Multi-physical field coupling test method and device for intelligent automobile hybrid network architecture

Abstract

This invention provides a multi-physics coupling test method and system for intelligent vehicle hybrid network architecture, belonging to the field of automotive electronics testing technology. The method includes: applying single-physics coupling, dual-physics coupling, and system-level multi-physics coupling loads through a tiered testing process, simultaneously acquiring electrical signal, optical signal performance parameters, and environmental parameters; calculating a unified signal health index (HI) and degradation rate based on a preset algorithm; analyzing the contribution of each physical field factor to performance degradation when the HI is below a health threshold or the degradation rate exceeds a rate threshold, and identifying the dominant failure factor; and intelligently matching the corresponding optimization strategy from a pre-built optimization strategy library. This invention solves the problems of media isolation, data fragmentation, and inefficient optimization in traditional testing methods, achieving unified cross-media testing, real-time health status assessment, and precise automated optimization, thus improving the reliability verification efficiency of intelligent vehicle hybrid network architecture.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics testing technology, and in particular to a multi-physics coupling testing method and electronic device for intelligent vehicle hybrid network architecture. Background Technology

[0002] As intelligent vehicles evolve towards connectivity and intelligence, the hybrid deployment of copper and fiber optic cables in vehicle network architecture has become mainstream. This hybrid architecture faces severe reliability challenges under complex multiphysics environments (temperature, vibration, salt spray, etc.), and the coupling effects of multiphysics may lead to system-level failures.

[0003] Existing testing methods have the following main problems: First, electrical and optical signal tests are isolated from each other, which cannot truly reflect the collaborative working state of the hybrid network in a unified environment; second, each parameter is collected and analyzed independently, lacking a unified data fusion mechanism, making it difficult to accurately assess the overall health status of the system; third, after problems are discovered, trial-and-error optimization relies on human experience, which is time-consuming, costly, and difficult to guarantee accuracy; fourth, there is a lack of a dynamic decision-making mechanism based on real-time performance changes, which may lead to irreversible damage to the device under test under extreme conditions.

[0004] Therefore, there is an urgent need for a multi-physics coupled testing method that can achieve unified testing across media, real-time health status assessment, accurate root cause localization, and intelligent optimization, in order to improve the reliability verification efficiency of intelligent vehicle hybrid network architecture. Summary of the Invention

[0005] In view of the technical defects and drawbacks existing in the prior art, the present invention provides a multi-physics coupling test method and electronic device for intelligent vehicle hybrid network architecture that overcomes or at least partially solves the above problems. The specific solution is as follows:

[0006] As a first aspect of the present invention, a multi-physics coupling test method for a hybrid network architecture of an intelligent vehicle is provided, the method comprising:

[0007] The hierarchical testing process is executed as follows: Based on the preset test logic, single-physics load, dual-physics coupled load, and system-level multi-physics coupled load are applied to the system under test in sequence, and the corresponding electrical signal performance parameters, optical signal performance parameters and environmental parameters are collected in real time.

[0008] Multi-source data fusion and health assessment: Time alignment and feature extraction are performed on the collected electrical signal performance parameters, optical signal performance parameters and environmental parameters. A unified signal health index representing the overall health status of the system is calculated based on a preset algorithm, and the degradation rate of the signal health index is calculated in real time.

[0009] Root cause analysis and decision-making: When the signal health index is lower than the preset health threshold or the degradation rate exceeds the preset rate threshold, the contribution of each physical field factor to the system performance degradation is analyzed based on the fused data in order to identify the dominant failure factor.

[0010] Perform adaptive optimization: Based on the identified dominant failure factors, intelligently match and execute the corresponding optimization strategies from the pre-built optimization strategy library to improve the reliability of the system under test.

[0011] In some embodiments, the tiered testing process executed in S1 based on preset test logic specifically includes:

[0012] Perform single-factor baseline testing: apply rated loads to each physics field independently and monitor the corresponding performance parameters of each physics field; if the performance parameters of all physics fields are within their single-factor threshold range, proceed to the two-factor coupled testing step; if the performance parameters of any physics field exceed its single-factor threshold range, pause the test and generate optimization suggestions for that physics field.

[0013] Perform a two-factor coupling test: Based on a predefined coupling coefficient α matrix, apply coupled loads consisting of two physical fields to the system under test in order of priority; the coupling coefficient α matrix includes at least the temperature-vibration coupling coefficient α_TV and the temperature-contamination coupling coefficient α_TP;

[0014] Monitor the interaction effects on system performance when applying each set of two-factor coupled loads;

[0015] If the interaction effect under the current two-factor coupled load is detected to exceed the preset interaction effect threshold, the two-factor coupled test is terminated, and the process proceeds to the step of failure root cause analysis and decision-making.

[0016] If, after traversing all two-factor combinations in the coupling coefficient α matrix, none of the monitored interaction effects exceed the interaction effect threshold, then the two-factor coupling test is deemed to have passed, and the process proceeds to the full-factor test step.

