing the changing trends of the electrical characteristics (such as voltage, current, and power) of the switching power supply under different operating states with duty cycle modulation. In-depth pattern analysis helps to discover the relationship between duty cycle modulation and the electrical characteristics of the switching power supply. Through the correlation analysis of features, the underlying patterns of system behavior can be revealed, providing more accurate input for the simulation of the working environment and risk prediction. Through in-depth pattern analysis and the generation of electrical parameter maps, a more comprehensive understanding of the working behavior of the switching power supply can be achieved, helping to identify potential operational risks. This map provides clear and effective data support for subsequent CNN model training and environmental simulation.

[0095] More specifically, the generated electrical parameter map is fed into a pre-trained lightweight convolutional neural network (CNN) model to simulate the operating environment of the switching power supply. The CNN model is trained using historical data to identify the operating environment characteristics under different electrical parameters. The trained CNN model is then used to infer the electrical parameter map, simulating the operating environment characteristics of the switching power supply under different conditions. The CNN model has powerful pattern recognition capabilities and can handle complex nonlinear relationships. By simulating the operating environment, the operating state of the switching power supply under various modulation behaviors can be effectively predicted, and potential environmental change trends can be discovered. The lightweight CNN model can quickly and efficiently simulate the operating environment, avoiding the computational bottlenecks that may occur in traditional methods. Through the simulation results, the performance of the switching power supply and its impact on the bus capacitor can be accurately evaluated, and potential faults or hazards can be identified in advance.

[0096] More specifically, the system retrieves the specifications and performance information of the bus capacitors and performs risk prediction based on the simulated operating environment characteristics. Combining the simulated environment characteristics and the specifications and performance of the bus capacitors, it predicts potential risks such as over-temperature and overload. Based on the risk prediction results, it analyzes the safe operating range of the bus capacitors and derives safety limit requirements that include multiple operating ranges and corresponding safety weights. Risk prediction helps identify potential safety hazards of the bus capacitors under different operating environments. Safe operating range analysis provides reliable operating range and risk management strategies for the practical application of bus capacitors, ensuring their safe operation. Through risk prediction and safe operating range analysis, it can provide detailed safety guidance for the coordinated operation of switching power supplies and bus capacitors. The system can adjust the operating status of the bus capacitors in a timely manner based on real-time simulation data to avoid faults or damage.

[0097] Understandably, continuously recording the duty cycle modulation behavior of the switching power supply ensures the accuracy and comprehensiveness of the foundational data for subsequent analysis. By extracting and deeply analyzing the duty cycle modulation characteristics, the complex relationship between the electrical characteristics of the switching power supply and its duty cycle can be identified, providing a reliable basis for simulation and prediction. The application of a lightweight CNN model allows for rapid simulation of the switching power supply's operating environment and prediction of the bus capacitor's safety under different conditions. Through risk prediction and safe operating range analysis, clear safety limitations can be provided for the combined application of the switching power supply and bus capacitor, effectively reducing the risk of system failure and equipment damage.

[0098] Preferably, the steps of predicting the thermal stress and aging rate of the bus capacitor based on the temperature characteristic information and the operating environment characteristics, performing load voltage adjustment optimization analysis on the bus capacitor based on the prediction results and the safety limit requirements, and simultaneously evaluating and monitoring the overall efficiency and stability of the switching power supply based on the operating environment characteristics to obtain optimized adjustment measures include:

[0099] S41: The original monitoring data sequence in the temperature feature information is used as the benchmark simulation object, and the key temperature monitoring features in the temperature feature information and the working environment characteristics are used as prediction tendency features. Based on the prediction tendency features, the benchmark simulation object is subjected to data expectation simulation and key feature extraction to generate the expected temperature features for future time periods.

[0100] S42: Based on the predicted temperature characteristics, predict the thermal stress and aging rate of the bus capacitor to obtain the thermal stress safety pressure and aging rate safety pressure of the bus capacitor under the current condition.

