A test method and system for a three-phase bridge IGBT driving circuit

Abstract

The present application relates to the technical field of circuit testing, and in particular to a kind of three-phase bridge IGBT drive circuit testing method and system, solve the technical problem that the performance of drive circuit under dynamic complex working condition cannot be accurately evaluated in the process of testing due to the mismatch between test condition and actual working condition in prior art.The method comprises: collecting the operation data set of three-phase bridge IGBT drive circuit;Operation data set includes switching loss data, load data and current waveform data;According to switching loss data and load data, determine the comprehensive abnormality indicator factor reflecting the abnormality of switching loss and load relationship;According to the possibility of abnormality caused by the action of protection circuit is evaluated according to current waveform data, and the circuit abnormality factor for characterizing the abnormal risk of circuit itself is determined by combining the comprehensive abnormality indicator factor;According to circuit abnormality factor, determine the test result of three-phase bridge IGBT drive circuit.

Description

Technical Field

[0001] This invention relates to the field of circuit testing technology, and specifically to a testing method and system for a three-phase bridge IGBT drive circuit. Background Technology

[0002] In the field of power electronics, the three-phase bridge insulated gate bipolar transistor (IGBT) drive circuit is a core component of various electrical equipment and is widely used in key equipment such as frequency converters, motor drives, and new energy power generation devices. Its performance directly determines the reliability of IGBT switching action, the stability of system operation, and overall energy efficiency. Performance testing of this circuit is a core guarantee link that runs through the entire life cycle of equipment research and development, production, and operation and maintenance.

[0003] In real-world operating scenarios, three-phase bridge IGBT drive circuits often face complex and ever-changing operating conditions. Motor drive systems in industrial production frequently start and stop, accelerate and decelerate, and change loads. For example, the load changes significantly when a crane lifts goods of different weights. At the same time, voltage fluctuations occur in industrial power grids due to the connection and disconnection of large equipment. These complex factors under real-world operating conditions will affect the performance of the drive circuit.

[0004] Most existing testing methods are limited to static or quasi-static conditions, focusing only on the performance of the drive circuit under relatively stable and ideal conditions. They fail to fully consider the complex operating conditions in actual operation, resulting in an inability to fully reflect the actual performance of the drive circuit in real working environments and making it difficult to detect potential fault risks in advance. Summary of the Invention

[0005] To address the technical problem in existing technologies where the performance of drive circuits under dynamic and complex operating conditions cannot be accurately evaluated due to the mismatch between test conditions and actual operating conditions, the present invention aims to provide a test method and system for three-phase bridge IGBT drive circuits. The specific technical solution adopted is as follows:

[0006] Firstly, a testing method for a three-phase bridge IGBT drive circuit is provided, comprising: acquiring an operational dataset of the three-phase bridge IGBT drive circuit; the operational dataset includes switching loss data, load data, and current waveform data; determining a comprehensive anomaly indicator factor reflecting the relationship between abnormal switching losses and load based on the switching loss data and load data; assessing the possibility of anomalies caused by the operation of the protection circuit based on the current waveform data, and determining a circuit anomaly factor to characterize the circuit's own anomaly risk based on the comprehensive anomaly indicator factor; and determining the test result of the three-phase bridge IGBT drive circuit based on the circuit anomaly factor.

[0007] Based on the above technical solution, in the test method for a three-phase bridge IGBT drive circuit provided by this invention, by collecting switching loss data, load data, and current waveform data during the operation of the three-phase bridge IGBT drive circuit, the relationship between switching loss and load is correlated to determine the comprehensive anomaly indicator factor. This breaks through the limitations of traditional static or quasi-static testing, enabling the capture of circuit anomalies under complex operating conditions. Furthermore, the possibility of anomalies caused by the operation of the protection circuit is evaluated through the current waveform data, and the comprehensive anomaly indicator factor is corrected. This effectively eliminates interference from faults not caused by the circuit itself, accurately characterizes the circuit's own anomaly risk, improves the accuracy of test results, enables the early detection of potential problems, and ensures the reliability of the actual operation of the drive circuit.

[0008] In conjunction with the first aspect above, in one possible implementation, the method for determining a comprehensive anomaly indicator factor reflecting the relationship between switch loss anomalies and load based on switch loss data and load data specifically includes: clustering load data into multiple clusters according to load similarity; determining at least one continuous time period corresponding to each cluster and the switch loss data within each continuous time period; for each continuous time period, analyzing the switch loss fluctuation characteristics and time trend characteristics within the continuous time period based on the corresponding switch loss data, and determining a first anomaly indicator value; the first anomaly indicator value is used to characterize the degree of anomaly in switch loss within a single continuous time period; for each cluster, determining a second anomaly indicator value for the cluster based on the first anomaly indicator value corresponding to at least one continuous time period; the second anomaly indicator value is used to characterize the overall degree of anomaly in switch loss corresponding to a single cluster; and determining a comprehensive anomaly indicator factor based on the load values ​​and second anomaly indicator values ​​of multiple clusters.

[0009] In conjunction with the first aspect above, in one possible implementation, the method for determining a comprehensive anomaly indicator factor based on the load values ​​and second anomaly indicator values ​​of multiple clusters specifically includes: taking the cluster center of each cluster as the representative load value of the cluster, analyzing the correlation between the representative load value and the second anomaly indicator value; dividing the representative load value sequence into multiple load intervals, analyzing the interval change trend and interval fluctuation amplitude of the second anomaly indicator value with load changes for each load interval, and determining the interval risk index; and determining the comprehensive anomaly indicator factor based on the interval risk indexes of multiple load intervals and their correlation.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for analyzing the correlation between the representative load value and the second anomaly indicator value specifically includes: constructing a sequence of representative load values ​​and a sequence of second anomaly indicator values ​​for multiple clusters according to time sequence; and using the correlation coefficient between the sequence of representative load values ​​and the sequence of second anomaly indicator values ​​as the characterization value of the correlation.

[0011] In conjunction with the first aspect above, in one possible implementation, the method for determining a comprehensive anomaly indicator factor based on interval risk indicators and correlation coefficients of multiple load intervals specifically includes: if the absolute value of the correlation coefficient is greater than or equal to a preset correlation threshold, the comprehensive anomaly indicator factor is determined by a weighted average of the interval risk indicators; the weight of the weighted average is positively correlated with the risk indicators of the corresponding interval; if the absolute value of the correlation coefficient is less than the preset correlation threshold, the comprehensive anomaly indicator factor is determined based on the maximum value of the interval risk indicators of multiple load intervals.