[0017] Perform full-factor testing: Apply system-level coupled loads of three or more physical fields simultaneously to the system under test to verify system-level reliability.

[0018] In some embodiments, performing a single-factor baseline test specifically includes:

[0019] Real-time monitoring and recording of the actual values ​​of each physical field when a rated load is applied individually;

[0020] The actual value is compared with a single-factor threshold range preset for the physical field;

[0021] If the actual value is within the corresponding single-factor threshold range, the physical field parameter is determined to meet the standard and is marked as a stable region.

[0022] If the actual value exceeds the corresponding single-factor threshold range, the physical field parameter is determined to be out of standard, the test is immediately suspended, and optimization suggestions for the out-of-standard physical field are generated.

[0023] The single-factor baseline test is considered passed and the two-factor coupling test step is triggered only if all tested physical fields are marked as stable regions.

[0024] In some embodiments, performing a full-factor test includes employing a progressive loading strategy, specifically including:

[0025] First loading stage: Simultaneously apply a temperature load, vibration load, and environmental pollution load at 50% of the rated intensity for a first preset time, and at the end of this stage, calculate the first signal health index HI_1 based on the collected electrical signal performance parameters and optical signal performance parameters;

[0026] First judgment and decision: If the first signal health index HI_1 is lower than the preset health threshold, the full factor test will be terminated immediately; if the first signal health index HI_1 is higher than or equal to the health threshold, the second loading stage will be entered.

[0027] Second loading stage: The temperature load, vibration load and pollution environment load are increased to 75% of the rated intensity and applied for a second preset time. At the end of this stage, the second signal health index HI_2 is calculated, and the first degradation rate is calculated based on HI_1 and HI_2.

[0028] Second judgment and decision: If the second signal health index HI_2 is lower than the health threshold or the first degradation rate exceeds the preset rate threshold, the full factor test is terminated immediately; if the second signal health index HI_2 is higher than or equal to the health threshold and the first degradation rate does not exceed the rate threshold, the third loading stage is entered.

[0029] Third loading stage: The temperature load, vibration load and pollution environment load are increased to 100% of the rated intensity and applied for a third preset time. At the end of this stage, the third signal health index HI_3 is calculated, and the second degradation rate is calculated based on HI_2 and HI_3.

[0030] Third judgment and decision: If the third signal health index HI_3 is lower than the health threshold or the second degradation rate exceeds the rate threshold, then the system is recorded as failing and the test is terminated.

[0031] In some embodiments, a unified signal health index HI is calculated based on a preset algorithm, specifically through the following mathematical formula:

[0032]

[0033] in, This represents the measured values ​​of the electrical signal performance parameters acquired in S1. This represents the reference value for the performance parameters of the electrical signal; This represents the measured values ​​of the optical signal performance parameters acquired in S1. This represents the reference value for the performance parameters of the optical signal; This represents the measured value of any other key environmental parameter collected in S1. This represents the baseline value for the environmental parameter; , , The preset weighting coefficients are used, and they satisfy the following conditions: + + =1.

[0034] In some embodiments, the degradation rate of the signal health index HI is calculated in real time, specifically as follows:

[0035] During any sustained loading phase of the tiered testing process, the degradation rate within that time window is calculated based on the defined signal health index HI using the following formula:

[0036]

[0037] Here, t1 and t2 are two different time points within this continuous loading phase, and t2>t1;

[0038] and These are the signal health indices calculated at time points t1 and t2, respectively.

[0039] The calculated degradation rate is used to determine whether it exceeds a preset rate threshold, thereby triggering root cause analysis of the failure.

[0040] In some embodiments, the contribution of various physical field factors to the degradation of system performance is analyzed. The analysis is based on various deviations that cause the signal health index HI to decrease, specifically including:

[0041] When analyzing the contribution of each physical field factor to the degradation of system performance, the contribution of each factor is determined by the proportion of the normalized deviation of each item in the formula for calculating the signal health index HI.

[0042] The contribution is calculated as follows:

[0043]

[0044]

[0045]

[0046] in,

[0047] The total deviation is calculated using the following formula: ;

[0048] The factor with the highest contribution was identified as the dominant failure factor.

[0049] In some embodiments, intelligent matching of corresponding optimization strategies from a pre-built optimization strategy library specifically includes:

[0050] Establish the optimization strategy library, which pre-stores the mapping relationship between different failure types and optimization strategies;

[0051] Based on the failure type of the locked dominant failure factor, the corresponding optimization strategy is matched and invoked from the optimization strategy library;

[0052] The mapping relationship between failure types and optimization strategies includes at least the following:

[0053] If the dominant failure factor is the degradation of electrical signal performance, the matching optimization strategies include upgrading to CAT8 twisted pair cabling and optimizing PCB grounding design.

[0054] If the dominant failure factor is mechanical vibration-related performance degradation, the matching optimization strategy includes adding a silicone damping ring at the connector.

[0055] If the dominant failure factor is the performance degradation caused by the synergistic effect of temperature and vibration, the matching optimization strategy includes simultaneously adding silicone damping rings and optimizing the heat dissipation airflow.

