Automatic test method and system for resistor

Abstract

The invention discloses an automatic test method and system for a resistor. The method comprises the following steps: acquiring a to-be-tested resistor; configuring a dynamic impulse voltage gradient parameter and a staged acquisition strategy for the to-be-tested resistor; based on the dynamic impulse voltage gradient parameter and a staged acquisition strategy, acquiring response voltage, response current and temperature data of the to-be-tested resistor under different impulse voltages as target data; performing feature extraction on the target data, and determining an anti-surge index and an impact response consistency index corresponding to the to-be-tested resistor based on the extracted features; generating a test report according to the anti-surge index and the impact response consistency index; the resistor performance data is acquired in multiple dimensions, and the resistor is accurately tested based on a resistor performance evaluation quantification system.

Description

Technical Field

[0001] This invention relates to the field of electrical variable measurement technology, and in particular to an automated testing method and system for resistors. Background Technology

[0002] As a core passive component in electronic circuits, resistors are widely used in key fields such as power electronics, new energy vehicles, industrial control, and aerospace. Their reliability under surge conditions directly determines the stability and lifespan of the entire system. With the rapid development of modern electronic equipment towards higher voltage, higher current, and higher frequency, the surge conditions faced by resistors are becoming increasingly complex. In practical applications, surge voltages not only fluctuate widely in amplitude but also often exhibit gradient characteristics such as stepped voltage increases, pulse superposition, and dynamic changes in duration. Furthermore, the rapid temperature rise, material aging, and parameter drift of resistors during surges further exacerbate the risk of performance degradation. Therefore, stringent testing requirements are placed on the surge resistance and surge response consistency of resistors.

[0003] Traditional testing methods often employ a single surge voltage mode with fixed amplitude and duration, which cannot simulate the dynamic gradient change characteristics of surge voltage in actual applications. This results in a single data acquisition strategy, one-sided acquisition of resistor dimensional information, and incomplete performance characterization. At the same time, there is a lack of a quantitative system for evaluating resistor performance, and human judgment is highly subjective, leading to inaccurate resistor testing.

[0004] Therefore, there is an urgent need for an automated testing method and system for resistors to solve the above problems. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the aforementioned technologies. Therefore, a first aspect of the present invention aims to provide an automated testing method for resistors, which collects resistor performance data from multiple dimensions and performs accurate testing of resistors based on a quantitative system for resistor performance evaluation.

[0006] A second objective of this invention is to provide an automated testing system for resistors.

[0007] To achieve the above objectives, a first aspect of the present invention provides an automated testing method for resistors, comprising: Obtain the resistor to be tested; Configure dynamic impulse voltage gradient parameters and a phased acquisition strategy for the resistor under test; Based on the dynamic impulse voltage gradient parameters and the phased acquisition strategy, the response voltage, response current and temperature data of the resistor under test under different impulse voltages are collected as target data. Feature extraction is performed on the target data, and the surge resistance index and impulse response consistency index of the resistor under test are determined based on the extracted features. A test report is generated based on the surge resistance index and the impact response consistency index.

[0008] Preferably, before feature extraction from the target data, the method further includes: data cleaning and noise reduction of the target data.

[0009] Preferably, the resistor under test is configured with dynamic impulse voltage gradient parameters and a staged acquisition strategy, including: Obtain the rated voltage and application scenario of the resistor to be tested; Based on the rated voltage of the resistor under test and the application scenario, a dynamic impulse voltage gradient parameter is configured; the dynamic impulse voltage gradient is set based on the impulse voltage value. A phased acquisition strategy is configured for the resistor under test based on dynamic impulse voltage gradient parameters.

[0010] Preferably, based on the dynamic impulse voltage gradient parameters and the staged acquisition strategy, the response voltage, response current, and temperature data of the resistor under test under different impulse voltages are acquired as target data, including: The dynamic impulse voltage gradient parameters are analyzed to determine the target impulse voltage gradient corresponding to the resistor under test; The phased acquisition strategy is analyzed to determine the target acquisition frequency and target acquisition duration for the resistor under test in each timing phase; the timing phases include before impact, during impact, and after impact; the target acquisition frequency includes the response voltage acquisition frequency, response current acquisition frequency, macroscopic temperature acquisition frequency, and microscopic temperature acquisition frequency. Based on the target impulse voltage gradient, target acquisition frequency, and target acquisition duration, the response voltage, response current, and macro-micro point position temperature data of the resistor under test are acquired to obtain the target data. Real-time monitoring of failure prediction conditions; if triggered, the current gradient test is stopped and marked; target data is stored according to the impact gradient-acquisition stage.

[0011] Preferably, feature extraction is performed on the target data, and the surge resistance index and impulse response consistency index of the resistor under test are determined based on the extracted features, including: Multi-dimensional temperature features are extracted from the temperature data in the target data, feature weights are determined by grey relational analysis, and the temperature influence coefficient of the resistor under test is calculated. The voltage stability factor is determined based on piecewise fitting of the response voltage in the target data; features are extracted from the response current data in the target data, and the current immunity factor is determined based on the extracted features; the surge protection index corresponding to the resistor under test is determined based on the voltage stability factor, the current immunity factor, and the temperature influence coefficient. Based on the response voltage curves, peak response voltages, and surge resistance index under different impulse voltages in the target data, the impulse response consistency index of the resistor under test is determined.

