Adaptive assessment method and apparatus for nuclear power plant component reliability, storage medium, and electronic device

Abstract

The present invention relates to an adaptive assessment method and apparatus for nuclear power plant component reliability, a storage medium, and an electronic device. The method comprises the following steps: constructing a Kriging initial model; gradually updating the Kriging initial model on the basis of first-layer samples of ICE to obtain a Kriging surrogate model; gradually updating the Kriging surrogate model on the basis of last-layer samples of the ICE; after the updating of the Kriging surrogate model is completed, obtaining a current Kriging model; and on the basis of the current Kriging model, assessing the reliability of a component to be assessed. In the present invention, first-layer adaptive Kriging gradually updates and explores a failure domain on the basis of the first-layer samples of the ICE until a set stopping criterion is satisfied, and on the basis of a currently constructed Kriging model, the last-layer samples of the ICE are also updated accordingly. Thus, it is ensured that an estimated failure probability converges unbiasedly to a real failure probability, and unnecessary updating of Kriging is avoided, thereby greatly improving calculation efficiency and saving calculation costs.

Description

Nuclear power plant component reliability adaptive evaluation method, device, storage medium and electronic equipment TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power plant component reliability analysis, and more particularly to a nuclear power plant component reliability adaptive evaluation method, device, storage medium and electronic equipment. BACKGROUND

[0002] In the operation of many devices in nuclear power plants, uncertain factors cause structural functional failure and lead to serious accidents, which is often a small failure probability problem. For nuclear power plant component reliability analysis, traditional reliability analysis methods such as MCS (Monte Carlo Simulation, MCS) require too many function function evaluation times. In addition, Kriging-based reliability analysis methods such as ALK-MCS (Active Learning Kriging (ALK) based on MCS) face the problem of too many candidate sample points, resulting in low computational efficiency. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a nuclear power plant component reliability adaptive evaluation method, device, storage medium and electronic equipment to solve the problems in the prior art.

[0004] The technical solution adopted by the present application to solve its technical problem is: constructing a nuclear power plant component reliability adaptive evaluation method, comprising the following steps:

[0005] Building a Kriging initial model;

[0006] Updating the Kriging initial model on the first layer sample of ICE step by step to obtain a Kriging surrogate model;

[0007] Updating the Kriging surrogate model on the last layer sample of ICE step by step;

[0008] After completing the update of the Kriging surrogate model, obtaining a current Kriging model;

[0009] Evaluating the reliability of the component to be evaluated based on the current Kriging model.

[0010] In the nuclear power plant component reliability adaptive evaluation method of the present application, the Kriging initial model includes:

[0011] Obtaining component data of the component to be evaluated;

[0012] performing data analysis processing based on the component data to obtain an original distribution density function of a to-be-evaluated variable of the to-be-evaluated component;

[0013] determining an initial sample point based on the original distribution density function;

[0014] constructing a training set based on the initial sample point;

[0015] performing training according to the training set to obtain the Kriging initial model.

[0016] In the nuclear power plant component reliability adaptive evaluation method, the step of gradually updating the Kriging initial model on the first layer sample of the ICE includes the following steps:

[0017] Step A1: determining a sample corresponding to a U learning function minimum value from the first layer sample of the ICE;

[0018] Step A2: determining whether the U learning function minimum value is greater than or equal to a preset value, if yes, performing Step A4, otherwise performing Step A3;

[0019] Step A3: adding the sample corresponding to the U learning function minimum value to the training set to retrain the Kriging initial model;

[0020] Step A4: performing the step of gradually updating the Kriging surrogate model on the last layer sample of the ICE.

[0021] In the nuclear power plant component reliability adaptive evaluation method, the step of constructing a training set based on the initial sample point includes:

[0022] performing calculation based on the initial sample point to obtain a real function function value;

[0023] constructing the training set based on the real function function value.