[0056] If the dominant failure factor is performance degradation related to salt spray contamination, the matching optimization strategies include shortening the cleaning cycle and spraying a nano-hydrophobic coating on the connector surface.

[0057] In some embodiments, the method is implemented through a closed-loop system consisting of an environment simulation layer, a data analysis layer, and a decision-making layer;

[0058] in,

[0059] The environmental simulation layer is used to execute the steps of the graded test process, apply the single physical field load, dual physical field coupled load and system-level multi-physical field coupled load through the multi-physics field loading device, and collect the electrical signal performance parameters, optical signal performance parameters and environmental parameters in real time through the synchronous acquisition unit.

[0060] The data analysis layer is used to perform the steps of multi-source data fusion and health assessment, receive data collected by the environment simulation layer, perform time-domain synchronization through the time alignment module, and calculate the unified signal health index and the degradation rate through the health calculation module.

[0061] The decision-making layer is used to perform the steps of failure root cause analysis and decision-making and the steps of adaptive optimization. It receives the results of the data analysis layer, controls the switching of the test process based on the coupling coefficient α matrix through a dynamic controller, and maps and calls the optimization strategy based on the contribution analysis results through an optimization matcher.

[0062] As a second aspect of the present invention, an electronic device is provided, comprising:

[0063] One or more processors;

[0064] Memory, used to store one or more programs;

[0065] When the one or more programs are executed by the one or more processors, the one or more processors implement the methods described above.

[0066] The present invention has the following beneficial effects:

[0067] This invention provides a multi-physics coupling test method for intelligent vehicle hybrid network architecture, constructing an adaptive, fully closed-loop test and optimization system. It fundamentally solves the three major bottleneck problems of traditional test methods: media isolation, data fragmentation, and inefficient optimization. Specifically, through S1 and S2, the traditionally independent tests of electrical signals (copper cables) and optical signals (fiber optics) are placed under a unified framework, and environmental parameters are synchronously collected and data is fused to generate a unified signal health index (HI), effectively avoiding test blind spots caused by media differences. Through S3, HI and its degradation rate are used as objective and quantitative decision-making criteria, replacing the traditional method that relies on human experience. When system performance deteriorates, it can automatically and quickly trigger root cause analysis, avoiding test sample damage or result distortion caused by response delay, thus improving the reliability and efficiency of testing. Through S4, test diagnosis and optimization measures are directly linked. Based on the identified dominant failure factors, the optimal solution (such as upgrading CAT8 cables, adding shock-absorbing rings, etc.) can be accurately matched and called from the pre-built strategy library. This changes the cumbersome process of iterating electrical and optical performance separately (≥7 times) in the past, shortening the comprehensive optimization cycle by more than 50% and greatly improving the efficiency of R&D and verification. Attached Figure Description

[0068] Figure 1 A schematic diagram of a multi-physics coupling test method for a hybrid network architecture of an intelligent vehicle provided in an embodiment of the present invention;

[0069] Figure 2 A schematic diagram illustrating the dynamic control of the test process provided in an embodiment of the present invention;

[0070] Figure 3 A schematic diagram illustrating the execution of a single-factor baseline test provided in an embodiment of the present invention;

[0071] Figure 4 A framework diagram of a closed-loop system consisting of an environment simulation layer, a data analysis layer, and a decision-making layer, provided for embodiments of the present invention;

[0072] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0073] 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.

[0074] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0075] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] To address at least one of the technical problems existing in the aforementioned related technologies, this invention provides a multi-physics coupling test method for intelligent vehicle hybrid network architecture. Figure 1 This is a flowchart illustrating a multi-physics coupling test method for a hybrid network architecture of an intelligent vehicle provided in an embodiment of the present invention. The method includes:

[0080] S1. Execute the graded test process: Based on the preset test logic, apply single-physical field load, dual-physical field coupled load and system-level multi-physical field coupled load to the system under test in sequence, and collect the corresponding electrical signal performance parameters, optical signal performance parameters and environmental parameters in real time.

[0081] S2. Perform multi-source data fusion and health assessment: Perform time alignment and feature extraction on the electrical signal performance parameters, optical signal performance parameters and environmental parameters collected in S1, calculate a unified signal health index that characterizes the overall health status of the system based on a preset algorithm, and calculate the degradation rate of the signal health index in real time.

[0082] S3. Perform root cause analysis and decision-making: When the signal health index is lower than the preset health threshold or the degradation rate exceeds the preset rate threshold, based on the data fused in S2, analyze the contribution of each physical field factor to the system performance degradation in order to identify the dominant failure factor.

[0083] S4. Perform adaptive optimization: Based on the dominant failure factors determined in S3, intelligently match the corresponding optimization strategy from the pre-built optimization strategy library and execute it to improve the reliability of the system under test.

