Life assessment methods for nuclear power materials and electronic equipment

Abstract

This application proposes a method and electronic device for assessing the lifespan of nuclear power materials, relating to the field of nuclear power materials technology. The method includes: acquiring first experimental data of sample nuclear power materials; acquiring feature data corresponding to input features from the first experimental data; analyzing the distribution characteristics of the input features based on the feature data; performing preliminary screening of the input features based on the distribution characteristic analysis results; performing correlation analysis between the lifespan features in the first experimental data and the pre-screened input features; selecting target input features from the pre-screened input features based on the correlation analysis results, and constructing training samples based on the target input features and the lifespan features corresponding to the target input features; training a lifespan prediction model according to the training samples; and inputting second experimental data of the nuclear power materials to be assessed into the trained lifespan prediction model to obtain the lifespan assessment results of the nuclear power materials to be assessed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power materials, and particularly relates to a life evaluation method of a nuclear power material and an electronic device. BACKGROUND

[0002] The pressurized water reactor is a reactor in which pressurized unboiling light water (i.e., ordinary water) is used as both a moderator and a coolant. As the mainstream reactor type of the current commercial nuclear power, the design life of the pressurized water reactor is generally 60 years. Further improving the life of the reactor has become the focus of related research. At the same time, the operating temperature of the new generation of advanced reactors shows an increasing trend, which puts higher requirements on the long-term reliability of the materials. The key component materials of the reactor pressure vessel, the heat exchanger pipe, the control rod drive mechanism and the like bear high temperature, high pressure and alternating load for a long time during service. Under such extreme working conditions, the materials may appear creep damage, fatigue failure, or a complex failure mode caused by the interaction of the two. Therefore, accurately evaluating the life of the nuclear power material under the interaction of creep and fatigue is of great significance to guarantee the safety of the nuclear power system, develop life extension strategies and select materials. SUMMARY

[0003] Therefore, the present application provides a life evaluation method of a nuclear power material and an electronic device.

[0004] In a first aspect, the present application provides a life evaluation method of a nuclear power material, comprising: obtaining first test data of a sample nuclear power material, wherein the first test data comprises first pure creep test data, first pure fatigue test data, first creep-fatigue test data with peak holding and first creep-fatigue test data with sequential loading of the sample nuclear power material under different test conditions; obtaining feature data corresponding to input features from the first test data; analyzing distribution features of the input features based on the feature data to obtain a distribution feature analysis result; performing preliminary screening on the input features according to the distribution feature analysis result; and performing correlation analysis on life features in the first test data and the input features after preliminary screening.

[0005] According to the correlation analysis result, target input features are selected from the input features after preliminary screening, and a training sample is constructed based on the target input features and life features corresponding to the target input features; a life prediction model is trained according to the training sample; second test data of a nuclear power material to be evaluated is input into the trained life prediction model, and a life evaluation result of the nuclear power material to be evaluated is obtained, wherein the second test data comprises second pure creep test data, second pure fatigue test data, second creep-fatigue test data with peak holding and second creep-fatigue test data with sequential loading of the nuclear power material to be evaluated under a set test condition.

[0006] In a second aspect, the present application provides an electronic device, comprising:

[0007] at least one processor; and

[0008] at least one memory having instructions stored thereon, the instructions, when executed by the at least one processor, alone or in combination, causing the electronic device to perform the method of the first aspect.

[0009] The life evaluation method of the nuclear power material provided by the present application introduces multiple data of the sample nuclear power material under different test conditions during model training: first pure creep test data, first pure fatigue test data, first creep-fatigue test data with peak holding, and first creep-fatigue test data with sequential loading, comprehensively considers the creep and fatigue damage mechanisms, and realizes accurate evaluation of the creep-fatigue life of the nuclear power material. On the other hand, the correlation between the distribution characteristics, the input characteristics and the life characteristics is coupled and analyzed, and the target input characteristics that meet the requirements of the correlation and the distribution state are retained. Such optimization is beneficial to guarantee the generalization ability and actual availability of the subsequent life prediction model. In addition, based on the distribution characteristics, the input characteristics are initially screened, and then the correlation analysis is performed in combination with the initial screening result. The method has the following advantages: since the distribution characteristics are relatively convenient and efficient to calculate, the redundant input characteristics can be effectively removed for preliminary screening, thereby reducing the workload for subsequent correlation analysis. BRIEF DESCRIPTION OF DRAWINGS

[0010] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this application, illustrate embodiments of the present application, and together with the description serve to explain the principle of the present application. In the drawings:

[0011] Figure 1 is a flowchart of a life evaluation method of a nuclear power material provided by an embodiment of the present application;

[0012] Figure 2 is a relationship matrix diagram of the life characteristics and the input characteristics;

[0013] Figure 3 is a corresponding scatter plot of PCA analysis;

[0014] Figure 4 is a flowchart of another life evaluation method of a nuclear power material provided by an embodiment of the present application;

[0015] Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to illustrate the technical solutions of the embodiments of the present application more clearly, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some examples or embodiments of the present application, and for those skilled in the art, the present application can be applied to other similar situations without creative effort on the basis of these drawings. Identical reference signs in the drawings represent identical structures or operations, unless otherwise clear from the context or otherwise indicated.

[0017] As shown in the present application, unless the context clearly indicates otherwise, the words "one", "an", "a", and / or "the" do not mean the singular, but can include the plural. Generally, the terms "comprising" and "including" are intended to mean that the steps and elements identified are included, but not to the exclusion of other steps or elements. The term "consisting essentially of" means that the steps and elements identified can be accompanied by other steps or elements that do not materially affect the basic and novel characteristics of the composition or method.

[0018] Meanwhile, specific words are used in the present application to describe the embodiments of the present application. The words "one embodiment", "an embodiment", and / or "some embodiments" mean that a certain feature, structure, or characteristic related to at least one embodiment of the present application is described. Therefore, it should be emphasized and noted that the words "one embodiment" or "an embodiment" or "an alternative embodiment" appearing in various places in the specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics of one or more embodiments of the present application can be properly combined.

