A state tracking method for digital twin system of nuclear power plant

Abstract

The present invention relates to the field of nuclear power simulation technology, and specifically to a method for tracking the state of a digital twin system of a nuclear power plant. The method comprises the following steps: data processing: obtaining the actual operation data of the power plant at a specified time, and processing the actual operation data to obtain tracking point data, and establishing a unit data mapping between the tracking point data and the thermal software calculation data; matching calculation: obtaining the closest IC according to the unit data mapping, resetting the IC and determining the model boundary parameter conditions of each subsystem model, and then performing system decoupling, and automatically starting calculation and parameter correction with agreed boundary parameter conditions, and iterating the calculation until the initialization calculation is completed. The beneficial effect is that the method can be used to analyze and extract power plant data closely related to the unit operation status from the unit operation data, and automatically complete the system parameter initialization and the decoupling, monotony and joint adjustment of the model, thereby laying the foundation for carrying out operation event analysis and thermal performance analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear power simulation, and in particular to a method for tracking the status of a digital twin system of a nuclear power plant. Background Art

[0002] At present, in the field of nuclear reactor operation, the operation of the unit mainly relies on the technical capabilities of the operators such as the main control room operators, and the handling of abnormal problems often needs to rely on personal experience and decision-making. Therefore, most of the operating events in nuclear power plants are related to human factors. On the other hand, with the long-term operation of the unit, there will be a large number of unit operation plan improvements and technical transformation needs, but the research and verification of nuclear power plant operation plans and technical transformation plans still lack technical means. At present, it mainly relies on single software calculations and identification tests of single equipment, or relies on expert reviews to make technical decisions. Therefore, it is necessary to study digital twin technology, and develop intelligent operation support tools through online intelligent detection, diagnosis and analysis technologies to solve the problems faced in the operation field.

[0003] To realize the operation digital twin of the nuclear reactor system, the digital twin system must have the same ability as the nuclear reactor system at a specified time. However, since the nuclear reactor system is a complex system, how to obtain key and sufficient data from its massive operation data and apply it to the simulation model so that the simulation model can automatically initialize to the same state as the nuclear reactor system is one of the difficulties. Therefore, a model state tracking method is urgently needed, which can achieve a steady-state simulation model consistent with the power plant operation data based on the actual operation data of the nuclear power plant at a certain moment. Summary of the invention

[0004] The purpose of the present invention is to provide a method for tracking the state of a digital twin system of a nuclear power plant, which can automatically make the simulation model reach the actual steady-state operating data state of the power plant at a specified time in a relatively short time, and can be used to establish a digital twin system of a nuclear power plant and perform intelligent operation and maintenance of the power plant.

[0005] The technical solution of the present invention is as follows: A method for tracking the state of a digital twin system of a nuclear power plant comprises the following steps:

[0006] S1 Data Processing:

[0007] Acquire actual operation data of the power plant at a specified time, process the actual operation data to obtain tracking point data, and establish unit data mapping between the tracking point data and thermal software calculation data;

[0008] S2 matching calculation:

[0009] The closest IC is obtained according to the unit data mapping, the IC is reset and the model boundary parameter conditions of each subsystem model are determined, and then the system is decoupled, and the calculation and parameter correction are started automatically after the boundary parameter conditions are agreed upon, and the calculation is iterated until the initialization calculation is completed.

[0010] The step S1 specifically includes the following steps:

[0011] S11 data management, performing one or more combinations of singular value removal, data completion, data smoothing, and data feature extraction on the actual operation data;

[0012] S12 data coordination, based on the principles of conservation of mass, conservation of energy, conservation of momentum, pressure conduction, and physical property compliance, the actual operating data after data governance is corrected.

[0013] In the step S11, the Raida criterion is used to remove singular values, and singular values ​​of the actual operating data are removed, that is, values ​​with deviations greater than 3σ are removed as singular values.

[0014] In step S11, data completion includes: if it is determined that the actual operation data is empty within a certain time period, tracing back in the external database until the time when there is data is traced back, and completing according to the data of the last time traced back to fill in the missing values ​​of the actual operation data.

[0015] In the step S11, the data smoothing process includes: performing direct shrinkage and stacking comparison on the actual operation data, and performing weighted smoothing calculation to smooth and balance the actual operation data, so as to reduce the impact of accidental data mutations.

