Multi-factor combination test method and device for intelligent automobile communication network

Abstract

The invention provides a multi-factor combination test method and device for an intelligent automobile communication network, and belongs to the technical field of automobile electronic tests.The method comprises the steps that a dynamic coupling model containing reference parameters is established; generating a multi-physical field combination test scheme based on the coupling coefficient matrix and executing a test; monitoring an actually measured coupling strength parameter in real time, and triggering optimization if the coupling strength parameter is continuously higher than a threshold value; dominant failure factors are determined through failure feature analysis, a hardware optimization scheme is matched and executed, and reference parameters are updated after verification is passed. By quantifying the multi-physical field coupling effect, dynamically adjusting the test scheme and actively triggering the optimization mechanism, the technical problems that a traditional single-factor test cannot simulate real and complex working conditions, is low in test efficiency and cannot realize preventive maintenance are solved, and the reliability test precision and efficiency of the intelligent automobile communication module are improved.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics testing technology, and in particular to a multi-physics field combined testing and optimization method and device for intelligent vehicle communication modules, especially to dynamic testing and adaptive optimization technology based on multi-physics field coupling effects. Background Technology

[0002] Intelligent vehicle communication modules (such as CAN bus, in-vehicle Ethernet, and gateway modules) are exposed to complex and variable environmental conditions during operation. Temperature, vibration, salt spray corrosion, and other multi-physical field factors often work synergistically, leading to communication signal degradation and even system failure. Traditional reliability testing methods often employ single-factor testing or fixed-combination testing, which cannot realistically simulate the synergistic effects of multi-physical field coupling in actual operating environments, resulting in significant deviations between test results and actual failure modes. While some multi-factor testing methods exist, they typically lack quantitative assessment of coupling effects, and test scheme design relies on experience, making it difficult to systematically cover high-risk combinations. Therefore, there is an urgent need for a testing method capable of quantifying multi-physical field coupling effects, dynamically adjusting test schemes, and achieving proactive preventative optimization. Summary of the Invention

[0003] In view of the technical defects and drawbacks existing in the prior art, embodiments of the present invention provide a multi-factor combination testing method and equipment for intelligent vehicle communication networks that overcomes or at least partially solves the above problems, the specific solution of which is as follows;

[0004] As a first aspect of the present invention, a multi-factor combination testing method for intelligent vehicle communication networks is provided, the method comprising:

[0005] Dynamic coupling modeling and risk ranking: Based on historical test data, the coupling strength parameters characterizing the multi-physics interaction effect are calculated to generate a coupling coefficient matrix, and the risk ranking of multi-physics test combinations is performed based on the coupling strength parameters to determine the test priority;

[0006] Priority testing and real-time monitoring: Based on the obtained risk ranking results, apply corresponding multiphysics combination conditions to the tested object in priority order, and monitor the coupling strength parameters in real time during the test;

[0007] Closed-loop optimization and model update: When the coupling strength parameter monitored in real time exceeds the preset threshold, the hardware of the object under test is optimized according to the failure feature matching optimization scheme, and the test is re-verified under the same multi-physics field combination conditions; if the verification is successful, the baseline parameters of the dynamic coupling model are updated.

[0008] In some embodiments, the dynamic coupling modeling and risk ranking specifically include:

[0009] Obtaining benchmark parameters: Based on historical single-factor test data, determine the benchmark parameters of each physical field under the benchmark state. The benchmark parameters include at least the benchmark signal quality S0, benchmark temperature T0, benchmark vibration V0, and benchmark salt spray concentration P0.

[0010] Calculate the coupling strength parameters: Based on the obtained baseline parameters, the coupling strength parameters between any two physical fields X and Y selected from temperature T, vibration V, and salt spray concentration P are calculated using the coupling strength calculation engine. The coupling strength parameter The calculation is based on the signal quality and physical field data acquired synchronously before and after the test, and the calculation formula is as follows:

[0011]

[0012] in, This indicates that under the condition that physical fields X and Y change together, with , The quality of synchronously acquired signals is relative to The change in signal quality is ΔS, which is the increment of the signal attenuation rate, in dB.

[0013] and These represent the changes in physical fields X and Y relative to their reference states, respectively.

[0014] Generate the coupling matrix: based on the calculated coupling strength parameters of all pairwise physical field combinations. Generate the coupling coefficient matrix;

[0015] Risk ranking is performed based on the coupling strength parameters in the generated coupling coefficient matrix. The numerical values ​​are used to rank the risk of all pairwise combinations of physics to be tested. The higher the value, the higher the priority of the corresponding test combination.

