Nuclear power system control method and device, electronic equipment, nuclear power system and medium

Abstract

The embodiment of the invention provides a nuclear power system control method and device, electronic equipment, a nuclear power system and a medium, and relates to the technical field of nuclear reactors. The method comprises the following steps: acquiring a first thermal parameter group of the nuclear power system under the current operation condition; fault detection is carried out according to the first thermal parameter set to obtain a detection result, the detection result is used for indicating that the steam pressure sensor is in a normal state or a fault state, and the detection result comprises a first fault type and a first fault degree of the steam pressure sensor under the condition that the detection result indicates the fault state; if the detection result indicates that the steam pressure sensor is in the fault state, the first steam pressure is corrected according to the first fault type and the first fault degree, and target steam pressure is obtained; and controlling the opening degree of an actuator according to the target steam pressure so as to control the discharge amount of steam entering the nuclear power device. According to the embodiment of the invention, the operation stability of the nuclear power system can be improved.

Description

Technical Field

[0001] The present application relates to the field of nuclear reactor technology, and in particular to a nuclear power system control method, device, electronic equipment, nuclear power system and medium. Background Art

[0002] For nuclear power systems, the most common steady-state operation mode is to control the average temperature of the primary coolant and the steam pressure of the secondary circuit to remain constant, that is, the dual constant operation mode. The steam pressure sensor is an important device for detecting steam pressure. Whether the device works normally and can accurately measure the steam pressure is crucial to maintaining the stability and safety of the nuclear reactor. However, since the steam pressure sensor is in a high temperature and high pressure environment for a long time, it is prone to failure. These failures may cause the output signal of the steam pressure sensor to deviate from the actual steam pressure, thereby affecting the judgment of the nuclear power system and making it difficult for the nuclear power system to maintain operational stability. Therefore, the existing nuclear power system control method has the problem of low operational stability of the nuclear power system. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a nuclear power system control method, device, electronic equipment, nuclear power system and medium, aiming to improve the operating stability of the nuclear power system.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application provides a nuclear power system control method, which is applied to a nuclear power system, wherein the nuclear power system includes a nuclear power plant, a steam pressure sensor and an actuator, wherein the steam pressure sensor is used to detect the steam pressure of the nuclear power plant, and the actuator is used to control the steam discharge amount entering the nuclear power plant, and the method includes:

[0005] Acquire a first thermal parameter group of the nuclear power system under a current operating condition, wherein the first thermal parameter group includes at least one parameter, and the at least one parameter includes a first steam pressure collected by the steam pressure sensor;

[0006] Perform fault detection according to the first thermal parameter group to obtain a detection result, wherein the detection result is used to indicate that the steam pressure sensor is in a normal state or a fault state, and when the detection result indicates a fault state, the detection result includes a first fault type and a first fault degree of the steam pressure sensor;

[0007] If the detection result indicates that the steam pressure sensor is in a fault state, the first steam pressure is corrected according to the first fault type and the first fault degree to obtain a target steam pressure;

[0008] The opening of the actuator is controlled according to the target steam pressure to control the steam discharge amount entering the nuclear power plant.

[0009] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a nuclear power system control device, which is applied to a nuclear power system. The nuclear power system includes a nuclear power plant, a steam pressure sensor and an actuator. The steam pressure sensor is used to detect the steam pressure of the nuclear power plant. The actuator is used to control the steam discharge amount entering the nuclear power plant. The device includes:

[0010] A parameter acquisition module, used for acquiring a first thermal parameter group of the nuclear power system under a current operating condition, wherein the first thermal parameter group includes at least one parameter, and the at least one parameter includes a first steam pressure collected by the steam pressure sensor;

[0011] a detection module, configured to perform fault detection according to the first thermal parameter group to obtain a detection result, wherein the detection result is used to indicate that the steam pressure sensor is in a normal state or a fault state, and when the detection result indicates a fault state, the detection result includes a first fault type and a first fault degree of the steam pressure sensor;

[0012] a correction module, configured to correct the first steam pressure according to the first fault type and the first fault degree to obtain a target steam pressure if the detection result indicates that the steam pressure sensor is in a fault state;

[0013] A control module is used to control the opening of the actuator according to the target steam pressure to control the steam discharge amount entering the nuclear power plant.

[0014] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the nuclear power system control method described in the first aspect when executing the computer program.