[0016] The data coordination in step S12 includes the following steps:

[0017] S121 performs redundancy analysis on various measurement parameters according to the main constraints of the system, performs measurement redundancy analysis on the system, and determines the system or corresponding measurement loop that can implement data coordination; if the system or the corresponding measurement loop has redundancy, it is determined that data coordination can be performed. The higher the redundancy, the higher the accuracy of the coordination calculation. The constraints in the data coordination problem of the thermal system mainly include: mass balance conditions, energy balance conditions, momentum balance conditions, physical property equations, etc.;

[0018] S122 performs parameter correction on the system or measurement loop that is judged to be capable of data coordination according to the principles of conservation of mass, conservation of energy, and conformity of physical properties.

[0019] The matching calculation in step S2 includes the following steps:

[0020] S21 creates an initial state of the simulation model to determine the initial operating conditions of the digital twin system, and further;

[0021] S22 subsystem steady-state calculation is used to start each simulation system model that has been filled with initial state and perform calculations respectively;

[0022] The S23 joint-adjustment steady-state calculation is used to couple and confirm the steady-state model achieved by the separate adjustments in steps until all simulation model systems are combined and reach the joint-adjustment steady-state. If the interface is judged to be mismatched or unstable after coupling, the unstable system will be decoupled and rolled back to the subsystem steady-state for re-correction until the system completes the joint-adjustment steady-state. The model initialization process is then judged to be completed and the tracking process is ended.

[0023] The step S21 of creating the initial state of the simulation model includes:

[0024] S211 obtains a reset IC and selects an IC with the most consistent parameter approximation with the tracking point data from pre-stored IC data as the reset IC, and resets the digital twin system with the reset IC;

[0025] The obtaining of the reset IC comprises the following steps:

[0026] S2111 pre-stores enough full range model ICs;

[0027] S2112 obtains and analyzes the core power parameters of the current reset data;

[0028] S2113 searching the model IC for the closest saved state based on the core power parameter;

[0029] S212 boundary parameter setting, for the reset digital twin system, adjusting the boundary parameters of each model in the digital twin system according to the tracking point data and the connection relationship between the systems to determine the boundary parameter conditions of each model;

[0030] S213 decouples the model and decomposes the adjusted digital twin model system into independent computing processes.

[0031] The step S21 comprises the following steps:

[0032] S221 subsystem operation confirmation is used to determine whether the initial state fitting of all simulation system model parameters is complete;

[0033] In S221, if each model meets the start-up conditions, all subsystem simulation models are loaded into the simulator platform and started to run. In this embodiment, further, whether the initial state fitting of the simulation system model parameters is complete is judged including the following steps:

[0034] S2211 determines whether the initial state of the model is complete, detects whether all non-zero values ​​of the simulation model have values, and if so, compares them with the preset reasonable interval to determine whether the non-zero value of the corresponding model is within the reasonable interval. If so, proceed to step S2212. If not, an external reminder is issued to prompt the user to analyze whether the simulation data is abnormal during the coordination and expansion process.

[0035] S2212 Model decoupling confirmation is used to confirm whether all simulation models are in a decoupling state. If they are not completely decoupled or the decoupling fails, a message will pop up and an error will be reported;

[0036] S222 non-key parameter tracking and correction, directly mapping the actual power plant operation data to the model data of non-key parameters, and correcting the real-time calculation results of non-key parameters of each simulation system;

[0037] S223 model operation startup and status monitoring: when it is determined that the model decoupling is completed, the simulation model operation is started, and the operation status of the model is monitored during the operation to ensure that the model operation is within the normal range and no abnormal deviation occurs. Key parameter tracking and correction are used to correct the key parameters of each simulation system to avoid long-term deviation from the expected target value during the calculation process of the key parameters.

[0038] The step S23 comprises the following steps:

[0039] S231 state record, used to store the results of the sub-regulation steady-state calculation in IC, record the current sub-regulation state, and determine whether it is necessary to fall back to the sub-regulation stage according to the system operation state. After coupling, when the joint adjustment calculation formula shows that some system calculations in the multi-objective coupling adjustment optimization deviate too much or fail, it will fall back to the state point recorded in this process, and set the current optimization parameter value to a disabled value or the current optimization direction to an incorrect direction. If instantaneous oscillation or instantaneous calculation failure occurs after coupling, it is necessary to further fall back to the sub-regulation stage to redefine the boundary parameters between subsystems and adjust the parameter distribution within the subsystem;

[0040] S232 subsystem coupling is used to gradually couple the simulation models of each subsystem, including opening the interface identifiers between the simulation models of each subsystem one by one, so that the interface program enters an online interactive state;

