Input parameter influence analysis method, system, medium and equipment for nuclear power plant accident consequence assessment

Abstract

The invention relates to an input parameter influence analysis method and system for nuclear power plant accident consequence evaluation, a medium and equipment. The method comprises the following steps: acquiring parameters required for evaluation; screening and setting parameters required by evaluation to generate a parameter information table; performing random sampling based on the input parameters to generate a random sampling matrix; calculating by using a random sampling matrix to obtain an accident consequence evaluation result; analyzing uncertainty and sensitivity based on the accident consequence evaluation result, and obtaining an uncertainty analysis result and a sensitivity analysis result of the input parameter; performing comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result; and determining an influence result of the input parameters according to the comprehensive analysis result. The uncertainty and sensitivity of the input parameters are analyzed, the influence degree of each parameter on the accident consequence evaluation result is effectively quantified, it is ensured that the nuclear power plant can make an accurate decision in time when a potential accident occurs, and the public and environment safety is effectively protected.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear safety analysis and accident consequence assessment, and more particularly, to a method, system, medium and equipment for input parameter influence analysis of nuclear power plant accident consequence assessment. BACKGROUND

[0002] The safe and stable operation of a nuclear power plant is crucial for public safety and environmental health. In the assessment of potential accident consequences of a nuclear power plant, accurate assessment of radioactive consequences plays a crucial role in developing a scientific and reasonable emergency response strategy, meeting regulatory requirements, and ensuring public safety. Currently, there are a variety of software on the market for assessing potential accident consequences of a nuclear power plant. These software calculate public radiation dose by inputting meteorological data, accident source terms and other parameters, and then assess the impact of potential nuclear accidents. However, due to the complexity of nuclear accident scenarios involving atmospheric diffusion, material migration and other processes, the input parameters of these assessment software often have uncertainties, and different parameters have different degrees of influence on the assessment results.

[0003] Although there has been some research on the influence of input parameters on the assessment results in the field of nuclear safety assessment software, there is still a lack of a systematic, comprehensive and widely applicable analysis method. This leads to difficulties in accurately grasping the reliability of the assessment results in practical applications, and makes it difficult to effectively determine the key influencing parameters, thereby affecting the safety decisions and emergency response plans of nuclear power plants. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a method, system, medium and equipment for input parameter influence analysis of nuclear power plant accident consequence assessment to solve the problems in the prior art.

[0005] The technical solution adopted by the present application to solve the technical problem is: a method for input parameter influence analysis of nuclear power plant accident consequence assessment is constructed, comprising the following steps:

[0006] Obtaining the required parameters for evaluation;

[0007] Filtering and setting the required parameters for evaluation, obtaining input parameters and value ranges of the input parameters, and generating a parameter information table based on the input parameters and the value ranges of the input parameters;

[0008] Based on the input parameters, a random sampling matrix is generated by random sampling;

[0009] Calling an accident consequence evaluation software, using the random sampling matrix for calculation, and obtaining an accident consequence evaluation result;

[0010] Based on the accident consequence evaluation result, an uncertainty analysis result of the input parameters is obtained by uncertainty analysis calculation.

[0011] performing sensitivity analysis based on the accident consequence evaluation result, to obtain a sensitivity analysis result of the input parameter;

[0012] performing comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result, to obtain a comprehensive analysis result;

[0013] determining an influence result of the input parameter according to the comprehensive analysis result.

[0014] In the input parameter influence analysis method for nuclear power plant accident consequence evaluation, the method further comprises:

[0015] outputting an optimized decision suggestion according to the comprehensive analysis result.

[0016] In the input parameter influence analysis method for nuclear power plant accident consequence evaluation, the generating a random sampling matrix based on the input parameter comprises:

[0017] determining a probability level and a confidence level of the input parameter;

[0018] performing calculation according to the probability level and the confidence level, to obtain a minimum number of calculation conditions;

[0019] determining a distribution type and a number of the input parameter;

[0020] generating the random sampling matrix according to the distribution type of the input parameter, the number of the input parameter and the minimum number of calculation conditions.

[0021] In the input parameter influence analysis method for nuclear power plant accident consequence evaluation, the calling an accident consequence evaluation software and performing calculation using the random sampling matrix to obtain an accident consequence evaluation result comprises:

[0022] performing calculation by taking the random sampling matrix as input data of the accident consequence evaluation software, to obtain the accident consequence evaluation result.