[0101] S43: Simulate the configuration of the load voltage regulation measures for the bus capacitor, and make corresponding corrections to the expected temperature characteristics based on the simulated configuration of the regulation measures, so as to re-evaluate the thermal stress safety pressure and aging rate safety pressure.

[0102] S44: Based on the evaluation results, a first round of value analysis is performed on the adjustment measures, and a second round of value analysis is performed on the adjustment measures based on the security restriction requirements. The results of the two rounds of value analysis are weighted and fused to generate the benchmark value characteristics of the adjustment measures.

[0103] S45: Based on the operating environment characteristics, the influence of the load voltage change of the bus capacitor on the other components of the switching power supply is predicted, and the influence effect analysis of the adjustment measures is performed accordingly based on the prediction results, so as to obtain the correlation influence characteristics of the other components of the switching power supply corresponding to the adjustment measures.

[0104] S46: Based on the aforementioned correlation influence characteristics, evaluate the overall efficiency and stability of the switching power supply for the adjustment measures to obtain the influence value characteristics of the adjustment measures;

[0105] S47: Using the baseline value characteristics and impact value characteristics of the regulation measures as monitoring conditions, the regulation measures are iteratively optimized to obtain optimized regulation measures.

[0106] Specifically, the original monitoring data sequence in the temperature characteristic information is used as the benchmark simulation object, and the key characteristics of temperature monitoring and working environment characteristics are used as predictive trend characteristics. Based on these predictive trend characteristics, the benchmark simulation object is simulated for expected data and key features are extracted to generate expected temperature characteristics for future time periods. Key features in the temperature monitoring data (such as temperature fluctuations and duration) are extracted and used for correlation analysis with working environment characteristics (such as ambient temperature and load changes) to construct an expected model of future temperature trends. The extraction of predictive trend characteristics is a prerequisite for the prediction of thermal stress and aging rate. By simulating temperature characteristics and working environment characteristics, accurate time series data support can be provided for subsequent thermal stress and aging rate analysis. By extracting and simulating expected temperature characteristics, a foundation can be provided for the prediction of thermal stress and aging rate of the bus capacitor, ensuring the accuracy and reliability of subsequent analysis.

[0107] More specifically, based on predicted temperature characteristics, the thermal stress and aging rate of the bus capacitor are predicted to obtain the safe thermal stress pressure and safe aging rate pressure of the bus capacitor under the current conditions. Combining the temperature change trend and the material characteristics of the bus capacitor, a thermal stress and aging rate model is used to predict and determine the thermal stress and aging risks that the bus capacitor may face under different load and temperature conditions. Thermal stress and aging rate are key factors affecting the lifespan and safety of the bus capacitor. By predicting these factors, potential performance degradation problems can be identified in advance during the design and operation stages, ensuring the reliability of the bus capacitor. Through thermal stress and aging rate prediction, safety warnings can be provided for the use of bus capacitors, reducing the risk of equipment failure and extending their service life.

[0108] More specifically, the simulation configuration of load voltage regulation measures for the bus capacitor is carried out, and the expected temperature characteristics are corrected accordingly based on the simulated regulation measures to reassess the thermal stress safety pressure and aging rate safety pressure. By adjusting the load voltage, the thermal response of the capacitor at different voltage levels is simulated, and the temperature prediction model is corrected to adjust the prediction results of thermal stress and aging rate. Adjusting the load voltage is a means to directly affect the temperature change of the bus capacitor. By simulating the temperature change after adjusting the load voltage, the assessment results of thermal stress and aging rate can be adjusted in real time to ensure that the temperature is controlled within a safe range. Through simulation and correction, the impact of different regulation measures on the bus capacitor can be accurately evaluated, providing data support for subsequent regulation optimization.

