A method and system for module division of a small reactor

Abstract

The application relates to a small modular reactor (SMR) module division method and system, comprising the following steps: determining correlation information of system components of an SMR system according to nuclear power design information, and constructing a comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components; performing hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system, and obtaining a hierarchical clustering result of the system components of the SMR system; determining a target function and a constraint condition of SMR system module division; coding all modules in the hierarchical clustering result of the system components of the SMR system, and optimizing the module division of the SMR system, to obtain a target module division scheme. Through the application, the module division design efficiency, design quality and applicability can be improved, the rationality of the module division can be optimized, and the difficulty of system upgrading and maintenance can be reduced, and the system scalability and flexibility can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power reactor system design, and more particularly to a small reactor module division method and system. BACKGROUND

[0002] Nuclear power, as a cost-effective power generation technology, can provide clean energy comparable to other renewable energy technologies. Small modular reactors (SMRs), as a new generation of nuclear energy technology, generally refer to nuclear reactors with an electric power of less than 300 Mwe. Due to their high flexibility, ease of deployment, and adaptability, SMRs have become an important direction for future nuclear energy development. Since the design of SMRs needs to adapt to limited space, and its design and construction involve multiple complex subsystems and components, it is essential to use an efficient and accurate module division design method to improve the overall efficiency and reliability of the system.

[0003] Traditional module division methods mainly rely on the experience of designers, and have strong subjectivity and uncertainty. At the same time, when dealing with complex systems, it is easy to cause insufficient module division and high module coupling, thereby increasing the difficulty of system upgrade and maintenance, and reducing the scalability and flexibility of the system. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a small reactor module division method and system to solve the problems in the prior art.

[0005] The technical solution adopted by the present application to solve the technical problem is: a small reactor module division method is constructed, comprising the following steps:

[0006] According to the nuclear power design information, the correlation information of the system components of the SMR system is determined, and the comprehensive correlation matrix of the system components of the SMR system is constructed according to the correlation information of the system components;

[0007] According to the comprehensive correlation matrix of the system components of the SMR system, hierarchical module division is performed to obtain the hierarchical clustering result of the system components of the SMR system;

[0008] The objective function and constraint condition of the SMR system module division are determined;

[0009] All modules in the hierarchical clustering result of the system components of the SMR system are coded, and the module division of the SMR system is optimized to obtain a target module division scheme.

[0010] In the module division method of the small reactor, the system components include various devices and components of the reactor and its auxiliary systems; the nuclear power design information includes nuclear power design specifications and design criteria of the SMR system;

[0011] The determining of the correlation information of the system components of the SMR system according to the nuclear power design information includes:

[0012] The classification and collection based on the nuclear power design specifications and the design criteria of the SMR system obtain the correlation information of the system components; the correlation information of the system components includes functional correlation between the system components, connection correlation between the system components and spatial correlation between the system components.

[0013] In the module division method of the small reactor, the construction of the comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components includes:

[0014] The functional correlation of the system components, the connection correlation of the system components and the spatial correlation between the system components are obtained;

[0015] The comprehensive correlation matrix of the system components is obtained based on the functional correlation of the system components, the connection correlation of the system components and the spatial correlation between the system components.

[0016] In the module division method of the small reactor, the functional correlation between the system components includes a first functional correlation evaluation index, a second functional correlation evaluation index, a third functional correlation evaluation index and a fourth functional correlation evaluation index;

[0017] The first functional correlation evaluation index is that the system components jointly and cooperatively complete the same sub-function, and the system components are indispensable; the correlation value of the first functional correlation evaluation index is 1;

[0018] The second functional correlation evaluation index is a strong functional auxiliary relationship; the correlation value of the second functional correlation evaluation index is 0.5-0.9;

[0019] The third functional correlation evaluation index is a weak functional auxiliary relationship; the correlation value of the third functional correlation evaluation index is 0.1-0.4;

[0020] The fourth functional correlation evaluation index is no functional relationship; the correlation value of the fourth functional correlation evaluation index is 0.

[0021] In the module division method of the small reactor, the connection correlation between the system components includes a first connection correlation evaluation index and a second connection correlation evaluation index;

[0022] The first connection-related evaluation index is direct physical connection, and the correlation value of the first connection-related evaluation index is 1;

[0023] The second connection-related evaluation index is no direct physical connection, and the correlation value of the second connection-related evaluation index is 0.

[0024] In the module division method of the small reactor, the spatial correlation between the system components includes a first spatial correlation evaluation index and a second spatial correlation evaluation index.

[0025] The first spatial correlation evaluation index is located in the same spatial region, and the correlation value of the first spatial correlation evaluation index is 1;

[0026] The second spatial correlation evaluation index is located in different cabins or spatial regions, and the correlation value of the second spatial correlation evaluation index is 0.

[0027] In the module division method of the small reactor, the hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system includes:

[0028] According to the comprehensive correlation matrix of the system components, a fuzzy hierarchical clustering method is used to perform hierarchical module division on the system components of the SMR system, and a hierarchical clustering result of the system components of the SMR system is obtained.

[0029] In the module division method of the small reactor, the hierarchical module division according to the comprehensive correlation matrix of the system components, using a fuzzy hierarchical clustering method to perform hierarchical module division on the system components of the SMR system, includes:

[0030] Based on the comprehensive correlation matrix of the system components, a distance matrix between components is calculated.

[0031] A fuzzy hierarchical clustering method is used to perform hierarchical clustering, and a hierarchical clustering tree of the system components is obtained.

[0032] A non-uniform granularity clustering function is used to combine clustering results at different levels, and a hierarchical clustering result of the system components is obtained.

[0033] In the module division method of the small reactor, the hierarchical clustering using a fuzzy hierarchical clustering method to obtain a hierarchical clustering tree of the system components includes:

[0034] Each system component is taken as an independent initial cluster.

[0035] In each iteration, the two clusters with the smallest distance are merged to generate a new cluster, and the distance between the new cluster and all other clusters is recalculated, and the step is repeated until all system components are merged into one cluster, generating the hierarchical clustering tree.

