Adaptive calibration method and device for electronic MOS tube array

Abstract

The present invention relates to the field of MOS tube array calibration, and in particular to an adaptive calibration method and device for an electronic MOS tube array. The method comprises the following steps: obtaining a real-time thermal imaging image of an electronic MOS tube array; performing discrete fitting of temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; collecting real-time electrical characteristic data of the electronic MOS tube array; performing local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; performing normalized load response mining based on the real-time electrical characteristic data, thereby constructing a normalized MOS tube array load response curve; and identifying the switching frequency of the real-time MOS tube array. The present invention improves the calibration accuracy and performance of the electronic MOS tube array.

Description

Technical Field

[0001] The present invention relates to the field of MOS tube array calibration, and in particular to an adaptive calibration method and device for an electronic MOS tube array. Background Art

[0002] As a core component of modern electronic devices, electronic MOS tube arrays are widely used in power amplification, switching power supplies, signal processing and other fields, playing an important role in electronic switching and amplification. With the continuous development of integrated circuit technology and the increasing complexity of electronic equipment functions, the application scope of electronic MOS tube arrays is also expanding, especially in high-frequency, high-speed, and high-precision electronic systems, playing a vital role. However, during the long-term operation of electronic MOS tube arrays, their performance is often affected by factors such as environmental changes, current fluctuations, and temperature increases, resulting in deviations in their working state and performance, thereby affecting the stability and reliability of the entire system.

[0003] In the traditional MOS tube array calibration method, each electronic MOS tube is usually calibrated independently by manual operation or external measuring instruments. Although this method can ensure the calibration accuracy of a single tube, it often has disadvantages such as low work efficiency, high labor costs, and inability to adapt to system changes in real time due to the complexity of the calibration process and susceptibility to human factors. In addition, the traditional method is particularly difficult for the calibration of large-scale MOS tube arrays, especially when the number of MOS tubes in the array is large and the working environment is complex. The traditional manual calibration method cannot respond to changes in the actual working state in a timely manner, resulting in a lag in the calibration results, affecting the accuracy and performance of the system.

[0004] In order to meet the above challenges, there is an urgent need for an adaptive electronic MOS tube array calibration method that can calibrate the MOS tube array in real time and accurately. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes an adaptive calibration method and device for an electronic MOS tube array to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides an adaptive calibration method for an electronic MOS tube array, comprising the following steps: Step S1: acquiring a real-time thermal imaging image of the electronic MOS tube array; performing discrete fitting of temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; Step S2: collecting real-time electrical characteristic data of the electronic MOS tube array; performing local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; Step S3: performing normalized load response mining based on the real-time electrical characteristic data, thereby constructing a normalized MOS tube array load response curve; Step S4: identifying the switching frequency of the real-time MOS tube array; performing a quantitative impact analysis of periodic changes on the switching frequency based on a normalized MOS tube array load response curve, and generating a load response-switching frequency change law; Step S5: performing mutation frequency identification on the switching frequency, and performing adaptive frequency compensation calculation, thereby adaptively adjusting frequency compensation parameters; Step S6: Perform reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation law, and construct an adaptive collaborative calibration control model based on the local current adaptive distribution strategy.

[0007] The present invention can monitor the temperature distribution of the electronic MOS tube array in real time and accurately through real-time thermal imaging images, avoiding performance degradation or device damage caused by excessive temperature. Not only can the static temperature distribution be obtained, but also the temperature change trend over time can be captured, providing dynamic temperature data support for further optimizing the current distribution strategy. The temperature distribution discrete fitting method helps to refine the temperature distribution information, making the analysis more accurate, and can identify local hot spots, providing a basis for subsequent current scheduling. Through real-time electrical characteristic data, the working state of the electronic MOS tube array can be obtained, and the change trend of its current, voltage and other parameters can be mastered. Combined with the temperature distribution change diagram and the electrical characteristic data, dynamic adjustment of local current distribution can be performed, which can avoid local overload and overheating, and effectively improve the stability and safety of the array. Through real-time data feedback, the working current of each MOS tube can be intelligently allocated to achieve optimal current distribution, ensure that each component is within its working range, and prevent energy efficiency loss and unnecessary energy consumption. By mining the load response law in the electrical characteristic data, a normalized load response curve can be generated, which provides an accurate benchmark for subsequent load analysis. By analyzing the normalized load response curve, the stability of the electronic MOS tube array can be evaluated to ensure that it can maintain stable operation under different load conditions. The normalized curve provides a basis for current distribution under different working conditions, and can be adaptively adjusted according to the actual load conditions, thereby improving the system's ability to respond to load changes. By real-time monitoring of the switching frequency, the working state of the MOS tube array can be timely understood to avoid system instability or energy efficiency loss caused by unreasonable switching frequency. By combining the load response curve, quantitative impact analysis of the switching frequency can reveal the law between the load and the switching frequency, helping to further optimize the adjustment of the switching frequency. By periodic analysis of the switching frequency changes, potential frequency fluctuation problems can be identified, and future frequency changes can be predicted to a certain extent, further improving the stability of the system. It can detect sudden changes in the switching frequency in a timely manner to avoid system instability or overheating caused by frequency fluctuations. Through frequency mutation identification, the system can automatically adjust the switching frequency parameters to achieve frequency compensation to ensure that the system remains stable under frequency fluctuations. Effective frequency compensation can ensure that the electronic MOS tube array can maintain the best working environment under different frequency conditions, avoiding over-frequency or insufficient frequency affecting the long-term operation of the system. By analyzing the changing rules of switching frequency and load response, reverse load compensation can be performed to ensure that frequency fluctuations do not affect the uniform distribution of loads, thereby maintaining the stability of the system. An adaptive collaborative calibration control model is established to achieve dynamic collaborative control of temperature, current, and frequency, so that the system can automatically adjust under different working environments and load conditions. The model can comprehensively consider factors such as temperature, current, and switching frequency, perform comprehensive optimization, avoid the impact of a single factor on system performance, and maximize the performance and efficiency of the electronic MOS tube array.

[0008] In this specification, an adaptive calibration device for an electronic MOS tube array is provided, which is used to perform the adaptive calibration method for an electronic MOS tube array as described above, comprising: The temperature distribution module is used to obtain a real-time thermal imaging image of the electronic MOS tube array; perform discrete fitting of the temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; The local current distribution module is used to collect real-time electrical characteristic data of the electronic MOS tube array; perform local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; A load response module, used to perform normalized load response mining based on the real-time electrical characteristic data, so as to construct a normalized MOS tube array load response curve; A quantitative analysis module is used to identify the switching frequency of the real-time MOS tube array; based on the normalized MOS tube array load response curve, the switching frequency is quantitatively analyzed for periodic changes, and a load response-switching frequency change law is generated; An adaptive frequency compensation module, used to identify the sudden change frequency of the switching frequency, perform adaptive frequency compensation calculation, and thus adaptive frequency compensation parameters; The collaborative calibration control module is used to perform reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation law, and to construct an adaptive collaborative calibration control model based on the local current adaptive distribution strategy.

[0009] The present invention monitors the temperature of the electronic MOS tube array through real-time thermal imaging images, and can clearly and timely understand the temperature change of each component, thereby preventing component damage or performance degradation caused by excessive temperature. Through discrete fitting of temperature distribution and dynamic change diagram, the temperature change trend can be seen in real time, providing accurate data support for subsequent current distribution and frequency adjustment. It can accurately identify local hot spots, help to make directional current adjustments, and avoid the local areas with excessively high temperatures from affecting the stability of the entire system. By dynamically adjusting the current distribution according to real-time electrical characteristics and temperature changes, it can avoid overloading or overheating of a single MOS tube and improve the balance of the system. It can adaptively distribute current according to the working status of different regions (such as temperature, current characteristics, etc.), ensure that each MOS tube works in its optimal range, and improve the overall current utilization efficiency. Through adaptive current distribution, the problem of heat accumulation caused by excessive local current is avoided, the influence of temperature on components is reduced, and the reliability of the system is improved. By mining the load response characteristics, the response curve of the MOS tube array under different load conditions can be deeply analyzed, and the stability of the array can be evaluated. The normalized load response curve provides the standard behavior of the system under different loads, helps to identify unbalanced loads, and optimizes the current distribution strategy. By normalizing the load response, the impact of load changes on the system can be predicted more accurately, and adjustments can be made in advance to avoid system shocks caused by sudden load changes. By real-time monitoring of the switching frequency, frequency fluctuations can be discovered in time to avoid efficiency loss or system instability caused by unreasonable frequency. The relationship between the switching frequency and the load response is quantitatively analyzed to reveal the periodic variation law of the two, providing theoretical support for further optimization of the switching frequency adjustment. Through the analysis of the load response-switching frequency variation law, the switching frequency can be adjusted to adapt to different load conditions, improve system efficiency, and reduce energy consumption. It can identify sudden changes in the switching frequency to avoid system instability or low energy efficiency due to excessive frequency fluctuations. When the frequency suddenly changes, the system can automatically compensate for the frequency to ensure that the frequency change does not have a negative impact on system performance. Adaptive frequency compensation enables the system to flexibly respond to frequency changes, avoid the risk of overload or instability in the system through compensation, and improve overall stability. Global optimization is performed by integrating factors such as temperature, current, voltage, and switching frequency to ensure the coordinated work of each module, thereby maximizing the performance of the system. Through the analysis of the load response law, reverse load compensation can be performed, so that the system can still maintain optimal current distribution and load balance when the frequency changes. The adaptive collaborative calibration control model can achieve intelligent coordination and optimization among different factors, dynamically adjust various parameters in a complex working environment, and ensure stable and efficient operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic flow chart of the steps of an adaptive calibration method for an electronic MOS tube array according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0012] The present application example provides an adaptive calibration method and device for an electronic MOS tube array. The execution subject of the adaptive calibration method and device for an electronic MOS tube array includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as a general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio image management system, an information management system, and a cloud data management system.