[0012] In conjunction with the first aspect above, in one possible implementation, the method for dividing the representative load value sequence into multiple load intervals specifically includes: extracting the statistical distribution characteristics of the representative load value sequence and determining the division criteria parameter based on the statistical distribution characteristics; dividing the representative load value sequence into multiple consecutive load intervals based on the division criteria parameter; each load interval corresponds to a non-overlapping load range, and the difference between the representative load values ​​within the load interval is within a preset range.

[0013] In conjunction with the first aspect above, in one possible implementation, the method for assessing the likelihood of an anomaly caused by the operation of the protection circuit based on current waveform data specifically includes: statistically analyzing the duration and magnitude of a single instance where the current value exceeds a preset allowable current fluctuation range from the current waveform data; determining the likelihood of an anomaly caused by the operation of the protection circuit based on the duration and magnitude of the single instance; the greater the proportion of the duration of the single instance to the acquisition time and the greater the magnitude of the single instance, the greater the likelihood.

[0014] In conjunction with the first aspect above, in one possible implementation, the method for determining the test result of the three-phase bridge IGBT drive circuit based on the circuit anomaly factor specifically includes: if the probability of an anomaly caused by the protection circuit operation is greater than or equal to a preset protection operation frequency threshold, the three-phase bridge IGBT drive circuit test is deemed unqualified; if the probability of an anomaly caused by the protection circuit operation is less than the preset protection operation frequency threshold, and the circuit anomaly factor is greater than or equal to the preset threshold, the three-phase bridge IGBT drive circuit test is deemed unqualified; if the probability of an anomaly caused by the protection circuit operation is less than the preset protection operation frequency threshold, and the circuit anomaly factor is less than the preset threshold, the three-phase bridge IGBT drive circuit test is deemed qualified.

[0015] In conjunction with the first aspect above, in one possible implementation, the method for collecting switching loss data of the three-phase bridge IGBT drive circuit specifically includes: collecting the collector-emitter voltage and collector current of the IGBT in the three-phase bridge IGBT drive circuit; determining the instantaneous power at each sampling moment based on the collector-emitter voltage and collector current; and determining the switching loss data based on the instantaneous power at multiple sampling moments and the time interval between adjacent sampling moments.

[0016] Secondly, a testing system for a three-phase bridge IGBT driver circuit is provided, comprising: a data acquisition module, an anomaly determination module, an interference elimination module, and a test judgment module; the data acquisition module is used to acquire the operating dataset of the three-phase bridge IGBT driver circuit; the operating dataset includes switching loss data, load data, and current waveform data; the anomaly determination module is used to determine a comprehensive anomaly indication factor reflecting the relationship between switching loss anomalies and load based on the switching loss data and load data; the interference elimination module is used to assess the possibility of anomalies caused by the operation of the protection circuit based on the current waveform data, and, in conjunction with the comprehensive anomaly indication factor, determine a circuit anomaly factor used to characterize the circuit's own anomaly risk; the test judgment module is used to determine the test results of the three-phase bridge IGBT driver circuit based on the circuit anomaly factor.

[0017] Thirdly, a test apparatus for a three-phase bridge IGBT drive circuit is provided, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the actions described in the first aspect and any possible implementation thereof. This test apparatus for the three-phase bridge IGBT drive circuit can be an electronic device or a chip within an electronic device.

[0018] Fourthly, a computer-readable storage medium is provided, which stores instructions that, when executed on a test apparatus for a three-phase bridge IGBT drive circuit, cause the test apparatus to perform the actions described in the first aspect and any possible implementation thereof.

[0019] Fifthly, a computer program product containing instructions is provided, which, when run on a test apparatus for a three-phase bridge IGBT drive circuit, causes the test apparatus for the three-phase bridge IGBT drive circuit to perform the actions described in the first aspect and any possible implementation thereof.

[0020] The present invention has the following beneficial effects:

[0021] By collecting switching loss data, load data, and current waveform data during the operation of the three-phase bridge IGBT drive circuit, the relationship between switching loss and load is correlated to determine the comprehensive anomaly indicator factor. This overcomes the limitations of traditional static or quasi-static testing, enabling the capture of circuit anomalies under complex operating conditions. Furthermore, the current waveform data is used to assess the possibility of anomalies caused by the protection circuit's operation and correct the comprehensive anomaly indicator factor. This effectively eliminates interference from non-circuit faults, accurately characterizes the circuit's own anomaly risks, improves the accuracy of test results, and enables the early detection of potential problems, ensuring the reliability of the drive circuit's actual operation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A system structure diagram of a test system for a three-phase bridge IGBT drive circuit provided in one embodiment of the present invention;

[0024] Figure 2 One of the flowcharts of a test method for a three-phase bridge IGBT drive circuit provided in an embodiment of the present invention;

[0025] Figure 3 The second flowchart illustrates a method for testing a three-phase bridge IGBT drive circuit according to an embodiment of the present invention.

[0026] Figure 4 The third flowchart of a test method for a three-phase bridge IGBT drive circuit provided in an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of the hardware structure of a test device for a three-phase bridge IGBT drive circuit provided in one embodiment of the present invention. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a test method and system for a three-phase bridge IGBT drive circuit proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0030] The following description, in conjunction with the accompanying drawings, details the specific scheme of the test method and system for a three-phase bridge IGBT drive circuit provided by this invention.

[0031] Please see Figure 1The diagram shows a system structure diagram of a test system for a three-phase bridge IGBT drive circuit according to an embodiment of the present invention. The test system for the three-phase bridge IGBT drive circuit includes: a data acquisition module 1, an anomaly determination module 2, an interference elimination module 3, and a test judgment module 4.

[0032] Among them, the data acquisition module 1 is the core of the system's data input, responsible for acquiring all key data during the operation of the three-phase bridge IGBT drive circuit, providing basic support for subsequent anomaly analysis and interference assessment. It can be implemented through a test platform that integrates sensors, signal acquisition equipment and data processing units.

[0033] In some implementations, data acquisition module 1 includes four sub-modules:

[0034] The voltage acquisition submodule 11 uses a high-precision voltage probe as a physical acquisition device, which is connected to the collector-emitter and gate of the IGBT respectively, and acquires the collector-emitter voltage and gate drive voltage signals in real time to ensure that the probe bandwidth and accuracy meet the requirements of fast voltage signal capture. The acquired voltage signal is transmitted to the switching loss calculation submodule 14.

