Relative entropy-based old product acceleration factor evaluation method and related equipment

Abstract

The invention provides an old product accelerated factor evaluation method based on relative entropy and related equipment, and the method comprises the steps: obtaining first performance parameter degradation data of a product in a natural storage test stage and second performance parameter degradation data of the product in an accelerated storage test stage; the method comprises the following steps: calculating a natural storage degradation rate and an accelerated storage degradation rate, carrying out sample expansion on the degradation rates to obtain a target degradation rate set comprising more sample degradation rates, calculating a relative entropy between the degradation rates in the target degradation rate set and the natural storage degradation rate based on a relative entropy algorithm, and minimizing the relative entropy. The acceleration factor corresponding to the minimum relative entropy is used as the target acceleration factor, the acceleration factor is optimal under the condition that the relative entropy is minimum, an acceleration test is carried out on the old product according to the optimal acceleration factor, and the reliability and accuracy of the acceleration test of the old product are improved.

Description

Technical Field

[0001] This application relates to the technical field, and in particular to a method and related equipment for evaluating the acceleration factor of old products based on relative entropy. Background Technology

[0002] Currently, in equipment storage life testing and evaluation, due to the high cost of complete machine-level products, extensive accelerated storage tests are conducted on numerous components and sub-assemblies during actual testing to obtain acceleration factors or storage life information. Storage life is then assessed based on these acceleration factors or storage life information. At present, the main approach is to analyze and evaluate accelerated test data. However, due to the limited number of products and the relatively limited range of test data available for evaluation, the accuracy of the analysis and evaluation is relatively low. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a method and related equipment for evaluating the acceleration factor of old products based on relative entropy, so as to solve some or all of the technical problems existing in the background art.

[0004] To achieve the above objectives, this application provides a method for evaluating the acceleration factor of legacy products based on relative entropy, comprising: Obtain the degradation data of the first performance parameter of the product during the natural storage test phase and the degradation data of the second performance parameter of the product during the accelerated storage test phase; Based on the degradation data of the first performance parameter and the degradation data of the second performance parameter, the natural storage degradation rate and the accelerated storage degradation rate are determined; A target degradation rate set is determined based on the natural storage degradation rate and the accelerated storage degradation rate; Based on the relative entropy algorithm, the target acceleration factor is determined according to the target degradation rate set and the natural storage degradation rate.

[0005] Optionally, determining the natural storage degradation rate based on the first performance parameter degradation data includes: Input the degradation data of the first performance parameter into the following formula to obtain the natural storage degradation rate; ; In the formula, In time The measured degradation data of the first performance parameter Let be the i-th measurement time point, n be the number of measurement time points, and K1 be the natural storage degradation rate.

[0006] Optionally, determining the accelerated storage degradation rate based on the second performance parameter degradation data includes: Input the degradation data of the second performance parameter into the following formula to obtain the accelerated storage degradation rate; ; In the formula, In time The measured degradation data of the second performance parameter Let be the i-th measurement time point, n be the number of measurement time points, and K2 be the accelerated storage degradation rate.

[0007] Optionally, determining the target degradation rate set based on the natural storage degradation rate and the accelerated storage degradation rate includes: The initial acceleration factor is determined based on the natural storage degradation rate and the accelerated storage degradation rate; The natural storage degradation rate is expanded based on the initial acceleration factor and the accelerated storage degradation rate to determine the degradation rate of newly added experimental data; The target degradation rate set is determined based on the natural storage degradation rate and the sample expansion degradation rate.

[0008] Optionally, determining the initial acceleration factor based on the natural storage degradation rate and the accelerated storage degradation rate includes: Substituting the natural storage degradation rate and the accelerated storage degradation rate into the following formula, the initial acceleration factor is obtained; ; In the formula, AF is the initial acceleration factor. This refers to the product's lifespan during the natural storage test phase. To extend the product's lifespan during the accelerated storage test phase, To accelerate the performance change rate constant during the storage test phase; This represents the performance change rate constant during the natural storage test phase.