[0024] In the nuclear power plant component reliability adaptive evaluation method, the step of gradually updating the Kriging surrogate model on the last layer sample of the ICE includes the following steps:

[0025] Step B1: performing ICE on the Kriging surrogate model;

[0026] Step B2: determining a sample corresponding to a U learning function minimum value from the last layer sample of the ICE;

[0027] Step B3: gradually updating the Kriging surrogate model based on the sample corresponding to the U learning function minimum value.

[0028] In the method for adaptively evaluating reliability of a nuclear power plant component, the step B3 comprises the following steps:

[0029] Step B31: calculating a relative error between the measured failure probability and the true failure probability;

[0030] Step B32: judging whether the relative error between the measured failure probability and the true failure probability meets a stopping criterion, if yes, executing step B34, otherwise executing step B33;

[0031] Step B33: adding the sample corresponding to the minimum value of the U learning function to the constructed training set, and updating the Kriging surrogate model;

[0032] Step B34: evaluating reliability of a component to be evaluated based on the current Kriging model.

[0033] In the method for adaptively evaluating reliability of a nuclear power plant component, the step of evaluating reliability of a component to be evaluated based on the current Kriging model comprises:

[0034] calculating a Kriging prediction value based on the current Kriging model;

[0035] determining an estimated weight of the ICE;

[0036] determining a sample number of samples in the last layer of the ICE;

[0037] calculating a failure probability of the component to be evaluated according to the Kriging prediction value, the estimated weight and the sample number;

[0038] evaluating reliability of the component to be evaluated based on the failure probability.

[0039] The application further provides a device for adaptively evaluating reliability of a nuclear power plant component, comprising:

[0040] a model construction unit configured to construct a Kriging initial model;

[0041] a first layer updating unit configured to gradually update the Kriging initial model on samples in a first layer of the ICE to obtain a Kriging surrogate model;

[0042] a second layer updating unit configured to gradually update the Kriging surrogate model on samples in a last layer of the ICE;

[0043] a model acquisition unit configured to acquire a current Kriging model after the updating of the Kriging surrogate model is completed;

[0044] a reliability evaluation unit configured to evaluate reliability of a component to be evaluated based on the current Kriging model.

[0045] The application further provides a storage medium storing a computer program, which is adapted to be loaded by a processor to execute the steps of the nuclear power plant component reliability adaptive evaluation method.

[0046] The application further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the nuclear power plant component reliability adaptive evaluation method by invoking the computer program stored in the memory.

[0047] The nuclear power plant component reliability adaptive evaluation method, device, storage medium and electronic device provided by the application have the following beneficial effects: comprising the following steps: constructing a Kriging initial model; updating the Kriging initial model on the first layer samples of the ICE step by step to obtain a Kriging surrogate model; updating the Kriging surrogate model on the last layer samples of the ICE step by step; obtaining a current Kriging model after completing the update of the Kriging surrogate model; and evaluating the reliability of a component to be evaluated based on the current Kriging model. The first layer adaptive Kriging updates the exploration failure domain on the first layer samples of the ICE step by step until the set stopping criterion is met, and the last layer samples of the ICE are updated based on the current constructed Kriging model, so that the estimated failure probability is unbiasedly converged to the true failure probability, unnecessary Kriging update is avoided, the calculation efficiency is greatly improved, and the calculation cost is saved. BRIEF DESCRIPTION OF DRAWINGS

[0048] The application will be further described below in combination with the drawings and embodiments, wherein:

[0049] Fig. 1 is a flowchart of the nuclear power plant component reliability adaptive evaluation method provided by the application;

[0050] Fig. 2 is a specific flowchart of the nuclear power plant component reliability adaptive evaluation method provided by the application;

[0051] Fig. 3 is a schematic diagram of a hydraulic pipeline provided by the application;

