Analog machine data upgrading method and device

Abstract

The invention provides an analog machine data upgrading method and device, and the method comprises the steps: obtaining a modeling parameter correction factor and a prediction factor related to the real-time operation of a nuclear power unit based on the parallel perception of the historical data and real-time operation state of the nuclear power unit; and performing evolution calculation on the modeling parameter correction factor and the prediction factor based on the group digital mirror image, determining a data file for upgrading, and performing data updating on the nuclear power unit according to the data file. The method comprises the steps of obtaining modeling parameter correction factors through parallel perception of historical data of a nuclear power unit; the real-time operation state of the nuclear power unit is sensed in parallel, predictive factors related to real-time operation of the nuclear power unit are obtained, then evolution calculation is carried out on digital mirror image modeling parameter correction factors and the predictive factors of the nuclear power unit, multi-Agent interaction is carried out based on an evolution result, and a data file used for upgrading is obtained. Therefore, efficient and accurate monitoring of the nuclear power unit is realized, and the system has wide application value.

Description

Technical Field

[0001] The present application relates to the field of energy data processing and application technology, and in particular to a simulator data upgrade method and device. Background Art

[0002] With the continuous development of industrial automation and simulation technology, simulators are increasingly used in process design, equipment troubleshooting, operator training and other fields. However, due to frequent changes in data during the production process, the process model parameters in the simulator often need to be updated in a timely manner to maintain simulation accuracy. The traditional manual update method is not only inefficient, but also difficult to fully cover all changing factors. Therefore, the research and development of adaptive update systems has become an important technical direction.

[0003] It should be noted that the above introduction to the technical background is only for the convenience of providing a clear and complete description of the technical solutions of the present application and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are well known to those skilled in the art simply because they are described in the background technology section of the present application. Summary of the invention

[0004] The embodiments of the present application provide a simulator data upgrade method and device.

[0005] The first embodiment of the present application provides a simulator data upgrade method, comprising:

[0006] Based on parallel perception of historical data of nuclear power units, modeling parameter correction factors are obtained;

[0007] Based on parallel perception of the real-time operating status of nuclear power units, prediction factors related to the real-time operation of nuclear power units are obtained;

[0008] Based on the preset digital image of the nuclear power unit, the modeling parameter correction factor and the prediction factor are evolved and calculated, a data file for upgrading is determined, and the data of the nuclear power unit is updated according to the data file.

[0009] The second embodiment of the present application provides a simulator data upgrade device, comprising:

[0010] A first acquisition module, the first acquisition module is used to acquire a modeling parameter correction factor based on parallel perception of historical data of nuclear power units;

[0011] A second acquisition module, the second acquisition module is used to acquire prediction factors related to the real-time operation of the nuclear power unit based on parallel perception of the real-time operation status of the nuclear power unit;

[0012] An update module is used to perform evolutionary calculations on the modeling parameter correction factor and the prediction factor based on a preset digital image of the nuclear power unit, determine a data file for upgrading, and update the data of the nuclear power unit according to the data file.

[0013] The third aspect embodiment of the present application proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the simulator data upgrade method proposed in the first aspect embodiment of the present application.

[0014] The fourth aspect embodiment of the present application proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute the method proposed in the first aspect embodiment of the present application.

[0015] The fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which implements the method proposed in the first aspect embodiment of the present application when executed by a processor in a communication device.

[0016] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0017] Through parallel perception of the historical data of nuclear power units, the correction factors of modeling parameters are obtained; through parallel perception of the real-time operating status of nuclear power units, the prediction factors related to the real-time operation of nuclear power units are obtained, and then the correction factors and prediction factors of the digital mirror modeling parameters of the nuclear power units are evolved. Based on multi-agent interaction on the evolution results, the data files for upgrading are obtained, thereby realizing efficient and accurate monitoring of nuclear power units, which has wide application value.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A schematic diagram of a simulator data upgrade method provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of another method for updating simulator data provided by an embodiment of the present application;

[0022] Figure 3 A schematic diagram of the structure of a simulator data upgrade device provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0026] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0027] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at..." or "when..." or "in response to determination".

[0028] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] It should be noted that the simulator data upgrade method provided in any embodiment of the present application can be executed alone, or in combination with possible implementation methods in other embodiments, or in combination with any technical solution in the relevant technology.