[0009] Optionally, the step of expanding the natural storage degradation rate based on the initial acceleration factor and the accelerated storage degradation rate to determine the expanded sample degradation rate includes: The acceleration factor and the accelerated storage degradation rate are input into the following formula to perform sample expansion of the natural storage degradation rate, resulting in the sample-expanded degradation rate: ； In the formula, As the initial acceleration factor, No. i The rate of degradation of each target For the first i This accelerates the degradation rate.

[0010] Optionally, determining the target degradation rate set based on the natural storage degradation rate and the sample expansion degradation rate includes: The natural storage degradation rate and the sample expansion degradation rate are fused to form the target degradation rate set.

[0011] Optionally, the determination of the target acceleration factor based on the relative entropy algorithm, according to the target degradation rate set and the natural storage degradation rate, includes: Based on the relative entropy algorithm, a relative entropy function is constructed according to the target degradation rate set and the natural storage degradation rate; Based on the initial acceleration factor, the relative entropy function is minimized, and the acceleration factor corresponding to the minimized relative entropy is taken as the target acceleration factor.

[0012] Optionally, determining the relative entropy based on the relative entropy algorithm, according to the target degradation rate set and the natural storage degradation rate, includes: The set of target degradation rates is made to follow a normal distribution, with its first probability density function being: ; In the formula, The mean, Let X be the variance, and X be the set of degradation rates of the target experimental data. For the first A set of target degradation rates; The natural storage degradation rate is made to follow a normal distribution, and its second probability density function is: ; In the formula, The mean, Let X be the variance, and X be the set of degradation rates of the target experimental data. For the first A natural storage degradation rate; The relative entropy function is constructed based on the first probability density function and the second probability density function using the following formula: ; In the formula, It is the distribution of natural storage degradation rate. It is the distribution of the degradation rate of the target experimental data.

[0013] Optionally, based on the initial acceleration factor, minimizing the relative entropy includes: The initial acceleration factor is used as the optimization variable of the relative entropy function, and the relative entropy function is iteratively optimized using the following formula to minimize the relative entropy of the relative entropy function. During the optimization process, the initial acceleration factor is adjusted using an iterative algorithm. ; In the formula, It is the distribution of natural storage degradation rate. It is the distribution of the degradation rate of the target experimental data.

[0014] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0015] Based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing a computer to perform the method described above.

[0016] As can be seen from the above, the method for evaluating the acceleration factor of old products based on relative entropy provided in this application obtains the degradation data of the first performance parameter of the product during the natural storage test and the degradation data of the second performance parameter of the product during the accelerated storage test. Based on the degradation data of the first performance parameter and the second parameter, the natural storage degradation rate and the accelerated storage degradation rate are calculated. Based on the natural storage degradation rate and the accelerated storage degradation rate, the degradation rate is expanded to obtain a target degradation rate set including more sample degradation rates. Based on the relative entropy algorithm, the relative entropy between the degradation rate in the target degradation rate set and the natural storage degradation rate is calculated and minimized. The acceleration factor corresponding to the minimized relative entropy is taken as the target acceleration factor. The acceleration factor is optimal when the relative entropy is minimized. The old product is accelerated to improve the reliability and accuracy of the accelerated testing of old products based on the optimal acceleration factor. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a method for evaluating acceleration factors of legacy products based on relative entropy, according to an embodiment of this application. Figure 2This is a schematic flowchart of the method for determining the target degradation rate set according to an embodiment of this application; Figure 3 This is a schematic diagram comparing natural storage experimental data and accelerated storage experimental data in an embodiment of this application; Figure 4 This is a schematic diagram showing the relationship between different acceleration factors and the numerical calculation results of the relative entropy of product distribution in an embodiment of this application; Figure 5 This is a schematic diagram of the apparatus structure for an old product acceleration factor evaluation method based on relative entropy according to an embodiment of this application; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] As described in the background art, in existing technologies, when conducting storage tests on older products, products that have been in service for a certain period of time are typically selected for accelerated storage testing. Furthermore, only the remaining equivalent years of service need to be tested, effectively saving on product manufacturing and testing costs. However, due to the small sample size and multi-stage nature of accelerated storage testing, the method of calculating the acceleration factor using data from multiple conventional constant stress accelerated storage tests is no longer applicable, and the acceleration model is also difficult to effectively cover the characteristics of multi-stage testing. Therefore, the key issue in assessing the storage life of older products that have been stored for a certain period of service is how to estimate the product's acceleration factor using limited test information. Generally, the acceleration factor can be calculated by comparing the average lifespan or average degradation rate of products at different stages; however, this method utilizes limited test data, resulting in low accuracy. Therefore, a scientifically sound and feasible method for assessing the storage life of older products based on single-stress accelerated storage testing is lacking.