[0013] See also Figures 1 to 4 The present invention provides an adaptive calibration method for an electronic MOS tube array, and the adaptive calibration method for an electronic MOS tube array comprises the following steps: Step S1: acquiring a real-time thermal imaging image of the electronic MOS tube array; performing discrete fitting of temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; Step S2: collecting real-time electrical characteristic data of the electronic MOS tube array; performing local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; Step S3: performing normalized load response mining based on the real-time electrical characteristic data, thereby constructing a normalized MOS tube array load response curve; Step S4: identifying the switching frequency of the real-time MOS tube array; performing a quantitative impact analysis of periodic changes on the switching frequency based on a normalized MOS tube array load response curve, and generating a load response-switching frequency change law; Step S5: performing mutation frequency identification on the switching frequency, and performing adaptive frequency compensation calculation, thereby adaptively adjusting frequency compensation parameters; Step S6: Perform reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation law, and construct an adaptive collaborative calibration control model based on the local current adaptive distribution strategy.

[0014] The present invention can monitor the temperature distribution of the electronic MOS tube array in real time and accurately through real-time thermal imaging images, avoiding performance degradation or device damage caused by excessive temperature. Not only can the static temperature distribution be obtained, but also the temperature change trend over time can be captured, providing dynamic temperature data support for further optimizing the current distribution strategy. The temperature distribution discrete fitting method helps to refine the temperature distribution information, making the analysis more accurate, and can identify local hot spots, providing a basis for subsequent current scheduling. Through real-time electrical characteristic data, the working state of the electronic MOS tube array can be obtained, and the change trend of its current, voltage and other parameters can be mastered. Combined with the temperature distribution change diagram and the electrical characteristic data, dynamic adjustment of local current distribution can be performed, which can avoid local overload and overheating, and effectively improve the stability and safety of the array. Through real-time data feedback, the working current of each MOS tube can be intelligently allocated to achieve optimal current distribution, ensure that each component is within its working range, and prevent energy efficiency loss and unnecessary energy consumption. By mining the load response law in the electrical characteristic data, a normalized load response curve can be generated, which provides an accurate benchmark for subsequent load analysis. By analyzing the normalized load response curve, the stability of the electronic MOS tube array can be evaluated to ensure that it can maintain stable operation under different load conditions. The normalized curve provides a basis for current distribution under different working conditions, and can be adaptively adjusted according to the actual load conditions, thereby improving the system's ability to respond to load changes. By real-time monitoring of the switching frequency, the working state of the MOS tube array can be timely understood to avoid system instability or energy efficiency loss caused by unreasonable switching frequency. By combining the load response curve, quantitative impact analysis of the switching frequency can reveal the law between the load and the switching frequency, helping to further optimize the adjustment of the switching frequency. By periodic analysis of the switching frequency changes, potential frequency fluctuation problems can be identified, and future frequency changes can be predicted to a certain extent, further improving the stability of the system. It can detect sudden changes in the switching frequency in a timely manner to avoid system instability or overheating caused by frequency fluctuations. Through frequency mutation identification, the system can automatically adjust the switching frequency parameters to achieve frequency compensation to ensure that the system remains stable under frequency fluctuations. Effective frequency compensation can ensure that the electronic MOS tube array can maintain the best working environment under different frequency conditions, avoiding over-frequency or insufficient frequency affecting the long-term operation of the system. By analyzing the changing rules of switching frequency and load response, reverse load compensation can be performed to ensure that frequency fluctuations do not affect the uniform distribution of loads, thereby maintaining the stability of the system. An adaptive collaborative calibration control model is established to achieve dynamic collaborative control of temperature, current, and frequency, so that the system can automatically adjust under different working environments and load conditions. The model can comprehensively consider factors such as temperature, current, and switching frequency, perform comprehensive optimization, avoid the impact of a single factor on system performance, and maximize the performance and efficiency of the electronic MOS tube array.

[0015] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of an adaptive calibration method for an electronic MOS tube array of the present invention. In this example, the steps of the adaptive calibration method for an electronic MOS tube array include: Step S1: acquiring a real-time thermal imaging image of the electronic MOS tube array; performing discrete fitting of temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; In this embodiment, a suitable thermal imaging device, such as an infrared thermal imager, is selected to ensure that it has high resolution and fast response characteristics. It is recommended to use a thermal imager with a resolution of at least 320x240 pixels to capture details and temperature changes. Calibrate the thermal imager before use. Standard objects with known temperatures can be used during the calibration process to ensure the measurement accuracy of the device. Ensure that the measurement error of the device is within the range of ±0.5°C to improve the reliability of the data. Before performing thermal imaging acquisition, ensure that the test environment is stable. Avoid strong winds, direct sunlight or other heat sources from interfering with the thermal imaging results. It is recommended to test in an environment with controlled temperature and humidity, such as under laboratory conditions. Ensure that the electronic MOS tube array operates under normal working conditions so that its thermal characteristics can be obtained in real time. The actual working conditions can be simulated by applying a rated load to ensure the authenticity of the thermal imaging data. Start the thermal imager and start real-time acquisition of thermal imaging images of the electronic MOS tube array. Set an appropriate sampling frequency, such as acquiring one frame of image per second, to capture dynamic temperature changes. During the acquisition process, keep the thermal imager stable to ensure that the image is clear and unblurred. A tripod can be used to fix the device to avoid jitter caused by manual operation. Store the acquired thermal imaging image data in a computer or data recorder to ensure the integrity and security of the image. You can choose a suitable image format (such as TIFF or JPEG) to save it for subsequent analysis. Preprocess the acquired thermal imaging images, including noise removal and contrast enhancement. You can use image processing software (such as MATLAB or ImageJ) to process it, and use median filtering or Gaussian filtering to remove noise and improve image quality. Adjust the brightness and contrast of the image to make the temperature distribution more obvious. Enhance the visualization of the temperature gradient in the image by adjusting the gamma value or histogram equalization method. Extract temperature data from the processed thermal imaging image. You can use image segmentation technology to divide the image into multiple regions for subsequent temperature distribution analysis. Use region growing method or threshold method to segment the image to ensure that different temperature regions can be clearly distinguished. Record the temperature value of each region and generate a temperature distribution map. Use discrete fitting methods (such as polynomial fitting or spline interpolation) to fit the extracted temperature data. Select a suitable model and calculate the fitting parameters based on the temperature distribution data. For example, a quadratic or cubic polynomial can be used to fit the temperature distribution to ensure that the fitting curve can accurately reflect the temperature change trend.

[0016] Step S2: collecting real-time electrical characteristic data of the electronic MOS tube array; performing local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; In this embodiment, a suitable electrical test device, such as a digital multimeter or an oscilloscope, is selected to ensure that it has real-time data acquisition capabilities. As required, the device should be able to measure current, voltage, and power with sufficient accuracy. It is recommended to use an oscilloscope with a bandwidth of at least 1MHz to capture high-frequency signals. Before data acquisition, the device is calibrated. Calibrate using a standard resistor or voltage source to ensure that the measurement error is within the specified range, such as ±0.1%. This step ensures that the data collected subsequently is accurate and reliable. Set the sampling frequency and time interval of the data acquisition system, for example, collect current and voltage data every 100 milliseconds. This frequency can accurately capture the transient characteristics of the MOS tube array in the working state. Configure the device to monitor the current and voltage of multiple pipelines at the same time, ensuring that the data of each pipeline can be recorded independently. If possible, use a multi-channel oscilloscope or data acquisition card to achieve this function. Start the data acquisition system to monitor the electrical characteristics of the electronic MOS tube array in real time. Record parameters such as the current (I), voltage (V), and calculated power (P = V × I) of each pipeline. During the acquisition process, maintain the stability of the equipment to avoid errors introduced by interference signals. You can set appropriate trigger conditions to ensure that data is recorded when the signal is stable. Store the real-time collected data in a computer or data logger, and record the current and voltage values ​​at each time point. It is recommended to store in CSV format for subsequent analysis. Combine the real-time electrical characteristic data with the dynamic temperature distribution change diagram to establish a comprehensive data set. Ensure that each current and voltage data has corresponding temperature data to analyze the rationality of local current distribution. Use the dynamic temperature distribution change diagram to identify the temperature characteristics of different regions, such as high temperature and low temperature regions, and record the requirements for current distribution in these regions. This step provides the basis for subsequent current distribution strategies. According to the temperature distribution characteristics, formulate a local adaptive current distribution strategy. The strategy should take into account the impact of temperature on the working performance of MOS tubes, such as reducing current in high temperature areas to avoid overheating, and increasing current in low temperature areas to improve efficiency. Set the priority and range of current distribution, such as limiting the current distribution in high temperature areas to a certain safety threshold. Determine a reasonable current distribution ratio based on historical data or simulation results. Implement local adaptive current distribution in electronic MOS tube arrays. Adjust the current of each pipe in real time through a controller or power management system, and dynamically adjust the distribution strategy according to temperature changes. Monitor the current distribution effect after implementation and record the current changes of each pipeline for subsequent analysis. Ensure real-time feedback during implementation so that the distribution strategy can be adjusted in time. Evaluate the effect of the local adaptive current distribution strategy and judge the effectiveness of the strategy by comparing the current distribution, power loss and temperature changes before and after implementation. Based on the evaluation results, further optimize the current distribution strategy, such as adjusting the current limit in the high-temperature area, or introducing new adaptive rules under specific conditions to ensure that the electronic MOS tube array operates in the best state.

