Performance test method for automobile connector

Abstract

The invention provides a performance testing method for an automobile connector, and belongs to the technical field of automobile testing. According to the method, specific environment data of an engine compartment, vehicle body electronics and a chassis are obtained, performance requirements of different test parts are divided by adopting a hierarchical classification algorithm, and a specific test parameter combination is determined. And optimizing the test scheme configuration by adjusting the parameter weight so as to further optimize the test scheme configuration. Therefore, corresponding test schemes can be matched for the automobile connector at different test parts and in different test environments, and differential test is further realized, so that the performance test of the connector is ensured to accord with a real result in an actual operation environment, and the test efficiency is improved. For example, the performance of the connector under extreme conditions such as high temperature, vibration, moisture or impact can be truly reflected, so that the pertinence, reliability and accuracy of the performance test of the connector are improved, and the overall stability of a vehicle system is guaranteed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of automobile testing, and in particular to a performance testing method for an automobile connector. Background Art

[0002] Currently, automotive connectors, as an integral component of automotive electronic systems, are directly related to vehicle safety, reliability, and overall performance. Testing automotive connectors is a crucial means of verifying their reliability and stability. Traditional testing methods often employ uniform testing standards and parameters, overlooking the diverse environments and performance requirements faced by automotive connectors at various test locations. This one-size-fits-all approach makes it difficult to fully assess connector performance. In particular, under complex operating conditions, test results can easily mismatch actual usage, leading to potential risks being overlooked. This, in turn, significantly reduces the targetedness and reliability of testing, potentially impacting the overall stability of the vehicle system.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides a performance testing method for an automotive connector, which can match corresponding connector testing solutions for different test locations, thereby achieving differentiated testing.

[0005] In a first aspect, an embodiment of the present application provides a performance testing method for an automotive connector, the method comprising: obtaining specific environmental data of different test parts of an automobile, and determining a set of environmental features corresponding to each of the test parts based on the specific environmental data; determining a performance requirement subset of each of the test parts based on the environmental feature set; determining a specific test parameter combination of each of the test parts based on the performance requirement subset of each of the test parts; determining simulated performance data based on the specific test parameter combination; determining key test scenarios based on the simulated performance data; determining real-time performance records based on the key test scenarios; obtaining dynamic response data based on the real-time performance records; judging the dynamic response data against a first preset performance threshold, and if the dynamic response data exceeds the first preset performance threshold, marking the current dynamic response data as an abnormal point; determining an abnormal performance distribution based on the abnormal point; determining a key performance defect point based on the abnormal performance distribution; and determining a test scheme configuration for different test parts based on the key performance defect point.

[0006] The embodiment of the present application provides a performance testing method for an automotive connector. By obtaining specific environmental data of the engine compartment, body electronics and chassis, a hierarchical classification algorithm is used to divide the performance requirements of different test parts and determine a specific test parameter combination. In addition, a test framework model is built based on different test parts, and a preliminary prediction is performed to obtain dynamic response data. Then, anomalies are identified through a data comparison and analysis method, and a regression analysis algorithm is used to trace the factors affecting the anomalies and determine the key performance defects. The test scheme configuration can also be optimized by adjusting the parameter weights to further optimize the test scheme configuration. In this way, the corresponding test schemes can be matched for different test parts and different test environments of the automotive connector, thereby achieving differentiated testing to ensure that the performance test of the connector conforms to the actual results under the actual operating environment, for example, it can truly reflect the performance of the connector under extreme conditions such as high temperature, vibration, humidity or impact, so as to improve the pertinence, reliability and accuracy of the performance test and ensure the overall stability of the vehicle system. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the traditional technology, the following briefly introduces the drawings required for use in the embodiments or the description of the traditional technology. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0008] Figure 1 It is a flowchart of a performance testing method for an automotive connector provided by an exemplary embodiment of the present application.

[0009] Figure 2 1 is a flow chart of a performance testing method for an automotive connector provided by another exemplary embodiment of the present application.

[0010] Figure 3 It is a flowchart of a performance testing method for an automotive connector provided by another exemplary embodiment of the present application.

[0011] Figure 4 3 is a flow chart of a method for testing the performance of an automotive connector provided by another exemplary embodiment of the present application.

[0012] Figure 5 3 is a flow chart of a method for testing the performance of an automotive connector provided by another exemplary embodiment of the present application.

[0013] Figure 6 3 is a flow chart of a method for testing the performance of an automotive connector provided by another exemplary embodiment of the present application.

[0014] Figure 73 is a flow chart of a method for testing the performance of an automotive connector provided by another exemplary embodiment of the present application.

[0015] Figure 8 3 is a flow chart of a method for testing the performance of an automotive connector provided by another exemplary embodiment of the present application. DETAILED DESCRIPTION

[0016] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of the embodiments of the present disclosure.

[0017] The terms "a," "an," and "the" are used to indicate the presence of one or more elements / components; the terms "including" and "having" are used to indicate an open-ended inclusiveness and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc. The terms "first," "second," etc. are used merely as labels and do not limit the quantity of the items to which they refer.

[0018] Currently, automotive connectors, as an integral component of automotive electronic systems, are directly related to vehicle safety, reliability, and overall performance. Testing automotive connectors is a crucial means of verifying their reliability and stability. Traditional testing methods often employ uniform testing standards and parameters, overlooking the diverse environments and performance requirements faced by automotive connectors at various test locations. This one-size-fits-all approach makes it difficult to fully assess connector performance. In particular, under complex operating conditions, test results can easily mismatch actual usage, leading to potential risks being overlooked. This, in turn, significantly reduces the targetedness and reliability of testing, potentially impacting the overall stability of the vehicle system.