[0030] The following describes the simulator data upgrade method and device according to an embodiment of the present application with reference to the accompanying drawings.

[0031] Figure 1 A flow chart of a simulator data upgrade method provided in an embodiment of the present application. Figure 1 As shown, the method includes but is not limited to the following steps:

[0032] S101, obtaining modeling parameter correction factors based on parallel perception of historical data of nuclear power units.

[0033] In a feasible implementation, historical data of nuclear power units can be obtained from the monitoring information system of the nuclear power plant. These historical data cover key information such as the operating status of the reactor, the parameters of the coolant system, and the overall performance of the primary system. The collected historical data is then preprocessed, including: cleaning the collected historical data to remove outliers, missing values, or duplicate data; standardizing or normalizing the historical data to ensure comparability between different parameters; and segmenting or classifying the data according to specific usage scenarios for subsequent analysis and modeling.

[0034] Parallel perception of historical data, where parallel perception introduces ACP theory (full name Artificial Systems, Computational Experiments, and Parallel Execution), organically combines artificial intelligence, computational experiments, and platform execution, and intelligently monitors the actual system process of the nuclear power system to improve the safety and reliability of the nuclear power system. Through parallel perception, the operating status of the nuclear power unit can be monitored in real time, including key parameters such as temperature, pressure, and vibration. When abnormal data is detected, the parallel perception system can immediately issue an early warning to remind the operator to take corresponding measures to avoid potential safety risks. Using parallel perception, the nuclear power unit can be diagnosed for faults, and the fault type and location can be accurately identified. At the same time, through the analysis and mining of historical data, possible future faults can be predicted, providing a reference for preventive maintenance. Parallel perception can also comprehensively consider factors such as the operating status and environmental conditions of the nuclear power unit, and provide intelligent decision-making support for operators to reduce operation and maintenance costs and improve the overall performance of the nuclear power unit.

[0035] The process of parallel perception of historical data also includes correlation analysis of historical data to determine the dependency between different parameters. According to the results of the correlation analysis, appropriate modeling parameter correction factors are selected. At the same time, parallel perception is used to calculate and optimize the modeling parameter correction factors to ensure that the model can more accurately reflect the actual operating status of the nuclear power unit.

[0036] S102, based on parallel perception of the real-time operating status of the nuclear power unit, obtaining prediction factors related to the real-time operation of the nuclear power unit.

[0037] In a feasible implementation, a parallel perception framework is the basis for obtaining prediction factors related to the real-time operation of nuclear power units. The framework generally includes: a sensor network (sensors deployed at various key locations of nuclear power units for real-time monitoring of key parameters of the units such as temperature, pressure, vibration, flow, etc.), a data acquisition and processing system (responsible for collecting sensor data and performing preprocessing such as denoising, filtering, and standardization to ensure data accuracy and consistency), a parallel computing platform (using high-performance computing resources to quickly analyze and process real-time data to support subsequent predictions and decisions), a prediction model library (containing a variety of prediction algorithms and models, such as time series analysis, machine learning, deep learning, etc., for generating prediction factors based on historical data and real-time data), etc.

[0038] When the real-time operating status of the nuclear power unit is perceived in parallel, it includes: cleaning and preprocessing the real-time data to remove outliers and noise; extracting features related to the operating status of the nuclear power unit, such as temperature change trends, vibration frequencies, flow fluctuations, etc.; then selecting a suitable prediction framework, such as the ARIMA framework based on time series, the support vector machine (SVM) based on machine learning, random forest, etc., or the neural network framework based on deep learning; then predicting the real-time data to generate prediction factors related to the real-time operation of the nuclear power unit; verifying the generated prediction factors to ensure their accuracy and reliability, which can be achieved by comparing with actual data, calculating prediction errors, etc.

[0039] Next, it can also include: analyzing the changing trends of prediction factors to timely discover potential failure risks and take corresponding preventive measures; adjusting the unit's operating strategy according to the changes in prediction factors to improve energy efficiency and reduce operation and maintenance costs; using prediction factors to formulate more accurate maintenance plans, reduce unplanned downtime, and improve the availability and reliability of the unit.

