Digital twin analysis method and device for life cycle mechanism of DCS equipment in nuclear industry

Abstract

The present application discloses a method and device for analyzing the life cycle mechanism of nuclear industry DCS equipment digital twin, which relates to the field of digital twin of nuclear industry DCS equipment. The method includes: obtaining information data of DCS electronic equipment; determining the remaining life prediction information according to the information data based on the remaining life prediction model; the remaining life prediction information is used to characterize the status information of DCS electronic equipment; the remaining life prediction model is determined according to the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical working environment data, the solid geometry model of DCS electronic equipment and the service working condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on historical electrical parameter data. The present application can quickly and accurately realize the life prediction analysis of nuclear industry DCS equipment.

Description

Technical Field

[0001] The present application relates to the field of digital twins of DCS equipment in the nuclear industry, and in particular to a digital twin analysis method and device for the life cycle mechanism of DCS equipment in the nuclear industry. Background Art

[0002] The nuclear safety-level digital control system (DCS) is the "brain and nervous system" of a nuclear power plant and the basic means of monitoring the operation process of a nuclear power plant. The design, manufacturing, operation, and maintenance of nuclear industry DCS equipment all need to take into account complex working conditions and environments, and have high safety and reliability requirements. According to surveys and statistics on power module failures in nuclear power plants, aging is the main cause of power module failures, accounting for more than 50%. However, in nuclear power plants and other industrial sectors, there is little research on the life and aging management of electronic equipment. Most electronic equipment is still in a passive management state, that is, corrective treatment is performed after failure. This measure has kept electronic equipment at the weak point of the system, affecting the reliability and economy of the operation of the unit or key circuits.

[0003] In 2002, Professor Michael Grieves proposed the Mirrored Space Model (MSM). In 2010, the National Aeronautics and Space Administration (NASA) first proposed the concept of digital twins, also known as "Simulation-Based System Engineering". NASA and the US Air Force applied digital twins to the health maintenance and remaining service life prediction of aircraft. The article proposed that the goal of digital twins is to replace the "traditional experience-based standard manuals" and realize virtual verification, fault prediction, life assessment and other functions. Specifically at the implementation level, the core of realizing the digital twin function is the digital twin mechanism model.

[0004] The life and aging management of traditional electronic equipment is mainly based on traditional aging, fatigue, life tests and experience accumulation, and also considers the working environment of electronic equipment. However, with the development of electronic equipment and the increase in the rate of update and iteration, the accumulation of electronic equipment life and aging database is relatively lagging, resulting in insufficient coverage of existing products.

[0005] There are three mainstream methods for constructing mechanism models of electronic equipment: ① The "experience + experiment" method, which requires consideration of test samples, test time, and product coverage, is costly and inefficient; ② Numerical calculation method. With the development of materials and processing technology, some constitutive models need to be optimized; Due to the need to consider cross-scale and cross-physical fields at the same time, coupled simulation technology needs to be developed; ③ AI and big data learning methods, data correlation screening is difficult and requires high computing resources.

[0006] Therefore, it is urgent to develop a low-cost, high-precision, high-scalability, and high-efficiency life mechanism model for electronic equipment to quickly and accurately realize the life prediction analysis of nuclear industry DCS equipment. Summary of the invention

[0007] The purpose of this application is to provide a digital twin analysis method and device for the life cycle mechanism of nuclear industry DCS equipment, which can quickly and accurately realize the life prediction analysis of nuclear industry DCS equipment.

[0008] To achieve the above objectives, this application provides the following solutions:

[0009] In the first aspect, the present application provides a digital twin analysis method for the life cycle mechanism of DCS equipment in the nuclear industry, including:

[0010] Acquire information data of DCS electronic equipment; the information data includes: working environment data and electrical parameter data;

[0011] Based on the remaining life prediction model, the remaining life prediction information is determined according to the information data; the remaining life prediction information is used to characterize the status information of the DCS electronic equipment; the remaining life prediction model is determined according to the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical working environment data, the solid geometry model of the DCS electronic equipment and the service working condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on historical electrical parameter data.