[0017] Step S3: performing normalized load response mining based on the real-time electrical characteristic data, thereby constructing a normalized MOS tube array load response curve; In this embodiment, load-related parameters such as current (I), voltage (V), and power (P) are extracted from real-time electrical characteristic data. Ensure that the extracted data is consistent with the temperature data recorded in the previous step in time for subsequent analysis. Clean the extracted data to remove outliers and noise. Statistical methods can be used, such as setting a standard deviation threshold to remove data points that deviate too far from the mean to ensure data quality. For example, if a current value exceeds 3 times the standard deviation of the normal range, it is considered an outlier and removed. Load response characteristics are extracted by analyzing the cleaned electrical characteristic data. This step can use regression analysis or machine learning methods (such as decision trees or support vector machines) to identify the response pattern of the load to current and voltage changes. Determine key characteristic variables, such as the effect of an increase in load current on the operating voltage of the MOS tube. A multivariate linear regression model can be constructed to analyze the relationship between voltage and current to obtain a mathematical expression for the load response. The extracted load response data is normalized by a standardization method to eliminate the difference in data under different load conditions. Common methods include Z-score standardization and Min-Max standardization. Select the Z-score normalization method and convert the response data under different load conditions into comparable standardized data through normalization. This process facilitates subsequent analysis and model building. Construct a load response curve based on the normalized data. By plotting the relationship between current and voltage, you can intuitively show the impact of load changes on the performance of the MOS tube array. Use visualization tools (such as Matplotlib or Excel) to generate a load response curve graph to indicate the current and voltage changes under different loads. Make sure the graph is clear and easy to read for easy analysis and comparison. Analyze the constructed load response curve to identify the key factors affecting the performance of the MOS tube with load changes. The response characteristics under different load conditions can be determined by analyzing the slope and shape of the curve. Perform a verification experiment to compare the theoretical load response curve with the load response measured in the actual experiment to determine whether the constructed curve accurately reflects the working characteristics of the MOS tube. If deviations are found, the data processing and model building process need to be re-evaluated.

[0018] Step S4: identifying the switching frequency of the real-time MOS tube array; performing a quantitative impact analysis of periodic changes on the switching frequency based on a normalized MOS tube array load response curve, and generating a load response-switching frequency change law; In this embodiment, a high-frequency oscilloscope or data acquisition system is used to monitor the gate signal of the MOS tube array in real time. This process requires that the sampling rate of the device is at least ten times the switching frequency to accurately capture the switching process. In the settings, you can choose to sample data every 10 milliseconds to ensure that there are enough data points for subsequent analysis. The switching frequency is identified by the zero-crossing detection method or the peak detection method. These methods can effectively capture the transient changes from the MOS tube from opening to closing. The timestamp of each switch is recorded, and the number of switches per unit time is calculated to obtain the switching frequency. During the identification process, the stability and repeatability of the monitoring signal are avoided to avoid inaccurate frequency identification due to signal noise. A threshold can be set, and when the frequency change exceeds this value, it is recorded as a new switching frequency to ensure accuracy. The real-time switching frequency data is integrated with the normalized MOS tube array load response curve constructed previously. The load conditions, temperature changes and electrical characteristics data corresponding to each switching frequency are recorded to provide a basis for subsequent analysis. The switching frequency data is cleaned to remove outliers and noise. Reasonable upper and lower limits can be set to eliminate data points that are significantly deviated from the normal range. Ensure the accuracy and reliability of the data for quantitative analysis. Use statistical analysis methods, such as linear regression or multivariate regression, to analyze the relationship between switching frequency and load response. According to the normalized load response curve, set the regression model, use the switching frequency as the independent variable and the load response as the dependent variable, and perform quantitative analysis. Calculate the coefficients of the regression model to determine the degree of influence of the switching frequency on the load response. By analyzing the slope and intercept of the regression equation, identify the specific impact of the switching frequency change on the performance of the MOS tube. Based on the results of the regression analysis, draw a load response-switching frequency change law graph. Use visualization tools to display the relationship between the switching frequency and the load response to form an intuitive chart to facilitate subsequent analysis and discussion. Ensure that the chart is clearly labeled and contains necessary axis descriptions and data point examples to facilitate understanding of the load response change law at different frequencies. Different colors or symbols can be used to distinguish the response characteristics under different load conditions. Conduct in-depth analysis of the generated load response-switching frequency change law to identify the performance of the MOS tube at different switching frequencies and its possible application scenarios. Analyze the shape and trend of the curve to evaluate the optimal working conditions at a specific frequency. Conduct a verification experiment to determine the accuracy of the model by comparing the actual load response with the predicted value of the theoretical model. If necessary, adjust model parameters or re-evaluate data processing to improve the reliability of predictions.

[0019] Step S5: performing mutation frequency identification on the switching frequency, and performing adaptive frequency compensation calculation, thereby adaptively adjusting frequency compensation parameters; In this embodiment, the real-time switching frequency data is analyzed to identify the mutation frequency. This can be achieved by calculating the derivative of the frequency change, that is, taking the first-order difference of the frequency value at each moment to detect the sharp change of the frequency. A threshold is set, and when the rate of change of the frequency exceeds the threshold, it is marked as a mutation frequency. The frequency data is smoothed using the moving average method to reduce the impact of noise on the identification of the mutation frequency. By setting the window size, such as the moving average of 5 data points, the frequency change trend can be observed more clearly. Ensure that the identification of the mutation frequency is as accurate as possible to avoid misjudgment. Record the timestamps of all identified mutation frequencies and their corresponding frequency values. A data table can be created containing the sequence number, time, frequency value and change rate of the mutation frequency for subsequent analysis and processing. Based on the identified mutation frequency, an adaptive frequency compensation model is established. The model should consider the impact of the mutation frequency on the circuit performance and set the compensation strategy, such as how to adjust the operating parameters to restore the normal working state after the mutation frequency occurs. Determine the input variables of the model, including the mutation frequency, the current operating temperature, the load conditions, etc. Through historical data analysis, identify which factors have a significant impact on the compensation effect, thereby optimizing the effectiveness of the model. Use regression analysis or machine learning methods (such as decision tree regression) to establish the compensation model. The model output should be the required compensation parameters, such as the amplitude of voltage and current adjustment. After the model is established, use real-time data to perform adaptive frequency compensation calculations. Input the identified mutation frequency into the model and calculate the corresponding compensation parameters. This step requires ensuring the accuracy and timeliness of real-time data. It is recommended to perform compensation calculations immediately after each mutation frequency is identified. Record the compensation parameters calculated each time, including the adjusted voltage and current values, for subsequent analysis and verification. Ensure that the results of the compensation calculation can be effectively reflected in the actual circuit to avoid system instability caused by calculation errors. After implementing adaptive frequency compensation, monitor the response of the system in real time and evaluate the effectiveness of the compensation measures. By comparing the switching frequency and load response before and after compensation, it is possible to determine whether the compensation measures are effective. If the compensation effect is not ideal, the parameters of the compensation model can be adjusted based on real-time feedback. Conduct multiple experiments, collect data, and use statistical analysis methods (such as variance analysis) to evaluate the compensation effect to ensure that the system can operate stably under different working conditions.

[0020] Step S6: Perform reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation law, and construct an adaptive collaborative calibration control model based on the local current adaptive distribution strategy.