[0022] The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0023] To solve the above technical problems, such as Figure 1 As shown, this application provides a method for evaluating the acceleration factor of legacy products based on relative entropy, including the following steps: Step 102: Obtain the first performance parameter degradation data of the product during the natural storage test phase and the second performance parameter degradation data of the product during the accelerated storage test phase.

[0024] In this step, the degradation data of the first performance parameter during the natural storage test phase can be environmental adaptability parameters, electrical parameters, physical characteristic parameters, etc. The degradation data of the second performance parameter during the accelerated storage test phase can also be environmental adaptability parameters, electrical parameters, physical characteristic parameters, etc. Specifically, the degradation data of the first and second performance parameters are of the same type. For example, if the first performance parameter degradation data is an electrical parameter (e.g., voltage), and the second performance parameter degradation data is also an electrical parameter (e.g., both are voltage), then the first and second performance parameters are the same electrical parameter.

[0025] For example, environmental adaptability parameters may include: Changes in thermal properties: including changes in heat distortion temperature, Vicat softening point, glass transition temperature, melt index, thermal stability, and thermogravimetric analysis. These parameters reflect the product's thermal stability during storage. Changes in chemical resistance: assessing the product's chemical resistance during storage, including changes in acid and alkali resistance, solvent resistance, oil resistance, salt water resistance, damp heat resistance, and oxidation resistance. These changes reveal the product's chemical compatibility during storage. Electrical parameters: including resistance, capacitance, dielectric strength, dielectric constant, volume resistivity, surface resistivity, and breakdown voltage. Physical property parameters: Changes in appearance: including color changes, surface gloss changes, oxidation level, blistering, cracking, and deformation. These changes directly reflect the product's physical aging during storage. Dimensional stability: assessing the product's dimensional changes during storage by measuring parameters such as length change rate, width change rate, thickness change rate, warpage, shrinkage rate, and coefficient of expansion. Mechanical properties include tensile strength retention, elongation at break change, impact strength retention, hardness change, flexural strength change, and compressive strength change. These parameters reflect the retention of the product's mechanical properties during storage.

[0026] Step 104: Determine the natural storage degradation rate and the accelerated storage degradation rate based on the first performance parameter degradation data and the second performance parameter degradation data.

[0027] In this step, the degradation data of the first performance parameter and the degradation data of the second performance parameter are input into the performance degradation trajectory model, and the natural storage degradation rate and the accelerated storage degradation rate are calculated by the corresponding formula in the performance degradation trajectory model.

[0028] Step 106: Determine the target degradation rate set based on the natural storage degradation rate and the accelerated storage degradation rate.

[0029] In this step, due to the limited number of experimental data samples, a large number of samples are needed to improve the accuracy of the experimental data. After acquiring the specific experimental data, it is also necessary to calculate the degradation rate corresponding to that sample. Therefore, in this embodiment, expanding the degradation rate sample is sufficient to expand the experimental data sample. The degradation rate sample is expanded based on the natural storage degradation rate and the accelerated storage degradation rate to determine the target degradation rate set. The acceleration factor ultimately used for accelerated testing of older products can be determined based on this target degradation rate set, thereby improving the reliability and accuracy of the accelerated testing.

