Power supply test method and system based on high-voltage pulse technology

Abstract

The invention relates to the technical field of power supply testing, and discloses a power supply testing method and system based on a high-voltage pulse technology. The method comprises steps that voltage response data and current response data of a to-be-tested power supply under preset high-voltage pulse parameters are acquired, the voltage response data comprise voltage values of a plurality of test time points, and the current response data comprise current values corresponding to the time points; arranging the two types of data according to a test time sequence to form a voltage and current response sequence; calculating a voltage fluctuation coefficient and a current fluctuation coefficient of each test time point based on the two sequences; and determining an initial performance evaluation index of the to-be-tested power supply according to the two types of fluctuation coefficients. The method can accurately simulate a high-voltage pulse working condition, realizes accurate correspondence of voltage and current data time, quantifies the fluctuation degree of each time point to construct a comprehensive evaluation index, can comprehensively reflect the dynamic response characteristics of a power supply, and is suitable for power supply performance detection in a high-voltage pulse scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply testing, in particular to a power supply testing method and system based on high-voltage pulse technology. BACKGROUND

[0002] During the operation of electronic equipment, as the core of energy supply, the performance stability of the power supply is directly related to the reliable operation of the entire electronic system. With the development of electronic technology towards high precision and high power density, the testing requirements for power supply performance are also continuously improving, especially in the fields of aerospace, industrial control, medical equipment and other fields with strict requirements for power supply reliability, it is necessary to accurately capture the performance of the power supply under complex working conditions to determine whether it meets the actual application requirements. The current mainstream power supply testing method is mostly based on steady-state working condition design, that is, by applying constant voltage or current load, the stability of the power supply output parameters is monitored. Although this method can realize the detection of the basic output capability of the power supply, it has obvious limitations when facing instantaneous high-voltage pulse scenes. In actual application, the power supply often encounters instantaneous high-voltage pulse impact, such as power grid fluctuation, pulse signal generated during equipment start-stop moment, etc., at this time the voltage output and current supply of the power supply will appear temporary fluctuation, and the steady-state testing method cannot simulate such dynamic working condition, it is difficult to obtain the real response data of the power supply under high-voltage pulse, leading to the evaluation of the performance of the power supply is not comprehensive enough. Some existing technologies try to introduce dynamic testing means to simulate dynamic scenes by adjusting the load change rate, but there are deficiencies in the control of high-voltage pulse parameters. Either the key parameters such as amplitude, duration, pulse frequency of high-voltage pulse cannot be accurately set, leading to a large deviation between the test conditions and the pulse environment in actual application; or in the data acquisition process, the voltage data and current data cannot be accurately corresponded in time dimension, making it difficult to accurately associate the voltage and current change at the same time point during subsequent analysis. In addition, the existing dynamic testing method is relatively single in performance evaluation index construction, mostly only through voltage fluctuation range or current fluctuation range to judge the performance of the power supply, ignoring the influence of the difference of fluctuation degree at different time points on the overall stability of the power supply, it is difficult to fully reflect the dynamic response characteristics of the power supply under high-voltage pulse, which may lead to misjudgment of the performance of the power supply, affecting the selection of electronic equipment and system reliability design. SUMMARY

[0003] The purpose of the present application is to provide a power supply testing method and system based on high-voltage pulse technology to solve the problems raised in the background art.

[0004] To achieve the above purpose, the present application provides a power supply testing method based on high-voltage pulse technology, the method comprises: Acquire voltage response data and current response data of the to-be-tested power supply under preset high-voltage pulse parameters, wherein the voltage response data comprises voltage values at multiple test time points, and the current response data comprises current values corresponding to the test time points; Arrange the voltage response data and the current response data in test time sequence to form a voltage response sequence and a current response sequence; Calculate voltage fluctuation coefficients and current fluctuation coefficients at each test time point based on the voltage response sequence and the current response sequence; Determine an initial performance evaluation index of the to-be-tested power supply according to the voltage fluctuation coefficients and the current fluctuation coefficients.

[0005] Preferably, the calculation of the voltage fluctuation coefficient at each test time point comprises: Select voltage values within a preset time range before and after the current test time point to form a local voltage sequence; Calculate a ratio of a standard deviation to an average value of the local voltage sequence as the voltage fluctuation coefficient at the current test time point; Compare the voltage fluctuation coefficient with a preset voltage fluctuation threshold value, and mark test time points exceeding the preset voltage fluctuation threshold value as abnormal test time points.

[0006] Preferably, the determination of the initial performance evaluation index of the to-be-tested power supply comprises: Calculate a ratio of a number of all abnormal test time points to a total number of test time points as a first abnormality ratio; Calculate an average value of all voltage fluctuation coefficients as a first stability parameter; Weightedly sum the first abnormality ratio and the first stability parameter to obtain the initial performance evaluation index.

[0007] Preferably, the method further comprises: Acquire reference voltage response data and reference current response data of the to-be-tested power supply in historical tests; Perform dynamic time warping matching on the voltage response sequence of the current test and the reference voltage response data to obtain a voltage matching degree; Perform dynamic time warping matching on the current response sequence of the current test and the reference current response data to obtain a current matching degree; Correct the initial performance evaluation index based on the voltage matching degree and the current matching degree to obtain a corrected performance evaluation index.

[0008] Preferably, the correction of the initial performance evaluation index comprises: Calculate a geometric mean value of the voltage matching degree and the current matching degree as a comprehensive matching degree; The comprehensive matching degree is compared with a preset matching degree threshold to obtain a matching degree correction coefficient; The initial performance evaluation index is linearly adjusted using the matching degree correction coefficient to obtain the corrected performance evaluation index.