[0021] In this embodiment, the adaptive frequency compensation parameters obtained in the previous step are collected, and the relevant load response-switching frequency change law data are sorted. These data should include load response characteristics at different switching frequencies, such as changes in current and voltage and their corresponding temperatures. Use statistical analysis software (such as Pandas in MATLAB or Python) to preliminarily sort the collected data to ensure the integrity and accuracy of the data. Check whether there are missing values ​​or outliers in the data, and perform corresponding processing to ensure the effectiveness of subsequent analysis. According to the load response-switching frequency change law, reverse load compensation analysis is performed. By constructing a mathematical model, the switching frequency and its corresponding load response are used as input to calculate the reverse compensation parameters. The specific method can use interpolation or curve fitting technology to interpolate existing data points to deduce the required compensation amount at a specific switching frequency. For example, a quadratic polynomial can be used to fit the relationship between the load response and the switching frequency to obtain a compensation function. Record the reverse compensation parameters calculated at each switching frequency and analyze their impact on circuit performance. This process helps to confirm the degree of influence of load changes at different frequencies on compensation requirements. Combine the reverse load compensation parameters with the local current adaptive allocation strategy to construct an adaptive collaborative calibration control model. The model should be able to adjust the current distribution in real time to achieve the best load response and frequency compensation. Determine the input variables of the model, such as real-time switching frequency, current load, current distribution ratio, etc. By monitoring and analyzing the input variables, adjust the operating current of each MOS tube in real time to ensure the best performance under different load conditions. Construct a control algorithm, such as PID control or fuzzy control, to dynamically adjust the current distribution strategy based on real-time feedback information. Ensure that the algorithm can quickly respond to frequency changes and load changes to optimize system performance. Verify the effectiveness of the adaptive collaborative calibration control model through experiments. Set different load conditions and switching frequencies, monitor the response of the system, and record the current distribution and load response data of each MOS tube. Analyze the performance changes after the implementation of the model and evaluate the impact of reverse load compensation and current distribution adjustment on system stability. Performance indicators (such as system efficiency, temperature change, power loss, etc.) can be used for comprehensive evaluation to judge the actual effect of the model. If it is found that the performance of the model does not meet expectations, it can be debugged according to the experimental data to further optimize the control algorithm and compensation parameters to improve the adaptability and accuracy of the model. All experimental results are recorded in the database, including reverse load compensation parameters, switching frequency changes, local current distribution and their corresponding performance indicators. This provides important data support for subsequent analysis and optimization.

[0022] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: obtaining a real-time thermal imaging image of the electronic MOS tube array; Step S12: performing global smooth transition processing on the real-time thermal imaging image, so as to construct a global smooth thermal imaging image; Step S13: performing temperature distribution analysis on the global smoothed thermal imaging image to obtain image temperature distribution characteristics; Step S14: Calculating the temperature dynamic distribution change based on the image temperature distribution characteristics, thereby generating image temperature dynamic distribution data; Step S15: Perform discrete fitting of temperature distribution according to the image temperature dynamic distribution data to construct a dynamic temperature distribution change graph.

[0023] In this embodiment, a suitable thermal imaging camera is selected to ensure that it has high resolution and fast response capabilities. The thermal imaging camera should be able to capture the temperature changes on the surface of the electronic MOS tube array and have real-time imaging capabilities. Ensure that the equipment is calibrated in good condition to improve the accuracy of imaging. The calibration process needs to be carried out under a known temperature source to ensure that the measurement error of the thermal imaging camera is within an acceptable range, such as ±0.5°C. Before acquiring the thermal imaging image, adjust the environmental conditions to ensure that there are no excessive interference factors in the imaging area, such as strong light sources or reflective objects. Shading curtains or other methods can be used to reduce environmental interference. Ensure that the MOS tube array operates under normal working conditions so that its temperature changes under different working conditions can be captured in real time. For example, tests can be performed under different load conditions to record thermal imaging images under high load and low load. Start the thermal imaging camera and start real-time monitoring and capturing thermal imaging images of the electronic MOS tube array. Set a reasonable frame rate, such as 30 frames / second, to ensure that rapidly changing temperature data can be captured. The captured image data is transmitted to a computer or data recording system in real time for subsequent processing and analysis. Ensure that the data storage system has sufficient storage capacity to store real-time image data for a period of time. After obtaining the real-time thermal imaging image, the image is first preprocessed, including denoising and contrast enhancement, to improve the effect of subsequent processing. A high-pass filter can be used to remove low-frequency noise to ensure that the main temperature features in the image are clearly visible. Gamma correction is applied to enhance the contrast of the image, which is particularly important for highlighting subtle temperature changes. Select a suitable smoothing algorithm, such as Gaussian smoothing or median filtering, to reduce the noise in the image while retaining important temperature information. Gaussian smoothing can effectively eliminate random noise, while median filtering maintains good clarity at the edges. Adjust smoothing parameters, such as the standard deviation of the Gaussian filter, to ensure the optimization of the smoothing effect. The optimal smoothing parameters can be determined experimentally to ensure that the details of the image are not overly blurred. By applying the selected smoothing algorithm, the real-time thermal imaging image is processed to generate a globally smoothed thermal imaging image. The processing time and algorithm performance are recorded for subsequent optimization. Ensure that the smoothed image can accurately reflect the temperature distribution characteristics of the electronic MOS tube array and reduce the error caused by noise. Extract temperature data from the globally smoothed thermal imaging image to generate a temperature distribution matrix. The temperature value of each pixel should accurately reflect the actual temperature of the point to ensure the reliability of the data. Use image processing techniques, such as threshold segmentation, to distinguish different temperature areas. You can set a specific temperature range to extract high and low temperature areas to help with subsequent analysis. Calculate the temperature distribution characteristics of the image, including statistical indicators such as temperature mean, standard deviation, maximum and minimum values. These indicators will provide basic data for subsequent analysis. Visualize the temperature distribution characteristics through heat maps to make temperature changes more visually intuitive. For example, use color mapping to display different temperature areas to help quickly identify hot and cold spots.Further analyze the temperature distribution characteristics of specific areas, such as specific parts of the electronic MOS tube array. The region growing algorithm or cluster analysis method can be used to identify the temperature abnormality area. The analysis results are recorded in the database for subsequent dynamic change calculations. Determine the parameters required to calculate the temperature dynamic distribution change, including time intervals, temperature change thresholds, etc. For example, the temperature change can be calculated every 1 second to capture rapidly changing thermal characteristics. Use historical temperature data as a reference to set the normal operating temperature range to help identify temperature anomalies. Perform time series analysis on the extracted temperature distribution data to calculate the temperature change at different time points. This can be achieved by analyzing the temperature difference between the current moment and the previous moment. Use linear regression or moving average methods to calculate the trend of temperature change to capture the dynamic temperature change over a long period of time. Generate temperature dynamic distribution data based on the results of the dynamic change calculation. These data will include the temperature distribution characteristics and change trends at each time point. Store the temperature dynamic distribution data in the database for subsequent analysis and visualization. Based on the dynamic temperature distribution data, select a suitable fitting model, such as a Gaussian model, normal distribution, or Laplace distribution, to facilitate discrete fitting of the temperature distribution. Determine the parameters of the fitting model, such as mean, variance, etc., to facilitate the subsequent fitting process. Apply the selected fitting model to perform discrete fitting on the temperature dynamic distribution data. This step will help simplify the temperature distribution characteristics and provide clearer data for subsequent analysis. Use the least squares method or other optimization algorithms to adjust the model parameters so that the fitting curve fits the actual temperature dynamic distribution data as closely as possible. Based on the fitting results, construct dynamic temperature distribution change graphs. These graphs will show the trend of temperature change over time and the temperature distribution characteristics under different conditions. Make sure the graphs are clear and easy to read, and mark important temperature thresholds and distribution characteristics for subsequent analysis and decision support.

[0024] In this embodiment, the specific steps of step S12 are: Performing brightness distribution recognition on the real-time thermal imaging image to extract image brightness distribution features; Perform brightness imbalance detection based on image brightness distribution characteristics and extract brightness imbalance area images; Performing histogram equalization on the image in the brightness imbalance area to obtain an image with optimized brightness distribution; Performing multi-grid division processing on the brightness distribution optimization image to generate a plurality of image grid units; Calculating the average pixel value of each grid cell of the multiple images one by one to generate an average pixel value of each grid cell; According to the average pixel value of each grid cell, the contrast analysis of adjacent cells is performed to identify the contrast deviation value between cells; Performing local contrast optimization based on the contrast deviation value between units, thereby generating local contrast optimized grid units; A global smooth transition process is performed on the local contrast optimized grid cells to construct a globally smooth thermal imaging image.