[0016] In some embodiments, the coupling coefficient matrix includes the temperature-vibration coupling strength parameter α. TV Temperature-salt spray coupling intensity parameter α TP and the vibration-salt spray coupling strength parameter α VP ;

[0017] Wherein, the α TV Used to quantify the coupling effect of the combined effects of high temperature and mechanical vibration on the reliability of communication line connections;

[0018] The α TP Used to quantify the aging effect of signal transmission media caused by the combined effects of high temperature environment and salt spray corrosion;

[0019] The α VP This is used to quantify the mutually accelerating destructive effect of mechanical vibration stress and salt spray corrosion on communication interfaces.

[0020] In some embodiments, priority testing and real-time monitoring specifically include:

[0021] Applying test conditions: The environmental loading device applies the physical field combination conditions to be tested to the object under test in sequence according to the risk ranking results;

[0022] Real-time monitoring and analysis: During the test process of applying each combination of physical field conditions, the current measured coupling strength parameters are continuously monitored and calculated at preset time intervals using a real-time analyzer;

[0023] Threshold Comparison and Decision: The measured coupling strength parameters calculated at each time interval are compared with the preset coupling strength threshold in real time, and the following decisions are made based on the comparison results:

[0024] If the measured coupling strength parameter is determined to be continuously higher than the coupling strength threshold, the current test is immediately terminated, and the closed-loop optimization and model update steps are executed.

[0025] If the measured coupling strength parameter does not continuously exceed the coupling strength threshold throughout the entire preset test period under the current physical field combination conditions, then the current physical field combination test is deemed to have passed the verification.

[0026] After determining that the current test has passed verification, determine whether all combinations of physical fields to be tested have been tested;

[0027] Test sequence progression:

[0028] If there are still untested combinations of physical fields, return to the step of applying test conditions, and the environmental loading device applies the next priority combination of physical fields conditions, repeating the process of real-time monitoring and analysis up to the threshold comparison and decision-making.

[0029] Once all combinations have been tested, the entire testing process ends.

[0030] In some embodiments, the closed-loop optimization and model update specifically include:

[0031] Optimization triggering and failure analysis: When it is determined that the measured coupling strength parameter is continuously higher than the coupling strength threshold, the failure feature rule base is activated; based on the physical field combination type that triggers the optimization, the signal degradation characteristics obtained by real-time monitoring, and the measured coupling strength parameter, failure feature analysis is performed to determine the dominant failure factor;

[0032] Optimization scheme execution: Based on the determined dominant failure factors, the corresponding hardware optimization scheme is matched from the failure feature rule base, and the hardware optimization scheme is executed through the measure executor to optimize the hardware of the tested object;

[0033] Optimization verification test: After hardware optimization is completed, the environment loading device is controlled to reapply the same combination of physical field conditions as when the optimization was triggered, and a retest is performed;

[0034] Verification of decisions and model evolution: During the retesting process, the retesting coupling strength parameters are monitored and calculated in real time using the real-time analyzer.

[0035] If the retested coupling strength parameter does not exceed the coupling strength threshold throughout the entire preset test period of the retest, the optimization is deemed effective, the test is passed, and the baseline parameters in the dynamic coupling model are updated based on the currently stable test parameters.

[0036] If the retested coupling strength parameter continues to be higher than the coupling strength threshold, then return to the optimization triggering and failure analysis step for iterative optimization until the retested coupling strength parameter meets the threshold requirement or reaches the preset maximum number of iterations.

[0037] In some embodiments, the failure feature rule base is pre-configured with a mapping relationship between failure factors and hardware optimization schemes;

[0038] The failure factors include at least one of temperature-dominated failure, vibration-dominated failure, and salt spray corrosion-dominated failure.

[0039] The mapping relationship is as follows:

[0040] When the failure is determined to be temperature-driven, the appropriate hardware optimization solution is a cooling system upgrade.

[0041] When the failure is determined to be vibration-dominant, the matching hardware optimization solution is an enhanced vibration damping structure solution;

[0042] When the failure is determined to be primarily caused by salt spray corrosion, the appropriate hardware optimization solution is an upgrade to the sealing and protection structure.

