Uncertainty analysis method and device for nuclear power mechanism model

Abstract

The invention discloses an uncertainty analysis method and device for a nuclear power mechanism model, and the method comprises the following steps: S1, selecting function modules from a function library according to the logic sequence of uncertainty analysis tasks, and constructing a function flow; s2, each function module defines an input interface and an output interface based on a unified shared data model, business data is automatically transmitted among function flows based on the shared data model, the reliability of business data transmission is ensured by utilizing intermediate storage and state management, and the business data state is updated in real time; s3, binding a triggering relationship among the functional modules of the functional flow through an event bus, sequentially triggering the functional modules, and automatically executing an analysis task; and S4, decoupling the function flow, and combining the function flow into a service through an API (Application Program Interface) to realize multiplexing of the function module. The uncertainty analysis method provided by the invention is high in data continuity and high in operation concentration.

Description

Technical Field

[0001] The present invention relates to the technical field of uncertainty analysis of nuclear power software, and in particular to an uncertainty analysis method and device for a nuclear power mechanical model. Background Art

[0002] In nuclear power plant nuclear fuel safety analysis, design, and operational assessment, nuclear power plant mechanical models are widely used for accurate system modeling and simulation prediction. Uncertainty analysis of complex nuclear power plant mechanical models is often required to assess the sensitivity of model input parameters to output results and the uncertainty propagation, ultimately gauging the safety of the nuclear power plant mechanical model's structural design. Uncertainty analysis encompasses multiple steps, including uncertainty measurement, sensitivity analysis, uncertainty propagation, and structural reliability optimization.

[0003] Traditional uncertainty analysis software is mostly function-oriented, with functions as the main body and analysis tasks as the workflow. It splits the business into multiple independent functional interfaces according to task logic, and uses different functional interfaces to call business data. Users need to frequently switch between multiple interfaces to complete a complete business process.

[0004] However, cross-screen operations require repeated data entry, and different functional entities have relatively discrete access to business data, resulting in discontinuity between business operations and business ideas. While existing technologies can connect functions in series, they still rely on multiple screen jumps, failing to fundamentally address the issues of data continuity and operational centralization. Summary of the Invention

[0005] In view of this, it is necessary to provide an uncertainty analysis method and device for nuclear power mechanism models to effectively solve the problem that the uncertainty analysis of nuclear power mechanism models relies on multi-interface jumps and cannot achieve data continuity and operation centralization.

[0006] The present invention provides an uncertainty analysis method for a nuclear power plant mechanical model, comprising the following steps:

[0007] Step S1: Select function modules from the function library to construct function flows according to the logical order of uncertainty analysis tasks;

[0008] Step S2: Each functional module defines an input interface and an output interface based on a unified shared data model. Business data is automatically transferred between functional flows based on the shared data model. Intermediate storage and state management are used to ensure the reliability of business data transfer and update the business data state in real time.

[0009] Step S3: Bind the trigger relationship between the functional modules of the functional flow through the event bus and trigger each functional module in turn to automatically execute the analysis task;

[0010] Step S4: decouple the functional flows and combine them into services through APIs to achieve reuse of functional modules.

[0011] Preferably, the step S1 specifically comprises: predefining and generating or customizing the functional flow according to the logical order of the uncertainty analysis task;

[0012] The generation of the predefined functional flow is specifically as follows: selecting functional modules that meet the analysis task from the functional library, configuring a fixed functional module execution order according to the logical order of the analysis task, arranging the selected functional modules based on the execution order to form a standardized process, and obtaining the predefined functional flow;

[0013] The generation of custom function flow is specifically as follows: manually select function modules from the function library according to the user's real-time needs, automatically check the data interface compatibility between the selected function modules, and prompt the user to make adjustments or supplements if they are incompatible. If they are compatible, generate the custom function flow based on the selected function modules.

[0014] Preferably, in step S2, each functional module defines an input interface and an output interface based on a unified shared data model, specifically:

[0015] Refine various types of analysis tasks, break down the analysis tasks into standardized steps, define the input interface, output interface and business rules of each standardized step based on the shared data model, encapsulate each standardized step into a functional module of a standardized interface, and store it in the functional library.

[0016] Preferably, in step S2, the business data is automatically transferred between functional flows based on the shared data model, specifically:

[0017] The data transfer rules between the functional modules are defined by the shared data model, and the business data is automatically transferred between the functional flows based on the data transfer rules.