[0035] The current acquisition submodule 12 uses a current probe or Rogowski coil as the core acquisition component, which is wrapped around the connecting wire of the IGBT collector to accurately acquire the collector current signal. At the same time, it is equipped with an oscilloscope with sufficient sampling rate and storage depth to synchronously acquire current waveform data. The current signal is sent to the switching loss calculation submodule 14, and the current waveform data is directly transmitted to the interference elimination module 3.

[0036] The load acquisition submodule 13 acquires load data in real time during the operation of the load device corresponding to the three-phase bridge IGBT drive circuit through a load sensor or a load monitoring device connected to the drive circuit, and transmits the acquired load data to the anomaly determination module 2.

[0037] The switching loss calculation submodule 14 uses an embedded processor or a dedicated data calculation chip as its core. It receives the voltage signal from the voltage acquisition submodule 11 and the current signal from the current acquisition submodule 12, calculates the instantaneous power at each acquisition moment, and then combines the instantaneous power at multiple sampling moments and the time interval between adjacent sampling moments to determine the switching loss data. Finally, it transmits the switching loss data to the anomaly determination module 2.

[0038] The anomaly determination module 2 is the core analysis unit of the system. It is responsible for mining the correlation anomaly characteristics between switching losses and load, and outputting comprehensive anomaly indicator factors. It can be implemented by an industrial control computer, a field-programmable gate array (FPGA) chip, or a microprocessor with high-speed data processing capabilities. It receives switching loss data and load data transmitted by the data acquisition module 1, and the processed results provide the core analysis basis for the interference elimination module 3.

[0039] In some implementations, the exception determination module 2 includes three sub-modules:

[0040] The load clustering and basic anomaly extraction submodule 21 uses a preset clustering algorithm to cluster the load data according to similarity, forming multiple clusters. At the same time, it determines at least one continuous time period corresponding to each cluster and the switching loss data within each time period. It analyzes the switching loss data of each continuous time period, extracts fluctuation characteristics and time trend characteristics, and calculates a first anomaly indication value that represents the degree of anomaly in the switching loss of a single time period. Then, it performs comprehensive processing on all the first anomaly indication values ​​corresponding to each cluster to obtain a second anomaly indication value that represents the overall degree of anomaly in the switching loss of the cluster. The cluster information and the second anomaly indication value are synchronously transmitted to the correlation analysis and interval risk calculation submodule 22.

[0041] The correlation analysis and interval risk calculation submodule 22 uses the cluster center of each cluster as the representative load value, constructs a representative load value sequence and a second anomaly indicator value sequence in time sequence, calculates the correlation coefficient between the two sequences through a preset correlation analysis algorithm, and uses it as the characterization value of the correlation between load and anomaly; extracts the statistical distribution characteristics of the representative load value sequence, determines the division basis parameters, divides the representative load value sequence into multiple continuous non-overlapping load intervals, and ensures that the difference of representative load values ​​within the intervals meets the preset requirements; for each load interval, analyzes the changing trend and fluctuation range of the second anomaly indicator value with load, calculates the interval risk index for each interval, and transmits the correlation coefficient and all interval risk indicators to the comprehensive anomaly indicator factor determination submodule 23.

[0042] After receiving the correlation coefficient and risk indicators of each interval, the comprehensive anomaly indicator determination submodule 23 first compares the absolute value of the correlation coefficient with the preset correlation threshold. If the absolute value of the correlation coefficient is greater than or equal to the preset correlation threshold, the comprehensive anomaly indicator is determined by the weighted average of the interval risk indicators, where the weight of the weighted average is positively correlated with the risk indicators of the corresponding interval. If the absolute value of the correlation coefficient is less than the preset correlation threshold, the comprehensive anomaly indicator is determined based on the maximum value of the interval risk indicators of multiple load intervals, and the factor is transmitted to the interference elimination module 3.

[0043] Interference elimination module 3 is used to eliminate false abnormal interference caused by the operation of the protection circuit, ensuring accurate characterization of the circuit's own abnormalities. It can be implemented through a signal processing module, a dedicated chip, or an integrated data processing unit. It receives the current waveform data from the data acquisition module 1 and the comprehensive abnormality indication factor from the abnormality determination module 2, and outputs the circuit abnormality factor to provide a basis for judgment for the test judgment module 4.

[0044] In some implementations, interference elimination module 3 includes three sub-modules:

[0045] The current fluctuation analysis submodule 31 receives the current waveform data transmitted by the current acquisition submodule 12, determines the allowable current fluctuation range based on the motor technical parameters corresponding to the three-phase bridge IGBT drive circuit, and counts the duration and magnitude of the single current value exceeding the allowable fluctuation range in the current waveform data. The statistical results are sent to the protection action probability assessment submodule 32.

[0046] The protection action probability assessment submodule 32 calculates the proportion of the single duration to the total acquisition time based on the single duration and single exceedance amplitude output by the current fluctuation analysis submodule 31. It then comprehensively assesses the probability of an anomaly caused by the protection circuit action in conjunction with the maximum exceedance amplitude. The higher the proportion and the larger the single exceedance amplitude, the higher the probability. The assessment result is transmitted to the circuit anomaly factor correction submodule 33.

[0047] The circuit anomaly factor correction submodule 33 receives the evaluation results from the protection action probability assessment submodule 32 and the comprehensive anomaly indication factor from the anomaly determination module 2. It eliminates the interference of protection circuit action on anomaly assessment through preset correction logic, and finally determines the circuit anomaly factor that characterizes the circuit's own anomaly risk. This factor is then transmitted to the test judgment module 4.

[0048] Test judgment module 4 is the system's result output unit, responsible for giving clear test conclusions based on circuit anomaly factors. It can be implemented through a microcontroller, industrial display, and linkage control interface. It receives circuit anomaly factors from interference elimination module 3, and the output test results can be directly used for subsequent equipment operation and maintenance or fault handling.

[0049] In some implementations, the test decision module 4 includes three sub-modules:

[0050] The threshold storage submodule 41 uses a non-volatile storage unit as a physical carrier to pre-store the anomaly judgment threshold determined by experimental verification and engineering practice, providing a unified reference standard for comparison judgment. The stored threshold is transmitted to the comparison judgment submodule 42.