[0084] This invention provides a multi-physics coupling testing method for intelligent vehicle hybrid network architecture, constructing an adaptive, fully closed-loop testing and optimization system. It fundamentally solves the three major bottleneck problems of traditional testing methods: media isolation, data fragmentation, and inefficient optimization. Specifically:

[0085] By using S1 and S2, the traditionally independent testing of electrical signals (copper cables) and optical signals (fiber optics) is placed under a unified framework, and the synchronous acquisition and data fusion of environmental parameters are introduced to generate a unified signal health index (HI), effectively avoiding test blind spots caused by differences in media.

[0086] S3 uses HI and its degradation rate as objective and quantitative decision-making criteria, replacing the traditional method that relies on human experience. When system performance deteriorates, it can automatically and quickly trigger root cause analysis, avoiding test sample corruption or result distortion caused by response delays, thus improving the reliability and efficiency of testing.

[0087] By directly linking test diagnostics with optimization measures through S4, the system can accurately match and call upon the optimal solution (such as upgrading CAT8 cables or adding vibration damping rings) from a pre-built strategy library based on the identified dominant failure factors. This changes the cumbersome process of iterating electrical and optical performance separately (≥7 times), shortening the overall optimization cycle by more than 50% and greatly improving R&D and verification efficiency.

[0088] See Figure 2As shown, in some embodiments, step S1 executes a tiered testing process based on preset test logic, specifically including:

[0089] S1.1 Perform single-factor baseline testing: Apply rated loads to each physical field independently and monitor the performance parameters corresponding to each physical field; if the performance parameters of all physical fields are within their single-factor threshold range, proceed to S1.2; if the performance parameters of any physical field exceed its single-factor threshold range, pause the test and generate optimization suggestions for that physical field.

[0090] S1.2 Perform a two-factor coupling test: Based on a predefined coupling coefficient α matrix, apply coupled loads consisting of two physical fields to the system under test in order of priority; the coupling coefficient α matrix includes at least the temperature-vibration coupling coefficient α_TV and the temperature-contamination coupling coefficient α_TP;

[0091] Monitor the interaction effects on system performance when applying each set of two-factor coupled loads;

[0092] If the interaction effect under the current two-factor coupling load is detected to exceed the preset interaction effect threshold, the two-factor coupling test is terminated and proceeds to step S3.

[0093] If, after traversing all two-factor combinations in the coupling coefficient α matrix, the monitored interaction effects do not exceed the interaction effect threshold, then the two-factor coupling test is deemed to have passed, and proceed to S1.3.

[0094] S1.3 Perform full-factor testing: Apply system-level coupled loads of three or more physical fields simultaneously to the system under test to verify system-level reliability.

[0095] The above embodiments specifically define the hierarchical testing process. By introducing a dynamic test path controlled by a coupling coefficient α matrix, efficient allocation of test resources and priority identification of high-risk failure modes are achieved. Specifically:

[0096] By employing the progressive testing logic of "single-factor → two-factor → full-factor" as specified in S1.1 to S1.3, and combining it with the α matrix to prioritize the risk of two-factor combinations, the system can prioritize testing the most destructive coupled scenarios (such as high temperature + salt spray). This strategy avoids the resource waste of traditional "carpet-bombing" full-factor testing and ensures that high-risk failure modes are exposed and resolved first.

[0097] By explicitly using the "preset interaction effect threshold" as the judgment criterion, a quantitative basis is provided for the vague concept of "weak or excessive interaction effect", making the decision to switch the test process (such as whether to enter the full factor test) more objective and repeatable, and enhancing the scientificity and comparability of the test results.

[0098] The process avoids the irreversible damage that may be caused by directly applying extreme operating conditions to the system under test. By first verifying single-factor stability, then exploring two-factor interactions, and finally conducting system-level extreme tests under controlled conditions, it effectively protects the expensive equipment under test. It is particularly suitable for reliability verification of critical communication components in intelligent vehicles to verify system-level reliability.

[0099] Optionally, when performing the full-factor test in S1.3, the applied load is a temperature load, a vibration load, and a salt spray pollution load;

[0100] The 50% rated strength, 75% rated strength, and 100% rated strength correspond to the following load combinations:

[0101] 50% of rated strength corresponds to: temperature 62.5°C, vibration 2.5 Grms, salt spray concentration 2.5 mg / cm³;

[0102] 75% of rated strength corresponds to: temperature 93.75°C, vibration 3.75 Grms, and salt spray concentration 3.75 mg / cm³;

[0103] 100% rated strength corresponds to: temperature 125°C, vibration 5 Grms, salt spray concentration 5 mg / cm³;

[0104] The first preset time, the second preset time, and the third preset time are all 10 minutes.

[0105] The above embodiments provide a standardized test condition for the method by precisely defining the specific values ​​of temperature, vibration, and salt spray concentration at 50%, 75%, and 100% intensity (e.g., 62.5°C / 2.5 Grms / 2.5 mg / cm³) and uniformly specifying the duration of each stage as 10 minutes. This allows different batches and different laboratories to conduct reliability tests on the same or different products under completely consistent conditions, and the results obtained (e.g., HI value, degradation rate) have high comparability and reliability.