[0012] Preferably, multi-dimensional temperature features are extracted based on temperature data in the target data, feature weights are determined through grey relational analysis, and the temperature influence coefficient corresponding to the resistor under test is calculated, including: Feature extraction is performed on the temperature data under different impulse voltage gradients in the target data to obtain the first feature corresponding to each impulse voltage gradient; the first feature includes the temperature change rate, temperature field uniformity index, temperature peak offset, and temperature recovery rate. The sequence composed of response voltage fluctuation coefficients is selected as the reference sequence, and the sequence composed of each type of temperature feature in the first feature is used as the comparison sequence. The reference sequence and comparison sequence are normalized. Calculate the correlation coefficient between the normalized reference sequence and each comparison sequence; The degree of correlation is determined based on the correlation coefficient; The feature weights are determined based on the correlation degree. The temperature influence coefficient is calculated based on the aforementioned feature weights.

[0013] Preferably, the voltage stability factor is determined based on piecewise fitting of the response voltage in the target data; features are extracted from the response current data in the target data, and a current immunity factor is determined based on the extracted features; the surge protection index corresponding to the resistor under test is determined based on the voltage stability factor, the current immunity factor, and the temperature influence coefficient, including: For each impact gradient, the response voltage data is divided based on the voltage drop interval to obtain several interval response voltage data. Exponential and linear fitting are performed on the response voltage data of each interval, and the fitting determination coefficient and the mean residual are calculated. The voltage stability factor is determined based on the fitting determination coefficient and the mean residual. For the response current data of each voltage drop range, kurtosis and skewness characteristics are extracted to determine the current immunity factor; Based on the voltage stability factor, current immunity factor, and temperature influence coefficient, the surge protection index of the resistor under test is determined by hierarchical weighted fusion.

[0014] Preferably, based on the response voltage curves, peak response voltages, and surge withstand index under different impulse voltages in the target data, the impulse response consistency of the resistor under test is constructed and calculated through dynamic time warping algorithm, coefficient of variation analysis, and threshold verification, including: The impact response voltage curves of each impact gradient are preprocessed with equal length, and the similarity between adjacent gradient curves is calculated by the dynamic time warping algorithm to obtain the average similarity. Extract the peak response voltage and surge protection index for each impact gradient, and calculate their coefficient of variation respectively; Based on the average similarity, the peak response voltage variation coefficient, and the surge resistance index variation coefficient, the surge response consistency index of the resistor under test is obtained by weighted summation.

[0015] Preferably, a test report is generated based on the surge resistance index and the impact response consistency index, including: Obtain the preset surge protection index threshold and consistency threshold range; The surge resistance index is compared with a preset surge resistance index threshold to obtain a first comparison result; the impulse response consistency index is compared with a consistency threshold range to obtain a second comparison result; based on the first comparison result and the second comparison result, the resistor to be tested is divided into three levels: qualified, to be re-inspected, and unqualified. The sample to be re-inspected is re-evaluated after multiple tests and the average value is taken. A test report is generated based on the average key performance indicators, grade classification results, and temperature field distribution diagram of the resistor under test.

[0016] To achieve the above objectives, a second aspect of the present invention provides an automated testing system for resistors, comprising: The acquisition module is used to acquire the resistor to be tested; The configuration module is used to configure dynamic impulse voltage gradient parameters and phased acquisition strategies for the resistor under test. The acquisition module is used to acquire the response voltage, response current and temperature data of the resistor under test under different impulse voltages based on the dynamic impulse voltage gradient parameters and the phased acquisition strategy, as target data; The determination module is used to extract features from the target data and determine the surge resistance index and impulse response consistency index of the resistor under test based on the extracted features. The generation module is used to generate a test report based on the surge resistance index and the impact response consistency index.

[0017] This invention provides an automated testing method and system for resistors. By configuring dynamic surge voltage gradient parameters, it can accurately simulate the complex characteristics of surge voltage in actual applications, such as step-by-step voltage increase, pulse superposition, and dynamic changes in duration. This overcomes the limitations of traditional fixed-parameter testing that is disconnected from real-world operating conditions. The phased acquisition strategy is designed for the entire lifecycle of surge impacts, including the initial instantaneous response, mid-term parameter stabilization, and late-term temperature rebound. This avoids the loss of key dynamic data caused by traditional single-acquisition modes and can fully capture the evolution trajectory of resistor performance. Simultaneously, it collects multi-dimensional data such as response voltage, response current, and temperature, compensating for the one-sidedness of traditional methods that only focus on electrical parameters. This achieves synergistic characterization of electrical and thermal performance, providing a comprehensive and accurate data foundation for subsequent feature extraction and performance evaluation. By extracting target data features and constructing two quantitative indicators—surge resistance index and surge response consistency index—it solves the problems of existing technologies that rely mainly on qualitative evaluation, are highly subjective, and have inconsistent judgment standards. Quantitative indicators can intuitively reflect the strength of a resistor's surge protection capability and the stability of its performance during surges. This not only enables horizontal comparisons between different products but also supports precise product grading and standardized screening, significantly improving the objectivity and rigor of quality control and preventing unqualified products from entering the market. The entire testing process (parameter configuration, data acquisition, feature extraction, index calculation, and report generation) is fully automated, requiring no manual intervention. This solves the problems of cumbersome and time-consuming traditional manual operations, greatly improving testing efficiency in mass production scenarios and meeting the needs of large-scale quality control. It also effectively avoids human error, ensuring the accuracy of test data and the consistency of test results, further enhancing the reliability of testing work.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of an automated testing method for a resistor according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the configuration of a data acquisition strategy according to an embodiment of the present invention; Figure 3This is a block diagram of an automated testing system for resistors according to an embodiment of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] Example 1: As Figure 1 As shown, an automated testing method for resistors includes steps S1-S5: S1: Obtain the resistor to be tested; S2: Configure dynamic impulse voltage gradient parameters and phased acquisition strategy for the resistor under test; S3: Based on the dynamic impulse voltage gradient parameters and the phased acquisition strategy, collect the response voltage, response current and temperature data of the resistor under test under different impulse voltages as target data; S4: Extract features from the target data and determine the surge resistance index and impulse response consistency index of the resistor under test based on the extracted features; S5: Generate a test report based on the surge resistance index and the impact response consistency index.