[0018] Preferably, in step S2, intermediate storage and state management are used to enhance the reliability of data transmission, specifically:

[0019] An intermediate storage with a unique identifier is allocated to each functional flow, and intermediate data during the execution of the functional flow is temporarily stored in the intermediate storage; if the functional flow is interrupted, historical data is reloaded according to the intermediate storage to ensure data continuity.

[0020] Preferably, the real-time updating of the service data status in step S2 is specifically as follows:

[0021] Real-time update of data status is achieved through the collaboration of event-driven and state persistence; when the previous functional module completes the calculation, it will publish an event carrying the latest status, and the event bus will broadcast the status event to related modules and the front-end interface. At the same time, the system uses the cache or database to store the status snapshot in real time. Each functional flow is identified by a unique identifier. When the data status changes, the status snapshot in the cache or database is updated to ensure that the terminal can restore the data through the unique identifier. The front-end monitors the changes in data status and refreshes the interface progress and result display in real time.

[0022] Preferably, the step S3 is specifically as follows:

[0023] The event bus operates through a publish-subscribe mechanism, with the functional module as the event producer, defining event types and standardizing event content based on task logic; during the execution of the functional flow, after the previous functional module completes data processing, it encapsulates the result data into an event and publishes the event carrying the result data to the specified message topic; the downstream module of the functional module subscribes to the relevant message topic as a consumer, listens to the event and triggers subsequent operations; when the downstream module is executed, it directly extracts the required data from the event, ensuring the real-time data transmission and process consistency between the functional modules of the functional flow.

[0024] Preferably, the functional flow is decoupled in step S4, specifically:

[0025] The functional flow is split into fine-grained modules according to the task logic, and the responsibility boundaries of each fine-grained module are clarified through domain-driven design; a standardized API interface is defined for each fine-grained module, and an interface document is generated at the same time; the fine-grained modules communicate with each other through API or event bus, and the data storage of each fine-grained module is isolated; the fine-grained modules are packaged into independent services using Docker containerization technology, and the communication strategy is managed through a service grid.

[0026] Preferably, the API combination service in step S4 is specifically:

[0027] The API address and health status of each functional module are managed through the service registration center, and the service is automatically registered when the functional module is started; the caller dynamically discovers the target service through the service name, and orchestrates the target service by predefining the module calling sequence through the configuration file, or uses the workflow engine to generate the calling link in real time, and orchestrates the target service based on the calling link.

[0028] The present invention also provides an uncertainty analysis device for a nuclear power mechanism model, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the uncertainty analysis method for the nuclear power mechanism model is implemented.

[0029] Compared with the existing technology, the present invention has the following beneficial effects: the uncertainty analysis method for nuclear power mechanical models shown in the present invention, after the user completes the function flow configuration, the system will automatically execute the analysis task, ensuring data continuity and realizing efficient reuse of function modules, without manual intervention, significantly improving the business continuity and operational efficiency of the software. First, the user directly selects function modules from the function library to construct the function flow, without the need to jump to multiple interfaces. The entire uncertainty analysis business process can be completed within a single interface, which improves business operation efficiency; function modules can be selected or adjusted on demand to avoid redundant functional interference. Second, through the shared data model and event-driven mechanism, business data is transmitted in real time, reducing repeated input and ensuring data continuity; the use of intermediate storage and event bus ensures that the process can be recovered after interruption, and data integrity is improved. Finally, the decoupled design of function modules and the use of API to call function modules can realize reuse across business needs, support customized business processes, and reduce development and maintenance costs. Through the above design, the present invention achieves a deep integration of business, data, and operations, providing enterprises with an efficient, flexible, and user-friendly uncertainty analysis solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0031] FIG1 is a flow chart of an embodiment of an uncertainty analysis method for a nuclear power plant mechanism model provided by the present invention;

[0032] Figure 2 for Figure 1 A schematic diagram of a thermal-hydraulic analysis model of a pressurized water reactor nuclear fuel assembly in the illustrated embodiment;

[0033] Figure 3 for Figure 1 A schematic diagram of a three-dimensional power partition model of fuel rods in the embodiment shown;

[0034] Figure 4 yes Figure 1 The illustrated embodiment is an architectural diagram of an uncertainty analysis software architecture embodiment. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0036] Example 1

[0037] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0038] See also Figure 1 In this embodiment, an uncertainty analysis method for a nuclear power mechanical model includes the following steps:

[0039] Step S1: Select function modules from the function library to construct function flows according to the logical order of uncertainty analysis tasks;

[0040] Step S2: Each functional module defines an input interface and an output interface based on a unified shared data model. Business data is automatically transferred between functional flows based on the shared data model. Intermediate storage and state management are used to ensure the reliability of business data transfer and update the business data state in real time.