[0051] The circuit anomaly factor of the receiving circuit anomaly factor correction submodule 33 and the preset threshold of the threshold storage submodule 41 are compared with those of the receiving circuit anomaly factor correction submodule 33. The probability of an anomaly caused by the protection circuit action is compared with the protection action frequency threshold and the circuit anomaly factor is compared with the preset threshold. If the probability of an anomaly caused by the protection circuit action is greater than or equal to the preset protection action frequency threshold, the three-phase bridge IGBT drive circuit test is deemed unqualified. If the probability of an anomaly caused by the protection circuit action is less than the preset protection action frequency threshold and the circuit anomaly factor is greater than or equal to the preset threshold, the three-phase bridge IGBT drive circuit test is deemed unqualified. If the probability of an anomaly caused by the protection circuit action is less than the preset protection action frequency threshold and the circuit anomaly factor is less than the preset threshold, the three-phase bridge IGBT drive circuit test is deemed qualified. The determination result is sent to the result output submodule 43.

[0052] The result output submodule 43 intuitively outputs the test conclusions of the comparison and judgment submodule 42 through physical devices such as industrial displays, indicator lights, or communication interfaces. At the same time, it can link with external fault diagnosis equipment or operation and maintenance systems to trigger fault diagnosis and location processes for unqualified cases and to provide continuous monitoring instructions for qualified cases, ensuring the engineering applicability of the test results.

[0053] Please see Figure 2 The diagram illustrates a method flowchart for testing a three-phase bridge IGBT drive circuit according to an embodiment of the present invention. The method includes:

[0054] S1. Collect the running data set of the three-phase bridge IGBT drive circuit.

[0055] Relying solely on static test data cannot cover the complex operating conditions of the actual operation of the drive circuit. Only by collecting multi-dimensional data during real-world operation can the actual performance of the circuit under different loads and switching frequencies be accurately reflected, avoiding the limitations of insufficient coverage of operating conditions in static testing. Based on this, the runtime dataset should include at least three types of data:

[0056] Switching loss data: Changes in switching losses are directly related to abnormal IGBT operating states. In some implementations, methods for collecting switching loss data include: collecting the collector-emitter voltage (which directly reflects the IGBT's operating state transition during switching) and collector current (reflecting the current carried during IGBT conduction) of the IGBT in the three-phase bridge IGBT drive circuit; determining the instantaneous power at each sampling moment based on the collector-emitter voltage and collector current, and the principle that power equals the product of current and voltage; determining the switching loss data, representing the energy consumed during IGBT switching, based on the instantaneous power at multiple sampling moments and the time interval between adjacent sampling moments (e.g., 10 ns). Specifically, for all sampling moments within the turn-on time range, the corresponding instantaneous power is accumulated and multiplied by the sampling time interval to obtain the turn-on loss; the same method is used to calculate the turn-off loss for the sampling moments within the turn-off time range; finally, the turn-on loss and turn-off loss are summed to obtain the total switching loss data.

[0057] Load Data: The performance of the drive circuit fluctuates with load changes, and the abnormal characteristics differ under different load conditions. On the load device (such as a crane) corresponding to the three-phase bridge IGBT drive circuit, a tension sensor with a range of 0-10t and an accuracy of ±1% is installed to collect load values ​​under different operating scenarios. For example, load data is collected for the daily, scattered cargo lifting operations of cranes in ordinary factory workshops (150-300 starts and stops per hour), while load data is also collected for cranes in high-intensity operating environments (300-600 starts and stops per hour), covering datasets of different load intensities.

[0058] Current waveform data: The fluctuation characteristics of the current waveform can reflect whether the current exceeds the allowable range, thus distinguishing between protection circuit operation and abnormality of the drive circuit itself. Based on the IGBT operating current (e.g., 0-300A) and frequency (e.g., 10kHz), select a current probe with a range of 0-500A and a bandwidth of 50MHz, paired with an oscilloscope with a sampling rate of 1GS / s and a storage depth of 10Mpts. Wrap the measuring jaws of the current probe around the connecting wires of the IGBT collector, ensuring the wires completely pass through the center of the jaws. During normal IGBT operation, activate the oscilloscope's acquisition function to collect current waveform data.

[0059] S2. Based on the switching loss data and load data, determine the comprehensive anomaly indicator factor that reflects the relationship between abnormal switching losses and load.

[0060] In one possible implementation, combining Figure 2 ,like Figure 3 As shown, the method in S2 described above can be specifically implemented through the following steps S21 to S24, which are explained in detail below:

[0061] S21. Cluster the load data into multiple clusters based on load similarity, and determine at least one continuous time period corresponding to each cluster and the switching loss data within each continuous time period.

[0062] The load on a motor changes dynamically with operating demands, and the switching loss characteristics of IGBTs differ under different loads. If all load data are directly mixed for analysis, it is easy to mask the abnormal characteristics under specific load conditions. By clustering loads, similar load conditions can be grouped into one category, and then the switching loss data for the corresponding continuous time period can be locked, enabling targeted analysis under different load scenarios and improving the accuracy of subsequent anomaly identification.

[0063] In some implementations, a preset K-means clustering algorithm (with load value as the core feature) is used, and Euclidean distance is used to characterize the similarity between load data (the smaller the distance, the higher the similarity). All load data are clustered to obtain multiple clusters (for example, the preset threshold for the number of clusters is 3, corresponding to light load, medium load, and heavy load). The loads at all times contained in each cluster are nearly consistent, and the dynamically changing load data are classified into a working condition group with unified features, which facilitates the subsequent independent analysis of the abnormal characteristics of switching losses for working conditions with different load intensities.

[0064] Iterate through all the time points contained in each cluster, group the load of multiple consecutive time points in the same cluster into a continuous time period, and record the length of each continuous time period. Lock the continuous running time period under the same load condition to avoid mixing loss data from scattered time periods and ensure that the subsequent analysis is of the switching loss characteristics of continuous operation under the same condition.

[0065] Based on the continuous time periods obtained from the division, the number of switching actions of the motor within the corresponding time period is retrieved, and the switching loss data corresponding to all switching actions within the time period is extracted.

[0066] S22. For each continuous time period, analyze the switching loss fluctuation characteristics and time trend characteristics within the continuous time period based on the corresponding switching loss data, and determine the first abnormal indication value.

