A durability test method for an electronic controller

Abstract

The present invention discloses a durability test method for an electronic controller, specifically relating to the technical field of electronic devices, and comprising the following steps: First, obtain and install a sample of the electronic controller on a test device connected with sensors and data recording devices; then conduct an accelerated aging experiment under extreme conditions and a normal operation experiment under standard working conditions to generate an accelerated aging data set and a normal operation data set respectively; next, analyze the data to generate an accelerated aging index and an operation fluctuation index; subsequently, input the two indexes into a machine learning model to generate a durability performance coefficient; finally, compare the coefficient with a preset standard to determine whether the durability performance of the sample meets the standard; The present invention adopts a method combining the accelerated aging index and the operation fluctuation index to comprehensively evaluate the durability performance of the electronic controller. The combination of the dual indexes can more comprehensively reflect the overall performance of the controller in actual use and avoid the deviation that may be brought by a single-index evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic devices, and more specifically, to a durability test method for an electronic controller. Background Art

[0002] In the design and manufacture of modern electronic devices, as a core component, the electronic controller undertakes many key functions such as control, monitoring, and communication. Therefore, the performance and durability of the electronic controller directly affect the stability and service life of the entire system. However, since the electronic controller often works in a complex and changeable environment, how to ensure its stability and reliability during long-term use has become an important research topic.

[0003] Existing durability test methods usually evaluate the performance of the electronic controller through single or limited environmental factors, and it is difficult to comprehensively reflect the durability performance of the electronic controller in the actual use environment, because in real applications, the electronic controller usually faces the combined action of multiple environmental factors. In addition, traditional test methods rely more on manual judgment and lack systematic and automated evaluation means, resulting in lower accuracy and consistency of test results. Therefore, a durability test method for an electronic controller is proposed here. Summary of the Invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A durability test method for an electronic controller, comprising the following steps:

[0006] Step 1: Obtain an electronic controller sample and install it on a test device. The test device is connected with multiple types of preset sensors and a data recording device, and monitor the performance of the electronic controller during the test;

[0007] Step 2: Simulate the long-term use environment through set extreme conditions and conduct an accelerated aging experiment on the electronic controller sample, and summarize the data recorded by the data recording device into an accelerated aging data set;

[0008] Step 3: Simulate the long-term use environment under set standard working conditions and conduct a normal operation experiment on the electronic controller sample, and summarize the data recorded by the data recording device into a normal operation data set;

[0009] Step 4: Conduct accelerated aging analysis based on the accelerated aging data set to generate an accelerated aging index, and conduct normal operation analysis based on the normal operation data set to generate an operation fluctuation index;

[0010] Step 5: Substitute the accelerated aging index and the operation fluctuation index into a pre-trained machine learning model together to generate a durability performance coefficient;

[0011] Step 6: Determine whether the durability performance of the electronic controller sample meets the preset standard based on the durability performance coefficient.

[0012] In a preferred embodiment, during the accelerated aging experiment and the normal operation experiment test, the electronic controller sample executes its designed functions and undergoes multiple operation cycles.

[0013] In a preferred embodiment, an operation cycle refers to the process in which the electronic controller sample repeatedly executes its normal work tasks during the experiment.

[0014] In a preferred embodiment, the acquisition logic of the accelerated aging index is as follows:

[0015] Obtain the total time of the accelerated aging test, the functions of multiple preset environmental factors changing with time, and the decay rates of multiple preset performance indicators changing with time from the accelerated aging dataset, and then substitute them into the following calculation formula together:

[0016] i is an index used to traverse all environmental factors, from the 1st environmental factor to the nth environmental factor, where n is the total number of environmental factors, wi represents the preset influence coefficient of environmental factor i, γi represents the inherent characteristics of the material corresponding to the electronic controller under the influence of environmental factor i, ∈i represents the sensitivity of the material corresponding to the electronic controller to environmental factor i under the influence of environmental factor i, T is the total time of the accelerated aging test, Ei(t) represents the function of environmental factor i changing with time, Di(t) represents the decay rate change function of the preset target performance indicator changing with time t under the influence of environmental factor i, and AAI represents the accelerated aging index.