[0052] Fig. 4 is a structural schematic diagram of the nuclear power plant component reliability adaptive evaluation device provided by the application. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the application will be described clearly and completely below in combination with the drawings of the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0054] In order to solve the problems of difficulty in reliability evaluation of nuclear power plant components, low efficiency in failure probability calculation and high cost in calculation, the present application provides a nuclear power plant component reliability adaptive evaluation method, which combines Kriging and adaptive double-layer Kriging of ICE (improved cross-entropy, ICE) for reliability evaluation. In order to avoid unnecessary updating of Kriging, a stopping criterion combining statistical characteristics of Kriging and ICE is proposed. The first-layer adaptive Kriging gradually updates the exploration failure domain on the first-layer samples of ICE until the set stopping criterion is met. The second-layer adaptive Kriging updates Kriging on the last-layer samples of ICE. Based on the currently constructed Kriging, the last-layer samples of ICE are also updated, so as to ensure that the estimated failure probability converges to the true failure probability without bias. The proposed stopping criterion ensures the accuracy of the calculation results while avoiding unnecessary updating of Kriging, which greatly improves the calculation efficiency and saves the calculation cost.

[0055] Referring to FIG. 1, FIG. 1 is a flowchart of a preferred embodiment of the nuclear power plant component reliability adaptive evaluation method provided by the present application.

[0056] Specifically, as shown in FIG. 1, the nuclear power plant component reliability adaptive evaluation method includes the following steps:

[0057] Step S101: Construct a Kriging initial model.

[0058] In some embodiments, constructing the Kriging initial model includes: obtaining component data of a component to be evaluated; performing data analysis and processing based on the component data to obtain an original distribution density function of an evaluation variable of the component to be evaluated; determining initial sample points based on the original distribution density function; constructing a training set based on the initial sample points; and training according to the training set (DoE, Design of Experiment) to obtain the Kriging initial model.

[0059] Optionally, in the embodiments of the present application, the number of initial sample points can be 12. The Kriging initial model can be represented by g(X).

[0060] The training set is constructed based on the initial sample points, including: calculating the true function function value based on the initial sample points; and constructing the training set based on the true function function value.

[0061] Step S102: Gradually update the Kriging initial model on the first-layer samples of ICE to obtain a Kriging surrogate model. The Kriging surrogate model here is the Kriging model after the first-layer update.

[0062] In some embodiments, the step of updating the Kriging surrogate model on the samples of the last layer of the ICE comprises the following steps:

[0063] Step A1: determining the sample corresponding to the minimum value of the U-learning function from the samples of the first layer of the ICE.

[0064] Step A2: determining whether the minimum value of the U-learning function is greater than or equal to a preset value, and if yes, performing step A4, otherwise performing step A3.

[0065] Optionally, in the embodiments of the present application, the expression of the U-learning function is:

[0066] ;

[0067] In the above formula, is the U-learning function; is the predicted mean of Kriging; is the predicted standard deviation of Kriging.

[0068] Optionally, in the embodiments of the present application, the preset value can be set to 2.

[0069] Step A3: adding the sample corresponding to the minimum value of the U-learning function into the training set to retrain the Kriging initial model.

[0070] Step A4: performing the step of updating the Kriging surrogate model on the samples of the last layer of the ICE.

[0071] Step S103: updating the Kriging surrogate model on the samples of the last layer of the ICE.

[0072] In some embodiments, the step of updating the Kriging surrogate model on the samples of the last layer of the ICE comprises the following steps:

[0073] Step B1: performing the ICE on the Kriging surrogate model.

[0074] Step B2: determining the sample corresponding to the minimum value of the U-learning function from the samples of the last layer of the ICE.

[0075] Step B3: updating the Kriging surrogate model based on the sample corresponding to the minimum value of the U-learning function.

[0076] In some embodiments, the step B3 comprises the following steps:

[0077] Step B31: calculating the relative error between the measured failure probability and the true failure probability.