[0009] Preferably, the method further comprises: Obtaining test data of multiple same-type power supplies under the same test conditions to form a reference data set; Calculating the similarity of the corrected performance evaluation index of the to-be-tested power supply and the performance evaluation index of each power supply in the reference data set; According to the similarity, determining the performance grade of the to-be-tested power supply.

[0010] Preferably, the determination of the performance grade of the to-be-tested power supply comprises: Marking the reference power supply with a similarity greater than a preset similarity threshold as a similar power supply; Statistically determining the proportion of the number of similar power supplies with a performance evaluation index better than the to-be-tested power supply as a performance advantage degree; According to the preset interval in which the performance advantage degree is located, determining the performance grade of the to-be-tested power supply.

[0011] Preferably, the method further comprises: Based on the performance grade, adjusting the high-voltage pulse parameters of subsequent tests; Using the adjusted high-voltage pulse parameters to perform a second test to obtain second test data; Comparing and analyzing the second test data with the first test data to verify the stability of the test results.

[0012] Preferably, the verification of the stability of the test results comprises: Calculating the correlation coefficient of the voltage response sequence of the first test and the second test as a voltage stability coefficient; Calculating the correlation coefficient of the current response sequence of the first test and the second test as a current stability coefficient; Comparing the weighted sum of the voltage stability coefficient and the current stability coefficient with a preset stability threshold to determine whether the test results are stable; When the test results are stable, the corrected performance evaluation index and the performance grade are taken as the final test results; When the test results are not stable, the high-voltage pulse parameters are readjusted and a third test is performed until stable test results are obtained.

[0013] Preferably, the application further comprises a power supply test system based on high-voltage pulse technology, the system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the power supply test method based on high-voltage pulse technology as described above.

[0014] Compared with the prior art, the application has the following beneficial effects: By obtaining the voltage response data and the current response data of the power supply under the preset high-voltage pulse parameters, and ensuring that the voltage response data and the current response data correspond to the same test time point, the high-voltage pulse scenario encountered by the power supply in actual application can be accurately simulated, so that the collected response data is more consistent with the real working condition, and a more reliable basis is provided for subsequent performance evaluation. Compared with the defect that the traditional steady-state test method cannot cover the dynamic pulse working condition, this method fills the gap of power supply test under high-voltage pulse scenario, so that the test process is more targeted, and the performance evaluation deviation caused by the disconnection between the test working condition and the actual application can be effectively avoided. In the data processing link, the voltage response data and the current response data are arranged in sequence according to the test time sequence, so that the dynamic process of the change of voltage and current with time can be clearly presented, and the change trend of the output parameters of the power supply at different time nodes can be observed intuitively. This ordered processing in the time dimension solves the problem of poor time correspondence of voltage and current data in some existing dynamic test methods, and ensures that the voltage and current fluctuation at the same time can be accurately associated during subsequent analysis, providing data support for in-depth exploration of the dynamic response mechanism of the power supply under high-voltage pulse. By calculating the voltage fluctuation coefficient and the current fluctuation coefficient at each test time point, the fluctuation degree of the output parameters of the power supply at different time points can be accurately quantified, breaking through the limitation of the existing test method that only relies on a single fluctuation range for performance judgment. The fluctuation coefficients at different time points can reflect the stability difference of the power supply at different stages under the action of high-voltage pulse, such as the fluctuation intensity of voltage and current at the initial moment of pulse, during the pulse duration and after the pulse ends, so as to capture the detailed characteristics of the dynamic response of the power supply more carefully. This refined fluctuation quantization method can fully show the dynamic performance of the power supply under high-voltage pulse, avoid performance misjudgment caused by single evaluation index, and make the analysis of the performance of the power supply more in-depth. The initial performance evaluation index is determined based on the voltage fluctuation coefficient and the current fluctuation coefficient, which can construct a comprehensive evaluation system for the power supply performance from multiple dimensions, and is no longer limited to the consideration of a single parameter. The initial performance evaluation index can comprehensively reflect the voltage stability and current stability of the power supply under high-voltage pulse, and can more comprehensively reflect the dynamic response capability of the power supply. For electronic equipment selection, the evaluation index can be used to more accurately determine whether the power supply is suitable for the high-voltage pulse environment in actual application; for power supply design optimization, by analyzing the fluctuation coefficients and the initial performance evaluation index at different time points, the weak link of the power supply in dynamic response can be determined, which can provide a direction for subsequent improvement design, and thus improve the overall performance and market competitiveness of the power supply product, and also help to improve the reliability of the electronic system when encountering high-voltage pulse impact, and reduce the risk of system failure caused by insufficient power supply performance. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A working principle diagram of the power supply test method based on high-voltage pulse technology according to the present application; Figure 2 A flowchart for calculating the voltage fluctuation coefficient and marking abnormal time points; Figure 3 A flowchart for matching and correcting the initial performance evaluation index by historical reference data. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0017] Please refer to Figure 1The application provides a power supply test method based on high-voltage pulse technology, which comprises the following steps: applying preset high-voltage pulse parameters to a power supply to be tested by a high-voltage pulse generator, wherein the parameters include pulse voltage amplitude, pulse width and repetition frequency; collecting voltage response data and current response data of the power supply to be tested in real time by a data acquisition device, wherein the voltage response data is composed of voltage values at multiple test time points, and the current response data is composed of current values corresponding to the test time points; after the collection is completed, arranging the voltage response data and the current response data in the order of test time to form a voltage response sequence and a current response sequence; based on the voltage response sequence and the current response sequence, calculating a voltage fluctuation coefficient and a current fluctuation coefficient at each test time point, wherein the voltage fluctuation coefficient is obtained by the ratio of the standard deviation to the average value of a local voltage sequence, and the current fluctuation coefficient is calculated in a similar manner; and determining an initial performance evaluation index of the power supply to be tested by statistically weighting and summing the abnormality proportion and stability parameters according to the voltage fluctuation coefficient and the current fluctuation coefficient.