[0041] S233 steady-state tracking parameter confirmation, obtain the main parameters of each model after the operation is completed, and compare them with the tracking data points to confirm whether the current model meets the technical requirements. If the technical requirements are met, the current model is considered to be valid for the tracking data and the tracking is completed; if the requirements are not met, return to the corresponding state save point in the state record for reconfirmation;

[0042] In S231, after the state recording process is completed in the separate steady-state calculation stage, the results of the separate steady-state calculation are stored in IC before the joint steady-state calculation process starts, and the current separate state is recorded to back up the pre-startup data for the subsequent subsystem simulation model coupling;

[0043] In S232, subsystem coupling is the process of gradually coupling the simulation models of each subsystem after the state recording process. After the coupling of the subsystem simulation models is completed, that is, after a period of stable operation, the main parameters of the model and the tracking data points are compared to confirm whether the current model meets the technical requirements. If the technical requirements are met, the current model is considered to be valid for the tracking data, that is, the tracking is completed.

[0044] The beneficial effect of the present invention is that the state tracking tool of the digital twin system of a nuclear power plant based on this method can automatically reach the actual steady-state operation data state of the power plant at a specified time in a relatively short time for the running simulation model, so that the digital twin system has the same capability as the state of the nuclear reactor system at a specified time. Using this method, the power plant data closely related to the unit operation status can be analyzed and extracted from the unit operation data, and the system parameter initialization and the decoupling, monotony and joint adjustment of the model can be automatically completed, thereby laying the foundation for carrying out operation event analysis and thermal performance analysis. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below with reference to specific embodiments.

[0046] The present invention discloses a method for tracking the state of a nuclear power plant digital twin system, comprising:

[0047] S1 Data Processing:

[0048] Acquire actual operation data of the power plant at a specified time, process the actual operation data to obtain tracking point data, and establish unit data mapping between the tracking point data and thermal software calculation data;

[0049] S2 matching calculation:

[0050] The closest IC is obtained according to the unit data mapping, the IC is reset and the model boundary parameter conditions of each subsystem model are determined, and then the system is decoupled, and the calculation and parameter correction are started automatically after the boundary parameter conditions are agreed upon, and the calculation is iterated until the initialization calculation is completed.

[0051] During the steady-state operation of the power plant, the measured steady-state data has a small vibration due to the disturbance of multiple complex factors. In the digital twin system, since the simulation model simplifies the disturbance factors, the actual simulation model steady-state cannot be calculated through the theoretical model to obtain data that is consistent with the fluctuating state of the power plant. The simulation model is also unable to identify steady-state information based on the fluctuating data source. Therefore, the data values ​​of the continuous power plant data segments required for the tracking point calculation should be noise-reduced and self-consistent.

[0052] In step S1, as a preferred solution, data governance and data coordination are performed on the actual operation data to obtain available tracking point data, wherein data governance is used to manage the quality of the actual operation data; the data coordination process mainly focuses on detecting parameter mismatches that may be introduced by the measurement error of the sensor, and calculating and verifying the parameters, which specifically includes the following steps:

[0053] S11 data management, performing one or more combinations of singular value removal, data completion, data smoothing, and data feature extraction on the actual operation data;

[0054] S12 Data coordination: correct the actual operation data after data management according to the principles of mass conservation, energy conservation, momentum conservation, pressure conduction, and physical property compliance;

[0055] Furthermore, in step S11, the Raida criterion is used to remove singular values, and singular values ​​of the actual operating data are removed, that is, values ​​with deviations greater than 3σ are removed as singular values.

[0056] And / or, in step S11, data completion includes: if it is determined that the actual operation data is empty within a certain time period, tracing back in the external database until the time when there is data is traced back, and completing according to the data of the last time traced back to fill in the missing values ​​of the actual operation data.

[0057] And / or, in step S11, the data smoothing process includes: performing direct shrinkage and stacking comparison on the actual operation data, and performing weighted smoothing calculations to smooth and balance the actual operation data (especially sensor data) to reduce the impact of accidental data mutations. For the actual operation data obtained, since the sampling samples are large, more than the number required for data coordination, and the data is subject to various interferences over a period of time. In order to condense the amount of data information itself, and not to ignore the useful information that the original measurement point sampling can bring, it is necessary to perform various smoothing processes before the data coordination calculation.