[0017] In a preferred embodiment, the integration in the calculation process of the accelerated aging index is implemented by the trapezoidal method or the Simpson method.

[0018] In a preferred embodiment, the expression of the decay rate change function is:

[0019] Pi0 represents the initial value of the preset target performance indicator at time t = 0 under the influence of environmental factor i, and Pi(t) represents the current value of the preset target performance indicator at time t under the influence of environmental factor i.

[0020] In a preferred embodiment, the acquisition logic of the operation fluctuation index is as follows:

[0021] Obtain the maximum and minimum values of each preset performance indicator from the normal operation dataset, then calculate the difference between the maximum and minimum values during the entire test period, and retain the performance indicators corresponding to the difference greater than or equal to the preset screening threshold;

[0022] Mark the retained performance indicators as Pj, where j represents the number of the retained performance indicators. For each retained performance indicator Pj, calculate its standard deviation σj during the test, and use the ratio of the standard deviation to the fluctuation range to measure the volatility of each performance indicator:

[0023] Fj represents the volatility of performance indicator j, and ΔPj represents the difference between the maximum and minimum values of performance indicator j during the entire test;

[0024] Perform a weighted average of the volatilities Fj corresponding to all retained performance indicators to obtain the operating fluctuation index NFI.

[0025] In a preferred embodiment, the acquisition logic of the durability performance coefficient is as follows:

[0026] Substitute the accelerated aging index and the operating fluctuation index into a pre-trained machine learning model. The machine learning model uses a linear regression model as a fitting tool, and the output result of the linear regression model is the durability performance coefficient.

[0027] In a preferred embodiment, determining whether the durability performance of the electronic controller sample meets the preset standard based on the durability performance coefficient means:

[0028] Obtain the durability performance coefficient of the electronic controller sample, and then compare it with the preset durability test range. If there is an intersection between the durability performance coefficient of the electronic controller sample and the preset durability test range, generate a normal signal, indicating that the durability performance of the electronic controller sample meets the preset standard. If there is no intersection between the durability performance coefficient of the electronic controller sample and the preset durability test range, generate an abnormal signal, indicating that the durability performance of the electronic controller sample does not meet the preset standard.

[0029] The technical effects and advantages of the present invention:

[0030] The present invention adopts a method that combines the accelerated aging index and the operating fluctuation index to comprehensively evaluate the durability performance of the electronic controller. The accelerated aging index reflects the performance degradation of the controller under extreme environments, while the operating fluctuation index evaluates the stability of the controller under normal usage conditions. The combination of these two indicators can more comprehensively reflect the overall performance of the controller in actual use, avoiding the deviation that may be brought by a single indicator evaluation.

[0031] The present invention automatically generates a durability performance coefficient by inputting an accelerated aging index and an operating fluctuation index into a pre-trained machine learning model. This durability performance coefficient can be automatically compared with a preset durability standard to determine whether a sample meets the quality requirements. This process reduces human intervention, decreases judgment errors, and greatly improves the efficiency and consistency of evaluation, and is particularly suitable for quality control in mass production.

[0032] The present invention ensures consistent evaluation of different batches and different samples through standardized testing and evaluation methods. The standardized judgment mechanism for durability performance not only improves the consistency of product quality but also enhances the reliability of the entire production process. This method is particularly suitable for the production of electronic controllers that require high reliability, such as in key fields like aerospace and automotive electronics. By automatically comparing the durability performance coefficients of each electronic controller sample, the present invention can screen out products that do not meet the durability standard and prevent unqualified products from entering the market. This is of great significance for maintaining the brand reputation of enterprises and reducing after-sales risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings;

[0034] Figure 1 It is a schematic diagram of a durability test method for an electronic controller in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Refer to Figure 1 The following embodiments are obtained:

[0037] Embodiment 1: A durability test method for an electronic controller, comprising the following steps:

[0038] Step 1: Obtain an electronic controller sample and install it on a test device. The test device is connected with multiple types of preset sensors and a data recording device to monitor the performance of the electronic controller during the test.