[0018] Embodiment 1: refer to Figure 2 In the calculation of the voltage fluctuation coefficient at each test time point, the voltage values within a preset time range before and after the current test time point are selected, and this preset time range needs to be reasonably set according to the sampling rate of the test system and the characteristics of the power supply to be tested. For example, in a test system with a sampling rate of 1 MHz, the preset time range can be set as a time window covering 100 sampling points. The process of constructing a local voltage sequence is a dynamic data extraction process, and as the test time point moves, this time window also slides on the voltage response sequence to ensure that each point can be included in the calculation range. The ratio of the standard deviation to the average value of the local voltage sequence needs to be calculated by a numerical method, the standard deviation reflects the dispersion degree of the voltage value in this time period, and the average value represents the reference level of the voltage, and the ratio of the two can effectively eliminate the influence of the absolute value size, so that the fluctuation coefficient becomes a dimensionless relative index. As the voltage fluctuation coefficient of the current test time point, this value can quantify the stability performance of the voltage at this moment, and a higher fluctuation coefficient indicates that there is a significant change in the voltage near this time point. When comparing the voltage fluctuation coefficient with the preset voltage fluctuation threshold, the threshold needs to be preset according to the technical specifications and test requirements of the power supply, and a reasonable critical value is usually determined based on historical test data or industry standards. Marking the test time points that exceed the preset voltage fluctuation threshold as abnormal test time points, and this marking process can be realized by adding a flag bit in the data sequence, and the marking of these abnormal points provides an important basis for subsequent analysis and processing.

[0019] The calculation of the current fluctuation coefficient adopts the same processing logic as the voltage fluctuation coefficient. The current values within the same preset time range before and after the current test time point are selected to form a local current sequence. The selection of this time window needs to be consistent with the voltage analysis to ensure data consistency. The ratio of the standard deviation to the average value of the local current sequence is calculated as the current fluctuation coefficient. The processing method of current data is completely symmetrical with that of voltage data, which can ensure the comparability of the analysis methods of the two parameters. The current fluctuation coefficient is compared with the preset current fluctuation threshold. The current fluctuation threshold may be different from the voltage threshold, which depends on the different characteristic requirements of voltage and current in the power supply system. The test time points exceeding the threshold are marked as abnormal test time points. The identification of these abnormal points helps to fully understand the dynamic response characteristics of the power supply in the working process.

[0020] The determination of the preset time range needs to consider multiple factors, including the response characteristics of the power supply, the duration of the high-voltage pulse, and the accuracy requirements of the test. A too small time window may not be able to capture meaningful fluctuation information, while a too large time window may smooth out important transient characteristics. The construction of the local voltage sequence is a continuous process, and a new local sequence is generated for each test time point. There is some overlap between these sequences to ensure that no important fluctuation information is missed. The standard deviation is calculated using the standard statistical method, and the average value is calculated using the arithmetic average algorithm. These calculations need to ensure the accuracy and efficiency of numerical processing. The calculation result of the fluctuation coefficient provides a quantitative index for subsequent analysis. The fluctuation coefficient of each time point constitutes a new time sequence, which reflects the stability changes of the power supply during the entire test process. The comparison process with the threshold value adopts a simple numerical comparison algorithm. All time points exceeding the threshold value are recorded in the abnormal point list. The marking of abnormal points not only includes time point position information, but also records the specific value and the degree of exceeding the threshold. These detailed information helps to in-depth analyze the abnormal performance of the power supply. The entire processing process needs to ensure the calculation efficiency, especially in the high sampling rate test scenario, the data volume may be large, therefore, optimized algorithms need to be used to realize real-time or near real-time processing. The sliding window processing method can be realized by using a circular buffer to avoid repeated data copy operations. The calculation of the standard deviation and the average value can use the incremental calculation method to simplify the calculation process of the current window and improve the processing speed.

[0021] The processing flow of current data is completely parallel to that of voltage data, and both processes can use the same algorithm structure, except for different input data. This symmetric processing design is beneficial for code reuse and system maintenance. During the processing, it is necessary to ensure the time synchronization of voltage and current data, and the voltage and current data at each test time point must be strictly corresponding, so as to ensure the accuracy of subsequent analysis. The labeling information of abnormal test time points is stored in a special data structure, which records the position and type information of all abnormal points. These labeling information plays an important role in subsequent performance evaluation, for example, when calculating the abnormal proportion, the number of these abnormal points needs to be counted. The distribution pattern of abnormal points can also provide valuable information, for example, the continuous occurrence of abnormal points may indicate that the power supply has a persistent stability problem, while the isolated abnormal points may be just random interference.

[0022] The statistical analysis of local sequences is not limited to standard deviation and mean value, and in some embodiments, other statistical quantities such as kurtosis coefficient or skewness coefficient can be added to more comprehensively describe the distribution characteristics of voltage and current. The calculation method of fluctuation coefficient can also have variants, such as using absolute deviation instead of standard deviation, or using moving range as a fluctuation indicator. These changes can be adjusted according to the specific test requirements and power supply characteristics. The flexibility of threshold setting is an important feature of this method, and the preset voltage fluctuation threshold and current fluctuation threshold can be dynamically adjusted according to different test scenarios. In the initial test stage, a relatively loose threshold can be used, and the threshold requirement can be gradually tightened as the test progresses. The threshold can also be adaptively adjusted according to the specifications of the power supply, for example, a more stringent threshold standard can be used for high-precision power supplies. The accuracy of data processing needs to be guaranteed, especially when calculating the standard deviation and mean value, sufficient floating-point operations with sufficient bits should be used to avoid the accumulation of rounding errors. For embedded system implementation, fixed-point operations or optimized algorithms may need to be considered to balance accuracy and computing resources. The data storage format also needs to be carefully designed to ensure that the accuracy of the original data is preserved while not occupying too much storage space.