[0106] See Figure 3 As shown, in some embodiments, performing a single-factor baseline test in S1.1 specifically includes:

[0107] Real-time monitoring and recording of the actual values ​​of each physical field when a rated load is applied individually;

[0108] The actual value is compared with a single-factor threshold range preset for the physical field;

[0109] If the actual value is within the corresponding single-factor threshold range, the physical field parameter is determined to meet the standard and is marked as the "stable region".

[0110] If the actual value exceeds the corresponding single-factor threshold range, the physical field parameter is determined to be out of standard, the test is immediately suspended, and optimization suggestions for the out-of-standard physical field are generated.

[0111] The single-factor baseline test is deemed passed and the two-factor coupling test in S1.2 is triggered only if all tested physical fields are marked as "stable regions".

[0112] The above embodiments refine the single-factor baseline test, establishing an accurate and reliable performance benchmark for the entire hierarchical testing system, and achieving early and rapid isolation of single-point faults. Specifically:

[0113] By rigorously monitoring the "actual values" of each physical field and comparing them with the "single-factor threshold range," and labeling the "stability region," a reliable normalized benchmark (x) is provided for evaluating performance changes in subsequent two-factor and full-factor tests (such as HI calculation). 10 x 20 (e0), which ensures the accuracy of coupling effect analysis and avoids misjudgment caused by baseline drift.

[0114] This method stipulates that once any single-factor parameter exceeds the standard, the test will be suspended and optimization suggestions will be generated. This allows faults caused by single physical fields, such as "the insulation impedance of copper cables decreases at high temperatures", to be quickly identified and eliminated in the early stages, preventing defective samples from entering the more complex and time-consuming coupled test stage, thus saving test resources.

[0115] The condition setting of "entering the next stage only if all physical fields are marked as 'stable regions'" constitutes an automated quality gate, ensuring that only systems that pass the basic reliability verification are eligible to undergo more stringent coupled reliability assessments, thereby improving the rigor and automation level of the entire testing process.

[0116] In some embodiments, performing a full-factor test in S1.3 includes employing a progressive loading strategy, specifically including:

[0117] S1.3.1 First loading stage: Simultaneously apply a temperature load, vibration load, and environmental pollution load at 50% of the rated intensity for a first preset time, and at the end of this stage, calculate the first signal health index HI_1 based on the collected electrical signal performance parameters and optical signal performance parameters;

[0118] S1.3.2 First Judgment and Decision: If the first signal health index HI_1 is lower than the preset health threshold, the full factor test is terminated immediately; if the first signal health index HI_1 is higher than or equal to the health threshold, proceed to S1.3.3.

[0119] S1.3.3 Second loading stage: The temperature load, vibration load and pollution environment load are increased to 75% of the rated intensity and applied for a second preset time. At the end of this stage, the second signal health index HI_2 is calculated, and the first degradation rate is calculated based on HI_1 and HI_2.

[0120] S1.3.4 Second Judgment and Decision: If the second signal health index HI_2 is lower than the health threshold or the first degradation rate exceeds the preset rate threshold, the full factor test is terminated immediately; if the second signal health index HI_2 is higher than or equal to the health threshold and the first degradation rate does not exceed the rate threshold, proceed to S1.3.5.

[0121] S1.3.5 Third loading stage: The temperature load, vibration load and pollution environment load are increased to 100% of the rated intensity and applied for a third preset time. At the end of this stage, the third signal health index HI_3 is calculated, and the second degradation rate is calculated based on HI_2 and HI_3.

[0122] S1.3.6 Third Judgment and Decision: If the third signal health index HI_3 is lower than the health threshold or the second degradation rate exceeds the rate threshold, the recording system fails and the test is terminated.

[0123] The above embodiments specifically define the incremental loading strategy for full-factor testing. By introducing a multi-level safety interruption mechanism based on quantification criteria, a balance between security, efficiency, and data completeness in extreme reliability testing is achieved. Specifically:

[0124] Through steps S1.3.2, S1.3.4, and S1.3.6, after each loading stage at 50%, 75%, and 100%, the "Signal Health Index (HI)" and "Degradation Rate" are used as the criteria for deciding whether to continue loading. Once the performance degradation exceeds the tolerance (HI falls below the threshold or the degradation rate exceeds the limit), the test is immediately terminated. This avoids the risk of irreversible damage to the device under test under extreme loads caused by the inability to accurately determine the system status in real time, as is common in traditional testing, thus protecting expensive automotive electronic components.

[0125] In some embodiments, the unified signal health index HI calculated in step S2 based on a preset algorithm is specifically implemented through the following mathematical formula:

[0126]

[0127] in, This represents the measured values ​​of the electrical signal performance parameters acquired in S1. This represents the reference value for the performance parameters of the electrical signal; This represents the measured values ​​of the optical signal performance parameters acquired in S1. This represents the reference value for the performance parameters of the optical signal; This represents the measured value of any other key environmental parameter collected in S1. This represents the baseline value for the environmental parameter; , , The preset weighting coefficients are used, and they satisfy the following conditions: + + =1.