[0023] In the input parameter influence analysis method for nuclear power plant accident consequence evaluation, the uncertainty analysis result of the input parameter is calculated by the following formula:

[0024]

[0025] wherein, U(k) represents the uncertainty corresponding to the kth iteration sampling parameter set; D is a result calculated by sequentially inputting the average value of the input parameter into the accident consequence evaluation software; D(k) is a dose calculated by using the kth group of sampling parameters.

[0026] In the input parameter influence analysis method for nuclear power plant accident consequence evaluation, the sensitivity analysis result comprises a single variable sensitivity analysis result and a global sensitivity analysis result.

[0027] The sensitivity analysis based on the accident consequence evaluation result comprises:

[0028] The single variable sensitivity analysis based on the accident consequence evaluation result comprises the single variable sensitivity analysis result.

[0029] The global sensitivity analysis based on the accident consequence evaluation result comprises the global sensitivity analysis result.

[0030] In the input parameter influence analysis method for nuclear power plant accident consequence evaluation, the influence result of the input parameter determined according to the comprehensive analysis result comprises:

[0031] The priority of each input parameter is determined according to the comprehensive analysis result, and the priority of each input parameter is obtained.

[0032] The influence result of the input parameter is determined according to the priority of each input parameter.

[0033] The application further provides an input parameter influence analysis system for nuclear power plant accident consequence evaluation, comprising:

[0034] A parameter acquisition unit is configured to acquire parameters required for evaluation.

[0035] A parameter screening and setting unit is configured to screen and set the parameters required for evaluation, obtain input parameters and a value range of the input parameters, and generate a parameter information table based on the input parameters and the value range of the input parameters.

[0036] A random sampling unit is configured to perform random sampling based on the input parameters and generate a random sampling matrix.

[0037] An accident consequence evaluation unit is configured to call accident consequence evaluation software, perform calculation using the random sampling matrix, and obtain an accident consequence evaluation result.

[0038] An uncertainty analysis unit is configured to perform uncertainty analysis calculation based on the accident consequence evaluation result and obtain an uncertainty analysis result of the input parameters.

[0039] A sensitivity analysis unit is configured to perform sensitivity analysis based on the accident consequence evaluation result and obtain a sensitivity analysis result of the input parameters.

[0040] a comprehensive analysis unit configured to perform comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result;

[0041] a parameter influence analysis unit configured to determine an influence result of the input parameter according to the comprehensive analysis result.

[0042] The application further provides a storage medium storing a computer program, wherein the computer program is adapted to be loaded by a processor to execute the steps of the input parameter influence analysis method for nuclear power plant accident consequence evaluation.

[0043] 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 input parameter influence analysis method for nuclear power plant accident consequence evaluation by calling the computer program stored in the memory.

[0044] The input parameter influence analysis method, system, medium and device for nuclear power plant accident consequence evaluation have the following beneficial effects: the required parameters for evaluation are obtained; the required parameters for evaluation are screened and set to generate a parameter information table; random sampling is performed based on the input parameters to generate a random sampling matrix; the random sampling matrix is used to perform calculation to obtain an accident consequence evaluation result; the uncertainty and sensitivity are analyzed based on the accident consequence evaluation result to obtain an uncertainty analysis result and a sensitivity analysis result of the input parameter; comprehensive analysis is performed based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result; and the influence result of the input parameter is determined according to the comprehensive analysis result. By analyzing the uncertainty and sensitivity of the input parameter, the influence degree of each parameter on the accident consequence evaluation result is effectively quantified, and the nuclear power plant can make accurate decisions in time when a potential accident occurs, thereby effectively protecting the safety of the public and the environment. BRIEF DESCRIPTION OF DRAWINGS

[0045] The application will be further described below with reference to the drawings and embodiments, wherein:

[0046] Figure 1 is a flowchart of an embodiment of the input parameter influence analysis method for nuclear power plant accident consequence evaluation provided by the application;

[0047] Figure 2 is a schematic diagram of a random sampling matrix provided by the application;

[0048] Figure 3 is a logic block diagram of the input parameter influence analysis system for nuclear power plant accident consequence evaluation provided by the application. DETAILED DESCRIPTION

[0049] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the present application.

[0050] The present application quantifies the sensitivity and uncertainty degree by systematically analyzing the influence of input parameters on the evaluation result, and determines the key influence parameters, thereby improving the accuracy and reliability of the potential accident consequence evaluation of the nuclear power plant, and providing solid data support and scientific basis for the safety management, environmental impact evaluation, emergency decision and monitoring of the nuclear power plant.