[0109] More specifically, a first-round value analysis is conducted on the adjustment measures based on the evaluation results, and a second-round value analysis is conducted based on the safety constraint requirements. The results of the two rounds of value analysis are weighted and integrated to generate the baseline value characteristics of the adjustment measures. The first-round value analysis is based on the predicted results of thermal stress and aging rate to evaluate the effect of the adjustment measures on improving the safety of the bus capacitors. The second-round analysis, combined with the safety constraint requirements, further analyzes the comprehensive effect of the adjustment measures. Value analysis helps to quantify the effect of the adjustment measures, ensuring that power supply efficiency and stability are improved while ensuring safety. The two rounds of analysis help to comprehensively evaluate the effectiveness of the adjustment measures from different perspectives. Through value analysis, the impact of each adjustment measure can be clarified, ensuring that the optimization scheme can improve the performance and service life of the bus capacitors under safety constraints.

[0110] More specifically, based on the characteristics of the operating environment, the impact of changes in the load voltage of the bus capacitor relative to other components of the switching power supply is inferred, and the impact effect analysis of the adjustment measures is performed. The correlation impact characteristics of the adjustment measures for other components of the switching power supply are generated. By inferring the impact of bus capacitor adjustment on other components (such as power management module, output current, etc.), correlation analysis is performed to generate the response characteristics of each component to the adjustment measures. Adjusting the load voltage of the bus capacitor not only affects its own performance, but may also affect the operating state of other components of the switching power supply. By analyzing the correlation impact of each component, it can be ensured that the adjustment measures will not lead to the performance degradation of other components. Impact effect analysis helps to identify the global impact of the adjustment measures, avoid adverse chain reactions caused by the adjustment measures, and ensure the stability of the entire switching power supply system.

[0111] More specifically, based on the characteristics of correlated effects, the overall efficiency and stability of the switching power supply are evaluated for the adjustment measures to obtain the impact value characteristics of the adjustment measures. Combining the characteristics of the correlated effects of each component, the comprehensive impact of the adjustment measures on the efficiency and stability of the switching power supply is analyzed to ensure that the adjustment measures can improve the overall performance of the power supply system. Evaluating the impact of the adjustment measures on the overall efficiency and stability is the key to ensuring the practical feasibility of the optimization scheme. Through this evaluation, it can be determined whether the adjustment measures can improve overall performance while improving safety. Through a comprehensive efficiency and stability evaluation, the feasibility of the adjustment measures on a global scale can be ensured, further optimizing the performance of the power supply system and avoiding system instability problems caused by local optimization.

[0112] More specifically, the baseline value characteristics and impact value characteristics of the regulation measures are used as monitoring conditions to iteratively optimize the regulation measures to obtain optimized regulation measures. Based on the aforementioned evaluation results, optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) are used to iteratively optimize the regulation measures to minimize risks and maximize performance. Iterative optimization can further improve the efficiency of the power supply system while ensuring safety and stability. Through multiple iterations, the optimal regulation measures are gradually found to ensure the best performance of the system in practical applications. Iterative optimization can automatically adjust the regulation measures for different operating conditions to ensure that the bus capacitor is always in the best working state under different environments, thereby improving the overall performance of the switching power supply.

[0113] Understandably, by simulating the expected temperature characteristics and extracting key features, the thermal stress and aging rate of the bus capacitor can be accurately predicted, providing support for subsequent safety analysis. Through multiple rounds of value analysis and impact effect analysis, it can be ensured that the adjustment measures can not only improve the safety of the bus capacitor but also optimize the efficiency and stability of the power supply system. Through impact analysis of the adjustment measures, adverse chain reactions can be identified and avoided, ensuring the efficient and stable operation of the entire switching power supply system. Through iterative optimization, the adjustment measures can be automatically adjusted according to real-time feedback, ensuring that the system always maintains optimal performance under different environments, achieving the optimal balance between safety and efficiency.