[0015] To achieve the above objectives, a fourth aspect of an embodiment of the present application provides a nuclear power system, including:

[0016] Nuclear power plants;

[0017] Steam pressure sensor;

[0018] Actuators; and

[0019] In the electronic device described in the third aspect, the steam pressure sensor is used to detect the steam pressure of the nuclear power device, and the actuator is used to control the steam discharge amount entering the nuclear power device.

[0020] To achieve the above objectives, the fifth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the nuclear power system control method described in the first aspect above.

[0021] The nuclear power system control method, device, electronic device, nuclear power system and medium proposed in the present application obtain the first thermal parameter group of the nuclear power system under the current operating condition, and perform fault detection according to the first thermal parameter group to obtain the detection result. If the detection result indicates that the steam pressure sensor is in a faulty state, the first steam pressure is corrected according to the first fault type and the first fault degree contained in the detection result to obtain the target steam pressure, and then the opening of the actuator is controlled according to the target steam pressure to control the steam discharge amount entering the nuclear power device. In the above manner, the steam pressure sensor can be fault-detected, and it can be determined whether the first steam pressure detected by the steam pressure sensor needs to be corrected according to the obtained detection result. When correction is required, the opening of the actuator is controlled according to the corrected target steam pressure, thereby controlling the steam discharge amount entering the nuclear power device. In this way, the possibility of the nuclear power system making an erroneous judgment due to the failure of the steam pressure sensor can be reduced, and the operating stability of the nuclear power system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a nuclear power system control method provided by an embodiment of the present application;

[0023] Figure 2 is a schematic diagram of the structure of a nuclear power system control device provided in an embodiment of the present application;

[0024] Figure 3 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0028] In order to solve the problems of the prior art, the embodiments of the present application provide a nuclear power system control method, device, electronic equipment, nuclear power system and medium, aiming to improve the operating stability of the nuclear power system.

[0029] The nuclear power system control method, device, electronic equipment, nuclear power system and medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the nuclear power system control method in the embodiments of the present application is described.

[0030] The nuclear power system control method provided in the embodiment of the present application relates to the field of nuclear reactor technology. The nuclear power system control method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the nuclear power system control method, etc., but is not limited to the above forms.

[0031] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0032] Figure 1 This is a flowchart of the nuclear power system control method provided in the embodiment of the present application. Please refer to Figure 1The nuclear power system control method provided in the embodiment of the present application is applied to the nuclear power system, and specifically to the electronic equipment in the nuclear power system. In addition, the nuclear power system also includes a nuclear power plant, a steam pressure sensor and an actuator. The steam pressure sensor is used to detect the steam pressure of the nuclear power plant, and the actuator is used to control the steam discharge amount entering the nuclear power plant. Figure 1 The method may include but is not limited to steps 101 to 104.

[0033] Step 101, obtaining a first thermal parameter group of the nuclear power system under a current operating condition, wherein the first thermal parameter group includes at least one parameter, and the at least one parameter includes a first steam pressure collected by the steam pressure sensor;

[0034] Step 102, performing fault detection according to the first thermal parameter group to obtain a detection result, wherein the detection result is used to indicate that the steam pressure sensor is in a normal state or a fault state, and when the detection result indicates a fault state, the detection result includes a first fault type and a first fault degree of the steam pressure sensor;

[0035] Step 103: if the detection result indicates that the steam pressure sensor is in a fault state, the first steam pressure is corrected according to the first fault type and the first fault degree to obtain a target steam pressure;

[0036] Step 104, controlling the opening of the actuator according to the target steam pressure to control the steam discharge amount entering the nuclear power plant.

[0037] The electronic device can obtain the first thermal parameter group of the nuclear power system under the current operating conditions. The first thermal parameter group may include parameters such as the reactor power setting value, the actual output power of the reactor, the coolant inlet temperature, the coolant outlet temperature, the steam generator feed water flow, the steam flow, the current opening and pressure drop of the actuator, the opening of the bypass valve, and the first steam pressure collected by the steam pressure sensor. Subsequently, according to the first thermal parameter group, the electronic device can perform fault detection and obtain a detection result. The detection result may indicate that the steam pressure sensor is in a normal state or a faulty state. Exemplarily, the first thermal parameter group can be input into a pre-constructed target fault detection model, and the fault detection is performed through the target fault detection model to obtain the detection result output by the target fault detection model. Alternatively, the first thermal parameter group and the theoretical thermal parameter group under the current operating conditions can be compared to determine the detection result.