[0039] Test plan development: First, develop a detailed test plan to identify the key functions of the electronic controller, the expected use environment, and the life goals. Select appropriate test conditions, including parameters such as temperature range, humidity, vibration frequency, voltage fluctuation, and operation cycle. Develop a test schedule and decide the type of data that needs to be collected during the test, such as voltage, current, temperature, response time, functional stability, etc.

[0040] Sample preparation and installation: Select representative samples of electronic controllers from the production line or batch and perform preliminary checks to ensure that they are working properly. Install these samples on the test equipment and connect the necessary sensors and data logging equipment to monitor the performance of the controllers during the test.

[0041] Step 2: simulate the long-term use environment by setting extreme conditions and conduct accelerated aging experiments on the electronic controller samples, and summarize the data recorded by the data recording device into an accelerated aging data set;

[0042] Accelerated aging testing: Start accelerated aging testing to simulate long-term use environments through set extreme conditions. Testing can include temperature cycling (such as repeated changes from extreme cold to extreme heat), vibration testing (simulating mechanical stress in operation), voltage fluctuations (testing controller performance when the power supply is unstable), and humidity testing (performance in high humidity environments). These conditions should be run at a planned time and frequency, and the performance of the controller should be continuously monitored throughout the process.

[0043] Intermediate checks and data recording: At different stages of the test, intermediate checks are performed to evaluate whether the controller functions properly. Relevant data such as voltage, current, response time and other parameters are collected and recorded.

[0044] During the accelerated aging experiment and normal operation experiment test, the electronic controller sample performs its designed functions and undergoes multiple operation cycles. The operation cycle refers to the process in which the electronic controller sample repeatedly performs its normal working tasks during the experiment, including but not limited to the following examples for easy understanding:

[0045] Function execution: Instruction processing: The controller is the abbreviation of the electronic controller sample, which receives and processes input signals or instructions. For example, it receives data from sensors and processes them, or controls mechanical or electrical equipment according to input instructions. Signal output: According to the processed data or instructions, the controller sends a control signal to drive the relevant equipment to perform corresponding operations. For example, it controls the start or stop of the motor, adjusts the operating status of the equipment, etc.

[0046] State Switching: The controller switches between different operating modes. For example, it switches from the standby mode to the working mode and then back to the standby mode. Each state switching may have an impact on the controller, and it is necessary to test its stability during multiple switches.

[0047] Load Variation: During the operation cycle, the controller may face different load conditions, such as different current, voltage, or power requirements. These variations may cause different reactions in the internal circuit, and it is necessary to evaluate the performance of the controller under various load conditions through the operation cycle.

[0048] Temperature Cycling: The controller operates at different temperatures, and its performance under high temperature, low temperature, and their rapid switching conditions is tested.

[0049] Humidity Cycling: The controller works under different humidity conditions to evaluate its tolerance in a humid environment.

[0050] Voltage Stress Cycling: The controller operates under different voltage conditions, including rapid switching of voltage increase and decrease, to test its tolerance to voltage fluctuations.

[0051] The multiple times in multiple operation cycles refer to repeated operations. In the experiment, the controller will repeatedly execute the same or similar operations to test its performance after long-term repeated operations. Each cycle represents a complete execution of functions, from receiving instructions, processing data to outputting results.

[0052] Step 3: Simulate the long-term use environment under the set standard working conditions and conduct a normal operation experiment on the electronic controller sample, and summarize the data recorded by the data recording device into a normal operation data set;

[0053] Test Condition Setting: Different from the accelerated aging test, the normal operation test is carried out under standard working conditions. These conditions should be as close as possible to the typical operation state of the controller in the actual application environment. Factors such as temperature, humidity, voltage, current, vibration, etc. should be set within the normal working range rather than extreme values.