[0114] Preferably, the step of adjusting the load voltage of the bus capacitor by driving the PFC boost circuit of the switching power supply according to the optimized adjustment measures includes:

[0115] The operating parameters of the PFC boost circuit of the switching power supply are deployed according to the optimization adjustment measures, so as to drive the PFC boost circuit to adjust the load voltage of the electrically connected bus capacitor, so that the load voltage of the bus capacitor is in the state corresponding to the optimization adjustment measures.

[0116] Specifically, based on optimized regulation measures, the operating parameters of the PFC boost circuit in the switching power supply are set and adjusted. These parameters include the input voltage range, output voltage target, current limit, and operating frequency. By configuring the operating parameters of the PFC boost circuit, it is ensured that the circuit can regulate the voltage under optimal operating conditions to adapt to the load voltage requirements of the bus capacitor. The operating parameters of the PFC boost circuit directly determine the voltage output characteristics of the power supply. Precise configuration of these parameters ensures that the regulation process is consistent with the optimized regulation measures, achieving the best load voltage control effect. Reasonable deployment of operating parameters ensures that the PFC boost circuit performs optimally when regulating the load voltage, avoiding unnecessary losses and power supply performance degradation.

[0117] More specifically, based on the set operating parameters, the PFC boost circuit regulates the load voltage of the connected bus capacitor. By precisely controlling the input current and output voltage through the PFC boost circuit, the load voltage of the bus capacitor is maintained at the target state specified by the optimized regulation measures. Through adjusting the voltage gain and feedback control mechanism of the boost circuit, precise regulation of the bus capacitor load voltage is achieved, ensuring that its voltage level meets the expected optimization requirements. When controlling the load voltage, the PFC boost circuit directly affects the thermal stress and aging rate of the bus capacitor. Precise control of the load voltage effectively avoids the risk of the capacitor operating at excessively high or low voltages, ensuring the stability of the power supply system. Accurate adjustment of the bus capacitor load voltage optimizes the capacitor's operating state, extends its service life, and maintains the high efficiency and stability of the switching power supply.

[0118] More specifically, based on real-time monitoring data and feedback mechanisms, the operating status of the PFC boost circuit is dynamically adjusted to ensure that the bus capacitor load voltage is always kept in the optimal state. If the voltage deviates from the optimized range, the circuit parameters are adjusted in a timely manner. Using real-time voltage and temperature monitoring data, the load voltage of the bus capacitor is automatically detected, and the PFC boost circuit is adjusted through a closed-loop control system to ensure that the voltage returns to the predetermined range. Real-time feedback and adjustment are the key to ensuring voltage control accuracy, enabling timely response to any sudden changes, such as load fluctuations or changes in ambient temperature, maintaining the stability and efficiency of the system. Through dynamic feedback and adjustment, the bus capacitor is always kept in the optimal load voltage state, preventing equipment failure or performance loss caused by voltage fluctuations.

[0119] Understandably, by precisely configuring and adjusting the operating parameters of the PFC boost circuit in real time, it is possible to ensure that the load voltage of the bus capacitor is always in an optimized state, reducing thermal stress and aging rate. Precise voltage regulation can improve the overall stability and efficiency of the switching power supply system, avoid unnecessary power failures or performance losses, and prevent capacitors from being damaged by excessively high or low voltages, thereby extending their service life and reducing maintenance costs. Combined with real-time monitoring data and feedback mechanisms, voltage regulation can be automatically optimized according to environmental changes, further enhancing the system's intelligence and adaptability.

