Performance verification method and system for automatic testing equipment of in-vehicle infotainment system

Abstract

The invention discloses a performance verification method and system for automatic testing equipment of a vehicle machine, and relates to the technical field of performance verification of the automatic testing equipment of the vehicle machine, and the method comprises the steps: obtaining initial performance baseline data of the automatic testing equipment of the vehicle machine under a preset reference load condition; establishing a corresponding relation table between the initial performance baseline data and the number of test channels; increasing the number of test channels according to a preset load increasing sequence, and collecting real-time performance monitoring data of the test equipment under the load level; calculating a comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data; and based on the comprehensive performance attenuation coefficient, constructing a dynamic performance attenuation curve of the test equipment, and when the comprehensive performance attenuation coefficient is greater than a preset threshold, outputting a performance verification report. The industrial problems of large environmental deviation, single evaluation dimension and early warning lag in the traditional test are solved, and the test efficiency, the equipment reliability and the maintenance decision accuracy are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of performance verification of vehicle-mounted automated test equipment, and in particular to a performance verification method and system for vehicle-mounted automated test equipment. Background Art

[0002] Current mainstream automated vehicle-based computer testing methods primarily rely on cloud-to-vehicle interface testing and performance testing under simulated load conditions. While these methods have improved testing efficiency to a certain extent, they still suffer from issues such as a single testing dimension, insufficient dynamic performance evaluation, and a lack of comprehensive multi-metric analysis capabilities. Existing technologies struggle to accurately simulate dynamic load variations in real-world scenarios, making it impossible to comprehensively assess the performance of vehicle-based computer systems in complex environments. This is particularly evident when analyzing performance degradation trends under multi-channel concurrent testing.

[0003] CN115658460A discloses an automated performance testing method and system based on the cloud-based interface with the vehicle-side computer. This method uses the Jmeter test tool to set parameter variables such as concurrency, port number, and IP address, and obtains test results for multiple interfaces in a single run, thereby improving testing efficiency. However, this method focuses primarily on performance testing at the interface level and lacks a multi-dimensional assessment of the overall performance of the vehicle-side computer system. In particular, it is unable to effectively monitor and analyze performance degradation under different load conditions, and does not involve the construction of dynamic performance curves and intelligent diagnostic functions.

[0004] CN105577475B proposes an automated performance testing system and method. This system implements client performance testing under simulated load conditions through the collaborative work of a requirements management module, an automation control module, and a test control module. Although this method implements the deployment of an automated test environment and the execution of test tasks, its test mode is relatively fixed, making it impossible to dynamically adjust the load strategy to adapt to the complexity of real-world scenarios. Furthermore, it lacks the ability to deeply analyze test data and diagnose the root causes of performance degradation, making it difficult to meet the needs of refined performance verification of vehicle-based systems. Summary of the Invention

[0005] In view of the problems of existing vehicle computer automation testing methods such as single testing dimension, insufficient dynamic performance evaluation, and lack of multi-index comprehensive analysis capabilities, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to realize the accurate evaluation of the dynamic performance attenuation of the vehicle-mounted automated test equipment under multi-channel concurrent conditions. By establishing the initial performance baseline data under the benchmark load conditions, adopting the nonlinear load increment strategy to simulate the real scenario, and calculating the comprehensive performance attenuation coefficient based on the fusion of multi-dimensional indicators, the dynamic performance attenuation curve is constructed and an intelligent diagnostic report is generated to improve the test accuracy and the reliability of equipment maintenance decisions.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a performance verification method for vehicle-mounted automated testing equipment, which includes:

[0009] Obtaining initial performance baseline data of the vehicle-mounted automated test equipment under preset benchmark load conditions, and establishing a corresponding relationship table between the initial performance baseline data and the number of test channels;

[0010] Increase the number of test channels according to the preset load increment sequence and collect real-time performance monitoring data of the test equipment at the load level;

[0011] Calculating a comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data;

[0012] Based on the comprehensive performance attenuation coefficient, a dynamic performance attenuation curve of the test equipment is constructed, and when the comprehensive performance attenuation coefficient is greater than a preset threshold, a performance verification report is output.

[0013] As a preferred embodiment of the performance verification method of the vehicle-mounted automated test equipment of the present invention, a dynamic performance attenuation curve of the test equipment is constructed based on the comprehensive performance attenuation coefficient. When the comprehensive performance attenuation coefficient is greater than a preset threshold, a performance verification report is output, including:

[0014] A rectangular coordinate system is constructed with the number of test channels as the horizontal axis and the comprehensive performance attenuation coefficient as the vertical axis;

[0015] Arrange the stored performance degradation analysis data in ascending order according to the number of test channels, connect the data points in sequence in the rectangular coordinate system, and generate a dynamic performance degradation curve, wherein the dynamic performance degradation curve is smoothed using a cubic spline interpolation method to eliminate local fluctuations;

[0016] Based on the dynamic performance attenuation curve, the comprehensive performance attenuation coefficient is compared with a preset threshold to generate a performance verification report.

