A method and system for testing the EMC conducted immunity of an automotive electronic device

Abstract

The application discloses a kind of automobile electronic equipment EMC transmission anti-interference degree test method and system, it is related to signal processing technical field, comprising: based on the electronic equipment component of the automobile to be tested, the anti-interference dynamic performance index of the electronic equipment component of the automobile to be tested is evaluated;Obtain the electronic equipment component failure event under each scene of automobile, evaluate the electronic equipment component failure distribution under each scene of automobile full life cycle, determine the interference source type of failure event, generate the electronic equipment component transmission anti-interference degree test task of the automobile to be tested is carried out several rounds of test to the automobile to be tested electronic equipment component anti-interference dynamic performance index, establish the electronic equipment component stall model of automobile, generate the electronic equipment component stall risk probability of test automobile, determine whether in tolerable operation threshold interval, if yes, determine no exception, if not, determine abnormality.The beneficial effects of the present application are that the accuracy of the test is significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, in particular to a method and system for EMC conducted immunity test of automotive electronic equipment. BACKGROUND

[0002] The existing EMC conducted immunity test of automotive electronic equipment relies on expert experience or fixed standard to identify interference sources, lacks systematic analysis based on actual fault data, adopts static priority and unified limit in test scheme, and fails to dynamically adjust in combination with different life cycle stages and scenes, lacks continuous optimization mechanism after test task generation, and cannot adapt to long-term performance changes, at the same time, the existing method is insufficient in analyzing frequency domain characteristics (such as conducted transient, continuous wave, etc.) of interference sources, which may cause key frequency bands to be ignored, and more importantly, the test resource allocation does not match the real risk, which may cause insufficient coverage of high fault risk scenes and excessive testing of low risk scenes, affecting the overall test efficiency and accuracy. SUMMARY

[0003] To solve the above technical problems, a method and system for EMC conducted immunity test of automotive electronic equipment are provided, which solve the above problems.

[0004] To achieve the above purposes, the technical scheme adopted by the present application is as follows:

[0005] A method for EMC conducted immunity test of automotive electronic equipment, comprising:

[0006] Based on the electronic equipment component configuration parameters of the test vehicle, the ideal anti-interference performance of the test vehicle is determined, the change trend of the ideal anti-interference performance is analyzed according to the design use cycle of the test vehicle, and the anti-interference dynamic performance index of the electronic equipment component of the test vehicle is evaluated;

[0007] Based on the Internet and the fault database of the vehicle enterprise background, the electronic equipment component fault events of the vehicle in each scene are obtained, the posterior probability of the electronic equipment component fault events of the vehicle in each scene is analyzed, and the fault distribution of the electronic equipment component of the test vehicle in each scene of the whole life cycle of the test vehicle is evaluated;

[0008] According to the fault distribution of the electronic equipment component of the test vehicle in each scene of the whole life cycle of the test vehicle, the interference source type of the fault event is determined, and the conducted immunity test task of the electronic equipment component of the test vehicle is generated;

[0009] According to the conducted immunity test task of the electronic equipment component of the test vehicle, the test vehicle is tested for several rounds, the running data of the electronic equipment component of the test vehicle and the anti-interference dynamic performance index of the electronic equipment component of the test vehicle are obtained, the stall model of the electronic equipment component of the vehicle is established, and the stall risk probability of the electronic equipment component of the test vehicle is generated;

[0010] determining whether the electronic device component stall risk probability of the test vehicle is in a tolerable operation threshold interval, if yes, determining no abnormality, if no, determining abnormality.

[0011] Preferably, the electronic device component configuration parameters of the test vehicle are acquired, and an electronic device component structured parameter array of the test vehicle is established; the electronic device component structured parameters include electrical parameters, PCB layout parameters, shielding measure parameters, and wire harness type parameters.

[0012] The electronic device component structured parameter array of the test vehicle is subjected to standardization processing.

[0013] An electronic device component structured parameter matrix of the test vehicle is established according to the electronic device component structured parameter array of the test vehicle by using AHP hierarchy, and a parameter vector value of each element in the matrix is calculated by using geometric mean, and subjective weights of the electronic device component structured parameters of the test vehicle are given.

[0014] The information entropy value of each adjacent parameter in the electronic device component structured parameter array of the test vehicle is analyzed by using entropy weight method, the variation coefficient of the electronic device component structured parameters of the test vehicle is determined, and objective weights of the electronic device component structured parameters of the test vehicle are given.

[0015] The weights of the electronic device component structured parameters of the test vehicle are given based on the subjective weights of the electronic device component structured parameters of the test vehicle and the objective weights of the electronic device component structured parameters of the test vehicle and the basic fusion weights.

[0016] Preferably, the electronic device component structured parameter weighted matrix of the test vehicle is established based on the weights of the electronic device component structured parameters of the test vehicle.

[0017] The electronic device component structured parameter weighted matrix of the test vehicle is subjected to normalization processing, the maximum value of each electronic device component structured parameter is screened out, and the ideal anti-interference performance parameter sequence of the electronic device component of the test vehicle is established.

[0018] The similarity between the electronic device component structured parameter array of the test vehicle and the ideal anti-interference performance parameter sequence of the electronic device component of the test vehicle is calculated, and the electronic device component structured parameter-ideal anti-interference performance correlation coefficient matrix of the test vehicle is established.

[0019] The ideal anti-interference performance of the electronic device component of the test vehicle is calculated according to weighted projection based on the electronic device component structured parameter-ideal anti-interference performance parameter correlation coefficient matrix of the test vehicle and the weights of the electronic device component structured parameters of the test vehicle.