[0054] Sample Selection and Installation: Select representative electronic controller samples and install them on the test platform or actual application equipment to ensure that the environmental conditions are consistent with the real use environment. Like the accelerated aging test, these samples should be equipped with sensors and data recording devices to monitor their performance during the test.

[0055] Long-Term Operation Test: Under standard conditions, continuously operate the controller and record its performance. This is usually a long process, which can last for weeks, months, or even longer. During the test, the controller should execute its designed functions and go through repeated operation cycles, such as switching, load variation, data processing, etc.

[0056] Regular inspections and data recording: During the test, regularly check the functions of the controller to ensure its normal operation throughout the test. Record key parameters such as response time, voltage, current, temperature, etc. Compared with the accelerated aging test, these parameters should remain within a more stable range because the test conditions are closer to the actual usage environment.

[0057] The acquisition logic of the accelerated aging index is as follows:

[0058] Obtain the total time of the accelerated aging test, the functions of multiple preset environmental factors changing with time, and the decay rates of multiple preset performance indicators changing with time from the accelerated aging dataset, and then substitute them into the following calculation formula together:

[0059] i is the index used to iterate through all environmental factors, from the 1st environmental factor to the nth environmental factor. In the formula, all preset environmental factors that may affect the aging of the electronic controller are taken into consideration, such as temperature, humidity, voltage, vibration, etc. n is the total number of these different environmental factors. wi represents the preset influence coefficient of environmental factor i, γi represents the inherent characteristics of the material corresponding to the electronic controller under the influence of environmental factor i, ∈i represents the sensitivity of the material corresponding to the electronic controller to environmental factor i under the influence of environmental factor i, T is the total time of the accelerated aging test, Ei(t) represents the function of environmental factor i changing with time, and the value is the sampled value at time t. Ei(t) represents the function of the preset target performance indicator changing with the decay rate over time t under the influence of environmental factor i, and the value is the decay rate value at time t. AAI represents the accelerated aging index. The larger the accelerated aging index, the faster the performance of the electronic controller decays during the accelerated aging test. This means that under extreme conditions (such as high temperature, humidity fluctuations, mechanical vibration, etc.), the key performance indicators of the controller (such as voltage stability, signal integrity, etc.) decrease more significantly. A higher AAI indicates that the controller may be more prone to failure or performance degradation during long-term use or under extreme conditions.

[0060] The decay rate Di(t) corresponding to Ei(t) for each environmental factor i is based on that specific environmental factor. For example, the decay rate D1(t) corresponding to the temperature E1(t) reflects the decay of the performance of the electronic controller under different temperature conditions; the decay rate D2(t) corresponding to the humidity E2(t) reflects the impact of humidity on the controller. Di(t) is a function of the decay rate change over time t of a specific performance metric (such as signal integrity, voltage stability, response time, etc.) of the electronic controller under a specific environmental factor i. It represents the degree of deterioration of this performance metric under the action of this environmental factor over time. It describes how a certain performance metric of the electronic controller changes over time under the action of this specific environmental factor i. In the formula, a specific environmental factor corresponds to a specific performance metric.

[0061] Suppose the temperature tolerance of the electronic controller is being tested, and E1(t) is the time-varying curve of the temperature. In this case, D1(t) may represent the decay rate of the voltage stability of the controller under the temperature condition. As time t progresses, the increasing temperature may cause the voltage output to become gradually unstable, which is how D1(t) changes over time. If the impact of humidity is being tested, E2(t) represents the time-varying curve of the humidity, and D2(t) may represent the decay rate of the signal integrity of the controller under the humidity condition.

[0062] In the accelerated aging experiment, a specific performance metric of the controller can be measured regularly, and its change over time can be recorded. Then, by comparing these measured values with the initial values, the decay rate can be calculated. The expression for the decay rate change function is:

[0063] Pi0 represents the initial value of the preset target performance metric at time t = 0 under the influence of environmental factor i, and Pi(t) represents the current value of the preset target performance metric at time t under the influence of environmental factor i.