[0017] As a preferred solution of the performance verification method of the vehicle-mounted automated test equipment of the present invention, wherein: based on the dynamic performance attenuation curve, comparing the comprehensive performance attenuation coefficient with a preset threshold, includes:

[0018] When the comprehensive performance degradation coefficient is greater than the preset threshold, the corresponding number of test channels is marked as a performance critical point and annotated with an identifier on the dynamic performance degradation curve. The safety margin index of the current device configuration is calculated and linked to the performance verification report.

[0019] If the dynamic performance attenuation curve shows a nonlinear turning feature, the composite analysis process is triggered and the analysis results are written into the performance verification report;

[0020] If the dynamic performance degradation curve shows an increase in response time and a decrease in data processing throughput near the critical point, and the test accuracy deviation value increases, an emergency maintenance alarm including hardware detection items and software optimization items will be generated, and the key degradation indicators will be marked in the performance verification report;

[0021] If the attenuation trajectory of the dynamic performance attenuation curve shows a stable linear change and the attenuation rates of each performance indicator maintain a fixed proportional relationship, an optimization suggestion alarm for the current test channel configuration is generated, and the load optimization suggestion is output in the performance verification report;

[0022] If the fluctuation amplitude of the dynamic performance attenuation curve is less than the preset fluctuation threshold and the attenuation rate of each indicator converges, it is marked as passed and the stability conclusion is recorded in the performance verification report;

[0023] When the safety margin indicator forms a specific correlation pattern with the real-time performance data, cross-validation is performed and the validation data is added to the performance validation report;

[0024] If the decay rate of the test channel's benchmark response time is more than twice the decay rate of the data processing throughput, a deep detection of the communication protocol stack is initiated, generating a composite alarm with communication parameter adjustment suggestions, and attaching a communication optimization plan to the performance verification report.

[0025] If the degree of degradation of the test accuracy deviation value is greater than the weighted average of the response time decay rate and the data processing throughput decay rate, the test script logic verification process is triggered, the test case optimization plan is output, and the test script revision suggestions are updated in the performance verification report.

[0026] As a preferred solution of the performance verification method of the vehicle-mounted automated test equipment of the present invention, it further includes:

[0027] When competition occurs among multiple test channel data, resource optimization analysis is performed and the analysis conclusions are integrated into the performance verification report.

[0028] If the response time decay rate of some channels is greater than that of other channels, a channel load redistribution plan is generated and a load balancing strategy is provided in the performance verification report.

[0029] As a preferred solution of the performance verification method of the vehicle-mounted automated test equipment of the present invention, wherein: calculating the comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data includes:

[0030] Based on the current number of test channels, matching initial performance baseline data is retrieved from the correspondence table, wherein the initial performance baseline data includes a benchmark response time, a benchmark data processing throughput, and a benchmark test accuracy deviation value; the real-time performance monitoring data includes the current test channel response time, the current data processing throughput, and the current test accuracy deviation value;

[0031] Calculating a response time decay rate using a logarithmic proportional method based on the current test channel response time and the benchmark response time;

[0032] Calculating a data processing throughput attenuation rate using a relative deviation method based on the current data processing throughput and the benchmark data processing throughput;

[0033] Calculating the test accuracy deviation degradation rate using an absolute value difference method based on the current test accuracy deviation value and the benchmark test accuracy deviation value;

[0034] Normalizing the response time decay rate, the data processing throughput decay rate, and the test accuracy deviation degradation rate, and calculating a comprehensive performance decay coefficient based on a preset weight coefficient;

[0035] The response time decay rate, the data processing throughput decay rate, the test accuracy deviation degradation rate, and the comprehensive performance decay coefficient are associated with the current number of test channels and stored in a performance decay analysis database.

[0036] As a preferred solution of the performance verification method of the vehicle-mounted automated test equipment of the present invention, the method for obtaining the real-time performance monitoring data is as follows:

[0037] According to the actual application scenario of the vehicle computer automated test equipment, a nonlinear load increment sequence is set, wherein the load increment sequence includes multiple load levels;

[0038] Loading a benchmark test script into the vehicle-mounted automated test device and increasing the number of test channels according to the nonlinear load increasing sequence;

[0039] At the same time, ensure that the computing resource allocation strategy of the test equipment is consistent with the actual operating environment, where the resource allocation strategy includes CPU scheduling strategy, memory allocation mechanism and network bandwidth limit;

[0040] Running the test task at the load level and collecting real-time performance monitoring data of the test device through the embedded performance monitoring module;

[0041] Dynamically adjust the growth rate of the number of test channels according to the changing trend of the real-time performance monitoring data;

[0042] The real-time performance monitoring data under the load level is associated with the corresponding number of test channels and stored to form a structured performance data set.