[0023] In this embodiment, the phases include pre-impact (0-10s), during impact (10s-10s+50μs), and post-impact (10s+50μs-70s).

[0024] In this embodiment, the temperature data includes the macroscopic temperature collected by the infrared thermal imager and the microscopic viewing temperature collected by the attached PT100; the PT100 attachment positions are: the sensor at the root of the pin is 3mm ± 0.2mm away from the pin-body joint, the sensor at the center of the body is located at the geometric center ± 0.3mm, and the sensor at the solder joint is 1mm ± 0.2mm away from the edge of the solder joint.

[0025] In this embodiment, a multi-condition adaptable acquisition system is constructed to provide equipment support for automated resistor testing. The acquisition system consists of a condition generation module, a parameter acquisition module, and a mechanical execution module. Each module achieves nanosecond-level synchronization through an FPGA. The condition generation module is a programmable impulse voltage source that supports three impulse modes: single pulse, continuous pulse, and gradient pulse. The parameter acquisition module includes a differential probe, a Rogowski coil, an infrared thermal imager, and an attached PT100, which are used to acquire response voltage, response current, and macro-micro positional temperature, respectively. The infrared thermal imager is disabled during the impulse phase (50μs). The mechanical execution module includes a six-axis robotic arm and an adaptive fixture, with the adaptive fixture having a built-in contact resistance sensor.

[0026] The working principle and beneficial effects of the above technical solution are as follows: By configuring dynamic impulse voltage gradient parameters, it can accurately simulate the complex characteristics of surge voltage in actual applications, such as step-by-step voltage increase, pulse superposition, and dynamic changes in duration, breaking the limitation of traditional fixed parameter testing being disconnected from real working conditions; the phased acquisition strategy is designed for the entire life cycle of surge impulses, including the initial instantaneous response, mid-term parameter stabilization, and late-term temperature rebound, avoiding the problem of key dynamic data loss caused by traditional single acquisition mode, and can completely capture the performance evolution trajectory of resistors; at the same time, it synchronously acquires multi-dimensional data of response voltage, response current, and temperature, making up for the one-sidedness of traditional focus only on electrical parameters, realizing the synergistic characterization of electrical and thermal performance, and providing a comprehensive and accurate data foundation for subsequent feature extraction and performance evaluation; by extracting target data features and constructing two quantitative indicators, the surge resistance index and impulse response consistency index, it solves the problems of existing technologies being mainly qualitative evaluation, highly subjective, and lacking unified judgment standards. Quantitative indicators can intuitively reflect the strength of a resistor's surge protection capability and the stability of its performance during surges. This not only enables horizontal comparisons between different products but also supports precise product grading and standardized screening, significantly improving the objectivity and rigor of quality control and preventing unqualified products from entering the market. The entire testing process is fully automated, requiring no manual intervention. This solves the problems of cumbersome and time-consuming traditional manual operations, greatly improving testing efficiency in mass production scenarios and meeting the needs of large-scale quality control. It also effectively avoids human error, ensuring the accuracy of test data and the consistency of test results, further enhancing the reliability of testing work.

[0027] Example 2: Before feature extraction from the target data, the method further includes: data cleaning and noise reduction of the target data.

[0028] In this embodiment, the data cleaning includes: removing data with a contact resistance >10mΩ; the data noise reduction includes: applying a 5th-order Butterworth low-pass filter to the response voltage / current data, applying a moving average filter to the temperature data before / after the impact, and applying wavelet threshold noise reduction to the temperature data during the impact.

[0029] The working principle and beneficial effects of the above technical solutions are as follows: data denoising can remove noise from the data, making the true characteristics of the data clearer; data cleaning can identify and correct errors, inconsistencies or duplicate information in the data; after data cleaning and denoising, the effective features in the data will be more prominent.

[0030] Example 3: As Figure 2 As shown, the dynamic impulse voltage gradient parameters and phased acquisition strategy are configured for the resistor under test, including steps S21-S23: S21: Obtain the rated voltage and application scenario of the resistor to be tested; S22: Configure dynamic impulse voltage gradient parameters based on the rated voltage of the resistor under test and the application scenario; the dynamic impulse voltage gradient is set based on the impulse voltage value. S23: Configure a phased acquisition strategy for the resistor under test based on dynamic impulse voltage gradient parameters.

[0031] In this embodiment, the application scenarios are as follows: industrial control resistors need to withstand 1.5-3 times the rated voltage surge; consumer electronics resistors need to withstand 1.2-1.8 times; and military resistors need to withstand 3.0-5.0 times.