[0030] Step 108: Based on the relative entropy algorithm, determine the target acceleration factor according to the target degradation rate set and the natural storage degradation rate.

[0031] In this step, the target degradation rate set includes a large number of degradation rates. The relative entropy between the target degradation rate set and the natural storage degradation rate is calculated based on the relative entropy algorithm. The acceleration factor corresponding to minimizing the relative entropy is taken as the target acceleration factor. The acceleration factor is optimal when the relative entropy is minimized. Acceleration tests are conducted on old products based on the optimal acceleration factor to improve the reliability and accuracy of the acceleration tests on old products.

[0032] Through steps 102-108, the degradation data of the first performance parameter of the product during the natural storage test and the degradation data of the second performance parameter of the product during the accelerated storage test are obtained. Based on the degradation data of the first and second performance parameter, the natural storage degradation rate and the accelerated storage degradation rate are calculated. Based on the natural storage degradation rate and the accelerated storage degradation rate, the degradation rate sample is expanded to obtain a target degradation rate set including more sample degradation rates. Based on the relative entropy algorithm, the relative entropy between the degradation rate in the target degradation rate set and the natural storage degradation rate is calculated and minimized. The acceleration factor corresponding to the minimized relative entropy is taken as the target acceleration factor. The acceleration factor is optimal when the relative entropy is minimized. Accelerated testing of old products is carried out based on the optimal acceleration factor to improve the reliability and accuracy of accelerated testing of old products.

[0033] In some embodiments, determining the natural storage degradation rate based on the first performance parameter degradation data includes: Input the degradation data of the first performance parameter into the following formula to obtain the natural storage degradation rate; ; In the formula, In time The measured degradation data of the first performance parameter Let be the i-th measurement time point, n be the number of measurement time points, and K1 be the natural storage degradation rate.

[0034] In this embodiment, the above formula is a formula for the performance degradation trajectory model. For example, the product's performance parameters and test time can be effectively fitted using the following mathematical models: Linear model: [1] Exponential model: [2] Logarithmic model: [3] In formulas [1] to [3]: P is the product performance parameter index; P0 is a constant, the initial value of the performance parameter; K is the constant of the performance change rate related to temperature; t is the test time.

[0035] For example, if the degradation curve of product performance parameters follows a linear model, then based on the above [1] linear model, according to the least squares method, its degradation rate calculation formula is expressed as: .

[0036] For example, suppose we conduct a natural storage test on product number 1 and obtain data on the degradation of the following performance parameters over time: Table 1

[0037] Based on the above data, the degradation rate K corresponding to serial number 1 in Table 1 is calculated to be 5.5096.

[0038] Calculation process: List the intermediate quantities required for the calculation:

[0039] Substitute into the formula to calculate the degradation rate: .

[0040] For example, the first performance parameter degradation data includes: , The time for the first natural storage test, The first performance parameter in the natural storage test, based on the performance degradation trajectory model, can be calculated as follows: ,in, For the set of natural storage degradation rates, Let be the natural storage degradation rate of the i-th time.

[0041] In some embodiments, determining the accelerated storage degradation rate based on the second performance parameter degradation data includes: Input the degradation data of the second performance parameter into the following formula to obtain the accelerated storage degradation rate; ; In the formula, In time The measured degradation data of the second performance parameter Let be the i-th measurement time point, n be the number of measurement time points, and K2 be the accelerated storage degradation rate.

[0042] In this embodiment, the above formula is a formula for the performance degradation trajectory model. For example, the product's performance parameters and test time can be effectively fitted using the following mathematical models: Linear model: [1] Exponential model: [2] Logarithmic model: [3] In formulas [1] to [3]: P is the product performance parameter index; P0 is a constant, the initial value of the performance parameter; K is the constant of the performance change rate related to temperature; t is the test time.

[0043] For example, if the degradation curve of product performance parameters follows a linear model, then based on the above [1] linear model, according to the least squares method, its degradation rate calculation formula is expressed as: .