[0036] In the module division method of the small modular reactor, the target function and the constraint condition of the SMR system module division include:

[0037] Based on the comprehensive correlation matrix and the hierarchical clustering result, the comprehensive correlation coefficient between system components, the number of modules, and the number of components in the module are determined;

[0038] According to the comprehensive correlation coefficient between system components, the number of modules, and the number of components in the module, the target function is determined;

[0039] The constraint condition is determined based on the constraint function and the target function.

[0040] In the module division method of the small modular reactor, the target function includes: coupling degree evaluation function, cohesion degree evaluation function, module number function, module size limit evaluation function, and module weight limit evaluation function;

[0041] The target function is determined according to the comprehensive correlation coefficient between system components, the number of modules, and the number of components in the module, including:

[0042] The coupling degree evaluation function is determined according to the comprehensive correlation coefficient between system components and the number of components in the module;

[0043] The cohesion degree evaluation function is determined according to the comprehensive correlation coefficient between system components and the number of modules;

[0044] The module number function is obtained according to the number of modules;

[0045] The module size limit evaluation function is determined according to the physical size of the module;

[0046] The module weight limit evaluation function is determined according to the total weight of the module and the predetermined upper limit of the weight.

[0047] In the module division method of the small modular reactor, the constraint condition includes: minimizing the coupling degree between modules, maximizing the cohesion degree within the module, minimizing the number of modules, component coverage constraint, and module size and weight limit constraint.

[0048] In the module division method of the small reactor, all modules in the hierarchical clustering result of the system components of the SMR system are encoded, and the target module division scheme is obtained by optimizing the system modularization division of the SMR system.

[0049] All modules in the hierarchical clustering result of the system components of the SMR system are encoded, and the target module division scheme is obtained by optimizing the system modularization division of the SMR system using the fast non-dominated sorting genetic algorithm.

[0050] In the module division method of the small reactor, all modules in the hierarchical clustering result of the system components of the SMR system are encoded, and the target module division scheme is obtained by optimizing the system modularization division of the SMR system using the fast non-dominated sorting genetic algorithm.

[0051] S4.1: Determine the encoding method;

[0052] S4.2: Encode all modules in the hierarchical clustering result according to the encoding method, and establish a component-module construction matrix;

[0053] S4.3: Randomly generate an initial population under the premise of meeting the constraint condition according to the encoding method and the component-module construction matrix;

[0054] S4.4: Non-dominated sorting is performed on the initial population to generate different non-dominated levels;

[0055] S4.5: The crowding degree of individuals in each non-dominated level is calculated to obtain the crowding degree of individuals in each non-dominated level;

[0056] S4.6: According to the non-dominated level and the crowding degree, selection, crossover and mutation operations are performed to generate new generation of offspring individuals;

[0057] S4.7: Combine the parent and child individuals to form a hybrid population, and re-perform non-dominated sorting and crowding degree calculation on the hybrid population to construct the next generation population;

[0058] S4.8: Determine the target solution of the previous generation;

[0059] S4.9: Repeat steps S4.4 to S4.8 until the iteration termination condition is met, and output the target module division scheme.

[0060] In the module division method of the small reactor, the encoding method includes a module encoding method and a scheme encoding method;

[0061] The module coding mode comprises: adopting a Boolean vector to represent each module; and each bit of the Boolean vector represents the presence or absence of a component.

[0062] The scheme coding mode comprises: adopting a Boolean vector to represent the entire module division scheme; and each bit of the Boolean vector represents the presence or absence of a module.

[0063] In the module division method of the small reactor, the method further comprises:

[0064] Performing feasibility analysis and verification based on the target module division scheme;

[0065] Determining whether the target module division scheme passes according to the analysis and verification results;

[0066] If yes, the target module division scheme is determined;

[0067] If no, the target module division scheme is reselected and determined.

[0068] The application further provides a module division system of a small reactor, comprising:

[0069] A comprehensive matrix construction unit is configured to determine the correlation information of system components of an SMR system according to nuclear power design information, and construct a comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components.

[0070] A module division unit is configured to perform hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system, and obtain a hierarchical clustering result of the system components of the SMR system.

[0071] A function condition determination unit is configured to determine a target function and a constraint condition of the module division of the SMR system.

[0072] A target scheme determination unit is configured to code all modules in the hierarchical clustering result of the system components of the SMR system, and optimize the module division of the SMR system to obtain a target module division scheme.

[0073] In the module division system of the small reactor, the system further comprises:

[0074] A target scheme verification unit is configured to:

[0075] Perform feasibility analysis and verification based on the target module division scheme;

[0076] Determine whether the target module division scheme passes according to the analysis and verification results;

[0077] If yes, the target module division scheme is determined;

[0078] If no, reselect to determine the target module division scheme.

[0079] The module division method and system of the small modular reactor provided by the application have the following beneficial effects: the method comprises the following steps: determining the correlation information of system components of an SMR system according to nuclear power design information, and constructing a comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components; performing hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system to obtain a hierarchical clustering result of the system components of the SMR system; determining a target function and a constraint condition of the module division of the SMR system; encoding all modules in the hierarchical clustering result of the system components of the SMR system, and optimizing the module division of the SMR system to obtain a target module division scheme. The method provided by the application can improve the design efficiency, design quality and applicability of the module division, and can also optimize the rationality of the module division, thereby reducing the difficulty of system upgrading and maintenance and improving the scalability and flexibility of the system. BRIEF DESCRIPTION OF DRAWINGS

[0080] The application will be further described below in combination with the drawings and embodiments, wherein:

[0081] Figure 1 Fig. 1 is a flowchart of the module division method of the small modular reactor provided by the application;

[0082] Figure 2 Fig. 2 is a detailed flowchart of the module division method of the small modular reactor provided by the application;

[0083] Figure 3 Fig. 3 is a principle block diagram of the module division system of the small modular reactor provided by the application;

[0084] Figure 4 Fig. 4 is a schematic diagram of the module division of the system provided by the application. DETAILED DESCRIPTION

[0085] The technical solutions in the embodiments of the application will be clearly and completely described below in combination with the drawings in 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 in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0086] In order to solve the problems in the prior art, the application provides a module division method of a small modular reactor, which can realize efficient and accurate module division of an SMR system, reduce the coupling degree between modules, improve the cohesion degree of the modules, and improve the maintainability and scalability of the system.