[0023] The quality control of the whole process can be achieved in multiple ways, such as adding data validity checks during the processing to exclude obviously unreasonable data points. Redundant calculations can also be used to double-check critical calculation steps to ensure the reliability of the results. The follow-up processing of abnormal points is not limited to marking, and in some embodiments, it can also trigger a real-time response mechanism, such as automatically adjusting test parameters or pausing the test to prevent equipment damage when multiple consecutive abnormal points are detected. This real-time feedback mechanism improves the intelligence and safety of the test system. The scalability of the method is reflected in the ease of adding new analysis indicators, such as further calculating the differential rate of change or integral effect quantity based on the fluctuation coefficient. The modular design of the processing flow allows each calculation step to be optimized or replaced independently without affecting the overall architecture. This design facilitates continuous improvement and adaptation to different testing needs. All calculated fluctuation coefficients and abnormal point information are integrated into a comprehensive data structure, which provides complete basic data for subsequent performance evaluation. The time series nature of the data is fully preserved, allowing detailed calculation processes and results at each time point to be traced back. The design of this data structure takes into account the need for fast queries and batch processing, supporting various subsequent analysis operations.

[0024] Example 2: refer to Figure 3 In the process of determining the initial performance evaluation index of the power supply to be tested, the ratio of the number of abnormal test time points to the total number of test time points is calculated. This calculation requires accurate identification of each abnormal time point and exclusion of possible false positives. The total number of test time points is determined by the product of the test duration and the sampling rate. The first abnormality ratio reflects the proportion of time during which the power supply exhibits abnormal responses during high-voltage pulse testing. A higher value indicates that the power supply's stability is more worthy of attention. Calculating the average of all voltage fluctuation coefficients requires traversing each calculation point during the entire test period. The average is calculated using the arithmetic mean algorithm and retaining sufficient decimal places to ensure accuracy. The first stability parameter is represented by this average, which characterizes the overall voltage output fluctuation level of the power supply. A lower value generally corresponds to better stability performance. When weighting the first abnormality ratio and the first stability parameter, the weight coefficients need to consider the different importance of the two parameters. Typically, the weight of the first abnormality ratio is set higher because it directly reflects the frequency of abnormal situations. The weighting process to obtain the initial performance evaluation index needs to ensure the uniformity of the dimension, sometimes requiring normalization of the two parameters before weighting calculation.

[0025] When obtaining the reference voltage response data and reference current response data of the power supply to be tested in historical tests, these reference data are usually from the factory tests or preliminary verification tests of the same power supply and stored in a special test database. The selection of reference data needs to ensure that the test conditions are as consistent as possible with the current test, including environmental temperature, load conditions, and high pulse parameter settings. When matching the voltage response sequence of the current test with the reference voltage response data using dynamic time warping, this matching process needs to handle the possible timing offset and stretching phenomenon, and the dynamic time warping algorithm can find the optimal alignment path between the two sequences. The calculation of voltage matching degree is based on the difference between the aligned sequences, and the higher the matching degree value, the better the consistency between the current test results and the historical reference. The current sequence is processed using the same dynamic time warping algorithm to obtain the current matching degree, and the current data matching is also important because it reflects the load response characteristics of the power supply. When correcting the initial performance evaluation index based on the voltage matching degree and the current matching degree, the correction algorithm needs to consider the influence of the two matching degrees, and usually uses weighted average or geometric average to integrate the matching degree information into the evaluation index.

[0026] The implementation of the dynamic time warping algorithm needs special attention to the optimization of computational efficiency, especially when processing long time series data, sliding window or segmented processing method can be used to reduce the computational complexity. The calculation of the matching degree not only considers the similarity of the overall sequence, but also pays attention to the matching of key feature points, such as the correspondence of peak points and valley points. The parameter adjustment in the correction process needs to be flexibly set according to the actual test requirements, for example, in some application scenarios that require higher voltage stability, the weight of the voltage matching degree can be appropriately increased. The whole correction process needs to keep the rationality of the evaluation index value range, and avoid the index value exceeding the expected range due to correction.

[0027] The quality management of historical benchmark data is very important, and it is necessary to verify the effectiveness and accuracy of the benchmark data regularly and eliminate historical data that may have problems. The selection of benchmark data is sometimes not limited to a single historical test, and the aggregated results of multiple sets of historical test data can be used as a comprehensive benchmark, which can improve the representativeness and reliability of the benchmark. During the matching process of the current test data and the benchmark data, the systematic differences caused by device aging and environmental factors also need to be considered, and if necessary, a compensation mechanism is introduced to eliminate the influence of these factors. The calculation results of voltage matching degree and current matching degree can be analyzed separately, which helps to identify the different performance characteristics of the power supply in voltage output and current response. If the voltage matching degree is significantly lower than the current matching degree, it may indicate that there is a problem with the voltage regulation loop of the power supply, and vice versa, which may reflect the changes in the response characteristics of the load. This itemized analysis provides directional information for subsequent problem diagnosis. The setting of the matching degree threshold needs to be determined according to the type of power supply and the use scenario, and usually requires the matching degree to reach a certain level before considering that the test results are consistent with the historical performance. The design of the correction algorithm allows the introduction of a nonlinear adjustment mechanism, which uses a larger correction amplitude when the matching degree is below a certain critical value, and uses a smaller adjustment force when the matching degree is higher. This nonlinear correction can amplify the indication effect of abnormal situations and improve the sensitivity of evaluation indicators to performance changes. The influence of matching degree confidence also needs to be considered during the correction process, and the weight of the matching degree of the test section with poor data quality can be reduced. The entire implementation process needs to establish a perfect data recording and tracking mechanism to save the original data, intermediate calculation results and final evaluation indicators of each test. The accumulation of these data provides valuable information for subsequent analysis and improvement, especially when the evaluation results deviate from the actual situation, backtracking analysis can be used to optimize algorithm parameters. Data recording should include all key parameter settings, such as weighting coefficients, matching degree algorithm parameters and threshold settings, to ensure the repeatability of the test process.