[0051] Reference Figure 1 In a preferred embodiment, the input parameter influence analysis method for the nuclear power plant accident consequence evaluation comprises the following steps:

[0052] Step S100: Obtain the evaluation required parameters.

[0053] Optionally, in the embodiments of the present application, the evaluation required parameters can include but are not limited to: meteorological data, accident source term data, reactor building height, reactor building vertical plane cross-sectional area, atmospheric diffusion coefficient, dry deposition velocity, breathing rate, etc. Among them, the obtaining of the evaluation required parameters can adopt various ways, such as directly calling from the database, or accessing from each monitoring system, etc., which is not limited in the present application.

[0054] Step S200: Screen and set the evaluation required parameters, obtain the input parameters and the value range of the input parameters, and generate the parameter information table based on the input parameters and the value range of the input parameters. Among them, the input parameters can be represented as [Input Parameter], the value range can be represented as [Value Range], and the distribution type can be represented as [DistributionType].

[0055] In some embodiments, the determination of the input parameters can be determined according to the physical process involved in the potential accident consequence evaluation of the nuclear power plant and the related research experience. These input parameters are the parameters that have important influence on the evaluation result. Optionally, these input parameters can include but are not limited to: reactor building height, reactor building vertical plane cross-sectional area, atmospheric diffusion coefficient, dry deposition velocity, breathing rate, etc. Among them, the value range of each input parameter can be set according to the respective physical characteristics, the possible variation range in actual operation and the availability of data, and the distribution type also needs to be determined. For example, the value range of the breathing rate is 5.56x10 -5 m3 / s~1.39x10 -3m3 / s, its distribution type is: logarithmic uniform distribution, the best estimate value is 3.14*10 -4 m3 / s. By screening out the input parameters which have important influence on the evaluation results, setting the value range and distribution type of each input parameter, and forming a detailed parameter information table. The parameter information table includes input parameters, value range and distribution type of input parameters.

[0056] Step S300: Random sampling based on input parameters to generate a random sampling matrix.

[0057] In some embodiments, the random sampling based on the input parameters to generate a random sampling matrix includes: determining the probability level and confidence level of the input parameters; calculating the minimum number of calculation conditions according to the probability level and confidence level; determining the distribution type and number of the input parameters; and performing random sampling according to the distribution type of the input parameters, the number of the input parameters, and the minimum number of calculation conditions to generate the random sampling matrix.

[0058] It should be noted that the minimum number of calculation conditions determines the number of rows of the random sampling matrix, and the number of input parameters determines the number of columns of the random sampling matrix. Therefore, before generating the random sampling matrix, the number of rows of the random sampling matrix is determined according to the minimum number of calculation conditions, and the number of columns of the random sampling matrix is determined according to the number of input parameters. Each column in the random sampling matrix is a random vector. Wherein, a random vector is generated by the following steps:

[0059] Step S01: dividing 1 into k same and non-overlapping intervals.

[0060] In this step, k is the minimum number of calculation conditions. The minimum number of calculation conditions can be calculated by the following formula:

[0061]

[0062] (1) In the formula:

[0063] m is the number of input parameters;

[0064] β is the confidence level, which is the reliability when estimating the population parameter. For example, under the 95% confidence level, the population mean falls between 10 and 20.

[0065] γ is the probability level, which is the value limit of the data under a certain probability level. For example, the P95 value of air pollutants is 200 μg / m 3 , which means that the pollution value is ≤200 for 95% of the time.

[0066] Step S02: randomly take a value in each interval to obtain a sequence.

[0067] Step S03: Substitute each value in the sequence into the inverse distribution function in turn for calculation to obtain a calculation result.

[0068] In this step, the inverse distribution function is determined by the distribution function type of the input parameter. The input parameter can be expressed as [Input Parameter], the distribution function type of the input parameter can be expressed as [Distribution Type], and the inverse distribution function can be expressed as [Inverse Probability Distribution].

[0069] Step S04: randomly shuffle the calculation results to obtain a random vector.

[0070] After a random vector is obtained through the above steps S01 to S04, steps S01 to S04 are repeated m times according to the number m of input parameters to obtain a random sampling matrix.

[0071] The specific expressions are as follows:

[0072]

[0073] in,

[0074] Step S400: calling the accident consequence evaluation software, using the random sampling matrix to perform calculations, and obtaining the accident consequence evaluation results.