[0058] In step S12, data coordination is mainly to solve the problem of correcting data that does not conform to physical mechanisms such as conservation of mass and conservation of energy generated during the operation of the nuclear power plant, so that the corrected parameters can conform to the physical mechanism equations. In this embodiment, data coordination includes the following steps:

[0059] S121 performs redundancy analysis on various measurement parameters according to the main constraints of the system, performs measurement redundancy analysis on the system, and determines the system or corresponding measurement loop that can implement data coordination; if the system or the corresponding measurement loop has redundancy, it is judged that data coordination can be performed. The higher the redundancy, the higher the accuracy of the coordination calculation. The constraints in the data coordination problem of the thermal system mainly include: mass balance conditions, energy balance conditions, momentum balance conditions, physical property equations, etc.

[0060] S122 performs parameter correction on the system or measurement loop that is judged to be capable of data coordination according to the principles of conservation of mass, conservation of energy, and conformity of physical properties.

[0061] In this embodiment, the thermal parameters of the secondary circuit can be corrected based on the reliability of the thermal system sensors and other measurement parameters of the secondary circuit. Usually, the measurement parameters with low reliability are calibrated based on the measurement parameters with the highest reliability. It includes two parts: basic calibration and thermal balance calibration. The basic calibration completes pressure calibration, temperature calibration, liquid level calibration and flow calibration. The thermal balance calibration completes the secondary energy balance calibration of the generator, the efficiency calibration of the turbine stage group and the thermal balance calibration of the condenser.

[0062] After the unit data mapping is completed, it goes to step S2 to assign the unit effective data to the corresponding model parameters, and store the new initial condition state based on the platform initial IC (initial condition). After importing the model calculation, the deviation is judged, and the parameters with large deviations are automatically adjusted, and the iterative calculation is completed until the initialization calculation is completed.

[0063] In this embodiment, as a preferred solution, step S2 matching calculation includes the following steps:

[0064] S21 creates an initial state of the simulation model to determine the initial operating conditions of the digital twin system. Furthermore, step S21 creates an initial state of the simulation model including:

[0065] S211 obtains a reset IC and selects an IC with the most consistent parameter approximation with the tracking point data from the pre-stored IC data as the reset IC, and resets the digital twin system with the reset IC;

[0066] S212 boundary parameter setting, for the digital twin system after reset, the boundary parameters of each model in the digital twin system are adjusted according to the tracking point data and the connection relationship between the systems to determine the boundary parameter conditions of each model.

[0067] S213 decouples the model and decomposes the adjusted digital twin model system into independent computing processes. In this embodiment, as a preferred solution, in step S211, obtaining the reset IC includes the following steps:

[0068] S2111 pre-stores enough full-range model ICs. Since the online initialization function considered in this project only includes online initialization during power operation, the pre-stored IC saves 51 IC states from full power to zero power reduction process at 2% power intervals and 20 IC states from zero power to 40% full power, a total of 71 pre-stored IC state points. According to the relationship between electric power Pe, average temperature Tavg of a circuit and IC number N, a search function relationship is established:

[0069] S2112 obtains and analyzes the core power parameters of the current reset data. In this embodiment, the current core power data can be obtained from the shared memory.

[0070] S2113 searching the model IC for the closest saved state based on the core power parameter;

[0071] Furthermore, in this embodiment, searching for the closest saved state includes the following steps: comparing the core power with the rated power to calculate the normalized power. If the normalized power is greater than 40%, searching for the IC closest to the power from full power downwards for reset; if the normalized power is less than 40%, first judging whether the process is a power increase or power decrease based on the power and temperature, and then searching for the closest IC based on the power after determining the process.

[0072] In step S212, the boundary parameter setting of each simulation system is jointly determined based on the tracking point data and the connection relationship between the systems. Each boundary parameter should have its own setting method. For each simulation system, it is only necessary to design its own output boundary parameters. The following lists the overall boundary parameters of the step system, and each system performs boundary parameter setting on the following parameters: 1) The boundary parameter setting of the main coolant system mainly includes the setting of the interface boundary with the core system, chemical volume system, residual drainage system, injection system, containment system, main feed water system, auxiliary feed water system, evaporator sewage system, main steam system and other systems; 2) The core system mainly includes the parameter setting of the boundary with the main coolant system; 3) The flow network system includes a single-phase flow network simulation model, a two-phase flow network simulation model and an equipment model. According to the difference in the output interface parameters, the boundary of the flow network system is divided into a pressure flow boundary and a thermal boundary, which correspondingly includes the pressure flow boundary output parameter setting and the thermal boundary output parameter setting. 4) The main boundaries of the power generation system include: the mechanical power transmitted from the turbine to the generator side through the main shaft (turbine control and regulation system), the turbine lubrication top shaft and the turning system; the initial voltage, frequency and phase angle of the equivalent generator (balance node type) of the external power grid; the initial active power and reactive power of the equivalent load of the external power grid.