[0087] Reference Figure 1 , Figure 1 The flowchart of a preferred embodiment of the module division method of the small reactor provided by the present application is shown.

[0088] Specifically, as shown in the figure, the module division method of the small reactor includes the following steps: Figure 1

[0089] Step S1: determining the relevance information of the system components of the SMR system according to the nuclear power design information, and constructing the comprehensive relevance matrix of the system components of the SMR system according to the relevance information of the system components.

[0090] In the embodiment of the present application, the SMR system is a small nuclear reactor with an electric power of less than 300MWe. The system components include various devices and components of the reactor and its auxiliary systems. Optionally, in the embodiment of the present application, the nuclear power design information includes the nuclear power design specification and the design criteria of the SMR system. The determination of the relevance information of the system components of the SMR system according to the nuclear power design information includes classification and summarization based on the nuclear power design specification and the design criteria of the SMR system to obtain the relevance information of the system components. Optionally, the relevance information of the system components includes the functional relevance between system components, the connection relevance between system components, and the spatial relevance between system components.

[0091] Specifically, the functional relevance between system components can be represented by , which reflects the degree of mutual cooperation or dependence of two components in the system function implementation process. System components with high functional relevance usually need to closely cooperate or frequently interact when performing tasks. Optionally, in the embodiment of the present application, the functional relevance between system components includes a first functional relevance evaluation index, a second functional relevance evaluation index, a third functional relevance evaluation index, and a fourth functional relevance evaluation index.

[0092] The first functional relevance evaluation index is that the system components jointly cooperate to complete the same sub-function, and the system components are indispensable; the relevance value of the first functional relevance evaluation index is 1. The second functional relevance evaluation index is a strong functional auxiliary relationship; the relevance value of the second functional relevance evaluation index is 0.5-0.9. The third functional relevance evaluation index is a weak functional auxiliary relationship; the relevance value of the third functional relevance evaluation index is 0.1-0.4. The fourth functional relevance evaluation index is no functional relationship; the relevance value of the fourth functional relevance evaluation index is 0.

[0093] The connection relevance between system components is represented by ​reflects the degree of direct association of components in physical structure. Optionally, in the embodiment of the present application, the connection correlation between system components includes: a first connection correlation evaluation index and a second connection correlation evaluation index. The first connection correlation evaluation index is: direct physical connection; the correlation value of the first connection correlation evaluation index is 1; the second connection correlation evaluation index is: no direct physical connection; the correlation value of the second connection correlation evaluation index is: 0.

[0094] The spatial correlation between system components is reflected by the relative distribution of components in the installation position. Optionally, in the embodiment of the present application, the spatial correlation between system components includes: a first spatial correlation evaluation index and a second spatial correlation evaluation index. The first spatial correlation evaluation index is: located in the same space area; the correlation value of the first spatial correlation evaluation index is 1; the second spatial correlation evaluation index is: located in different cabins or space areas; the correlation value of the second spatial correlation evaluation index is 0.

[0095] In the embodiment of the present application, the construction of the comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components includes: obtaining the functional correlation of the system components, the connection correlation of the system components and the spatial correlation between the system components; based on the functional correlation of the system components, the connection correlation of the system components and the spatial correlation between the system components, the comprehensive correlation matrix of the system components is obtained. Specifically, the comprehensive correlation matrix of the system components can be composed of the functional correlation between the system components, the connection correlation between the system components and the spatial correlation between the system components, and the specific function expression is as follows:

[0096] (1).

[0097] (1) in which, is a comprehensive correlation matrix function, , , respectively, the weight coefficients of the functional correlation, the connection correlation and the spatial correlation. At the same time, the set of system components of the SMR system is defined as .

[0098] Step S2: hierarchical module division is performed according to the comprehensive correlation matrix of the system components of the SMR system, and the hierarchical clustering result of the system components of the SMR system is obtained.

[0099] In the embodiment of the present application, the hierarchical module division is performed according to the comprehensive correlation matrix of the system components of the SMR system, and the hierarchical clustering result of the system components of the SMR system is obtained, including: according to the comprehensive correlation matrix of the system components, the hierarchical module division of the system components of the SMR system is performed by using a fuzzy hierarchical clustering method, and the hierarchical clustering result of the system components of the SMR system is obtained.

[0100] The fuzzy hierarchical method includes: based on the comprehensive correlation matrix of the system components, a distance matrix between the components is calculated; the hierarchical clustering is performed by using the fuzzy hierarchical clustering method, and a hierarchical clustering tree of the system components is obtained; and the hierarchical clustering results of different levels are combined by using a non-uniform granularity clustering function, and the hierarchical clustering result of the system components is obtained. Specifically, the distance matrix between the system components is calculated first, then the hierarchical clustering is performed by using the fuzzy hierarchical clustering method, the hierarchical clustering tree of the system components is constructed, and finally the hierarchical clustering results of different levels are combined by using the non-uniform granularity clustering function, and the module set including all the modules is counted. By combining the clustering results of different levels, the relationship between each component can be better understood, that is, the effects of comprehensive coverage (ensuring that all components are reasonably classified into a certain module without omission), flexible adjustment (some components have close relationships and are suitable to be placed together; some components have not so close relationships and are suitable to be separated. The hierarchical clustering of different levels helps us to flexibly adjust the size and content of each module), and more reasonable module division (combining the information of large and small categories, ensuring that each module can meet the overall functional requirements and handle specific requirements in details) can be achieved.

[0101] Specifically, after the classification of all the components is completed (that is, all the components are classified into different modules), module counting needs to be performed. By counting the modules, the completeness of the module division can be ensured (confirming that all the components are included in a certain module without omitting any part), the module division can be optimized (by counting and analyzing these modules, we can consider which modules have close relationships between the components (high cohesion), and which modules have less dependency between the modules (low coupling). In this way, the module division can be adjusted so that each module has good internal cooperation and high independence between the modules, which is convenient for maintenance and upgrading), and preparation for further optimization can be made (after counting the module set, a genetic algorithm (NSGA-II) is used to further optimize the module division. The algorithm can find the optimal module division scheme according to the set target (such as reducing the coupling degree, improving the cohesion, reducing the number of modules, etc.).