[0020] Preferably, the full lifecycle test data of known test vehicles is obtained, and the K-Means clustering algorithm is used to divide the full lifecycle test data of known test vehicles according to several usage dimensions to generate hierarchical full lifecycle test data of known test vehicles.

[0021] Based on the full lifecycle test data of known test vehicles across different layers, the factors affecting the decline in conducted immunity under the known test vehicle layer dimensions are labeled; the factors affecting the decline in conducted immunity include: temperature, humidity, and vibration intensity;

[0022] Based on the known factors affecting the reduction of conducted immunity in various dimensions of the test vehicle, the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle are calculated.

[0023] Using the entropy weight method, the distribution proportion of stress variables affecting the reduction of conducted immunity in each dimension of the vehicle is calculated, and weights are assigned to stress variables affecting the reduction of conducted immunity in each dimension of the vehicle.

[0024] Based on the weights of the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle and the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle, calculate the comprehensive acceleration factor of the vehicle's impact on the reduction of conducted immunity.

[0025] An observation window is established based on the timestamps of the full life cycle test data of the known test vehicles in the hierarchical dimensions. The comprehensive acceleration factor of the decline in the vehicle's immunity due to the influence of transmission is used as the observation object to generate a time series of the comprehensive acceleration factor of the decline in the vehicle's immunity due to the influence of transmission throughout the entire life cycle of the known test vehicles.

[0026] Based on the ARMA autoregressive moving average model, using the known time series of the comprehensive acceleration factor affecting the decline of conducted immunity throughout the entire life cycle of the test vehicle and the ideal anti-interference performance of the electronic equipment components of the test vehicle as inputs, the influence trend of the comprehensive acceleration factor affecting the decline of conducted immunity on the ideal anti-interference performance of the electronic equipment components per unit time is predicted, and dynamic performance indicators of anti-interference of the electronic equipment components of the test vehicle are generated.

[0027] Preferably, based on the Internet and the vehicle manufacturer's back-end fault database, electronic component failure events in various scenarios of the vehicle life cycle are marked, and a feature dataset of electronic component failure events in various scenarios of the vehicle life cycle is established.

[0028] Based on the feature dataset of electronic component failure events in various scenarios of the automotive life cycle, Bayesian probability is used to calculate the posterior probability of electronic component failure events in each scenario of each life cycle.

[0029] Preferably, based on the posterior probability of electronic device component failure events in each scenario under each life cycle, the Weibull distribution is used for fitting to verify the temporal distribution of electronic device component failure events in each scenario under each life cycle.

[0030] Using KDE kernel density estimation, the geographical distribution of faults in electronic device components under each lifecycle and scenario is statistically analyzed to generate the spatial distribution of electronic device component fault events under each lifecycle and scenario.

[0031] By using conditional independence testing, we can verify the dependency between each scenario and electronic device component in the temporal distribution of electronic device component failure events in each scenario under each life cycle.

[0032] The conditional independence test was used to verify the dependency between electronic components and failure events in the spatial distribution of electronic component failure events in each scenario of each life cycle.

[0033] Based on the dependencies between various scenarios and electronic device components, and the dependencies between electronic device components and fault events, a three-dimensional data array of scenario-electronic device component-fault event is established to determine the fault distribution of electronic device components in various scenarios throughout the entire life cycle of the test vehicle.

[0034] Preferably, based on the fault distribution of electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle, the matching degree between the fault mode of each electronic equipment component and the known interference source type is calculated and divided to generate the electronic equipment component fault mode-interference source type matrix for various scenarios throughout the entire life cycle of the test vehicle.

[0035] As further information: known interference source types include, but are not limited to: conducted transients, radiated immunity, and conducted continuous waves;

[0036] Based on the fault mode-interference source type matrix of electronic equipment components in various scenarios throughout the test vehicle's life cycle, the frequency of occurrence of interference source type for each electronic equipment component fault mode is statistically analyzed relative to the overall proportion of the same type of fault mode, and a weight is assigned to the attention interference source type for each electronic equipment component fault mode in various scenarios throughout the test vehicle's life cycle.

[0037] Preferably, the time and frequency domains of the fault mode-interference source type matrix of electronic equipment components under various scenarios throughout the entire life cycle of the test vehicle are extracted using Fast Fourier Transform to obtain the feature parameters of the interference source type of the fault mode of electronic equipment components under various scenarios throughout the entire life cycle of the test vehicle.

[0038] Based on the posterior probability of electronic component failure events in each scenario of each life cycle and the weight of the interference source type of the electronic component failure mode in each scenario of the entire life cycle of the test vehicle, the test priority of electronic components in each scenario of the entire life cycle of the test vehicle is calculated.

[0039] Based on the standard confidence interval, we screen the failure modes of electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle, focusing on the characteristic parameters of the interference source type, and establish test limits for electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle.

[0040] Test sequences are constructed based on the test priorities and test limits of electronic components in various scenarios throughout the test vehicle's lifecycle, generating conducted immunity test tasks for the electronic components of the test vehicle.

[0041] Preferably, based on the conducted immunity test task of the electronic components of the test vehicle, several rounds of tests are carried out on the test vehicle to obtain the operating data of the electronic components of the test vehicle;

[0042] Based on the operating data of the electronic components of the test vehicle, the actual tolerance value of the electronic components of the test vehicle and the conducted immunity test limit of the electronic components are marked, and the immunity margin of the electronic components of the test vehicle is calculated.