[0025] In this embodiment, before brightness distribution recognition is performed, basic preprocessing is performed on the real-time thermal imaging image, including denoising and color correction. The random noise in the image is removed by using median filtering or Gaussian filtering, which can improve the accuracy of subsequent analysis. Ensure that the contrast and brightness of the thermal imaging image are moderate to facilitate subsequent brightness analysis. The image quality can be optimized by adjusting the gamma value or using a linear contrast enhancement method. The distribution characteristics of the brightness in the image are identified by calculating the brightness histogram of the image. The horizontal axis of the histogram represents the brightness value (from black to white), and the vertical axis represents the number of pixels corresponding to the brightness value. The shape and distribution of the histogram are analyzed, and statistical features such as the mean, variance, maximum and minimum values ​​of the brightness are calculated. These features can reflect the overall brightness level and contrast of the image. Extract feature data of the image brightness distribution, such as the brightness mean and standard deviation, which will provide a basis for subsequent brightness imbalance detection. Further analyze the skewness and kurtosis of the brightness distribution to identify whether the image has brightness imbalance. If the skewness deviates significantly from 0, it means that the image brightness distribution is uneven and there may be an imbalance. Design a brightness imbalance detection algorithm based on the extracted brightness distribution features. Set a threshold to determine which areas in the image have brightness values ​​that exceed a preset range. For example, you can set ±20% of the brightness mean as the imbalance judgment standard. Use binarization to mark the areas with brightness imbalance in the image. Use Otsu's threshold segmentation method to automatically determine the optimal binarization threshold. For the areas marked as imbalanced, apply the connected component labeling algorithm to extract the contours and positions of these areas. Ensure that the extracted areas can accurately reflect the brightness imbalance. Record the area, position, and brightness characteristics of each brightness imbalance area for subsequent processing and analysis. Create a new image that only shows the brightness imbalance areas. These areas can be marked with different colors (such as red) to facilitate quick identification by users. Overlay the imbalance area image with the original image to generate a composite image containing brightness imbalance information for subsequent analysis and processing. Select an appropriate histogram equalization method to process the brightness imbalance area image. Common equalization methods include global histogram equalization and adaptive histogram equalization (CLAHE). Global histogram equalization is suitable for the overall brightness balance of the image, while adaptive histogram equalization can better preserve local details and is suitable for areas with large brightness changes. Perform histogram equalization on the image in the brightness imbalance area, calculate the brightness value after equalization, and update the image data. Record the changes in the histogram before and after the equalization process to ensure that the brightness distribution of the processed image is more uniform. Generate the brightness distribution optimization image after processing and compare the effects before and after processing. Display the brightness distribution changes of the image through visualization to ensure that the optimization effect is significant. Store the brightness distribution optimization image in the database for subsequent use. Determine the grid division standard based on the image resolution and analysis requirements.For example, divide the image into 10×10 grids to ensure that the size of each grid unit is moderate and can effectively capture local features. Calculate the size of each grid unit so that it can be processed uniformly during processing. For example, if the original image is 800x800 pixels, the size of each grid unit is 80x80 pixels. Divide the brightness distribution optimized image according to the set standard to generate multiple image grid units. The position of each grid unit can be marked with a rectangular box to facilitate subsequent processing. Record the position information of each grid unit for subsequent analysis and calculation. Visualize the divided grid units to ensure that each grid is clearly visible in the brightness distribution optimized image. Different grid units can be distinguished by different colors or lines. For each grid unit, calculate its average pixel value. This can be done by traversing each pixel in the grid unit, accumulating its value and dividing it by the total number of pixels in the grid. For example, if a grid unit contains 6400 pixels, the calculation formula is: average value = (pixel value 1 + pixel value 2 + ... + pixel value 6400) / 6400. Calculate all grid cells one by one and record the average pixel value of each grid. These values ​​will help analyze local contrast and brightness changes. Ensure data accuracy during calculations to prevent deviations caused by incorrect pixel values. Store the average pixel value of each grid cell in a data structure (such as an array or dictionary) for subsequent analysis and comparison. After the calculation is completed, generate a report containing the average pixel values ​​of all grid cells for subsequent contrast analysis. Determine the contrast calculation method between adjacent grid cells. Contrast deviation can be evaluated using a simple difference method or a standard deviation method. For example, contrast deviation can be defined as the difference between the average values ​​of adjacent cells. Set a threshold for contrast deviation to determine whether there is a significant difference in contrast between adjacent grid cells. Calculate each pair of adjacent grid cells and record the difference in their average pixel values. For example, if the average value of grid cell A is 150 and the average value of grid cell B is 130, the contrast deviation is 20. Store the calculation results in a data structure for subsequent analysis. Visualize the contrast deviation results to generate a contrast deviation map. Display the contrast changes between different grid cells through heat maps or bar charts to facilitate identification of significant contrast problems. By analyzing the results, the grid cells with large contrast deviations are identified to provide a basis for subsequent local contrast optimization. According to the contrast deviation values ​​obtained by analysis, a local contrast optimization strategy is formulated. Grid cells with large contrast deviations require stronger contrast enhancement. Specific enhancement parameters are set. For example, for grids with deviation values ​​greater than a certain threshold, local histogram equalization and other methods are used for optimization. Local contrast enhancement is performed on grid cells that need to be optimized. Methods such as adaptive histogram equalization (CLAHE) are used to ensure that image details are retained while enhancing contrast.Record the contrast changes of each optimized grid unit for subsequent effect evaluation. Evaluate the effect of local contrast optimization and ensure the effectiveness of the optimization measures by comparing the contrast values ​​and image quality before and after optimization. Select a suitable smoothing method to reduce the noise in the image and improve the overall visual effect. Common global smoothing methods include Gaussian filtering and bilateral filtering. Gaussian filtering is suitable for removing random noise, while bilateral filtering can retain edge information while smoothing the image. Perform global smoothing on the optimized image to ensure that important details are not lost while reducing noise. Adjust the filtering parameters, such as the standard deviation of the Gaussian kernel, to achieve the best smoothing effect. Record the image comparison before and after processing to confirm the effect of smoothing. Generate a globally smoothed thermal imaging image and compare it with the original image. Ensure that the brightness distribution of the smoothed image is uniform, the details are clear, and it can effectively reflect the temperature change. Save the generated globally smoothed thermal imaging image for subsequent application and analysis.

[0026] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: collecting real-time electrical characteristic data of the electronic MOS tube array; Step S22: Calculating the current parameters of each pipeline based on the real-time electrical characteristic data to generate the current parameters of each pipeline; Step S23: Detect abnormal temperature changes on the dynamic temperature distribution change diagram to identify abnormal temperature distribution areas; Step S24: locating the abnormal area of ​​the electronic MOS tube array based on the abnormal temperature distribution area, and marking the abnormal temperature distribution pipe; Step S25: matching the current parameters of the abnormal temperature distribution pipeline according to the current parameters of each pipeline to obtain the abnormal pipeline current parameters; Step S26: Perform local adaptive current distribution on the abnormal pipeline current parameters, thereby constructing a local current adaptive distribution strategy.

[0027] In this embodiment, a suitable electronic test device, such as a digital multimeter, an oscilloscope or a data acquisition system, is selected to ensure that it can collect the electrical characteristic data of the electronic MOS tube array in real time. The equipment needs to have high precision and fast sampling capabilities to accurately capture transient current changes. Before data acquisition, ensure that the equipment is calibrated to improve the measurement accuracy. During the calibration process, a known standard resistor or a standard voltage source can be used for comparison to ensure that the measurement error is within an acceptable range, such as ±0.1%. Set the sampling frequency and data storage format of the data acquisition system. Select an appropriate sampling frequency, such as 1kHz, to capture rapidly changing current signals. Configure the data acquisition system so that it can monitor the current and voltage characteristics of multiple pipelines at the same time, ensuring that the real-time data of each pipeline can be recorded independently. Start the real-time data acquisition system and start monitoring the electrical characteristics of the electronic MOS tube array. Record parameters such as the current (I), voltage (V) and calculated power (P = V× I) of each pipeline. During the acquisition process, monitor the integrity and validity of the data in real time to ensure that there is no data loss or abnormality. The data acquisition system can be set to automatically alarm when an abnormality is detected for timely processing. After acquiring real-time electrical characteristic data, data cleaning is first performed to remove outliers and noise data. Current values ​​that exceed the specified range can be removed using statistical methods, such as setting a threshold of ±3 standard deviations. Ensure that the timestamps of the voltage and current data of each pipeline are consistent to facilitate subsequent calculations and comparisons. Calculate the current parameters for each pipeline. The current parameters of each pipeline can be calculated using Ohm's law (I = V / R), where R is the resistance of the pipeline. Record the current parameters of each pipeline and store them in a data structure for subsequent analysis. For example, generate a list containing the current values ​​of each pipeline for further processing. Visualize the calculated current parameters and generate a chart of the current change over time. Analyze the current characteristics of each pipeline through the chart to determine whether there are abnormal conditions, such as a sudden increase or decrease in current. Record the current parameters and change trends of each pipeline to lay the foundation for subsequent anomaly detection and analysis. Select a suitable anomaly detection algorithm to analyze the dynamic temperature distribution change chart. Statistical methods, such as the Z-score method, can be used to set a threshold for temperature change to identify abnormal temperatures. Set the normal temperature range and the allowed variation, for example, set the normal temperature between 20°C and 60°C, and anything outside this range is considered abnormal. Input the dynamic temperature distribution data into the selected detection algorithm to identify areas of abnormal temperature variation. For example, if the temperature of an area exceeds the set upper or lower limit, it is marked as abnormal. Record the coordinates and temperature values ​​of the detected abnormal area for subsequent positioning and analysis. Generate a heat map containing abnormal temperature areas to intuitively display the areas of abnormal changes. Highlight abnormal areas through color coding for quick identification. Select the appropriate positioning algorithm based on the abnormal temperature areas previously identified.An algorithm based on region growing can be used to determine the boundaries and locations of abnormal regions. The initial point of region growth is set as the center of the abnormal temperature region so that it can be quickly expanded to the surrounding area. For the identified abnormal temperature region, the selected algorithm is applied to locate it and the boundaries of the abnormal region are marked. The coordinates and regional features of the mark, such as area and temperature features, are recorded. The marking information is superimposed on the original temperature distribution map to generate a composite image containing the marked abnormal region for subsequent analysis. The positioning results are stored in a database, including the features of the abnormal region and related temperature data, for subsequent analysis and processing. According to the current parameters of each pipeline, the matching criteria are set. A reasonable matching threshold can be set based on the relationship between temperature change and current change. For example, a linear relationship between current and temperature is set under normal working conditions, and matching is performed when it exceeds this range. For pipelines with abnormal temperatures detected, matching is performed based on their current parameters. The difference between the current parameters and the set standards is compared to determine whether there is an abnormality. The current parameters of each abnormal pipeline are recorded and compared with the normal range to generate a report of the matching results. The matching results are visualized, and the current parameter comparison between normal and abnormal pipelines is displayed through charts or heat maps to facilitate rapid identification of problems. Based on the current parameters and temperature characteristics of abnormal pipelines, a local adaptive current distribution strategy is formulated. Algorithmic models can be used to dynamically adjust current distribution according to real-time data. Determine the principles of current distribution, such as giving priority to ensuring the stability of key pipelines while controlling the current of abnormal pipelines within a safe range. According to the formulated strategy, adaptively distribute current to abnormal pipelines. Monitor the current distribution effect in real time, and dynamically adjust the distribution strategy to ensure that the current distribution of each pipeline is reasonable. Record the results of each current distribution in order to analyze the effectiveness of different strategies. Establish a feedback mechanism to evaluate the temperature changes and current stability after current distribution, and regularly optimize the current distribution strategy. Store and analyze the effects and data after implementation to form historical data of adaptive current distribution for subsequent optimization and decision support.