[0043] As a preferred solution of the performance verification method of the vehicle-mounted automated test equipment of the present invention, the method for establishing the correspondence table is as follows:

[0044] Configuring a preset benchmark load condition on the vehicle-mounted automated test equipment, wherein the preset benchmark load condition includes a fixed test instruction frequency, a standard data packet, and a constant number of test channels;

[0045] Based on the preset benchmark load condition, inputting an initial number of test channels N into the vehicle-based automated test equipment, starting a benchmark test of the vehicle-based automated test equipment, and running the test continuously for X hours;

[0046] Record the benchmark response time, benchmark data processing throughput and benchmark test accuracy deviation of the test channel through the built-in timer and data acquisition module;

[0047] Repeating the benchmark test for a constant number of test channels until a maximum number of benchmark tests is reached, eliminating outliers, and calculating an average value of the benchmark response time, benchmark data processing throughput, and benchmark test accuracy deviation value as the initial performance baseline data;

[0048] The initial performance baseline data is classified according to the number of test channels to generate a corresponding relationship table.

[0049] In a second aspect, an embodiment of the present invention provides a performance verification system for vehicle-mounted automated testing equipment, comprising:

[0050] A performance baseline data acquisition module is used to obtain initial performance baseline data of the vehicle-mounted automated test equipment under preset benchmark load conditions and establish a corresponding relationship table between the initial performance baseline data and the number of test channels;

[0051] The load control and data acquisition module is used to increase the number of test channels according to a preset load increment sequence and collect real-time performance monitoring data of the test equipment under load level;

[0052] a load curve simulation module, configured to calculate a comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data;

[0053] The performance alarm and reporting module constructs a dynamic performance attenuation curve of the test equipment based on the comprehensive performance attenuation coefficient, and outputs a performance verification report when the comprehensive performance attenuation coefficient is greater than a preset threshold.

[0054] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the performance verification method of the vehicle-mounted automated testing equipment as described in the first aspect of the present invention are implemented.

[0055] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the performance verification method of the vehicle-mounted automated testing equipment as described in the first aspect of the present invention are implemented.

[0056] Compared with the existing technology, the beneficial effects of the present invention are as follows: by establishing the initial performance baseline data and the corresponding relationship table under the benchmark load conditions, the reliability and repeatability of the test data are ensured; a nonlinear load increment strategy is used to dynamically collect real-time performance data, effectively simulating the load impact in real scenarios, and significantly improving the test coverage and environmental adaptability; a comprehensive performance attenuation coefficient is calculated by combining multi-dimensional indicator algorithms to comprehensively evaluate the performance degradation trend of the equipment in terms of latency, throughput and accuracy; a dynamic performance attenuation curve is constructed based on cubic spline interpolation and combined with multi-condition decision rules to achieve a leap from single judgment to intelligent diagnosis, and can accurately identify various performance degradation patterns and generate targeted optimization solutions. The present invention solves industry problems such as large environmental deviations, single evaluation dimensions, and delayed warnings in traditional testing, and greatly improves test efficiency, equipment reliability, and maintenance decision accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0058] Figure 1 This is a flow chart of the performance verification method of the vehicle-mounted automated testing equipment of this embodiment. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0062] Example 1

[0063] Reference Figure 1 , which is the first embodiment of the present invention, provides a performance verification method for vehicle-mounted automated testing equipment, comprising:

[0064] S1: Obtain initial performance baseline data of the vehicle-mounted automated test equipment under preset benchmark load conditions, and establish a corresponding relationship table between the initial performance baseline data and the number of test channels.

[0065] S1.1: Configure preset benchmark load conditions on the vehicle-mounted automated test equipment, where the preset benchmark load conditions include a fixed test command frequency, a standard data packet, and a constant number of test channels;

[0066] It should be noted that the fixed test instruction frequency is 100 instructions per second, the standard data packet size is 1KB, and the number of constant test channels increases from 1 to n (n is the maximum number of channels supported by the device).

[0067] S1.2: Based on the preset benchmark load conditions, input the initial number of test channels (N) to the vehicle-based automated test equipment, start the benchmark test of the vehicle-based automated test equipment, and run it continuously for X hours.

[0068] It should be noted that N is the test channel reference value under the preset reference load condition; X is the preset stability test duration.

[0069] S1.3: Record the benchmark response time, benchmark data processing throughput, and benchmark test accuracy deviation of the test channel through the built-in timer and data acquisition module;

[0070] It should be noted that the response time is the time difference between the instruction being sent and the result being returned; the data processing throughput is the number of data packets successfully processed per unit time; and the test accuracy deviation value is the error rate between the expected result and the actual result.