[0043] Based on the dynamic performance index of the electronic components of the test vehicle and the immunity margin of the electronic components of the test vehicle, a time-varying covariate vector sample array is established.

[0044] Based on Cox regression, a model for the shutdown of electronic components in automobiles is established, with a time-varying covariate vector sample array as input and the probability of the electronic components in automobiles shutting down as output.

[0045] Furthermore, an EMC conducted immunity test system for automotive electronic devices, used to implement the EMC conducted immunity test method for automotive electronic devices as described above, includes:

[0046] Performance evaluation module, fault distribution evaluation module, test task generation module, shutdown risk analysis module, and judgment module;

[0047] The performance evaluation module is used to determine the ideal anti-interference performance of the test vehicle based on the structural parameters of the electronic equipment components of the test vehicle, analyze the changing trend of the ideal anti-interference performance according to the design and use cycle of the test vehicle, and evaluate the dynamic anti-interference performance index of the electronic equipment components of the test vehicle.

[0048] The fault distribution assessment module is used to obtain electronic component failure events in various automotive scenarios based on the Internet and the vehicle manufacturer's back-end fault database, analyze the posterior probability of electronic component failure events in various automotive scenarios, and assess the fault distribution of electronic components in various scenarios throughout the entire life cycle of the test vehicle.

[0049] The test task generation module is electrically connected to the fault distribution assessment module. The test task generation module is used to determine the interference source type of the fault event based on the fault distribution of electronic components in various scenarios throughout the entire life cycle of the test vehicle, and generate the conducted immunity test task of the electronic components of the test vehicle.

[0050] The shutdown risk analysis module is electrically connected to the performance evaluation module and the test task generation module. The shutdown risk analysis module is used to conduct several rounds of tests on the test vehicle based on the conducted immunity test task of the electronic components of the test vehicle, obtain the operating data of the electronic components of the test vehicle and the dynamic performance index of the electronic components of the test vehicle, establish a shutdown model of the electronic components of the vehicle, and generate the shutdown risk probability of the electronic components of the test vehicle.

[0051] The judgment module is electrically connected to the shutdown risk analysis module. The judgment module is used to determine whether the shutdown risk probability of the electronic equipment components of the test vehicle is within the tolerable operating threshold range. If yes, it is determined that there is no abnormality; if no, it is determined that there is an abnormality.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] This invention proposes a test scheme for the EMC conducted immunity of automotive electronic devices. Through multi-dimensional data fusion and dynamic analysis, it achieves accurate assessment and optimization of the EMC conducted immunity of automotive electronic devices: a dynamic prediction model for anti-interference performance is constructed based on component structural parameters, and a full life cycle fault distribution analysis is performed by combining the Internet and vehicle manufacturer fault databases. Targeted test tasks are generated through intelligent identification of interference sources. During the test, component operating data is monitored in real time, a shutdown risk probability model is established, and intelligent judgment of abnormal states is achieved. This realizes closed-loop optimization from assessment to actual testing, significantly improving the accuracy of the test. Attached Figure Description

[0054] Figure 1 A flowchart of a test method for EMC conducted immunity of automotive electronic devices;

[0055] Figure 2 A framework diagram of an EMC conducted immunity test system for automotive electronic devices; Detailed Implementation

[0056] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0057] Reference Figure 1 As shown, a method for testing the EMC conducted immunity of automotive electronic devices includes:

[0058] Step 1: Based on the structural parameters of the electronic equipment components of the test vehicle, determine the ideal anti-interference performance of the test vehicle, analyze the changing trend of the ideal anti-interference performance according to the design and service life of the test vehicle, and evaluate the dynamic anti-interference performance index of the electronic equipment components of the test vehicle.

[0059] Step one includes the following:

[0060] Step 101: Obtain the structural parameters of the electronic equipment components of the test vehicle and establish a structured parameter array for the electronic equipment components of the test vehicle; the structured parameters of the electronic equipment components include: electrical parameters, PCB layout parameters, shielding measures parameters, and wiring harness type parameters;

[0061] The structured parameter arrays of electronic components in the test vehicle are standardized.

[0062] Using the AHP hierarchy, a structured parameter matrix of the electronic equipment components of the test vehicle is established according to the structured parameter array of the electronic equipment components of the test vehicle. The parameter vector value of each element in the matrix is ​​calculated using geometric mean, and subjective weights are assigned to the structured parameters of the electronic equipment components of the test vehicle.

[0063] Using the entropy weight method, the information entropy value of each neighboring parameter in the structured parameter array of the electronic equipment components of the test vehicle is analyzed, the variation coefficient of the structured parameters of the electronic equipment components of the test vehicle is determined, and objective weights are assigned to the structured parameters of the electronic equipment components of the test vehicle.

[0064] The structured parameters of the electronic equipment components of the test vehicle are weighted based on their subjective weights, objective weights, and fundamental fusion weights, as follows:

[0065]

[0066] in, To test the weight value of the j-th structured parameter of an electronic component in a car, To test the subjective weight value of the j-th structured parameter of an electronic component in a car, To test the objective weight value of the j-th structured parameter of an automotive electronic component, Basic fusion weight

[0067] As a further development, the basic fusion weight value (0.6) is based on experimental verification through extensive experiments (500+ sets of EMC test data in typical scenarios). At the same time, the fusion weight can retain 60% of the expert experience judgment (AHP weight) to ensure compliance with the laws of engineering physics; and introduce 40% of the data-driven correction (entropy weight method) to effectively suppress subjective bias.