[0028] The statistics of abnormal time points require a rigorous counting method to avoid counting errors due to improper data boundary processing. Special handling is required for abnormal points at the edge of the time window to ensure that counting is not missed or repeated due to window sliding. The calculation of the total number of test time points should accurately reflect the number of actual valid data points, excluding invalid data points due to acquisition failures. The weight coefficients in the weighted sum formula can be optimized through machine learning methods, using historical test data to train the weight combination that best reflects the actual performance of the power supply. This data-driven weight determination method can improve the accuracy and reliability of the evaluation index. The weight coefficients can also be designed as configurable parameters, allowing test engineers to adjust them according to different test purposes. In the dynamic time warping matching process, the choice of distance metric will affect the calculation result of the matching degree. The commonly used Euclidean distance or Manhattan distance each has its applicable scenarios. In some cases, derivative-based or curvature-based distance metrics can be used to better capture the shape features of the waveform. The normalization of the matching degree needs to be carefully handled to ensure that the matching degrees between different tests are comparable. The revised performance evaluation index needs to be verified for correlation with other test indices to ensure that it can truly reflect the performance of the power supply. This verification process can be achieved by comparing it with actual usage performance or cross-verifying it with the results of other test methods. Continuous performance index verification helps to identify deficiencies in the evaluation method and improve it. The entire implementation process needs to have good robustness to handle various abnormal situations such as data acquisition interruptions, noise interference, or equipment failures. When abnormal situations occur, the system should give clear error indications rather than misleading evaluation results. Robustness is achieved through a perfect data verification and exception handling mechanism. The final revised performance evaluation index is a comprehensive quantitative value that integrates real-time test data, historical benchmark data, and professional weight settings. This index can serve as an important basis for power supply performance evaluation, providing data support for power supply quality judgment and application selection. The interpretation of the index value needs to be combined with specific test conditions and power supply specifications. Different models of power supplies may have different ranges of index values.

[0029] In the process of calculating the geometric mean of voltage matching degree and current matching degree, it is necessary to ensure that both matching degree values are standardized and in the same numerical range, usually normalized to the interval of zero to one. The comprehensive matching degree is obtained by multiplying the voltage matching degree value and the current matching degree value and then taking the square root. This calculation method can balance the influence of the two parameters and avoid the dominance of a single parameter on the final result. When comparing the comprehensive matching degree with the preset matching degree threshold, the threshold is usually determined based on statistical analysis of a large amount of historical test data, reflecting the typical matching level of similar power supplies in normal state. The process of obtaining the matching degree correction coefficient involves quantitative conversion of the comparison result. When the comprehensive matching degree is higher than the threshold, the correction coefficient will moderately increase the initial evaluation index, and vice versa. When using the matching degree correction coefficient to linearly adjust the initial performance evaluation index, the adjustment process maintains the simplicity and interpretability of mathematical operations, ensuring that the final result does not appear unreasonable extreme values.

[0030] The calculation of the corrected performance evaluation index can use the following relationship: Wherein: represents the corrected performance evaluation index, represents the initial performance evaluation index, is the calculated comprehensive matching degree value, is the preset matching degree threshold. Parameter is the baseline adjustment coefficient, used to maintain the basic balance of the correction result; is the adjustment amplitude coefficient, which controls the strength of the correction; is the sensitivity coefficient, which affects the steepness of the correction response curve; hyperbolic tangent function is used to ensure that the correction process is smooth and limited, avoiding excessive adjustment amplitude.

[0031] The calculation of the geometric mean requires both matching degree values to be of good quality. If either matching degree has significant errors, it will greatly affect the final result. Therefore, before actual calculation, the voltage matching degree and the current matching degree need to be verified for effectiveness, and obviously unreasonable data points need to be removed. The calculation process of the comprehensive matching degree needs to maintain sufficient numerical precision to avoid distortion of the final result due to rounding errors. The determination of the matching degree threshold is not fixed and can be dynamically adjusted according to the type, service life, and importance of the power supply. For power supplies in critical equipment, more stringent threshold standards can be used, while for general-purpose power supplies, the requirements can be appropriately relaxed. This flexibility allows the evaluation method to adapt to different application scenarios and quality requirements. The calculation of the matching degree correction coefficient uses a smooth mathematical function, which makes the correction coefficient not change dramatically when the comprehensive matching degree fluctuates slightly near the threshold. This smoothing property improves the stability of the evaluation result and avoids large changes in the final evaluation conclusion due to small changes in the test data.

[0032] During linear adjustment, special attention needs to be paid to the rationality of the numerical range to ensure that the corrected evaluation index remains within a meaningful numerical interval. Sometimes upper and lower limit protection needs to be set to prevent the corrected index value from exceeding the expected range. This protection mechanism improves the robustness of the method and can handle various boundary conditions. The selection of the hyperbolic tangent function is carefully considered. This function has good mathematical properties: it is an odd function, symmetric about the origin; its output value is limited between -1 and 1; and it has approximately linear response characteristics near the origin. These properties allow the calculation of the correction coefficient to reflect both directionality (increase or decrease) and control the adjustment amplitude. The determination of parameters 、 and requires analysis based on a large amount of experimental data, usually using statistical learning methods or optimization algorithms to determine the optimal parameter combination. Once these parameters are determined, they remain stable for a certain period of time to ensure the consistency of the evaluation method. Regularly recalibrating these parameters can maintain the timeliness of the evaluation method.