[0038] The first fault type is used to indicate the representation form of the first fault degree, and the first fault degree is used to indicate the deviation degree of the collected first steam pressure compared to the theoretical steam pressure under the current operating condition. If the detection result indicates that the steam pressure sensor is in a faulty state, the detection result includes the first fault type and the first fault degree of the steam pressure sensor. Exemplarily, the first fault type can be a constant gain fault or a constant deviation fault. If the first fault type is a constant gain fault, the first fault degree can take a value in the range of 0.4 to 1.6. If the first fault type is a constant deviation fault, the first fault degree can take a value in the range of -1 MPa to 1 MPa. Then, according to the first fault type and the first fault degree, the electronic device can correct the first steam pressure to obtain the target steam pressure, and then generate an opening control signal according to the target steam pressure and the current opening of the actuator, and send the opening control signal to the actuator to achieve control of the opening of the actuator. During the control process, illustratively, the electronic device can pre-determine the corresponding relationship between the opening of the actuator and the steam pressure, so that during the control process, the target steam pressure can be matched with multiple preset steam pressures in the corresponding relationship, thereby obtaining the target opening corresponding to the target steam pressure, and then generating a corresponding opening control signal according to the target opening and the current opening of the actuator. Among them, the corresponding relationship includes multiple preset steam pressures and the opening corresponding to each preset steam pressure. The actuator is used to control the steam discharge amount entering the nuclear power plant. Therefore, by controlling the opening of the actuator, the steam discharge amount entering the nuclear power plant can be controlled.

[0039] In addition, before the correction of the first steam pressure is completed, in order to reduce the possibility that the electronic device controls the opening of the actuator according to the wrong first steam pressure, after the detection result is obtained, the opening of the actuator can be feedforward controlled according to the first fault degree and the generalized predictive control algorithm to offset the additional action of the actuator caused by the steam pressure sensor failure. Among them, the design of the generalized predictive control algorithm includes controlled autoregressive integral moving average model modeling, prediction model construction and rolling optimization.

[0040] In the steps 101 to 104 shown in the embodiment of the present application, the first thermal parameter group of the nuclear power system under the current operating condition is obtained, and fault detection is performed according to the first thermal parameter group to obtain the detection result. If the detection result indicates that the steam pressure sensor is in a faulty state, the first steam pressure is corrected according to the first fault type and the first fault degree contained in the detection result to obtain the target steam pressure, and then the opening of the actuator is controlled according to the target steam pressure to control the steam discharge amount entering the nuclear power device. In the above manner, the steam pressure sensor can be fault-detected, and it can be determined whether the first steam pressure detected by the steam pressure sensor needs to be corrected according to the obtained detection result. When correction is required, the opening of the actuator is controlled according to the target steam pressure obtained after correction, so as to control the steam discharge amount entering the nuclear power device. In this way, the possibility of the nuclear power system making an erroneous judgment due to the failure of the steam pressure sensor can be reduced, and the operation stability of the nuclear power system can be improved.

[0041] In some embodiments, performing fault detection according to the first thermal parameter group to obtain a detection result includes:

[0042] Fault detection is performed according to the pre-built target fault detection model and the first thermal parameter group to obtain the detection result.

[0043] In order to improve the fault detection efficiency, the steam pressure sensor can be tested for faults by using the pre-built target fault detection model and the first thermal parameter group to obtain the test results. Specifically, the electronic device can input the first thermal parameter group into the target fault detection model to obtain the test results output by the target fault detection model. In addition, the electronic device can obtain the data and feedback results in the detection process in real time to update the target fault detection model according to the data and feedback results in the detection process, thereby improving the accuracy and generalization ability of the target fault detection model.

[0044] In some embodiments, the target fault detection model is obtained according to the following process:

[0045] Constructing a sample data set, wherein the sample data set includes a training sample set and a test sample set;

[0046] According to the training sample set, the initial fault detection model is trained until the loss function of the initial fault detection model converges to obtain an intermediate fault detection model;

[0047] Testing the intermediate fault detection model according to the test sample set to obtain a test result, wherein the test result includes a detection accuracy rate of the intermediate fault detection model;

[0048] If the detection accuracy is greater than a preset accuracy, the intermediate fault detection model is determined as the target fault detection model.