[0019] In an embodiment of the present disclosure, a performance test method for an automobile connector is provided. Figure 1 The performance testing method of the automotive connector shown in the figure may include the following steps:

[0020] Step S110: Acquire specific environmental data of different test parts of the vehicle, where the test parts include the engine compartment, body electronics, and vehicle chassis, and the specific environmental data include temperature, vibration, and humidity;

[0021] Step S120: determining an environmental feature set corresponding to each test location based on specific environmental data;

[0022] Step S130: determining a performance requirement subset for each test location based on the environmental feature set;

[0023] Step S140: determining a specific test parameter combination for each test site according to the performance requirement subset of each test site;

[0024] Step S150: determining simulation performance data according to a specific test parameter combination;

[0025] Step S160: determining a key test scenario based on the simulated performance data;

[0026] Step S170: determining real-time performance records based on key test scenarios;

[0027] Step S180: obtaining dynamic response data based on the real-time performance record;

[0028] Step S190: comparing the dynamic response data with a first preset performance threshold, and if the dynamic response data exceeds the first preset performance threshold, marking the current dynamic response data as an abnormal point;

[0029] Step S200: determining abnormal performance distribution according to abnormal points;

[0030] Step S210: determining key performance defect points based on abnormal performance distribution;

[0031] Step S220: Determine the test plan configuration for different test locations based on the key performance defects.

[0032] According to the performance testing method for automobile connectors provided by the present disclosure, the method can obtain specific environmental data of different test parts of an automobile, and determine the environmental feature set corresponding to each test part based on the specific environmental data; determine the performance requirement subset of each test part based on the environmental feature set; determine the specific test parameter combination of each test part based on the performance requirement subset of each test part; determine simulated performance data based on the specific test parameter combination; determine key test scenarios based on the simulated performance data; determine real-time performance records based on the key test scenarios; obtain dynamic response data based on the real-time performance records; judge the dynamic response data against a first preset performance threshold, and if the dynamic response data exceeds the first preset performance threshold, mark the current dynamic response data as an abnormal point; determine an abnormal performance distribution based on the abnormal point; determine key performance defect points based on the abnormal performance distribution; and determine the test plan configuration for different test parts based on the key performance defect points.

[0033] By acquiring specific environmental data from the engine compartment, body electronics, and chassis, a hierarchical classification algorithm is used to categorize the performance requirements of different test locations and determine specific test parameter combinations. Furthermore, a test framework model is constructed based on the different test locations, and preliminary predictions are performed to obtain dynamic response data. Data comparison and analysis methods are then used to identify anomalies, and regression analysis algorithms are used to trace the factors influencing these anomalies and identify key performance defects. Test plan configurations can also be optimized by adjusting parameter weights to further optimize the test plan configuration. This allows for matching corresponding test plans for different test locations and environments in automotive connectors, enabling differentiated testing. This ensures that connector performance tests align with actual results under actual operating conditions. For example, this can truly reflect the connector's performance under extreme conditions such as high temperature, vibration, humidity, or shock. This improves the targeted, reliable, and accurate nature of performance testing and ensures the overall stability of the vehicle system.

[0034] The following describes in detail the steps of the performance testing method for an automotive connector provided by the embodiment of the present disclosure:

[0035] In one embodiment of the present disclosure, in step S110, specific environmental data is acquired for different test locations of the vehicle, including the engine compartment, body electronics, and chassis. The specific environmental data includes temperature, vibration, and humidity. Specifically, by acquiring specific environmental data for the engine compartment, body electronics, and chassis, a database source can be pre-built to facilitate data extraction during subsequent testing.

[0036] In one embodiment of the present disclosure, in step S120, determining the environmental feature set corresponding to each test site according to the specific environmental data further includes the following steps: Figure 2 The specific contents are as follows:

[0037] Step S230: determining an initial environmental information set based on specific environmental data of different test locations;

[0038] Step S235: Classify the specific environmental data based on the initial environmental information set to determine condition parameter groups for different test locations;

[0039] Step S240: grouping the condition parameters of different test locations and performing feature processing on the specific environmental data to determine initial feature sets for the different test locations;

[0040] Step S245: performing pattern recognition on the specific environmental data based on the initial feature set to determine the feature distribution of different test locations under different condition parameters;

[0041] Step S250: judging whether there are abnormal fluctuations in different test parts according to the characteristic distribution;

[0042] Step S255: If it is determined that abnormal fluctuation exists, an in-depth analysis is performed on the specific environmental data of the current test location to determine the environmental information corresponding to the current abnormal fluctuation, where the environmental information corresponding to the abnormal fluctuation includes the area and condition parameters of the abnormal fluctuation;

[0043] Step S260: determining a supplementary feature set based on the region and condition parameters of the abnormal fluctuation;

[0044] Step S265: Supplementing the feature set based on the initial feature set to determine the final environmental feature set.

[0045] In this way, through the pre-built database source, data such as temperature, vibration frequency and humidity of the engine compartment, body electronics and chassis are obtained, and the set of environmental characteristics corresponding to each test part is determined.