[0068] 102. Based on the structured parameter weights of the electronic equipment components of the test vehicle, establish a weighted matrix of the structured parameters of the electronic equipment components of the test vehicle;

[0069] The weighted matrix of the structured parameters of the electronic components of the test vehicle is normalized, the maximum value of the structured parameter of each electronic component is selected, and the ideal anti-interference performance parameter sequence of the electronic components of the test vehicle is established.

[0070] Calculate the similarity between the structured parameter array of the electronic components of the test vehicle and the ideal anti-interference performance parameter sequence of the electronic components of the test vehicle, and establish the correlation coefficient matrix between the structured parameters and the ideal anti-interference performance of the electronic components of the test vehicle.

[0071] Based on the correlation coefficient matrix between the structured parameters and ideal anti-interference performance parameters of the electronic equipment components of the test vehicle and the weights of the structured parameters of the electronic equipment components of the test vehicle, the ideal anti-interference performance of the electronic equipment components of the test vehicle is calculated by weighted projection as follows:

[0072]

[0073] in, To test the ideal interference immunity of the i-th electronic component in a car, The correlation coefficient between the j-th structured parameter and the ideal anti-interference performance of the i-th electronic component of a car is used to test the vehicle's electronic components.

[0074] Step 103: Obtain known test vehicle full life cycle test data, and use K-Means clustering algorithm to divide the known test vehicle full life cycle test data according to several usage dimensions to generate known test vehicle hierarchical full life cycle test data.

[0075] As a further point, since the conducted immunity test of automobiles is conducted indoors using a frequency transmitter to simulate high-frequency signal interference to test the vehicle, the actual usage scenarios of automobiles vary greatly. Therefore, the single high-frequency signal test results in poor simulation of immunity when facing complex usage scenarios. Therefore, multiple usage dimensions are used to supplement the data in multiple dimensions: according to the usage environment, usage frequency, and usage load intensity, in order to maximize the confidence of the fitted performance change trend.

[0076] Based on the known test vehicle's layered full lifecycle test data, the factors affecting the decline in conducted immunity under the known test vehicle layered dimensions are labeled; the factors affecting the decline in conducted immunity of the vehicle include: temperature, humidity, and vibration intensity;

[0077] Based on the known factors affecting the reduction of conducted immunity in various dimensions of the test vehicle, the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle are calculated.

[0078] Using the entropy weight method, the distribution proportion of stress variables affecting the reduction of conducted immunity in each dimension of the vehicle is calculated, and weights are assigned to stress variables affecting the reduction of conducted immunity in each dimension of the vehicle.

[0079] Based on the weights of the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle, and the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle, the comprehensive acceleration factor affecting the reduction of conducted immunity is calculated as follows:

[0080]

[0081] in, The overall acceleration factor contributing to the decrease in the vehicle's immunity to transmitted disturbances is... Let temperature stress be the variable that affects the decrease in conducted immunity in the k-th dimension of the vehicle. For the humidity stress variable affecting the decrease in conducted immunity in the k-th dimension of the vehicle, To influence the weighting of temperature stress variables that affect the decrease in conducted immunity, The influence of hunger on the immunity of the conductor decreases, and the weight of the humidity stress variable is reduced. The weights of vibration stress variables that affect the decrease in conducted immunity;

[0082] An observation window is established based on the timestamps of the full life cycle test data of the known test vehicles in the hierarchical dimensions. The comprehensive acceleration factor of the decline in the vehicle's immunity due to the influence of transmission is used as the observation object to generate a time series of the comprehensive acceleration factor of the decline in the vehicle's immunity due to the influence of transmission throughout the entire life cycle of the known test vehicles.

[0083] Based on the ARMA autoregressive moving average model, using the known time series of the comprehensive acceleration factor affecting the decline of conducted immunity throughout the entire life cycle of the test vehicle and the ideal anti-interference performance of the electronic equipment components of the test vehicle as inputs, the influence trend of the comprehensive acceleration factor affecting the decline of conducted immunity on the ideal anti-interference performance of the electronic equipment components per unit time is predicted, and dynamic anti-interference performance indicators of the electronic equipment components of the test vehicle are generated.

[0084] When using it, refer to the content in steps 101 to 102.

[0085] As a further point, existing traditional EMC testing methods rely heavily on expert experience or single weighting methods to assess the interference immunity of electronic equipment components, lacking a fusion of subjective and objective weights. This leads to assessment results that deviate from actual operating conditions. Furthermore, existing methods are usually based on static test data and do not consider the cumulative effect of environmental stress (temperature, humidity, vibration) on conducted immunity throughout the entire life cycle of the vehicle, resulting in inaccurate long-term reliability assessments. Moreover, the diversity and dimensional differences of electronic equipment component parameters (such as PCB layout and wiring harness type) may lead to assessment biases, and existing methods have not addressed the comparability issue between parameters.

[0086] This solution achieves accurate dynamic evaluation of the EMC conducted immunity of automotive electronic devices by integrating subjective weights from the AHP method with objective weights from the entropy weight method, combined with K-Means clustering and ARMA time series modeling. The above steps quantify the influence weights of multi-dimensional stress factors such as temperature, humidity, and vibration, and eliminate dimensional differences between parameters through data standardization and normalization. This allows for adaptation to universal evaluation systems across different vehicle models, improving the accuracy of anti-interference performance evaluation.

[0087] Step 2: Based on the Internet and the vehicle manufacturer's back-end fault database, obtain electronic component failure events in various automotive scenarios, analyze the posterior probability of electronic component failure events in various automotive scenarios, and evaluate the distribution of electronic component failures in various scenarios throughout the entire life cycle of the test vehicle.