[0032] In this embodiment, dynamic impulse voltage gradient parameters are configured based on the rated voltage of the resistor under test and the application scenario, as shown in Table 1: Table 1 In this embodiment, the acquisition strategy, i.e., the acquisition frequency, is shown in Table 2: Table 2 In this embodiment, during the pre-impact stage (0 to 10s): the operating condition generation module outputs 0V, and the parameter acquisition module acquires static response parameters and initial temperature at a preset frequency; during the impact stage (10s to 10s+50μs): the FPGA sends a synchronization signal, the operating condition generation module outputs the impact voltage corresponding to the gradient, and at the same time, the parameter acquisition module switches to 20MHz high-frequency acquisition to capture the transient peak value of the response voltage / current, and the thermocouple acquires the micro temperature at 10MHz; during the post-impact stage (10s+50μs to 70s): the operating condition generation module stops outputting the impact voltage, and the parameter acquisition module acquires recovery process parameters and temperature drop data at a frequency of 500Hz until the temperature recovers to the initial value ±0.5℃.

[0033] The working principle and beneficial effects of the above technical solution are as follows: It obtains the rated voltage and application scenario of the resistor under test, enabling subsequent testing to be closely integrated with the actual usage of the resistor; the dynamic impulse voltage gradient is set based on the impulse voltage value, allowing adjustment of test conditions according to different voltage impulse situations; the resistance value of the resistor may change under different voltage impulses, and this change may be linear or non-linear; a phased acquisition strategy is configured for the resistor under test based on the dynamic impulse voltage gradient parameters; this phased acquisition method avoids continuous high-frequency data acquisition throughout the entire test process, thereby reducing unnecessary data acquisition; configuring the dynamic impulse voltage gradient parameters according to the resistor's rated voltage ensures that the voltage impulse during the test is within the resistor's tolerance range, avoiding damage to the resistor due to excessively high voltage impulses.

[0034] Example 4: Based on the dynamic impulse voltage gradient parameters and the staged acquisition strategy, the response voltage, response current, and temperature data of the resistor under test under different impulse voltages are collected as target data, including: The dynamic impulse voltage gradient parameters are analyzed to determine the target impulse voltage gradient corresponding to the resistor under test; The phased acquisition strategy is analyzed to determine the target acquisition frequency and target acquisition duration for the resistor under test in each timing phase; the timing phases include before impact, during impact, and after impact; the target acquisition frequency includes the response voltage acquisition frequency, response current acquisition frequency, macroscopic temperature acquisition frequency, and microscopic temperature acquisition frequency. Based on the target impulse voltage gradient, target acquisition frequency, and target acquisition duration, the response voltage, response current, and macro-micro point position temperature data of the resistor under test are acquired to obtain the target data. Real-time monitoring of failure prediction conditions; if triggered, the current gradient test is stopped and marked; target data is stored according to the impact gradient-acquisition stage.

[0035] In this embodiment, the failure prediction conditions include current > 200A and duration > 1μs; temperature rise > 150℃ / s; voltage < 0.1V and current > 10 times the rated current.

[0036] The working principle and beneficial effects of the above technical solution are as follows: Analyzing the dynamic impulse voltage gradient parameters to determine the target impulse voltage gradient ensures that the collected data accurately corresponds to the actual set impulse voltage conditions; by analyzing the phased acquisition strategy, the target acquisition frequency and target acquisition duration for each time sequence stage (before, during, and after the impulse) are determined, enabling targeted data acquisition based on the characteristic changes of the resistor at different stages; acquiring response voltage, response current, and macro-micro positional temperature data allows for monitoring the resistor's performance from multiple dimensions; real-time monitoring of failure prediction conditions stops the current gradient test and marks the condition once triggered; this mechanism avoids unnecessary testing when the resistor already shows signs of failure, saving testing time and resources, preventing further damage to the resistor, and ensuring the safety of testing equipment and personnel; through comprehensive and accurate data acquisition and orderly storage management, in-depth analysis of the resistor's performance under different impulse voltages is possible.

[0037] Example 5: Feature extraction is performed on the target data, and the surge resistance index and impulse response consistency index of the resistor under test are determined based on the extracted features, including: Multi-dimensional temperature features are extracted from the temperature data in the target data, feature weights are determined by grey relational analysis, and the temperature influence coefficient of the resistor under test is calculated. The voltage stability factor is determined based on piecewise fitting of the response voltage in the target data; features are extracted from the response current data in the target data, and the current immunity factor is determined based on the extracted features; the surge protection index corresponding to the resistor under test is determined based on the voltage stability factor, the current immunity factor, and the temperature influence coefficient. Based on the response voltage curves, peak response voltages, and surge resistance index under different impulse voltages in the target data, the impulse response consistency index of the resistor under test is determined.

[0038] In this embodiment, the multi-dimensional temperature characteristics include the temperature change rate, the temperature field uniformity index, the temperature peak offset, and the temperature recovery rate.

[0039] The working principle and beneficial effects of the above technical solution are as follows: By extracting multi-dimensional temperature features, the influence of temperature on resistor performance can be captured from multiple perspectives; for example, in addition to the average temperature, features such as the rate of temperature change and the temperature fluctuation range can also be considered; by determining the feature weights using grey relational analysis, the influence of each temperature feature on resistor performance can be quantified, making the calculated temperature influence coefficient more accurately reflect the actual situation; temperature is one of the important factors affecting resistor performance, and accurate calculation of the temperature influence coefficient helps to fully consider the role of temperature in the calculation of surge resistance index; by determining the voltage stability factor based on piecewise fitting of the response voltage, the stability of the resistor at different voltage stages can be analyzed more meticulously; by determining the surge resistance index based on the voltage stability factor, current immunity factor, and temperature influence coefficient, a comprehensive evaluation from multiple electrical parameters and environmental factors is achieved; by determining the impulse response consistency index based on the response voltage curve, peak response voltage, and surge resistance index under different impulse voltages, the performance stability of the resistor under different impulse conditions can be evaluated.