[0102] The distance matrix D between the system components is:

[0103] (2).

[0104] (2) In the formula, the comprehensive correlation matrix , represents the distance between the set components and components , and the smaller the distance, the higher the correlation between the two components, and the more likely to be classified into a class in the clustering process.

[0105] In the embodiments of the application, the mean linkage method can be used to calculate the distance between system components or modules, and the specific formula (3) can be seen.

[0106] (3).

[0107] (3) In the formula, respectively represent the number of components contained in the module and , Cm (module m): represents the mth module. Cn (module n): represents the nth module. d mn represents the distance between module m and module n. d mn is used to measure the similarity or correlation between two modules. In hierarchical clustering, the smaller the distance, the higher the similarity of the two modules, and the more likely to be merged into a larger module.

[0108] Formula (3) is to calculate the distance between modules using the mean linkage method (Average Linkage Method). The specific steps are as follows:

[0109] 1. Initial stage: each component is an independent module, and there are multiple single-component modules at the beginning.

[0110] 2. Calculate the distance between modules: use the mean linkage method to calculate the distance d mn between any two modules Cm and Cn. The calculation formula of the mean linkage method is usually: , where |Cm| and |Cn| are the number of components contained in modules Cm and Cn, respectively, and d(i, j) is the distance between components i and j.

[0111] 3. Merge modules: find the two modules Cm and Cn with the smallest distance dmn, and merge them into a new module. Update the module set and recalculate the distance between the new module and other modules.

[0112] 4. Repeat iteration: continue module merging until the stop condition is met (for example, all components are merged into a module, or the predetermined number of modules is reached).

[0113] This method captures multi-level relationships: by calculating the distance between different modules, the relationships between components at different levels can be effectively captured. This facilitates module partitioning at different granularities, ensuring that modules cover both overall functionality and specific detailed requirements. Optimizing module partitioning: by continuously merging modules with the smallest distance, the cohesion (the tightness of components within a module) and the coupling (the degree of dependency between modules) of modules can be gradually optimized, improving the overall performance and maintainability of the system. Providing a foundation for subsequent optimization: statistically analyzing the module set containing all modules ensures that all components are reasonably assigned to a module. This provides a comprehensive foundation for further optimization using genetic algorithms (such as NSGA-II).

[0114] In this embodiment of the invention, the hierarchical clustering method using fuzzy hierarchical clustering to obtain a hierarchical clustering tree of system components includes: treating each system component as an independent initial cluster; merging the two clusters with the smallest distance in each iteration to generate a new cluster, recalculating the distance between the new cluster and all other clusters, and repeating this step until all system components are merged into one cluster to generate the hierarchical clustering tree.

[0115] Step S3: Determine the objective function and constraints for the SMR system module partitioning.

[0116] In this embodiment of the invention, determining the objective function and constraints for the SMR system module partitioning includes: determining the comprehensive correlation coefficient between system components, the number of modules, and the number of components within each module based on the comprehensive correlation matrix and the hierarchical clustering results; determining the objective function based on the comprehensive correlation coefficient between system components, the number of modules, and the number of components within each module; and determining the constraints based on the constraint function and the objective function. The objective function includes: a coupling degree evaluation function, a cohesion evaluation function, a module number function, a module size constraint evaluation function, and a module weight constraint evaluation function.

[0117] In this embodiment of the invention, determining the objective function based on the comprehensive correlation coefficient between the system components, the number of modules, and the number of components in each module includes: determining the coupling degree evaluation function based on the comprehensive correlation coefficient between the system components and the number of components in each module; determining the cohesion evaluation function based on the comprehensive correlation coefficient between the system components and the number of modules; obtaining the module number function based on the number of modules; determining the module size limitation evaluation function based on the physical dimensions of the modules; and determining the module weight limitation evaluation function based on the total weight of the modules and a predetermined weight limit.

[0118] In the embodiment of the present application, the constraint conditions include: minimizing inter-module coupling degree, maximizing intra-module cohesion degree, minimizing module quantity, component coverage constraint, and module size and weight limit constraint.

[0119] Specifically, in step S3, the objective function of the system module division optimization mainly considers three aspects: (1) the tightness of inter-module connection, i.e., coupling degree; (2) the tightness of intra-module connection, i.e., cohesion degree; and (3) optimizing module quantity to improve the overall construction of the system, i.e., module quantity. Module size limit: ensure that the physical size of each module does not exceed the predetermined design specification and space limit, specifically including the geometric size parameters of the length, width, and height of the module; module weight limit: ensure that the total weight of each module does not exceed the designed bearing capacity, considering the needs of transportation, installation, and structural support.

[0120] The coupling degree evaluation function is represented by C, and its specific expression is:

[0121] (4).

[0122] In formula (4), C is the comprehensive correlation coefficient of components in module A and module B, and N A and N B represent the number of components contained in module A and module B, respectively. The cohesion degree evaluation function can be represented by C, and its expression is:

[0123]

[0124] (5).

[0125] In formula (5), C is the comprehensive correlation coefficient of components in module A and module B, and N A and N B represent the number of components contained in module A and module B, respectively.

[0126] The module quantity function can be represented by Q, and its expression is:

[0127] (6).

[0128] In formula (6), Q represents the module quantity.

[0129] The module size limit evaluation function Q S is:

[0130] ​​​​​​​​​​​​​​​(7).

[0131] wherein, respectively represent the length, width and height of the module . respectively are predetermined upper limits of the length, width and height.

[0132] The module weight limit evaluation function is:

[0133] (8).

[0134] wherein represents the total weight of the module . is a predetermined upper limit of the weight.

[0135] The constraint is a constraint condition established to ensure that all components in the system are divided into modules, wherein the constraint condition is used to ensure that all system components in the system are divided into corresponding modules, and the constraint function can be represented as:

[0136] (9).

[0137] In equation (9), C represents a set of all components in the system. X represents a set of modules. respectively represent empty sets. That is, in the intersection formula, ⋂Mi∈X, Mi=∅ means that no component belongs to multiple modules at the same time.