[0012] Optionally, the method for determining the physical damage mechanism model specifically includes:

[0013] Constructing a solid geometric model of a DCS electronic device; the solid geometric model is a physical geometric model determined based on a simulation method;

[0014] determining physical boundaries of operating conditions and loads based on the solid geometric model;

[0015] Based on the solid geometric model and the physical boundary, multi-physical field calculation is performed according to historical working condition environment data to determine calculation results; the calculation results include: electromagnetic field calculation results, temperature field calculation results, vibration / vibration calculation results and fatigue calculation results;

[0016] Based on DCS electronic equipment, multi-physical fields are used to conduct service condition tests according to historical working condition environment data to obtain test results; the test results include: electromagnetic field test results, temperature field test results, vibration / vibration test results and fatigue test results;

[0017] According to the calculation results and the test results, multi-physical field coupling calculations are performed based on stress / strain data to obtain the physical damage mechanism model.

[0018] Optionally, according to the calculation results and the test results, a multi-physical field coupling calculation is performed based on stress / strain data to obtain the physical damage mechanism model, including:

[0019] Performing regression analysis based on the calculation results and the test results to determine the digital twin baseline;

[0020] Based on the envelope data condition, the digital twin mechanism model is determined by using the Monte Carlo or Latin hypercube method according to the digital twin baseline; the digital twin mechanism model includes: a digital twin electromagnetic field mechanism model, a digital twin temperature field mechanism model, a digital twin vibration / vibration mechanism model and a digital twin fatigue mechanism model;

[0021] Based on the stress / strain data, multi-physical field coupling calculation is performed on the digital twin mechanism model to obtain the physical damage mechanism model.

[0022] Optionally, the expression of the physical damage mechanism model is:

[0023] ;

[0024] in, is the function corresponding to the physical damage mechanism model; is the function corresponding to the digital twin electromagnetic field mechanism model; is the function corresponding to the digital twin temperature field mechanism model; is the function corresponding to the digital twin vibration / vibration mechanism model; is the function corresponding to the digital twin fatigue mechanism model; is the functional relationship of the physical damage mechanism model.

[0025] Optionally, the method for determining the electrical parameter damage model specifically includes:

[0026] Convert historical electrical parameter data into time-based electrical parameter data to obtain converted electrical parameter data;

[0027] Performing correlation analysis and information screening processing on the converted electrical parameter data and the historical electrical parameter data to obtain screened parameter data;

[0028] The screening parameter data are subjected to multivariate regression analysis to determine a regression equation; the expression of the regression equation is:

[0029] ;

[0030] The electrical parameter damage model is determined according to the screening parameter data and the regression equation; the expression of the electrical parameter damage model is:

[0031] ;

[0032] in, is the regression equation; Design matrix for regression; is the parameter vector; is an n-dimensional random column vector; is the function corresponding to the electrical parameter damage model; For over time Changing current; For over time Changing voltage; It is the functional relationship of the electrical parameter damage model.

[0033] Optionally, the remaining life prediction model is expressed as:

[0034] ;

[0035] in, is the function corresponding to the remaining life prediction model; is the function corresponding to the physical damage mechanism model; is the function corresponding to the electrical parameter damage model; For over time Decreasing function of change; For over time An increasing function of change.

[0036] In the second aspect, the present application provides a digital twin analysis device for the life cycle mechanism of DCS equipment in the nuclear industry, including:

[0037] An information data acquisition module is used to acquire information data of DCS electronic equipment; the information data includes: working environment data and electrical parameter data;

[0038] The prediction module is used to determine the remaining life prediction information according to the information data based on the remaining life prediction model; the remaining life prediction information is used to characterize the status information of the DCS electronic equipment; the remaining life prediction model is determined according to the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical working condition environment data, the solid geometry model of the DCS electronic equipment and the service working condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on historical electrical parameter data.

[0039] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0040] The present application provides a method and device for analyzing the life cycle mechanism of DCS equipment in the nuclear industry by digital twins, by acquiring information data of DCS electronic equipment; based on the remaining life prediction model, the remaining life prediction information is determined according to the information data to characterize the status information of the DCS electronic equipment. Among them, the remaining life prediction model is determined based on the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical operating environment data, the solid geometry model of the DCS electronic equipment and the service operating condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on historical electrical parameter data. The physical damage mechanism model and the electrical parameter damage model take into account high efficiency while ensuring the accuracy of the model, and ensure scalability during the service process; based on the remaining life prediction model, the life prediction analysis of DCS equipment in the nuclear industry can be realized quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 This is a flow chart of the digital twin analysis method for the life cycle mechanism of DCS equipment in the nuclear industry;

[0043] Figure 2 Design a flow chart for the digital twin analysis method of the life cycle mechanism of DCS equipment in the nuclear industry in practical applications;

[0044] Figure 3 This is a schematic diagram of the process of determining the digital twin mechanism model;

[0045] Figure 4A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0048] like Figure 1 As shown, the present application provides a digital twin analysis method for the life cycle mechanism of DCS equipment in the nuclear industry, the method comprising:

[0049] Step 100: Obtain information data of DCS electronic equipment. The information data includes: working environment data and electrical parameter data.