[0019] Unless otherwise specifically stated, the relative arrangements of the components and steps in these embodiments, numerical expressions, and numerical values are not meant to limit the scope of the present application. Meanwhile, it should be understood that the sizes of the parts shown in the drawings are not drawn in accordance with the actual proportional relationship. The techniques, methods, and devices known to those skilled in the relevant art can not be discussed in detail, but should be considered as part of the specification under appropriate circumstances. In all examples shown and discussed herein, any specific value should be interpreted as merely exemplary, and not as a limitation. Therefore, other examples of exemplary embodiments can have different values. It should be noted that similar reference signs and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0020] In addition, although the terms used in the present application are selected from commonly known terms, some terms mentioned in the specification of the present application can be selected by the applicant according to his or her judgment, and the detailed meanings of the terms are described in the relevant part of the description. In addition, the present application is not only required to be understood by the actual terms used, but also by the meaning implied by each term.

[0021] Flowcharts are used in this application to illustrate the operations performed by the apparatus or device according to the embodiments of the application. It should be understood that the preceding or following operations are not necessarily performed in sequence. Instead, various steps can be processed in reverse order or simultaneously. Meanwhile, other operations can be added to these processes, or one or more steps of these processes can be removed.

[0022] Nuclear power materials are widely used in key components such as reactor pressure vessels, heat exchanger pipes, control rod drive mechanisms, etc. Such materials are usually under extreme conditions such as high temperature, high pressure, alternating load during service, and long-term operation may cause creep damage, fatigue failure, or complex failure modes caused by the interaction of the two. Therefore, accurately assessing the life of nuclear power materials under creep-fatigue interaction is of great significance to the safety of nuclear power systems, life extension strategy development, and material selection.

[0023] However, the typical test commonly used in the industry for creep-fatigue life assessment is a strain-controlled test with peak and valley retention. This test introduces less creep damage and cannot reflect the influence of precipitate growth on creep performance, resulting in the existing creep-fatigue interaction diagram calculated by the existing scheme mainly located in the fatigue damage dominant area, which cannot comprehensively cover the various mechanisms of creep-fatigue interaction. Therefore, the existing creep-fatigue test data cannot accurately and comprehensively reflect the life consumption process of nuclear power materials.

[0024] To alleviate at least one of the above problems, see Figure 1 The embodiments of the present application propose a life assessment method for nuclear power materials, comprising:

[0025] S10: Obtain first test data of a sample nuclear power material, the first test data including first pure creep test data, first pure fatigue test data, first creep-fatigue test data with peak value retention, and first creep-fatigue test data with sequential loading of the sample nuclear power material under different test conditions.

[0026] Currently, the typical test commonly used in the industry for obtaining creep-fatigue test data through creep-fatigue interaction testing is a strain-controlled creep-fatigue interaction test with peak and valley retention. This test introduces less creep damage (usually fatigue damage dominates, for example, fatigue damage accounts for 90%, and creep damage accounts for only 10%), cannot reflect the influence of precipitate growth on creep performance, and is inconsistent with the actual service damage process of nuclear power materials. Accordingly, the creep-fatigue test data obtained through this test cannot accurately and comprehensively reflect the life consumption process of nuclear power materials.

[0027] In order to alleviate the above problems, the application provides a method for obtaining creep-fatigue test data. In an embodiment, the creep-fatigue test data is the first creep-fatigue test data of the above sequential loading, and the obtaining process comprises: under a first preset service temperature zone and a first preset stress condition, applying a preset number of cycles of cyclic fatigue test to a sample nuclear power material to form a pre-fatigue damage, performing constant stress creep loading on the pre-fatigue damaged sample nuclear power material until the sample nuclear power material breaks or continues to a first preset termination time, and outputting the creep life of the sample nuclear power material under the first preset service temperature zone, the first preset stress condition, and the pre-fatigue damage.

[0028] Unlike the fatigue damage in the traditional creep-fatigue interaction test, in this way, more creep damage can be introduced during the entire test process. The pre-fatigue damaged sample nuclear power material is subjected to constant stress creep loading, so that the stress remains relatively high during the entire creep process, and the creep time is relatively long. This is conducive to the gradual growth or aggregation of precipitates (such as carbides and intermetallic compounds) in the sample nuclear power material during the creep process, resulting in changes in the structure of the sample nuclear power material, thereby accelerating the process of creep damage. More creep damage is introduced during the test process, so that the first creep-fatigue test data of the above sequential loading obtained at the end can better reflect the influence of the interaction of creep and fatigue on the service life of the sample nuclear power material during the actual service process, and improve the accuracy of the data.

[0029] In an embodiment, the obtaining process of the first pure creep test data comprises: under a second preset service temperature zone and a second preset stress condition, performing constant stress creep loading on the sample nuclear power material until the sample nuclear power material breaks or continues to a second preset termination time, and outputting the breaking time of the sample nuclear power material under the second preset service temperature zone and the second preset stress condition.

[0030] In an embodiment, the obtaining process of the first pure fatigue test data comprises: under a third preset service temperature zone and a fatigue loading condition, applying a cyclic strain to the sample nuclear power material until the sample nuclear power material breaks or meets a third preset termination condition, and outputting the fatigue life of the sample nuclear power material under the third preset service temperature zone and the fatigue loading condition.

[0031] In an embodiment, the obtaining process of the first creep-fatigue test data with peak value retention comprises: under a fourth preset service temperature zone and a preset strain amplitude condition, applying a cyclic strain with a preset retention time to the sample nuclear power material until the sample nuclear power material breaks or meets a fourth preset termination condition, and outputting the breaking cycle number of the sample nuclear power material under the fourth preset service temperature zone, the preset strain amplitude condition, and the preset retention time.

[0032] The following is an example of N (N greater than 1) austenitic stainless steel nuclear grade 316 materials (hereinafter referred to as 316 materials) of the same batch of sample nuclear power materials as the standard size, to illustrate the acquisition process of the first creep-fatigue test data, the first pure creep test data, the first pure fatigue test data, and the first creep-fatigue test data with peak value retention.