[0071] S1.4: Repeat the benchmark test with a constant number of test channels until the maximum number of benchmark tests is reached, remove outliers, and calculate the average of the benchmark response time, benchmark data processing throughput, and benchmark accuracy deviation as the initial performance baseline data;

[0072] S1.5: Classify the initial performance baseline data by the number of test channels and generate a corresponding relationship table.

[0073] Preferably, as shown in Table 1, initial performance baseline data of the vehicle-mounted automated test equipment under different numbers of test channels are established as a reference benchmark for subsequent performance degradation analysis; Table 1 lists the three key performance indicators under each configuration in order from 1 to n (the maximum number of supported channels), namely, benchmark response time, benchmark data processing throughput, and benchmark test accuracy deviation value; these data are obtained by taking the average value of multiple tests under preset benchmark load conditions, and are representative and stable; the benchmark response time reflects the time efficiency of the device in processing a single instruction, and the smaller the value, the faster the response; the benchmark data processing throughput measures the number of data packets that can be processed per unit time, reflecting the processing capability of the device; the benchmark test accuracy deviation value reflects the error rate between the test result and the expected value, and is an important indicator of performance stability.

[0074] Table 1. Correspondence between initial performance baseline data and the number of test channels

[0075] Number of test channels Benchmark response time Benchmark data processing throughput Benchmark accuracy deviation 1 15ms 950 packs / s 20％ 2 18ms 1800 packs / s 25％ 4 22ms 3550 packages / s 28％ 8 31ms 6900 packs / s 35％ 12 48ms 9800 packs / s 42％ 16 67ms 12500 packages / s 50％ ... ... ... ... n Tn Dn Pn

[0076] S2: Increase the number of test channels according to the preset load increment sequence, and collect real-time performance monitoring data of the test equipment under the load level.

[0077] Real-time performance monitoring data includes the current test channel response time, current data processing throughput, and current test accuracy deviation value; the preset load increment sequence adopts a nonlinear growth mode to simulate the load fluctuation characteristics of actual test scenarios;

[0078] S2.1: Set a nonlinear load increment sequence based on the actual application scenario of the vehicle-mounted automated test equipment, where the load increment sequence includes multiple load levels;

[0079] Specifically, the step of setting a nonlinear load increase sequence includes determining a load grading node based on a benchmark response time inflection point in the initial performance baseline data, specifically including: extracting the number of test channels with a benchmark response time growth rate greater than 20% from a correspondence table, recorded as a first critical value N1; extracting the number of test channels when the benchmark data processing throughput drops to 90% of the peak value, recorded as a second critical value N2; dividing the interval [N1, N2] into at least 3 equidistant subintervals, with each subinterval serving as a first-level load level.

[0080] Furthermore, a nonlinear growth function model is constructed, including: in the interval (0, N1], a linear growth mode is adopted, and the number of test channels increases according to the incremental step size ΔN = floor (0.1 × n) of the number of test channels; in the interval (N1, N2], an exponential growth mode is adopted, and the number of test channels increases according to the incremental step size ΔN = floor (N i ×0.25) increases, where Ni is the current number of channels; in the (N2,n] interval, a step-by-step growth mode is adopted, and the number of test channels is increased by an incremental step of ΔN = floor (0.05 × n) channels at each level.

[0081] Furthermore, a dynamic adjustment mechanism is set up, including: if the response time of the current test channel exceeds the benchmark response time by 150%, the incremental step size ΔN of the number of test channels at the next level will be halved; if the current data processing throughput is lower than 70% of the benchmark data processing throughput, the increment will be paused and the current load level will be maintained for 5 minutes; if the current test accuracy deviation value exceeds the benchmark test accuracy deviation value by 200% for three consecutive times, it will fall back to the previous load level.

[0082] Preferably, generating a load level identifier includes: assigning a unique identifier L to each load level x,y , where x represents the interval number (1 corresponds to the (0, N1] interval, 2 corresponds to the (N1, N2] interval, and 3 corresponds to the (N2, n] interval), and y represents the sequential number within the interval.

[0083] Specifically, a load increment test is performed, including: initializing the number of test channels to floor(0.05×n); selecting an increment step ΔN of the corresponding number of test channels according to the current interval; dynamically adjusting the actual increment according to the above-mentioned adjustment mechanism; and stably running each load level for at least 1 / 10 of X hours.

[0084] Furthermore, a structured performance data set is recorded, including: recording a timestamp, a load level identifier, and each time a load level changes. x,y , current test channel response time, current data processing throughput and current test accuracy deviation value; when the adjustment mechanism is triggered, the adjustment type and the number of test channels after adjustment are additionally recorded.