[0050] Step 200: Based on the remaining life prediction model, the remaining life prediction information is determined according to the information data. The remaining life prediction information is used to characterize the state information of the DCS electronic equipment; the remaining life prediction model is determined according to the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical working condition environment data, the solid geometry model of the DCS electronic equipment and the service working condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on the historical electrical parameter data.

[0051] In one embodiment, a method for determining a physical damage mechanism model specifically includes:

[0052] Construct a solid geometry model of DCS electronic equipment; the solid geometry model is a physical geometry model determined based on simulation methods.

[0053] Determine the physical boundaries of working conditions and loads based on the solid geometry model; perform multi-physics field calculations based on the solid geometry model and physical boundaries according to historical working environment data to determine the calculation results; the calculation results include: electromagnetic field calculation results, temperature field calculation results, vibration / vibration calculation results and fatigue calculation results.

[0054] Based on DCS electronic equipment, multi-physical fields are used to conduct service condition tests according to historical working environment data to obtain test results; the test results include: electromagnetic field test results, temperature field test results, vibration / vibration test results and fatigue test results.

[0055] According to the calculation results and test results, multi-physical field coupling calculations are performed based on stress / strain data to obtain the physical damage mechanism model.

[0056] In one embodiment, according to the calculation results and the test results, a multi-physics field coupling calculation is performed based on the stress / strain data to obtain a physical damage mechanism model, including:

[0057] Regression analysis is performed based on the calculation results and test results to determine the digital twin baseline. Based on the envelope data conditions, the digital twin mechanism model is determined using the Monte Carlo or Latin hypercube method according to the digital twin baseline. The digital twin mechanism model includes: digital twin electromagnetic field mechanism model, digital twin temperature field mechanism model, digital twin vibration / vibration mechanism model and digital twin fatigue mechanism model.

[0058] Based on the stress / strain data, multi-physical field coupling calculation is performed on the digital twin mechanism model to obtain the physical damage mechanism model.

[0059] Specifically, the expression of the physical damage mechanism model is:

[0060] ;

[0061] in, is the function corresponding to the physical damage mechanism model; is the function corresponding to the digital twin electromagnetic field mechanism model; is the function corresponding to the digital twin temperature field mechanism model; is the function corresponding to the digital twin vibration / vibration mechanism model; is the function corresponding to the digital twin fatigue mechanism model; is the functional relationship of the physical damage mechanism model.

[0062] In one embodiment, a method for determining an electrical parameter damage model specifically includes:

[0063] The historical electrical parameter data is converted into time-based electrical parameter data to obtain converted electrical parameter data.

[0064] Correlation analysis and information screening are performed on the converted electrical parameter data and the historical electrical parameter data to obtain screened parameter data.

[0065] Perform multiple regression analysis on the screening parameter data to determine the regression equation; the expression of the regression equation is:

[0066] ;

[0067] The electrical parameter damage model is determined based on the screening parameter data and the regression equation; the expression of the electrical parameter damage model is:

[0068] ;

[0069] in, is the regression equation; Design matrix for regression; is the parameter vector; is an n-dimensional random column vector; is the function corresponding to the electrical parameter damage model; For over time Changing current; For over time Changing voltage; It is the functional relationship of the electrical parameter damage model.

[0070] In one embodiment, the expression of the remaining life prediction model is:

[0071] ;

[0072] in, is the function corresponding to the remaining life prediction model; is the function corresponding to the physical damage mechanism model; is the function corresponding to the electrical parameter damage model; For over time Decreasing function of change; For over time An increasing function of change.

[0073] The purpose of the present invention is to 1. provide a method for modeling a life cycle digital twin mechanism model of nuclear industry DCS electronic equipment. 2. provide a low-cost, high-precision, high-scalability, and high-efficiency construction process for the life cycle mechanism model of electronic equipment. 3. The life cycle digital twin mechanism model of nuclear industry DCS electronic equipment is deployed on the digital twin platform to provide early design verification functions for designers, and the reduced-order operating load and mechanism model are packaged and deployed in the health management system to provide decision support for operation and maintenance personnel.