[0033] The process of the first pure creep test data includes setting the temperature to 650℃, 700℃, etc. key service temperature zone (i.e. the second preset service temperature zone described above), and selecting different levels of initial stress as the second preset stress condition based on existing literature and previous endurance strength evaluation results. Each group of temperature-stress combination test has no less than 3 repeated samples. According to different temperatures and different stress conditions, the 316 materials are subjected to constant stress creep loading until the 316 materials fracture or continue to the second preset termination time, and the creep fracture time of the 316 materials under different temperatures and stress conditions (i.e. the fracture time of the sample nuclear power materials under the second preset service temperature zone and the second preset stress condition) is output, etc.

[0034] The acquisition process of the first pure fatigue test data includes setting the temperature to 650℃, 700℃, etc. key service temperature zone (i.e. the third preset service temperature zone described above), and selecting multiple strain amplitudes as the fatigue loading condition in the strain control mode based on the previous fatigue performance evaluation results of the 316 materials. The strain amplitudes are 0.18%, 0.2%, 0.4%, 0.6%, 0.8%, 1.0%, and 1.2%. Under the third preset service temperature zone and the fatigue loading condition, the 316 materials are subjected to cyclic strain until the 316 materials fracture or meet the third preset termination condition, and the stress-strain hysteresis curve and the fatigue life of the 316 materials under different temperatures and strain amplitudes (i.e. the fatigue life of the sample nuclear power materials under the third preset service temperature zone and the fatigue loading condition) are output, etc.

[0035] The acquisition process of the first creep-fatigue test data with peak value retention includes setting the temperature to 650℃, 700℃, etc. key service temperature zone (i.e. the fourth preset service temperature zone described above), and selecting a typical 0.8% strain amplitude in the preset strain amplitude condition. The peak strain setting retention time is from 0.110 minutes to 1060 minutes (i.e. the preset retention time described above). Under the fourth preset service temperature zone and the preset strain amplitude condition, the 316 materials are subjected to cyclic strain with a retention time from 0.110 minutes to 1060 minutes until the 316 materials fracture or meet the fourth preset termination condition, and the fracture cycle number of the 316 materials under the fourth preset service temperature zone, the preset strain amplitude condition, and the preset retention time is output, etc.

[0036] The acquisition process of the first creep-fatigue test data loaded in sequence includes: setting the temperature to 650°C, 700°C, and other key service temperature zones (i.e., the first preset service temperature zone described above), selecting the same peak stress as the stress at 1000h of the creep fracture time in the first preset stress condition, and performing a preset cycle (100, 1000, 3000, 10000, 30000, and 100000 cycles) of fatigue test on the 316 material to form different pre-fatigue damage. The 316 material with different pre-fatigue damage is subjected to constant stress creep loading until the 316 material breaks, and the creep life under different temperatures, the first preset stress condition, and different fatigue pre-damage (i.e., the creep life of the sample nuclear power material under the first preset service temperature zone, the first preset stress condition, and the pre-fatigue damage) is output, and the like.

[0037] It should be noted that in the above examples, the first preset service temperature zone, the second preset service temperature zone, the third preset service temperature zone, and the fourth preset service temperature zone are all exemplified as the same value, such as 650°C, 700°C, etc. However, in other examples, the first preset service temperature zone, the second preset service temperature zone, the third preset service temperature zone, and the fourth preset service temperature zone can also be different or partially the same, and the present application does not make specific limitations thereto.

[0038] In one embodiment, after obtaining the first test data of the sample nuclear power material, some preprocessing can be performed on the first test data, such as statistical consistency test (such as standard deviation, skewness), abnormal data rejection, and the like. The subsequent steps S11-S16 are performed using the preprocessed first test data.

[0039] S11: Obtain feature data corresponding to the input feature from the first test data.

[0040] S12: Analyze the distribution characteristics of the input feature based on the feature data.

[0041] In one embodiment, the input feature can include temperature, maximum / minimum strain, loading speed, hold time, cycle number, and the like. The preset feature is pre-set, and the specific input features can be set according to the requirements.

[0042] The feature data corresponding to the input feature described above can be understood as the specific numerical value of each input feature, for example, the input feature "temperature" corresponds to how many degrees Celsius. In one embodiment, the feature data corresponding to the input feature can be obtained from the first test data, and statistical analysis is performed on these feature data, thereby determining the distribution characteristics of the input feature. The distribution characteristics can include mean, data range, and dispersion degree, and the dispersion degree can be obtained by calculating the standard deviation.

[0043] Exemplarily, assuming that the input features include temperature, strain, loading speed, holding time, cycle number (Nf), statistical analysis is performed on the feature data corresponding to each input feature, and the distribution feature analysis result can be as shown in Table 1. Among them: the range is the maximum minus the minimum, reflecting the data fluctuation range; the mean is the average, representing the central tendency; the standard deviation is an index of dispersion, and the larger the standard deviation, the greater the dispersion; the coefficient of variation (CV) is the standard deviation divided by the mean.

[0044] Table 1

[0045] Feature name Range Mean Standard deviation Coefficient of variation (CV) Temperature (°C) 50 676.25 25.39 0.038 Strain (%) 0.88 0.50 0.259 0.518 Loading speed 0 0.001 0 0 Soak time (min) 100 19.44 35.10 1.81 Cycle number (Nf) 340405 41245 84145 2.04

[0046] S13: Preliminary screening of the input features according to the distribution feature analysis result.

[0047] In an embodiment, the distribution feature analysis result includes statistical indicators such as mean, standard deviation, range, etc., and the preliminary screening of the input features according to the distribution feature analysis result in step S13 includes: quantifying the distribution form of each input feature by using the statistical indicators such as mean, standard deviation, range, coefficient of variation, etc., determining the input features with abnormal distribution (hereinafter referred to as distribution abnormal features), and then combining the number of distribution abnormal features to filter out the distribution abnormal features with a number > L (L is for example 2) and retain the distribution abnormal features with 0 > number ≤ L. Among them, the distribution abnormal features can include input features with abnormal distribution narrowness (such as coefficient of variation ≤ lower limit value of coefficient of variation), input features with extreme outliers (which can be determined according to a preset distribution rule). Among them, the lower limit value of the coefficient of variation can be for example 0.1~0.2. Exemplarily, for an input feature: CV < 0.1~0.2: the feature fluctuation is extremely small, and the data is extremely concentrated, such features are generally considered redundant or lack of discrimination, and can be generally considered for removal for machine learning and regression modeling. CV > 0.2: it is generally considered that the feature has a certain volatility and discrimination ability, and can be included in subsequent analysis. CV > 0.5: the fluctuation is large, and it is worth attention to distinguish different samples, but attention should be paid to the abnormal high CV caused by extreme outliers, in which case, manual assistance can be provided for the screening of input features. If CV is much greater than 1 (such as greater than 2, 3), it also needs to be combined with the rationality of the data distribution itself to judge whether it is caused by abnormal distribution, in which case, manual assistance can be provided for the screening of input features.