[0046] For example, engine compartment temperature data was collected via a temperature sensor with a set threshold of 120 degrees Celsius and a frequency of once per minute. The system obtained an average high temperature value of 85.3 degrees Celsius over a month, with a peak of 118.7 degrees Celsius. Vibration data was collected via an accelerometer with a frequency range of 5 to 50 Hz. Analysis revealed that the primary vibration frequency in the engine compartment was concentrated at 20 Hz, with a peak amplitude of 2.5g. Humidity data was collected via a humidity sensor with a set threshold of 80%, resulting in an average humidity of 65.2% and a peak of 82.1%. For the body electronics area, the system collected an average temperature of 45.6 degrees Celsius, a vibration frequency concentrated at 10 Hz, an amplitude of 1.2g, and an average humidity of 55.3%. The chassis area had an average temperature of 30.2 degrees Celsius, a vibration frequency of 15 Hz, an amplitude of 1.8g, and a peak humidity of 90.4%.

[0047] Subsequently, clustering algorithms (such as K-means) are used to classify the above temperature data, humidity data, and vibration data, and the environmental parameters are divided into three major feature sets: high temperature, vibration, and humidity. The weight of the characteristics of each area is calculated. For example, the high temperature weight of the engine compartment is 0.6, the vibration weight is 0.3, and the humidity weight is 0.1. The comprehensive environmental characteristic value is obtained through the weighted average algorithm to provide a basis for the subsequent connector selection. Next, the system standardizes the environmental feature set of each area and uses the Z-score method to normalize the data to a range of mean 0 and standard deviation 1 to ensure the comparability of different parameters. For example, the standardized value of the high temperature data in the engine compartment is 1.25, the vibration value is 0.87, and the humidity value is 0.33. Finally, the system matches the feature set with the connector tolerance standard database, setting the high-temperature resistance standard to 130 degrees Celsius, the vibration resistance standard to 3g, and the humidity resistance standard to 85%. The analysis shows that the engine compartment needs to use a connector that prioritizes high-temperature resistance, the body electronics need to balance vibration and temperature resistance, and the chassis needs to prioritize moisture resistance. This forms a complete mapping logic between environmental characteristics and application requirements to ensure design accuracy.

[0048] In one embodiment of the present disclosure, in step S130, determining the performance requirement subset of each test location according to the environmental feature set further includes the following steps: Figure 3 The specific contents are as follows:

[0049] Step S310: Based on the environmental feature set, a hierarchical classification algorithm is used to divide the performance requirements of different test locations to determine an initial performance requirement subset;

[0050] Step S320: comparing the deviation between the environmental characteristic data in the initial performance requirement subset and the performance requirement threshold with a preset deviation value;

[0051] Step S330: If it is determined that the predetermined deviation value is not exceeded, the current initial performance requirement subset is determined as the final performance requirement subset;

[0052] Step S340: If it is determined that the deviation exceeds the preset value, the initial performance requirement subset is re-divided into performance requirements to determine a supplementary performance requirement subset, and the current supplementary performance requirement subset is determined as the final performance requirement subset.

[0053] Specifically, the performance requirement subset of each test part is determined by obtaining the operating data of the automobile connector in various environments from the environmental characteristic database.

[0054] For example, a data acquisition system is used to acquire environmental data from the vehicle's engine compartment, interior, chassis, and external interfaces. This data, such as specific values ​​such as temperatures ranging from -40°C to 125°C, humidity ranging from 20% to 95%, and vibration frequencies ranging from 10Hz to 2000Hz, is then stored as a structured dataset. This data is then processed using a hierarchical classification algorithm, specifically a decision tree-based classification method, to classify environmental characteristics into categories such as high temperature and high humidity, or low temperature and high vibration. For example, through information gain calculation, temperature is prioritized as the first-level classification basis, with temperatures above 80°C classified as high temperature, below -20°C as low temperature, and the rest as normal temperature. Within the high temperature category, humidity is then used as the second-level classification basis, with humidity greater than 80% classified as a high humidity and high temperature subcategory, resulting in a preliminary classification of environmental categories. Performance requirements are further mapped based on the functional requirements of each vehicle part. For example, the engine compartment corresponds to the high-temperature and high-humidity category and must meet heat resistance requirements of 125°C and a waterproof rating of IP67. The chassis corresponds to the high-vibration category and must meet vibration resistance requirements of 20G. An algorithm automatically generates a subset of performance requirements for each part. For example, the engine compartment subset includes parameters for high-temperature resistance and corrosion resistance, while the chassis subset includes parameters for vibration resistance and shock resistance. The analysis compares the environmental data for each part with the performance requirements and calculates a deviation value, which can be preset to 10%. For example, the deviation between the actual engine compartment temperature of 120°C and the performance requirement threshold of 125°C is 4%, ensuring reasonable classification. If the deviation exceeds the preset deviation value of 10%, the machine learning model readjusts the classification threshold. Ultimately, a subset of performance requirements for each part is formed, ensuring a logically rigorous and data-driven approach.

[0055] In one embodiment of the present disclosure, in step S140, determining a specific test parameter combination for each test site according to the performance requirement subset of each test site further includes the following steps: Figure 4 The specific contents are as follows:

[0056] Step S410: determining a test index data range for each test location based on the performance requirement subset, where the test index data range includes a temperature range, a vibration frequency range, and a humidity range;

[0057] Step S420: Matching the test index data range with a preset test parameter library to determine the corresponding test index. The preset test parameter library includes test indexes under various environments, including temperature resistance test, vibration resistance test, and moisture resistance test.

[0058] Step S430: Determine a specific test parameter combination for each test site based on the test indicators.