[0074] In practical applications, such as Figure 2 As shown, the design idea of ​​the method mentioned in this application includes the following steps:

[0075] S1: Collect the working conditions of nuclear industry DCS electronic equipment during service.

[0076] S2: Establish the geometric model of the physical entity of nuclear industry DCS electronic equipment, establish the physical boundaries of working conditions and loads, and perform numerical calculations.

[0077] S2 includes the following steps:

[0078] S21: Numerical calculation of electromagnetic fields of DCS electronic equipment in nuclear industry.

[0079] S22: Numerical calculation of temperature field of DCS electronic equipment in nuclear industry.

[0080] S23: Numerical calculation of vibration and shock of DCS electronic equipment in the nuclear industry.

[0081] S24: Numerical calculation of fatigue of DCS electronic equipment in nuclear industry.

[0082] S3: The prototype products of nuclear industry DCS electronic equipment are tested in accordance with the service conditions collected in S1.

[0083] S3 includes the following steps:

[0084] S31: Compare the electromagnetic field test and numerical calculation result curves of nuclear industry DCS electronic equipment to confirm the reliability of the numerical simulation calculation process.

[0085] S32: Compare the temperature field test of nuclear industry DCS electronic equipment with the numerical calculation result curve to confirm the reliability of the numerical simulation calculation process.

[0086] S33: Compare the vibration and shock test of nuclear industry DCS electronic equipment with the numerical calculation result curve to confirm the reliability of the numerical simulation calculation process.

[0087] S34: Compare the fatigue test and numerical calculation result curves of nuclear industry DCS electronic equipment to confirm the reliability of the numerical simulation calculation process.

[0088] S4: Establish physical mechanism models of electromagnetic field, temperature field, vibration and shock, fatigue, etc. of nuclear industry DCS electronic equipment.

[0089] S4 includes the following steps:

[0090] S41: Perform regression analysis based on S21 and S31, verify various parameter settings, and establish the electromagnetic field digital twin baseline of nuclear industry DCS electronic equipment.

[0091] S42: Perform regression analysis based on S22 and S32, verify various parameter settings, and establish a digital twin baseline for the temperature field of nuclear industry DCS electronic equipment.

[0092] S43: Perform regression analysis based on S23 and S33, verify various parameter settings, and establish a vibration and shock digital twin baseline for nuclear industry DCS electronic equipment.

[0093] S44: Perform regression analysis based on S24 and S34, verify various parameter settings, and establish fatigue digital twin baselines for nuclear industry DCS electronic equipment.

[0094] S45: Based on the digital twin baseline of nuclear industry DCS equipment, comprehensive consideration of the envelope operating data, Monte Carlo or Latin hypercube methods, and model training are used to establish the functions corresponding to the digital twin electromagnetic field mechanism model. , Function corresponding to the digital twin temperature field mechanism model , Digital Twin Vibration / shock Function corresponding to the mechanism model , Function corresponding to the digital twin fatigue mechanism model , the model training process is as follows Figure 3 shown.

[0095] S5: Based on stress / strain data, perform coupled calculations of multiple physical fields.

[0096] S6: Based on S5, a physical damage mechanism model in the time domain is established:

[0097] .

[0098] S7: Current collection based on DCS electronic equipment status monitoring function ,Voltage ,temperature And other electrical parameter information.

[0099] S8: Convert the status information of nuclear industry DCS electronic equipment into data based on time parameters, including: current ,Voltage ,temperature wait.

[0100] S9: Perform correlation analysis based on the information from S7-S8, and filter out parameter data related to electronic equipment damage or life from the information collected by the DCS electronic equipment status monitoring function.

[0101] S10: Perform multiple regression analysis on relevant parameter data based on S9.

[0102] S10 includes the following steps:

[0103] S101: Consider the common influence of various related parameters on the damage of electronic equipment, and establish the relationship between each factor and temperature.

[0104] .

[0105] in ， Current is the relevant parameter ,Voltage wait; is an unknown constant; is a random variable that follows a standard normal distribution.

[0106] S102: Based on S101, regression analysis is performed using the parameter data of S9 as a sample to construct The linear regression model is the regression equation :

[0107] .