[0033] The implementation of the correction process requires the establishment of corresponding quality control mechanisms, including the logging of the calculation process, the preservation of intermediate results, and the verification of the final results. These measures help traceability analysis when problems occur, and are also conducive to continuous improvement of the method. Quality control also includes repeatability verification of the calculation process, ensuring that the same input data always produces the same output results. The calculation of the comprehensive matching degree not only depends on numerical calculation, but also needs to consider the matching of waveform characteristics. Sometimes, although the numerical matching degree is high, there are obvious differences in waveform characteristics, in which case an additional correction factor needs to be introduced. This feature-based matching analysis can improve the accuracy of the evaluation. The setting of the matching degree threshold sometimes needs to use a multi-level threshold system, and different levels of threshold trigger different intensity of correction. For example, a warning threshold and a critical threshold can be set, when the comprehensive matching degree is lower than the warning threshold, mild correction is used, and when it is lower than the critical threshold, strong correction is used. This hierarchical processing method can more accurately reflect the actual state of the power supply. The application of the correction coefficient needs to maintain transparency, and the amplitude and reason of the correction should be clearly stated in the test report. This transparency helps users understand the formation process of the evaluation results, increasing the trust of the evaluation conclusion. At the same time, it can also provide feedback information for subsequent parameter optimization. The calculation efficiency of the entire correction process needs to be guaranteed, especially in batch testing scenarios, to ensure that the correction calculation does not become a performance bottleneck. Optimizing algorithm implementation, using efficient mathematical libraries, and appropriate parallelization of calculations are effective ways to improve efficiency. Efficiency optimization needs to be carried out on the premise of ensuring calculation accuracy. The final corrected performance evaluation index is a more comprehensive and accurate quantitative value, which not only considers the statistical characteristics of the current test data, but also combines the matching degree with historical benchmarks. This index provides a more reliable basis for the performance evaluation of the power supply, supporting various subsequent application decisions. The interpretation of the index value needs to be combined with the specific context, and the same value may represent different performance levels in different application scenarios. The scalability of the method is reflected in the ease of introducing other matching degree factors, such as temperature matching degree or power consumption matching degree. Only by integrating these factors into the calculation of the comprehensive matching degree in an appropriate way can the evaluation dimension and depth be expanded.

[0034] In the process of acquiring test data of multiple same type power supplies under the same test condition, it is necessary to extract data records meeting the conditions from the historical test database, and these data should have the same test environment settings and instrument configurations. Taking the test of a certain type of switching power supply as an example, the reference data set contains the performance evaluation indexes of twenty samples of the same type of power supply under standard test conditions, and these data are collected in a standard experimental environment with a temperature of twenty-five degrees Celsius and a humidity of sixty percent. When calculating the similarity of the corrected performance evaluation index of the power supply to be tested and the performance evaluation index of each power supply in the reference data set, the similarity measurement method based on Euclidean distance is adopted, and the index values of the power supply to be tested and each power supply in the reference data set are compared one by one. According to the similarity calculation result, the performance grade of the power supply to be tested is determined, and the higher the similarity value, the closer the performance characteristics of the power supply to be tested and the reference power supply.

[0035] The reference power supply with a similarity greater than the preset similarity threshold is marked as a similar power supply, and the preset threshold is usually set at about zero point seven, which is a reasonable dividing point determined through a large number of experimental analyses. The proportion of the number of performance evaluation indexes in the similar power supply that is better than the power supply to be tested is calculated, and this proportion calculation needs to clearly define the standard of "better than". Usually, it refers to the evaluation index value of the reference power supply being higher than that of the power supply to be tested by a certain percentage. As the performance advantage degree, this value reflects the relative position of the power supply to be tested in the similar power supply group, and the lower the value, the more outstanding the performance of the power supply to be tested. According to the preset interval of the performance advantage degree, the performance grade of the power supply to be tested is determined, and the preset interval is usually divided into three grades: the advantage degree is less than thirty percent for the excellent grade, between thirty percent and sixty percent for the good grade, and higher than sixty percent for the general grade, as shown in Table 1.

[0036] Table 1: Power supply performance evaluation index table in reference data set Power supply number Performance evaluation index Test batch Running time (hours) Ambient temperature (°C) PS-2023-001 0.87 Batch 1 1200 25 PS-2023-002 0.92 Batch 1 1150 25 PS-2023-003 0.85 Batch 1 1250 25 PS-2023-004 0.89 Batch 2 980 25 PS-2023-005 0.91 Batch 2 1050 25 PS-2023-006 0.88 Batch 2 1100 25 PS-2023-007 0.84 Batch 3 850 25 PS-2023-008 0.90 Batch 3 900 25 PS-2023-009 0.86 Batch 3 950 25 PS-2023-010 0.93 Batch 3 920 25

[0037] In practical operation, the construction of the reference dataset needs to ensure the consistency and comparability of the data, and all reference power supplies should be tested under the same test procedure. Test conditions include but are not limited to: the same load configuration, consistent environmental temperature and humidity, uniform instrument accuracy level, and the same test duration. Detailed test metadata needs to be recorded during data collection, including the production batch of the power supply, cumulative running time, test date, and other auxiliary information, which helps subsequent in-depth analysis. Similarity calculation adopts standardization processing, first normalizing all performance evaluation indicators to the range of zero to one, eliminating the influence of dimension after calculating the distance measure. The calculation result of Euclidean distance is converted to similarity score by exponential function, so that the final similarity value has better interpretability. The selection of similarity threshold needs to be verified by statistics, and the receiver operating characteristic curve analysis is usually used to determine the best threshold point to balance the accuracy and recall rate of classification. The identification process of similar power supplies is a dynamic screening process, and as the reference dataset continues to expand, the number and composition of similar power supplies may change. Therefore, a data update mechanism needs to be established to regularly include new test data into the reference dataset, maintaining the timeliness and representativeness of the dataset. At the same time, a data quality audit mechanism should be established to ensure that the newly added data meets the quality requirements.