[0044] Specific examples can be taken from the specific examples of the above embodiments. For instance, the first performance parameter degradation data includes: ,in, Let i be the time for the i-th accelerated storage test. To accelerate the assessment of the first performance parameter in the storage test, the natural storage degradation rate can be calculated based on the performance degradation trajectory model as follows: ,in, To accelerate the storage degradation rate set, Let be the i-th accelerated storage degradation rate.

[0045] In some embodiments, such as Figure 2 As shown, determining the target degradation rate set based on the natural storage degradation rate and the accelerated storage degradation rate includes: Step 202: Determine the initial acceleration factor based on the natural storage degradation rate and the accelerated storage degradation rate.

[0046] In this step, for example, the product performance parameter degradation curve follows a linear model, such as... Figure 3 As shown, the degradation rate of product performance parameters before and after time t is the same during the natural storage test phase, while the degradation rate of product performance parameters increases significantly during the accelerated test phase. The acceleration factor reflects the conversion relationship between the lifespan information obtained in accelerated life testing and the lifespan information under actual usage conditions. In accelerated storage testing, the acceleration factor is often considered to remain constant throughout the test; that is, for a given acceleration stress level T1, its acceleration factor relative to the actual storage usage temperature stress Ts is a constant, defined as the ratio of a certain storage lifespan characteristic of the product under accelerated stress level to that under a baseline storage stress level, i.e., the ratio of time or rate. Therefore, the formula for calculating its degradation rate can be: In the formula, AF is the initial acceleration factor; The time constant for performance changes during the natural storage test phase. To accelerate the performance change time constant during the storage test phase, To accelerate the performance change rate constant during the storage test phase; This represents the performance change rate constant during the natural storage test phase.

[0047] The step of determining the initial acceleration factor based on the natural storage degradation rate and the accelerated storage degradation rate includes: Substituting the natural storage degradation rate and the accelerated storage degradation rate into the following formula, the initial acceleration factor is obtained; ; In the formula, AF is the initial acceleration factor; This refers to the product's lifespan during the natural storage test phase. To extend the product's lifespan during the accelerated storage test phase, To accelerate the performance change rate constant during the storage test phase; This represents the performance change rate constant during the natural storage test phase.

[0048] Step 204: Expand the natural storage degradation rate by using the initial acceleration factor and the accelerated storage degradation rate to determine the expanded degradation rate.

[0049] In this step, due to the small sample size of the experimental data, a large number of experimental data samples are needed to improve the accuracy of the experimental data. After acquiring the specific experimental data, it is also necessary to calculate the degradation rate corresponding to that experimental data sample. Therefore, in this embodiment, expanding the degradation rate sample is sufficient to expand the experimental data sample. The degradation rate can be expanded using the following formula. ； In the formula, No. i The rate of degradation of each target For the first i This accelerates the rate of storage degradation.

[0050] The step of expanding the accelerated storage degradation rate based on the initial acceleration factor to determine the expanded degradation rate includes: The acceleration factor and the accelerated storage degradation rate are input into the following formula to perform sample expansion of the natural storage degradation rate, resulting in the sample-expanded degradation rate: ； In the formula, No. i The rate of degradation of each target For the first i This accelerates the rate of storage degradation.

[0051] For example, natural storage degradation rates include: The sample is expanded to: The expanded sample is based on the initial acceleration factor and can therefore be summarized as accelerating the storage degradation rate.

[0052] Step 206: Determine the target degradation rate set based on the natural storage degradation rate and the sample expansion degradation rate.

[0053] In this step, determining the target degradation rate set based on the natural storage degradation rate and the sample expansion degradation rate includes: fusing the natural storage degradation rate and the sample expansion degradation rate to form the target degradation rate set.

[0054] Based on the above embodiments, considering that natural storage test data better reflects the product's storage life characteristics, a sample expansion coefficient k is set, ranging from 1 to 1.5, and the newly added samples are obtained by weighted averaging of different data. If the sample expansion coefficient is set to 1, the natural storage degradation rate and the sample expansion degradation rate (accelerated phase data) are fused, which can be expressed as: X is the target degradation rate set.