[0048] For example, as shown in Table 1, assuming that the lower limit value of the coefficient of variation is 0.1 and L is 2, the coefficient of variation of the input feature "temperature" in Table 1 is less than the lower limit value of the coefficient of variation, so it can be determined as a distribution abnormal feature, but the input feature "temperature" has only two in the initial data set, and the data amount is small, so the input feature "temperature" is retained, and subsequent correlation analysis is combined to determine again.

[0049] S14: Perform correlation analysis between the lifetime characteristics in the first experimental data and the input characteristics after initial screening.

[0050] In one embodiment, step S14 includes: obtaining lifetime data corresponding to the lifetime characteristics from the first experimental data, and calculating the correlation coefficient between the characteristic data of the input characteristics after initial screening and the lifetime data according to the following calculation formula. :

[0051] .

[0052] Where Cov is the covariance, and X is the variance. i Y is the input feature, Var is the lifetime feature, and Var is the variance.

[0053] The lifetime data corresponding to the aforementioned lifetime characteristics can be understood as the specific numerical values ​​of each lifetime characteristic. In one embodiment, lifetime characteristics may refer to features related to the lifetime of nuclear power materials. The specific lifetime characteristics can be preset, and lifetime characteristics may include, for example, fatigue life, cycle count, creep life, fracture time, etc.

[0054] S15: Based on the correlation analysis results, target input features are selected from the initially screened input features, and training samples are constructed based on the target input features and their corresponding lifetime features. There are multiple training samples, each consisting of a lifetime feature and multiple corresponding target input features.

[0055] In one embodiment, the correlation analysis results include the aforementioned correlation coefficients. Step S15 includes: determining the input features whose correlation coefficients meet the threshold condition (e.g., the correlation coefficients need to be greater than the set threshold) as target input features.

[0056] Alternatively, in another embodiment, the correlation analysis result is a correlation matrix representing lifetime characteristics versus input characteristics, such as... Figure 2 As shown, there are 13 features, including stress, left deformation, right deformation, average deformation, temperature rise, preheating time, holding temperature, holding time, high temperature, medium temperature, low temperature, displacement, and creep fracture life. Creep fracture life is the life feature, and the rest are input features. To identify the input features most strongly correlated with the life features and reduce redundancy, analysis was performed. Figure 2It can be found that the correlation coefficient between the five characteristics of high temperature, low temperature, medium temperature, holding time and temperature rise is 1, the correlation coefficient between holding time and creep rupture life is -0.38, which is stronger than the other four characteristics, so it is taken as the key feature, and the other four characteristics are regarded as redundant and removed from the data set. Similarly, left deformation, right deformation and average deformation are deformation characteristics with strong correlation (such as the difference between the correlation coefficients is less than 0.2), and the average deformation (the correlation coefficient between the average deformation and the rupture life is greater than that of other characteristics) is selected as the main feature, so that the other two redundant features are removed. By analogy, the stress, holding temperature, preheating time, average deformation and displacement are finally selected as the target input features.

[0057] By implementing steps S13-S15, the coupling analysis of the distribution characteristics (such as dispersion) and the correlation between the input features and the life characteristics is carried out, the features with abnormally narrow distribution or extreme outliers are automatically filtered, the target input features which are highly correlated with the life characteristics and have good distribution representation are retained, and the generalization ability and actual availability of the subsequent life prediction model are guaranteed through such optimization. In addition, by performing initial screening of input features based on distribution characteristics, and then performing correlation analysis based on the initial screening results, the following advantages are obtained: since the distribution characteristics are easy and efficient to calculate, the initial screening of features can effectively eliminate redundant input features, thereby reducing the workload of subsequent correlation analysis.

[0058] S16: Train the life prediction model according to the training samples, and input the second test data of the nuclear power material to be evaluated into the trained life prediction model to obtain the life evaluation result of the nuclear power material to be evaluated. The second test data includes second pure creep test data, second pure fatigue test data, second creep fatigue test data with peak holding, and second creep fatigue test data with sequential loading of the nuclear power material to be evaluated under the set test conditions. The life prediction model may be, for example, an artificial neural network (ANN) model.

[0059] The first test data (first pure creep test data, first pure fatigue test data, first creep-fatigue test data with peak value retention, and first creep-fatigue test data with sequential loading) obtained by the embodiments of the present application can comprehensively cover various mechanisms of creep-fatigue interaction: fatigue damage dominance, creep damage dominance, and creep-fatigue interaction, and can reflect the bilinear characteristics in the creep-fatigue interaction diagram, thereby solving the problem that the existing scheme calculates the creep-fatigue interaction diagram mainly in the fatigue damage dominant area, and having the advantage of comprehensively reflecting the life consumption process of the nuclear power material. In addition, the first creep-fatigue test data with sequential loading can better reflect the interaction of creep and fatigue in the actual service process. Therefore, training the life prediction model based on such first test data can make the life assessment result finally output by the life prediction model more consistent with the actual service damage process of the nuclear power material, and more accurate.

[0060] The life prediction model is a neural network model, such as an ANN model. At present, in order to ensure the accuracy of the output result of the neural network model, a large number of high-fidelity training samples are usually needed to perform machine learning on the neural network model. However, in the actual industrial scene of the nuclear power material, the available creep-fatigue interaction data (such as the first creep-fatigue test data with peak value retention and the first creep-fatigue test data with sequential loading) is very limited, and the neural network model is difficult to produce effective output results under a small sample size.