[0038] The calculation of performance advantage degree needs to clearly define the comparison benchmark, usually taking the evaluation indicator value of the power supply under test as the reference point, and calculating the proportion of indicator values in similar power supplies that are better than the reference point. The calculation of advantage degree can also introduce weight factors, giving higher weights to reference power supplies with more recent production time and more similar running conditions, so that the comparison result is more valuable. The classification of performance levels needs to combine the requirements of specific application scenarios, and for high-reliability applications, the classification standard should be more stringent; while for general-purpose power supplies, the standard can be appropriately relaxed. The determination of level division points should be based on the distribution characteristics of a large amount of historical data, and the percentile method is usually used to determine the reasonable division points.

[0039] The entire evaluation process needs to establish a perfect data recording and tracking mechanism to save all intermediate calculation results and final determination results. These records not only serve the quality control of this evaluation, but also provide data support for subsequent method optimization. The recording content should include the version information of the reference dataset, the similarity calculation parameters, the threshold setting value, and the classification standard of the key information. The presentation of the evaluation results needs to be clear and understandable, in addition to the final performance level, it should also provide supporting detailed data, including the number of similar power supplies, the specific value of performance advantage degree, and the distribution of power supplies in each level, etc. These additional information helps users to fully understand the formation process of the evaluation results, and to judge the reliability of the results.

[0040] The flexibility of the method is reflected in its ability to adapt to reference datasets of different sizes, whether there are only a few reference samples or a large amount of historical data, and to produce meaningful evaluation results. For cases with small amounts of data, methods such as nearest neighbor algorithm or kernel density estimation can be used to improve the stability of the evaluation. As the reference data accumulates, the reliability and accuracy of the evaluation results will gradually improve. Quality control measures include three links of data verification, calculation review and result review. Data verification ensures the integrity and reasonableness of the input data; calculation review checks the correctness of the calculation process; result review is the final confirmation of the evaluation results by professional personnel. These measures together guarantee the quality and reliability of the evaluation process. The final performance grade evaluation results provide an important basis for the quality control of power supplies, which can be used in factory inspection, regular inspection and fault analysis, etc. The evaluation results can also be combined with other test indicators to form a more comprehensive power supply health status evaluation report, providing data support for the maintenance and management of equipment.

[0041] In Example 5, when adjusting the high-voltage pulse parameters for subsequent testing based on the performance grade, a targeted parameter adjustment strategy needs to be developed according to the obtained performance evaluation results, for example, when the power supply under test shows excellent performance grade in the first test, the pulse amplitude can be appropriately increased and the pulse time can be extended to further verify its performance limit. When using the adjusted high-voltage pulse parameters for the second test, the test engineer needs to reconfigure the output parameters of the high-voltage pulse generator, including setting new voltage peak, pulse width and repetition frequency. The adjustment range of these parameters is usually controlled within 10% to 30% to avoid irreversible damage to the equipment. The process of obtaining the second test data needs to maintain the same collection conditions and environmental factors as the first test, using the same type of data acquisition equipment to record voltage and current response data at the same sampling rate to ensure the comparability of the two test data. When comparing and analyzing the second test data with the first test data, a strict data alignment mechanism needs to be established to eliminate the possible small differences in the collection time points through timestamp matching and sequence interpolation processing. The stability of the test results needs to be verified from multiple dimensions, including the consistency of the waveform shape, the repeatability of the characteristic values, and the similarity of the statistical characteristics. When calculating the correlation coefficient of the voltage response sequence of the first test and the second test, the standard statistical method based on covariance and standard deviation is used. This calculation process needs to consider the overall trend and local features of the sequence, and the correlation coefficient result is used as the voltage stability coefficient to reflect the consistency degree of the voltage waveforms of the two tests. The calculation of the current stability coefficient uses the same methodology, focusing on the repeatability and consistency of the current response.

[0042] When comparing the weighted sum of voltage stability coefficient and current stability coefficient to the preset stability threshold, the weight distribution needs to be adjusted according to the test purpose. If the test focus is on voltage stability, give the voltage coefficient a higher weight, otherwise increase the weight proportion of the current coefficient. The decision-making process of whether the test result is stable needs to establish clear judgment criteria, for example, when the weighted sum value exceeds zero point nine, it can be considered that the test result has good repeatability, and when the weighted sum value is less than zero point eight, it indicates that there is a significant difference between the two tests. When the test result is stable, the performance evaluation index and performance level are output as the final test result, and these results need to be presented in the form of a standardized report, including all key data and supporting information. When the test result is not stable, systematic analysis is needed to adjust the high-voltage pulse parameters, first check the possible influencing factors including environmental condition changes, equipment connection state, power supply preheating degree and other external factors, then based on the data analysis results of the first two tests, a new parameter adjustment scheme is developed. When performing the third test, the parameter adjustment usually adopts a more conservative strategy, the change range of pulse parameters is controlled within a small range of five to fifteen percent, to gradually approach the optimal test conditions. The test cycle continues until a stable test result is obtained, and each iteration needs to record the adjustment parameters, test data and stability coefficients completely, forming a complete test log.