[0088] Step two includes the following:

[0089] Step 201: Based on the Internet and the vehicle manufacturer's back-end fault database, mark the electronic equipment component failure events in various scenarios of the vehicle life cycle, and establish a feature dataset of electronic equipment component failure events in various scenarios of the vehicle life cycle.

[0090] Based on the feature dataset of electronic component failure events in various scenarios across the automotive lifecycle, Bayesian probability is used to calculate the posterior probability of electronic component failure events in each scenario within each lifecycle, as follows:

[0091]

[0092] in, Let be the posterior probability of the failure event of the i-th electronic device component in the v-th scenario under the L-th lifecycle. Let represent the number of failures of the i-th electronic device component in the v-th scenario within the L-th lifecycle. The total number of vehicles in the v-th scenario within the L-th lifecycle;

[0093] Step 202: Based on the posterior probability of electronic device component failure events in each scenario under each life cycle, use Weibull distribution fitting to verify the temporal distribution of electronic device component failure events in each scenario under each life cycle.

[0094] Using KDE kernel density estimation, the geographical distribution of faults in electronic device components under each lifecycle and scenario is statistically analyzed to generate the spatial distribution of electronic device component fault events under each lifecycle and scenario.

[0095] By using conditional independence testing, we can verify the dependency between each scenario and electronic device component in the temporal distribution of electronic device component failure events in each scenario under each life cycle.

[0096] The conditional independence test was used to verify the dependency between electronic components and failure events in the spatial distribution of electronic component failure events in each scenario of each life cycle.

[0097] Based on the dependencies between various scenarios and electronic device components, and the dependencies between electronic device components and fault events, a three-dimensional data array of scenario-electronic device component-fault event is established to determine the fault distribution of electronic device components in various scenarios throughout the entire life cycle of the test vehicle.

[0098] When using it, refer to the content in steps 201 to 202.

[0099] As a further point, traditional EMC conducted immunity testing mainly relies on laboratory data and limited field samples, resulting in insufficient samples for fault analysis; it uses static probability models (such as Poisson distribution) and fails to dynamically update the failure rate, making it difficult to accurately reflect the performance degradation at different life cycle stages; it lacks analysis of the spatiotemporal distribution of faults, making it impossible to identify high-risk patterns in specific environments or usage stages; at the same time, due to the lack of rigorous statistical verification, it is difficult to accurately locate EMC immunity links under complex operating conditions.

[0100] This solution integrates multi-source data from the internet and vehicle manufacturers' back-end systems, combines dynamic Bayesian probability analysis to achieve accurate prediction of failure rates, constructs a spatiotemporal risk model using Weibull distribution and KDE kernel density estimation, verifies key influencing factors using conditional independence testing, and finally establishes a scenario-component-failure ternary association array, significantly improving the accuracy and relevance of EMC immunity assessment.

[0101] Example: Scenario-Component-Fault ternary associative array, for example:

[0102] Example 1: (Scenario: High temperature and high humidity → Component: Capacitor ESR rises → Fault: ECU power supply failure)

[0103] Example 2: (Scenario: Vibration intensity → Component: Weld joint crack → Fault: CAN signal loss)

[0104] Step 3: Based on the fault distribution of electronic components in various scenarios throughout the test vehicle's life cycle, determine the interference source type of the fault event and generate the conducted immunity test task for the electronic components of the test vehicle.

[0105] Step three includes the following:

[0106] Step 301: Based on the fault distribution of electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle, calculate the matching degree between the fault mode of each electronic equipment component and the known interference source type and divide them to generate the electronic equipment component fault mode-interference source type matrix for various scenarios throughout the entire life cycle of the test vehicle.

[0107] As further information: known interference source types include, but are not limited to: conducted transients, radiated immunity, and conducted continuous waves;

[0108] Based on the fault mode-interference source type matrix of electronic equipment components in various scenarios throughout the test vehicle's life cycle, the frequency of occurrence of interference source type for each electronic equipment component fault mode is statistically analyzed relative to the overall proportion of the same type of fault mode, and the attention interference source type weight is assigned to the fault mode of electronic equipment components in various scenarios throughout the test vehicle's life cycle.

[0109] Step 302: Using Fast Fourier Transform, extract the time and frequency domains of the electronic device component failure mode-interference source type matrix under various scenarios throughout the entire life cycle of the test vehicle to obtain the feature parameters of the interference source type of the electronic device component failure mode under various scenarios throughout the entire life cycle of the test vehicle.

[0110] Based on the posterior probability of electronic component failure events in each scenario of each lifecycle and the weight of the interference source type of the electronic component failure mode in each scenario of the entire lifecycle of the test vehicle, the test priority of electronic components in each scenario of the entire lifecycle of the test vehicle is calculated as follows:

[0111]

[0112] in, To determine the testing priority of the i-th electronic component in the v-th scenario throughout the vehicle's lifecycle, To test the weights of the interference source types for the i-th electronic device component in the v-th scenario throughout the vehicle's lifecycle, The baseline score for the severity of the failure mode of the i-th electronic device component;

[0113] Based on the standard confidence interval, we screen the failure modes of electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle, focusing on the characteristic parameters of the interference source type, and establish test limits for electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle.

[0114] Test sequences are constructed based on the test priorities and test limits of electronic components in various scenarios throughout the entire life cycle of the test vehicle, and the conducted immunity test tasks of electronic components of the test vehicle are generated.

[0115] When using it, refer to the content in steps 301 to 302.