[0040] S103, performing evolution calculation on the modeling parameter correction factor and the prediction factor based on the preset digital image of the nuclear power unit, determining the data file for upgrading, and updating the data of the nuclear power unit according to the data file.

[0041] In a feasible implementation, the digital image of a nuclear power unit refers to high-precision digital modeling of a nuclear power unit, including detailed representations of various aspects such as its physical structure, operating parameters, and control system; initializing the modeling parameters and prediction models in the digital image of the nuclear power unit according to the actual operating status and historical data of the nuclear power unit; setting the initial conditions of the evolutionary calculation, including population size, genetic algorithm parameters (such as crossover rate, mutation rate), number of iterations, etc. Specifically, genetic algorithms or other evolutionary algorithms (such as particle swarm algorithms, neural networks, etc.) can be used to iteratively optimize the modeling parameter correction factors in the digital image of the nuclear power unit; by comparing the differences between the model output and the actual operating data under different parameter combinations, the advantages and disadvantages of the modeling parameter correction factors are evaluated; according to the evaluation results, an excellent combination of modeling parameter correction factors is selected as an alternative object for updating the modeling parameters in the digital image.

[0042] Similarly, genetic algorithms or other evolutionary algorithms (such as particle swarm algorithms, neural networks, etc.) can be used to substitute prediction factors into the prediction model in the digital image of the nuclear power unit for iterative optimization; by analyzing the prediction ability of the prediction model for future states, the advantages and disadvantages of the prediction factors can be evaluated; prediction factors with strong prediction ability can be used as alternatives to improve the accuracy of the digital image's prediction of future states.

[0043] Through the evolution calculation of the modeling parameter correction factor and the prediction factor, the candidate with the highest fitness is selected from the final iteration as the optimal solution, and these optimal solutions will be used as the final values ​​of the modeling parameter correction factor and the prediction factor. This process can also include multi-agent interaction on the evolution results, for example, defining a data collection agent, which is responsible for collecting data from the evolution calculation process, including modeling parameter correction factors, prediction factors, etc.; defining a data analysis agent, which conducts in-depth analysis of the collected data and evaluates the effectiveness and accuracy of the evolution results; defining a decision agent, which makes decisions based on the analysis results and determines which data files can be used for upgrading; an execution agent, which is responsible for executing the decision and applying the selected data files to the digital mirror or actual system of the nuclear power unit; setting standard communication protocols and data formats to ensure that information can be transmitted efficiently and accurately between agents to achieve message distribution and overall control mechanisms to coordinate the workflow of each agent.

[0044] Then, based on the optimal solution, a data file for upgrading the digital image is generated. The data file should contain all necessary parameters and configuration information to ensure that the digital image can accurately reflect the actual operating status and performance of the nuclear power unit. Before applying the data file to the nuclear power unit, a simulation test is carried out to verify its effectiveness and safety to ensure that the data file will not cause abnormal operation of the nuclear power unit or safety hazards.

[0045] Apply the verified data files to the digital image of the nuclear power unit to update parameters and optimize the model; make necessary adjustments and optimizations to the control system, monitoring system, etc. of the nuclear power unit based on the updated digital image; ensure that the upgrade process proceeds smoothly without interfering with the normal operation of the unit; through this process, the digital image of the nuclear power unit can be continuously optimized and upgraded to improve the operating efficiency and safety of the unit.

[0046] In summary, the correction factors of modeling parameters are obtained by parallel perception of the historical data of nuclear power units; the prediction factors related to the real-time operation of nuclear power units are obtained by parallel perception of the real-time operation status of nuclear power units. Then, the correction factors and prediction factors of the digital mirror modeling parameters of the nuclear power units are evolved, and multi-agent interaction is performed on the evolution results to obtain the data files for upgrading, thereby realizing efficient and accurate monitoring of nuclear power units, which has wide application value.

[0047] Figure 2 This is a flow chart of another simulator data upgrade method provided in the embodiment of the present application. Figure 2 As shown, the method includes but is not limited to the following steps:

[0048] S201, based on parallel sensing, obtains historical data of nuclear power units under various operating conditions.

[0049] For further introduction to step S201, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0050] S202, calculating historical data based on a pre-trained nuclear power unit mechanism model to obtain a modeling parameter correction factor of a preset nuclear power unit digital image.