[0120] Secondly, the present invention provides a control power supply for a switching power supply, which employs a control method for a switching power supply as described in any one of the first aspects.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A control method for a switching power supply, characterized in that, include: The feedback signal of the switching power supply is sampled in real time, and the sampling signal is analyzed and adjusted based on preset standards in order to modulate the duty cycle of the power switching transistor of the switching power supply. Continuous acquisition and feature analysis of temperature data of bus capacitors in switching power supply are performed to obtain temperature feature information composed of original monitoring data sequence and key temperature monitoring features; Based on the operation record of the duty cycle modulation of the switching power supply, the working environment of the bus capacitor is simulated to generate the working environment characteristics and safety limitation requirements of the bus capacitor. Based on the temperature characteristics and the working environment characteristics, the thermal stress and aging rate of the bus capacitor are predicted. Combining the prediction results with the safety limit requirements, the load voltage of the bus capacitor is adjusted and optimized. At the same time, based on the working environment characteristics, the overall efficiency and stability of the switching power supply are evaluated and monitored to obtain optimized adjustment measures. The optimized adjustment measures drive the PFC boost circuit of the switching power supply to adjust the load voltage of the bus capacitor.

2. The control method for a switching power supply as described in claim 1, characterized in that, The steps of real-time sampling of the feedback signal from the switching power supply and analysis of adjustment measures based on preset standards to modulate the duty cycle of the power switching transistors of the switching power supply include: The voltage or current signal is selected as the feedback signal. The feedback signal of the switching power supply is collected in real time through the pre-installed sensing module, and a timestamp is generated for each collected feedback signal to form a feedback monitoring sequence. The feedback monitoring sequence is subjected to periodic and trend pattern mining to generate the expected pattern of the feedback signal of the switching power supply, and the duty cycle modulation algorithm of the preset standard is adaptively deployed according to the expected pattern of the feedback signal. The feedback signal at the current moment is analyzed for differences based on the standard reference signal of the preset standard to obtain signal difference characteristics. At the same time, the duty cycle modulation algorithm after adaptive parameter deployment is used to calculate the modulation measures for the signal difference characteristics to obtain the duty cycle modulation measures. The power switching transistor of the switching power supply is then modulated with the duty cycle based on the duty cycle modulation measures.

3. The control method for a switching power supply as described in claim 1, characterized in that, The steps for continuously acquiring and analyzing temperature data of the bus capacitors in a switching power supply to obtain temperature feature information composed of the original monitoring data sequence and key temperature monitoring features include: The temperature of the bus capacitor is monitored in real time by a temperature sensor pre-set in the switching power supply. The temperature signals monitored at each time are converted from analog to digital form. At the same time, a corresponding timestamp is assigned to the digital temperature signals so that the temperature signals at each time are arranged into the original monitoring data sequence according to the time sequence relationship. Multi-dimensional feature mining is performed on the original monitoring data sequence to retrieve the corresponding key temperature monitoring features from the pre-built bus capacitor temperature feature knowledge base. The original monitoring data sequence is combined with the key temperature monitoring features to form temperature feature information.

4. The control method for a switching power supply as described in claim 3, characterized in that, The steps of performing multi-dimensional feature mining on the original monitoring data sequence to retrieve the corresponding key temperature monitoring features from the pre-built bus capacitor temperature feature knowledge base include: The original monitoring data sequence is subjected to multi-dimensional analysis of statistical characteristics, time-domain characteristics, rate of change, and outliers to obtain the data time-series characteristics of the bus capacitor. The original monitoring data sequence is subjected to Fourier transform, wavelet transform, and dynamic time warping sequence depth mining to obtain the temperature fluctuation pattern of the bus capacitor. The data time series features are matched with the temperature fluctuation pattern based on a pre-built knowledge base of bus capacitor temperature features, and the results of the information matching are vectorized to generate an information matching feature matrix. Principal component analysis is used to reduce the dimensionality of the information matching feature matrix, and an adjacency matrix is ​​constructed for the dimensionality-reduced information matching feature matrix. By performing cluster analysis on the adjacency matrix, the results of the cluster analysis are used as a basis to extract key matching information by vector clustering of the information matching feature matrix. Based on the key matching information, the corresponding key temperature monitoring features are retrieved from the bus capacitor temperature feature knowledge base.