[0041] Step S3: Bind the trigger relationship between the functional modules of the functional flow through the event bus and trigger each functional module in turn to automatically execute the analysis task;

[0042] Step S4: decouple the functional flows and combine them into services through APIs to achieve reuse of functional modules.

[0043] The present invention designs an automatic uncertainty analysis system for nuclear power mechanical models. It adopts a modular architecture and a business-driven functional flow mechanism to realize customized configuration of uncertainty analysis functional flows of nuclear power mechanical models, automated execution of analysis tasks, data continuity management, and improved reusability of functional modules.

[0044] This embodiment proposes an uncertainty analysis method for nuclear power mechanical models, specifically for typical nuclear power systems such as nuclear fuel models. Through standardized functional modules and event-driven mechanisms, it implements an uncertainty analysis process with customized configuration of functional flows and automated execution of analysis tasks.

[0045] Figure 2 and Figure 3 Examples of models constructed using this method are shown respectively.

[0046] The thermal hydraulic analysis case of nuclear fuel assembly is used as the modeling object of nuclear power mechanism model, such as Figure 2 Figure 2 shows a simplified thermal-hydraulic analysis model for an AFA3G pressurized water reactor nuclear fuel assembly based on a 5×5 array structure. This model was constructed using a 3D solid geometry reconstruction method. The main body of the model is discretized using a regular hexahedral mesh, with a total mesh count of millions, effectively balancing the requirements for computational accuracy and efficiency.

[0047] based on Figure 3The three-dimensional power partition model of the fuel rod shown in the figure adopts the radial layered thermal coupling method to divide the fuel rod cross section into three regions with different thermal characteristics: core region A, transition region B, and edge region C. Figure 3 shown.

[0048] The thermal-hydraulic analysis model for the AFA3G pressurized water reactor nuclear fuel assembly integrates 15 parameters. The input parameters include 14 important parameters such as the coolant inlet flow rate, the power factors of the three regions A, B, and C, and the output parameter is the coolant pressure drop.

[0049] The automatic uncertainty analysis system for nuclear power mechanical models adopts a shared data model and event-driven mechanism to ensure the continuity of data flow and the real-time response of the system. It supports users to select or combine analysis processes on demand, and can also call predefined analysis templates to achieve custom configuration of functional flows, automated execution of analysis tasks, analysis process failure rollback and efficient reuse of functional modules. It is widely applicable to scenarios such as nuclear power model design verification and reliability assessment.

[0050] The automatic uncertainty analysis framework for nuclear power mechanism models is as follows: Figure 4 The following describes the specific steps in detail.

[0051] The step S1 specifically includes: pre-defining and generating or custom-generating the functional flow according to the logical sequence of the uncertainty analysis task;

[0052] The generation of the predefined functional flow is specifically as follows: selecting functional modules that meet the analysis task from the functional library, configuring a fixed functional module execution order according to the logical order of the analysis task, arranging the selected functional modules based on the execution order to form a standardized process, and obtaining the predefined functional flow;

[0053] The generation of custom function flow is specifically as follows: manually select function modules from the function library according to the user's real-time needs, automatically check the data interface compatibility between the selected function modules, and prompt the user to make adjustments or supplements if they are incompatible. If they are compatible, generate the custom function flow based on the selected function modules.

[0054] Each task requirement corresponds to a single user interface, which dynamically loads optional function modules to form a functional flow. For example, in the "Nuclear Fuel Uncertainty Study" interface, function modules can be selected from the function library according to the business logic sequence to construct a functional flow. Functional flows are predefined or dynamically generated by function modules based on task logic. Users select or adjust functional flows through drop-down menus, tabs, or flowcharts.

[0055] A predefined functional flow is a standardized process based on an analysis task, with a fixed execution order for functional modules configured in advance. The specific implementation steps are as follows: select functional modules that match the task logic from the function library and arrange them in a logical order to form a predefined functional flow.