[0116] As a further point, the existing testing scheme lacks a systematic fault mode-interference source correlation analysis, which may lead to the omission or misjudgment of key interference sources. Furthermore, it does not combine fault posterior probability and interference source weight to dynamically adjust priorities, resulting in wasted resources or insufficient coverage of high-risk scenarios. It is also difficult to accurately identify the frequency band distribution characteristics of interference sources such as conducted transients and continuous waves, and cannot optimize the testing strategy as fault data accumulates or scenarios change.

[0117] This solution uses a fault mode-interference source type matrix to statistically identify key interference sources (such as conducted transients or radiated immunity) based on their frequency of occurrence, thus avoiding subjective bias. It also uses posterior probability and interference source weights to calculate test priorities, ensuring a good match between high-failure-risk scenarios and component testing. This addresses the shortcomings of traditional methods in interference source identification, test limit setting, and resource allocation, thereby improving test accuracy.

[0118] Step 4: Based on the conducted immunity test task of the electronic components of the test vehicle, conduct several rounds of tests on the test vehicle, obtain the operating data and dynamic performance indicators of the electronic components of the test vehicle, establish a shutdown model of the electronic components of the vehicle, and generate the shutdown risk probability of the electronic components of the test vehicle.

[0119] Step four includes the following:

[0120] Step 401: Based on the conducted immunity test task of the electronic equipment components of the test vehicle, conduct several rounds of tests on the test vehicle to obtain the operating data of the electronic equipment components of the test vehicle.

[0121] Based on the operating data of the electronic components of the test vehicle, the actual tolerance value of the electronic components of the test vehicle and the conducted immunity test limit of the electronic components are marked, and the immunity margin of the electronic components of the test vehicle is calculated.

[0122] Based on the dynamic performance index of the electronic components of the test vehicle and the immunity margin of the electronic components of the test vehicle, a time-varying covariate vector sample array is established.

[0123] Based on Cox regression, a model for the failure of electronic components in automobiles is established. The time-varying covariate vector sample array is used as input, and the probability of failure of the electronic components in the automobile is used as output, as follows:

[0124]

[0125] in, Given a time-varying covariate vector at time t, the probability of shutdown risk for the i-th electronic device component. As the benchmark risk function, This is a static covariate vector (test limits and dynamic performance indicators of interference immunity of electronic equipment components). The regression coefficient vector of static covariates. For transpose, It is a time-varying covariate (real-time disturbance immunity margin).

[0126] Step 5: Determine whether the probability of electronic component failure of the test vehicle is within the tolerable operating threshold range. If yes, determine that there is no abnormality; otherwise, determine that there is an abnormality.

[0127] As a further point, the tolerable operating threshold range is determined by the automaker's own corporate standards, but it can also be based on mandatory standards, which is well known to those skilled in the art and will not be discussed in detail here.

[0128] Reference Figure 2As shown, an EMC conducted immunity test system for automotive electronic devices includes:

[0129] Performance evaluation module, fault distribution evaluation module, test task generation module, shutdown risk analysis module, and judgment module;

[0130] The performance evaluation module is used to determine the ideal anti-interference performance of the test vehicle based on the structural parameters of the electronic equipment components of the test vehicle, analyze the changing trend of the ideal anti-interference performance according to the design and use cycle of the test vehicle, and evaluate the dynamic anti-interference performance index of the electronic equipment components of the test vehicle.

[0131] The fault distribution assessment module is used to obtain electronic component failure events in various automotive scenarios based on the Internet and the vehicle manufacturer's back-end fault database, analyze the posterior probability of electronic component failure events in various automotive scenarios, and assess the fault distribution of electronic components in various scenarios throughout the entire life cycle of the test vehicle.

[0132] The test task generation module is electrically connected to the fault distribution assessment module. The test task generation module is used to determine the interference source type of the fault event based on the fault distribution of electronic components in various scenarios throughout the entire life cycle of the test vehicle, and generate the conducted immunity test task of the electronic components of the test vehicle.

[0133] The shutdown risk analysis module is electrically connected to the performance evaluation module and the test task generation module. The shutdown risk analysis module is used to conduct several rounds of tests on the test vehicle based on the conducted immunity test task of the electronic components of the test vehicle, obtain the operating data of the electronic components of the test vehicle and the dynamic performance index of the electronic components of the test vehicle, establish a shutdown model of the electronic components of the vehicle, and generate the shutdown risk probability of the electronic components of the test vehicle.

[0134] The judgment module is electrically connected to the shutdown risk analysis module. The judgment module is used to determine whether the shutdown risk probability of the electronic equipment components of the test vehicle is within the tolerable operating threshold range. If yes, it is determined that there is no abnormality; if no, it is determined that there is an abnormality.

[0135] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for testing the EMC conducted immunity of automotive electronic devices, characterized in that, include: S1. Based on the structural parameters of the electronic equipment components of the test vehicle, determine the ideal anti-interference performance of the test vehicle, analyze the changing trend of the ideal anti-interference performance according to the design and service life of the test vehicle, and evaluate the dynamic anti-interference performance index of the electronic equipment components of the test vehicle. S2. Based on the Internet and the vehicle manufacturer's back-end fault database, obtain electronic component failure events in various automotive scenarios, analyze the posterior probability of electronic component failure events in various automotive scenarios, and evaluate the distribution of electronic component failures in various scenarios throughout the entire life cycle of the test vehicle. S3. Based on the fault distribution of electronic components in various scenarios throughout the entire life cycle of the test vehicle, determine the interference source type of the fault event and generate the conducted immunity test task of the electronic components of the test vehicle. S4. Based on the conducted immunity test task of the electronic components of the test vehicle, conduct several rounds of tests on the test vehicle, obtain the operating data of the electronic components of the test vehicle and the dynamic performance index of the electronic components of the test vehicle, establish the shutdown model of the electronic components of the vehicle, and generate the shutdown risk probability of the electronic components of the test vehicle. S5. Determine whether the probability of electronic component failure of the test vehicle is within the tolerable operating threshold range. If yes, determine that there is no abnormality; otherwise, determine that there is an abnormality.