[0028] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: performing voltage statistics at multiple time points based on the real-time electrical characteristic data to generate voltage parameters at multiple time points; Step S32: performing real-time voltage load calculation according to voltage parameters at multiple time points to obtain voltage loads at multiple time points; Step S33: performing a time-series load fluctuation analysis on the voltage load at multiple time points to generate a time-series load fluctuation feature; Step S34: performing long-term operation load response evolution according to the time series load fluctuation characteristics, thereby generating long-term operation load response data of the MOS tube array; Step S35: performing normalized load response mining based on the long-term operation load response data of the MOS tube array, thereby constructing a normalized MOS tube array load response curve.

[0029] In this embodiment, before performing multi-point voltage statistics, the real-time electrical characteristic data is first cleaned to remove abnormal values ​​and noise data. A statistical method is used, such as setting a threshold, to remove data whose voltage value exceeds a preset range to ensure the validity of the data. Ensure that the voltage data at each time point has a corresponding timestamp, and the time interval of the data should be uniform. For example, it can be set to record voltage data once every minute for subsequent analysis. Extract voltage parameters from the real-time data set and group them according to the set time interval (for example, every minute or every second). Record the voltage value at each time point and store it in a data structure, such as an array or a database table. Calculate the voltage mean and standard deviation at each time point to analyze the voltage change. This will provide basic data for subsequent load calculations. Generate a statistical report containing voltage parameters at multiple time points, and the report should include information such as the voltage value, mean, standard deviation, etc. at each time point. These statistical data will be used for subsequent real-time voltage load calculations. Through visualization tools, draw a curve chart of voltage changes over time to intuitively display the voltage change trend, which is convenient for quickly identifying abnormal situations. According to the relationship between voltage and current, a voltage load calculation model is established. Use the formula P=V×I, where P is power, V is voltage, and I is current. Calculate the load based on the known voltage and real-time current data. Determine the real-time sampling frequency of the current to ensure the real-time nature of the voltage load calculation. For example, the current data can be sampled once a second to facilitate synchronization with the voltage data. For each voltage parameter at a time point, calculate the corresponding voltage load in real time. Multiply the real-time voltage by the current current to obtain the real-time load value and record it. During the calculation process, ensure that all data units are consistent, such as using kilowatts (kW) as the power unit, and record the calculated load value and its change trend. Perform time series analysis on the voltage load data at multiple time points to calculate the load fluctuation amplitude, frequency, and period characteristics. Frequency domain analysis methods such as Fourier transform can be used to identify the frequency components of load fluctuations. Calculate the mean, standard deviation, and coefficient of variation of the load to help identify the laws and characteristics of load fluctuations. For example, a larger standard deviation may mean that the load fluctuation is larger. Use methods such as cluster analysis to identify different patterns of load fluctuations. For example, load fluctuations can be divided into three types: high fluctuations, medium fluctuations, and low fluctuations to help further analysis. Record the time period and frequency of each fluctuation mode to facilitate subsequent response evolution analysis. Generate a visual report of the load fluctuation characteristics, and display the trend and pattern of load fluctuations through charts. You can use bar charts or line charts to intuitively display the changes in different fluctuation modes. Build a long-term load response evolution model based on the time-series load fluctuation characteristics. The model should take into account the periodicity and trend of load fluctuations, and be able to reflect the changes in load in different time periods. Set model parameters, including the average value, fluctuation amplitude, and response time of the load, to simulate the changes in long-term load operation. Use the established model to simulate and analyze long-term load operation.By inputting historical load data, predicting possible changes in future loads, and generating long-term load response data. Record the load change trend during the simulation process and identify possible abnormal situations, such as the risk of excessive or low load. Store the generated long-term load response data in the database for subsequent analysis and query. The data should include the load change trend and prediction results in different time periods. Based on the long-term load response data, establish a normalized load response model. The model should be able to reflect the load characteristics of the MOS tube array under different working conditions and extract the laws of normal load response. Set model parameters, including the load mean and variation range under normal working conditions, to ensure that the model can accurately reflect the characteristics of normal loads. Mine the long-term load response data to identify the characteristics of normal load response. Data mining techniques, such as cluster analysis and association rule analysis, can be used to mine the key characteristics and laws of the load. Record the mined normal load response characteristics, including normal load range, typical load mode, etc., for subsequent application. Based on the mined normal load response characteristics, construct a normalized MOS tube array load response curve. The curve should be able to reflect the trend of load changes over time and mark abnormal points and key characteristics. Through visualization tools, a load response curve is generated to intuitively display the changes in normal load, providing a basis for subsequent load management and optimization.

[0030] In this embodiment, step S4 includes the following steps: Step S41: identifying the switching frequency of the real-time MOS tube array; Step S42: calculating the average switching frequency range of the switching frequency to generate an average switching frequency range; Step S43: calculating the standardized switching frequency within the cycle according to the average switching frequency range to obtain a standardized switching frequency curve; Step S44: performing dynamic switching frequency change analysis on the standardized switching frequency curve to generate a dynamic switching frequency change feature; Step S45: performing a quantitative impact analysis of periodic changes on the dynamic switching frequency change characteristics according to the normalized MOS tube array load response curve, and generating a load response-switching frequency change law.

[0031] In this embodiment, a high-frequency oscilloscope or data acquisition system is used to set a suitable sampling rate to capture the switching state of the MOS tube array. To ensure that the switching frequency can be accurately identified, the sampling rate should be at least 10 times the switching frequency. Ensure that the device is well connected and can monitor the gate signal of the MOS tube in real time. This signal is usually used to indicate the switching state (on or off) of the MOS tube and is the basis for analyzing the switching frequency. Start the data acquisition system and start capturing the switching signal of the MOS tube. Record the switching process within a certain time range, and usually select a complete signal of several cycles for frequency analysis. Real-time monitoring and ensure data integrity to prevent data loss. The system can be set to issue an alarm when a signal abnormality is detected. Analyze the collected switching signal and use the zero-crossing detection method to identify the switching frequency. This method detects the crossing point of the signal from high to low or from low to high, calculates the number of crossings per unit time, and thus obtains the switching frequency. Record the change of the switching frequency and generate a time series of frequency data to provide a basis for subsequent analysis. Before calculating the average switching frequency, the identified switching frequency data is first cleaned to remove abnormal values ​​and noise. Reasonable thresholds can be set to remove frequency values ​​that are out of the normal range. Ensure that the timestamps of the remaining frequency data are consistent for subsequent calculation and analysis. Calculate the average value of the switching frequency. A simple arithmetic average can be used to add all valid switching frequency values ​​and divide by the number of valid values. At the same time, calculate the standard deviation of the switching frequency to evaluate the fluctuation of the switching frequency. These two values ​​will serve as the basis for the subsequent average range calculation. Based on the calculated average frequency and standard deviation, generate an average range of the switching frequency. For example, the range can be set to "average frequency ± 1 standard deviation" to capture frequency fluctuations under normal operation. Record and store the average switching frequency range for subsequent use and analysis. Determine the calculation method for the standardized switching frequency. Common standardization methods include Z-score standardization and Min-Max standardization. Z-score standardization is suitable for situations where the data distribution is relatively normal, while Min-Max standardization is suitable for situations where the data range is known. Select the Z-score standardization method for switching frequency calculation, the formula is: 𝑍= (X−μ) / σ, where X is the switching frequency value, μ is the average frequency, and σ is the standard deviation. Perform standardized calculations on the switching frequency value at each time point. Store the calculated standardized values ​​in an array or data table for subsequent analysis. Record the standardized frequency values ​​and ensure the accuracy and completeness of the data. Construct a standardized switching frequency curve based on the standardized switching frequency data. Draw a curve chart using a visualization tool to intuitively display the trend of standardized frequency changes over time. Perform dynamic change analysis on the standardized switching frequency curve to calculate the rate and amplitude of frequency change.Moving average or sliding window analysis can be used to smooth the curve and extract the change characteristics. Identify the peaks and valleys of frequency changes and record the time points and amplitudes when they occur. Use cluster analysis or pattern recognition methods to identify different modes of switching frequency changes. For example, frequency changes can be divided into types such as fast changes, slow changes, and stable states. Record the frequency and duration of each mode for subsequent analysis and comparison. Visualize the dynamic switching frequency change characteristics and display the trend and pattern of frequency changes through charts. Line charts or heat maps can be used to intuitively display the changes of different modes. Generate a report containing dynamic change characteristics for subsequent research and analysis. Collect and integrate the normalized MOS tube array load response curve and dynamic switching frequency change characteristic data. Ensure that the two sets of data have the same time base for comparative analysis. Determine an analysis method, such as regression analysis or correlation analysis, to evaluate the impact of switching frequency changes on load response. Perform quantitative analysis on the integrated data. For example, calculate the correlation coefficient between dynamic switching frequency changes and load response to quantify the relationship between the two. Analyze how switching frequency changes affect load response by establishing a mathematical model. For example, a linear regression model can be used to evaluate the impact of frequency changes on the load. The analysis results can be visualized to show the relationship between the switching frequency change and the load response through a graph. A scatter plot or linear regression graph can be used to intuitively show the relationship between the two.