[0043] In some embodiments, failure characteristic analysis is performed to determine the dominant failure factors, specifically including:

[0044] Based on the physical field combination type of trigger optimization, determine the candidate set of physical fields to be analyzed;

[0045] For each physical field factor in the candidate set, the sensitivity of the signal degradation feature to the change of that factor is calculated, whereby the sensitivity is the ratio of the rate of change of the degradation feature monitored in real time to the rate of change of the corresponding physical field.

[0046] Based on the overall measured coupling strength parameter values ​​monitored in the current test, the contribution weight of each physical field factor to signal degradation is calculated. The formula for calculating the contribution weight is: contribution weight of each physical field factor = sensitivity of the factor × measured coupling strength parameter.

[0047] The physical field factor with the highest contribution weight is determined as the dominant failure factor.

[0048] In some embodiments, updating the baseline parameters in the dynamic coupling model specifically includes:

[0049] The signal quality, temperature, vibration, and salt spray concentration corresponding to the current successful test are used as the new reference signal quality, new reference temperature, new reference vibration, and new reference salt spray concentration, respectively, to update the reference parameters in the dynamic coupling model.

[0050] In some embodiments, the object under test includes a CAN bus communication module, an in-vehicle Ethernet communication module, or an in-vehicle gateway module in an intelligent vehicle communication network; wherein, the in-vehicle gateway module includes an optical network and routing device.

[0051] As a second aspect of the present invention, an electronic device is provided, comprising:

[0052] One or more processors;

[0053] Memory, used to store one or more programs;

[0054] When the one or more programs are executed by the one or more processors, the one or more processors implement the methods described above.

[0055] The present invention has the following beneficial effects:

[0056] This invention achieves systematic reliability testing and performance improvement of intelligent vehicle communication modules under complex environmental conditions by establishing a dynamic coupling model, performing multi-physics field combination tests, and conducting real-time monitoring and adaptive optimization based on coupling strength parameters. Specifically, it upgrades traditional single-factor or fixed combination tests to dynamic combination tests based on coupling effects, which can more realistically simulate the harsh operating conditions of multi-physics field synergy in actual operating environments. By monitoring coupling strength parameters in real time, potential failure risks can be dynamically identified, and optimization mechanisms can be proactively triggered before failure occurs, realizing a shift from "passive detection" to "proactive prevention." At the same time, by updating benchmark parameters to achieve adaptive evolution of the model, the test system can track the performance state changes of the tested object after optimization, forming a closed-loop feedback, which significantly improves the accuracy of testing and the effectiveness of reliability verification, providing technical support for the long-term reliability assurance of intelligent vehicle communication systems. Attached Figure Description

[0057] Figure 1 A flowchart illustrating a multi-physics combination testing and optimization method for an intelligent vehicle communication module provided in an embodiment of the present invention;

[0058] Figure 2 A flowchart illustrating the dynamic coupling modeling and risk ranking process provided in this embodiment of the invention;

[0059] Figure 3 A flowchart illustrating the priority testing and real-time monitoring process provided in an embodiment of the present invention;

[0060] Figure 4 A schematic diagram illustrating the closed-loop optimization and model update process provided in an embodiment of the present invention;

[0061] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0062] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0063] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0064] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0065] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0066] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0067] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0068] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a multi-factor combination test method for intelligent vehicle communication networks. Figure 1 This is a flowchart illustrating a multi-factor combination testing method for intelligent vehicle communication networks provided in an embodiment of the present invention. The method includes:

[0069] S1. Dynamic Coupling Modeling and Risk Ranking: Based on historical test data, calculate the coupling strength parameters that characterize the interaction effects of multi-physics fields to generate a coupling coefficient matrix, and rank the multi-physics field test combinations based on the coupling strength parameters to determine the test priority.

[0070] S2. Priority Testing and Real-time Monitoring: Based on the risk ranking results obtained in S1, apply corresponding multiphysics combination conditions to the tested object in priority order, and monitor the coupling strength parameters in real time during the test.

[0071] S3. Closed-loop optimization and model update: When the coupling strength parameter monitored in real time in S2 exceeds the preset threshold, the test object is optimized in hardware according to the failure feature matching optimization scheme, and retested under the same multi-physics field combination conditions; if the verification is successful, the baseline parameters of the dynamic coupling model in S1 are updated.