[0138] Equation (9) can ensure the completeness and uniqueness of module division. Specifically: 1. Completeness, , explanation: the union set of all modules Mi is equal to the entire component set C, ensuring that every component in the system is assigned to a module and no component is missed. 2. Uniqueness: , explanation: the intersection set of all modules Mi is empty, meaning that no component is assigned to multiple modules, ensuring that each component belongs to only one module, avoiding the sharing of the same component by multiple modules, thereby simplifying the dependency relationship between modules and reducing the complexity of the system.

[0139] With this function constraint, the following can be achieved: 1. Improve the rationality of module division: Ensure that each component has a clear attribution, avoid the increase of coupling degree between modules due to the dispersion or repeated allocation of components, and affect the maintainability and scalability of the system. 2. Optimize the optimization process of genetic algorithm: When using genetic algorithm (such as NSGA-II) to optimize module division, formula (9) as a constraint condition can guide the algorithm to consider only the module division scheme that meets the integrity and uniqueness in the search process, thereby improving the quality and practicality of the optimization result. 3. Enhance the feasibility and reliability of system design: Through strict component allocation rules, ensure the feasibility of the module division scheme in practical application, reduce errors and inconsistencies in the design process, and improve the overall reliability of system design.

[0140] Step S4: encode all modules in the hierarchical clustering result of the system components of the SMR system, and optimize the module division of the SMR system to obtain a target module division scheme.

[0141] In the embodiment of the application, encoding all modules in the hierarchical clustering result of the system components of the SMR system and optimizing the module division of the SMR system to obtain a target module division scheme includes: according to the division result of the fuzzy hierarchical clustering of the system components of the SMR system, encoding the modules, and using the fast non-dominated sorting genetic algorithm (NSGA-II) to optimize the system modularization division to obtain a target module division scheme.

[0142] Further, according to the division result of the fuzzy hierarchical clustering of the system components of the SMR system, encoding the modules, and using the fast non-dominated sorting genetic algorithm (NSGA-II) to optimize the system modularization division to obtain a target module division scheme includes:

[0143] S4.1: Determine the encoding method.

[0144] In the embodiment of the application, the encoding method includes a module encoding method and a scheme encoding method.

[0145] The module encoding method includes representing each module with a Boolean vector; each bit of the Boolean vector represents the presence or absence of a component. For a module , it is represented by a vector = , where =1 indicates that the component exists in the module , =0 represents that the component does not exist in the module The method can accurately indicate the presence or absence of each component in each module, allows the optimization algorithm to finely adjust and divide the internal structure of the module, and is suitable for scenarios where the system is complex and the module division requires fine granularity.

[0146] The scheme coding mode includes: using a Boolean vector to represent the entire module division scheme; each bit of the Boolean vector represents the presence or absence of a certain module. That is, the module division scheme , =1 represents that the component module exists in the scheme X, =0 represents that the module does not exist in the scheme X. This method uses a single Boolean vector to represent the entire module division scheme, reduces the complexity of coding, and is suitable for scenarios where the number of system components is large and the division scheme needs to be quickly generated.

[0147] It should be noted that in actual application, which coding mode to choose can be determined according to project requirements. The present application does not make specific limitations.

[0148] S4.2: Encode all modules in the hierarchical clustering result according to the coding mode, and establish a component-module construction matrix.

[0149] Specifically, according to the fuzzy hierarchical clustering division result of the system components of the SMR system and the determined coding mode, a component-module construction matrix Z is established. Based on this, the constraint function (9) can be equivalent to (10), that is:

[0150] (10).

[0151] S4.3: Randomly generate an initial population under the premise of meeting the constraint condition according to the coding mode and the component-module construction matrix.

[0152] Specifically, after the construction matrix of S4.2 is equivalent, a certain scale of initial population can be randomly generated under the premise of meeting the constraint condition according to the determined coding mode. At the same time, the objective function value of each individual (X)、 (X)、 (X)、 (X)、 (X) and set a suitable number of iterations.

[0153] S4.4: Non-dominated sorting is performed on the initial population to generate different non-dominated levels.

[0154] S4.5: Crowding Calculation: Crowding is calculated for individuals in each non-dominated level to obtain the crowding level of individuals in each non-dominated level. By calculating the crowding level of individuals in each non-dominated level, population diversity can be maintained.

[0155] S4.6: Crossover and Mutation: Based on the non-dominated hierarchy and the crowding degree, select the next generation of parent individuals and perform crossover and mutation operations on them to generate a new generation of offspring individuals.

[0156] S4.7: Generate a new population: Merge parent and offspring individuals to form a mixed population, and re-sort and calculate crowding in the mixed population to construct the next generation population. The next generation population can be constructed by selecting the top few best individuals from the obtained population.

[0157] S4.8: Determine the target solution for the previous generation.

[0158] In this embodiment of the invention, the target solution of the previous generation can be obtained using an elitist strategy. This target solution is the optimal solution of the previous generation. This method ensures that the quality of the solution set continues to improve.

[0159] S4.9: Repeat steps S4.4 to S4.8 until the iteration termination condition is met, and output the target module partitioning scheme.

[0160] Furthermore, such as Figure 1 As shown, the modular partitioning method for this small reactor also includes the following steps:

[0161] Step S5: Perform feasibility analysis and verification based on the target module partitioning scheme; determine whether the target module partitioning scheme passes the analysis and verification results; if yes, determine the target module partitioning scheme; if no, reselect to determine the target module partitioning scheme.

[0162] refer to Figure 3 The present invention also provides a modular partitioning system for small reactors, which can be applied to the modular partitioning method for small reactors disclosed in the embodiments of the present invention. Specifically, as shown... Figure 3 As shown, the modular partitioning system of this small reactor includes:

[0163] The integrated matrix construction unit 301 is used to determine the correlation information of the system components of the SMR system based on the nuclear power design information, and to construct the integrated correlation matrix of the system components of the SMR system based on the correlation information of the system components.

[0164] The module division unit 302 is configured to perform hierarchical module division according to a comprehensive correlation matrix of system components of the SMR system, to obtain a hierarchical clustering result of the system components of the SMR system.

[0165] The function condition determination unit 303 is configured to determine a target function and a constraint condition of the module division of the SMR system.