[0061] In order to alleviate the above problems, the step S15 of constructing the training sample based on the target input feature and the life feature corresponding to the target input feature comprises: constructing an original data set based on the target input feature and the life feature corresponding to the target input feature, decomposing the original data set into at least one data cluster by using an affinity propagation clustering algorithm, constructing and training a generative adversarial network model (including a generator and a discriminator) corresponding to each data cluster for each data cluster, respectively, merging the data generated by the final generator corresponding to each data cluster into a generated data set, and the final generator is the generator corresponding to the adversarial network model when the adversarial network model training reaches a convergence state. Further, the training sample is constructed based on the generated data set and the original data set, so as to expand the original data set, and the accurate evaluation of the life of the nuclear power material under the limited creep-fatigue interaction test data is realized.

[0062] Wherein, the generative adversarial network model is, for example, a GAN model, including a generator G (Generator) and a discriminator D (Discriminator), the objective of the generator G is to learn to generate a sample G(z) similar to the real data x ~ pdata(x) from the random noise z ~ pz(z), and the objective of the discriminator D is to distinguish the real data x and the generated data G(z). The GAN model related architecture design includes:

[0063] 1. Generator G: input dimension = noise 16 dimension + condition (key statistics); 2-4 layers of full connection; the width of the hidden layer is linearly enlarged with the number of cluster samples; the activation uses LeakyReLU; the output layer has no / linear activation.

[0064] 2. Discriminator D: 2-4 layers of full connection; spectral normalization; activation LeakyReLU; add Dropout 0.1 to prevent overfitting.

[0065] 3. Loss function of the generator G and the discriminator D, both of which adopt the loss function of the currently known GAN model.

[0066] 4. Learning rate: 1e-4 to 3e-4 (Adam optimizer, β1=0.5, β2=0.9), batch size 16.

[0067] 5. Training strategy: discriminator: generator = 5:1 update, accelerate the convergence of the discriminator.

[0068] First, the affinity propagation (AP) clustering algorithm is used to perform unsupervised clustering on the original data set. Unlike algorithms such as K-Means, which require a pre-specified number of clusters (k), the AP clustering algorithm can automatically determine the optimal number of clusters and cluster centers based on the similarity between data points. The purpose of this step is to divide data samples with similar intrinsic distribution characteristics into the same subset (cluster). In this way, the complex multi-modal data set is decomposed into several simpler and more single-distributed sub-data sets, one data set corresponding to one data cluster.

[0069] Further, independent training of GANs for each cluster: for each data cluster generated in the AP clustering, an independent GAN model is constructed and trained, and the architecture of each GAN (including the number of network layers, the number of neurons, the activation function, the optimizer, and the learning rate of the generator G and the discriminator D) is configured according to the data characteristics of the corresponding cluster. Each GAN only needs to learn a simpler data distribution, thus reducing the training difficulty and significantly improving the quality and realism of the generated data. Specifically.

[0070] The generator loss and discriminator loss will oscillate wildly in the early stages of training, but as training progresses, they should gradually enter a dynamic equilibrium and stabilize within a certain numerical range (equivalent to the adversarial network model reaching convergence). This indicates that the generator and discriminator have reached Nash Equilibrium, meaning that the generator's generative ability and the discriminator's discriminative ability mutually constrain and enhance each other, and the model has not experienced problems such as mode collapse or gradient vanishing. The data included in the above-mentioned generated dataset are the generator output data corresponding to the convergence state of the adversarial network model.

[0071] In one embodiment, constructing training samples based on the generated dataset and the original dataset includes: performing data distribution analysis and feature correlation analysis on the generated dataset and the original dataset; based on the results of the data distribution analysis and feature correlation analysis, if it is determined that the generated dataset matches the original dataset in terms of feature correlation and data distribution, then the generated dataset is determined as the target generated dataset, and training samples are constructed according to the target generated dataset and the original dataset. In this way, the target generated dataset used to construct the training samples can be more consistent with the original dataset in terms of statistical characteristics, thus ensuring the quality and reliability of the expanded training samples.

[0072] One feasible approach is data distribution analysis, which includes histogram analysis and principal component analysis (PCA). Histogram analysis is primarily used to verify the consistency of marginal distributions between the generated and original datasets, while PCA is primarily used to verify the consistency of global structure between the generated and original datasets. For example, the histogram analysis process and decision-making process are as follows:

[0073] Analyze each input feature in the generated dataset and the original dataset, and plot histograms for each input feature in the original dataset and the generated dataset. The histograms visually reflect the numerical distribution characteristics of a single feature (such as shape, peak position, and data range). Compare the shape (e.g., whether they are all normal or uniform), peak position (e.g., if the "holding time" of the input feature in the original dataset is 0.8, the input feature in the generated dataset should also be around 0.8, with a fluctuation of 0.1 allowed), and data range (the interval between the minimum and maximum values). If the distribution characteristics such as shape, peak position, and data range are similar (e.g., similarity greater than 90%), the dataset is considered to have passed; otherwise, it is considered to have failed. Generate the histogram distribution analysis results representing whether the dataset has passed or failed.

[0074] For example, the analysis and judgment process of principal component analysis is as follows:

[0075] Generate a centralized data matrix from the generated dataset or the original dataset. (where N is the number of samples and D is the feature dimension), its covariance matrix Defined as:

[0076] .

[0077] Performing eigenvalue decomposition on the covariance matrix ∑ yields the eigenvectors W = [w1, w2, w3]. D ] and eigenvalues ​​Λ=diag[λ1, λ2, λ D ], where each w i (representing the i-th eigenvector) is the "principal component direction", corresponding to the direction with the largest variance, each λ i (Representing the i-th eigenvalue) reflects the "importance" of the corresponding principal component, i.e., the magnitude of its variance.

[0078] The projection matrix is ​​constructed by selecting the first K (K=2 or 3, for easy 2D / 3D visualization) eigenvectors with the largest eigenvalues. Generate the dataset and each data point x in the original dataset. i K-dimensional projection y i Represented as:

[0079] .