[0073] Among them, for the secondary circuit main steam-water system, it mainly includes the boundary setting of the steam system, condenser, condensate system, deaerator and water supply system: a) Steam system boundary: The steam system will be initialized as a whole based on the pipe network system from the steam generator outlet to the low-pressure cylinder last-stage blade outlet. Therefore, the steam system boundary parameter setting is mainly to determine the boundary with the steam generator and the boundary with the condenser; b) The condenser is a device that connects the two-phase flow network and the single-phase flow network system. The boundary parameter setting of the condenser is mainly to determine the boundary between the condenser and the steam system and the boundary with the condensate system. Boundary; c) The condensate system is a pipe network system from the condensate outlet of the condenser to the inlet of the deaerator. Therefore, the boundary parameter setting of the condensate system is mainly to determine the boundary with the deaerator and the boundary with the condenser; d) The deaerator is a device connecting the condensate system and the feed water system. The boundary parameter setting of the deaerator is mainly to determine the boundary between the deaerator and the condensate system and the boundary with the feed water system; e) The feed water system is a pipe network system from the outlet of the deaerator to the feed water inlet of the steam generator. Therefore, the boundary parameter setting of the feed water system is mainly to determine the boundary with the deaerator and the boundary with the steam generator.

[0074] In the digital twin model system, the connection identifiers between systems are composed of a series of logical variables. The connection identifier is named in the system name_system name format, where the former system name is the current simulation system, and the latter system name is the connected simulation system. For example, in the main coolant system, the connection identifier with the core system is named RCP_CRS. When the connection identifier is in the false state, it means that the parameters between the simulation systems are not transmitted to each other, and the interface parameter values ​​are the set boundary values. When the connection identifier is in the true state, the boundary parameters between the simulation systems begin to be transmitted to each other. Therefore, in step S213, when it is judged that the connection identifier is in the false state, the boundary parameter interaction code between the simulation systems will be skipped, and the parameters will not be transmitted.

[0075] Step S22: the subsystem steady-state calculation is used to start each simulation system model that has been filled with the initial state, and perform calculations respectively, including:

[0076] S221 subsystem operation confirmation is used to determine whether the initial state fitting of all simulation system model parameters is complete;

[0077] S222 non-key parameter tracking and correction, directly mapping the actual power plant operation data to the model data of non-key parameters, and correcting the real-time calculation results of non-key parameters of each simulation system;

[0078] S223 Model operation start and status monitoring: When it is determined that the model decoupling is complete, the simulation model operation is started. The operation status of the model is monitored during the operation to ensure that the model operation is within the normal range and no abnormal deviation occurs. If a model abnormality occurs, the abnormal simulation model will be frozen and a message will pop up to prompt confirmation.

[0079] During the subsystem steady-state calculation process, the system will execute three processes: subsystem operation, parameter tracking and correction (coarse adjustment), and key parameter tracking and correction (fine adjustment). The coarse adjustment and fine adjustment processes involve the back-substitution process of the joint adjustment stage. Each process in this stage must wait for all simulation model systems to complete before entering the next process.

[0080] In step S221, if each model meets the start-up conditions, all subsystem simulation models are loaded into the simulator platform and started to run. In this embodiment, further, whether the initial state fitting of the simulation system model parameters is complete is judged including the following steps:

[0081] S2211 determines whether the initial state of the model is complete, detects whether all non-zero values ​​of the simulation model have values, and if so, compares them with the preset reasonable interval to determine whether the non-zero value of the corresponding model is within the reasonable interval. If so, proceed to step S2212. If not, an external reminder is issued to prompt the user to analyze whether the simulation data is abnormal during the coordination and expansion process.

[0082] S2212 Model decoupling confirmation is used to confirm whether all simulation models are in a decoupling state. If they are not completely decoupled or the decoupling fails, a message will pop up and an error will be reported;

[0083] In this embodiment, step S2211 performs detection of gas phase temperature, liquid phase temperature, pressure, and cavitation fraction for the node type, and performs detection of liquid phase flow rate and gas phase flow rate for the connecting pipe type. When the non-zero values ​​of the corresponding models at each position of each system meet the requirements, it is judged that the initial states of each type are complete.