[0108] in, is the regression design matrix, is the parameter vector, is an n-dimensional random column vector.

[0109] S103: Model parameter estimation, obtaining unknown parameter vector The value of , we get the regression equation.

[0110] S104: Test the regression effect of the regression model, including deviation analysis, goodness of fit analysis, etc.

[0111] S11: Establish a time-based electrical parameter damage mechanism model based on the analysis results of S9-S10:

[0112] .

[0113] S12: Based on the models of S6 and S11, the physical damage mechanism model and the electrical parameter damage mechanism model are weighted respectively to build a model for predicting the remaining life of the equipment. :

[0114] .

[0115] in, is the function corresponding to the remaining life prediction model; is the function corresponding to the physical damage mechanism model; is the function corresponding to the electrical parameter damage model; For over time Decreasing function of change; For over time An increasing function of change.

[0116] S13: Apply the model to the design phase for verification.

[0117] S13 includes the following steps:

[0118] S131: In the early design stage, by selecting different options from the drop-down menu of the design parameter menu, design risks can be identified and early optimization design can be performed.

[0119] S132: In the early design stage, design risks are identified by dragging and dropping or directly entering different options in the operating parameter menu as a preliminary verification of product performance.

[0120] S14: Apply the mechanism model to the equipment service and operation stage to achieve real-time monitoring and risk prediction functions.

[0121] S14 includes the following steps:

[0122] S141: By dragging the working condition variables, the status of the equipment under different working load conditions is displayed in real time.

[0123] S142: By automatically identifying working conditions, the equipment status under different working load conditions is displayed in real time, and risk locations are predicted.

[0124] S15: The reduced-order operating loads and mechanism models are packaged and deployed in the equipment health management system.

[0125] In summary, this application establishes a construction process of a low-cost, high-precision, high-scalability and high-efficiency electronic equipment life mechanism model based on digital twin technology. This mechanism model can realize the real-time diagnosis and prediction capabilities of digital twins in applications; the mechanism model is deployed on the digital twin platform to provide designers with early design verification functions, and the reduced-order working load and mechanism model are packaged and deployed in the health management system to provide decision support for operation and maintenance personnel. First, based on single test data and numerical simulation results, a physical damage mechanism model of electronic equipment is established, which improves the accuracy of numerical calculations while reducing the test cost by 80%, while ensuring the scalability of the model in the design stage; then, the electrical parameter data in the equipment status monitoring is correlated and regressed to establish an electrical parameter damage mechanism model. The electrical parameter damage mechanism model is modeled separately, which takes into account high efficiency while ensuring the accuracy of the model, and ensures scalability during service; finally, the physical damage of electronic equipment and the electrical parameter damage mechanism model are integrated to establish a model for predicting the remaining life of the equipment.

[0126] Based on the same inventive concept, the embodiment of the present application also provides a nuclear industry DCS equipment life mechanism digital twin analysis device for implementing the above-mentioned nuclear industry DCS equipment life mechanism digital twin analysis method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the nuclear industry DCS equipment life mechanism digital twin analysis device provided below can refer to the limitations of the nuclear industry DCS equipment life mechanism digital twin analysis method in the above text, and will not be repeated here.

[0127] In an exemplary embodiment, a digital twin analysis device for life cycle mechanism of DCS equipment in nuclear industry is provided, including:

[0128] The information data acquisition module is used to acquire the information data of the DCS electronic equipment; the information data includes: working environment data and electrical parameter data.

[0129] The prediction module is used to determine the remaining life prediction information according to the information data based on the remaining life prediction model; the remaining life prediction information is used to characterize the status information of the DCS electronic equipment; the remaining life prediction model is determined based on the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical working environment data, the solid geometry model of the DCS electronic equipment and the service working condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on historical electrical parameter data.

[0130] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a digital twin analysis method for the life mechanism of DCS equipment in the nuclear industry is implemented.