[0085] S2.2: Load the benchmark test script into the vehicle automated test equipment and increase the number of test channels in a nonlinear load increment sequence.

[0086] S2.3: Ensure that the computing resource allocation strategy of the test equipment is consistent with the actual operating environment. The resource allocation strategy includes CPU scheduling strategy, memory allocation mechanism, and network bandwidth limit.

[0087] S2.4: Run the test task at the load level and collect real-time performance monitoring data of the test equipment through the embedded performance monitoring module;

[0088] S2.5: Dynamically adjust the growth rate of the number of test channels based on the changing trend of real-time performance monitoring data;

[0089] It should be noted that if the response time of the current test channel exceeds the preset threshold or the current data processing throughput drops sharply, the load increment rate will be reduced or the addition of new test channels will be suspended;

[0090] S2.6: Associate and store the real-time performance monitoring data at the load level with the corresponding number of test channels to form a structured performance data set;

[0091] It should be noted that the structured performance data set includes a complete record of timestamp, load level identifier, current test channel response time, current data processing throughput, and current test accuracy deviation value.

[0092] S3: Calculate the comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data.

[0093] S3.1: Based on the current number of test channels, query the corresponding relationship table for matching initial performance baseline data, where the initial performance baseline data includes benchmark response time, benchmark data processing throughput, and benchmark test accuracy deviation value; the real-time performance monitoring data includes the current test channel response time, current data processing throughput, and current test accuracy deviation value;

[0094] S3.2: Based on the current test channel response time and the benchmark response time, the response time decay rate is calculated using the logarithmic proportional method. The specific formula is as follows:

[0095]

[0096] Among them, R d is the response time decay rate, T c is the current response time, T b is the benchmark response time, N c is the current number of test channels, an integer value, N is the maximum number of channels supported by the device, α is the channel impact factor, ranging from [0.1, 0.5], β is the time attenuation coefficient, ranging from [0.01, 0.1], and t is the test duration.

[0097] Example

[0098] S3.3: Based on the current data processing throughput and the benchmark data processing throughput, the data processing throughput attenuation rate is calculated using the relative deviation method. The specific formula is as follows:

[0099]

[0100] Among them, P d is the data processing throughput decay rate, D c is the current data processing throughput, D bis the benchmark data processing throughput, γ is the throughput time adjustment coefficient, and its value range is [0.001, 0.01].

[0101] S3.4: Based on the current test accuracy deviation value and the benchmark test accuracy deviation value, use the absolute value difference method to calculate the test accuracy deviation degradation rate. The specific formula is as follows:

[0102]

[0103] Among them, A d is the test accuracy deviation degradation rate, E c is the current test accuracy deviation value, E b is the benchmark test accuracy deviation value, δ is the accuracy adjustment coefficient, and its value range is [0.1, 0.3].

[0104] S3.5: Normalize the response time decay rate, data processing throughput decay rate, and test accuracy deviation degradation rate, and calculate a comprehensive performance decay coefficient based on a preset weight coefficient;

[0105]

[0106] Among them, C d is the comprehensive performance attenuation coefficient, w1 is the response time attenuation rate weight coefficient, w2 is the throughput attenuation rate weight coefficient, w3 is the accuracy deviation degradation rate weight coefficient, and λ is the fluctuation adjustment coefficient, with a value range of [0.1, 0.2].

[0107] As an example, the effectiveness of the performance degradation calculation formula was verified using a set of simulated test data. The test environment set the maximum number of channels supported by the device to 100, the test duration to 6 hours (21,600 seconds), the channel impact factor to 0.3, the time degradation coefficient to 0.05, the throughput time adjustment coefficient to 0.005, the accuracy adjustment coefficient to 0.2, and the current number of test channels to 60. The baseline performance data were: response time 10ms, data processing throughput 2000 packets / second, and test accuracy deviation value 0.5%; the real-time monitoring data were: response time 25ms, data processing throughput 1200 packets / second, and test accuracy deviation value 1.8%. The calculated results are as follows: response time degradation rate 0.804; data processing throughput degradation rate 9.41; test accuracy deviation degradation rate 3.003; and overall performance degradation coefficient 0.823.

[0108] Furthermore, the analysis results show that when the device operates under a 60-channel load, all performance indicators show a significant decline; the response time approaches the critical threshold, the data processing capability decreases significantly, and the test accuracy deviation exceeds the normal range.

[0109] S3.6: Associate the response time decay rate, the data processing throughput decay rate, the test accuracy deviation degradation rate, and the comprehensive performance decay coefficient with the current number of test channels and store them in a performance decay analysis database.

[0110] S4: Based on the comprehensive performance attenuation coefficient, a dynamic performance attenuation curve of the test equipment is constructed. When the comprehensive performance attenuation coefficient is greater than a preset threshold, a performance verification report is output.