[0072] This invention achieves systematic reliability testing and performance improvement of intelligent vehicle communication modules under complex environmental conditions by establishing a dynamic coupling model, performing multi-physics field combination tests, and conducting real-time monitoring and adaptive optimization based on coupling strength parameters. Specifically, it upgrades traditional single-factor or fixed combination tests to dynamic combination tests based on coupling effects, which can more realistically simulate the harsh operating conditions of multi-physics field synergy in actual operating environments. By monitoring coupling strength parameters in real time, potential failure risks can be dynamically identified, and optimization mechanisms can be proactively triggered before failure occurs, realizing a shift from "passive detection" to "proactive prevention." At the same time, by updating benchmark parameters to achieve adaptive evolution of the model, the test system can track the performance state changes of the tested object after optimization, forming a closed-loop feedback, which significantly improves the accuracy of testing and the effectiveness of reliability verification, providing technical support for the long-term reliability assurance of intelligent vehicle communication systems.

[0073] refer to Figure 2 As shown, in some embodiments, step S1, dynamic coupling modeling and risk ranking, specifically includes:

[0074] S11. Obtaining reference parameters: Based on historical single-factor test data, determine the reference parameters of each physical field under the reference state. The reference parameters include at least the reference signal quality S0, the reference temperature T0, the reference vibration V0, and the reference salt spray concentration P0.

[0075] S12. Calculate the coupling strength parameters: Based on the baseline parameters obtained in S11, the coupling strength parameters between any two physical fields X and Y selected from temperature T, vibration V, and salt spray concentration P are calculated using the coupling strength calculation engine. The coupling strength parameter The calculation is based on the signal quality and physical field data acquired synchronously before and after the test, and the calculation formula is as follows:

[0076]

[0077] in, This indicates that under the condition that physical fields X and Y change together, with , The quality of synchronously acquired signals is relative to The change in signal quality is ΔS, which is the increment of the signal attenuation rate, in dB.

[0078] and These represent the changes in physical fields X and Y relative to their reference states, respectively.

[0079] S13. Generate the coupling matrix: Based on the coupling strength parameters of all pairwise physical field combinations calculated in S12. Generate the coupling coefficient matrix;

[0080] S14. Perform risk ranking: Based on the coupling strength parameters in the coupling coefficient matrix generated in S13. The numerical values ​​are used to rank the risk of all pairwise combinations of physics to be tested. The higher the value, the higher the priority of the corresponding test combination.

[0081] Among them, the physical field change and Each has a corresponding physical unit; when physical field X or Y is temperature, the unit of change is ℃; when physical field X or Y is vibration, the unit of change is Grms; when physical field X or Y is salt spray concentration, the unit of change is mg / m³.

[0082] The above embodiments provide quantifiable decision-making basis for multiphysics combination testing by constructing baseline parameters, calculating coupling strength parameters, generating coupling coefficient matrices, and performing risk ranking. Specifically, by establishing calculation formulas for baseline states and coupling strength parameters, the abstract multiphysics coupling effect is transformed into measurable numerical indicators, achieving an objective quantitative assessment of coupling strength; by generating a coupling coefficient matrix, the coupling strength relationship of different physics combinations can be systematically characterized, providing a data foundation for test scheme design; and risk ranking based on coupling strength parameters can prioritize testing high-risk combinations, optimize test resource allocation, and improve test efficiency.

[0083] In some embodiments, in step S13, the coupling coefficient matrix includes the temperature-vibration coupling strength parameter α. TV Temperature-salt spray coupling intensity parameter α TP and the vibration-salt spray coupling strength parameter α VP ;

[0084] Wherein, the α TV Used to quantify the coupling effect of the combined effects of high temperature and mechanical vibration on the reliability of communication line connections;

[0085] The α TP Used to quantify the aging effect of signal transmission media caused by the combined effects of high temperature environment and salt spray corrosion;

[0086] The α VP This is used to quantify the mutually accelerating destructive effect of mechanical vibration stress and salt spray corrosion on communication interfaces.

[0087] The following are the temperature-vibration coupling strength parameters α. TV Calculation example:

[0088] Reference conditions are set as follows: T0 = 25℃, V0 = 2 Grms, S0 = 0.5 dB;

[0089] Test conditions: T = 80℃, V = 5 Grms, S = 1.2 dB;

[0090] The changes are: ΔT = 55℃, ΔV = 3 Grms, ΔS = 0.7 dB;

[0091] Substitute into the formula to calculate:

[0092] .

[0093] refer to Figure 3 As shown, in some embodiments, step S2, priority testing and real-time monitoring, specifically includes:

[0094] S21. Apply test conditions: The environmental loading device applies the physical field combination conditions to be tested to the object under test in sequence according to the risk ranking results.