[0166] The target scheme determination unit 304 is configured to encode all modules in the hierarchical clustering result of the system components of the SMR system, and optimize the module division of the SMR system, to obtain a target module division scheme.

[0167] Further, as shown in the figure, Figure 3 the module division system of the small reactor further includes:

[0168] The target scheme verification unit 305 is configured to perform feasibility analysis and verification based on the target module division scheme, and determine whether the target module division scheme passes according to the analysis and verification result; if yes, the target module division scheme is determined; if no, the target module division scheme is reselected and determined. The feasibility analysis and verification of the target module division scheme can refer to an existing scheme, and the present application is not limited in this regard.

[0169] The following will be described in combination with specific embodiments.

[0170] As shown in the figure, Figure 2

[0171] Step 1: According to the nuclear power design specification and the SMR system design criterion, an SMR system component correlation index system is constructed.

[0172] In this embodiment, the radioactive waste gas system (the full name in English may be Radioactive Waste Gas System, WGS) of a certain SMR is modularly divided. The system is distributed in four different space regions, and the sizes are about 11.5m×4m, 4m×3m, 4m×3m and 9m×4m respectively. According to the design and function of the WGS, 27 main components (see the table, indicating the name) are selected to form a component set . According to the system function, the physical structure connection relationship and the installation position between the components, the functional correlation , the connection correlation and the space correlation matrix are constructed respectively.

[0173] According to the comprehensive correlation matrix formula (1), the weight coefficients , and ​The comprehensive correlation matrix R of WGS is calculated with the settings of 0.3, 0.3 and 0.4 respectively, as shown in Table 1.

[0174] Second step: According to the system component correlation characteristics of WGS, the fuzzy hierarchical clustering algorithm is used for clustering analysis of the system components of WGS.

[0175] According to the comprehensive correlation matrix in the first step, the distance matrix is calculated by formula (2) .

[0176] According to the distance matrix D, the clustering analysis is carried out by formula (3), and the clustering number is drawn as shown in Figure 4 . Then the clustering results of each level are combined to obtain a module set containing all modules, and the total number of modules is 42.

[0177] Third step: According to the design specification of WGS in nuclear power design specification, the optimization objective function of system module division is determined as formula (4)-(8), and the constraint function is formula (9).

[0178] Fourth step: According to the WGS modularization division result, NSGA-II is used to optimize the system modularization division, and the optimal modularization division scheme is solved.

[0179] S4.1, develop coding method, including module coding, scheme coding.

[0180] S4.2, according to the coding method in S4.1, the WGS module set is coded, and the construction matrix is established as , see Table (1), and the constraint condition (formula 9) is further established, see formula (10).

[0181] S4.3, according to the coding method, the initial population is generated under the premise of meeting the constraint condition, the population size is set to 30, and the iteration number is set to 100. At the same time, the objective function value of each individual is calculated (X)、 (X)、 (X)、 (X)、 (X) .

[0182] S4.4, non-dominated sorting: the population is quickly sorted in a non-dominated way to generate different non-dominated levels.

[0183] S4.5, calculate the crowding degree: calculate the crowding degree of each individual in the non-dominated level to maintain the diversity of the population.

[0184] S4.6, crossover and variation: based on non-dominated sorting and congestion, the next generation of parent individuals is selected, and crossover and variation operations are performed on them to generate new generation of offspring individuals. The crossover probability is set to 0.8. The mutation probability is 0.2.

[0185] S4.7, generate new population: combine parent and offspring individuals to form a mixed population, and perform non-dominated sorting and congestion calculation on the mixed population to select the top several excellent individuals to form the next generation population.

[0186] S4.8, adopt the elite strategy to retain the optimal solution of the last generation to ensure that the solution set quality continues to improve.

[0187] S4.9, repeat steps S4.4 to S4.8 until the iteration termination condition is met, the iteration results are shown in Table 1, and the final stable calculation results are:

[0188] , , , , .

[0189] The module code is: 000000 000000 000000 100000 000011 000000 000111 10, and the optimal module division scheme is shown in the following Table 1.

[0190] Table 1 Target module division scheme (optimal module division scheme)

[0191]

[0192] Step 5: According to the optimal module division scheme obtained by optimization in the fourth step, through three-dimensional modeling and comprehensive analysis of the system operation, physical structure connection and installation position of each component, etc., the efficiency, rationality and scientificity of the optimal module division scheme are finally verified and determined. Among them, the optimal module division scheme is as shown in Figure 4 .

[0193] The following effects can be achieved by using the present application:

[0194] 1. Comprehensive and systematic improvement: By constructing a comprehensive correlation matrix, the functional correlation, connection correlation and spatial correlation between the components of the SMR system are comprehensively considered, and the correlation degree between the components is quantified comprehensively. This systematic analysis method ensures comprehensive coverage in the module division process, and avoids the omission or repetition of modules caused by incomplete analysis in traditional methods.

[0195] 2. High cohesion and low coupling of module division: The fuzzy hierarchical clustering method is used for hierarchical module division, and the NSGA-II algorithm is used to optimize the coupling degree between modules and the cohesion degree within modules, achieving high cohesion and low coupling of module division. High cohesion enhances the collaborative working ability of internal components of the module, low coupling reduces the dependency between modules, and improves the overall performance and maintainability of the system.

[0196] 3. Efficient implementation of multi-objective optimization: By introducing the NSGA-II algorithm, the coupling degree between modules, the cohesion degree within modules, the number of modules, and the size and weight restrictions of modules can be optimized simultaneously under the same optimization framework, obtaining a set of globally optimal module division schemes. Fast non-dominated sorting and congestion calculation effectively maintain the diversity of the population, improving the comprehensiveness and applicability of the optimization results.

[0197] 4. Enhanced maintainability and scalability of the system: The optimized module division scheme significantly improves the maintainability and scalability of the system by reducing the coupling degree between modules and improving the cohesion degree within modules. In the subsequent maintenance, upgrading and expansion process, the system can be more conveniently modified and optimized independently, reducing the impact on the overall system.

[0198] 5. Improved automation and intelligence: Combining fuzzy hierarchical clustering and genetic algorithms, the module division process is automated, reducing the dependence on designers' subjective experience. Automated module division improves the objectivity and consistency of the design, shortens the design cycle, and effectively improves the design efficiency.