[0111] S4.1: Construct a rectangular coordinate system with the number of test channels as the horizontal axis and the comprehensive performance attenuation coefficient as the vertical axis;

[0112] S4.2: Arrange the stored performance degradation analysis data in ascending order by the number of test channels, connect the data points in sequence in a rectangular coordinate system, and generate a dynamic performance degradation curve. The dynamic performance degradation curve is smoothed using a cubic spline interpolation method to eliminate local fluctuations.

[0113] S4.3: Based on the dynamic performance attenuation curve, compare the comprehensive performance attenuation coefficient with the preset threshold and generate a performance verification report.

[0114] In an optional embodiment, when the comprehensive performance attenuation coefficient is greater than a preset threshold, the corresponding number of test channels is marked as a performance critical point and annotated with an identifier on the dynamic performance attenuation curve, the safety margin index of the current equipment configuration is calculated, and at the same time associated with the performance verification report; if the dynamic performance attenuation curve shows a nonlinear turning feature, the composite analysis process is triggered, and the analysis results are written into the performance verification report; if the dynamic performance attenuation curve shows an increase in response time and a decrease in data processing throughput near the critical point, and the test accuracy deviation value expands, an emergency maintenance alarm including hardware detection items and software optimization items is generated, and key degradation indicators are marked in the performance verification report; if the attenuation trajectory of the dynamic performance attenuation curve shows a stable linear change and the attenuation rate of each performance indicator maintains a fixed proportional relationship, an optimization suggestion alarm is generated for the current test channel configuration, and the load optimization suggestion is output in the performance verification report; if the fluctuation amplitude of the dynamic performance attenuation curve is less than the preset fluctuation threshold and the attenuation rate of each indicator converges, it is marked as performance verification passed, and the stability conclusion is recorded in the performance verification report.

[0115] It should be noted that the preset threshold is determined based on the performance tolerance analysis results of the vehicle-mounted automated test equipment during long-term operation; the preset fluctuation threshold is determined based on the natural fluctuation range of each performance indicator under the same load level when the system is in normal operation.

[0116] In an optional implementation, when the safety margin indicator forms a specific correlation pattern with the real-time performance data, cross-validation is performed and the verification data is added to the performance verification report; if the decay rate of the test channel benchmark response time is more than twice the decay rate of the data processing throughput, the communication protocol stack deep detection is started, a composite alarm with communication parameter adjustment suggestions is generated, and a communication optimization plan is attached to the performance verification report; if the degree of degradation of the test accuracy deviation value is greater than the weighted average of the response time decay rate and the data processing throughput decay rate, the test script logic verification process is triggered, the test case optimization plan is output, and the test script revision suggestion is updated in the performance verification report.

[0117] It should be noted that the test script logic verification process includes: calling the test script set under the corresponding number of test channels, and extracting the logical structure modules in each test script. The logical structure modules include judgment structures, loop structures and asynchronous call structures, and constructing a logical structure mapping table; based on the logical structure mapping table, combined with the execution records of each script before and after the test accuracy deviation value mutation, compare and analyze the call frequency and execution time changes of each logical structure module to generate a script logic anomaly indicator matrix; based on the script logic anomaly indicator matrix, identify the logical structure modules with call changes or sudden increases in time within the abnormal interval, and mark them as candidate logical anomaly points; perform cross-script comparative analysis on the candidate logical anomaly points. If the logical structure shows similar abnormal performance in multiple scripts, it will be marked as a key verification logic unit; associate all key verification logic units with the original test script, output structure reconstruction suggestions, call path optimization suggestions and logic boundary condition revision suggestions, and update them to the performance verification report as test script revision suggestions.

[0118] In an optional implementation, when competition occurs among the data of multiple test channels, resource optimization analysis is performed, and the analysis conclusions are integrated into the performance verification report; if the response time decay rate of some channels is greater than the response time decay rate of other channels, a channel load redistribution plan is generated, and a load balancing strategy is provided in the performance verification report.

[0119] Preferably, if the data processing throughput of a specific channel combination shows correlated attenuation, data routing optimization suggestions are output, and data transmission optimization measures are supplemented in the performance verification report; if the test accuracy deviation values ​​of all channels show the same degradation pattern, the benchmark test data review process is triggered, and the benchmark data reference value is corrected in the performance verification report.

[0120] Furthermore, this embodiment also provides a performance verification system for vehicle-mounted automated test equipment, including: a performance baseline data acquisition module, used to obtain the initial performance baseline data of the vehicle-mounted automated test equipment under preset benchmark load conditions, and establish a correspondence table between the initial performance baseline data and the number of test channels; a load control and data acquisition module, used to increase the number of test channels according to a preset load increment sequence, and collect real-time performance monitoring data of the test equipment under the load level; a load curve simulation module, used to calculate the comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data; a performance alarm and reporting module, based on the comprehensive performance attenuation coefficient, constructs a dynamic performance attenuation curve of the test equipment, and outputs a performance verification report when the comprehensive performance attenuation coefficient is greater than a preset threshold.