[0040] Example 6: Extract multi-dimensional temperature features based on temperature data in the target data, determine feature weights through grey relational analysis, and calculate the temperature influence coefficient corresponding to the resistor under test, including: Feature extraction is performed on the temperature data under different impulse voltage gradients in the target data to obtain the first feature corresponding to each impulse voltage gradient; the first feature includes the temperature change rate, temperature field uniformity index, temperature peak offset, and temperature recovery rate. The sequence composed of response voltage fluctuation coefficients is selected as the reference sequence, and the sequence composed of each type of temperature feature in the first feature is used as the comparison sequence. The reference sequence and comparison sequence are normalized. Calculate the correlation coefficient between the normalized reference sequence and each comparison sequence; The degree of correlation is determined based on the correlation coefficient; The feature weights are determined based on the correlation degree. The temperature influence coefficient is calculated based on the aforementioned feature weights.

[0041] In this embodiment, a first feature is extracted for each impact gradient (G1-G5) during the pre-impact, during-impact, and post-impact stages: Rate of temperature change During the impact phase only, the instantaneous rate of change was calculated from the three temperature data points collected by PT100 over time. Take the maximum value of the three rates of change as the value for that stage. ; The rate of temperature change; , Represents temperature values ​​at consecutive points in time; , Indicates adjacent time points; Temperature field uniformity index ( ): Calculate the coefficient of variation of the temperature field based on temperature field data acquired by a thermal imager, only 10 seconds after the impact. , ; The standard deviation of the temperature field; This is the average value of the temperature field; Temperature peak offset The difference between the peak temperature during impact and the initial temperature before impact; ; This represents the temperature peak offset. This represents the maximum temperature during the impact. The initial temperature before impact; the temperature recovery rate, in the post-impact phase, is the time it takes for the temperature to drop from its peak value back to 80% of its initial temperature. ; This is the time it takes for the temperature to drop back to 80% of its initial value. This represents the temperature recovery rate.

[0042] In this embodiment, the correlation between temperature characteristics and electrical parameter fluctuations is calculated using grey relational analysis (GRA), and the weights of each characteristic are determined: the response voltage fluctuation coefficient is selected. As a reference sequence Temperature features as comparison sequences ; ; ; ; Normalize all sequences (range [0,1]): ; For normalized reference sequence values; This represents the original value of the j-th type of temperature characteristic; The maximum value of the j-th type feature; The minimum value of the reference sequence; The maximum value of the reference sequence is used; the correlation coefficient is calculated: ; This represents the normalized temperature characteristic value of type j. The value is 0.5; the correlation degree is calculated as follows: Determine the weights: Based on the aforementioned feature weights and the 99th percentile value of this temperature characteristic for resistors of the same type in the historical database. Calculate the temperature influence coefficient: based on ;in, The actual value of the j-th type feature; Example: If w1 = 0.2 ( w2=0.15 w3=0.4 w4=0.25 ), a certain sample X1=5℃ / s (X 1max =20℃ / s), X2=0.3 (X 2max =0.5), X3=80℃ (X 3max =120℃), X4=2℃ / s (X 4max =4℃ / s), then =0.532.

[0043] The working principle and beneficial effects of the above technical solution are as follows: Multiple primary features are extracted from temperature data under different impulse voltage gradients, such as temperature change rate, temperature field uniformity index, temperature peak offset, and temperature recovery rate, which can describe the temperature characteristics of the resistor during surge impact from multiple perspectives; feature extraction is performed for different impulse voltage gradients, considering the influence of surge intensity on the resistor's temperature characteristics; a sequence composed of response voltage fluctuation coefficients is selected as a reference sequence, and sequences composed of various temperature features are used as comparison sequences. Correlation coefficients and correlation degrees are calculated through grey relational analysis, quantifying the degree of correlation between each temperature feature and response voltage fluctuations; the temperature influence coefficient is calculated based on the determined feature weights, comprehensively considering the impact of various temperature features on resistor performance; by determining feature weights through multi-dimensional temperature feature extraction and grey relational analysis to calculate the temperature influence coefficient, the impact of temperature on resistor performance can be comprehensively and accurately evaluated, providing a more reliable basis for resistor performance evaluation, quality control, and circuit design, and possessing significant practical value.

[0044] Example 7: Determine the voltage stability factor based on piecewise fitting of the response voltage in the target data; extract features from the response current data in the target data, and determine the current immunity factor based on the extracted features; determine the surge protection index corresponding to the resistor under test based on the voltage stability factor, the current immunity factor, and the temperature influence coefficient, including: For each impact gradient, the response voltage data is divided based on the voltage drop interval to obtain several interval response voltage data. Exponential and linear fitting are performed on the response voltage data of each interval, and the fitting determination coefficient and the mean residual are calculated. The voltage stability factor is determined based on the fitting determination coefficient and the mean residual. For the response current data of each voltage drop range, kurtosis and skewness characteristics are extracted to determine the current immunity factor; Based on the voltage stability factor, current immunity factor, and temperature influence coefficient, the surge protection index of the resistor under test is determined by hierarchical weighted fusion.