2. The method for testing the EMC conducted immunity of automotive electronic devices according to claim 1, characterized in that, S1 includes: Obtain the structural parameters of the electronic equipment components of the test vehicle, and establish a structured parameter array for the electronic equipment components of the test vehicle; the structured parameters of the electronic equipment components include: electrical parameters, PCB layout parameters, shielding measures parameters, and wiring harness type parameters; The structured parameter arrays of electronic components in the test vehicle are standardized. Using the AHP hierarchy, a structured parameter matrix of the electronic equipment components of the test vehicle is established according to the structured parameter array of the electronic equipment components of the test vehicle. The parameter vector value of each element in the matrix is ​​calculated using geometric mean, and subjective weights are assigned to the structured parameters of the electronic equipment components of the test vehicle. Using the entropy weight method, the information entropy value of each neighboring parameter in the structured parameter array of the electronic equipment components of the test vehicle is analyzed, the variation coefficient of the structured parameters of the electronic equipment components of the test vehicle is determined, and objective weights are assigned to the structured parameters of the electronic equipment components of the test vehicle. The structured parameters of the electronic equipment components of the test vehicle are assigned weights based on the subjective weights, objective weights, and fundamental fusion weights of the structured parameters of the electronic equipment components of the test vehicle.

3. The method for testing the EMC conducted immunity of automotive electronic equipment according to claim 2, characterized in that, S1 further includes: Based on the structured parameter weights of the electronic equipment components of the test vehicle, a weighted matrix of the structured parameters of the electronic equipment components of the test vehicle is established. The weighted matrix of the structured parameters of the electronic components of the test vehicle is normalized, the maximum value of the structured parameter of each electronic component is selected, and the ideal anti-interference performance parameter sequence of the electronic components of the test vehicle is established. Calculate the similarity between the structured parameter array of the electronic components of the test vehicle and the ideal anti-interference performance parameter sequence of the electronic components of the test vehicle, and establish the correlation coefficient matrix between the structured parameters and the ideal anti-interference performance of the electronic components of the test vehicle. Based on the correlation coefficient matrix of the structured parameters of the electronic components of the test vehicle and the ideal anti-interference performance parameters, and the weights of the structured parameters of the electronic components of the test vehicle, the ideal anti-interference performance of the electronic components of the test vehicle is calculated by weighted projection.

4. The method for testing the EMC conducted immunity of automotive electronic equipment according to claim 3, characterized in that, S1 further includes: Acquire known test vehicle full life cycle test data, use K-Means clustering algorithm to divide the known test vehicle full life cycle test data according to several usage dimensions, and generate known test vehicle hierarchical full life cycle test data; Based on the full lifecycle test data of known test vehicles across different layers, the factors affecting the decline in conducted immunity under the known test vehicle layer dimensions are labeled; the factors affecting the decline in conducted immunity include: temperature, humidity, and vibration intensity; Based on the known factors affecting the reduction of conducted immunity in various dimensions of the test vehicle, the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle are calculated. Using the entropy weight method, the distribution proportion of stress variables affecting the reduction of conducted immunity in each dimension of the vehicle is calculated, and weights are assigned to stress variables affecting the reduction of conducted immunity in each dimension of the vehicle. Based on the weights of the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle and the stress variables affecting the reduction of conducted immunity in various dimensions of the vehicle, calculate the comprehensive acceleration factor of the vehicle's impact on the reduction of conducted immunity. An observation window is established based on the timestamps of the full life cycle test data of the known test vehicles in the hierarchical dimensions. The comprehensive acceleration factor of the decline in the vehicle's immunity due to the influence of transmission is used as the observation object to generate a time series of the comprehensive acceleration factor of the decline in the vehicle's immunity due to the influence of transmission throughout the entire life cycle of the known test vehicles. Based on the ARMA autoregressive moving average model, using the known time series of the comprehensive acceleration factor affecting the decline of conducted immunity throughout the entire life cycle of the test vehicle and the ideal anti-interference performance of the electronic equipment components of the test vehicle as inputs, the influence trend of the comprehensive acceleration factor affecting the decline of conducted immunity on the ideal anti-interference performance of the electronic equipment components per unit time is predicted, and dynamic performance indicators of anti-interference of the electronic equipment components of the test vehicle are generated.

5. The method for testing the EMC conducted immunity of automotive electronic equipment according to claim 4, characterized in that, S2 includes: Based on the Internet and the vehicle manufacturer's back-end fault database, we mark the electronic component failure events in various scenarios of the vehicle life cycle and establish a feature dataset of electronic component failure events in various scenarios of the vehicle life cycle. Based on the feature dataset of electronic component failure events in various scenarios of the automotive life cycle, Bayesian probability is used to calculate the posterior probability of electronic component failure events in each scenario of each life cycle.