[0128] The above embodiments specifically define the calculation formula for the Signal Health Index (HI), normalizing heterogeneous multi-physics parameters into a unified, quantifiable health indicator, thus solving the core challenge of multi-source data fusion. Specifically, the formula uses weighting coefficients ( , , This method normalizes heterogeneous parameters characterizing electrical signal performance, optical signal performance, and environmental impact (such as bit error rate, periodicity, and salt spray concentration) and merges them into a single health index HI ranging from 0 to 1. This provides a unified metric for parameters that were previously incomparable, offering an objective and quantitative basis for overall system status assessment and reliability comparison between different systems.

[0129] In some embodiments, the degradation rate of the signal health index HI is calculated in real time in step S2, specifically as follows:

[0130] During any sustained loading phase of the tiered testing process, the degradation rate within that time window is calculated based on the defined signal health index HI using the following formula:

[0131]

[0132] Here, t1 and t2 are two different time points within this continuous loading phase, and t2>t1;

[0133] and These are the signal health indices calculated at time points t1 and t2, respectively.

[0134] The calculated degradation rate is used in S3 to determine whether it exceeds a preset rate threshold, thereby triggering root cause analysis of the failure.

[0135] The above embodiments specifically define the calculation method for the degradation rate, introducing dynamic change trends as a key indicator for performance evaluation, and realizing predictive diagnosis from static "health state" to dynamic "health trend". Specifically, this method not only focuses on the instantaneous absolute value of HI, but also on its rate of change over time (degradation rate). Even if the current HI value is still above the safety threshold, if a sustained and high degradation rate (e.g., 1% / min) is detected, the system can issue an early warning and intervene in the analysis. This changes the passive situation of only taking action when parameters exceed the limit. The degradation rate is a key indicator for measuring system robustness and lifespan. By comparing the degradation rates under different load levels or different designs, the reliability margin of the design can be evaluated more profoundly. For example, a design with a degradation rate of only 0.1% / min under 75% load has far better reliability than another design with a degradation rate of 0.8% / min under the same load, even though their current HI values ​​may be the same. This provides a deeper insight for design optimization.

[0136] In some embodiments, the contribution of each physical field factor to the system performance degradation is analyzed in step S3. The analysis is based on various deviations that cause the signal health index HI to decrease, specifically including:

[0137] Calculate the proportion of each normalized deviation in the formula for the signal health index HI at the S3 trigger time to determine the contribution of each factor.

[0138] The contribution is calculated as follows:

[0139]

[0140]

[0141]

[0142] in,

[0143] The total deviation is calculated using the following formula: ;

[0144] The factor with the highest contribution was identified as the dominant failure factor.

[0145] The above embodiments specifically define a contribution-based root cause analysis method, which accurately traces system-level performance degradation to specific physical field failure factors, achieving quantitative and precise fault location. Specifically, this method calculates the proportion of each factor's deviation in the total deviation, transforming the qualitative "primary cause" into a quantitative "contribution percentage" (e.g., electrical signal factors contribute 92.3%). This changes the traditional model of relying on expert experience for vague judgments, making the root cause analysis results objective, repeatable, and indisputable, greatly improving the accuracy and efficiency of diagnosis.

[0146] In some embodiments, the step S4, which involves intelligently matching the corresponding optimization strategy from a pre-built optimization strategy library, specifically includes:

[0147] Establish the optimization strategy library, which pre-stores the mapping relationship between different failure types and optimization strategies;

[0148] Based on the failure type of the dominant failure factor locked by S3, the corresponding optimization strategy is matched and invoked from the optimization strategy library;

[0149] The mapping relationship between failure types and optimization strategies includes at least the following:

[0150] If the dominant failure factor is the degradation of electrical signal performance, the matching optimization strategies include upgrading to CAT8 twisted pair cabling and optimizing PCB grounding design.

[0151] If the dominant failure factor is mechanical vibration-related performance degradation, the matching optimization strategy includes adding a silicone damping ring at the connector.

[0152] If the dominant failure factor is the performance degradation caused by the synergistic effect of temperature and vibration, the matching optimization strategy includes simultaneously adding silicone damping rings and optimizing the heat dissipation airflow.

[0153] If the dominant failure factor is performance degradation related to salt spray contamination, the matching optimization strategies include shortening the cleaning cycle and spraying a nano-hydrophobic coating on the connector surface.

[0154] The above embodiments specifically define the intelligent matching rules for optimization strategies, establish a precise optimization knowledge base based on failure mechanism mapping, and achieve seamless automated connection from diagnostic results to optimization measures. Specifically, by pre-building a "failure type-optimization strategy" mapping library, the system can automatically match the most appropriate optimization measures (such as "upgrading to CAT8 twisted pair") based on the specific failure type identified by root cause analysis (such as "deterioration of electrical signal performance"). This avoids the large number of iterations required by traditional trial-and-error methods, shortens the optimization decision-making process from hours to minutes, and ensures the effectiveness of optimization measures.