[0056] like Figure 4 As shown, this embodiment creates a "Nuclear Fuel Uncertainty Research" task based on the analysis task requirements and configures a fixed function module execution order. "Nuclear Fuel Uncertainty Analysis" first calls the uncertainty measurement function module to perform uncertainty measurement on the nuclear fuel parameter data and determine the uncertainty of the nuclear fuel model input parameters. Secondly, the sensitivity analysis function module is called to perform sensitivity analysis on the nuclear fuel input parameters, measure the impact of the input parameters on the nuclear fuel model, and screen the input parameters. Then, the uncertainty propagation function module is called to analyze the uncertainty propagation analysis of the nuclear fuel model to calculate the uncertainty of the nuclear fuel model. Then, the structural reliability analysis function module is called to perform reliability measurement on the nuclear fuel model through the structural reliability analysis tool. Finally, the structural reliability optimization design function module is called to perform structural reliability optimization design and optimize the reliability of the nuclear fuel model by optimizing the input parameter data.

[0057] Store predefined process configuration information in a database or configuration file. For example, information includes module order, data interfaces, and event triggering rules. Users can simply call predefined templates. For example, a nuclear fuel uncertainty analysis process can be encapsulated as a predefined template called "Nuclear Fuel Uncertainty Analysis Process." If a similar model is encountered, the "Nuclear Fuel Uncertainty Analysis Process" template can be directly called for reuse.

[0058] After the predefined functional flow is generated, the event bus is used to automatically trigger the predefined functional flow, and the shared data model is used to realize data transmission between various functional modules.

[0059] For example, "Nuclear Fuel Uncertainty Analysis" uses an event bus to automatically trigger predefined processes. After the uncertainty measurement module completes calculations, the sensitivity analysis module automatically triggers calculations. After the sensitivity analysis module completes calculations, the uncertainty propagation module automatically triggers calculations. Therefore, within the predefined process, the event bus binds the trigger relationships between all functional modules. "Nuclear Fuel Uncertainty Analysis" uses a shared data model to transfer data between functional modules. The calculation results of the uncertainty measurement module automatically serve as input to the sensitivity analysis module. Simultaneously, the calculation results of the sensitivity analysis module also automatically serve as input to the uncertainty propagation module. Within the predefined process, data interaction between all functional modules is implemented using the shared data model described above.

[0060] The core concept of dynamic function flow generation is to flexibly combine functional modules based on real-time user needs to generate customized processes that adapt to individual or temporary business objectives. First, the user independently selects a function module, manually selecting a module from the function library. For example, skipping the "Sensitivity Analysis Function Module" and directly selecting the "Uncertainty Propagation Function Module." Real-time compatibility verification: The system automatically verifies the compatibility of data interfaces between modules. If the verification fails, the user is prompted to adjust the modules or supplement the missing data interfaces. Function flow generation and execution: A temporary function flow configuration is generated, binding the event bus and data transfer rules. If the user needs to save the current function flow, such as a "custom nuclear fuel optimization process," the configuration can be stored in the database. Dynamic execution and monitoring: During execution, the event bus triggers real-time collaboration between modules. If a module fails to execute, the event bus triggers a rollback mechanism, returning to the previous state.

[0061] Specifically, in step S2, each functional module defines an input interface and an output interface based on a unified shared data model, specifically:

[0062] Refine various types of analysis tasks, break down the analysis tasks into standardized steps, define the input interface, output interface and business rules of each standardized step based on the shared data model, encapsulate each standardized step into a functional module of a standardized interface, and store it in the functional library.

[0063] First, analyze the tasks and standardize them to refine the business requirements. For example, consider the "nuclear fuel uncertainty analysis" task, breaking down the requirements into standardized steps. After breaking down the standardized steps, define the input and output data interfaces and business rules for each step. Finally, encapsulate these into functional modules with standardized interfaces and store them in a function library. Based on business requirements, the "nuclear fuel uncertainty analysis" task can be broken down into functional modules with standardized interfaces of varying granularity. For example, these modules include an uncertainty measurement module, a sensitivity analysis module, an uncertainty propagation module, a structural reliability analysis module, and a structural reliability optimization design module. These functional modules with standardized interfaces are stored in the function library.