[0049] The electronic device can pre-build a target fault detection model. Specifically, the electronic device can pre-configure a variety of operating conditions and obtain the second thermal parameter group under different operating conditions, the second thermal parameter group includes but is not limited to the reactor power setting value, the actual output power of the reactor, the coolant inlet temperature, the coolant outlet temperature, the steam generator feed water flow, the steam flow, the second steam pressure, the actuator opening and pressure drop, the bypass valve opening and other parameters, and further based on the data generation program of the simulation platform, generate sample data according to multiple second thermal parameter groups and configured fault labels, so as to construct a sample data set including a training sample set and a test sample set according to the sample data. The sample data includes input data and output data, wherein the input data is the second thermal parameter group or the third thermal parameter group, and the output data is the normal label corresponding to the second thermal parameter group, or the fault label corresponding to the third thermal parameter group.

[0050] In this way, according to the training sample set, the electronic device can train the initial fault detection model. The initial fault detection model can adopt a time domain convolutional neural network, a multi-layer long short-term memory network or a convolutional neural network. Taking the time domain convolutional neural network as an example, the initial fault detection model can include an input layer, a time domain convolutional neural network layer, a flattening layer, a fully connected layer, a normalized index layer and an output layer. Among them, the input layer can preprocess the input data to make it a data form that can be received by the time domain convolutional neural network layer. The time domain convolutional neural network layer can include multiple residual blocks, and the residual blocks are connected by residual connections to solve the problem of gradient disappearance caused by the increase in network depth. The time domain convolutional neural network layer extracts time features from the data processed by the input layer through the residual block and converts it into a high-dimensional feature vector. The flattening layer is used to flatten the multi-dimensional feature vector extracted by the time domain convolutional neural network layer into a one-dimensional feature vector, which will be input into the fully connected layer for subsequent operations. The fully connected layer is generally composed of one or more layers of neurons, and activation functions are generally used between layers to introduce nonlinear factors and improve the learning ability of the model. The fully connected layer is mainly used to map the one-dimensional feature vector input by the flattening layer to the label space for subsequent classification by the classifier. The normalized exponential layer performs mathematical calculations through the normalized exponential function, which can convert a set of arbitrary real numbers into a set of real numbers representing probability distribution. The output layer can obtain output data based on the probability vector given by the normalized exponential layer.

[0051] During the training process, according to the input data in the training sample set, forward reasoning can be performed through the initial fault detection model to obtain the output data of the initial fault detection model, and then the loss value of the initial fault detection model can be determined according to the output data of the initial fault detection model and the output data and loss function included in the training sample set. Subsequently, the optimizer gradient can be set to zero, and back propagation can be performed to update the model parameters of the initial fault detection model. After the model parameters are updated, if the loss function of the initial fault detection model converges (that is, the loss value fluctuates within a preset range or the loss value is less than a preset threshold), then an intermediate fault detection model can be obtained. If the loss function of the initial fault detection model does not converge, the step of forward reasoning based on the input data in the training sample set is re-executed.

[0052] Furthermore, the intermediate fault detection model can be tested according to the test sample set to obtain the test results. The test results include the detection accuracy of the intermediate fault detection model. When the detection accuracy is greater than the preset accuracy, the electronic device can determine the intermediate fault detection model as the target fault detection model. The preset accuracy can be adjusted according to the actual detection accuracy requirements. In addition, the test results can also include the confusion matrix and macro-average of the intermediate fault detection model. According to the detection accuracy, confusion matrix and macro-average of the intermediate fault detection model, the performance of the intermediate fault detection model can be comprehensively evaluated, and then the target fault detection model can be determined based on the evaluation results. In this way, the construction of the target fault detection model can be completed, which is convenient for the subsequent fault detection of the steam pressure sensor through the target fault detection model.