[0055] Furthermore, a likelihood function can be constructed based on the target degradation rate set, resulting in better data after the likelihood function solution. This means a more optimized acceleration factor can be obtained. Using the most suitable acceleration factor for accelerated product testing improves the accuracy and reliability of the test results.

[0056] In some embodiments, determining the target acceleration factor based on the relative entropy algorithm, according to the target degradation rate set and the natural storage degradation rate, includes the following steps: Step 302: Based on the relative entropy algorithm, construct a relative entropy function according to the target degradation rate set and the natural storage degradation rate.

[0057] In this step, the target degradation rate set includes multiple degradation rates, which can include samples expanded based on natural storage degradation rates as well as samples expanded based on accelerated storage degradation rates. It comprises a large number of degradation rates. Based on the relative entropy algorithm, the relative entropy between the target degradation rate set and the natural storage degradation rate is calculated, including multiple relative entropies. Minimizing the relative entropy yields the optimal acceleration factor.

[0058] The construction of the relative entropy function based on the relative entropy algorithm, according to the target degradation rate set and the natural storage degradation rate, includes: The set of target degradation rates is made to follow a normal distribution, with its first probability density function being: ; In the formula, The mean, Let X be the variance, and X be the set of degradation rates of the target experimental data. For the first A set of target degradation rates; The natural storage degradation rate is made to follow a normal distribution, and its second probability density function is: ; In the formula, The mean, Let X be the variance, and X be the set of degradation rates of the target experimental data. For the first A natural storage degradation rate; The relative entropy function is constructed based on the first probability density function and the second probability density function using the following formula: In the formula, For relative entropy, It is the distribution of natural storage degradation rate. It is the distribution of the degradation rate of the target experimental data.

[0059] Specifically, since the data conforms to a normal distribution and is continuous, the calculation is performed based on the continuous relative entropy calculation formula. The first probability density function and the second probability density function are input into the relative entropy calculation formula to obtain the relative entropy.

[0060] The formula for calculating its continuous relative entropy is: In the formula, These are the probability density functions of distributions P and Q, respectively. Let P represent the first probability density function, and let P represent the degradation rate in the target degradation rate set. Let represent the second probability density function, and Q represent the natural storage degradation rate. Its calculation formula can also be: ; This represents the range of values ​​for a one-dimensional random variable X under continuous conditions. The range of X should be... .

[0061] Step 304: Based on the initial acceleration factor, minimize the relative entropy function, and take the acceleration factor corresponding to the minimized relative entropy as the target acceleration factor.

[0062] Specifically, based on the initial acceleration factor, minimizing the relative entropy includes: using the initial acceleration factor as the optimization variable of the relative entropy function, and iteratively optimizing the relative entropy function using the following formula to minimize the relative entropy of the relative entropy function. During the optimization process, an iterative algorithm is used to adjust the initial acceleration factor. ; In the formula, It is the distribution of natural storage degradation rate. The target is the degradation rate distribution of the experimental data. In other words, the goal is to minimize the relative entropy between the natural storage stage distribution P and the target degradation rate Q. Specific steps include: Parameterizing the acceleration factor: Using the initial acceleration factor AF as the variable to be optimized, and associating it with the distribution parameters (such as mean and variance) of the natural and accelerated storage stages. Numerical optimization: Using iterative algorithms (such as gradient descent, Newton's method, etc.) to adjust AF, calculating the corresponding relative entropy value until convergence to the minimum. During the optimization process, the value of the acceleration factor is continuously adjusted to minimize the relative entropy between the target degradation rate set obtained based on this acceleration factor and the natural storage degradation rate. Result verification: Checking whether the relative entropy is stable confirms the rationality of the acceleration factor estimation.

[0063] An electronic product was stored and served for 13 years. The degradation data obtained from the natural storage test are shown in Table 2.