[0059] Specifically, for the processing of the performance requirement subset, the system first automatically extracts the environmental characteristic data of each part. For example, the temperature range faced by the key part A of a certain equipment during operation is -20℃ to 60℃, the vibration frequency is 5Hz to 50Hz, and the humidity range is 30% to 85%. The system matches this data with the preset test parameter library, which stores test indicators under various environmental conditions. For example, the test indicator corresponding to the temperature range of -30℃ to 70℃ is the temperature resistance test, the vibration frequency of 5Hz to 60Hz corresponds to the vibration resistance test, and the humidity of 30% to 90% corresponds to the moisture resistance test. The matching algorithm uses Euclidean distance to calculate the proximity of each environmental feature to the indicator in the parameter library. The formula is:

[0060]

[0061] Among them, T, V, and H represent temperature, vibration frequency, and humidity respectively, and the one with the smallest calculated D value is the best match. For example, the D value of part A is 3.5, which matches the temperature resistance test index. Next, the system determines the specific test parameter combination of each part based on the matching results. For part A, combined with its environmental characteristics and matching indicators, it generates a temperature test range of -25°C to 65°C (taking ±5°C of the environmental range as a safety margin), a vibration frequency test range of 5Hz to 55Hz (the upper limit is increased by 5Hz to cover potential risks), and a humidity threshold of 88% (slightly higher than the actual upper limit to ensure the severity of the test). The analysis process is completed through the system's built-in logic judgment module. If a parameter exceeds the index range in the library, the backup parameter library is automatically called for secondary matching. For example, if the humidity threshold exceeds 90%, the extreme humidity test index library is called to ensure the rationality of the parameter combination. This process is also linked to the equipment operation log. The system will read the environmental data fluctuations in the past year in the log. For example, if it is found that the temperature of part A has briefly reached 62°C, the test upper limit will be automatically adjusted to 67°C to adapt to the historical extreme value, forming a closed-loop logic for parameter optimization to ensure that the test parameters meet current needs and cover historical risks.

[0062] In one embodiment of the present disclosure, in step S150, determining the simulation performance data according to the specific test parameter combination further includes the following steps: Figure 5 The specific contents are as follows:

[0063] Step S510: determining an initial test scenario according to a specific test parameter combination;

[0064] Step S520: in the initial test scenario, establish a test framework model for each test part according to the specific test combination;

[0065] Step S530: Determine simulation performance data according to the test framework model;

[0066] Step S540: determining a simulation performance prediction curve chart based on the simulation performance data;

[0067] Step S550: Determine a simulation performance analysis report according to the simulation performance prediction curve.

[0068] Specifically, in the process of constructing a test framework model based on part stratification and predicting connector performance, the authors first defined test scenarios using specific test parameter combinations. For different connector parts (such as the contact, housing, and seal), the temperature range was set to -40°C to 85°C, the humidity range was set to 20% to 90%, and the vibration frequency was set to 10Hz to 200Hz. Using these parameter combinations, 1,000 sets of test condition data were generated. An algorithm automatically selected the 50 parameter combinations with the highest coverage, ensuring that the test scenarios fully covered extreme environments. Next, a test framework model based on part stratification was constructed. Finite element analysis was used to divide the connector into three layers (contact, housing, and seal). A three-dimensional model was created for each layer with a mesh accuracy of 0.1mm. Material parameters, such as the conductivity of the contact was set to 5.96e7 S / m, the tensile strength of the housing was set to 400MPa, and the elastic modulus of the seal was set to 2.5MPa. The software then automatically calculated the stress distribution and deformation of each part under different parameter combinations, forming a layered performance database. Subsequently, simulation technology was used to predict the performance of the connector in various environmental conditions. Based on the Monte Carlo algorithm, 10,000 random simulations were performed on 50 groups of parameter combinations. The average contact resistance of the contact end at a high temperature of 85°C was calculated to be 0.002 ohms, the peak displacement of the shell at a vibration of 200Hz was 0.05mm, and the leakage rate of the sealing ring at a humidity of 90% was 0.01%. A performance prediction curve was generated. The analysis results showed that the resistance fluctuation rate of the contact end at high temperature was 5%, and the material conductivity needed to be optimized. Finally, the simulation performance data was sorted out, and the simulation results were clustered and analyzed using a data mining algorithm. The performance data were divided into three categories: excellent, good, and poor. 60% of the data had a contact resistance below 0.003 ohms, 75% had a displacement peak below 0.06 mm, and 80% had a leakage rate below 0.02%. A performance report was generated based on the analysis results, automatically identifying the sealing ring performance as a weak link under high temperature and high humidity environments. It was recommended that the elastic modulus be optimized to 3.0 MPa to improve the sealing performance. The front-end logic formed a closed loop through parameter screening, simulation prediction, and data analysis to ensure the effectiveness of the test framework and prediction accuracy.

[0069] In one embodiment of the present disclosure, in step S160, determining the key test scenario based on the simulated performance data further includes the following steps: Figure 6 The specific contents are as follows:

[0070] Step S610: Acquire data points of a collection device from the simulated performance data, where the collection device includes a temperature sensor, an acceleration sensor, and a humidity sensor;

[0071] Step S620: determining high-risk data points based on the data points collected by the device;

[0072] Step S630: Prioritize high-risk data points to determine key test scenarios.