[0121] This embodiment also provides a computer device suitable for the e-commerce management method based on big data analysis, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the e-commerce management method based on big data analysis proposed in the above embodiment.

[0122] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises 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 the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0123] This embodiment further provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the method for verifying the performance of the vehicle-mounted automated test equipment proposed in the above embodiment is implemented.

[0124] In summary, the present invention ensures the reliability and repeatability of test data by establishing initial performance baseline data and corresponding relationship tables under benchmark load conditions; adopts a nonlinear load increment strategy to dynamically collect real-time performance data, effectively simulates load impacts in real scenarios, and significantly improves test coverage and environmental adaptability; calculates the comprehensive performance attenuation coefficient through a combination of multi-dimensional indicator algorithms to comprehensively evaluate the performance degradation trend of the equipment in terms of latency, throughput, and accuracy; constructs a dynamic performance attenuation curve based on cubic spline interpolation and combines it with multi-condition decision rules to achieve a leap from single judgment to intelligent diagnosis, and can accurately identify various performance degradation patterns and generate targeted optimization solutions. The present invention solves industry problems such as large environmental deviations, single evaluation dimensions, and delayed warnings in traditional testing, and greatly improves test efficiency, equipment reliability, and maintenance decision accuracy.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A performance verification method for vehicle-mounted automated testing equipment, characterized by: include, Obtaining initial performance baseline data of the vehicle-mounted automated test equipment under preset benchmark load conditions, and establishing a corresponding relationship table between the initial performance baseline data and the number of test channels; Increase the number of test channels according to the preset load increment sequence and collect real-time performance monitoring data of the test equipment at the load level; Calculating a comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data; Based on the comprehensive performance attenuation coefficient, a dynamic performance attenuation curve of the test equipment is constructed, and when the comprehensive performance attenuation coefficient is greater than a preset threshold, a performance verification report is output.

2. The performance verification method of vehicle-mounted automated testing equipment according to claim 1, characterized in that: Based on the comprehensive performance attenuation coefficient, a dynamic performance attenuation curve of the test equipment is constructed. When the comprehensive performance attenuation coefficient is greater than a preset threshold, a performance verification report is output, including: A rectangular coordinate system is constructed with the number of test channels as the horizontal axis and the comprehensive performance attenuation coefficient as the vertical axis; Arrange the stored performance degradation analysis data in ascending order according to the number of test channels, connect the data points in sequence in the rectangular coordinate system, and generate a dynamic performance degradation curve, wherein the dynamic performance degradation curve is smoothed using a cubic spline interpolation method to eliminate local fluctuations; Based on the dynamic performance attenuation curve, the comprehensive performance attenuation coefficient is compared with a preset threshold to generate a performance verification report.

3. The performance verification method of vehicle-mounted automated testing equipment according to claim 2, characterized in that: Comparing the comprehensive performance attenuation coefficient with a preset threshold based on the dynamic performance attenuation curve includes: When the comprehensive performance degradation coefficient is greater than the preset threshold, the corresponding number of test channels is marked as a performance critical point and annotated with an identifier on the dynamic performance degradation curve. The safety margin index of the current device configuration is calculated and linked to the performance verification report. If the dynamic performance attenuation curve shows a nonlinear turning feature, the composite analysis process is triggered and the analysis results are written into the performance verification report; If the dynamic performance degradation curve shows an increase in response time and a decrease in data processing throughput near the critical point, and the test accuracy deviation value increases, an emergency maintenance alarm including hardware detection items and software optimization items will be generated, and the key degradation indicators will be marked in the performance verification report; If the attenuation trajectory of the dynamic performance attenuation curve shows a stable linear change and the attenuation rates of each performance indicator maintain a fixed proportional relationship, an optimization suggestion alarm for the current test channel configuration is generated, and the load optimization suggestion is output in the performance verification report; If the fluctuation amplitude of the dynamic performance attenuation curve is less than the preset fluctuation threshold and the attenuation rate of each indicator converges, it is marked as passed and the stability conclusion is recorded in the performance verification report; When the safety margin indicator forms a specific correlation pattern with the real-time performance data, cross-validation is performed and the validation data is added to the performance validation report; If the decay rate of the test channel's benchmark response time is more than twice the decay rate of the data processing throughput, a deep detection of the communication protocol stack is initiated, generating a composite alarm with communication parameter adjustment suggestions, and attaching a communication optimization plan to the performance verification report. If the degree of degradation of the test accuracy deviation value is greater than the weighted average of the response time decay rate and the data processing throughput decay rate, the test script logic verification process is triggered, the test case optimization plan is output, and the test script revision suggestions are updated in the performance verification report.