[0067] Under the same load, the working environment and electrical conditions of IGBTs are relatively consistent, and the switching losses should remain stable without significant changes. If the switching losses fluctuate greatly or change in a trend under this condition, it often corresponds to a circuit abnormality. Therefore, analyzing the fluctuation and time trend of switching losses over a continuous period of time is the core basis for identifying anomalies in a single time period.

[0068] The first anomaly indication value is used to characterize the degree of anomaly in switching losses within a single continuous time period.

[0069] In some implementations, the first exception indicator value is represented as:

[0070]

[0071] In the formula, and These represent the maximum and minimum values ​​of the switching loss data for consecutive time periods, respectively. This represents the average value of switching loss data corresponding to a continuous time period. The fluctuation range of switching loss is quantified by calculating the ratio of the difference between the maximum and minimum values ​​of switching loss data corresponding to a continuous time period to the average value. and To pre-determine the weighting coefficients and satisfy α+β=1, for example, when it is necessary to reflect that trend changes (long-term anomalies) are more important than fluctuations (short-term disturbances), α can be set to 0.4 and β to 0.6; if If the sum is 0, then A = 0.

[0072] b represents the absolute value of the slope of the fitted straight line for the switching loss data corresponding to the continuous time period. Specifically, the switching loss data corresponding to the continuous time period is linearly fitted using a preset least squares fitting algorithm to obtain a straight line showing the change of loss over time. The absolute value of the slope of this straight line is then extracted to capture the trend of loss change.

[0073] t represents the duration of a continuous time period; This represents the change in switching losses caused by a trend over a continuous time period, which is further divided by the average loss to obtain... It indicates the relative degree of change in wear level relative to the current operating condition, and is more in line with the actual operating status of the current operating condition.

[0074] Furthermore, fluctuation amplitude reflects the short-term dispersion of switching losses (such as transient interference), while trend changes reflect the long-term gradual nature of losses (such as aging and performance degradation). Both can indicate circuit anomalies, but the degree of risk differs: if the fluctuation amplitude is large but the trend change is small, it may be transient interference, with a lower risk; if the trend change is large but the fluctuation amplitude is small, it may be slow aging, with a moderate risk; if both increase simultaneously, it indicates a serious circuit anomaly, with the highest risk. To comprehensively evaluate the contribution of the two anomalies and avoid ignoring anomalies when a single factor is zero, a weighted summation is used to calculate the first anomaly indication value A. The weighting coefficients α and β are used to adjust the contribution of fluctuation and trend, respectively, and α + β = 1. The larger the value of A, the higher the degree of anomaly in the switching losses within that continuous time period. Specifically, if the average value of the switching loss data... If the result is zero, then let A = 0.

[0075] It should be noted that in actual operating scenarios, switching losses do not change strictly linearly with time. They are influenced by multiple factors (such as IGBT junction temperature fluctuations, slight deviations in gate drive voltage, instantaneous load fluctuations, and electromagnetic interference in the circuit). Their variation patterns include both near-linear trends and random fluctuations. Specifically, if the IGBT experiences progressive failures such as aging or deterioration of the cooling system performance, the switching losses will show a slow upward / downward trend. In this case, the absolute value 'b' of the slope of the linearly fitted line can characterize this long-term trend. Furthermore, the superposition of other interference factors mentioned above will cause the switching losses to fluctuate randomly around the trend line (e.g., at one moment, due to a sudden increase in junction temperature, the loss briefly exceeds the trend line; at the next moment, due to a slight decrease in load, the loss briefly falls below the trend line). Therefore, the "difference between the maximum and minimum values" is usually different from the "product of the slope and time".

[0076] S23. For each cluster, determine the second anomaly indicator value of the cluster based on the first anomaly indicator value corresponding to at least one consecutive time period.

[0077] The second anomaly indicator value is used to characterize the overall anomaly degree of switching loss corresponding to a single cluster.

[0078] In some implementations, all consecutive time periods under the same cluster are traversed, the average of the first anomaly indication values ​​corresponding to all consecutive time periods is calculated, and the average is recorded as the second anomaly indication value of the cluster. This value is used to characterize the overall anomaly degree of switching loss corresponding to a single cluster. The anomaly characteristics of all time periods within the cluster are integrated to obtain the overall anomaly index under the load condition.

[0079] S24. Determine the comprehensive anomaly indicator factor based on the load values ​​of multiple clusters and the second anomaly indicator value.

[0080] The three-phase bridge IGBT drive circuit will dynamically switch between different loads. Analyzing only the anomalies of a single load condition cannot cover the entire operating scenario. By associating the relationship between the load and the anomaly and splitting the load range to quantify the risk differences, a comprehensive anomaly indication covering the entire operating condition can be obtained.

[0081] In some implementations, the first step is to use the cluster center of each cluster as the representative load value of the cluster and analyze the correlation between the representative load value and the second anomaly indicator value. Specifically, this includes: constructing a sequence of representative load values ​​and a sequence of second anomaly indicators for multiple clusters according to time sequence; using the correlation coefficient (such as the Pearson correlation coefficient) between the representative load value sequence and the second anomaly indicator value sequence as the characterization value of the correlation, thus clarifying the correlation trend between load changes and anomaly degree.

[0082] The second step is to divide the representative load value sequence into multiple load intervals, and analyze the interval change trend and interval fluctuation range of the second anomaly indicator value with load changes for each load interval to determine the interval risk index.

[0083] The method for dividing multiple load intervals includes: extracting statistical distribution features of the representative load value sequence and determining the division criteria parameters based on the statistical distribution features; dividing the representative load value sequence into multiple consecutive load intervals according to the division criteria parameters; each load interval corresponds to a non-overlapping load range, and the difference between the representative load values ​​within the load interval is within a preset range. For example, the statistical distribution features include quartiles Q1, Q2, and Q3, with Q1 and Q3 determined as the division criteria parameters. Loads less than Q1 are classified as light load intervals, loads between Q1 and Q3 are classified as medium load intervals, and loads greater than Q3 are classified as heavy load intervals.