[0064] The software architecture of the uncertainty automatic analysis system for nuclear power plant mechanical models ensures the continuity of business data. Business data is automatically transferred between functional flows, eliminating duplicate data input. In this system, the automatic transfer of business data between functional flows is achieved through the following mechanisms.

[0065] Specifically, in step S2, the business data is automatically transferred between functional flows based on the shared data model, specifically as follows:

[0066] The data transfer rules between the functional modules are defined by the shared data model, and the business data is automatically transferred between the functional flows based on the data transfer rules.

[0067] An analytical task-oriented software architecture ensures the continuity of business data, enabling automatic data transfer between functional flows and avoiding duplicate data entry. In this architecture, automatic data transfer between functional flows is achieved through a shared data model and event-driven development. The shared data model defines data transfer rules between modules, and an event bus is used to bind module trigger relationships.

[0068] All functional modules define input and output interfaces based on a unified shared data model, ensuring data format consistency across modules. Loosely coupled communication between modules is achieved through an event bus. For example, when the sensitivity analysis module completes data processing, it proactively publishes an event containing the result data. The uncertainty propagation module listens for this event and automatically triggers execution, extracting the required data directly from the event without manual intervention. This mechanism ensures real-time data transfer between modules and process continuity.

[0069] Specifically, in step S2, intermediate storage and state management are used to enhance the reliability of data transmission, specifically:

[0070] An intermediate storage with a unique identifier is allocated to each functional flow, and intermediate data during the execution of the functional flow is temporarily stored in the intermediate storage; if the functional flow is interrupted, historical data is reloaded according to the intermediate storage to ensure data continuity.

[0071] The system uses intermediate storage and state management to enhance the reliability of data transmission. Each business process instance is assigned a unique identifier Session ID, and the intermediate data generated during the execution process is temporarily stored in the intermediate storage. If the process is interrupted due to user suspension or system failure, the historical data can be reloaded according to the Session ID when it is restored to ensure data continuity. For example, in the "Nuclear Fuel Uncertainty Analysis" process, the results of the "Sensitivity Analysis Module" are not only transmitted to the downstream module through the event bus, but are also cached; even if the process is paused midway, the user can still restore the data directly from the cache after restarting, avoiding repeated operations. This design combines the efficiency of event-driven and the fault tolerance of intermediate storage, ultimately achieving seamless automatic transmission of business data and full process automation.

[0072] Specifically, the real-time updating of the business data status in step S2 is as follows:

[0073] Real-time update of data status is achieved through the collaboration of event-driven and state persistence; when the previous functional module completes the calculation, it will publish an event carrying the latest status, and the event bus will broadcast the status event to related modules and the front-end interface. At the same time, the system uses the cache or database to store the status snapshot in real time. Each functional flow is identified by a unique identifier. When the data status changes, the status snapshot in the cache or database is updated to ensure that the terminal can restore the data through the unique identifier. The front-end monitors the changes in data status and refreshes the interface progress and result display in real time.

[0074] The real-time update mechanism is implemented through the collaboration of event-driven and state persistence. When a module completes its calculation, it publishes an event carrying the latest status, such as {"step": "PropagationModeling", "status": "Completed", "data": {...}}. The event bus broadcasts the status to relevant modules and the front-end interface. At the same time, the system uses a cache or database to store state snapshots in real time, and each functional flow instance is identified by a unique Session ID. Each time the status changes, the corresponding record in the cache or database is updated to ensure that data can be restored through the Session ID after an interruption. The front-end monitors status changes through WebSocket or long polling, refreshing the interface progress and result display in real time. For example, after the "Propagation Modeling Module" is completed, the event triggers a status update of session:12345 in Redis. The front-end interface synchronously displays the results and prepares to start the next module.

[0075] Specifically, the data status includes: process status, data content, status mark and abnormal status; the process status is the current execution step and progress, the data content includes the original input data, intermediate calculation results and final output, the status mark includes the model execution status and the dependency relationship between modules, and the abnormal status includes error code, log and interruption recovery point.

[0076] In the analysis task-oriented software architecture, the real-time update mechanism is implemented in the following way: the data status covers the dynamic information during the execution of the business process, which specifically includes four dimensions: one is the process status, including the current execution step and progress, for example, the current execution step is "sensitivity analysis in progress" and the progress is "step 3 / 5"; the second is the data content, including the original input data, intermediate calculation results and final output, such as the JSON format data of the sensitivity analysis results; the third is the status mark, such as the success or failure status of the module execution, and the dependency relationship between modules, such as "reliability optimization design" depends on the results of "reliability analysis"; the fourth is the abnormal status, including error codes, logs and interruption recovery points. For example, in the "core uncertainty analysis" process, the data status may record that the current step is "structural reliability analysis", the intermediate data contains the propagation modeling results, and is marked as "optimization design to be executed".