[0064] γi represents the inherent characteristics of the material corresponding to the electronic controller under the influence of environmental factor i. It is a parameter related to the material characteristics in the electronic controller, reflecting the inherent characteristics of the material under the influence of specific environmental factors. Usually a fixed value, it is used to adjust the non-linear response in the formula. In the accelerated aging index formula, γi is named the material constant, and the material constant γi is mainly used to adjust the attenuation rate of the controller in the face of different environmental factors. For example, under high-temperature conditions, the characteristics of a certain material may cause it to age faster or slower, which is reflected in the material constant γi. The material constant γi is usually obtained through experimental data or by referring to relevant literature. For example, by conducting a series of temperature tests on the material, the aging characteristics of the material at different temperatures can be determined. The environmental sensitivity coefficient ∈i represents the sensitivity of the material corresponding to the electronic controller to the environmental factor i under the influence of environmental factor i. The environmental sensitivity coefficient ∈i represents the sensitivity of the material of the electronic controller to specific environmental factors (such as temperature, humidity, etc.), reflecting the response degree of the material when the environmental factor changes. The environmental sensitivity coefficient ∈i affects the attenuation rate in the formula and is the performance intensity of the controller under a certain environmental factor i, which is used to adjust the degree of influence of the environmental factor on the material aging in the formula. If the environmental sensitivity coefficient ∈i is large, it means that the material is very sensitive to the change of environmental factor i, and its performance will change significantly with the fluctuation of the environment.

[0065] Integration is used to calculate the cumulative amount of a certain function within a certain interval. If the function Ei(t) is continuous, the mathematical integration can be directly used. However, in actual tests, usually discrete test data points are obtained instead of a continuous function. Numerical methods need to be used to approximately calculate the integration. Numerical integration is a method used to approximately calculate the integration, especially when there is only a set of discrete experimental data points. In this case, the standard integration calculation cannot be directly carried out, but numerical methods need to be used for approximation. The integration in the accelerated aging index calculation is realized through the trapezoidal method or the Simpson's method. The trapezoidal method approximates the integral value by dividing the area under the function curve into multiple trapezoids and then calculating the sum of the areas of these trapezoids. The Simpson's method divides the area under the function curve into multiple parabolic-shaped regions instead of simple trapezoids. The corresponding trapezoidal method or Simpson's method is selected according to the user's requirement for data accuracy. The accuracy of the Simpson's method is better than that of the trapezoidal method, but it occupies more computing resources.

[0066] Step 4: Conduct accelerated aging analysis based on the accelerated aging data set to generate an accelerated aging index, and conduct normal operation analysis based on the normal operation data set to generate an operation fluctuation index.

[0067] Obtain data from the normal operation dataset: Under normal working conditions, the electronic controller will continuously execute its designed functions and generate a series of operation data. This data usually includes the key performance indicators of the controller, such as voltage, current, temperature, response time, etc. The data should be continuously collected over a period of time to capture the normal fluctuations of the system.

[0068] Select the performance indicators to be analyzed: Select several key performance indicators from the normal operation dataset for analysis. The selected indicators should be able to reflect the core performance characteristics of the electronic controller, such as signal integrity, response time, voltage stability, etc. These indicators are the basis for calculating the operation fluctuation index.