[0084] The following uses temperature detection as an example to illustrate the initial state integrity of the model. First, based on the temperature and load curves of each component in the system during operation, the temperature range at each position is estimated as the corresponding reasonable interval (as shown in Table 1 below); then the temperature value of the corresponding position in the initial state operating condition of the simulation model is obtained and compared with the reasonable interval. If the temperature value is within the reasonable interval, the initial state of the model is judged to be complete.

[0085] Table 1 Temperature and load curve

[0086] Location unit Temperature lower limit Temperature limit Core exit ℃ 291.4 329.8 Average temperature ℃ 291.4 310.0 core entrance ℃ 291.4 292.4 Steam generator steam temperature ℃ 283.6 291.4

[0087] In step S222, for the main coolant system model, the adjustment order of each equipment parameter is as follows: pressure vessel → hot pipe section → stabilizer → pressure relief box → evaporator → transition section pump and cold pipe section; in the core system model, the axis power distribution, control rod value and critical boron concentration parameters are corrected respectively; in the flow network system model: the flow network model correction process is carried out in the following order: (1) equipment status; (2) main process parameter error calculation and correction, including: a) pipeline conductance; b) turbine stage group efficiency curve; c) regulating valve characteristic curve and pipeline conductance; d) heat exchanger heat transfer coefficient; e) pump characteristic curve; in the power generation system model, for the main generator simulation model, including no-load curve characteristic distribution and generator additional loss correction; for the external power grid load model, including the correction of the external power grid load active initial value and the external power grid load reactive initial value; for the excitation system model, including the correction of the excitation resistance of the excitation machine. The model correction of the steam turbine control system includes parameter correction of three types of control algorithms: 1. The output value of the RS trigger at that time, which affects the status of each valve, switch command and the number of steps of automatic control; 2. The output value of the counter, which affects the accuracy of time and logic; 3. The output value of the PID controller, which affects the opening status of the valve and controller.

[0088] S223 key parameter tracking and correction is used to correct the key parameters of each simulation system to avoid the key parameters deviating from the expected target value for a long time during the calculation process.

[0089] In this embodiment, the key parameters include but are not limited to: the primary circuit average temperature, pressurizer water level, primary circuit flow and other key parameters of the reactor primary circuit, as well as the main steam flow, evaporator water level, main feed water flow and other key parameters of the secondary circuit. It is necessary to monitor the calculated values ​​of each key parameter in real time to prevent the calculation process from deviating from the expected target value for a long time. When the key parameter deviates from the expected target value for more than 10 seconds, it is necessary to automatically intervene and load the adjustment module to correct the key parameter. As a preferred solution, the correction method may include adjusting the flow rate using parameters such as energy loss coefficient and conductance, adjusting the temperature using temperature, internal energy or enthalpy value initialization, adjusting the pressure distribution using pressure, temperature or internal energy initialization, adjusting the liquid level using cavitation fraction and liquid level initialization, etc.

[0090] Step S23 of the joint debugging steady-state calculation is used to couple and confirm the steady-state model achieved by the separate debugging in steps until all simulation model systems are fully combined and reach the joint debugging steady state. If it is determined that the interface does not match or becomes unstable after coupling, the unstable system will be decoupled and rolled back to the subsystem steady state for re-correction until the system completes the joint debugging steady state. It is then determined that the model initialization process is completed and the tracking process ends.

[0091] As a preferred solution, in this embodiment, the joint regulation steady-state calculation includes the following steps:

[0092] S231 state record is used to store the results of the separate adjustment steady-state calculation in IC, record the current separate adjustment state, and determine whether it is necessary to fall back to the separate adjustment stage according to the system operation state. When after coupling, if the joint adjustment calculation formula shows that some system calculations in the multi-objective coupling adjustment optimization deviate too much or fail, it will fall back to the state point recorded in this process, and set the current optimization parameter value to a disabled value or the current optimization direction to an incorrect direction. If instantaneous oscillation or instantaneous calculation failure occurs after coupling, it is necessary to further fall back to the separate adjustment stage to redefine the boundary parameters between subsystems and adjust the parameter distribution within the subsystem.

[0093] S232 subsystem coupling is used to gradually couple the simulation models of each subsystem, including opening the interface identifiers between the simulation models of each subsystem one by one, so that the interface program enters an online interactive state;

[0094] S233 steady-state tracking parameter confirmation, obtain the main parameters of each model after operation, and compare them with the tracking data points to confirm whether the current model meets the technical requirements. If it meets the technical requirements, the current model is considered to be valid for the tracking data and the tracking is completed; if it does not meet the requirements, it will fall back to the corresponding state save point in the state record for reconfirmation.