[0073] Specifically, when processing simulated performance data to optimize the operation of the sensor acquisition module in the actual test equipment, the data analysis system first automatically extracts key indicators from the simulated data. For example, in a simulated test, it was found that the response time of a certain sensor was extended to 2.5 seconds when the temperature exceeded 85 degrees Celsius, exceeding the normal range of 1.5 seconds. The system marked this condition as a high-risk condition. The data analysis algorithm used a weighted scoring model, assigning weights of 0.4, 0.3, and 0.3 to the temperature, response time, and failure frequency, respectively. The calculated comprehensive risk index was 0.85, which was far higher than the risk threshold of 0.6 and was automatically classified as the highest priority risk point. The system then sorted all high-risk conditions based on the risk index and generated a priority list. For example, the temperature condition of 85 degrees Celsius ranked first, and the condition with a failure rate of 15% under a humidity of 90% ranked second with a risk index of 0.72. The sorting algorithm used a quick sort method to ensure processing efficiency. Subsequently, the system automatically determines key test scenarios for high-risk conditions in the priority list, combining historical test data and the equipment operating environment. For example, for the high-risk condition of a temperature of 85 degrees Celsius, the system analyzes the failure probability of the sensor acquisition module in similar scenarios in the past 100 tests and finds that the failure probability is 20%. Combined with the prediction model, it is inferred that the failure probability increases by 2% for every increase of 1 degree Celsius in the temperature range of 85 to 90 degrees Celsius. The test scenario is generated as an extreme test plan with a continuous operation of 2 hours and a temperature set to 88 degrees Celsius to ensure that the risk peak is covered.

[0074] Optionally, key test scenarios can be connected to the equipment resource database to automatically match suitable test equipment. For example, the HT-300 device model that supports high-temperature testing is selected, with a rated temperature range of -20 to 100 degrees Celsius to ensure test feasibility. At the same time, a test plan file is generated, including scenario parameters and expected result thresholds, such as the response time must not exceed 2.0 seconds. A complete logical chain from data analysis to scenario determination is constructed to ensure that each link is automatically handled by the system to improve efficiency and accuracy.

[0075] In one embodiment of the present disclosure, steps S170 to S190 are as follows: determining real-time performance records based on key test scenarios; obtaining dynamic response data based on the real-time performance records; comparing the dynamic response data with a first preset performance threshold, and if the dynamic response data exceeds the first preset performance threshold, marking the current dynamic response data as an abnormal point.

[0076] Specifically, for key test scenarios, real-time data acquisition technology is used to capture dynamic response data of connectors under high temperature, vibration, and humidity conditions from test equipment, and to record real-time performance. Data acquisition is achieved by deploying a high-precision sensor network. Specifically, temperature, vibration, and humidity sensors monitor environmental parameters separately. For example, a high-temperature environment is set at 85 degrees Celsius, a vibration frequency of 10 Hz, and a relative humidity of 90%. These sensors collect data in real time at a sampling rate of 100 times per second and upload the data to a cloud server via a wireless transmission module. Next, in the data processing phase, a time series analysis algorithm is used to clean and extract features from the collected data. For example, a sliding window averaging method is used to remove noise, with a window size of 5 seconds. The mean and standard deviation of the temperature, vibration acceleration, and humidity values ​​within each window are calculated to produce a smoothed dynamic response curve. Finally, in the performance evaluation phase, a preset threshold model is used for anomaly detection. For example, a temperature threshold of 90 degrees Celsius, a vibration acceleration threshold of 5 meters per second squared, and a humidity rate of change threshold of 5% per minute are used. If any of these indicators exceeds a first preset performance threshold, the record is automatically marked as an anomaly. The abnormal records and normal records are stored in the distributed database as real-time performance records.

[0077] Optionally, a regression analysis algorithm can be used to predict the performance degradation trend of the connector. A linear regression model can be used to fit the data from the past hour. A calculated slope value such as 0.02 indicates a slow decline in performance. In the distributed database, the data retention period is set to 30 days, and the fluctuations of various indicators are displayed in real time through a dynamic dashboard. For example, the temperature change over time is presented in the form of a line graph. If an abnormal point is found, an alarm email is automatically triggered to notify the relevant system. The email content includes the specific value of the abnormal indicator, such as a temperature of 92 degrees Celsius and the time of occurrence, to ensure the continuity of subsequent analysis. Through the above method, a complete closed loop is formed from data collection to performance evaluation to result presentation, ensuring comprehensive monitoring of connector performance in the test scenario.

[0078] In one embodiment of the present disclosure, in step S200 , an abnormal performance distribution is determined based on the abnormal points.

[0079] Specifically, in the processing of real-time performance records, the system first automatically collects server response time data, for example, recording it once every minute. Assuming that the data collected in a certain period is response time of 2.5 seconds, 3.1 seconds, 2.8 seconds, 4.2 seconds, and 3.9 seconds, this data will be stored in the database for subsequent analysis. Next, using a data comparison analysis method, the collected response time is compared with a first preset performance threshold. Assuming that the first preset performance threshold is that the response time does not exceed 3.0 seconds, the system will go through the data one by one, using a simple comparison algorithm. If a response time is greater than 3.0 seconds, it will be recorded as an abnormality. For example, 2.5 seconds and 2.8 seconds are normal, while 3.1 seconds, 4.2 seconds, and 3.9 seconds are marked as abnormal points, with an abnormality ratio of 60%. The system then automatically matches the abnormal points with the threshold range for analysis and generates an abnormal performance distribution report. For example, by statistically analyzing the time distribution of abnormal points, it is found that the abnormalities are mostly concentrated in peak hours (such as 2 pm to 4 pm). The average response time of the abnormal points is calculated to be 3.73 seconds. Further analysis may be related to excessive server load. To establish a rigorous logical relationship, the system also correlates business data. For example, if user access volume during peak hours is 5,000 visits per minute, far exceeding the normal average of 2,000 visits per minute, the system infers that the anomaly may be caused by a traffic surge. Finally, the system generates a visual chart showing the distribution of anomalies and their correlation with business traffic, automatically triggering an alarm mechanism to notify relevant modules to optimize resource allocation, such as dynamically adding server instances to ensure response times return to thresholds. This process forms a complete technical processing chain, from data collection to anomaly analysis to business correlation, ensuring the accuracy and timeliness of system performance monitoring.