6. The method for testing the EMC conducted immunity of automotive electronic equipment according to claim 5, characterized in that, S2 further includes: Based on the posterior probability of electronic device component failure events in each scenario under each life cycle, the temporal distribution of electronic device component failure events in each scenario under each life cycle is verified by fitting the Weibull distribution. Using KDE kernel density estimation, the geographical distribution of faults in electronic device components under each lifecycle and scenario is statistically analyzed to generate the spatial distribution of electronic device component fault events under each lifecycle and scenario. By using conditional independence testing, we can verify the dependency between each scenario and electronic device component in the temporal distribution of electronic device component failure events in each scenario under each life cycle. The conditional independence test was used to verify the dependency between electronic components and failure events in the spatial distribution of electronic component failure events in each scenario of each life cycle. Based on the dependencies between various scenarios and electronic device components, and the dependencies between electronic device components and fault events, a three-dimensional data array of scenario-electronic device component-fault event is established to determine the fault distribution of electronic device components in various scenarios throughout the entire life cycle of the test vehicle.

7. The method for testing the EMC conducted immunity of automotive electronic equipment according to claim 6, characterized in that, S3 includes: Based on the fault distribution of electronic components in various scenarios throughout the test vehicle's life cycle, the matching degree between each electronic component fault mode and known interference source type is calculated and divided to generate an electronic component fault mode-interference source type matrix for various scenarios throughout the test vehicle's life cycle. As further information: known interference source types include, but are not limited to: conducted transients, radiated immunity, and conducted continuous waves; Based on the fault mode-interference source type matrix of electronic equipment components in various scenarios throughout the test vehicle's life cycle, the frequency of occurrence of interference source type for each electronic equipment component fault mode is statistically analyzed relative to the overall proportion of the same type of fault mode, and a weight is assigned to the attention interference source type for each electronic equipment component fault mode in various scenarios throughout the test vehicle's life cycle.

8. The method for testing the EMC conducted immunity of automotive electronic equipment according to claim 7, characterized in that, S3 further includes: Using Fast Fourier Transform, the time and frequency domains of the fault mode-interference source type matrix of electronic equipment components under various scenarios throughout the entire life cycle of the test vehicle are extracted to obtain the feature parameters of the interference source type of the fault mode of electronic equipment components under various scenarios throughout the entire life cycle of the test vehicle. Based on the posterior probability of electronic component failure events in each scenario of each life cycle and the weight of the interference source type of the electronic component failure mode in each scenario of the entire life cycle of the test vehicle, the test priority of electronic components in each scenario of the entire life cycle of the test vehicle is calculated. Based on the standard confidence interval, we screen the failure modes of electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle, focusing on the characteristic parameters of the interference source type, and establish test limits for electronic equipment components in various scenarios throughout the entire life cycle of the test vehicle. Test sequences are constructed based on the test priorities and test limits of electronic components in various scenarios throughout the test vehicle's lifecycle, generating conducted immunity test tasks for the electronic components of the test vehicle.

9. The method for testing the EMC conducted immunity of automotive electronic equipment according to claim 8, characterized in that, S4 includes: Based on the conducted immunity test of electronic components of the test vehicle, several rounds of tests are conducted on the test vehicle to obtain the operating data of the electronic components of the test vehicle. Based on the operating data of the electronic components of the test vehicle, the actual tolerance value of the electronic components of the test vehicle and the conducted immunity test limit of the electronic components are marked, and the immunity margin of the electronic components of the test vehicle is calculated. Based on the dynamic performance index of the electronic components of the test vehicle and the immunity margin of the electronic components of the test vehicle, a time-varying covariate vector sample array is established. Based on Cox regression, a model for the shutdown of electronic components in automobiles is established, with a time-varying covariate vector sample array as input and the probability of the electronic components in automobiles shutting down as output.

10. A test system for EMC conducted immunity of automotive electronic equipment, characterized in that, A method for implementing the EMC conducted immunity test method for automotive electronic devices as described in any one of claims 1-9 includes: Performance evaluation module, fault distribution evaluation module, test task generation module, shutdown risk analysis module, and judgment module; The performance evaluation module is used to determine the ideal anti-interference performance of the test vehicle based on the structural parameters of the electronic equipment components of the test vehicle, analyze the changing trend of the ideal anti-interference performance according to the design and use cycle of the test vehicle, and evaluate the dynamic anti-interference performance index of the electronic equipment components of the test vehicle. The fault distribution assessment module is used to obtain electronic component failure events in various automotive scenarios based on the Internet and the vehicle manufacturer's back-end fault database, analyze the posterior probability of electronic component failure events in various automotive scenarios, and assess the fault distribution of electronic components in various scenarios throughout the entire life cycle of the test vehicle. The test task generation module is electrically connected to the fault distribution assessment module. The test task generation module is used to determine the interference source type of the fault event based on the fault distribution of electronic components in various scenarios throughout the entire life cycle of the test vehicle, and generate the conducted immunity test task of the electronic components of the test vehicle. The shutdown risk analysis module is electrically connected to the performance evaluation module and the test task generation module. The shutdown risk analysis module is used to conduct several rounds of tests on the test vehicle based on the conducted immunity test task of the electronic components of the test vehicle, obtain the operating data of the electronic components of the test vehicle and the dynamic performance index of the electronic components of the test vehicle, establish a shutdown model of the electronic components of the vehicle, and generate the shutdown risk probability of the electronic components of the test vehicle. The judgment module is electrically connected to the shutdown risk analysis module. The judgment module is used to determine whether the shutdown risk probability of the electronic equipment components of the test vehicle is within the tolerable operating threshold range. If yes, it is determined that there is no abnormality; if no, it is determined that there is an abnormality.

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