[0064] Table 2 Product Parameter Performance Parameter Degradation Information

[0065] Three electronic products were subjected to a 6000-hour accelerated storage test at 100°C. The degradation data obtained from the accelerated storage test are shown in Table 3.

[0066] Table 3 Product Parameters, Performance Parameters, and Degradation Information

[0067] Based on the product's natural and accelerated storage test information, and using the rate ratio and relative entropy methods, the acceleration factor calculation results corresponding to the product parameters are shown in Table 4. The calculation results are compared to... Figure 4 As shown.

[0068] Table 4 Summary of Acceleration Factor Calculation Results for Product Parameter 1

[0069] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0070] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] Based on the same inventive concept, and corresponding to any of the above embodiments, this application also provides an apparatus for evaluating an old product acceleration factor based on relative entropy.

[0072] refer to Figure 5 The aforementioned device for evaluating the acceleration factor of legacy products based on relative entropy includes: The acquisition module 402 is configured to acquire first performance parameter degradation data of the product during the natural storage test phase and second performance parameter degradation data of the product during the accelerated storage test phase. The first determining module 404 is configured to determine the natural storage degradation rate and the accelerated storage degradation rate based on the first performance parameter degradation data and the second performance parameter degradation data. The second determining module 406 is configured to determine a target degradation rate set based on the natural storage degradation rate and the accelerated storage degradation rate; The third determining module 408 is configured to determine the target acceleration factor based on the relative entropy algorithm, according to the target degradation rate set and the natural storage degradation rate.

[0073] Specifically, by acquiring the degradation data of the first performance parameter of the product during the natural storage test and the degradation data of the second performance parameter of the product during the accelerated storage test, the natural storage degradation rate and the accelerated storage degradation rate are calculated based on the first and second performance parameter degradation data. Then, based on the natural and accelerated storage degradation rates, the degradation rate sample is expanded to obtain a target degradation rate set with more sample degradation rates. Using a relative entropy algorithm, the relative entropy between the degradation rate in the target degradation rate set and the natural storage degradation rate is calculated and minimized. The acceleration factor corresponding to the minimized relative entropy is taken as the target acceleration factor. The acceleration factor is optimal when the relative entropy is minimized. Accelerated testing of older products is then conducted based on the optimal acceleration factor, improving the reliability and accuracy of accelerated testing of older products.

[0074] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0075] The apparatus of the above embodiments is used to implement a corresponding method for evaluating the acceleration factor of old products based on relative entropy in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0076] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for evaluating an old product acceleration factor based on relative entropy as described in any of the above embodiments.

[0077] Figure 6 This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0078] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0079] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0080] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0081] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0082] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0083] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0084] The electronic device described above is used to implement a corresponding method for evaluating the acceleration factor of an older product based on relative entropy in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0085] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute a legacy product acceleration factor evaluation method based on relative entropy as described in any of the above embodiments.

[0086] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0087] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute a method for evaluating the acceleration factor of an old product based on relative entropy as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0088] It is understood that before using the technical solutions of the various embodiments in this application, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0089] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations described in this application.

[0090] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0091] It is understood that the above notification and user authorization process is merely illustrative and does not limit the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0092] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0093] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0094] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0095] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating the acceleration factor of legacy products based on relative entropy, characterized in that, include: Obtain the degradation data of the first performance parameter of the product during the natural storage test phase and the degradation data of the second performance parameter of the product during the accelerated storage test phase; Based on the degradation data of the first performance parameter and the degradation data of the second performance parameter, the natural storage degradation rate and the accelerated storage degradation rate are determined; A target degradation rate set is determined based on the natural storage degradation rate and the accelerated storage degradation rate; Based on the relative entropy algorithm, the target acceleration factor is determined according to the target degradation rate set and the natural storage degradation rate.

2. The method according to claim 1, characterized in that, The step of determining the natural storage degradation rate based on the degradation data of the first performance parameter includes: Input the degradation data of the first performance parameter into the following formula to obtain the natural storage degradation rate; ； In the formula, In time The measured degradation data of the first performance parameter Let be the i-th measurement time point, n be the number of measurement time points, and K1 be the natural storage degradation rate.