[0053] In some embodiments, constructing a sample data set includes:

[0054] Acquire a plurality of second thermal parameter groups of the nuclear power system under a plurality of operating conditions within a preset historical time period, wherein the plurality of operating conditions include steady-state conditions and transient conditions, and the parameters in each of the second thermal parameter groups are related to each of the operating conditions of the nuclear power system;

[0055] According to the second thermal parameter group under each of the operating conditions and the preconfigured multiple second fault types and multiple second fault degrees, a fault simulation is performed on the nuclear power system under each of the operating conditions to obtain multiple third thermal parameter groups under each of the operating conditions and a fault label corresponding to each of the third thermal parameter groups, wherein the fault label is used to indicate the second fault type and the second fault degree corresponding to each of the third thermal parameter groups;

[0056] Obtaining a plurality of fault sample data according to a plurality of third thermal parameter groups under a plurality of the operating conditions and the fault labels respectively corresponding to the plurality of third thermal parameter groups under a plurality of the operating conditions;

[0057] configuring a normal label for each second thermal parameter group under the operating condition, and obtaining a plurality of normal sample data according to a plurality of second thermal parameter groups under the plurality of operating conditions and the normal label corresponding to each second thermal parameter group;

[0058] Obtaining a plurality of sample data according to the plurality of fault sample data and the plurality of normal sample data;

[0059] Dividing the plurality of sample data according to a preset ratio to obtain the training sample set and the test sample set;

[0060] The sample data set is obtained according to the training sample set and the test sample set.

[0061] The electronic device can construct a sample data set. Specifically, first, a preset historical time period and multiple operating conditions can be configured. The preset historical time period can be determined according to the actual data volume requirements. Multiple operating conditions may include steady-state conditions and transient conditions, and transient conditions may include step-change load, linear variable load, and load rejection. Exemplarily, steady-state conditions may include three conditions: 100% full power (Full Power, FP), 60% FP, and 30% FP. The step-change load may be -10% step-change load, including three conditions: 100% FP-90% FP, 60% FP-50% FP, and 30% FP-20% FP. The linear variable load may be -10% / min linear variable load, including 100% FP-30% FP, 100% FP-60% FP, and 60% FP-30% FP. Load rejection includes 100% FP-30% FP. The division of operating conditions is not limited to the aforementioned examples and can be adjusted according to actual conditions.

[0062] In this way, the electronic device can obtain multiple second thermal parameter groups of the nuclear power system under multiple operating conditions in a preset historical time period. Each second thermal parameter group corresponds to an operating condition, and the parameters in each second thermal parameter group are related to an operating condition corresponding to each second thermal parameter group. Each second thermal parameter group includes but is not limited to the reactor power setting value, the actual output power of the reactor, the coolant inlet temperature, the coolant outlet temperature, the steam generator feed water flow, the steam flow, the second steam pressure, the actuator opening and pressure drop, the bypass valve opening and other parameters. Then, after pre-configuring the simulation platform with data such as the fault introduction time, the simulation time, and the fault object, according to the second thermal parameter group under each operating condition, the pre-configured multiple second fault types, and the pre-configured multiple second fault degrees, the electronic device can simulate the fault of the nuclear power system under each operating condition through the simulation platform, obtain multiple third thermal parameter groups under each operating condition and the fault labels corresponding to each third thermal parameter group, and obtain multiple fault sample data according to the multiple third thermal parameter groups under multiple operating conditions and the fault labels corresponding to the multiple third thermal parameter groups under multiple operating conditions. The multiple second fault types may include a constant gain fault and a constant deviation fault, and each second fault type has a corresponding multiple second fault degrees. The fault label is used to indicate the second fault type and second fault degree corresponding to each third thermal parameter group, and each fault sample data includes a third thermal parameter group under an operating condition, and a fault label corresponding to the third thermal parameter group.

[0063] Furthermore, the electronic device can configure a normal label for the second thermal parameter group under each operating condition, and construct multiple normal sample data according to multiple second thermal parameter groups under multiple operating conditions and the normal label corresponding to each second thermal parameter group, each normal sample data includes a second thermal parameter group under an operating condition, and a normal label corresponding to a second thermal parameter group. In this way, multiple sample data (including multiple fault sample data and multiple normal sample data) can be obtained. After dividing the multiple sample data according to a preset ratio (for example, 8:2), a training sample set and a test sample set can be obtained, and then a sample data set containing the training sample set and the test sample set can be obtained.

[0064] The electronic device can also further process the sample data set, that is, delete useless features, null values, outliers, duplicate values, etc. in the sample data set through data cleaning methods, and then normalize and adjust the time step according to the extreme values ​​of the data features to eliminate the impact of magnitude on data analysis.

[0065] Through the above steps, a sample data set can be constructed, so that a target fault detection model can be constructed based on the sample data set in the future.