4. The performance verification method of vehicle-mounted automated testing equipment according to claim 3, characterized in that: Also includes, When competition occurs among multiple test channel data, resource optimization analysis is performed and the analysis conclusions are integrated into the performance verification report. If the response time decay rate of some channels is greater than that of other channels, a channel load redistribution plan is generated and a load balancing strategy is provided in the performance verification report.

5. The performance verification method of vehicle-mounted automated testing equipment according to claim 4, characterized in that: Calculating a comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data includes: Based on the current number of test channels, matching initial performance baseline data is retrieved from the correspondence table, wherein the initial performance baseline data includes a benchmark response time, a benchmark data processing throughput, and a benchmark test accuracy deviation value; the real-time performance monitoring data includes the current test channel response time, the current data processing throughput, and the current test accuracy deviation value; Calculating a response time decay rate using a logarithmic proportional method based on the current test channel response time and the benchmark response time; Calculating a data processing throughput attenuation rate using a relative deviation method based on the current data processing throughput and the benchmark data processing throughput; Calculating the test accuracy deviation degradation rate using an absolute value difference method based on the current test accuracy deviation value and the benchmark test accuracy deviation value; Normalizing the response time decay rate, the data processing throughput decay rate, and the test accuracy deviation degradation rate, and calculating a comprehensive performance decay coefficient based on a preset weight coefficient; The response time decay rate, the data processing throughput decay rate, the test accuracy deviation degradation rate, and the comprehensive performance decay coefficient are associated with the current number of test channels and stored in a performance decay analysis database.

6. The performance verification method of vehicle-mounted automated testing equipment according to claim 5, characterized in that: The method for obtaining the real-time performance monitoring data is: According to the actual application scenario of the vehicle computer automated test equipment, a nonlinear load increment sequence is set, wherein the load increment sequence includes multiple load levels; Loading a benchmark test script into the vehicle-mounted automated test device and increasing the number of test channels according to the nonlinear load increasing sequence; At the same time, ensure that the computing resource allocation strategy of the test equipment is consistent with the actual operating environment, where the resource allocation strategy includes CPU scheduling strategy, memory allocation mechanism and network bandwidth limit; Running the test task at the load level and collecting real-time performance monitoring data of the test device through the embedded performance monitoring module; Dynamically adjust the growth rate of the number of test channels according to the changing trend of the real-time performance monitoring data; The real-time performance monitoring data under the load level is associated with the corresponding number of test channels and stored to form a structured performance data set.

7. The performance verification method of vehicle-mounted automated testing equipment according to claim 5, characterized in that: The method for establishing the correspondence table is: Configuring a preset benchmark load condition on the vehicle-mounted automated test equipment, wherein the preset benchmark load condition includes a fixed test instruction frequency, a standard data packet, and a constant number of test channels; Based on the preset benchmark load condition, inputting an initial number of test channels N into the vehicle-based automated test equipment, starting a benchmark test of the vehicle-based automated test equipment, and running the test continuously for X hours; Record the benchmark response time, benchmark data processing throughput and benchmark test accuracy deviation of the test channel through the built-in timer and data acquisition module; Repeating the benchmark test for a constant number of test channels until a maximum number of benchmark tests is reached, eliminating outliers, and calculating an average value of the benchmark response time, benchmark data processing throughput, and benchmark test accuracy deviation value as the initial performance baseline data; The initial performance baseline data is classified according to the number of test channels to generate a corresponding relationship table.

8. A performance verification system for vehicle-mounted automated test equipment, based on the performance verification method for vehicle-mounted automated test equipment according to any one of claims 1 to 7, characterized in that: include, A performance baseline data acquisition module is used to obtain initial performance baseline data of the vehicle-mounted automated test equipment under preset benchmark load conditions and establish a corresponding relationship table between the initial performance baseline data and the number of test channels; The load control and data acquisition module is used to increase the number of test channels according to a preset load increment sequence and collect real-time performance monitoring data of the test equipment under load level; a load curve simulation module, configured to calculate a comprehensive performance attenuation coefficient of the real-time performance monitoring data relative to the initial performance baseline data; The performance alarm and reporting module constructs a dynamic performance attenuation curve of the test equipment based on the comprehensive performance attenuation coefficient, and outputs a performance verification report when the comprehensive performance attenuation coefficient is greater than a preset threshold.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the performance verification method of the vehicle-mounted automated testing equipment according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the performance verification method of vehicle-mounted automated testing equipment according to any one of claims 1 to 7 are implemented.

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