[0069] The acquisition logic of the operation fluctuation index is as follows:

[0070] Logic for selecting performance indicators: Obtain the maximum and minimum values of each preset performance indicator from the normal operation dataset, and then calculate the difference between the maximum and minimum values during the entire test period. Retain the performance indicators whose difference is greater than or equal to the preset screening threshold;

[0071] Mark the retained performance indicators as Pj, where j represents the number of the retained performance indicator. For each retained performance indicator Pj, calculate its standard deviation σj during the test period. Use the ratio of the standard deviation to the fluctuation range to measure the volatility of each performance indicator:

[0072] Fj represents the volatility of performance indicator j. This ratio represents the relative volatility of the performance indicator, that is, within the entire fluctuation range, how much the performance indicator changes. ΔPj represents the difference between the maximum and minimum values of performance indicator j during the entire test period;

[0073] Perform a weighted average of the volatilities Fj corresponding to all retained performance indicators to obtain the operation fluctuation index NFI. The calculation formula is as follows:

[0074] vi represents the preset weight coefficient corresponding to the retained performance indicator j, which is used to balance the influence degree of different performance indicators j on the result. N represents the total number of retained performance indicators. The higher the calculated NFI value, the greater the volatility of the electronic controller under normal operation conditions and the poorer the stability. On the contrary, a lower NFI value indicates that the controller performs stably and has less fluctuation under normal operation conditions.

[0075] Step Five: Substitute the accelerated aging index and the operation fluctuation index into the pre-trained machine learning model to generate the durability performance coefficient. The acquisition logic of the durability performance coefficient is as follows:

[0076] Substitute the accelerated aging index and the operation fluctuation index into the pre-trained machine learning model. The machine learning model uses a linear regression model as the fitting tool. The output result of the linear regression model is the durability performance coefficient. The expression of the linear regression model is:

[0077] DPC = β0 - β1 * NFI - β2 * AAI; both β1 and β2 are positive values used to adjust the influence of the corresponding parameters on the calculation result. β0 is the intercept term, representing the durability performance coefficient when AAI = 0 and NFI = 0, which is the basic durability under certain ideal conditions. When AAI increases, it indicates accelerated aging and poorer durability, so DPC should decrease, which is represented by the -β2 * AAI term. When NFI increases, it indicates poorer stability and reliability, and DPC should also decrease, which is represented by the -β1 * NFI term. The larger the durability performance coefficient DPC, generally the stronger the overall durability of the electronic controller. This coefficient synthesizes the influence of the accelerated aging index and the operation fluctuation index, reflecting the overall performance of the controller under long-term use and various environmental conditions. A higher DPC indicates that the controller can maintain more stable performance and has a lower failure rate during long-term operation and under extreme conditions. Therefore, the larger DPC is, generally the better the durability and reliability of the controller.

[0078] Step 6: Judge whether the durability performance of the electronic controller sample meets the preset standard based on the durability performance coefficient. Obtain the durability performance coefficient of the electronic controller sample, and then compare it with the preset durability test range. If there is an intersection between the durability performance coefficient of the electronic controller sample and the preset durability test range, a normal signal is generated, indicating that the durability performance of the electronic controller sample meets the preset standard. If there is no intersection between the durability performance coefficient of the electronic controller sample and the preset durability test range, an abnormal signal is generated, indicating that the durability performance of the electronic controller sample does not meet the preset standard. The preset durability test range refers to a reasonable durability range defined in the experimental design or quality standard. This is usually set based on industry standards, experimental data, or product design requirements, indicating that the durability of a qualified product should be within this range. Through this judgment method based on the durability performance coefficient, the durability of the electronic controller can be automatically evaluated, reducing the error of manual judgment. This is very important for improving efficiency and consistency in large-scale production or quality control processes. This method can help manufacturers quickly screen out products with insufficient durability on the production line, thus avoiding putting unqualified products on the market. This is crucial for maintaining product quality and brand reputation. By standardizing the durability performance judgment, it can ensure the consistent evaluation of different batches and different samples, enhancing the consistency and reliability of product quality.