3. The method according to claim 1, characterized in that, The step of determining the accelerated storage degradation rate based on the degradation data of the second performance parameter includes: Input the degradation data of the second performance parameter into the following formula to obtain the accelerated storage degradation rate; ； In the formula, In time The measured degradation data of the second performance parameter Let be the i-th measurement time point, n be the number of measurement time points, and K2 be the accelerated storage degradation rate.

4. The method according to claim 1, characterized in that, The step of determining the target degradation rate set based on the natural storage degradation rate and the accelerated storage degradation rate includes: The initial acceleration factor is determined based on the natural storage degradation rate and the accelerated storage degradation rate; The natural storage degradation rate is expanded based on the initial acceleration factor and the accelerated storage degradation rate to determine the degradation rate of newly added experimental data; The target degradation rate set is determined based on the natural storage degradation rate and the sample expansion degradation rate.

5. The method according to claim 4, characterized in that, The step of determining the initial acceleration factor based on the natural storage degradation rate and the accelerated storage degradation rate includes: Substituting the natural storage degradation rate and the accelerated storage degradation rate into the following formula, the initial acceleration factor is obtained; ； In the formula, AF is the initial acceleration factor. This refers to the product's lifespan during the natural storage test phase. To extend the product's lifespan during the accelerated storage test phase, To accelerate the performance change rate constant during the storage test phase; This represents the performance change rate constant during the natural storage test phase.

6. The method according to claim 4, characterized in that, The step of expanding the natural storage degradation rate based on the initial acceleration factor and the accelerated storage degradation rate to determine the expanded degradation rate includes: The acceleration factor and the accelerated storage degradation rate are input into the following formula to perform sample expansion of the natural storage degradation rate, resulting in the sample-expanded degradation rate: ； In the formula, As the initial acceleration factor, No. i The rate of degradation of each target For the first i This accelerates the degradation rate.

7. The method according to claim 4, characterized in that, The step of determining the target degradation rate set based on the natural storage degradation rate and the sample expansion degradation rate includes: The natural storage degradation rate and the sample expansion degradation rate are fused to form the target degradation rate set.

8. The method according to claim 7, characterized in that, The relative entropy-based algorithm, which determines the target acceleration factor based on the target degradation rate set and the natural storage degradation rate, includes: Based on the relative entropy algorithm, a relative entropy function is constructed according to the target degradation rate set and the natural storage degradation rate; Based on the initial acceleration factor, the relative entropy function is minimized, and the acceleration factor corresponding to the minimized relative entropy is taken as the target acceleration factor.

9. The method according to claim 8, characterized in that, The construction of the relative entropy function based on the relative entropy algorithm, according to the target degradation rate set and the natural storage degradation rate, includes: The set of target degradation rates is made to follow a normal distribution, with its first probability density function being: ； In the formula, The mean, Let X be the variance, and X be the set of degradation rates of the target experimental data. For the first A set of target degradation rates; The natural storage degradation rate is made to follow a normal distribution, and its second probability density function is: ； In the formula, The mean, Let X be the variance, and X be the set of degradation rates of the target experimental data. For the first A natural storage degradation rate; The relative entropy function is constructed based on the first probability density function and the second probability density function using the following formula: ； In the formula, It is the distribution of natural storage degradation rate. It is the distribution of the degradation rate of the target experimental data.

10. The method according to claim 9, characterized in that, Minimizing the relative entropy based on the initial acceleration factor includes: The initial acceleration factor is used as the optimization variable of the relative entropy function, and the relative entropy function is iteratively optimized using the following formula to minimize the relative entropy of the relative entropy function. During the optimization process, the initial acceleration factor is adjusted using an iterative algorithm. ； In the formula, It is the distribution of natural storage degradation rate. It is the distribution of the degradation rate of the target experimental data.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 10.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method described in any one of claims 1 to 10.

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