[0199] The module division method of the small reactor of the application has unique technical advantages, and other alternative solutions have certain limitations in technical implementation and cannot achieve the effect of the application. Among them, the technical limitations of the common alternative method are as follows: the traditional experience division method is difficult to ensure the optimality and rationality of module division, which may lead to problems such as system performance degradation, maintenance difficulty, cost increase, etc. The module division method of a single optimization algorithm is difficult to handle multi-objective optimization problems when dealing with module division of complex systems: such algorithms are usually optimized for single or limited number of targets, and it is difficult to consider multiple conflicting optimization targets such as module coupling degree, module cohesion degree and module number in the same framework; easy to fall into local optimal solution: single algorithm is easy to fall into local optimal solution due to limited search space, strong initial solution dependence, etc., and cannot effectively obtain the globally optimal module division scheme. The rule-based or heuristic design method has limitations in rules: the formulation of rules usually depends on experience and existing assumptions, and it is difficult to cover all cases in complex systems and cannot flexibly respond to changing conditions in actual layout; lack of dynamic adjustment ability: such methods lack adaptive mechanisms and cannot dynamically adjust according to actual conditions and changes in optimization targets, making it difficult to achieve global optimal module division of complex systems; unable to integrate multiple targets and multiple constraints: rule-based methods usually focus on a single optimization target and are difficult to consider multiple optimization targets and complex constraints during the design process, resulting in insufficient comprehensiveness and optimization of the module division scheme.

[0200] Obviously, the application does not have the above limitations. The application accurately reflects the function, connection and spatial relationship between components by establishing a comprehensive correlation matrix, ensures the rationality of module division, and comprehensively considers the multiple correlations between components; NSGA-II is used to simultaneously optimize the coupling degree between modules, the cohesion degree of modules and the number of modules in the same optimization framework, obtain the globally optimal module division scheme, and effectively handle the multi-objective optimization problem; through the chromosome adjustment mechanism and constraint processing method, the generated scheme meets the various constraint conditions in actual application, has high feasibility and practicality, and can flexibly respond to complex constraint conditions. Therefore, the application has unique technical advantages and innovation when solving the SMR modular division problem, can meet the needs of actual engineering applications, and other alternative solutions are difficult to achieve the same effect.

[0201] Specifically, the specific cooperation process between each unit in the module division system of the small reactor here can refer to the module division method of the small reactor described above, which will not be described here.

[0202] 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 module division method of the small reactor according to any one of the above. Specifically, according to the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, an embodiment of the present application includes 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 an embodiment, the computer program can be downloaded and installed by the electronic device and executed to perform the above functions defined in the method of the embodiment of the present application. The electronic device in the present application can be a notebook, a desktop, a tablet computer, a smart phone, or the like terminal, or can be a server.

[0203] 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 module division method of the small reactor 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 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.

[0204] The above computer readable medium can be included in the above electronic device; or can exist separately and not be assembled into the electronic device.

[0205] The various embodiments described in this specification are presented by way of example, and each embodiment describes a specific feature or combination of features that can be included in one or more embodiments of the present application. Each embodiment is intended to be limiting only to the extent recited in the specific embodiment description. In addition, any feature described in relation to one embodiment can be combined with any other feature described in relation to any other embodiment.

[0206] Those skilled in the art will further appreciate that the units and algorithms described in the examples presented herein can be implemented in electronic hardware, computer software, or any combination thereof. To clearly illustrate this interchangeability of hardware and software, various components will be described herein generally in terms of their functionality, without reference to the particular manner in which they are implemented. As hardware and software are both functional equivalents, the particular implementation can be determined by the design constraints imposed on a particular application. Skilled artisans will appreciate that the replacement of one component for another in this manner typically requires only routine design choices that are well within the scope of one skilled in the art, and that such design choices do not result in changes to the underlying functionality of the systems described herein.

[0207] 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. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC.

[0208] The above embodiments are only for illustrating the technical concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and to implement it accordingly, and cannot limit the protection scope of the present application. Any equivalent changes and modifications made within the scope of the claims of the present application shall be included in the scope of the claims of the present application.

Claims

1. A method of dividing a small reactor into modules, characterized by, The method comprises the following steps: determining the correlation information of the system components of the SMR system according to nuclear power design information, and constructing a comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components; the system components comprise various devices and components of the reactor and its auxiliary system; the nuclear power design information comprises nuclear power design specifications and design criteria of the SMR system; the determination of the correlation information of the system components of the SMR system according to the nuclear power design information comprises: classifying and summarizing based on the nuclear power design specifications and the design criteria of the SMR system to obtain the correlation information of the system components; the correlation information of the system components comprises functional correlation between system components, connection correlation between system components, and spatial correlation between system components; performing hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system to obtain a hierarchical clustering result of the system components of the SMR system; determining the objective function and the constraint condition of the module division of the SMR system; encoding all modules in the hierarchical clustering result of the system components of the SMR system, and optimizing the module division of the SMR system to obtain a target module division scheme.

2. The method of claim 1, wherein, the construction of the comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components comprises: obtaining the functional correlation of the system components, the connection correlation of the system components, and the spatial correlation between the system components; summarizing based on the functional correlation of the system components, the connection correlation of the system components, and the spatial correlation between the system components to obtain the comprehensive correlation matrix of the system components.

3. The method of claim 1, wherein, the functional correlation between the system components comprises a first functional correlation evaluation index, a second functional correlation evaluation index, a third functional correlation evaluation index, and a fourth functional correlation evaluation index; the first functional correlation evaluation index is that the system components jointly cooperate to complete the same sub-function, and the system components are indispensable; the correlation value of the first functional correlation evaluation index is 1; the second functional correlation evaluation index is a strong functional auxiliary relationship; the correlation value of the second functional correlation evaluation index is 0.5-0.9; the third functional correlation evaluation index is a weak functional auxiliary relationship; the correlation value of the third functional correlation evaluation index is 0.1-0.4; the fourth functional correlation evaluation index is no functional relationship; the correlation value of the fourth functional correlation evaluation index is 0.