[0045] In this embodiment, for each impact gradient's impact phase, the response voltage data is divided based on the voltage drop interval to obtain several interval response voltage data, including: For each impact gradient, the impact phase is divided according to the pressure drop interval, every 0.1U n As an interval, piecewise fitting and residual calculation are performed within each interval. The piecewise fitting includes: based on the voltage change trend, exponential fitting is used for the first half of the impact (0-25μs). ; To fit the voltage value; , , The parameters are used for fitting; the second half of the impact (25-50 μs) is fitted using linear fitting. ; The voltage value is a linear fit. , The fitting parameters are used; residual calculation: the residual between the actual response voltage and the fitted value in each interval. ; Calculate the mean residual; coefficient of determination for fit : ; This is the actual response voltage; To fit the voltage value; The actual average voltage; voltage stability factor. : ; Extract kurtosis features from the response current for each voltage drop range. : ; This is a single-point response current; This is the average current. A value greater than 0 indicates the presence of current spikes; A value less than 0 indicates a smooth current distribution; skewness characteristics are extracted from the response current for each voltage drop range. : ; The larger the absolute value, the more asymmetrical the current distribution; current immunity factor : .

[0046] In this embodiment, based on the voltage stability factor, current immunity factor, and temperature influence coefficient, the surge protection index of the resistor under test is determined by hierarchical weighted fusion. ;in, This indicates a temperature-related interference correction term, which reduces the negative impact of temperature on surge response; The value range is [0,1].

[0047] The working principle and beneficial effects of the above technical solution are as follows: The response voltage data of each impact gradient stage is divided based on the voltage drop interval to obtain several interval response voltage data, which are then subjected to exponential and linear fitting respectively; the fitting determination coefficient and residual mean are calculated, and the voltage stability factor is determined based on these indicators; the fitting determination coefficient measures the degree of fit of the fitted curve to the data, while the residual mean reflects the deviation between the actual data and the fitted curve; kurtosis and skewness characteristics are extracted from the response current data of each voltage drop interval; kurtosis describes the peak of the data distribution, and skewness reflects the asymmetry of the data distribution; based on the voltage stability factor, current immunity factor, and temperature influence coefficient, the surge protection index corresponding to the resistor under test is determined through hierarchical weighted fusion.

[0048] Example 8: Based on the response voltage curves, peak response voltage, and surge resistance index under different impulse voltages in the target data, the impulse response consistency of the resistor under test is constructed and calculated through dynamic time warping algorithm, coefficient of variation analysis, and threshold verification, including: The impact response voltage curves of each impact gradient are preprocessed with equal length, and the similarity between adjacent gradient curves is calculated by the dynamic time warping algorithm to obtain the average similarity. Extract the peak response voltage and surge protection index for each impact gradient, and calculate their coefficient of variation respectively; Based on the average similarity, the peak response voltage variation coefficient, and the surge resistance index variation coefficient, the surge response consistency index of the resistor under test is obtained by weighted summation.

[0049] In this embodiment, the response voltage curves of each impact gradient are preprocessed to the same length, and the similarity between adjacent gradient curves is calculated using a dynamic time warping algorithm to obtain the average similarity, including: For the response voltage curves of five impact gradients (G1-G5), the DTW algorithm was used to calculate the similarity between the curves. Curve preprocessing included: truncating the response voltage curve of each gradient to an equal length; constructing a distance matrix; and calculating the curves. and Euclidean distance of each data point ; , Index the data points; Find the path with the minimum cumulative distance using dynamic programming. Similarity calculation: ;in, For the number of data points; Maximum response voltage; Average similarity: .

[0050] In this embodiment, the peak response voltage and surge protection index of each impact gradient are extracted, and their coefficients of variation are calculated, including: Response voltage peak variation coefficient: ; The peak standard deviation, Peak mean; Surge resistance index coefficient of variation: ; for Standard deviation for Mean.

[0051] In this embodiment, based on the average similarity, the peak response voltage variation coefficient, and the surge resistance index variation coefficient, the surge response consistency index corresponding to the resistor under test is obtained by weighted summation, including: ; The working principle and beneficial effects of the above technical solution are as follows: The response voltage curves under each impact gradient are preprocessed to the same length, allowing for comparison of response voltage curves under different impact gradients within a unified dimension; the DTW algorithm is used to calculate the similarity between adjacent gradient curves and obtain the average similarity; the peak response voltage and surge protection index for each impact gradient are extracted, and their coefficients of variation are calculated respectively; the coefficient of variation is a relative indicator measuring the dispersion of data, eliminating the influence of data mean and dimensions, and can more objectively reflect the fluctuation of data; by calculating the coefficients of variation of the peak response voltage and surge protection index, the stability and consistency of these two important indicators under different impact gradients can be clearly understood; based on the average similarity, the coefficient of variation of the peak response voltage, and the coefficient of variation of the surge protection index, a weighted summation is used to obtain the impact response consistency index corresponding to the resistor under test; this method, which comprehensively considers multiple factors, can comprehensively evaluate the impact response consistency of the resistor from different perspectives.