[0051] Optionally, as an example, based on preprocessing the historical data, the first intermediate data is obtained, wherein the preprocessing includes operations such as filling missing values, replacing abnormal values, removing duplicate values, format conversion, and data dimension reduction. Then, the first intermediate data is calculated based on the pre-trained nuclear power unit mechanism model to obtain the modeling parameter correction factor of the preset nuclear power unit digital image. Specifically, according to the operating principle of the nuclear power unit, the mechanism model is determined, and the nuclear power unit uses the heat energy generated by nuclear fission to generate electricity. In the nuclear power unit, nuclear fuel (such as uranium-235) undergoes fission reaction in the reactor, releasing a large amount of heat energy. This heat energy is transferred to the steam generator through a coolant (such as water) to generate steam. Steam drives the turbine to rotate, which in turn drives the generator to generate electricity. Therefore, the mechanism model can be subdivided into a reactor model, a coolant model, a steam generator model, a turbine and generator model, and a control system and protection system model. After obtaining the mechanism model, the mechanism model is trained based on the simulation parameters of the obtained mechanism model to obtain the first model that minimizes the loss parameters. The first intermediate data is calculated based on the first model to obtain a modeling parameter correction factor of a preset digital image of a nuclear power unit.

[0052] After the modeling parameter correction factor is obtained, the modeling parameter correction factor is evaluated based on the digital image of the nuclear power unit, an error result is obtained, and the digital image of the nuclear power unit is updated based on the error result.

[0053] For further introduction to step S202, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0054] S203, based on parallel sensing, obtain the real-time operation data of the nuclear power unit.

[0055] For further introduction to step S203, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0056] S204, calculating the real-time operation data based on the pre-trained nuclear power unit mechanism model to obtain prediction factors related to the real-time operation of the nuclear power unit.

[0057] Optionally, as an example, based on distributed processing of real-time operation data, the second intermediate data is obtained. Specifically, the distributed system architecture of the nuclear power unit is identified, and multiple nodes of the distributed system architecture are obtained. The distributed system consists of multiple independent computer nodes or servers, which share resources, data and tasks, and complete specific functions through communication and coordination. In the distributed system architecture of the nuclear power unit, these nodes may include servers, workstations, control units, etc., which work together to realize the monitoring, control and management of the nuclear power unit. The distributed architecture includes: a data acquisition layer (responsible for collecting various parameters and data during the operation of the nuclear power unit, such as temperature, pressure, flow, etc.), a data processing layer (processing and analyzing the collected data to extract useful information and features), a control layer (based on the processed data and information, the nuclear power unit is controlled and managed in real time to ensure the stable operation of the unit), and a monitoring layer (based on the processed data and information, the nuclear power unit is controlled and managed in real time to ensure the stable operation of the unit). Corresponding nodes are set according to the architecture composition of the distributed system, and each node has specific functions and tasks.

[0058] Then, based on multiple nodes, the real-time operation data is parallelized to obtain the second intermediate data. Parallel computing is a method of using multiple processors or computing nodes to process data simultaneously, aiming to improve computing speed and efficiency. In the distributed system of nuclear power units, large amounts of operation data can be processed in real time through parallel computing, thereby quickly obtaining useful information and features. Specifically, the real-time operation data is divided into multiple subsets according to certain rules (such as timestamp, data type, geographic location, etc.), and each subset is assigned to a computing node for processing to ensure the balance of data division to avoid the situation where some nodes are overloaded while other nodes are idle; according to the result of data division, the corresponding computing tasks are assigned to each computing node, and each node is responsible for processing the data subset assigned to it and performing corresponding computing operations; computing tasks are executed on each computing node at the same time, and the computing power of the node is used to process and analyze the data. During the calculation process, the nodes may need to transmit and synchronize data through the communication network to ensure the correctness and consistency of the calculation; after all computing nodes complete the computing tasks, the calculation results of each node are summarized and integrated. During the summary process, the results may need to be further processed and analyzed to extract useful information and features; after the processing and analysis of the above steps, the second intermediate data is finally obtained. These data may include processed real-time operation data, feature extraction results, analysis results, etc., that is, the second intermediate data will serve as the basis for subsequent decision-making, optimization and control.