[0080] Projecting the original dataset and the generated dataset into y i The original and generated datasets are plotted as K-dimensional scatter plots. The shapes (whether the clustering patterns of the points are consistent, e.g., if the original dataset shows an "elliptical distribution," the generated dataset should also have similar elliptical patterns), ranges (whether the coverage areas of the points on the coordinate axes overlap, e.g., if the original dataset's range on the PCA1 axis is [-3, 3], the generated dataset should also be within this range), and densities (whether the density distribution of the points is consistent, e.g., if the original dataset is dense in a certain area, the generated dataset should also be dense in that area). The criteria for judgment are that the shapes, ranges, and densities of the scatter plots of the original and generated datasets should highly overlap, without any deviation of the generated data from the original data area or the formation of new artificial clusters. The two scatter plots can be manually checked to see if they meet the judgment criteria. If they do, the dataset passes the judgment; otherwise, it fails, and the principal component analysis results indicating whether the judgment passes are output. Alternatively, image analysis techniques can be used to compare the shapes, ranges, and densities of the two scatter plots. If they are similar, the dataset passes the judgment; otherwise, it fails, and the principal component analysis results indicating whether the judgment passes are output.

[0081] For example, the above scatter plot can be, for instance, as shown in the example... Figure 3 As shown, Figure 3Hollow circles correspond to the generated dataset, and solid circles correspond to the original dataset. As can be seen from the figure, the generated dataset and the original dataset are almost completely overlapping in the figure. The shape, range and density of the scatter plot are basically the same, indicating that the difference between the two is small.

[0082] As a feasible approach, the correlation analysis results between the above features may include any one or more of the following: generating correlation coefficient matrices for the dataset and the original dataset respectively, and heatmaps of the correlation coefficient matrices. The correlation analysis and determination process between features is as follows:

[0083] Calculate the Pearson correlation coefficient matrix between all pairs of features in both the generated and original datasets. For two variables X and Y, the Pearson correlation coefficient is... for:

[0084]

[0085] Where N is the number of samples. and These are the means of X and Y, respectively. The criterion is that the first correlation coefficient matrix of the generated dataset should maintain a high degree of similarity to the second correlation coefficient matrix of the original dataset in both structure and value. Quantitatively, the mean absolute error (MAE) of the difference between corresponding elements of the first and second correlation coefficient matrices can be calculated. If this value is lower than a preset threshold (e.g., 0.2), it indicates that the generated dataset successfully reproduces the linear relationship structure between variables in the original dataset, and is thus deemed acceptable; otherwise, it is deemed unacceptable, and the feature correlation analysis results representing whether the judgment is acceptable are output.

[0086] Alternatively, intuitively, heatmaps corresponding to the first and second correlation coefficient matrices can be generated. If the color patterns and textures of the two heatmaps are similar (e.g., the similarity reaches 90%), the heatmap passes the test; otherwise, it fails. The correlation analysis results between features representing whether the test passes or fails are then output.

[0087] In this embodiment, the data distribution analysis results include histogram distribution analysis results and principal component analysis results. On one hand, when both histogram distribution analysis and principal component analysis results pass the characterization judgment, it is determined that the data distribution of the generated dataset matches that of the original dataset. This ensures that the internal patterns (marginal distribution) of single features and the correlation patterns (global structure) between features in the subsequently selected target generated dataset are consistent with the original dataset. If only the single feature distribution is similar, but the relationships between features are chaotic (e.g., the generated data are isolated clusters), it will still lead to inaccurate samples. On the other hand, when the correlation analysis results between features pass the characterization judgment, it is determined that the feature correlation of the generated dataset matches that of the original dataset. Further, combining the correlation analysis results between features and the data distribution analysis results, when both the feature correlation and data distribution of the generated dataset match those of the original dataset, the generated dataset is determined as the target generated dataset for subsequent training sample construction. By comprehensively considering the distribution characteristics and feature correlations between the generated dataset and the original dataset, the quality and reliability of the target generated dataset used for subsequent training sample construction are ensured. See also... Figure 4 This application also proposes another method for lifetime assessment of nuclear power materials, wherein the sample nuclear power materials include a first sample nuclear power material and a second sample nuclear power material. This method is applicable in... Figure 1 Based on the previous embodiment, the following steps S17 to S21 are added, and before performing step S10, the method further includes:

[0088] Step S17: Obtain nuclear power materials of the same size and batch as multiple initial sample nuclear power materials, and perform heat treatment on multiple initial sample nuclear power materials to eliminate residual stress (such as stress introduced by welding and processing) and microstructure deviations (such as uneven grain size and phase composition differences).

[0089] Step S18: Perform X-ray residual stress analysis (e.g., XRD). The microstructure and residual stress of the nuclear power materials in each initial sample after heat treatment were quantitatively characterized by electron backscatter diffraction (EBSD) and electron backscatter diffraction (EBSD) analysis.

[0090] Step S19: Obtain microstructure characteristic factors related to the heat treatment state from the quantitative characterization, and analyze the dispersion of these microstructure characteristic factors. These microstructure characteristic factors include residual stress factors (such as the mean and standard deviation of surface residual stress) and microstructure factors (such as grain size, dislocation density, phase ratio, etc.). Methods for analyzing the dispersion of microstructure characteristic factors include, for example, analysis of variance (ANOVA) and F-test, mainly used to compare the differences in microstructure characteristic factors among the initial nuclear power materials samples.

[0091] Step S20: Based on the dispersion analysis results, screen the first sample nuclear power material and the second sample nuclear power material from multiple initial sample nuclear power materials. The first sample nuclear power material is the initial sample nuclear power material whose dispersion meets the first condition, and the second sample nuclear power material is the initial sample nuclear power material whose dispersion meets the second condition.