[0095] S22. Real-time monitoring and analysis: During the test process of applying each combination of physical field conditions, the current measured coupling strength parameters are continuously monitored and calculated at preset time intervals by a real-time analyzer.

[0096] S23. Threshold Comparison and Decision: The measured coupling strength parameters calculated at each time interval are compared with the preset coupling strength threshold in real time, and the following decision is made based on the comparison results:

[0097] If the measured coupling strength parameter is determined to be continuously higher than the coupling strength threshold, the current test is terminated immediately, and step S3 is executed.

[0098] If the measured coupling strength parameter does not continuously exceed the coupling strength threshold throughout the entire preset test period under the current physical field combination conditions, then the current physical field combination test is deemed to have passed the verification.

[0099] After determining that the current test has passed verification, determine whether all combinations of physical fields to be tested have been tested;

[0100] S24, Test sequence progression:

[0101] If there are still untested combinations of physical fields, return to step S21, where the environment loading device applies the next priority combination of physical fields, and repeat the process from S22 to S23.

[0102] Once all combinations have been tested, the entire testing process ends.

[0103] In the above embodiments, intelligent control of the testing process is achieved by setting a coupling strength threshold, monitoring the measured coupling strength parameters in real time, and comparing them with the threshold for decision-making. Specifically, by introducing a coupling strength threshold as a judgment criterion, a clear trigger condition is provided for "when optimization is needed," avoiding the uncertainty caused by subjective judgment and improving the automation level and decision reliability of the testing system. The mechanism of real-time monitoring and comparison with the threshold can promptly capture risk signals of abnormally high coupling strength and initiate intervention measures before or in the early stages of failure.

[0104] refer to Figure 4 As shown, in some embodiments, step S3 includes:

[0105] S31. Optimization Triggering and Failure Analysis: When it is determined in step S23 that the measured coupling strength parameter is continuously higher than the coupling strength threshold, the failure feature rule base is activated; based on the physical field combination type that triggers the optimization, the signal degradation characteristics obtained by real-time monitoring, and the measured coupling strength parameter, failure feature analysis is performed to determine the dominant failure factor;

[0106] S32. Optimization scheme execution: Based on the determined dominant failure factors, match the corresponding hardware optimization scheme from the failure feature rule base, and execute the hardware optimization scheme through the measure executor to optimize the hardware of the tested object.

[0107] S33. Optimization verification test: After the hardware optimization is completed, control the environment loading device to reapply the same combination of physical fields as when the optimization was triggered, and perform a retest;

[0108] S34. Verification of Decisions and Model Evolution: During the retesting process, the retesting coupling strength parameters are monitored and calculated in real time using the real-time analyzer.

[0109] If the retested coupling strength parameter does not exceed the coupling strength threshold throughout the entire preset test period of the retest, the optimization is deemed effective, the test is passed, and the baseline parameters in the dynamic coupling model are updated based on the currently stable test parameters.

[0110] If the retested coupling strength parameter continues to be higher than the coupling strength threshold, then return to step S31 for iterative optimization until the retested coupling strength parameter meets the threshold requirement or reaches the preset maximum number of iterations.

[0111] The above embodiments, by setting a trigger mechanism of "triggering optimization if the measured coupling strength parameter is continuously higher than a threshold" and specifying the optimization process (failure feature analysis → matching optimization scheme → executing optimization → verification and update), make the optimization steps operable. Specifically: by setting the condition of "continuously higher than the threshold," false triggering caused by instantaneous interference is avoided, improving the accuracy of optimization decisions; the optimization process is decomposed into analysis, matching, execution, and verification, giving the optimization operation a clear execution path and verification standard; by locating the dominant failure factor through failure feature analysis, the root cause of the problem can be accurately identified, avoiding blind optimization; and the verification step ensures the effectiveness of optimization measures and prevents resource waste caused by ineffective optimization.

[0112] In some embodiments, in step S31, the failure feature rule base is pre-set with a mapping relationship between failure factors and hardware optimization schemes;

[0113] The failure factors include at least one of temperature-dominated failure, vibration-dominated failure, and salt spray corrosion-dominated failure.

[0114] The mapping relationship is as follows:

[0115] When the failure is determined to be temperature-driven, the appropriate hardware optimization solution is a cooling system upgrade.

[0116] When the failure is determined to be vibration-dominant, the matching hardware optimization solution is an enhanced vibration damping structure solution;

[0117] When the failure is determined to be primarily caused by salt spray corrosion, the appropriate hardware optimization solution is an upgrade to the sealing and protection structure.