[0032] In this embodiment, the specific steps of step S5 are: Step S51: performing mutation frequency identification on the switching frequency to obtain mutation switching frequency parameters; Step S52: performing a standard frequency deviation analysis on the sudden change switching frequency parameter according to the standardized switching frequency curve to generate a frequency deviation value; Step S53: performing deviation attribution inference on the frequency deviation value to generate frequency deviation attribution data; Step S54: performing adaptive frequency compensation calculation based on the frequency deviation attribution data, thereby adaptively adjusting the frequency compensation parameters.

[0033] In this embodiment, before performing mutation frequency identification, the switching frequency data is first preprocessed to remove noise and outliers. Median filtering or moving average method can be used to smooth the data to ensure accurate identification of mutation points. Ensure that the timestamps of the data are consistent for subsequent analysis. Organize the switching frequency data into a time series to facilitate observation and analysis of the frequency change trend. Select a suitable mutation frequency detection algorithm, such as CUSUM (cumulative sum control chart) or gradient detection method. These methods can effectively identify mutation points in the data and facilitate capturing frequency mutations. Determine the mutation threshold and set the appropriate sensitivity according to the actual situation to identify the real frequency mutation. For example, a threshold can be set, and when the frequency change exceeds the threshold, it is considered that a mutation has occurred. According to the selected algorithm, the switching frequency data is analyzed to identify the mutation frequency parameters. Record the time when the mutation occurs, the frequency values ​​before and after the mutation, and the mutation amplitude. The identified mutation frequency parameters are stored in a data structure to form a list of mutation frequency parameters for subsequent analysis and processing. Use the previously calculated standardized switching frequency curve as a benchmark to ensure that the curve can accurately reflect the frequency characteristics under normal working conditions. For each mutation frequency parameter, calculate its deviation value from the standardized switching frequency. Deviation calculation formula is: Deviation = mutation frequency − standardized frequency. Calculate the deviation value of mutation frequency parameters one by one and record the deviation of each mutation frequency. Ensure data accuracy during analysis to prevent inaccurate deviation due to calculation errors. Organize the deviation values ​​into a data table for subsequent statistical analysis and visualization. Generate a frequency deviation chart through a visualization tool to show the deviation between the mutation frequency and the standardized frequency. This can help quickly identify which frequency mutations are significant for subsequent analysis. Record the results of the deviation analysis for subsequent attribution and compensation calculations. Select a suitable attribution method, such as a multivariate linear regression model based on regression analysis, or use machine learning methods (such as decision trees and random forests) to determine the cause of frequency deviation. Determine possible factors that affect frequency deviation, such as operating temperature, voltage fluctuations, load changes, etc., for comprehensive attribution analysis. Integrate the frequency deviation value with the data of related factors to form a data set containing multiple features. Ensure that each mutation frequency has corresponding environmental conditions and electrical characteristics data. Train and analyze the data through the selected attribution model to identify the main factors affecting frequency deviation. Record the degree of influence of each factor on the frequency deviation to generate frequency deviation attribution data. Organize the results of attribution inference into a report, and record the attribution analysis results of each mutation frequency, including the influencing factors and their weights. The attribution results can be displayed as a chart through a visualization tool to facilitate the identification of the main influencing factors and help with subsequent frequency compensation calculations. According to the results of the attribution analysis, formulate an adaptive frequency compensation strategy. The compensation strategy should consider the main factors affecting the frequency and set a reasonable compensation range and method.Determine the compensation goal, such as reducing the impact of mutation frequency on system performance and restoring it to the standardized frequency range. For each mutation frequency, calculate based on the attribution data and compensation strategy to determine the frequency value that needs to be compensated. For example, the compensation value can be calculated based on the size of the frequency deviation and the weight of the influencing factors. Update the system's control parameters and implement frequency compensation to ensure that the system runs stably at the new frequency. After implementing compensation, monitor the changes in switching frequency in real time and evaluate the compensation effect. Record the compensated frequency value and compare it with the standardized frequency to ensure the effectiveness of the compensation measures.

[0034] In this embodiment, the specific steps of step S6 are: Step S61: performing reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation rule, thereby obtaining a reverse load compensation value; Step S62: performing load adaptive calibration on the electronic MOS tube array according to the reverse load compensation value to obtain an adaptive load calibration strategy; Step S63: Perform intelligent collaborative control optimization on the adaptive load calibration strategy and the local current adaptive distribution strategy to construct an adaptive collaborative calibration control model.

[0035] In this embodiment, the load response-switching frequency change law is integrated with the adaptive frequency compensation parameters to form a comprehensive data set. Ensure that each compensation parameter has corresponding load response and switching frequency data for subsequent analysis. Through data cleaning, remove outliers and missing data to ensure the accuracy and reliability of the data. Statistical methods, such as Z-score, can be used to identify and eliminate abnormal data. Establish a reverse load compensation analysis model. The model should take into account the relationship between load change and switching frequency, and use regression analysis or other statistical methods to establish the model. For example, a linear regression model can be used to describe how the load response affects the switching frequency. Determine the key variables in the model, including load change, frequency change, and compensation parameters, to ensure that the model can accurately reflect the relationship between these variables. Based on the established model, calculate the reverse load compensation value. The reverse compensation value is obtained by inputting the data of load response and switching frequency change into the model. This value represents the amount of compensation required under specific load conditions. Record the reverse load compensation value calculated each time, and generate a report containing the compensation value and its corresponding load condition for subsequent calibration analysis. According to the reverse load compensation value, formulate a load adaptive calibration strategy. The strategy should specify how to adjust the operating parameters of the electronic MOS tube array to maintain optimal performance under different load conditions. Set the calibration goals, such as ensuring stable switching frequency under specific loads, reducing energy consumption, or improving system efficiency. In practical applications, the electronic MOS tube array is calibrated using reverse load compensation values. Specifically, it includes adjusting the operating voltage, current distribution, and switching frequency of the MOS tube. Monitor the system response after each calibration to ensure that the adjusted parameters can effectively improve the load adaptability. A real-time data monitoring system can be used to collect feedback data to ensure the effectiveness of the calibration. Evaluate the effectiveness of the calibration strategy by comparing the system performance indicators before and after calibration, such as efficiency, stability, and response time, to determine the effectiveness of the calibration. Design an intelligent collaborative control model framework that can integrate the adaptive load calibration strategy with the local current adaptive distribution strategy. The model should have real-time monitoring, data analysis, and decision support functions. Determine the input variables of the model, such as load conditions, switching frequency, real-time current, etc., and the output variables, such as adjusted current distribution and calibration parameters. Develop a collaborative control algorithm to ensure that the calibration strategy and current distribution strategy can be adjusted in real time during operation. For example, a fuzzy logic controller or an adaptive control algorithm can be used to adjust the parameters based on real-time data. Set control rules and strategies to ensure that the system can respond quickly and adjust to achieve optimal performance under different operating conditions. Verify the effect of the intelligent collaborative control model in actual operation. Evaluate the relationship between the model's output and actual system performance through a real-time data monitoring system to ensure the accuracy of the model. Based on the verification results, gradually optimize the model parameters and control strategies to ensure that the system can maintain stable and efficient operation under various load conditions.

[0036] In this embodiment, an adaptive calibration device for an electronic MOS transistor array is provided, which is used to execute the adaptive calibration method for an electronic MOS transistor array as described above, and includes: The temperature distribution module is used to obtain a real-time thermal imaging image of the electronic MOS tube array; perform discrete fitting of the temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; The local current distribution module is used to collect real-time electrical characteristic data of the electronic MOS tube array; perform local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; A load response module, used to perform normalized load response mining based on the real-time electrical characteristic data, so as to construct a normalized MOS tube array load response curve; A quantitative analysis module is used to identify the switching frequency of the real-time MOS tube array; based on the normalized MOS tube array load response curve, the switching frequency is quantitatively analyzed for periodic changes, and a load response-switching frequency change law is generated; An adaptive frequency compensation module, used to identify the sudden change frequency of the switching frequency, perform adaptive frequency compensation calculation, and thus adaptive frequency compensation parameters; The collaborative calibration control module is used to perform reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation law, and to construct an adaptive collaborative calibration control model based on the local current adaptive distribution strategy.