[0080] In one embodiment of the present disclosure, in step S210, determining the key performance defect points according to the abnormal performance distribution further includes the following steps: Figure 7 The specific contents are as follows:

[0081] Step S710: determining an abnormal point set according to abnormal performance distribution;

[0082] Step S720: determining a significant abnormal point set based on the abnormal point set;

[0083] Step S730: Determine key performance defect points based on the set of significant abnormal points.

[0084] Specifically, in step S710, during the analysis of the abnormal performance distribution, the data acquisition system can extract abnormal points from the performance test results. Specifically, the abnormal points can be obtained from the abnormal performance distribution and formed into an abnormal point set. For example, the abnormal point set can be formed by obtaining 20 test points in the abnormal performance distribution where the contact resistance of the connector exceeds a first preset performance threshold. The first preset performance threshold can be twice the standard contact resistance value.

[0085] Specifically, in step S720, determining a significant abnormal point set based on the abnormal point set further includes the following steps:

[0086] Determine whether an abnormal point in the abnormal point set exceeds a second preset performance threshold; if an abnormal point in the abnormal point set is determined to exceed the second preset threshold, determine the abnormal point in the current abnormal point set as a significant abnormal point; and determine a significant abnormal point set based on the significant abnormal point. For example, in an abnormal point set consisting of 20 abnormal points, determine whether each abnormal point exceeds a second preset performance threshold, where the second preset performance threshold may be 3 times the standard value of contact resistance. If it is determined that the contact resistance of 10 of the abnormal points is greater than 3 times the standard value of contact resistance, then determine the current 10 abnormal points as significant abnormal points and form a significant abnormal point set.

[0087] Specifically, in step S730, determining key performance defect points based on the set of significant abnormal points also includes the following steps:

[0088] Based on the set of significant anomaly points, the test parameters and environmental conditions corresponding to the significant anomaly points are obtained; a regression analysis model is established based on the test parameters and environmental conditions; variables with a high correlation with the anomaly cause are identified based on the regression analysis model; and key performance defects are identified based on the variables with a high correlation with the anomaly cause. For example, for each significant anomaly point in the set of significant anomaly points, the environmental conditions and test parameters corresponding to 10 significant anomaly points are extracted from the test records. The environmental conditions can be temperature, humidity, and vibration frequency. The test parameters can be current and voltage. For example, the environmental conditions corresponding to one significant anomaly point are temperature 80°C, humidity 70%, and vibration frequency 150Hz, and the test parameters are current 20A and voltage 36V. A regression analysis model is established using the corresponding temperature 80°C, humidity 70%, vibration frequency 150Hz, current 20A, and voltage 36V as independent variables and contact resistance as the dependent variable. The regression analysis model then calculates the correlation coefficient between each independent and dependent variable. Based on the correlation coefficient, the factor with a high correlation with the anomaly cause is identified. That is, a larger correlation coefficient indicates a higher correlation with the anomaly cause. For example. Regression analysis shows a high correlation coefficient between temperature, vibration frequency, and contact resistance anomalies. Consequently, significant anomalies corresponding to these variables are identified as key performance defects. This facilitates precise location of key performance defects, improving defect location and ensuring comprehensive and business-relevant analysis.

[0089] In one embodiment of the present disclosure, in step S220, the test scheme configuration of different test locations is determined according to the key performance defect points as follows: Figure 8 The specific contents are as follows:

[0090] Step S810: Screening model parameters in the test framework model based on key performance defects;

[0091] Step S820: performing weight adjustment on the filtered model parameters to determine an adjusted weight distribution;

[0092] Step S830: determining an adjusted parameter combination set according to the adjusted weight distribution;

[0093] Step S840: performing simulation verification on the parameter combination set to determine performance data;

[0094] Step S850: determining whether the performance data is better than the historical performance data;

[0095] Step S860: If the current performance data is determined to be better than the historical performance data, the current performance data is used as the optimized performance data;

[0096] Step S870: Determine an optimized test solution configuration based on the optimized performance data.

[0097] According to the above steps of the present disclosure, in the face of performance requirements in different application environments, the test parameter combinations can be recombined by adjusting the parameter weights to achieve differentiated test parameters and standards, thereby achieving optimization of different test scheme configurations for different test parts, realizing precise testing of different vehicle parts, improving the accuracy and efficiency of automotive connector performance evaluation, and providing strong support for product quality improvement.

[0098] Step S810: Screening the model parameters in the test framework model according to key performance defects.

[0099] Specifically, the model parameters in the test framework model may include temperature, vibration frequency, humidity, contact resistance, etc. Based on the key performance defects mentioned above, one or more of temperature, vibration frequency, humidity, and contact resistance can be determined as key model parameters to facilitate weight adjustment.

[0100] Step S820: performing weight adjustment on the filtered model parameters to determine an adjusted weight distribution.