[0155] See Figure 4As shown, in some embodiments, the method is implemented through a closed-loop system consisting of an environment simulation layer, a data analysis layer, and a decision-making layer;

[0156] in,

[0157] The environment simulation layer is used to execute S1, which applies the single-physics load, dual-physics coupled load and system-level multi-physics coupled load through the multi-physics loading device, and collects the electrical signal performance parameters, optical signal performance parameters and environmental parameters in real time through the synchronous acquisition unit.

[0158] The data analysis layer is used to execute S2, receive data collected by the environment simulation layer, perform time-domain synchronization through the time alignment module, and calculate the unified signal health index (HI) and the degradation rate through the health calculation module.

[0159] The decision-making layer is used to execute S3 and S4, receive the results of the data analysis layer, control the switching of the test process based on the coupling coefficient α matrix through the dynamic controller, and map and call the optimization strategy based on the contribution analysis results through the optimization matcher.

[0160] The above embodiments clarify that the entire method is implemented using a specific three-layer closed-loop system architecture. This architecture solidifies the automated closed-loop process of "testing-evaluation-decision-optimization" at the system implementation level, ensuring the efficient and reliable execution and engineering implementation of the method. Specifically, the clearly defined environmental simulation layer, data analysis layer, and decision-making layer decompose the complex multi-physics coupled testing task into three specialized stages: load application, data fusion, and intelligent decision-making. The environmental simulation layer focuses on the control and synchronous acquisition of high-precision loads, the data analysis layer focuses on the processing and feature extraction of heterogeneous data, and the decision-making layer focuses on test process scheduling and optimization strategy matching.

[0161] 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 coupling test method for a smart car hybrid network architecture 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.

[0162] 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).

[0163] 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.

[0164] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0165] 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 any of the multi-physics coupling testing methods for intelligent vehicle hybrid network architectures described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0166] 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 multi-physics coupling test methods for intelligent vehicle hybrid network architectures.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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 method for multi-physical field coupling test of intelligent vehicle hybrid network architecture, characterized in that, The method includes: The hierarchical testing process is executed as follows: Based on the preset test logic, single-physics load, dual-physics coupled load, and system-level multi-physics coupled load are applied to the system under test in sequence, and the corresponding electrical signal performance parameters, optical signal performance parameters and environmental parameters are collected in real time. Multi-source data fusion and health assessment: Time alignment and feature extraction are performed on the collected electrical signal performance parameters, optical signal performance parameters and environmental parameters. A unified signal health index representing the overall health status of the system is calculated based on a preset algorithm, and the degradation rate of the signal health index is calculated in real time. Root cause analysis and decision-making: When the signal health index is lower than the preset health threshold or the degradation rate exceeds the preset rate threshold, the contribution of each physical field factor to the system performance degradation is analyzed based on the fused data in order to identify the dominant failure factor. Perform adaptive optimization: Based on the identified dominant failure factors, intelligently match and execute the corresponding optimization strategies from the pre-built optimization strategy library to improve the reliability of the system under test.

2. The method of claim 1, wherein, The S1 process, which executes a tiered testing procedure based on preset testing logic, specifically includes: Perform single-factor baseline testing: apply rated loads to each physics field independently and monitor the corresponding performance parameters of each physics field; if the performance parameters of all physics fields are within their single-factor threshold range, proceed to the two-factor coupled testing step; if the performance parameters of any physics field exceed its single-factor threshold range, pause the test and generate optimization suggestions for that physics field. Perform a two-factor coupling test: Based on a predefined coupling coefficient α matrix, apply coupled loads consisting of two physical fields to the system under test in order of priority; the coupling coefficient α matrix includes at least the temperature-vibration coupling coefficient α_TV and the temperature-contamination coupling coefficient α_TP; Monitor the interaction effects on system performance when applying each set of two-factor coupled loads; If the interaction effect under the current two-factor coupled load is detected to exceed the preset interaction effect threshold, the two-factor coupled test is terminated, and the process proceeds to the step of failure root cause analysis and decision-making. If, after traversing all two-factor combinations in the coupling coefficient α matrix, none of the monitored interaction effects exceed the interaction effect threshold, then the two-factor coupling test is deemed to have passed, and the process proceeds to the full-factor test step. Perform full-factor testing: Apply system-level coupled loads of three or more physical fields simultaneously to the system under test to verify system-level reliability.

3. The method of claim 2, wherein, Performing a single-factor baseline test specifically includes: Real-time monitoring and recording of the actual values ​​of each physical field when a rated load is applied individually; The actual value is compared with a single-factor threshold range preset for the physical field; If the actual value is within the corresponding single-factor threshold range, the physical field parameter is determined to meet the standard and is marked as a stable region. If the actual value exceeds the corresponding single-factor threshold range, the physical field parameter is determined to be out of standard, the test is immediately suspended, and optimization suggestions for the out-of-standard physical field are generated. The single-factor baseline test is considered passed and the two-factor coupling test step is triggered only if all tested physical fields are marked as stable regions.