[0066] In some embodiments, before training the initial fault detection model according to the training sample set, the method further includes:

[0067] In response to the selection instruction of the initial fault detection model, the following steps are performed:

[0068] Setting the activation function of the initial fault detection model as a target activation function;

[0069] Setting the loss function of the initial fault detection model as a target loss function;

[0070] Setting the optimizer of the initial fault detection model to a target optimization algorithm;

[0071] Setting the learning rate of the initial fault detection model to a target value;

[0072] The selection instruction includes an identifier of the target activation function, an identifier of the target loss function, an identifier of the target optimization algorithm, and the target value;

[0073] After the initial fault detection model is trained according to the training sample set, and before the loss function of the initial fault detection model converges, the method further includes:

[0074] Determining a loss value of the initial fault detection model according to the loss function;

[0075] When the loss value is less than or equal to a preset loss value, a learning rate of the initial fault detection model is reduced.

[0076] Before training the initial fault detection model according to the training sample set, the electronic device can also receive a selection instruction for the initial fault detection model, and in response to the selection instruction, adjust the training strategy of the initial fault detection model to ensure that the model can capture key features through effective training and has a certain generalization ability. Specifically, the selection instruction includes the identification of the target activation function (e.g., ReLU), the identification of the target loss function (e.g., cross entropy loss function), the identification of the target optimization algorithm (e.g., Adaptive Moment Estimation, Adam), and the target value (e.g., 0.001). Then, according to the identification of the target activation function, the identification of the target loss function, the identification of the target optimization algorithm, and the target value contained in the selection instruction, the electronic device can set the activation function of the initial fault detection model to the target activation function, the loss function of the initial fault detection model to the target loss function, the optimizer of the initial fault detection model to the target optimization algorithm, and the learning rate of the initial fault detection model to the target value.

[0077] In order to alleviate the problem of detection accuracy jitter that occurs when the training process is close to convergence, it is necessary to add a learning rate decay strategy to make the convergence process stable by reducing the learning rate. Specifically, after starting to train the initial fault detection model and before the loss function of the initial fault detection model converges, the loss value of the initial fault detection model is determined based on the loss function, the output data of the initial fault detection model, and the output data included in the training sample set, and when the loss value is less than or equal to the preset loss value, the learning rate of the initial fault detection model is reduced. The preset loss value should be greater than the preset threshold (i.e., the aforementioned value used to determine whether the initial fault detection model has converged), and can be set according to actual conditions.

[0078] In addition, during the back propagation process of the model, due to the deep model architecture, there may be a gradient explosion problem. Therefore, a gradient clipping strategy can be added during training, and its threshold can be set to 1.

[0079] In some embodiments, the first fault type includes a constant gain fault or a constant deviation fault. When the first fault type is a constant gain fault, the first fault degree is used to describe the gain amplitude. When the first fault type is a constant deviation fault, the first fault degree is used to describe the deviation amplitude.

[0080] According to the first fault type and the first fault degree, the first steam pressure is corrected to obtain a target steam pressure, including:

[0081] If the first fault type is a constant gain fault, adjusting the first steam pressure according to the gain amplitude to obtain the target steam pressure;

[0082] If the first fault type is a constant deviation fault, the deviation amplitude is subtracted from the first steam pressure to obtain the target steam pressure.

[0083] When the detection result indicates that the steam pressure sensor is in a fault state, the detection result includes a first fault type and a first fault degree. At this time, the electronic device can correct the first steam pressure according to the first fault type and the first fault degree to obtain the target steam pressure. Specifically, when the first fault type is a constant gain fault, the first fault degree is used to describe the gain amplitude, then the first steam pressure can be adjusted according to the gain amplitude, that is, the first steam pressure is divided by the gain amplitude to obtain the target steam pressure. When the first fault type is a constant deviation fault, the first fault degree is used to describe the deviation amplitude, then the first steam pressure can be subtracted from the deviation amplitude to obtain the target steam pressure. Through the aforementioned steps, the correction of the first steam pressure can be completed to obtain the target steam pressure, so that the opening of the actuator can be controlled according to the target steam pressure later.