[0118] The above embodiments provide a standardized decision-making basis for optimization matching by pre-setting a failure feature rule base and specifically defining the mapping relationship between failure factors and hardware optimization solutions (temperature-dominant → heat dissipation upgrade, vibration-dominant → shock absorption enhancement, salt spray-dominant → sealing protection).

[0119] In some embodiments, step S31, "performing failure characteristic analysis to determine the dominant failure factor," specifically includes:

[0120] S311. Based on the physical field combination type of trigger optimization, determine the candidate set of physical fields to be analyzed;

[0121] S312. For each physical field factor in the candidate set, calculate the sensitivity of the signal degradation feature to the change of that factor, whereby the sensitivity is the ratio of the rate of change of the degradation feature monitored in real time to the rate of change of the corresponding physical field.

[0122] S313. Based on the measured coupling strength parameter, calculate the contribution weight of each physical field factor to the current signal degradation. The formula for calculating the contribution weight is: contribution weight of each physical field factor = sensitivity of the factor × measured coupling strength parameter.

[0123] S314. The physical field factor with the highest contribution weight is determined as the dominant failure factor.

[0124] The following is a specific example of determining the dominant failure factor in a coupled temperature-vibration-salt spray test:

[0125] In this embodiment, the test object is set to undergo a high-severity test with the following physical field combination: temperature 85°C, vibration 5 Grms, and salt spray concentration 5 mg / m³. Real-time monitoring shows that the signal attenuation rate deteriorates from a reference value of 0.5 dB to 2.0 dB within 120 seconds. The calculated measured coupling strength parameters are then used to determine the optimal parameters. The value is 0.015, consistently higher than the coupling strength threshold. (Setting to 0.01) triggered the optimization mechanism, proceeding to step S31, where the failure characteristic analysis step is executed, specifically:

[0126] S311. Determine the candidate set of physical fields:

[0127] The test combination that triggers optimization includes three physical fields: temperature (T), vibration (V), and salt spray (P). Therefore, the candidate set of physical fields to be analyzed is {T, V, P}.

[0128] S312. Calculate the sensitivity of each physical field factor:

[0129] Sensitivity definition: Sensitivity = Rate of change of signal degradation characteristic / Rate of change of corresponding physical field. In this example, the degradation characteristic is the signal attenuation rate (dB), and the rate of change is the average value over the test period.

[0130] Calculation process:

[0131] Sensitivity to temperature T, S_T:

[0132] Signal attenuation rate change = (2.0 - 0.5) dB / 120 s ≈ 0.0125 dB / s

[0133] Rate of temperature change = (85 - 25) ℃ / 120 s ≈ 0.5 ℃ / s

[0134] S_T = 0.0125 / 0.5 = 0.025 (dB / s) / (℃ / s) = 0.025 dB / ℃

[0135] Sensitivity S_V to vibration V:

[0136] Vibration change rate = (5 - 2) Grms / 120 s ≈ 0.025 Grms / s

[0137] S_V = 0.0125 / 0.025 = 0.5 (dB / s) / (Grms / s) = 0.5 dB / Grms

[0138] Sensitivity to salt spray P, S_P:

[0139] Salt spray concentration change rate = (5 - 0) mg / m³ / 120 s ≈ 0.0417 mg / (m³·s)

[0140] S_P = 0.0125 / 0.0417 ≈ 0.3 (dB / s) / (mg / (m³·s)) = 0.3 dB·m³ / mg

[0141] S313. Calculate the contribution weight of each physical field factor:

[0142] Calculation formula: Contribution weight = Sensitivity × Measured coupling strength parameter (α measured = 0.015)

[0143] Calculation process:

[0144] The contribution weight of temperature T is W_T = S_T × = 0.025 × 0.015 = 0.000375;

[0145] The contribution weight of vibration V is W_V = S_V × = 0.5 × 0.015 = 0.0075;

[0146] The contribution weight of salt spray P is W_P = S_P × = 0.3 × 0.015 = 0.0045;

[0147] S314. Determine the dominant failure factor:

[0148] Comparing contribution weights: W_V (0.0075) > W_P (0.0045) > W_T (0.000375)

[0149] Judgment result: Vibration factor (V) has the highest contribution weight. Therefore, vibration is determined to be the dominant failure factor.