[0101] Specifically, the weights of the selected model parameters, such as vibration frequency, temperature, and humidity, are adjusted using the Analytic Hierarchy Process (AHP) or the Entropy Method (EMP) to more accurately assign each model parameter's weight to the key performance defect, thereby accurately reflecting the importance of each parameter to the key performance defect. The AHP method uses expert evaluation to break down complex problems into several levels, then compares them pairwise to calculate weights. The EMP method determines weights by calculating the degree of dispersion of the indicators; the greater the dispersion, the greater the weight. These two methods can be used to adjust the weight distribution of model parameters and conduct a comprehensive assessment of multiple model parameters. For example, the weight assigned to vibration frequency is 0.2, the weight assigned to temperature is 1.3, and the weight assigned to humidity is 0.5, forming an adjusted weight distribution.

[0102] Step S830: determining an adjusted parameter combination set according to the adjusted weight distribution.

[0103] Specifically, after the adjusted weight distribution is determined, the magnitudes of the model parameters such as vibration frequency, temperature, and humidity are changed. The changed model parameters are rearranged and combined to obtain an adjusted parameter combination, which constitutes a parameter combination set.

[0104] Step S840: Perform simulation verification on the parameter combination set to determine performance data.

[0105] Specifically, based on the adjusted model parameters, a test framework model based on part stratification was reconstructed. The finite element analysis method was used to divide the connector into three layers (contact end, shell, and seal ring). A three-dimensional model was established for each layer, with the meshing accuracy controlled at 0.1mm. Material parameters such as the conductivity of the contact end was set to 5.96e7 S / m, the tensile strength of the shell was 400MPa, and the elastic modulus of the seal ring was 2.5MPa. The software automatically calculated the stress distribution and deformation of each part under different parameter combinations to form a layered performance database. Subsequently, simulation technology was used to predict the performance of the connector under various parts of the environment. Based on the Monte Carlo algorithm, 10,000 random simulations were performed on 50 groups of parameter combinations to output performance data.

[0106] Step S850: determining whether the performance data is better than the historical performance data;

[0107] Step S860: If the current performance data is determined to be better than the historical performance data, the current performance data is used as the optimized performance data;

[0108] Step S870: Determine the optimized test solution configuration according to the optimized performance data.

[0109] Specifically, the current performance data is determined to be superior to the simulated performance data from the first simulation. If so, the current performance data is used as the optimized performance data, and the optimized test plan configuration is determined based on the optimized performance data. This test plan configuration includes model parameters adjusted using weight distribution. By repeating these steps, the test plan configuration can be continuously optimized, improving the accuracy and efficiency of automotive connector performance evaluation and providing strong support for product quality improvement.

[0110] Embodiments of the present disclosure also provide an electronic device comprising one or more processors and a memory resource, represented by a memory, for storing instructions executable by the processors, such as an application. The application stored in the memory may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor is configured to execute the instructions to perform the aforementioned automotive connector performance testing method.

[0111] The electronic device may further include a power supply component configured to perform power management of the electronic device, a wired or wireless network interface configured to connect the electronic device to a network, and an input / output (I / O) interface. The electronic device may be operated based on an operating system stored in the memory, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0112] In one embodiment, a computer device is also provided, which may be a server. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for testing the performance of an automotive connector is implemented.

[0113] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication, where the wireless communication may be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for decorating a building. The display unit of the computer device is used to form a visually visible image, and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0114] A non-temporary computer-readable storage medium is also provided in an embodiment of the present disclosure. When the instructions in the storage medium are executed by the processor of the above-mentioned electronic device, the above-mentioned electronic device is capable of executing a method for testing the performance of an automobile connector, including: obtaining specific environmental data of different test parts of the automobile, the test parts including the engine compartment, body electronics, and automobile chassis, and the specific environmental data including temperature, vibration, and humidity; determining an environmental feature set corresponding to each test part based on the specific environmental data; determining a performance requirement subset for each test part based on the environmental feature set; determining a specific test parameter combination for each test part based on the performance requirement subset for each test part; determining simulated performance data based on the specific test parameter combination; determining a key test scenario based on the simulated performance data; determining a real-time performance record based on the key test scenario; obtaining dynamic response data based on the real-time performance record; judging the dynamic response data against a first preset performance threshold, and if the dynamic response data exceeds the first preset performance threshold, marking the current dynamic response data as an abnormal point; determining an abnormal performance distribution based on the abnormal point; determining a key performance defect point based on the abnormal performance distribution; and determining a test scheme configuration for different test parts based on the key performance defect point.

[0115] The present disclosure may take the form of a computer program product implemented on one or more storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include but are not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0116] It should be noted that although the steps of the method of the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all steps must be performed to achieve the desired results. Additional or alternative steps, such as omitting certain steps, combining multiple steps into one step, and / or decomposing a step into multiple steps, should all be considered part of this disclosure.

[0117] It should be understood that the present disclosure is not limited in its application to the detailed structure and arrangement of the components set forth in this specification. The present disclosure is capable of other embodiments and can be implemented and executed in a variety of ways. The aforementioned variations and modifications fall within the scope of the present disclosure. It should be understood that the present disclosure disclosed and defined in this specification extends to all alternative combinations of two or more individual features mentioned or evident in the text and / or the drawings. All of these different combinations constitute multiple alternative aspects of the present disclosure. The embodiments of this specification illustrate the best mode known for implementing the present disclosure and will enable those skilled in the art to utilize the present disclosure.