[0075] In some embodiments, calling the accident consequence evaluation software and using the random sampling matrix to perform calculations to obtain the accident consequence evaluation results includes: using the arranged random sampling matrix as input data of the accident consequence evaluation software to perform calculations to obtain the accident consequence evaluation results. Specifically, the initial random sampling matrix is ​​randomly arranged by row, and each row X[i,:] (e.g. Figure 2 (as shown), and X is used as the input data for the accident consequence assessment software. The random sampling matrix is ​​composed of random vectors, which can be permuted or not. For example, the random vectors can be permuted to obtain a random sampling matrix, or the random sampling matrix can be obtained without permutation. The permutation or non-permutation has no effect on the final result.

[0076] In an embodiment of the present invention, the arranged random sampling matrix is ​​used as input data for the accident consequence evaluation software for calculation. The accident consequence evaluation result is obtained by inputting each row into the accident consequence evaluation software in sequence for calculation, and a corresponding accident consequence evaluation result is obtained each time. It is not a whole input.

[0077] Specifically, after the random sampling of the input parameters is completed, the nuclear power plant potential accident consequence evaluation software (i.e., the accident consequence evaluation software) can be directly called to perform calculation using the multiple sets of input data X generated by the sampling to obtain corresponding accident consequence evaluation results, i.e., output results (such as relative air concentration X / Q, effective dose, thyroid dose, etc.). In the present application, the number n of single output results can be automatically identified, and the output results can be stored as a matrix D of k rows and n columns, which has an adaptation function of an interface of the accident consequence evaluation software and can automatically process data interaction and format conversion.

[0078] Step S500: performing uncertainty analysis calculation based on the accident consequence evaluation results to obtain uncertainty analysis results of the input parameters.

[0079] Specifically, the output results are calculated by the accident consequence evaluation software, and the uncertainty analysis results of the input parameters are calculated according to the uncertainty calculation formula, so as to realize the uncertainty analysis results of the input parameters. The uncertainty analysis results of the input parameters are calculated by the following formula:

[0080]

[0081] (2) In the formula, U(k) represents the uncertainty corresponding to the parameter set of the kth iteration sampling; D is the result calculated by sequentially inputting the average value of the input parameters into the accident consequence evaluation software; and D(k) is the dose calculated by using the kth set of sampling parameters.

[0082] The deviation between the kth calculation value and the best estimation value can be obtained by formula (2). A distribution can be composed of the series of U(k), and the meaningful quantities are the average value of U(k) and U0.67, U0.95, etc., i.e., the U values when 67% and 95% of the U distribution appear, which gives the uncertainty values of the mode under the confidence levels of 67% and 95%.

[0083] Step S600: performing sensitivity analysis based on the accident consequence evaluation results to obtain sensitivity analysis results of the input parameters.

[0084] Optionally, in the embodiments of the present application, the sensitivity analysis results include single-variable sensitivity analysis results and global sensitivity analysis results.

[0085] In some embodiments, the sensitivity analysis based on the accident consequence evaluation results to obtain the sensitivity analysis results of the input parameters includes: performing single-variable sensitivity analysis based on the accident consequence evaluation results to obtain single-variable sensitivity analysis results; and performing global sensitivity analysis based on the accident consequence evaluation results to obtain global sensitivity analysis results.

[0086] Specifically, the output result of the calculation is subjected to single variable sensitivity analysis and global sensitivity analysis. The single variable sensitivity analysis calculates the influence of the change of each input parameter on the output variance, and determines the sensitivity index, i.e. the sensitivity analysis result of each input parameter. The global sensitivity analysis is based on Sobol method to calculate the sensitivity index, analyzes the interaction effect between the input parameters, generates a sensitivity analysis report, and determines the influence and importance ranking of each input parameter on the accident consequence evaluation.

[0087] Single variable sensitivity analysis:

[0088] Specifically, the sampling and accident consequence evaluation software can be repeatedly called to calculate m times (the number of sensitivity analysis parameters) to obtain m random sampling matrices X, m sets of output results T calculated by the accident consequence evaluation software, and corresponding coding 1, 2, 3,..., m, the random sampling matrix is numbered as X1, X2,..., Xm, and the result calculated by the consequence evaluation software is numbered as T1, T2,..., Tm.