[0037] The present invention monitors the temperature of the electronic MOS tube array through real-time thermal imaging images, and can clearly and timely understand the temperature change of each component, thereby preventing component damage or performance degradation caused by excessive temperature. Through discrete fitting of temperature distribution and dynamic change diagram, the temperature change trend can be seen in real time, providing accurate data support for subsequent current distribution and frequency adjustment. It can accurately identify local hot spots, help to make directional current adjustments, and avoid the local areas with excessively high temperatures from affecting the stability of the entire system. By dynamically adjusting the current distribution according to real-time electrical characteristics and temperature changes, it can avoid overloading or overheating of a single MOS tube and improve the balance of the system. It can adaptively distribute current according to the working status of different regions (such as temperature, current characteristics, etc.), ensure that each MOS tube works in its optimal range, and improve the overall current utilization efficiency. Through adaptive current distribution, the problem of heat accumulation caused by excessive local current is avoided, the influence of temperature on components is reduced, and the reliability of the system is improved. By mining the load response characteristics, the response curve of the MOS tube array under different load conditions can be deeply analyzed, and the stability of the array can be evaluated. The normalized load response curve provides the standard behavior of the system under different loads, helps to identify unbalanced loads, and optimizes the current distribution strategy. By normalizing the load response, the impact of load changes on the system can be predicted more accurately, and adjustments can be made in advance to avoid system shocks caused by sudden load changes. By real-time monitoring of the switching frequency, frequency fluctuations can be discovered in time to avoid efficiency loss or system instability caused by unreasonable frequency. The relationship between the switching frequency and the load response is quantitatively analyzed to reveal the periodic variation law of the two, providing theoretical support for further optimization of the switching frequency adjustment. Through the analysis of the load response-switching frequency variation law, the switching frequency can be adjusted to adapt to different load conditions, improve system efficiency, and reduce energy consumption. It can identify sudden changes in the switching frequency to avoid system instability or low energy efficiency due to excessive frequency fluctuations. When the frequency suddenly changes, the system can automatically compensate for the frequency to ensure that the frequency change does not have a negative impact on system performance. Adaptive frequency compensation enables the system to flexibly respond to frequency changes, avoid the risk of overload or instability in the system through compensation, and improve overall stability. Global optimization is performed by integrating factors such as temperature, current, voltage, and switching frequency to ensure the coordinated work of each module, thereby maximizing the performance of the system. Through the analysis of the load response law, reverse load compensation can be performed, so that the system can still maintain optimal current distribution and load balance when the frequency changes. The adaptive collaborative calibration control model can achieve intelligent coordination and optimization among different factors, dynamically adjust various parameters in a complex working environment, and ensure stable and efficient operation of the system.

[0038] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0039] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An adaptive calibration method for an electronic MOS tube array, characterized in that: The following steps are involved: Step S1: obtaining a real-time thermal imaging image of the electronic MOS tube array; Performing discrete fitting of temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; Step S2: collecting real-time electrical characteristic data of the electronic MOS tube array; performing local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; Step S3: performing normalized load response mining based on the real-time electrical characteristic data, thereby constructing a normalized MOS tube array load response curve; Step S4: identifying the switching frequency of the real-time MOS tube array; performing a quantitative impact analysis of periodic changes on the switching frequency based on a normalized MOS tube array load response curve, and generating a load response-switching frequency change law; Step S5: performing mutation frequency identification on the switching frequency, and performing adaptive frequency compensation calculation, thereby adaptively adjusting frequency compensation parameters; Step S6: Perform reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation law, and construct an adaptive collaborative calibration control model based on the local current adaptive distribution strategy.

2. The method for self-adapting calibration of an electronic MOS tube array according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: obtaining a real-time thermal imaging image of the electronic MOS tube array; Step S12: performing global smooth transition processing on the real-time thermal imaging image, so as to construct a global smooth thermal imaging image; Step S13: performing temperature distribution analysis on the global smoothed thermal imaging image to obtain image temperature distribution characteristics; Step S14: Calculating the temperature dynamic distribution change based on the image temperature distribution characteristics, thereby generating image temperature dynamic distribution data; Step S15: Perform discrete fitting of temperature distribution according to the image temperature dynamic distribution data to construct a dynamic temperature distribution change graph.

3. The method for self-adapting calibration of an electronic MOS tube array according to claim 2, characterized in that: The specific steps of step S12 are: Performing brightness distribution recognition on the real-time thermal imaging image to extract image brightness distribution features; Perform brightness imbalance detection based on image brightness distribution characteristics and extract brightness imbalance area images; Performing histogram equalization on the image in the brightness imbalance area to obtain an image with optimized brightness distribution; Performing multi-grid division processing on the brightness distribution optimization image to generate a plurality of image grid units; Calculating the average pixel value of each grid cell of the multiple images one by one to generate an average pixel value of each grid cell; According to the average pixel value of each grid cell, the contrast analysis of adjacent cells is performed to identify the contrast deviation value between cells; Performing local contrast optimization based on the contrast deviation value between units, thereby generating local contrast optimized grid units; A global smooth transition process is performed on the local contrast optimized grid cells to construct a globally smooth thermal imaging image.

4. The method for self-adapting calibration of an electronic MOS tube array according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: collecting real-time electrical characteristic data of the electronic MOS tube array; Step S22: Calculating the current parameters of each pipeline based on the real-time electrical characteristic data to generate the current parameters of each pipeline; Step S23: Detect abnormal temperature changes on the dynamic temperature distribution change diagram to identify abnormal temperature distribution areas; Step S24: locating the abnormal area of ​​the electronic MOS tube array based on the abnormal temperature distribution area, and marking the abnormal temperature distribution pipe; Step S25: matching the current parameters of the abnormal temperature distribution pipeline according to the current parameters of each pipeline to obtain the abnormal pipeline current parameters; Step S26: Perform local adaptive current distribution on the abnormal pipeline current parameters, thereby constructing a local current adaptive distribution strategy.

5. The method for self-adapting calibration of an electronic MOS tube array according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing voltage statistics at multiple time points based on the real-time electrical characteristic data to generate voltage parameters at multiple time points; Step S32: performing real-time voltage load calculation according to voltage parameters at multiple time points to obtain voltage loads at multiple time points; Step S33: performing a time-series load fluctuation analysis on the voltage load at multiple time points to generate a time-series load fluctuation feature; Step S34: performing long-term operation load response evolution according to the time series load fluctuation characteristics, thereby generating long-term operation load response data of the MOS tube array; Step S35: performing normalized load response mining based on the long-term operation load response data of the MOS tube array, thereby constructing a normalized MOS tube array load response curve.

6. The method for self-adapting calibration of an electronic MOS tube array according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: identifying the switching frequency of the real-time MOS tube array; Step S42: calculating the average switching frequency range of the switching frequency to generate an average switching frequency range; Step S43: calculating the standardized switching frequency within the cycle according to the average switching frequency range to obtain a standardized switching frequency curve; Step S44: performing dynamic switching frequency change analysis on the standardized switching frequency curve to generate a dynamic switching frequency change feature; Step S45: performing a quantitative impact analysis of periodic changes on the dynamic switching frequency change characteristics according to the normalized MOS tube array load response curve, and generating a load response-switching frequency change law.

7. The method for self-adapting calibration of an electronic MOS tube array according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing mutation frequency identification on the switching frequency to obtain mutation switching frequency parameters; Step S52: performing a standard frequency deviation analysis on the sudden change switching frequency parameter according to the standardized switching frequency curve to generate a frequency deviation value; Step S53: performing deviation attribution inference on the frequency deviation value to generate frequency deviation attribution data; Step S54: performing adaptive frequency compensation calculation based on the frequency deviation attribution data, thereby adaptively adjusting the frequency compensation parameters.

8. The method for self-adapting calibration of an electronic MOS tube array according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: performing reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation rule, thereby obtaining a reverse load compensation value; Step S62: performing load adaptive calibration on the electronic MOS tube array according to the reverse load compensation value to obtain an adaptive load calibration strategy; Step S63: Perform intelligent collaborative control optimization on the adaptive load calibration strategy and the local current adaptive distribution strategy to construct an adaptive collaborative calibration control model.

9. An adaptive calibration device for an electronic MOS tube array, characterized in that: The method for performing the adaptive calibration of the electronic MOS tube array according to claim 1 comprises: The temperature distribution module is used to obtain a real-time thermal imaging image of the electronic MOS tube array; perform discrete fitting of the temperature distribution on the real-time thermal imaging image to construct a dynamic temperature distribution change diagram; The local current distribution module is used to collect real-time electrical characteristic data of the electronic MOS tube array; perform local adaptive current distribution according to the dynamic temperature distribution change diagram and the real-time electrical characteristic data, thereby constructing a local current adaptive distribution strategy; A load response module, used to perform normalized load response mining based on the real-time electrical characteristic data, so as to construct a normalized MOS tube array load response curve; A quantitative analysis module is used to identify the switching frequency of the real-time MOS tube array; based on the normalized MOS tube array load response curve, the switching frequency is quantitatively analyzed for periodic changes, and a load response-switching frequency change law is generated; An adaptive frequency compensation module, used to identify the sudden change frequency of the switching frequency, perform adaptive frequency compensation calculation, and thus adaptive frequency compensation parameters; The collaborative calibration control module is used to perform reverse load compensation analysis on the adaptive frequency compensation parameters according to the load response-switching frequency variation law, and to construct an adaptive collaborative calibration control model based on the local current adaptive distribution strategy.

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