[0084] Then, since the relationship between load and switching losses is approximately linearly positively correlated under normal operating conditions—that is, as the load increases, the IGBT collector current increases, leading to increased switching losses—the second anomaly indicator (an indicator of the degree of anomaly in switching losses) typically shows a slow, approximately linear upward trend as the representative load value increases. Based on this, for each load range, a pre-defined least squares fitting algorithm is used to fit a straight line showing the change of the second anomaly indicator with the representative load value. The absolute value of the slope of this line is extracted to characterize the trend of anomaly changes with load within the range. Simultaneously, the range of the second anomaly indicator within the range (i.e., the difference between the maximum and minimum values) is calculated to characterize the fluctuation amplitude of anomaly within the range. Only when both increase simultaneously (both showing a significant trend and exhibiting drastic fluctuations) is it considered a high-risk scenario for the load range. Therefore, the product of the trend and the fluctuation amplitude is used as the range risk indicator, decomposing the entire load range into ranges with unified characteristics, thus quantifying the risk level of each load segment.

[0085] The third step involves determining a comprehensive anomaly indicator factor based on the interval risk indicators of multiple load intervals and the aforementioned correlations. Specifically, this includes:

[0086] Set a preset correlation threshold θ (e.g., θ=0.3). Compare the absolute value of the correlation coefficient f, |f|, with θ.

[0087] If |f|≥θ, a significant correlation is considered between load and anomaly. In this case, the comprehensive anomaly indicator C is calculated using a weighted average of interval risk indicators, with the weights positively correlated with the interval risk indicators, thus highlighting the impact of high-risk intervals. The calculation formula is as follows:

[0088]

[0089] In the formula, N is the number of load intervals. This is the interval risk indicator for the i-th load interval.

[0090] If |f| < θ, the correlation between load and anomaly is considered weak, potentially indicating a severe runaway fault. In this case, it is directly classified as high risk, and the comprehensive anomaly indicator factor C is taken as the maximum value of the risk index in each interval. And multiplied by a penalty coefficient greater than 1 (For example, if the value is 2), it is represented as:

[0091]

[0092] By employing the above methods, we can avoid both the underestimation of risk due to low correlation and the reduction of the overall risk value due to extremely low risk in a single interval.

[0093] S3. Based on the current waveform data, assess the possibility of abnormality caused by the operation of the protection circuit, and combine the comprehensive abnormality indicator factors to determine the circuit abnormality factors used to characterize the abnormality risk of the circuit itself.

[0094] In one possible implementation, combining Figure 2 ,like Figure 4 As shown, the method in S3 above for assessing the possibility of an anomaly caused by the operation of the protection circuit based on the current waveform data can be specifically implemented through the following S31 to S32, which are explained in detail below:

[0095] S31. From the current waveform data, calculate the duration and magnitude of each instance where the current value exceeds the preset allowable current fluctuation range.

[0096] Under stable load, the amplitude of the IGBT collector current should be kept within a relatively fixed range. The characteristics of the current exceeding this range (duration and magnitude of the exceedance) are the core basis for distinguishing between the protection circuit action and the drive circuit itself. The protection circuit action usually causes the current to exceed the range in a short time and regularly, while the current abnormality of the drive circuit fault is often more continuous and has no obvious pattern.

[0097] In some implementations, a reference current value for the corresponding load is obtained from the motor technical manual (e.g., a reference current value of 50A for a light load condition). ±5% of this reference value is set as the allowable current fluctuation range (i.e., 47.5A~52.5A). The times when the current value exceeds the allowable fluctuation range are recorded, and consecutive exceedances are merged to obtain multiple exceedance periods. The duration of each period is extracted. Simultaneously, the absolute value of the difference between the current value at each exceedance time and the boundary of the allowable fluctuation range is calculated as the exceedance amplitude.

[0098] S32. Determine the probability of an abnormality caused by the operation of the protection circuit based on the duration and magnitude of the single occurrence.

[0099] Current anomalies caused by protection circuit operation are typically characterized by a high proportion of exceeding the limit in duration and a large exceedance in magnitude. By integrating the quantitative characteristics of these two dimensions, the probability that the current anomaly is caused by the protection circuit operation can be accurately assessed, avoiding misjudging the protection operation as a fault in the drive circuit itself.

[0100] The greater the proportion of a single duration to the total collection time, and the greater the magnitude of the single exceedance, the higher the likelihood of a positive outcome.

[0101] In some implementations, the probability of an anomaly caused by the operation of the protection circuit is represented as:

[0102]

[0103] In the formula, This indicates the longest duration during which the current amplitude exceeds the allowable fluctuation range within a continuous time period; t represents the duration of the continuous time period. The longest duration of current exceeding the allowable fluctuation range is the proportion of the total duration of the time period. The larger the proportion, the higher the proportion of time with abnormal current, and it is positively correlated with the probability of the protection circuit operating.

[0104] This indicates the maximum extent to which the current amplitude exceeds the allowable fluctuation range within a continuous time period. The larger the value, the stronger the current abnormality, and it is positively correlated with the probability of the protection circuit operating. I is the current reference value, such as 1A, used to eliminate dimensions.

[0105] Among them, the typical characteristics of the protection circuit operation are accurately captured by two maximum values, avoiding the masking of key abnormal signals by averaged data, and adapting to the short-term, sudden and extreme characteristics of the protection circuit operation.

[0106] A typical protection action occurs only when the current simultaneously exceeds the allowable range for a high percentage of consecutive durations and a large single-time exceedance (e.g., during overcurrent-triggered protection, the current will briefly and drastically exceed the range). If only a single characteristic is significant (e.g., a high percentage of duration but a small amplitude, which may indicate a slight load fluctuation; or a large amplitude but an extremely short duration, which may indicate a transient interference), it is not a protection circuit action. Based on this, the probability D of an anomaly caused by protection circuit action is obtained by calculating the product of the longest duration percentage and the maximum exceedance amplitude. The larger the value, the higher the probability that the current anomaly in the current continuous time period is caused by protection circuit action.

[0107] Furthermore, the comprehensive anomaly indicator factor C includes abnormal interference caused by the operation of the protection circuit (the protection operation is a normal protection behavior of the circuit, not a fault of the drive circuit itself). Therefore, the comprehensive anomaly indicator factor C needs to be modified in combination with the possibility of the protection circuit operation to eliminate the abnormal influence of non-circuit itself in order to accurately obtain the anomaly risk indicator focused on the drive circuit itself.

[0108] After calculating the probability D of an anomaly caused by the protection circuit operation for all consecutive time periods, the maximum value of D in each cluster is taken as the probability of protection circuit operation for that cluster. The maximum value of this probability among all clusters is denoted as... Preset protection action probability threshold (This can be adjusted according to the actual application scenario, for example) =0.5), then the overall probability index P of the protection circuit operation is calculated as follows:

[0109]

[0110] The closer P is to 1, the higher the overall probability of the protection circuit activating.