[0078] Optionally, in the embodiments of the present application, the relative error between the measured failure probability and the true failure probability can be calculated by the following formula:

[0079] ;

[0080] In the above formula, is the relative error between the measured failure probability and the true failure probability; I is the counter; represents the Kriging prediction, is the estimated weight of ICE, and are the upper bounds of two types of uncertainty samples; represents the ICE sample, represents the distribution family parameter.

[0081] Step B32: Determine whether the relative error between the measured failure probability and the true failure probability meets the stopping criterion. If yes, execute step B34; otherwise, execute step B33.

[0082] wherein the stopping criterion is: ≤ 0.02. That is, when ≤ 0.02, the stopping criterion is met.

[0083] Step B33: Add the sample corresponding to the minimum value of the U learning function to the constructed training set, and update the Kriging surrogate model.

[0084] Step B34: Evaluate the reliability of the component to be evaluated based on the current Kriging model.

[0085] Step S104: Obtain the current Kriging model after completing the update of the Kriging surrogate model.

[0086] wherein the current Kriging model here is the Kriging model updated on the last layer sample of ICE.

[0087] Step S105: Evaluate the reliability of the component to be evaluated based on the current Kriging model.

[0088] In some embodiments, evaluating the reliability of the component to be evaluated based on the current Kriging model comprises: calculating the Kriging prediction value based on the current Kriging model; determining the estimated weight of ICE; determining the sample quantity of the last layer sample of ICE;

[0089] calculating the failure probability of the component to be evaluated according to the Kriging prediction value, the estimated weight and the sample quantity; and evaluating the reliability of the component to be evaluated based on the failure probability.

[0090] wherein the failure probability of the component to be evaluated can be calculated by the following formula:

[0091] ;

[0092] In the above formula, N is the number of the last layer samples of ICE, is the failure indication function related to Kriging prediction. Wherein, The expression of is:

[0093] ;

[0094] Specifically, as shown in FIG. 2, in one specific embodiment, the nuclear power plant component reliability adaptive evaluation method of the present application comprises the following steps:

[0095] Step 1: Constructing Kriging initial model.

[0096] In this step, first, according to the original distribution density function of the considered variable 12 initial sample points are extracted, the true function function values are calculated, and they are constituted into a training set (Design of Experiment, DoE). The initial Kriging model constructed by DoE .

[0097] Step 2: Selecting the sample x* corresponding to the minimum value of U learning function from the first layer ICE sample, judging whether the minimum U learning function value is greater than or equal to 2. If the condition is met, step 4 is performed, otherwise, step 3 is executed.

[0098] Step 3: Adding x* to DoE, retraining Kriging model and returning to step 2.

[0099] Step 4: Executing ICE based on the Kriging model constructed at present, and taking the last layer sample of ICE as a candidate sample point.

[0100] Step 5: Selecting the sample x* with the minimum U learning function, judging whether the proposed stopping criterion is met. If the condition is met, step 7 is executed, otherwise, step 6 is executed. The proposed stopping criterion is:

[0101] ;

[0102] Step 6: Adding x* to DoE and updating Kriging. Return to step 4.

[0103] Step 7: Calculating failure probability .

[0104] The present application is described below with hydraulic pipeline as the component to be evaluated.

[0105] As shown in FIG. 3, the hydraulic pipeline is a pipeline conveying fluid in the hydraulic system, and is one of the key components in many devices of the nuclear power plant. As shown in FIG. 3, the hydraulic pipeline is made of a functionally graded material pipeline, the leftmost material is Ti-6Al-4V, and the rightmost material is SiC. The pipeline is filled with hydraulic oil, the density of the hydraulic oil is , and the velocity of the hydraulic oil is U. In the normal operation process of the nuclear power plant, the hydraulic pipeline is subjected to external excitation . When the external excitation is very close to the natural frequency of the hydraulic pipeline, the hydraulic pipeline resonates and is damaged.