[0089] The ith column of the ith random sampling matrix is increased (or decreased) by p%, and the accident consequence evaluation software is input to obtain the calculation result H, which is numbered as H1, H2,..., Hm. The value of p is determined as follows: for the first estimation mode, the calculation is increased (or decreased) by 5%, if the average increase of the result is greater than 5%, it indicates that the result is relatively sensitive to parameter change, then the second calculation is reduced to 2.5%, the final calculation result is saved; if the average increase of the result is less than 5%, it indicates that the result is not sensitive to parameter change, then the second calculation is increased to 10%, the calculation result is compared; the calculation is similar to each increase of 5%.

[0090] T1, T2,..., Tm, H1, H2,..., Hm are respectively calculated by column, and the variance is recorded as VT1, VT2,..., VTm, VH1, VH2,..., VHm, which are all 1 row n column matrices. M1(i) = (VH1(i)-VT1(i)) / VT1(i); i takes 1, 2,..., n, M1(i) is the single variable sensitivity analysis result of the first input parameter for the ith output result, and similarly, the single variable sensitivity analysis results of all input parameters can be obtained.

[0091] Global sensitivity analysis:

[0092] The number of input parameters and the value range of each input parameter are sampled by using the built-in saltelli sampling method, and the accident consequence evaluation software is called to calculate the accident consequence evaluation result. The Sobol sensitivity index is calculated by using the result.

[0093] Step S700: comprehensive analysis is performed based on the uncertainty analysis result and the sensitivity analysis result, and a comprehensive analysis result is obtained.

[0094] Specifically, the input parameters in the potential accident consequence assessment of the nuclear power plant are comprehensively and systematically analyzed in combination with the uncertainty analysis result, the single-variable sensitivity analysis result and the global sensitivity analysis result, that is, the uncertainty information of the parameters obtained in the uncertainty analysis result is compared and fused with the single-variable sensitivity index and the global sensitivity index of each input parameter in the sensitivity analysis result to obtain a comprehensive analysis result. Specifically, the sensitivity indexes calculated by the single-variable sensitivity analysis and the global sensitivity analysis are compared, and the input parameters with the same results calculated by the two methods are fused to obtain a final sensitivity fusion result. By comparing the uncertainty analysis result and the sensitivity fusion result of different input parameters, it can be determined which input parameter has the most significant impact on the assessment result and which input parameter has greater uncertainty, thereby providing support for the reliability of the assessment result.

[0095] Step S800: determining the impact result of the input parameter according to the comprehensive analysis result.

[0096] In some embodiments, determining the impact result of the input parameter according to the comprehensive analysis result includes: prioritizing each input parameter according to the comprehensive analysis result to obtain the priority of each input parameter; and determining the impact result of the input parameter according to the priority of each input parameter. Specifically, according to the sensitivity analysis result of each input parameter, it can be determined which input parameter has a greater impact on the assessment result. For example, the sensitivity of the input parameter can be calculated according to the average of the single-variable sensitivity analysis result and the global sensitivity analysis result, and then the sensitivity can be sorted from large to small according to the size. The priority ranking reflects the importance of each parameter in the accident consequence assessment, which can help decision makers identify key parameters. For those parameters with high sensitivity and greater uncertainty, more attention should be paid in actual operation to ensure the safety of the nuclear power plant and the accuracy of the emergency response.

[0097] Further, the input parameter impact analysis method for the nuclear power plant accident consequence assessment further includes: outputting an optimization decision suggestion according to the comprehensive analysis result.

[0098] Specifically, through in-depth analysis of the comprehensive analysis result, targeted decision suggestions can be provided based on the comprehensive analysis result. For example, when the uncertainty of some input parameters is high, the system can suggest increasing the collection of related data or adjusting the relevant control strategy. Through multi-angle analysis, the safety management measures and emergency response strategies of the nuclear power plant are optimized to ensure that the nuclear power plant can make the most reasonable response when a potential accident occurs. For another example, the uncertainty can be calculated to determine whether an unacceptable uncertainty is generated. For example, if the uncertainty of 67% exceeds three times the nominal value or is less than 1 / 3 of the nominal value, it needs to be highly valued. Through sensitivity analysis, it can be determined which input parameters have a greater impact on the result, and the main control method is provided.

[0099] Reference Figure 3 The application further provides a nuclear power plant accident consequence evaluation input parameter influence analysis system.