[0150] In some embodiments, in step S3, updating the baseline parameters in the dynamic coupling model specifically includes:

[0151] The signal quality, temperature, vibration, and salt spray concentration corresponding to the current test passing are used as the new reference signal quality, new reference temperature, new reference vibration, and new reference salt spray concentration, respectively, to update the reference parameters in the dynamic coupling model.

[0152] The updated baseline parameters are used to calculate the coupling strength parameters in subsequent test cycles to reflect the optimized performance state of the tested object and realize the adaptive evolution of the dynamic coupling model.

[0153] In some embodiments, the object under test includes a CAN bus communication module, an in-vehicle Ethernet communication module, or an in-vehicle gateway module in an intelligent vehicle communication network.

[0154] The vehicle gateway module includes an optical network and routing equipment. The tested object is susceptible to the effects of multiple physical field couplings such as temperature, vibration, and salt spray in the intelligent vehicle operating environment, which leads to the degradation of communication signals. Therefore, it is necessary to conduct multi-factor combination testing and optimization through the method described above.

[0155] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement a multi-physics combination testing and optimization method for an intelligent vehicle communication module as described in any of the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0156] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0157] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0158] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0159] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in the multiphysics combination testing and optimization method for any of the intelligent vehicle communication modules described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0160] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes any of the above-described methods for multi-physics combination testing and optimization of intelligent vehicle communication modules.

[0161] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0162] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0163] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0164] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0165] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0166] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0167] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0168] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0169] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0170] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A multi-factor combination test method for intelligent vehicle communication networks, characterized in that, The method includes: Dynamic coupling modeling and risk ranking: Based on historical test data, the coupling strength parameters characterizing the multi-physics interaction effect are calculated to generate a coupling coefficient matrix, and the risk ranking of multi-physics test combinations is performed based on the coupling strength parameters to determine the test priority; Priority testing and real-time monitoring: Based on the obtained risk ranking results, apply corresponding multiphysics combination conditions to the tested object in priority order, and monitor the coupling strength parameters in real time during the test; Closed-loop optimization and model update: When the coupling strength parameter monitored in real time exceeds the preset threshold, the hardware of the object under test is optimized according to the failure feature matching optimization scheme, and the test is re-verified under the same multi-physics field combination conditions; if the verification is successful, the baseline parameters of the dynamic coupling model are updated.

2. The method according to claim 1, characterized in that, The dynamic coupling modeling and risk ranking specifically include: Obtaining benchmark parameters: Based on historical single-factor test data, determine the benchmark parameters of each physical field under the benchmark state. The benchmark parameters include at least the benchmark signal quality S0, benchmark temperature T0, benchmark vibration V0, and benchmark salt spray concentration P0. Calculate the coupling strength parameters: Based on the obtained baseline parameters, the coupling strength parameters between any two physical fields X and Y selected from temperature T, vibration V, and salt spray concentration P are calculated using the coupling strength calculation engine. The coupling strength parameter The calculation is based on the signal quality and physical field data acquired synchronously before and after the test, and the calculation formula is as follows: in, This indicates that under the condition that physical fields X and Y change together, with , The quality of synchronously acquired signals is relative to The change in signal quality is ΔS, which is the increment of the signal attenuation rate, in dB. and These represent the changes in physical fields X and Y relative to their reference states, respectively. Generate the coupling matrix: based on the calculated coupling strength parameters of all pairwise physical field combinations. Generate the coupling coefficient matrix; Risk ranking is performed based on the coupling strength parameters in the generated coupling coefficient matrix. The numerical values ​​are used to rank the risk of all pairwise combinations of physics to be tested. The higher the value, the higher the priority of the corresponding test combination.

3. The method according to claim 2, characterized in that, The coupling coefficient matrix includes the temperature-vibration coupling strength parameter α. TV Temperature-salt spray coupling intensity parameter α TP and the vibration-salt spray coupling strength parameter α VP ; Wherein, the α TV Used to quantify the coupling effect of the combined effects of high temperature and mechanical vibration on the reliability of communication line connections; The α TP Used to quantify the aging effect of signal transmission media caused by the combined effects of high temperature environment and salt spray corrosion; The α VP This is used to quantify the mutually accelerating destructive effect of mechanical vibration stress and salt spray corrosion on communication interfaces.