[0095] In step S231, after the state recording process is completed in the separate adjustment steady-state calculation stage, the result of the separate adjustment steady-state calculation is stored in IC before the joint adjustment steady-state calculation process starts, and the current separate adjustment state is recorded to backup the pre-start data for the subsequent subsystem simulation model coupling. In this embodiment, the process will continue to use the IC save function of the simulation platform, and set the logic of whether to roll back to the separate adjustment stage. When an abnormality occurs in the subsequent coupling process or the parameter confirmation process fails, it will roll back to the state point recorded in this process, and the logic of this stage will confirm whether to further roll back to the separate adjustment stage.

[0096] In step S232, subsystem coupling is the process of gradually coupling the simulation models of each subsystem after the state recording process. In this process, the interface identifiers between the simulation models of each subsystem will be opened one by one, so that the interface program enters the online interactive state. Furthermore, in this embodiment, the coupling order of the subsystem models is main coolant system-core physical system-main steam system-condensate system-main feed water system-electrical system-control system. The continuous operation time of each system coupling needs to be greater than 1 minute, and ensure that the deviation of boundary parameters and main operating parameters within 1 minute is not greater than 0.5%.

[0097] In step S232, after the subsystem simulation model coupling is completed, that is, after a period of stable operation, the main parameters of the model are compared with the tracking data points to confirm whether the current model meets the technical requirements. If the technical requirements are met, the current model is considered to be valid for the tracking data, that is, the tracking is completed. If the requirements are not met, it will fall back to the state save point for reconfirmation.

Claims

1. A method for tracking the status of a digital twin system of a nuclear power plant, characterized in that: The steps include: S1 Data Processing: Acquire actual operation data of the power plant at a specified time, process the actual operation data to obtain tracking point data, and establish unit data mapping between the tracking point data and thermal software calculation data; S2 matching calculation: According to the unit data mapping, the closest initial condition IC is obtained, and after the model boundary parameter conditions of each subsystem model are determined, the system is decoupled, and the calculation and parameter correction are started automatically after the boundary parameter conditions are agreed upon, and the calculation is iterated until the initialization calculation is completed; The matching calculation in step S2 includes the following steps: S21 creates an initial state of a simulation model to determine an initial operating condition of the digital twin system; S22 subsystem steady-state calculation is used to start each simulation system model that has been filled with initial state and perform calculations respectively; S23 joint adjustment steady-state calculation is used to couple and confirm the steady-state model achieved by the sub-adjustment in steps until all simulation model systems are fully combined and reach the joint adjustment steady state. If it is judged that the interface does not match or is unstable after coupling, the unstable system will be decoupled and fall back to the subsystem steady state for re-correction until the system completes the joint adjustment steady state. The model initialization process is judged to be completed and the tracking process is ended. The step S21 of creating the initial state of the simulation model includes: S211 obtains a reset IC and selects an IC with the most consistent parameter approximation with the tracking point data from pre-stored IC data as the reset IC, and resets the digital twin system with the reset IC; The obtaining of the reset IC comprises the following steps: S2111 pre-stores enough full range model ICs; S2112 obtains and analyzes the core power parameters of the current reset data; S2113 searching the model IC for the closest saved state based on the core power parameter; S212 boundary parameter setting, for the reset digital twin system, adjusting the boundary parameters of each model in the digital twin system according to the tracking point data and the connection relationship between the systems to determine the boundary parameter conditions of each model; S213 decouples the model and decomposes the adjusted digital twin model system into independent computing processes.

2. A method for tracking the status of a nuclear power plant digital twin system according to claim 1, characterized in that: The step S1 specifically includes the following steps: S11 data management, performing one or more combinations of singular value removal, data completion, data smoothing, and data feature extraction on the actual operation data; S12 data coordination, based on the principles of conservation of mass, conservation of energy, conservation of momentum, pressure conduction, and physical property compliance, the actual operating data after data governance is corrected.

3. A method for tracking the status of a nuclear power plant digital twin system according to claim 2, characterized in that: In the step S11, the Raida criterion is used to remove singular values, and singular values ​​of the actual operating data are removed, that is, values ​​with deviations greater than 3σ are removed as singular values.

4. A method for tracking the status of a nuclear power plant digital twin system according to claim 2, characterized in that: In step S11, data completion includes: if it is determined that the actual operation data is empty within a certain time period, tracing back in the external database until the time when there is data is traced back, and completing according to the data of the last time traced back to fill in the missing values ​​of the actual operation data.