[0059] After obtaining the second intermediate data, the second intermediate data is calculated based on the pre-trained nuclear power unit mechanism model to obtain prediction factors related to the real-time operation of the nuclear power unit. Specifically, the second intermediate data is first cleaned to remove outliers, missing values, and duplicate values ​​to ensure the accuracy and consistency of the data; then the data is pre-processed, such as data normalization, feature extraction, etc., to extract useful information and features to provide a basis for subsequent calculations and analyses; the processed second intermediate data is used as input data for the mechanism model; the input data is calculated using the pre-trained mechanism model to obtain prediction factors related to the real-time operation of the nuclear power unit, which may include key parameters such as reactor power, coolant temperature, and steam flow; the calculation results are analyzed to evaluate the prediction performance and accuracy of the model. If there is a large deviation between the prediction results and the actual data, the model needs to be further adjusted and optimized.

[0060] For further introduction to step S204, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0061] S205, performing evolution calculation on the modeling parameter correction factor and the prediction factor based on the preset digital image of the nuclear power unit, determining the data file for upgrading, and updating the data of the nuclear power unit according to the data file.

[0062] Optionally, as an example, according to the data structure of the modeling parameter correction factor and the prediction factor, an algorithm for performing evolutionary calculation is determined; according to the algorithm, the digital image of the nuclear power unit iteratively optimizes the modeling parameter correction factor and the prediction factor to obtain evolutionary results related to the change trend and potential impact of the nuclear power unit; based on multi-agent interaction of the evolutionary results, a data file for upgrading is determined. Specifically, according to the distributed system architecture of the nuclear power unit, multiple agents are configured in the distributed system; based on the interaction of the multiple agents on the evolutionary results, the optimal solution is obtained, and the data file for upgrading is determined according to the optimal solution.

[0063] After the data file for upgrading is obtained, the data file is verified to obtain a data packet compatible with the configuration of the nuclear power unit; and the data of the nuclear power unit is updated according to the data packet.

[0064] For further introduction to step S205, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0065] In summary, the correction factors of modeling parameters are obtained by parallel perception of the historical data of nuclear power units; the prediction factors related to the real-time operation of nuclear power units are obtained by parallel perception of the real-time operation status of nuclear power units. Then, the correction factors and prediction factors of the digital mirror modeling parameters of the nuclear power units are evolved, and multi-agent interaction is performed on the evolution results to obtain the data files for upgrading, thereby realizing efficient and accurate monitoring of nuclear power units, which has wide application value.

[0066] Figure 3 This is a schematic diagram of the structure of a simulator data upgrade device provided in an embodiment of the present application. Figure 3 As shown, the simulator data upgrading device 300 comprises:

[0067] A first acquisition module 301, the first acquisition module 301 is used to acquire a modeling parameter correction factor based on parallel perception of historical data of nuclear power units;

[0068] A second acquisition module 302, the second acquisition module 302 is used to acquire prediction factors related to the real-time operation of the nuclear power unit based on parallel perception of the real-time operation status of the nuclear power unit;

[0069] The updating module 303 is used to perform evolution calculations on the modeling parameter correction factors and prediction factors based on the preset digital image of the nuclear power unit, determine the data file for upgrading, and update the data of the nuclear power unit according to the data file.

[0070] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0071] like Figure 4 As shown, the electronic device 400 includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a memory 406 to a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0072] The following components are connected to the I / O interface 405: a memory 406 including a hard disk, etc.; and a communication part 407 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., and the communication part 407 performs communication processing via a network such as the Internet; a drive 408 is also connected to the I / O interface 405 as needed.

[0073] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 407. When the computer program is executed by the processor 401, the above-mentioned functions defined in the method of the present application are executed.

[0074] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 401 of the electronic device 400 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0075] In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0076] Figure 5 It is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 5As shown, the electronic device 500 includes a processor 501 and a memory 502. The memory 502 is used to store program codes, and the processor 501 is connected to the memory 502 and is used to read program codes from the memory 502 to implement the simulator data upgrade method in the above embodiment.

[0077] Optionally, the number of processors 501 may be one or more.

[0078] Optionally, the electronic device may further include an interface 503, and the number of the interface 503 may be multiple. The interface 503 may be connected to an application program and may receive data from an external device such as a sensor.