[0106] For the above hydraulic pipeline, due to the existence of uncertainty, the resonance failure probability of the hydraulic pipeline needs to be evaluated. The resonance failure function of the hydraulic pipeline is , when , it is determined that the pipe has resonance failure. Table 1 shows the distribution information of the geometric information and mechanical properties of the hydraulic pipeline, and also includes other uncertain factors to be considered. Table 2 shows the calculation results of different methods.

[0107] Table 1 Uncertainty distribution information

[0108]

[0109] Table 2 Example analysis results

[0110]

[0111] By comparing with the reference value, it can be seen that the subset simulation (SS) and the improved cross-entropy (ICE) can calculate the failure probability of the hydraulic pipeline, but the error of the failure probability obtained by calling tens of thousands of function functions is large. Compared with ALK-MCS, the method provided in the present application only calls 78 function functions to obtain the accurate failure probability, and the error of the failure probability obtained by calling 186 function functions of ALK-MCS is greater than that of the method provided in the present application.

[0112] Referring to FIG. 4, the present application also provides a nuclear power plant component reliability adaptive evaluation device. Specifically, as shown in FIG. 4, the nuclear power plant component reliability adaptive evaluation device comprises:

[0113] A model construction unit 401 is configured to construct a Kriging initial model.

[0114] A first layer updating unit 402 is configured to gradually update the Kriging initial model on the first layer samples of the ICE to obtain a Kriging surrogate model.

[0115] A second layer updating unit 403 is configured to gradually update the Kriging surrogate model on the last layer samples of the ICE.

[0116] The model obtaining unit 404 is configured to obtain the current Kriging model after the updating of the Kriging surrogate model is completed.

[0117] The reliability evaluating unit 405 is configured to evaluate the reliability of the component to be evaluated based on the current Kriging model.

[0118] Specifically, the cooperation operation process between the units in the nuclear power plant component reliability adaptive evaluation device can refer to the nuclear power plant component reliability adaptive evaluation method described above, and will not be described here.

[0119] In addition, the electronic device of the present application comprises a memory and a processor; the memory is configured to store a computer program; the processor is configured to execute the computer program to realize the nuclear power plant component reliability adaptive evaluation method of any one of the above. Specifically, according to the embodiments of the present application, the process described above with reference to the flow chart can be realized as a computer software program. For example, the embodiments of the present application comprise a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program codes for executing the method shown in the flow chart. In such embodiments, the computer program can be downloaded and installed by the electronic device and executed to perform the above-mentioned functions defined in the method of the embodiments of the present application. The electronic device in the present application can be a notebook, a desktop, a tablet computer, a smart phone, etc. terminal, and can also be a server.

[0120] In addition, the present application also provides a storage medium storing a computer program, which is executed by a processor to implement the reliability adaptive evaluation method of the nuclear power plant component according to any one of the above embodiments. Specifically, it should be noted that the storage medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the above two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0121] The above computer readable medium can be contained in the above electronic device; or can exist separately and not be assembled into the electronic device.

[0122] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0123] Those skilled in the art will further realize that the mere conception of the examples described herein is not inducing any patentable instrument, and that each example presents only one illustrative aspect of the present application. The present application is thus deemed to cover any and all adaptations or variations of preferred examples. It is intended to embrace each and every possible modification and change as fall within the scope of the present application. However, it is to be understood that no limitation of the scope of the application is intended by the method or algorithm steps disclosed herein. It is contemplated that those steps can be implemented by either hardware, software, or combinations thereof, in one application or another depending on the particular application or design constraints.

[0124] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM, flash memory, ROM, electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. The present application is not limited by these implementations but is intended to cover any and all possible implementations that fall within the scope of the appended claims.

[0125] The above examples are merely illustrative of the present application. It is contemplated that other variations that fall within the scope of the present application will occur to those skilled in the art. Accordingly, the application is not limited to the specific examples described above, but only by the scope of the appended claims.