[0131] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0132] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0133] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0135] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0136] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A digital twin analysis method for the life cycle mechanism of DCS equipment in the nuclear industry, characterized in that: The digital twin analysis method for the life cycle mechanism of nuclear industry DCS equipment includes: Acquire information data of DCS electronic equipment; the information data includes: working environment data and electrical parameter data; Based on the remaining life prediction model, the remaining life prediction information is determined according to the information data; the remaining life prediction information is used to characterize the status information of the DCS electronic equipment; the remaining life prediction model is determined according to the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical working condition environment data, the solid geometry model of the DCS electronic equipment and the service working condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on historical electrical parameter data; The expression of the physical damage mechanism model is: ； in, is the function corresponding to the physical damage mechanism model; is the function corresponding to the digital twin electromagnetic field mechanism model; is the function corresponding to the digital twin temperature field mechanism model; is the function corresponding to the digital twin vibration / vibration mechanism model; is the function corresponding to the digital twin fatigue mechanism model; is the functional relationship of the physical damage mechanism model; The expression of the remaining life prediction model is: ； in, is the function corresponding to the remaining life prediction model; is the function corresponding to the physical damage mechanism model; is the function corresponding to the electrical parameter damage model; For over time a decreasing function of change; For over time An increasing function of change.

2. The digital twin analysis method for the life cycle mechanism of nuclear industry DCS equipment according to claim 1 is characterized in that: The method for determining the physical damage mechanism model specifically includes: Constructing a solid geometric model of a DCS electronic device; the solid geometric model is a physical geometric model determined based on a simulation method; determining physical boundaries of operating conditions and loads based on the solid geometric model; Based on the solid geometric model and the physical boundary, multi-physical field calculation is performed according to historical working condition environment data to determine calculation results; the calculation results include: electromagnetic field calculation results, temperature field calculation results, vibration / vibration calculation results and fatigue calculation results; Based on DCS electronic equipment, multi-physical fields are used to conduct service condition tests according to historical working condition environment data to obtain test results; the test results include: electromagnetic field test results, temperature field test results, vibration / vibration test results and fatigue test results; According to the calculation results and the test results, multi-physical field coupling calculations are performed based on stress / strain data to obtain the physical damage mechanism model.

3. The digital twin analysis method for the life cycle mechanism of nuclear industry DCS equipment according to claim 2 is characterized in that: According to the calculation results and the test results, multi-physical field coupling calculation is performed based on stress / strain data to obtain the physical damage mechanism model, including: Performing regression analysis based on the calculation results and the test results to determine the digital twin baseline; Based on the envelope data condition, the digital twin mechanism model is determined by using the Monte Carlo or Latin hypercube method according to the digital twin baseline; the digital twin mechanism model includes: a digital twin electromagnetic field mechanism model, a digital twin temperature field mechanism model, a digital twin vibration / vibration mechanism model and a digital twin fatigue mechanism model; Based on the stress / strain data, multi-physical field coupling calculation is performed on the digital twin mechanism model to obtain the physical damage mechanism model.

4. The digital twin analysis method for the life cycle mechanism of nuclear industry DCS equipment according to claim 1 is characterized in that: The method for determining the electrical parameter damage model specifically includes: Convert historical electrical parameter data into time-based electrical parameter data to obtain converted electrical parameter data; Performing correlation analysis and information screening processing on the converted electrical parameter data and the historical electrical parameter data to obtain screened parameter data; The screening parameter data are subjected to multivariate regression analysis to determine a regression equation; the expression of the regression equation is: ； The electrical parameter damage model is determined according to the screening parameter data and the regression equation; the expression of the electrical parameter damage model is: ； in, is the regression equation; Design matrix for regression; is the parameter vector; is an n-dimensional random column vector; is the function corresponding to the electrical parameter damage model; For over time Changing current; For over time Changing voltage; It is the functional relationship of the electrical parameter damage model.

5. A digital twin analysis device for the life cycle mechanism of DCS equipment in the nuclear industry, characterized in that: The digital twin analysis device for the life cycle mechanism of a nuclear industry DCS equipment is used to execute the digital twin analysis method for the life cycle mechanism of a nuclear industry DCS equipment as described in any one of claims 1 to 4; The nuclear industry DCS equipment life cycle mechanism digital twin analysis device includes: An information data acquisition module is used to acquire information data of DCS electronic equipment; the information data includes: working environment data and electrical parameter data; The prediction module is used to determine the remaining life prediction information according to the information data based on the remaining life prediction model; the remaining life prediction information is used to characterize the status information of the DCS electronic equipment; the remaining life prediction model is determined according to the physical damage mechanism model and the electrical parameter damage model; the physical damage mechanism model is a mathematical model determined by multi-physical field coupling simulation calculation based on historical working condition environment data, the solid geometry model of the DCS electronic equipment and the service working condition test; the electrical parameter damage model is a mathematical model obtained after regression analysis based on historical electrical parameter data.

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