[0077] Specifically, the step S3 is as follows:

[0078] The event bus operates through a publish-subscribe mechanism, with the functional module as the event producer, defining event types and standardizing event content based on task logic; during the execution of the functional flow, after the previous functional module completes data processing, it encapsulates the result data into an event and publishes the event carrying the result data to the specified message topic; the downstream module of the functional module subscribes to the relevant message topic as a consumer, listens to the event and triggers subsequent operations; when the downstream module is executed, it directly extracts the required data from the event, ensuring the real-time data transmission and process consistency between the functional modules of the functional flow.

[0079] Functional flows trigger subsequent steps via an event bus, updating data status in real time to automate analysis tasks. Data from unfinished processes is temporarily stored in a cache or database for subsequent function flow invocation. In an automated uncertainty analysis system for nuclear power plant mechanical models, automated analysis tasks are achieved through the generation of an event bus, the definition of data status, and a real-time update mechanism.

[0080] The construction of an event bus relies on mature message-based middleware technology. Its core is to provide a loosely coupled communication framework between modules. First, event types are defined based on business logic, and event content, including metadata such as data payload, timestamp, and process identifier, is standardized. The event bus operates through a publish-subscribe mechanism: functional modules, acting as event producers, encapsulate the resulting data as events after completing their tasks and publish them to designated message topics. Downstream modules, acting as consumers, subscribe to related topics, listen for events, and trigger subsequent actions. To ensure reliability, the event bus requires message persistence, retry mechanisms, and dead-letter queues. Kafka ensures message loss through persistent storage. For example, in a nuclear power analysis tool, when the "sensitivity analysis module" completes its calculations, it publishes an event containing nuclear fuel parameters and process IDs to the Kafka topic "analysis-results." The downstream "propagation modeling module" subscribes to this topic and automatically initiates calculations, ensuring seamless integration.

[0081] The real-time update mechanism is implemented through the collaboration of event-driven and state persistence. When the module completes the calculation, it will publish an event carrying the latest status, and the event bus will broadcast the status to related modules and the front-end interface. At the same time, the system uses the cache or database to store status snapshots in real time, and each process instance is identified by a unique Session ID. Every time the status changes, the corresponding record in the cache or database will be updated to ensure that the data can be restored through the Session ID after an interruption. The front-end monitors status changes through long polling and refreshes the interface progress and result display in real time. For example, after the "propagation modeling module" is completed, the event triggers the status update of session:12345 in Redis, and the front-end interface synchronously displays the results and prepares to start the next module.

[0082] This mechanism combines the efficient communication of the event bus with the fault tolerance of intermediate storage. It not only ensures the continuity of business data and the traceability of operations, but also supports the high availability requirements of complex scenarios, ultimately achieving full-process automation and real-time response to user operations.

[0083] During back-end implementation, it's necessary to decouple functional flow modules and combine services through APIs to achieve efficient reuse of functional modules. In an automated uncertainty analysis system for nuclear power mechanical models, efficient reuse of functional flow modules is achieved through decoupling and service combination.

[0084] Specifically, the functional flow is decoupled in step S4, specifically as follows:

[0085] The functional flow is split into fine-grained modules according to the task logic, and the responsibility boundaries of each fine-grained module are clarified through domain-driven design; a standardized API interface is defined for each fine-grained module, and an interface document is generated at the same time; the fine-grained modules communicate with each other through API or event bus, and the data storage of each fine-grained module is isolated; the fine-grained modules are packaged into independent services using Docker containerization technology, and the communication strategy is managed through a service grid.