Claims

1. A performance testing method for an automotive connector, characterized in that: include: Acquire specific environmental data for different test areas of the vehicle, including the engine compartment, body electronics, and chassis. The specific environmental data includes temperature, vibration, and humidity. Determine the environmental feature set corresponding to each of the test locations according to the specific environmental data; Determining a performance requirement subset for each of the test locations based on the set of environmental characteristics; determining a specific test parameter combination for each of the test sites according to a subset of performance requirements for each of the test sites; determining simulated performance data according to the specific test parameter combination; determining a key test scenario based on the simulated performance data; Determine real-time performance records based on the key test scenarios; Obtain dynamic response data based on real-time performance records; Comparing the dynamic response data with a first preset performance threshold, and if the dynamic response data exceeds the first preset performance threshold, marking the current dynamic response data as an abnormal point; determining an abnormal performance distribution based on the abnormal points; determining key performance defect points based on the abnormal performance distribution; Determine the test plan configuration for different test locations based on the key performance defects.

2. The performance testing method according to claim 1, wherein: The determining of the environmental feature set corresponding to each of the test locations according to the specific environmental data includes: determining an initial environmental information set based on the specific environmental data of different test locations; Classifying the specific environmental data according to the initial environmental information set to determine condition parameter groups for different test locations; performing feature processing on the specific environmental data according to the condition parameters of different test locations to determine initial feature sets for different test locations; Determining, based on the initial feature set, pattern recognition on the specific environmental data to determine feature distributions of different test locations under different condition parameters; Determining whether there are abnormal fluctuations in different test parts based on the characteristic distribution; If it is determined that there is an abnormal fluctuation, an in-depth analysis is performed on the specific environmental data of the current test location to determine the environmental information corresponding to the current abnormal fluctuation, wherein the environmental information corresponding to the abnormal fluctuation includes the area and condition parameters of the abnormal fluctuation; Determining a supplementary feature set according to the area of ​​the abnormal fluctuation and the condition parameter; The final environmental feature set is determined according to the initial feature set and the supplementary feature set.

3. The performance testing method according to claim 1, wherein: Determining the performance requirement subset of each test location according to the set of environmental characteristics includes: Based on the set of environmental characteristics, a hierarchical classification algorithm is used to divide the performance requirements of different test locations to determine an initial performance requirement subset; Comparing the deviation between the environmental characteristic data in the initial performance requirement subset and the performance requirement threshold with a preset deviation value; If it is determined that the preset deviation value is not exceeded, the current initial performance requirement subset is determined as the final performance requirement subset; If it is determined that the predetermined deviation value is exceeded, the performance requirements of the initial performance requirement subset are re-divided to determine a supplementary performance requirement subset, and the current supplementary performance requirement subset is determined as the final performance requirement subset.

4. The performance testing method according to claim 1, wherein: Determining a specific test parameter combination for each test site according to the performance requirement subset of each test site includes: Determine a test index data range for each test location based on the performance requirement subset, wherein the test index data range includes a temperature range, a vibration frequency range, and a humidity range; Matching the test index data range with a preset test parameter library to determine the corresponding test index, wherein the preset test parameter library includes test indexes under various environments, including temperature resistance test, vibration resistance test, and moisture resistance test; A specific test parameter combination for each test site is determined according to the test indicators.

5. The performance testing method according to claim 1, wherein: Determining the simulation performance data according to the specific test parameter combination includes: determining an initial test scenario according to the specific test parameter combination; Under the initial test scenario, establishing a test framework model for each of the test locations according to a specific test combination; determining simulation performance data according to the test framework model; Wherein, after determining the simulation performance data according to the test framework model, the method further includes: Determining a simulation performance prediction curve graph based on the simulation performance data; A simulation performance analysis report is determined based on the simulation performance prediction curve graph.

6. The performance testing method according to claim 1, wherein: Determining a key test scenario based on the simulated performance data includes: Acquiring data points of a collection device from the simulated performance data, the collection device including a temperature sensor, an acceleration sensor, and a humidity sensor; Identify high-risk data points based on the data points collected by the device; Prioritize the high-risk data points to determine focused testing scenarios.

7. The performance testing method according to claim 1, characterized in that: Determining key performance defect points according to the abnormal performance distribution includes: Determining an abnormal point set according to the abnormal performance distribution; Determine a significant abnormal point set based on the abnormal point set; Key performance defect points are determined based on the set of significant abnormal points.

8. The performance testing method according to claim 7, characterized in that: Determining a significant abnormal point set based on the abnormal point set includes: Determine whether the abnormal point in the abnormal point set exceeds the second preset performance threshold If it is determined that the abnormal point in the abnormal point set exceeds the second preset performance threshold, the abnormal point in the current abnormal point set is determined as a significant abnormal point; A significant abnormal point set is determined based on the significant abnormal points.

9. The performance testing method according to claim 7, characterized in that: Determining key performance defect points based on the set of significant abnormal points includes: Acquire test parameters and environmental conditions corresponding to the significant abnormal points according to the significant abnormal point set; Establishing a regression analysis model based on the test parameters and the environmental conditions; Determining variables with a high correlation with abnormality causes based on the regression analysis model; Determine key performance defect points based on variables with a high correlation with the cause of the abnormality.

10. The performance testing method according to claim 5, characterized in that: After determining the test scheme configurations for different test locations according to the key performance defect points, the method further includes: Screening model parameters in the test framework model according to the key performance defects; The filtered model parameters are weighted to determine the adjusted weight distribution; Determining an adjusted parameter combination set according to the adjusted weight distribution; Performing simulation verification on the parameter combination set to determine performance data; Determining whether the performance data is better than historical performance data; If it is determined that the performance data is better than the historical performance data, the current performance data is used as the optimized performance data; An optimized test solution configuration is determined based on the optimized performance data.

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