[0052] Example 9: Based on the surge resistance index and the impulse response consistency index, a test report is generated, including: Obtain the preset surge protection index threshold and consistency threshold range; The surge resistance index is compared with a preset surge resistance index threshold to obtain a first comparison result; the impulse response consistency index is compared with a consistency threshold range to obtain a second comparison result; based on the first comparison result and the second comparison result, the resistor to be tested is divided into three levels: qualified, to be re-inspected, and unqualified. The sample to be re-inspected is re-evaluated after multiple tests and the average value is taken. A test report is generated based on the average key performance indicators, grade classification results, and temperature field distribution diagram of the resistor under test.

[0053] In this embodiment, the resistor under test is classified into three levels—qualified, requiring re-inspection, and unqualified—based on the first comparison result and the second comparison result. If the surge withstand index is greater than the first preset surge withstand index threshold and the impulse response consistency index is within the consistency threshold range, the resistor under test is qualified; if the surge withstand index is greater than or equal to the second preset surge withstand index threshold and less than the first preset surge withstand index threshold, and the impulse response consistency index is within the consistency threshold range, the resistor under test needs to be re-inspected; if the surge withstand index is less than the second preset surge withstand index threshold or the impulse response consistency index is not within the consistency threshold range, the resistor under test is unqualified.

[0054] The working principle and beneficial effects of the above technical solution are as follows: a test report is generated based on the average key indicators, grade classification results, and temperature field distribution diagram of the resistor under test, integrating various information such as the resistor's performance data, quality grade, and temperature distribution; the average key indicators can intuitively reflect the overall performance level of the resistor; the grade classification results clarify the quality status of the resistor; and the temperature field distribution diagram helps to analyze the heat distribution of the resistor during operation, providing a reference for further optimizing the design and use of the resistor.

[0055] To achieve the above objectives, such as Figure 3 As shown, a second aspect of the present invention provides an automated testing system for resistors, comprising: The acquisition module is used to acquire the resistor to be tested; The configuration module is used to configure dynamic impulse voltage gradient parameters and phased acquisition strategies for the resistor under test. The acquisition module is used to acquire the response voltage, response current and temperature data of the resistor under test under different impulse voltages based on the dynamic impulse voltage gradient parameters and the phased acquisition strategy, as target data; The determination module is used to extract features from the target data and determine the surge resistance index and impulse response consistency index of the resistor under test based on the extracted features. The generation module is used to generate a test report based on the surge resistance index and the impact response consistency index.

[0056] The working principle and beneficial effects of the above technical solution are as follows: By configuring dynamic impulse voltage gradient parameters, it can accurately simulate the complex characteristics of surge voltage in actual applications, such as step-by-step voltage increase, pulse superposition, and dynamic changes in duration, breaking the limitation of traditional fixed parameter testing being disconnected from real working conditions; the phased acquisition strategy is designed for the entire life cycle of surge impulses, including the initial instantaneous response, mid-term parameter stabilization, and late-term temperature rebound, avoiding the problem of key dynamic data loss caused by traditional single acquisition mode, and can completely capture the performance evolution trajectory of resistors; at the same time, it synchronously acquires multi-dimensional data of response voltage, response current, and temperature, making up for the one-sidedness of traditional focus only on electrical parameters, realizing the synergistic characterization of electrical and thermal performance, and providing a comprehensive and accurate data foundation for subsequent feature extraction and performance evaluation; by extracting target data features and constructing two quantitative indicators, the surge resistance index and impulse response consistency index, it solves the problems of existing technologies being mainly qualitative evaluation, highly subjective, and lacking unified judgment standards. Quantitative indicators can intuitively reflect the strength of a resistor's surge protection capability and the stability of its performance during surges. This not only enables horizontal comparisons between different products but also supports precise product grading and standardized screening, significantly improving the objectivity and rigor of quality control and preventing unqualified products from entering the market. The entire testing process is fully automated, requiring no manual intervention. This solves the problems of cumbersome and time-consuming traditional manual operations, greatly improving testing efficiency in mass production scenarios and meeting the needs of large-scale quality control. It also effectively avoids human error, ensuring the accuracy of test data and the consistency of test results, further enhancing the reliability of testing work.

[0057] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An automated testing method for resistors, characterized in that, include: Obtain the resistor to be tested; Configure dynamic impulse voltage gradient parameters and a phased acquisition strategy for the resistor under test; Based on the dynamic impulse voltage gradient parameters and the phased acquisition strategy, the response voltage, response current and temperature data of the resistor under test under different impulse voltages are collected as target data. Feature extraction is performed on the target data, and the surge resistance index and impulse response consistency index of the resistor under test are determined based on the extracted features. A test report is generated based on the surge resistance index and the impact response consistency index.

2. The automated testing method for resistors as described in claim 1, characterized in that, Before feature extraction from the target data, the process also includes data cleaning and noise reduction of the target data.

3. The automated testing method for resistors as described in claim 1, characterized in that, Configure dynamic impulse voltage gradient parameters and a phased acquisition strategy for the resistor under test, including: Obtain the rated voltage and application scenario of the resistor to be tested; Based on the rated voltage of the resistor under test and the application scenario, a dynamic impulse voltage gradient parameter is configured; the dynamic impulse voltage gradient is set based on the impulse voltage value. A phased acquisition strategy is configured for the resistor under test based on dynamic impulse voltage gradient parameters.