[0086] The core goal of decoupling is to reduce direct dependencies between modules, enabling them to become independent, reusable services. First, functional flows are broken down into fine-grained modules based on business logic, such as the "Sensitivity Analysis Module" and the "Uncertainty Propagation Module." Domain-Driven Design (DDD) is used to clearly define the responsibilities of each module, including input, output, and processing logic. Next, standardized API interfaces are defined for each module, specifying request parameters, response formats, and error codes. Interface documentation is generated using OpenAPI / Swagger to ensure transparent calling rules. Modules communicate via APIs or event buses, prohibiting direct access to each other's internal state. Data storage is also isolated. Finally, Docker containerization technology is used to package modules into independent services, enabling on-demand deployment and horizontal scalability. Communication policies, such as circuit breaking and rate limiting, are managed through the service mesh Istio. For example, the "Sensitivity Analysis Module" is encapsulated as a standalone service, providing an interface called / analyze-sensitivity. Other modules only need to call this API without having to understand its internal implementation.

[0087] Specifically, in step S4, services are combined through APIs, specifically: the API address and health status of each functional module are managed through the service registration center, and the service is automatically registered when the functional module is started; the caller dynamically discovers the target service through the service name, and orchestrates the target service by predefining the module calling sequence through the configuration file, or uses the workflow engine to generate a call link in real time, and orchestrates the target service based on the call link.

[0088] The goal of service composition is to dynamically integrate decoupled modules into a complete business function flow. First, the module's API address and health status are managed through a service registry. Modules are automatically registered upon startup, and callers dynamically discover their targets using the service name. Service orchestration can be divided into two approaches: static orchestration, which predefines the module call sequence through a configuration file and is suitable for standardized processes; and dynamic orchestration, which utilizes a workflow engine to generate call chains in real time, supporting conditional branching and looping logic.

[0089] API calls can be made synchronously via HTTP / RPC or asynchronously via an event bus. For example, in the "Nuclear Fuel Uncertainty Analysis" process, the backend first calls the / analyze-sensitivity API, which then triggers execution via a Kafka event called / model-propagation. To ensure reliability, retry mechanisms and distributed transaction management are implemented. If a process fails, compensation logic is triggered to roll back data.

[0090] Through decoupling and API combination, the system can not only flexibly respond to complex business needs, but also ensure high availability and maintainability, ultimately reducing development and operation costs.

[0091] In summary, in this embodiment, the user accesses the uncertainty analysis auxiliary analysis tool interface, creates a "Nuclear Fuel Uncertainty Analysis" task based on business needs, and customizes the functional flow of "Uncertainty Measurement Module - Sensitivity Analysis Module - Uncertainty Propagation Module - Structural Reliability Analysis Module - Structural Reliability Optimization Design Module." The system automatically executes the analysis task for "Nuclear Fuel Uncertainty Analysis" through a continuous flow of business data. It first performs a sensitivity analysis on the nuclear fuel data, then measures uncertainty, models uncertainty propagation, calculates the structural reliability of the nuclear fuel model data, and finally optimizes the nuclear fuel model data to increase its reliability.

[0092] The present invention illustrates an automatic uncertainty analysis system for nuclear power plant mechanism models. After the user completes the functional flow configuration, the system automatically executes the analysis task, ensuring data continuity and enabling efficient reuse of functional modules without manual intervention, significantly improving the software's business continuity and operational efficiency. Users can complete the entire uncertainty analysis business process within a single interface without having to navigate between multiple interfaces, improving business operational efficiency. Functional modules can be selected or adjusted on demand to avoid redundant functional interference. Through a shared data model and event-driven mechanism, business data is transmitted in real time, reducing duplicate input by 90% and ensuring data continuity. Intermediate storage and an event bus ensure process recovery after interruptions, improving data integrity. The decoupled design of functional modules, using APIs to call functional modules, enables reuse across business requirements, supports customized business processes, and reduces development and maintenance costs. The event-driven architecture supports process rollback, automatically returning to the previous state if the current task fails. Through the above design, the present invention achieves a deep integration of business, data, and operations, providing enterprises with an efficient, flexible, and user-friendly software architecture solution.

[0093] Example 2

[0094] This embodiment provides an uncertainty analysis device for a nuclear power mechanism model, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the uncertainty analysis method for the nuclear power mechanism model described in Example 1.

[0095] The uncertainty analysis device for the nuclear power mechanism model provided in this embodiment is used to implement the uncertainty analysis method for the nuclear power mechanism model. Therefore, the technical effects possessed by the uncertainty analysis method for the nuclear power mechanism model are also possessed by the uncertainty analysis device for the nuclear power mechanism model, which will not be repeated here.

[0096] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in the present invention should be covered by the present invention.