[0111] Next, a threshold for frequent protection actions is preset. (For example =0.8), determine whether the probability index P of the protection circuit operation exceeds this threshold:

[0112] If P≥ If the protection circuit is found to be operating frequently, a fault code indicating frequent protection operation will be directly output, and the three-phase bridge IGBT drive circuit test will be deemed unqualified, thus ending the test process.

[0113] If P < The modified comprehensive anomaly indicator factor is expressed as:

[0114]

[0115] In the formula, C represents the comprehensive abnormality indicator factor under full load conditions, including interference from the operation of the protection circuit.

[0116] It is the negative operation of the protection circuit operation probability index P, used to correct the comprehensive anomaly indicator factor C, to offset the interference of protection circuit operation on anomaly assessment, and to obtain the circuit anomaly factor Y, which characterizes the circuit's own anomaly risk, thus achieving accurate quantification of the circuit's own anomaly.

[0117] S4. Determine the test results of the three-phase bridge IGBT drive circuit based on the circuit abnormality factor.

[0118] In some implementations, if the test is already deemed unqualified due to frequent protection actions in S32, the test process ends; otherwise, the following judgment is made: if the circuit abnormality factor is greater than or equal to the preset threshold, the three-phase bridge IGBT drive circuit test is deemed unqualified; if the circuit abnormality factor is less than the preset threshold, the three-phase bridge IGBT drive circuit test is deemed qualified.

[0119] Specifically, the circuit anomaly factor is first mapped to the [0, 1] interval using a preset normalization algorithm (such as min-max normalization). Since the circuit anomaly factor Y is the anomaly risk indicator of the drive circuit itself after eliminating interference from the protection circuit, but the value range of Y differs under different test scenarios, a clear pass / fail standard can be established by unifying the judgment benchmark through normalization and comparing it with preset thresholds.

[0120] Based on a large amount of engineering test data and the fault critical characteristics of IGBT drive circuits, the judgment threshold can be preset to 0.6.

[0121] If the normalized circuit anomaly factor is ≥0.6, the probability of the circuit failing due to its own anomaly is high, and the three-phase bridge IGBT drive circuit is judged to be unqualified. At this time, the abnormal characteristics of the current switching loss and current waveform can be matched with the typical abnormal characteristic library of faulty components (such as gate drive resistors and IGBT chips) to locate the abnormal components (for example, matching the performance degradation of the IGBT gate drive resistor). After repairing / replacing the damaged or poorly performing components, the test process is re-executed to verify the repair effect.

[0122] If the normalized circuit anomaly factor is less than 0.6, the three-phase bridge IGBT drive circuit is deemed to have passed the test. The IGBT's operating data set is continuously monitored to track the circuit's operating status in real time and to detect potential anomalies in advance.

[0123] Based on the above technical solution, by collecting switching loss data, load data, and current waveform data during the operation of the three-phase bridge IGBT drive circuit, the relationship between switching loss and load is correlated to determine the comprehensive anomaly indicator factor. This overcomes the limitations of traditional static or quasi-static testing, enabling the capture of circuit anomalies under complex operating conditions. Furthermore, the current waveform data is used to assess the possibility of anomalies caused by the protection circuit's operation and correct the comprehensive anomaly indicator factor. This effectively eliminates interference from faults not inherent to the circuit itself, accurately characterizes the circuit's own anomaly risks, improves the accuracy of test results, and enables the early detection of potential problems, ensuring the reliability of the actual operation of the drive circuit.

[0124] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0125] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0126] In this embodiment of the invention, the testing device for a three-phase bridge IGBT drive circuit can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0127] This invention also provides a hardware structure diagram of a test device for a three-phase bridge IGBT drive circuit, see [link / reference]. Figure 5 The test device 500 for the three-phase bridge IGBT drive circuit includes a processor 501, and optionally, a memory 502 connected to the processor 501.

[0128] In the first possible implementation, see Figure 5 The test device 500 for the three-phase bridge IGBT drive circuit also includes a transceiver 503. The processor 501, memory 502, and transceiver 503 are connected via a bus. The transceiver 503 is used to communicate with other devices or communication networks. Optionally, the transceiver 503 may include a transmitter and a receiver. The device in the transceiver 503 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of the present invention. The device in the transceiver 503 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of the present invention.

[0129] Based on the first possible implementation method Figure 5 The schematic diagram shown can be used to illustrate the structure of the test device for the three-phase bridge IGBT drive circuit involved in the above embodiments.

[0130] in, Figure 5 The diagram can also illustrate the system chip in the test device for the three-phase bridge IGBT drive circuit. In this case, the actions performed by the test device for the three-phase bridge IGBT drive circuit can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0131] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in this embodiment can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0132] The processor in this invention may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a standalone semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a System-on-a-Chip (SoC), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), or logic circuits that implement dedicated logic operations.