[0092] Both the first and second conditions can be preset based on experimental data or theoretical analysis and can be adjusted as needed later. For example, the first condition can be that the fluctuation is less than 30%, and the second condition can be that the fluctuation is equal to 30%. Assuming multiple initial sample nuclear power materials include initial sample nuclear power material 01, initial sample nuclear power material 02, and initial sample nuclear power material 03, according to the dispersion analysis results, if the tissue characteristic factor of initial sample nuclear power material 01 is found to have a fluctuation exceeding 30% compared to the other two initial sample nuclear power materials, it can be removed and not used as a sample nuclear power material for subsequent step S10. If the tissue characteristic factor of initial sample nuclear power material 02 is found to have a fluctuation less than 30% compared to the other two initial sample nuclear power materials (i.e., meeting the first condition), it is retained as the first sample nuclear power material and used as one of the sample nuclear power materials for subsequent step S10. If the microstructure characteristic factor of the initial sample nuclear power material 03 is found to fluctuate by 30% compared to the other two initial sample nuclear power materials (i.e., meeting the second condition), it is not directly eliminated, but retained as the second sample nuclear power material, serving as one of the sample nuclear power materials for subsequent step S10. Its corresponding microstructure characteristic factor is associated with the identifier (such as material number) of the initial sample nuclear power material 03 and stored in the sample material library. When subsequent step S10 obtains the first test data of the initial sample nuclear power material 03, it retrieves the corresponding microstructure characteristic factor of the initial sample nuclear power material 03 from the sample database, and corrects the initial lifetime characteristics in the first test data of the initial sample nuclear power material 03 using this microstructure characteristic factor. Subsequent steps are then performed based on the corrected lifetime characteristics. This approach eliminates microstructure deviations in sample nuclear power materials from different batches and with different heat treatment histories, establishing an equivalent screening mechanism based on heat treatment status.

[0093] Step S21: Obtain the tissue characteristic factor corresponding to the second sample nuclear power material, and associate the tissue characteristic factor corresponding to the second sample nuclear power material with the second sample nuclear power material and store it in the sample material library.

[0094] In one embodiment, the lifetime characteristics in step S14 above include sub-lifetime characteristics corresponding to the second sample nuclear power material. The sub-lifetime characteristics are values ​​corrected based on the microstructure characteristics factor corresponding to the second sample nuclear power material stored in the sample material library, which eliminates the microstructure deviation of sample nuclear power materials under different batches and different heat treatment histories.

[0095] Specifically, the first experimental data mentioned above includes initial lifetime characteristics. Assuming that the sample nuclear power material in step S10 includes a second sample nuclear power material, after obtaining the first experimental data of the second sample nuclear power material by executing step S10, the microstructure characteristic factor corresponding to the second sample nuclear power material can be obtained from the sample database. The initial lifetime data corresponding to the second sample nuclear power material can be corrected by the microstructure characteristic factor. The subsequent steps all use the corrected lifetime characteristics. The microstructure characteristic factor characterizing the heat treatment state of the material can be filtered out during the entire lifetime assessment process. Lifetime equivalence prediction can be achieved between nuclear power materials of different batches and different heat treatment histories, thereby improving the stability and generalization ability of the subsequent lifetime prediction model.

[0096] As a feasible approach, correcting the initial lifetime data using tissue characteristic factors includes: measuring tissue characteristic factors and lifetime for several batches of sample materials, establishing the relationship between lifetime deviation and tissue characteristic factors, and obtaining a correction function F(S) to characterize the decay rate of lifetime due to tissue characteristic factors (0 < F ≤ 1), which is also the correction factor. This correction factor can be used to correct the lifetime.

[0097] By executing steps S17 to S21, it is possible to effectively ensure that the microstructure and stress deviations of each sample nuclear power material are within a reasonable range before executing subsequent steps S10 to S16. Furthermore, in addition to directly eliminating abnormal initial sample nuclear power materials (such as those with fluctuations exceeding 30%), lifetime equivalence prediction can be achieved between the same material from different batches and with different heat treatment histories by filtering microstructural characteristic factors that characterize the material's heat treatment state in lifetime prediction. This improves the stability and generalization ability of subsequent model predictions.

[0098] This application also provides a chip, including a circuit system configured to perform the life assessment method for nuclear power materials described above. The chip includes a Field Programmable Gate Array (FPGA) chip, a Complex Programmable Logic Device (CPLD) chip, and an Application Specific Integrated Circuit (ASIC) chip, etc.

[0099] Figure 5This is a simplified block diagram of an electronic device 500 suitable for implementing embodiments of this application. For example, the life assessment method for nuclear power materials described above can be implemented by the electronic device 500. As shown, the electronic device 500 includes one or more processors 510, one or more memories 520 coupled to the processors 510, and one or more communication modules 540 coupled to the processors 510.

[0100] Communication module 540 is used for bidirectional communication. Communication module 540 has at least one antenna to facilitate communication. The communication interface can represent any interface necessary for communication with other network elements.

[0101] Processor 510 can be of any type suitable for a local technology network, and as a non-limiting example, can include one or more of the following: general-purpose computer, special-purpose computer, microprocessor, digital signal processor (DSP), and processor based on a multi-core processor architecture. Electronic device 500 can have multiple processors, such as application-specific integrated circuit (ASIC) chips, which are timely driven to a clock that synchronizes with the main processor.

[0102] Memory 520 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM), electrically programmable read-only memory (EPROM), flash memory, hard disk, optical disc (CD), digital video disc (DVD), and other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, random access memory (RAM) and other volatile memories that do not persist during power-off periods.

[0103] Computer program 530 includes computer-executable instructions that are executed by the associated processor 510. Program 530 may be stored in ROM 524. Processor 510 may perform any appropriate actions and processes by loading program 530 into RAM 522.

[0104] The embodiments of this application can be implemented by program 530, enabling electronic device 500 to execute the reference. Figure 1 or Figure 4 Any process disclosed in the discussion. Embodiments of this application may also be implemented by hardware or by a combination of software and hardware.

[0105] In some embodiments, program 530 may be tangibly contained in a computer-readable medium, which may be contained in an electronic device 500 (e.g., memory 520) or other storage device accessible to the electronic device 500. The electronic device 500 may load program 530 from the computer-readable medium into RAM 522 for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. Program 530 is stored on the computer-readable medium.

[0106] Generally, the various embodiments of this application can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while others may be implemented in firmware or software, which may be executed by a controller, microprocessor, or other electronic device. Although various aspects of the embodiments of this application are shown and described as block diagrams, flowcharts, or other graphical representations, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other electronic devices, or some combination thereof.

[0107] This application also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in a program module, which execute in a device on a target real or virtual processor to perform the aforementioned references. Figure 1 or Figure 4 The method described herein. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of a program module can be combined or separated among program modules as needed. The machine-executable instructions used in the program module can execute on a local or distributed device. In a distributed device, the program module can reside on both local and remote storage media.