[0079] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0080] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0081] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0082] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0083] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A durability test method for an electronic controller, characterized in that: The following steps are involved: Step 1: Obtain an electronic controller sample and install it on a test device, which is connected to multiple types of preset sensors and data recording devices to monitor the performance of the electronic controller during the test; Step 2: simulate the long-term use environment by setting extreme conditions and conduct accelerated aging experiments on the electronic controller samples, and summarize the data recorded by the data recording device into an accelerated aging data set; Step 3: Simulate a long-term use environment under set standard working conditions and conduct a normal operation experiment on the electronic controller sample, and summarize the data recorded by the data recording device into a normal operation data set; Step 4: Perform accelerated aging analysis based on the accelerated aging data set to generate an accelerated aging index, and perform normal operation analysis based on the normal operation data set to generate an operation fluctuation index; Step 5: Substitute the accelerated aging index and the operation fluctuation index into the pre-trained machine learning model to generate a durability performance coefficient; Step 6: judging whether the durability performance of the electronic controller sample reaches a preset standard based on the durability performance coefficient; The logic for obtaining the accelerated aging index is: The total time of the accelerated aging test, the functions of multiple preset environmental factors changing with time, and the decay rates of multiple preset performance indicators changing with time are obtained from the accelerated aging data set, and then substituted into the following calculation formula: i is an index used to traverse all environmental factors, from the first environmental factor to the nth environmental factor, n is the total number of environmental factors, wi represents the preset influence coefficient of environmental factor i, γi represents the inherent characteristics of the material corresponding to the electronic controller under the influence of environmental factor i, ∈i represents the sensitivity of the material corresponding to the electronic controller to the environmental factor i under the influence of environmental factor i, T is the total time of the accelerated aging test, Ei(t) represents the function of environmental factor i changing with time, Di(t) represents the attenuation rate change function of the preset target performance indicator under the influence of environmental factor i with time t, and AAI represents the accelerated aging index; The logic for obtaining the running volatility index is: Obtain the maximum and minimum values ​​of each preset performance indicator from the normal operation data set, then calculate the difference between the maximum and minimum values ​​during the entire test period, and retain the performance indicators corresponding to the difference being greater than or equal to the preset screening threshold; The retained performance indicators are marked as Pj, where j represents the number of the retained performance indicators. For each retained performance indicator Pj, its standard deviation σj during the test period is calculated, and the volatility of each performance indicator is measured using the ratio of the standard deviation to the fluctuation range: Fj represents the volatility of performance indicator j, ΔPj represents the difference between the maximum and minimum values ​​of performance indicator j during the entire test period; The volatility Fj corresponding to all retained performance indicators is weighted averaged to obtain the operating volatility index NFI; The logic for obtaining the durability performance coefficient is: The accelerated aging index and the operation fluctuation index are substituted into the pre-trained machine learning model. The machine learning model uses the linear regression model as a fitting tool, and the output result of the linear regression model is the durability performance coefficient.

2. A durability testing method for an electronic controller according to claim 1, characterized in that: In the accelerated aging and normal operation tests, the electronic controller samples perform their designed functions and go through multiple operation cycles.

3. A durability testing method for an electronic controller according to claim 2, characterized in that: An operating cycle is the process by which an electronic controller sample repeatedly performs its normal operating tasks during an experiment.

4. A durability testing method for an electronic controller according to claim 3, characterized in that: Integration during the calculation of the accelerated aging index is achieved using the trapezoidal method or the Simpson method.

5. A durability testing method for an electronic controller according to claim 4, characterized in that: The expression of the decay rate change function is: Pi0 represents the initial value of the preset target performance indicator under the influence of environmental factor i at time t=0, and Pi(t) represents the current value of the preset target performance indicator under the influence of environmental factor i at time t.

6. A durability testing method for an electronic controller according to claim 5, characterized in that: Judging whether the durability performance of the electronic controller sample meets the preset standard based on the durability performance coefficient means: The durability performance coefficient of the electronic controller sample is obtained, and then compared with the preset durability test range. If the durability performance coefficient of the electronic controller sample and the preset durability test range have an intersection, a normal signal is generated, indicating that the durability performance of the electronic controller sample meets the preset standard. If the durability performance coefficient of the electronic controller sample and the preset durability test range do not have an intersection, an abnormal signal is generated, indicating that the durability performance of the electronic controller sample does not meet the preset standard.

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