4. The automated testing method for resistors as described in claim 1, characterized in that, Based on the dynamic impulse voltage gradient parameters and the phased acquisition strategy, the response voltage, response current, and temperature data of the resistor under test under different impulse voltages are collected as target data, including: The dynamic impulse voltage gradient parameters are analyzed to determine the target impulse voltage gradient corresponding to the resistor under test; The phased acquisition strategy is analyzed to determine the target acquisition frequency and target acquisition duration for the resistor under test in each timing phase; the timing phases include before impact, during impact, and after impact; the target acquisition frequency includes the response voltage acquisition frequency, response current acquisition frequency, macroscopic temperature acquisition frequency, and microscopic temperature acquisition frequency. Based on the target impulse voltage gradient, target acquisition frequency, and target acquisition duration, the response voltage, response current, and macro-micro point position temperature data of the resistor under test are acquired to obtain the target data. Real-time monitoring of failure prediction conditions; if triggered, the current gradient test is stopped and marked; target data is stored according to the impact gradient-acquisition stage.

5. The automated testing method for resistors as described in claim 1, characterized in that, Feature extraction is performed on the target data, and based on the extracted features, the surge resistance index and impulse response consistency index of the resistor under test are determined, including: Multi-dimensional temperature features are extracted from the temperature data in the target data, feature weights are determined by grey relational analysis, and the temperature influence coefficient of the resistor under test is calculated. The voltage stability factor is determined based on piecewise fitting of the response voltage in the target data; features are extracted from the response current data in the target data, and the current immunity factor is determined based on the extracted features; the surge protection index corresponding to the resistor under test is determined based on the voltage stability factor, the current immunity factor, and the temperature influence coefficient. Based on the response voltage curves, peak response voltages, and surge resistance index under different impulse voltages in the target data, the impulse response consistency index of the resistor under test is determined.

6. The automated testing method for a resistor as described in claim 5, characterized in that, Multi-dimensional temperature features are extracted from the temperature data in the target data. Feature weights are determined through grey relational analysis, and the temperature influence coefficient corresponding to the resistor under test is calculated, including: Feature extraction is performed on the temperature data under different impulse voltage gradients in the target data to obtain the first feature corresponding to each impulse voltage gradient; the first feature includes the temperature change rate, temperature field uniformity index, temperature peak offset, and temperature recovery rate. The sequence composed of response voltage fluctuation coefficients is selected as the reference sequence, and the sequence composed of each type of temperature feature in the first feature is used as the comparison sequence. The reference sequence and comparison sequence are normalized. Calculate the correlation coefficient between the normalized reference sequence and each comparison sequence; The degree of correlation is determined based on the correlation coefficient; The feature weights are determined based on the correlation degree. The temperature influence coefficient is calculated based on the aforementioned feature weights.

7. The automated testing method for a resistor as described in claim 6, characterized in that, The voltage stability factor is determined based on piecewise fitting of the response voltage in the target data; Feature extraction is performed on the response current data in the target data, and the current immunity factor is determined based on the extracted features; The surge protection index of the resistor under test is determined based on the voltage stability factor, current immunity factor, and temperature influence coefficient, including: For each impact gradient, the response voltage data is divided based on the voltage drop interval to obtain several interval response voltage data. Exponential and linear fitting are performed on the response voltage data of each interval, and the fitting determination coefficient and the mean residual are calculated. The voltage stability factor is determined based on the fitting determination coefficient and the mean residual. For the response current data of each voltage drop range, kurtosis and skewness characteristics are extracted to determine the current immunity factor; Based on the voltage stability factor, current immunity factor, and temperature influence coefficient, the surge protection index of the resistor under test is determined by hierarchical weighted fusion.

8. The automated testing method for a resistor as described in claim 7, characterized in that, Based on the response voltage curves, peak response voltage, and surge resistance index under different impulse voltages in the target data, the impulse response consistency of the resistor under test is constructed and calculated through dynamic time warping algorithm, coefficient of variation analysis, and threshold verification, including: The impact response voltage curves of each impact gradient are preprocessed with equal length, and the similarity between adjacent gradient curves is calculated by the dynamic time warping algorithm to obtain the average similarity. Extract the peak response voltage and surge protection index for each impact gradient, and calculate their coefficient of variation respectively; Based on the average similarity, the peak response voltage variation coefficient, and the surge resistance index variation coefficient, the surge response consistency index of the resistor under test is obtained by weighted summation.

9. The automated testing method for a resistor as described in claim 1, characterized in that, Based on the surge resistance index and impact response consistency index, a test report is generated, including: Obtain the preset surge protection index threshold and consistency threshold range; The surge resistance index is compared with a preset surge resistance index threshold to obtain a first comparison result; the impulse response consistency index is compared with a consistency threshold range to obtain a second comparison result; based on the first comparison result and the second comparison result, the resistor to be tested is divided into three levels: qualified, to be re-inspected, and unqualified. The sample to be re-inspected is re-evaluated after multiple tests and the average value is taken. A test report is generated based on the average key performance indicators, grade classification results, and temperature field distribution diagram of the resistor under test.

10. A system applying the automated testing method for a resistor as described in any one of claims 1-9, characterized in that, include: The acquisition module is used to acquire the resistor to be tested; The configuration module is used to configure dynamic impulse voltage gradient parameters and phased acquisition strategies for the resistor under test. The acquisition module is used to acquire the response voltage, response current and temperature data of the resistor under test under different impulse voltages based on the dynamic impulse voltage gradient parameters and the phased acquisition strategy, as target data; The determination module is used to extract features from the target data and determine the surge resistance index and impulse response consistency index of the resistor under test based on the extracted features. The generation module is used to generate a test report based on the surge resistance index and the impact response consistency index.

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