[0100] As Figure 3 shown, the nuclear power plant accident consequence evaluation input parameter influence analysis system comprises:

[0101] A parameter acquisition unit 301 is configured to acquire parameters required for evaluation.

[0102] A parameter screening and setting unit 302 is configured to screen and set the parameters required for evaluation, obtain input parameters and a value range of the input parameters, and generate a parameter information table based on the input parameters and the value range of the input parameters.

[0103] A random sampling unit 303 is configured to perform random sampling based on the input parameters to generate a random sampling matrix.

[0104] An accident consequence evaluation unit 304 is configured to call an accident consequence evaluation software and perform calculation using the random sampling matrix to obtain an accident consequence evaluation result.

[0105] An uncertainty analysis unit 305 is configured to perform uncertainty analysis calculation based on the accident consequence evaluation result to obtain an uncertainty analysis result of the input parameters.

[0106] A sensitivity analysis unit 306 is configured to perform sensitivity analysis based on the accident consequence evaluation result to obtain a sensitivity analysis result of the input parameters.

[0107] A comprehensive analysis unit 307 is configured to perform comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result.

[0108] A parameter influence analysis unit 308 is configured to determine an influence result of the input parameters according to the comprehensive analysis result.

[0109] The application can achieve the following advantages:

[0110] Enhancing the reliability of assessment results: By conducting in-depth analysis of the uncertainty and sensitivity of input parameters, the present application can effectively quantify the degree of influence of each parameter on the assessment results of accident consequences. This quantitative analysis provides a solid theoretical basis for the reliability of the assessment results. Compared with traditional methods of nuclear power plant accident consequence assessment, the present application can significantly reduce the assessment deviation caused by parameter uncertainty, and improve the accuracy and reliability of the assessment results.

[0111] Providing accurate safety decision support: The present application can quickly identify key influencing parameters by comprehensively analyzing the influence of input parameters on accident consequence assessment, and provide clear safety decision guidance for decision makers. This provides scientific basis for the safety management, environmental impact assessment, emergency response plan, etc. of nuclear power plants, so as to ensure that the nuclear power plant can make accurate decisions in time when a potential accident occurs, and effectively protect the safety of the public and the environment.

[0112] Improving the efficiency of emergency response: Combined with the results of sensitivity analysis and uncertainty analysis, the present application can quickly identify the key influencing factors and uncertainty sources of accident consequences, and help nuclear power plant emergency management personnel to prioritize the most important and most uncertain factors when facing unexpected situations. Through accurate decision support, the efficiency and effectiveness of nuclear power plant emergency response can be greatly improved, and the potential risk in the event of an accident can be reduced.

[0113] Optimizing the safety management strategy of nuclear power plants: The present application not only can identify the assessment results of potential accident consequences of nuclear power plants, but also can help to optimize the daily safety management strategy of nuclear power plants. Through the sensitivity and uncertainty analysis of input parameters, management personnel can more accurately adjust the operation strategy and emergency measures, optimize resource allocation, and improve the overall safety and stability of nuclear power plants.

[0114] Specifically, the specific cooperation process between each unit in the input parameter influence analysis system for nuclear power plant accident consequence assessment can refer to the above-mentioned input parameter influence analysis method for nuclear power plant accident consequence assessment, which will not be repeated here.

[0115] In addition, an electronic device of the present application includes a memory and a processor; the memory is configured to store a computer program; and the processor is configured to execute the computer program to implement the input parameter influence analysis method for nuclear power plant accident consequence evaluation according to any one of the above. Specifically, according to the embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product including a computer program carried on a computer readable medium, the computer program including program code for executing the method shown in the flowchart. 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, desktop, tablet computer, smart phone, or other terminal, or can be a server.

[0116] In addition, a storage medium of the present application has a computer program stored thereon, and the computer program is executed by a processor to implement the input parameter influence analysis method for nuclear power plant accident consequence evaluation according to any one of the above. Specifically, it should be noted that the storage medium of the present application described above can be a computer readable signal medium or a computer readable storage medium or any combination of the above. 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.

[0117] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0118] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0119] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0120] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0121] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the present invention and implement it accordingly. They are not intended to limit the scope of protection of the present invention. All equivalent variations and modifications within the scope of the claims of the present invention are intended to be covered by the claims of the present invention.