5. A method for tracking the status of a nuclear power plant digital twin system according to claim 2, characterized in that: In the step S11, the data smoothing process includes: performing direct shrinkage and stacking comparison on the actual operation data, and performing weighted smoothing calculation to smooth and balance the actual operation data, so as to reduce the impact of accidental data mutations.

6. A method for tracking the status of a nuclear power plant digital twin system according to claim 2, characterized in that: The data coordination in step S12 includes the following steps: S121 performs redundancy analysis on various measurement parameters according to the main constraints of the system, performs measurement redundancy analysis on the system, and determines the system or corresponding measurement loop that can implement data coordination; if the system or the corresponding measurement loop has redundancy, it is determined that data coordination can be performed. The higher the redundancy, the higher the accuracy of the coordination calculation. The constraints in the data coordination problem of the thermal system mainly include: mass balance condition, energy balance condition, momentum balance condition, and physical property equation; S122 performs parameter correction on the system or measurement loop that is judged to be capable of data coordination according to the principles of conservation of mass, conservation of energy, and conformity of physical properties.

7. A method for tracking the status of a nuclear power plant digital twin system according to claim 1, characterized in that: The step S21 comprises the following steps: S221 subsystem operation confirmation is used to determine whether the initial state fitting of all simulation system model parameters is complete; In S221, if each model meets the start-up conditions, all subsystem simulation models are loaded into the simulator platform and started to run. Further, whether the initial state fitting of the simulation system model parameters is complete is judged, including the following steps: S2211 determines whether the initial state of the model is complete, and detects whether all non-zero values ​​of the simulation model have values. If they have values, they are compared with the preset reasonable interval to determine whether the non-zero value of the corresponding model is within the reasonable interval. If they are within the reasonable interval, the process proceeds to step S2212. If they are not within the reasonable interval, an external reminder is issued to prompt the user to analyze whether there are any abnormalities in the coordination and expansion process of the simulation data. S2212 Model decoupling confirmation is used to confirm whether all simulation models are in a decoupling state. If they are not completely decoupled or the decoupling fails, a message will pop up and an error will be reported; S222 non-key parameter tracking and correction, directly mapping the actual power plant operation data to the model data of non-key parameters, and correcting the real-time calculation results of non-key parameters of each simulation system; S223 model operation startup and status monitoring: when it is determined that the model decoupling is completed, the simulation model operation is started, and the operation status of the model is monitored during the operation to ensure that the model operation is within the normal range and no abnormal deviation occurs. Key parameter tracking and correction are used to correct the key parameters of each simulation system to avoid long-term deviation from the expected target value during the calculation process of the key parameters.

8. A method for tracking the status of a nuclear power plant digital twin system according to claim 1, characterized in that: The step S23 comprises the following steps: S231 state record, used to store the results of the sub-regulation steady-state calculation in IC, record the current sub-regulation state, and determine whether it is necessary to fall back to the sub-regulation stage according to the system operation state. After coupling, when the joint adjustment calculation formula shows that some system calculations in the multi-objective coupling adjustment optimization deviate too much or fail, it will fall back to the state point recorded in this process, and set the current optimization parameter value to a disabled value or the current optimization direction to an incorrect direction. If instantaneous oscillation or instantaneous calculation failure occurs after coupling, it is necessary to further fall back to the sub-regulation stage to redefine the boundary parameters between subsystems and adjust the parameter distribution within the subsystem; S232 subsystem coupling is used to gradually couple the simulation models of each subsystem, including opening the interface identifiers between the simulation models of each subsystem one by one, so that the interface program enters an online interactive state; S233 steady-state tracking parameter confirmation, obtain the main parameters of each model after the operation is completed, and compare them with the tracking data points to confirm whether the current model meets the technical requirements. If the technical requirements are met, the current model is considered to be valid for the tracking data and the tracking is completed; if the requirements are not met, return to the corresponding state save point in the state record for reconfirmation; In S231, after the state recording process is completed in the separate steady-state calculation stage, the results of the separate steady-state calculation are stored in IC before the joint steady-state calculation process starts, and the current separate state is recorded to back up the pre-startup data for the subsequent subsystem simulation model coupling; In S232, the subsystem coupling is the process of gradually coupling the subsystem simulation models after the state recording process. After the subsystem simulation model coupling is completed, that is, after a period of stable operation, the main parameters of the model are Compare the data with the tracking data points to confirm whether the current model meets the technical requirements. If it meets the technical requirements, The current model is considered to be valid for the tracking data, that is, the tracking is completed.