[0108] The program code used to perform the methods of this application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on a machine, partially on a machine, partially on a remote machine, partially on a remote machine, or entirely on a remote machine or server as a standalone software package.

[0109] In the context of this application, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, etc.

[0110] Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or apparatuses, or any suitable combination thereof. More specific examples of computer-readable storage media include electrical connections having one or more wires, portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0111] Furthermore, although the operations are described in a specific order, this should not be construed as requiring that these operations be performed in the specific order or sequence shown, or that all of the operations shown be performed to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these details should not be construed as limiting the scope of this application, but rather as descriptions of features specific to particular embodiments. Certain features described in the context of a single embodiment may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0112] Although this application has been described in language specific to structural features and / or methodological behavior, it should be understood that the application as defined in the appended claims is not necessarily limited to the specific features or behaviors described above. Rather, the specific features and actions described above are disclosed as exemplary forms for implementing the claims.

Claims

1. A method for life assessment of a nuclear power material, characterized by, The method comprises the following steps: obtaining first test data of a sample nuclear power material, wherein the first test data comprises first pure creep test data, first pure fatigue test data, first creep-fatigue test data with peak holding and first creep-fatigue test data with sequential loading of the sample nuclear power material under different test conditions; obtaining feature data corresponding to input features from the first test data; analyzing distribution characteristics of the input features based on the feature data to obtain a distribution characteristic analysis result; preliminarily screening the input features according to the distribution characteristic analysis result; performing correlation analysis on life characteristics in the first test data and the preliminarily screened input features; selecting target input features from the preliminarily screened input features according to a correlation analysis result, and constructing a training sample based on the target input features and life characteristics corresponding to the target input features; training a life prediction model according to the training sample; inputting second test data of a nuclear power material to be evaluated into the trained life prediction model to obtain a life evaluation result of the nuclear power material to be evaluated, wherein the second test data comprises second pure creep test data, second pure fatigue test data, second creep-fatigue test data with peak holding and second creep-fatigue test data with sequential loading of the nuclear power material to be evaluated under a set test condition, wherein the step of constructing a training sample based on the target input features and life characteristics corresponding to the target input features comprises: constructing an original data set based on the target input features and life characteristics corresponding to the target input features; decomposing the original data set into at least one data cluster by using an affinity propagation clustering algorithm; constructing and training a generative adversarial network model corresponding to each data cluster respectively, wherein the generative adversarial network model comprises a generator and a discriminator; merging data generated by final generators corresponding to each data cluster into a generated data set, wherein the final generator is a generator corresponding to the adversarial network model when the adversarial network model is trained to reach a convergence state; constructing a training sample based on the generated data set and the original data set.

2. The method of claim 1, wherein, The process of obtaining the first creep-fatigue test data with sequential loading comprises: applying a preset number of cycles of cyclic fatigue test to the sample nuclear power material under a first preset service temperature zone and a first preset stress condition to form a pre-fatigue damage; performing constant stress creep loading on the sample nuclear power material with pre-fatigue damage until the sample nuclear power material breaks or continues to a first preset termination time, and outputting the creep life of the sample nuclear power material under the first preset service temperature zone, the first preset stress condition and the pre-fatigue damage.

3. The method of claim 1, wherein, The process of constructing a training sample based on the generated data set and the original data set comprises: performing data distribution analysis and feature correlation analysis on the generated data set and the original data set to obtain a data distribution analysis result and a feature correlation analysis result; If it is determined that the feature correlation and the data distribution of the generated data set match those of the original data set based on the data distribution analysis result and the feature correlation analysis result, the generated data set is determined as a target generated data set. A training sample is constructed according to the target generated data set and the original data set.

4. The method of claim 3, wherein, The data distribution analysis result includes a histogram distribution analysis result and a principal component analysis result of the generated data set and the original data set.

5. The method of claim 3, wherein, The feature correlation analysis result is determined based on a correlation coefficient matrix corresponding to the generated data set and the original data set.

6. The method of claim 1, wherein, The sample nuclear power material includes a first sample nuclear power material and a second sample nuclear power material, and the method further includes: Obtaining nuclear power materials of the same size and batch as a plurality of initial sample nuclear power materials; Performing heat treatment on the plurality of initial sample nuclear power materials to eliminate residual stress and organizational deviation; Quantitatively characterizing the microstructure and residual stress of each initial sample nuclear power material after heat treatment by X-ray residual stress analysis and electron backscatter diffraction analysis; Obtaining an organizational characteristic factor related to the heat treatment state from the quantitative characterization, and analyzing the dispersion of the organizational characteristic factor; Based on the dispersion analysis result, the first sample nuclear power material and the second sample nuclear power material are selected from the plurality of initial sample nuclear power materials, the first sample nuclear power material is an initial sample nuclear power material whose dispersion satisfies a first condition, and the second sample nuclear power material is an initial sample nuclear power material whose dispersion satisfies a second condition; Obtaining the organizational characteristic factor corresponding to the second sample nuclear power material, and storing the organizational characteristic factor corresponding to the second sample nuclear power material in association with the identification of the second sample nuclear power material in a sample material library.

7. The method of claim 6, wherein, The life feature includes a sub-life feature corresponding to the second sample nuclear power material, and the sub-life feature is a numerical value corrected based on the organizational characteristic factor corresponding to the second sample nuclear power material stored in the sample material library.

8. The method of claim 1, wherein, The first pure creep test data acquisition process includes: Under a second preset service temperature zone and a second preset stress condition, the sample nuclear power material is subjected to constant stress creep loading until the sample nuclear power material is fractured or until a second preset termination time; The fracture time of the sample nuclear power material under the second preset service temperature zone and the second preset stress condition is output.

9. The method of claim 1, wherein, The first pure fatigue test data acquisition process includes: Under a third preset service temperature zone and fatigue loading conditions, the sample nuclear power material is subjected to cyclic strain until the sample nuclear power material is fractured or a third preset termination condition is met, and the fatigue life of the sample nuclear power material under the third preset service temperature zone and the fatigue loading conditions is output.

10. An electronic device, comprising: Comprise: At least one processor; And At least one memory having instructions stored thereon, which, when executed by the at least one processor alone or collectively, cause the electronic device to perform the method of any one of claims 1-9.

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