[0079] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0080] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for upgrading simulator data, characterized in that: include: Based on parallel perception of historical data of nuclear power units, correction factors of modeling parameters are obtained; Based on parallel perception of the real-time operating status of nuclear power units, prediction factors related to the real-time operation of nuclear power units are obtained; Based on the preset digital image of the nuclear power unit, the modeling parameter correction factor and the prediction factor are evolved and calculated, a data file for upgrading is determined, and the data of the nuclear power unit is updated according to the data file.

2. The method according to claim 1, characterized in that: The method of obtaining the modeling parameter correction factor based on parallel sensing of the historical data of the nuclear power unit includes: Based on parallel sensing, historical data of nuclear power units under various operating conditions are obtained; Based on preprocessing the historical data, obtaining first intermediate data; Calculating the first intermediate data based on a pre-trained nuclear power unit mechanism model to obtain a modeling parameter correction factor of a preset nuclear power unit digital image; The modeling parameter correction factor is evaluated based on the digital image of the nuclear power unit to obtain an error result, and the digital image of the nuclear power unit is updated based on the error result.

3. The method according to claim 2, characterized in that The calculating the first intermediate data based on the pre-trained nuclear power unit mechanism model to obtain the modeling parameter correction factor of the preset nuclear power unit digital image includes: Determine the mechanism model based on the operating principle of the nuclear power unit; Based on the obtained simulation parameters of the mechanism model, the mechanism model is trained to obtain a first model that minimizes the loss parameters; The first intermediate data is calculated based on the first model to obtain a modeling parameter correction factor of a preset digital image of a nuclear power unit.

4. The method according to claim 1, characterized in that: The method of obtaining prediction factors related to the real-time operation of the nuclear power unit based on parallel perception of the real-time operation status of the nuclear power unit includes: Based on parallel sensing, obtain real-time operation data of nuclear power units; Based on the distributed processing of the real-time operation data, obtaining second intermediate data; The second intermediate data is calculated based on a pre-trained nuclear power unit mechanism model to obtain prediction factors related to the real-time operation of the nuclear power unit.

5. The method according to claim 4, characterized in that The step of obtaining the second intermediate data based on distributed processing of the real-time operation data includes: Identifying a distributed system architecture of a nuclear power unit and obtaining a plurality of nodes of the distributed system architecture; Based on the multiple nodes, the real-time operation data is calculated in parallel to obtain second intermediate data.

6. The method according to claim 1, characterized in that The method of performing evolution calculation on the modeling parameter correction factor and the prediction factor based on the preset digital image of the nuclear power unit to determine the data file for upgrading includes: Determining an algorithm for performing evolutionary calculation according to the data structure of the modeling parameter correction factor and the prediction factor; According to the algorithm, the digital image of the nuclear power unit iteratively optimizes the modeling parameter correction factor and the prediction factor to obtain evolution results related to the change trend and potential impact of the nuclear power unit; Based on multi-agent interaction on the evolution result, a data file for upgrading is determined.

7. The method according to claim 6, characterized in that The step of determining the data file for upgrading based on multi-agent interaction on the evolution result includes: According to the distributed system architecture of nuclear power units, multiple agents are configured in the distributed system; Based on the interaction of the multiple Agents on the evolution results, an optimal solution is obtained, and a data file for upgrading is determined according to the optimal solution.

8. The method according to any one of claims 1 to 7, characterized in that The step of updating the data of the nuclear power unit according to the data file comprises: Based on data verification of the data file, obtaining a data packet compatible with the configuration of the nuclear power unit; The data of the nuclear power unit is updated according to the data packet.

9. A simulator data upgrade device, characterized in that: include: A first acquisition module, the first acquisition module is used to acquire a modeling parameter correction factor based on parallel perception of historical data of nuclear power units; A second acquisition module, the second acquisition module is used to acquire prediction factors related to the real-time operation of the nuclear power unit based on parallel perception of the real-time operation status of the nuclear power unit; An update module is used to perform evolutionary calculations on the modeling parameter correction factor and the prediction factor based on a preset digital image of the nuclear power unit, determine a data file for upgrading, and update the data of the nuclear power unit according to the data file.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 8.

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