Claims

1. A method for analyzing the impact of input parameters on nuclear power plant accident consequence assessment, characterized in that: The following steps are involved: Obtain the parameters required for evaluation; Screening and setting the parameters required for the evaluation, obtaining input parameters and value ranges of the input parameters, and generating a parameter information table based on the input parameters and the value ranges of the input parameters; Perform random sampling based on the input parameters to generate a random sampling matrix; Calling accident consequence evaluation software, using the random sampling matrix to perform calculations, and obtaining accident consequence evaluation results; Performing uncertainty analysis calculation based on the accident consequence evaluation result to obtain uncertainty analysis results of the input parameters; Performing a sensitivity analysis based on the accident consequence assessment results to obtain sensitivity analysis results of the input parameters; Perform a comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result; The impact of the input parameters is determined based on the comprehensive analysis results.

2. The input parameter impact analysis method for nuclear power plant accident consequence assessment according to claim 1, characterized in that: The method further comprises: Output optimization decision suggestions based on the comprehensive analysis results.

3. The input parameter impact analysis method for nuclear power plant accident consequence assessment according to claim 1, characterized in that: The performing random sampling based on the input parameters to generate a random sampling matrix includes: determining a probability level and a confidence level for the input parameter; Performing calculations based on the probability level and the confidence level to obtain a minimum number of calculation conditions; Determining the distribution type and number of the input parameters; The random sampling matrix is ​​generated according to the distribution type of the input parameters, the number of the input parameters, and the minimum number of calculation conditions.

4. The input parameter impact analysis method for nuclear power plant accident consequence assessment according to claim 1, characterized in that: The calling of the accident consequence evaluation software and the use of the random sampling matrix to perform calculations to obtain the accident consequence evaluation results include: The random sampling matrix is ​​used as input data of the accident consequence evaluation software to perform calculations to obtain the accident consequence evaluation results.

5. The input parameter impact analysis method for nuclear power plant accident consequence assessment according to claim 1, characterized in that: The uncertainty analysis results of the input parameters are calculated by the following formula: Where U(k) represents the uncertainty corresponding to the k-th iterative sampling parameter set; D is the result calculated by inputting the average value of the input parameters into the accident consequence assessment software in sequence; and D(k) is the dose calculated using the k-th set of sampling parameters.

6. The input parameter impact analysis method for nuclear power plant accident consequence assessment according to claim 1, characterized in that: The sensitivity analysis results include: univariate sensitivity analysis results and global sensitivity analysis results; The performing of sensitivity analysis based on the accident consequence assessment result to obtain the sensitivity analysis result of the input parameter includes: Performing a univariate sensitivity analysis based on the accident consequence evaluation result to obtain the univariate sensitivity analysis result; A global sensitivity analysis is performed based on the accident consequence evaluation result to obtain the global sensitivity analysis result.

7. The input parameter impact analysis method for nuclear power plant accident consequence assessment according to claim 1, characterized in that: Determining the impact of the input parameters according to the comprehensive analysis results includes: Prioritizing each input parameter according to the comprehensive analysis result to obtain the priority of each input parameter; The impact results of the input parameters are determined according to the priorities of the input parameters.

8. An input parameter impact analysis system for nuclear power plant accident consequence assessment, characterized in that: include: A parameter acquisition unit, used to obtain parameters required for evaluation; a parameter screening and setting unit, configured to screen and set parameters required for the evaluation, obtain input parameters and value ranges of the input parameters, and generate a parameter information table based on the input parameters and the value ranges of the input parameters; A random sampling unit, configured to perform random sampling based on the input parameters to generate a random sampling matrix; An accident consequence evaluation unit, configured to call the accident consequence evaluation software, perform calculations using the random sampling matrix, and obtain an accident consequence evaluation result; An uncertainty analysis unit, configured to perform uncertainty analysis calculation based on the accident consequence assessment result to obtain an uncertainty analysis result of the input parameter; A sensitivity analysis unit, configured to perform a sensitivity analysis based on the accident consequence assessment result to obtain a sensitivity analysis result of the input parameter; A comprehensive analysis unit, configured to perform a comprehensive analysis based on the uncertainty analysis result and the sensitivity analysis result to obtain a comprehensive analysis result; A parameter impact analysis unit is used to determine the impact result of the input parameter based on the comprehensive analysis result.

9. A storage medium, characterized in that: The storage medium stores a computer program, which is suitable for being loaded by a processor to execute the steps of the input parameter impact analysis method for nuclear power plant accident consequence assessment according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the steps of the input parameter impact analysis method for nuclear power plant accident consequence assessment as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

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