4. The method according to claim 2, characterized in that, Priority testing and real-time monitoring specifically include: Applying test conditions: The environmental loading device applies the physical field combination conditions to be tested to the object under test in sequence according to the risk ranking results; Real-time monitoring and analysis: During the test process of applying each combination of physical field conditions, the current measured coupling strength parameters are continuously monitored and calculated at preset time intervals using a real-time analyzer; Threshold Comparison and Decision: The measured coupling strength parameters calculated at each time interval are compared with the preset coupling strength threshold in real time, and the following decisions are made based on the comparison results: If the measured coupling strength parameter is determined to be continuously higher than the coupling strength threshold, the current test is immediately terminated, and the closed-loop optimization and model update steps are executed. If the measured coupling strength parameter does not continuously exceed the coupling strength threshold throughout the entire preset test period under the current physical field combination conditions, then the current physical field combination test is deemed to have passed the verification. After determining that the current test has passed verification, determine whether all combinations of physical fields to be tested have been tested; Test sequence progression: If there are still untested combinations of physical fields, return to the step of applying test conditions, and the environmental loading device applies the next priority combination of physical fields conditions, repeating the process of real-time monitoring and analysis up to the threshold comparison and decision-making. Once all combinations have been tested, the entire testing process ends.

5. The method according to claim 4, characterized in that, The closed-loop optimization and model update specifically include: Optimization triggering and failure analysis: When it is determined that the measured coupling strength parameter is continuously higher than the coupling strength threshold, the failure feature rule base is activated; based on the physical field combination type that triggers the optimization, the signal degradation characteristics obtained by real-time monitoring, and the measured coupling strength parameter, failure feature analysis is performed to determine the dominant failure factor; Optimization scheme execution: Based on the determined dominant failure factors, the corresponding hardware optimization scheme is matched from the failure feature rule base, and the hardware optimization scheme is executed through the measure executor to optimize the hardware of the tested object; Optimization verification test: After hardware optimization is completed, the environment loading device is controlled to reapply the same combination of physical field conditions as when the optimization was triggered, and a retest is performed; Verification of decisions and model evolution: During the retesting process, the retesting coupling strength parameters are monitored and calculated in real time using the real-time analyzer. If the retested coupling strength parameter does not exceed the coupling strength threshold throughout the entire preset test period of the retest, the optimization is deemed effective, the test is passed, and the baseline parameters in the dynamic coupling model are updated based on the currently stable test parameters. If the retested coupling strength parameter continues to be higher than the coupling strength threshold, then return to the optimization triggering and failure analysis step for iterative optimization until the retested coupling strength parameter meets the threshold requirement or reaches the preset maximum number of iterations.

6. The method according to claim 5, characterized in that, The failure feature rule base has a pre-set mapping relationship between failure factors and hardware optimization schemes; The failure factors include at least one of temperature-dominated failure, vibration-dominated failure, and salt spray corrosion-dominated failure. The mapping relationship is as follows: When the failure is determined to be temperature-driven, the appropriate hardware optimization solution is a cooling system upgrade. When the failure is determined to be vibration-dominant, the matching hardware optimization solution is an enhanced vibration damping structure solution; When the failure is determined to be primarily caused by salt spray corrosion, the appropriate hardware optimization solution is an upgrade to the sealing and protection structure.

7. The method according to claim 5, characterized in that, Failure characteristic analysis is conducted to identify the dominant failure factors, specifically including: Based on the physical field combination type of trigger optimization, determine the candidate set of physical fields to be analyzed; For each physical field factor in the candidate set, the sensitivity of the signal degradation feature to the change of that factor is calculated, whereby the sensitivity is the ratio of the rate of change of the degradation feature monitored in real time to the rate of change of the corresponding physical field. Based on the measured coupling strength parameter, the contribution weight of each physical field factor to the current signal degradation is calculated. The formula for calculating the contribution weight is: contribution weight of each physical field factor = sensitivity of the factor × measured coupling strength parameter. The physical field factor with the highest contribution weight is determined as the dominant failure factor.

8. The method according to claim 1, characterized in that, Updating the baseline parameters in the dynamic coupling model specifically includes: The signal quality, temperature, vibration, and salt spray concentration corresponding to the current successful test are used as the new reference signal quality, new reference temperature, new reference vibration, and new reference salt spray concentration, respectively, to update the reference parameters in the dynamic coupling model.

9. The method according to claim 1, characterized in that, The tested object includes a CAN bus communication module, an in-vehicle Ethernet communication module, or an in-vehicle gateway module in an intelligent vehicle communication network; wherein, the in-vehicle gateway module includes an optical network and routing equipment.

10. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-9.

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