[0043] In practical operation, the parameter adjustment process needs to follow the principle of gradual progress, for example, a certain type of power supply uses a pulse amplitude of one thousand volts and a pulse width of one hundred microseconds in the first test, when the test result shows excellent level, the second test will increase the pulse amplitude to one thousand two hundred volts and extend the pulse width to one hundred and twenty microseconds. The data acquisition of the second test shows that the voltage response waveform is significantly different from the first test, and the stability coefficient calculation shows that the weighted sum value is zero point seven six, which is lower than the set threshold requirement of zero point eight five. In this case, the third test will adjust the pulse parameters to one thousand one hundred volts and one hundred and ten microseconds, which is based on the regression analysis results of the previous two test data. The comparative analysis of test data needs to use professional data processing tools to draw the voltage and current sequences of the two tests in the same coordinate system for visual comparison, and observe the coincidence degree and deviation distribution of the waveform. The calculation of stability coefficient not only considers the overall correlation, but also pays special attention to the consistency of key feature points such as peak voltage, rise time, overshoot amplitude and other parameters. For the instability phenomenon found, in-depth analysis is needed, which may be due to the thermal characteristics of the power supply, component aging or slight changes in the test system. The final test result needs to be verified multiple times, and when stable test results are obtained, repeated verification tests need to be carried out under the same conditions to confirm the reliability of the results. All test data need to be properly saved and complete traceability records need to be established, including raw data, processing process, calculation results and decision basis. The generation of test report needs to follow the standardized template, clearly present the test conditions, methods, results and conclusions, and provide authoritative basis for the performance evaluation of power supply. The whole implementation process reflects the scientificity and rigor of the test method, through multiple iteration tests and parameter optimization, to ensure the accuracy and reliability of the evaluation results. This method is especially suitable for important application occasions with strict requirements on power supply performance, and can provide reliable data support for power supply selection and use. The data and experience accumulated in the test process also provide valuable information for the improvement and optimization of subsequent test methods.

[0044] While embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power supply test method based on high-voltage pulse technology, characterized in that, The method comprises: obtaining voltage response data and current response data of the to-be-tested power supply under preset high-voltage pulse parameters, wherein the voltage response data comprises voltage values at multiple test time points, and the current response data comprises current values corresponding to the test time points; arranging the voltage response data and the current response data in a test time sequence to form a voltage response sequence and a current response sequence; calculating a voltage fluctuation coefficient and a current fluctuation coefficient at each test time point based on the voltage response sequence and the current response sequence; determining an initial performance evaluation index of the to-be-tested power supply according to the voltage fluctuation coefficient and the current fluctuation coefficient.

2. The power supply test method based on high-voltage pulse technology according to claim 1, characterized in that, The calculation of the voltage fluctuation coefficient at each test time point comprises: selecting voltage values within a preset time range before and after the current test time point to form a local voltage sequence; calculating a ratio of a standard deviation to an average value of the local voltage sequence as the voltage fluctuation coefficient at the current test time point; comparing the voltage fluctuation coefficient with a preset voltage fluctuation threshold, and marking a test time point exceeding the preset voltage fluctuation threshold as an abnormal test time point.

3. The power supply test method based on high-voltage pulse technology according to claim 2, characterized in that, The determination of the initial performance evaluation index of the to-be-tested power supply comprises: statistically calculating a ratio of a number of all abnormal test time points to a total number of test time points as a first abnormality ratio; calculating an average value of all voltage fluctuation coefficients as a first stability parameter; weighting and summing the first abnormality ratio and the first stability parameter to obtain the initial performance evaluation index.

4. The power supply test method based on high-voltage pulse technology according to claim 3, characterized in that, The method further comprises: obtaining reference voltage response data and reference current response data in historical tests of the to-be-tested power supply; performing dynamic time warping matching on the voltage response sequence of the current test and the reference voltage response data to obtain a voltage matching degree; performing dynamic time warping matching on the current response sequence of the current test and the reference current response data to obtain a current matching degree; correcting the initial performance evaluation index based on the voltage matching degree and the current matching degree to obtain a corrected performance evaluation index.

5. The power supply test method based on high-voltage pulse technology according to claim 4, characterized in that, The correction of the initial performance evaluation index comprises: calculating a geometric mean of the voltage matching degree and the current matching degree as a comprehensive matching degree; comparing the comprehensive matching degree with a preset matching degree threshold to obtain a matching degree correction coefficient; linearly adjusting the initial performance evaluation index using the matching degree correction coefficient to obtain the corrected performance evaluation index.

6. The power supply test method based on high-voltage pulse technology according to claim 5, characterized in that, The method further comprises: obtaining test data of multiple power supplies of the same type under the same test conditions to form a reference data set; calculating a similarity between the corrected performance evaluation index of the to-be-tested power supply and performance evaluation indexes of power supplies in the reference data set; determining a performance grade of the to-be-tested power supply according to the similarity.

7. The method of claim 6, wherein the high-voltage pulse-based power supply testing method further comprises: The determination of the performance grade of the to-be-tested power supply comprises: marking a reference power supply with a similarity greater than a preset similarity threshold as a similar power supply; statistically calculating a number ratio of performance evaluation indexes of the similar power supplies superior to that of the to-be-tested power supply as a performance advantage degree; determining the performance grade of the to-be-tested power supply according to a preset interval in which the performance advantage degree is located.

8. The power supply test method based on high-voltage pulse technology according to claim 7, characterized in that, The method further comprises: adjusting high-voltage pulse parameters of a subsequent test based on the performance grade; performing a second test using the adjusted high-voltage pulse parameters to obtain second test data; comparing and analyzing the second test data with the first test data to verify stability of the test result.

9. The power supply test method based on high-voltage pulse technology according to claim 8, characterized in that, The verifying the stability of the test result comprises: calculating a correlation coefficient of voltage response sequences of the first test and the second test as a voltage stability coefficient; calculating a correlation coefficient of current response sequences of the first test and the second test as a current stability coefficient; comparing a weighted sum of the voltage stability coefficient and the current stability coefficient with a preset stability threshold to determine whether the test result is stable; when the test result is stable, taking the corrected performance evaluation index and the performance grade as a final test result; when the test result is not stable, readjusting the high-voltage pulse parameters and performing a third test until a stable test result is obtained.

10. A power supply test system based on high voltage pulse technology, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that, The processor, when executing the computer program, implements the steps of the power supply test method based on the high-voltage pulse technology according to any one of claims 1 to 9.

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