Claims

1. An adaptive reliability assessment method for nuclear power plant components, characterized in that, Includes the following steps: Construct the initial Kriging model; The initial Kriging model is progressively updated on the first layer of ICE samples to obtain the Kriging proxy model; The Kriging agent model is progressively updated on the last layer of samples in ICE; After completing the update of the Kriging agent model, the current Kriging model is obtained; The reliability of the component to be evaluated is assessed based on the current Kriging model.

2. The adaptive reliability assessment method for nuclear power plant components according to claim 1, characterized in that, The construction of the initial Kriging model includes: Obtain the component data of the component to be evaluated; Data analysis and processing are performed based on the component data to obtain the original distribution density function of the variable to be evaluated for the component to be evaluated; Initial sample points are determined based on the original distribution density function; A training set is constructed based on the initial sample points; The initial Kriging model is obtained by training the model based on the training set.

3. The adaptive reliability assessment method for nuclear power plant components according to claim 2, characterized in that, The stepwise updating of the initial Kriging model on the first layer of ICE samples to obtain the Kriging surrogate model includes the following steps: Step A1: Determine the sample corresponding to the minimum value of the U learning function from the first layer samples of the ICE; Step A2: Determine whether the minimum value of the U learning function is greater than or equal to a preset value. If yes, proceed to step A4; otherwise, proceed to step A3. Step A3: Add the sample corresponding to the minimum value of the U learning function to the training set and retrain the initial Kriging model; Step A4: Perform the stepwise update of the Kriging agent model on the last layer of samples in ICE.

4. The adaptive reliability assessment method for nuclear power plant components according to claim 2, characterized in that, The construction of the training set based on the initial sample points includes: The actual function value is obtained by calculating based on the initial sample points; The training set is constructed based on the actual function values.

5. The adaptive reliability assessment method for nuclear power plant components according to claim 1, characterized in that, The stepwise updating of the Kriging agent model on the last layer of samples in ICE includes the following steps: Step B1: Execute ICE on the Kriging agent model; Step B2: Determine the sample corresponding to the minimum value of the U learning function from the last layer of samples in the ICE; Step B3: Update the Kriging proxy model step by step based on the samples corresponding to the minimum value of the U learning function.

6. The adaptive reliability assessment method for nuclear power plant components according to claim 5, characterized in that, Step B3 includes the following steps: Step B31: Calculate the relative error between the measured failure probability and the actual failure probability; Step B32: Determine whether the relative error between the measured failure probability and the actual failure probability meets the stopping criterion. If yes, proceed to step B34; otherwise, proceed to step B33. Step B33: Add the sample corresponding to the minimum value of the U learning function to the constructed training set and update the Kriging proxy model; Step B34: Evaluate the reliability of the component to be evaluated based on the current Kriging model.

7. The adaptive reliability assessment method for nuclear power plant components according to claim 1, characterized in that, The reliability assessment of the component to be evaluated based on the current Kriging model includes: Calculate the Kriging prediction value based on the current Kriging model; Determine the estimated weights for ICE; Determine the number of samples in the last layer of the ICE; The failure probability of the component to be evaluated is calculated based on the Kriging prediction, the estimated weight, and the sample size. The reliability of the component to be evaluated is assessed based on the failure probability.

8. An adaptive reliability assessment device for nuclear power plant components, characterized in that, include: Model building unit, used to build the initial Kriging model; The first-layer update unit is used to progressively update the Kriging initial model on the first-layer samples of ICE to obtain the Kriging proxy model. The second-layer update unit is used to progressively update the Kriging agent model on the last layer of samples in the ICE. The model acquisition unit is used to obtain the current Kriging model after the update of the Kriging proxy model is completed; The reliability assessment unit is used to assess the reliability of the component to be assessed based on the current Kriging model.

9. A storage medium, characterized in that, The storage medium stores a computer program adapted for loading by a processor to perform the steps of the adaptive assessment method for the reliability of nuclear power plant components as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the adaptive assessment method for the reliability of nuclear power plant components as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

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