Claims

1. A method for uncertainty analysis of nuclear power mechanical models, characterized in that: The following steps are involved: Step S1: Select function modules from the function library to construct function flows according to the logical order of uncertainty analysis tasks; Step S2: Each functional module defines an input interface and an output interface based on a unified shared data model. Business data is automatically transferred between functional flows based on the shared data model. Intermediate storage and state management are used to ensure the reliability of business data transfer and update the business data state in real time. Step S3: Bind the trigger relationship between the functional modules of the functional flow through the event bus and trigger each functional module in turn to automatically execute the analysis task; Step S4: decouple the functional flows and combine them into services through APIs to achieve reuse of functional modules.

2. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: The step S1 specifically includes: pre-defining and generating or custom-generating the functional flow according to the logical sequence of the uncertainty analysis task; The generation of the predefined functional flow is specifically as follows: selecting functional modules that meet the analysis task from the functional library, configuring a fixed functional module execution order according to the logical order of the analysis task, arranging the selected functional modules based on the execution order to form a standardized process, and obtaining the predefined functional flow; The generation of custom function flow is specifically as follows: manually select function modules from the function library according to the user's real-time needs, automatically check the data interface compatibility between the selected function modules, and prompt the user to make adjustments or supplements if they are incompatible. If they are compatible, generate the custom function flow based on the selected function modules.

3. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: In step S2, each functional module defines an input interface and an output interface based on a unified shared data model, specifically: Refine various types of analysis tasks, break down the analysis tasks into standardized steps, define the input interface, output interface and business rules of each standardized step based on the shared data model, encapsulate each standardized step into a functional module of a standardized interface, and store it in the functional library.

4. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: In step S2, the business data is automatically transferred between functional flows based on the shared data model, specifically: The data transfer rules between the functional modules are defined by the shared data model, and the business data is automatically transferred between the functional flows based on the data transfer rules.

5. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: In step S2, intermediate storage and state management are used to enhance the reliability of data transmission, specifically: An intermediate storage with a unique identifier is allocated to each functional flow, and intermediate data during the execution of the functional flow is temporarily stored in the intermediate storage; if the functional flow is interrupted, historical data is reloaded according to the intermediate storage to ensure data continuity.

6. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: The real-time updating of the business data status in step S2 is specifically as follows: Real-time update of data status is achieved through the collaboration of event-driven and state persistence; when the previous functional module completes the calculation, it will publish an event carrying the latest status, and the event bus will broadcast the status event to related modules and the front-end interface. At the same time, the system uses the cache or database to store the status snapshot in real time. Each functional flow is identified by a unique identifier. When the data status changes, the status snapshot in the cache or database is updated to ensure that the terminal can restore the data through the unique identifier. The front-end monitors the changes in data status and refreshes the interface progress and result display in real time.

7. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: The step S3 is specifically as follows: The event bus operates through a publish-subscribe mechanism, with the functional module as the event producer, defining event types and standardizing event content based on task logic; during the execution of the functional flow, after the previous functional module completes data processing, it encapsulates the result data into an event and publishes the event carrying the result data to the specified message topic; the downstream module of the functional module subscribes to the relevant message topic as a consumer, listens to the event and triggers subsequent operations; when the downstream module is executed, it directly extracts the required data from the event, ensuring the real-time data transmission and process consistency between the functional modules of the functional flow.

8. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: In step S4, the functional flow is decoupled, specifically: The functional flow is split into fine-grained modules according to the task logic, and the responsibility boundaries of each fine-grained module are clarified through domain-driven design; a standardized API interface is defined for each fine-grained module, and an interface document is generated at the same time; the fine-grained modules communicate with each other through API or event bus, and the data storage of each fine-grained module is isolated; the fine-grained modules are packaged into independent services using Docker containerization technology, and the communication strategy is managed through a service grid.

9. The uncertainty analysis method for a nuclear power mechanical model according to claim 1, characterized in that: In step S4, the service is combined through the API, specifically: The API address and health status of each functional module are managed through the service registration center, and the service is automatically registered when the functional module is started; the caller dynamically discovers the target service through the service name, and orchestrates the target service by predefining the module calling sequence through the configuration file, or uses the workflow engine to generate the calling link in real time, and orchestrates the target service based on the calling link.

10. An uncertainty analysis device for a nuclear power mechanical model, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the uncertainty analysis method for the nuclear power mechanical model as described in any one of claims 1 to 9 is implemented.

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