5. The control method for a switching power supply as described in claim 1, characterized in that, The steps for simulating the operating environment of the bus capacitor based on the duty cycle modulation operation record of the switching power supply to generate the operating environment characteristics and safety limitation requirements of the bus capacitor include: The operation behavior of the power switching transistor of the switching power supply to modulate the duty cycle based on the sampled signal is continuously recorded to obtain the operation record of the duty cycle modulation performed by the switching power supply. The duty cycle modulation curve is plotted based on the operation record, and preliminary features such as adjustment time, change amplitude, and fluctuation frequency are extracted from the duty cycle modulation curve. Then, based on the preliminary feature extraction, in-depth analysis of the correlation between the preliminary features is performed to generate an electrical parameter display spectrum of the switching power supply. The electrical parameter display map is fed into a pre-trained lightweight CNN model, which simulates the working environment of the electrical parameter display map to generate the working environment characteristics of the switching power supply. The specifications and performance information of the bus capacitor are retrieved, and risk prediction is performed on the specifications and performance information based on the characteristics of the working environment. The results of the risk prediction are used as constraints to analyze the safe working range of the bus capacitor, resulting in safety limit requirements composed of several working ranges and corresponding safety weights for each working range.

6. The control method for a switching power supply as described in claim 1, characterized in that, Based on the temperature characteristics and the operating environment characteristics, the thermal stress and aging rate of the bus capacitor are predicted. Combining the prediction results with the safety limitations, the load voltage of the bus capacitor is adjusted and optimized. Simultaneously, based on the operating environment characteristics, the overall efficiency and stability of the switching power supply are evaluated and monitored to obtain optimized adjustment measures. The steps include: The original monitoring data sequence in the temperature feature information is used as the benchmark simulation object, and the key temperature monitoring features and working environment characteristics in the temperature feature information are used as prediction tendency features. Based on the prediction tendency features, the benchmark simulation object is subjected to data expectation simulation and key feature extraction to generate expected temperature features for future time periods. Based on the expected temperature characteristics, the thermal stress and aging rate of the bus capacitor are predicted to obtain the thermal stress safety pressure and aging rate safety pressure of the bus capacitor under the current condition. A simulation configuration of adjustment measures for the load voltage of the bus capacitor is performed, and the expected temperature characteristics are corrected accordingly based on the adjustment measures of the simulation configuration, so as to re-evaluate the thermal stress safety pressure and aging rate safety pressure. The first round of value analysis is conducted on the adjustment measures based on the evaluation results, and the second round of value analysis is conducted on the adjustment measures based on the security restriction requirements. The results of the two rounds of value analysis are weighted and fused to generate the benchmark value characteristics of the adjustment measures. Based on the operating environment characteristics, the impact of the load voltage change of the bus capacitor on the other components of the switching power supply is inferred. Based on the inference results, the impact effect analysis of the adjustment measures is carried out accordingly to obtain the correlation impact characteristics of the other components of the switching power supply on the adjustment measures. Based on the aforementioned correlation impact characteristics, the overall efficiency and stability of the switching power supply are evaluated using the adjustment measures to obtain the impact value characteristics of the adjustment measures; Using the baseline value characteristics and impact value characteristics of the aforementioned regulatory measures as monitoring conditions, the regulatory measures are iteratively optimized to obtain optimized regulatory measures.

7. The control method for a switching power supply as described in claim 1, characterized in that, The steps for adjusting the load voltage of the bus capacitor by driving the PFC boost circuit of the switching power supply according to the optimized adjustment measures include: The operating parameters of the PFC boost circuit of the switching power supply are deployed according to the optimization adjustment measures, so as to drive the PFC boost circuit to adjust the load voltage of the electrically connected bus capacitor, so that the load voltage of the bus capacitor is in the state corresponding to the optimization adjustment measures.

8. A switching power supply, characterized in that, The control method for a switching power supply as described in any one of claims 1-7 is adopted.

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