4. The method of claim 1, wherein, the connection correlation between the system components comprises a first connection correlation evaluation index and a second connection correlation evaluation index; the first connection correlation evaluation index is direct physical connection; the correlation value of the first connection correlation evaluation index is 1; the second connection correlation evaluation index is no direct physical connection; the correlation value of the second connection correlation evaluation index is 0.

5. The method of claim 1, wherein, the spatial correlation between the system components comprises a first spatial correlation evaluation index and a second spatial correlation evaluation index; the first spatial correlation evaluation index is located in the same spatial region; the correlation value of the first spatial correlation evaluation index is 1; The second space-related evaluation index is located in different cabins or space regions, and the correlation value of the second space-related evaluation index is 0.

6. The method of claim 1, wherein, The hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system comprises: The hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system comprises:

7. The method of claim 6, wherein, The hierarchical module division according to the comprehensive correlation matrix of the system components of the SMR system comprises: The distance matrix between components is calculated based on the comprehensive correlation matrix of the system components. The hierarchical clustering tree of the system components is obtained by using the fuzzy hierarchical clustering method. The hierarchical clustering result of the system components is obtained by combining the clustering results of different levels by using the non-uniform granularity clustering function.

8. The method of claim 7, wherein, The hierarchical clustering tree of the system components is obtained by using the fuzzy hierarchical clustering method. Each system component is taken as an independent initial cluster. In each iteration, the two clusters with the smallest distance are merged to generate a new cluster, the distance between the new cluster and all other clusters is recalculated, and the step is repeatedly executed until all system components are merged into one cluster to generate the hierarchical clustering tree.

9. The method of claim 1, wherein, The target function and constraint condition of the SMR system module division are determined. Based on the comprehensive correlation matrix and the hierarchical clustering result, the comprehensive correlation coefficient between system components, the number of modules and the number of components in the modules are determined. The target function is determined according to the comprehensive correlation coefficient between system components, the number of modules and the number of components in the modules. The constraint condition is determined based on the constraint function and the target function.

10. The method of claim 9, wherein, The target function comprises a coupling degree evaluation function, a cohesion degree evaluation function, a module number function, a module size limit evaluation function and a module weight limit evaluation function. The target function is determined according to the comprehensive correlation coefficient between system components, the number of modules and the number of components in the modules. The coupling degree evaluation function is determined according to the comprehensive correlation coefficient between system components and the number of components in the modules. The cohesion degree evaluation function is determined according to the comprehensive correlation coefficient between system components and the number of modules. The module number function is obtained according to the number of modules. The module size limit evaluation function is determined according to the physical size of the module. The module weight limit evaluation function is determined according to the total weight of the module and the predetermined upper limit of the weight.

11. The method of claim 9, wherein, The constraint condition comprises minimizing the coupling degree between modules, maximizing the cohesion degree within modules, minimizing the number of modules, component coverage constraint and module size and weight limit constraint.

12. The method of claim 1, wherein, The all modules in the hierarchical clustering result of the system components of the SMR system are encoded, and the module division of the SMR system is optimized to obtain the target module division scheme. The all modules in the hierarchical clustering result of the system components of the SMR system are encoded, and the module division of the SMR system is optimized to obtain the target module division scheme.

13. The method of claim 12, wherein, The all modules in the hierarchical clustering result of the system components of the SMR system are encoded, and the module division of the SMR system is optimized to obtain the target module division scheme. S4.1: determining an encoding mode; S4.2: encoding all modules in the hierarchical clustering result according to the encoding mode, and establishing a component-module construction matrix; S4.3: randomly generating an initial population under the premise of meeting the constraint condition according to the encoding mode and the component-module construction matrix; S4.4: non-dominant sorting of the initial population to generate different non-dominant levels; S4.5: calculating the crowding degree of the individuals in each non-dominant level to obtain the crowding degree of the individuals in each non-dominant level; S4.6: selecting, crossing and mutating according to the non-dominant level and the crowding degree to generate new generation of offspring individuals; S4.7: combining the parent and child individuals to form a hybrid population, and re-performing non-dominant sorting and crowding degree calculation on the hybrid population to construct the next generation population; S4.8: determining the target solution of the last generation; S4.9: repeating steps S4.4 to S4.8 until the iteration termination condition is met, and outputting the target module division scheme.

14. The method of claim 13, wherein, The encoding mode includes a module encoding mode and a scheme encoding mode; The module encoding mode includes representing each module by a Boolean vector; each bit of the Boolean vector represents the presence or absence of a component; The scheme encoding mode includes representing the entire module division scheme by a Boolean vector; each bit of the Boolean vector represents the presence or absence of a module.

15. The method of claim 1-14, wherein, Further comprising: Based on the target module division scheme, performing feasibility analysis and verification; According to the analysis and verification results, it is judged whether the target module division scheme passes or not; If yes, the target module division scheme is determined; If not, the target module division scheme is reselected and determined.

16. A modular division system for a small reactor, characterized by, Comprising: A comprehensive matrix construction unit is configured to determine the correlation information of the system components of the SMR system according to nuclear power design information, and construct a comprehensive correlation matrix of the system components of the SMR system according to the correlation information of the system components; The system components include various devices and components of the reactor and its auxiliary system; the nuclear power design information includes nuclear power design specifications and design criteria of the SMR system; The correlation information of the system components of the SMR system is determined according to the nuclear power design information, which includes: The correlation information of the system components includes functional correlation between system components, connection correlation between system components, and spatial correlation between system components. The module division unit is configured to perform hierarchical module division on the system components of the SMR system according to the comprehensive correlation matrix of the system components of the SMR system, and obtain a hierarchical clustering result of the system components of the SMR system. The function condition determination unit is configured to determine a target function and a constraint condition of the module division of the SMR system. The target scheme determination unit is configured to encode all modules in the hierarchical clustering result of the system components of the SMR system, and optimize the module division of the SMR system to obtain a target module division scheme.

17. The modular partitioning system for a small reactor of claim 16, wherein, Further comprising: The target scheme verification unit is configured to: perform feasibility analysis and verification based on the target module division scheme; determine whether the target module division scheme passes according to an analysis and verification result; if yes, determine the target module division scheme; if no, reselect to determine the target module division scheme.

Images