4. The method of claim 2, wherein, Performing full-factor testing includes employing a progressive loading strategy, specifically including: First loading stage: Simultaneously apply a temperature load, vibration load, and environmental pollution load at 50% of the rated intensity for a first preset time, and at the end of this stage, calculate the first signal health index HI_1 based on the collected electrical signal performance parameters and optical signal performance parameters; First judgment and decision: If the first signal health index HI_1 is lower than the preset health threshold, the full factor test will be terminated immediately; if the first signal health index HI_1 is higher than or equal to the health threshold, the second loading stage will be entered. Second loading stage: The temperature load, vibration load and pollution environment load are increased to 75% of the rated intensity and applied for a second preset time. At the end of this stage, the second signal health index HI_2 is calculated, and the first degradation rate is calculated based on HI_1 and HI_2. Second judgment and decision: If the second signal health index HI_2 is lower than the health threshold or the first degradation rate exceeds the preset rate threshold, the full factor test is terminated immediately; if the second signal health index HI_2 is higher than or equal to the health threshold and the first degradation rate does not exceed the rate threshold, the third loading stage is entered. Third loading stage: The temperature load, vibration load and pollution environment load are increased to 100% of the rated intensity and applied for a third preset time. At the end of this stage, the third signal health index HI_3 is calculated, and the second degradation rate is calculated based on HI_2 and HI_3. Third judgment and decision: If the third signal health index HI_3 is lower than the health threshold or the second degradation rate exceeds the rate threshold, then the system is recorded as failing and the test is terminated.

5. The method of claim 1, wherein, A unified signal health index (HI) is calculated based on a preset algorithm, specifically through the following mathematical formula: wherein, represents the measured value of the electrical signal performance parameter collected in S1, represents the reference value of the electrical signal performance parameter; represents the measured value of the optical signal performance parameter collected in S1, represents the reference value of the optical signal performance parameter; represents the measured value of any other key environmental parameter collected in S1, represents the reference value of the environmental parameter; is a preset weight coefficient, and satisfies = 1.​​​​ 6. The method of claim 5, wherein, The degradation rate of the signal health index HI is calculated in real time, specifically as follows: During any sustained loading phase of the tiered testing process, the degradation rate within that time window is calculated based on the defined signal health index HI using the following formula: Here, t1 and t2 are two different time points within this continuous loading phase, and t2 > t1; and These are the signal health indices calculated at time points t1 and t2, respectively. The calculated degradation rate is used to determine whether it exceeds a preset rate threshold, thereby triggering root cause analysis of the failure.

7. The method according to claim 5, characterized in that, The contribution of various physical field factors to the system performance degradation is analyzed, based on the various deviations that lead to a decrease in the signal health index HI, specifically including: When analyzing the contribution of each physical field factor to the degradation of system performance, the contribution of each factor is determined by the proportion of the normalized deviation of each item in the formula for calculating the signal health index HI. The contribution is calculated as follows: in, The total deviation is the sum of the individual deviations, calculated as: ; The factor with the highest contribution was identified as the dominant failure factor.

8. The method of claim 1, wherein, Intelligent matching of corresponding optimization strategies from a pre-built optimization strategy library, specifically including: Establish the optimization strategy library, which pre-stores the mapping relationship between different failure types and optimization strategies; Based on the failure type of the locked dominant failure factor, the corresponding optimization strategy is matched and invoked from the optimization strategy library; The mapping relationship between failure types and optimization strategies includes at least the following: If the dominant failure factor is the degradation of electrical signal performance, the matching optimization strategies include upgrading to CAT8 twisted pair and optimizing PCB grounding design. If the dominant failure factor is mechanical vibration-related performance degradation, the matching optimization strategy includes adding a silicone damping ring at the connector. If the dominant failure factor is the performance degradation caused by the synergistic effect of temperature and vibration, the matching optimization strategy includes simultaneously adding silicone damping rings and optimizing the heat dissipation airflow. If the dominant failure factor is performance degradation related to salt spray contamination, the matching optimization strategies include shortening the cleaning cycle and spraying a nano-hydrophobic coating on the connector surface.

9. The method according to any one of claims 1 to 8, characterized in that, The method is implemented through a closed-loop system consisting of an environment simulation layer, a data analysis layer, and a decision-making layer. in, The environmental simulation layer is used to execute the steps of the graded test process, apply the single physical field load, dual physical field coupled load and system-level multi-physical field coupled load through the multi-physics field loading device, and collect the electrical signal performance parameters, optical signal performance parameters and environmental parameters in real time through the synchronous acquisition unit. The data analysis layer is used to perform the steps of multi-source data fusion and health assessment, receive data collected by the environment simulation layer, perform time-domain synchronization through the time alignment module, and calculate the unified signal health index and the degradation rate through the health calculation module. The decision-making layer is used to perform the steps of failure root cause analysis and decision-making and the steps of adaptive optimization. It receives the results of the data analysis layer, controls the switching of the test process based on the coupling coefficient α matrix through a dynamic controller, and maps and calls the optimization strategy based on the contribution analysis results through an optimization matcher.

10. An electronic device, comprising: 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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