[0133] The memory in the embodiments of the present invention may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable read-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0134] This invention also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0135] This invention also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0136] This invention also provides a chip, which includes a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0137] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0138] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0139] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A test method for a three-phase bridge IGBT drive circuit, characterized in that, include: Collect the operating dataset of the three-phase bridge IGBT drive circuit; the operating dataset includes switching loss data, load data, and current waveform data; The load data is clustered into multiple clusters based on load similarity, and at least one continuous time period corresponding to each cluster and the switching loss data within each continuous time period are determined. For each continuous time period, analyze the fluctuation characteristics and time trend characteristics of the switching loss within the continuous time period based on the corresponding switching loss data, and determine the first anomaly indication value. The first anomaly indication value is used to characterize the degree of anomaly in switching losses within a single continuous time period; For each cluster, a second anomaly indicator value for the cluster is determined based on a first anomaly indicator value corresponding to at least one consecutive time period. The second anomaly indicator value is used to characterize the overall anomaly degree of switching loss corresponding to a single cluster; The cluster center of each cluster is used as the representative load value of the cluster, and a sequence of representative load values ​​and a second anomaly indicator value sequence of multiple clusters are constructed in time sequence. The correlation coefficient between the load value sequence and the second anomaly indicator value sequence is used as the characterization value representing the association between the load value and the second anomaly indicator value. The representative load value sequence is divided into multiple load intervals. For each load interval, the interval variation trend and interval fluctuation range of the second anomaly indicator value with load change are analyzed to determine the interval risk index. If the absolute value of the correlation coefficient is greater than or equal to the preset correlation threshold, a comprehensive anomaly indicator factor reflecting the relationship between abnormal switching losses and load is determined by the weighted average of the interval risk indicators; the weight of the weighted average is positively correlated with the risk indicators of the corresponding interval. If the absolute value of the correlation coefficient is less than the preset correlation threshold, the comprehensive anomaly indicator factor is determined based on the maximum value of the interval risk index of multiple load intervals. For each consecutive time period, the duration and magnitude of each instance where the current value exceeds the preset allowable current fluctuation range are statistically analyzed from the current waveform data. Based on the duration and magnitude of each single occurrence, the probability of an anomaly caused by the protection circuit action is determined for each consecutive time period; the greater the proportion of the duration of each single occurrence to the collection time and the greater the magnitude of the single occurrence, the greater the probability. For each cluster, the maximum value of the probability of an anomaly caused by the operation of the protection circuit in at least one consecutive time period is taken as the probability of the protection circuit operation for the corresponding cluster. The overall probability index of protection circuit operation is determined based on the ratio of the maximum probability of protection circuit operation in multiple clusters to a preset protection operation probability threshold; the overall probability index is used to characterize the overall probability of protection circuit operation. If the overall probability index is greater than or equal to the preset frequent protection action threshold, the test of the three-phase bridge IGBT drive circuit is deemed unqualified. If the overall probability index is less than the preset protection action frequent threshold, the comprehensive anomaly indicator factor is corrected according to the overall probability index to offset the interference of protection circuit action on anomaly assessment and to determine the circuit anomaly factor used to characterize the circuit's own anomaly risk. If the overall probability index is less than the preset protection action frequent threshold and the circuit abnormality factor is greater than or equal to the preset threshold, the three-phase bridge IGBT drive circuit is determined to be unqualified. If the overall probability index is less than the preset protection action frequent threshold and the circuit abnormality factor is less than the preset threshold, the three-phase bridge IGBT drive circuit is deemed to have passed the test.

2. The test method for the three-phase bridge IGBT drive circuit according to claim 1, characterized in that, The sequence of representative load values ​​is divided into multiple load intervals, including: Extract the statistical distribution features of the representative load value sequence, and determine the division criteria parameters based on the statistical distribution features; According to the division criteria parameters, the representative load value sequence is divided into multiple consecutive load intervals; each load interval corresponds to a non-overlapping load range, and the difference between the representative load values ​​within the load interval is within a preset range.

3. The test method for the three-phase bridge IGBT drive circuit according to claim 1, characterized in that, Data on switching losses of the three-phase bridge IGBT drive circuit was collected, including: Collector-emitter voltage and collector current of the IGBT in the three-phase bridge IGBT drive circuit are collected; The instantaneous power at each acquisition moment is determined based on the collector-emitter voltage and collector current. The switching loss data is determined based on the instantaneous power at multiple sampling times and the time interval between adjacent sampling times.

4. A test system for a three-phase bridge IGBT drive circuit, characterized in that, include: Data acquisition module, anomaly detection module, interference elimination module, and test judgment module; The data acquisition module is used to acquire the operating dataset of the three-phase bridge IGBT drive circuit; the operating dataset includes switching loss data, load data, and current waveform data. The anomaly determination module is used to cluster the load data into multiple clusters according to load similarity, determine at least one continuous time period corresponding to each cluster and the switching loss data within each continuous time period; for each continuous time period, analyze the switching loss fluctuation characteristics and time trend characteristics within the continuous time period based on the corresponding switching loss data, and determine the first anomaly indication value. The first anomaly indication value is used to characterize the degree of anomaly in switching losses within a single continuous time period; For each cluster, a second anomaly indicator value for the cluster is determined based on a first anomaly indicator value corresponding to at least one consecutive time period. The second anomaly indication value is used to characterize the overall anomaly degree of switching loss corresponding to a single cluster; the cluster center of each cluster is used as the representative load value of the cluster, and a sequence of representative load values ​​and a second anomaly indication value sequence of multiple clusters are constructed according to the time sequence. The correlation coefficient between the load value sequence and the second anomaly indication value sequence is used as a representation of the relationship between the load value and the second anomaly indication value. The load value sequence is divided into multiple load intervals, and the interval variation trend and fluctuation range of the second anomaly indication value with load change are analyzed for each load interval to determine the interval risk index. If the absolute value of the correlation coefficient is greater than or equal to a preset correlation threshold, a comprehensive anomaly indication factor reflecting the relationship between switch loss anomaly and load is determined by the weighted average of the interval risk indexes. The weight of the weighted average is positively correlated with the risk index of the corresponding interval. If the absolute value of the correlation coefficient is less than the preset correlation threshold, the comprehensive anomaly indication factor is determined based on the maximum value of the interval risk indexes of multiple load intervals. The interference elimination module is used to, for each continuous time period, statistically analyze the current waveform data to determine the duration and magnitude of each instance where the current value exceeds a preset allowable current fluctuation range; based on the duration and magnitude, determine the probability of an anomaly caused by protection circuit operation for each continuous time period; the greater the proportion of the duration to the acquisition time and the greater the magnitude, the greater the probability; for each cluster, the maximum probability of an anomaly caused by protection circuit operation for at least one continuous time period is taken as the probability of protection circuit operation for the corresponding cluster; based on the ratio of the maximum probability of protection circuit operation in multiple clusters to a preset protection operation probability threshold, determine the overall probability index of protection circuit operation; the overall probability index is used to characterize the overall probability of protection circuit operation; if the overall probability index is less than a preset protection operation frequency threshold, then the comprehensive anomaly indicator factor is corrected based on the overall probability index to offset the interference of protection circuit operation on anomaly assessment, and a circuit anomaly factor is determined to characterize the circuit's own anomaly risk. The test determination module is used to determine that the three-phase bridge IGBT drive circuit fails the test if the overall probability index is greater than or equal to a preset frequent protection action threshold; if the overall probability index is less than the preset frequent protection action threshold and the circuit abnormality factor is greater than or equal to a preset threshold, the three-phase bridge IGBT drive circuit fails the test; if the overall probability index is less than the preset frequent protection action threshold and the circuit abnormality factor is less than the preset threshold, the three-phase bridge IGBT drive circuit passes the test.

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