[0084] The following is an example of the nuclear power system control method provided in the embodiment of the present application. The nuclear power system control method includes the following steps:

[0085] Step 1, based on the established simulation platform, set the steady-state condition and transient condition, select the signals corresponding to multiple thermal parameters under different operating conditions as the input signals of the initial fault detection model, and collect them by writing a data generation program to construct sample data sets of different positions, types and degrees. Assume that the operating conditions are divided into steady-state conditions and transient conditions, the steady-state conditions include 100% FP, 60% FP and 30% FP, the step load can be -10% step load, including 100% FP-90% FP, 60% FP-50% FP and 30% FP-20% FP, the linear variable load can be -10% / min linear variable load, including 100% FP-30% FP, 100% FP-60% FP and 60% FP-30% FP, and the load rejection includes 100% FP-30% FP. Furthermore, assuming that the fault degree corresponding to the constant deviation fault ranges from -1 MPa to 1 MPa, and the fault degree corresponding to the constant gain fault ranges from 0.4 to 1.6, then, under the conditions of different fault degrees and different times of the steam pressure sensor, a total of 3210 sets of data can be generated, and the data shape of each set of data is the number of characteristic signals (i.e., the characteristic dimension of the thermal parameter group) × time step. The number of characteristic signals is 12, and the time step is 6000 (the simulation time is 600 seconds, and the sampling time is set to 0.1 seconds). Each set of data reflects the values ​​of 12 characteristic signals at different positions under 6000 time steps. Furthermore, the simulation time setting, fault introduction time setting, fault object setting, etc. can be initialized, and at the same time, the simulation programs of different fault types can be written and packaged, including no fault, constant deviation fault, and constant gain fault. Subsequently, the set simulation time, fault introduction time, fault object, and packaged function are used to generate data in the order of different fault types and different fault degrees. Finally, the sample data of various fault labels and their corresponding labels are saved according to the specified data shape to obtain a sample data set for subsequent offline training of the initial fault diagnosis model.

[0086] Step 2: Preprocess the sample data set, delete some useless features, null values, outliers, duplicate values, etc. in the sample data set through data cleaning methods, and then normalize and adjust the time step according to the extreme values ​​of the data features to eliminate the impact of magnitude on data analysis, improve the accuracy of the model and speed up the convergence of model training. Finally, divide the sample data set to obtain the training sample set and test sample set required for offline training.

[0087] Step 3, you can set a sliding window. The sliding window is mainly used to input the format of the signal data specified by offline training to the initial fault detection model. The format of the signal data is defined as: time step × number of feature signals in this scheme. The time step of the offline training data is set to 2991, the number of feature signals is 12, and the sampling time is set to 0.1s, that is, a set of feature signals is taken every 0.1s.

[0088] Step 4: Select the time-domain convolutional neural network with excellent performance in the field of time series to build the initial fault detection model, and the initial fault detection model is built using the Pytorch framework. The architecture of the initial fault detection model based on the time-domain convolutional neural network has six layers, including the input layer, the time-domain convolutional neural network layer, the flattening layer, the fully connected layer, the normalized index layer, and the output layer.

[0089] Step 5, design the specific structure of the model and the corresponding training strategy according to the characteristics of the data and model, including the network hierarchy and related hyperparameters, activation function type, loss function selection, gradient clipping strategy, parameter optimization strategy, etc., to ensure that the model can capture key features through effective training and has a certain generalization ability. To avoid the problem of gradient disappearance, the ReLU activation function is used as the activation function; fault detection is a multi-classification task, and the cross entropy loss function is used as the loss function; in the parameter optimization process, the Adam algorithm is selected as the optimizer of this solution, and the learning rate is set to 0.001. In the back propagation process of the model, due to the deep model architecture, the gradient explosion problem may occur, so the gradient clipping strategy is added during training, and its threshold is set to 1; in order to alleviate the problem of accuracy jitter near convergence during training, it is necessary to add a learning rate decay strategy to make the convergence process stable by reducing the learning rate.

[0090] Step 6. During the training process, the input data format of the initial fault detection model is a tensor of n×m×k, where n is the number of data and can be specified arbitrarily. Each piece of data contains the values ​​corresponding to k feature signals under m time steps, m is the time step and can be specified arbitrarily, and k is the number of feature signals, which can be set to 12 in the embodiment of the present application. There are 4 residual block structures in the initial fault detection model, each with a different number of output channels (number of extracted features): the number of output channels of the first layer is 128, the number of output channels of the second layer is 128, the number of output channels of the third layer is 512, and the number of output channels of the last layer is 256. The convolution kernel in the residual block is set to 3, and the dropout is set to 0.2. After the input data enters the initial fault detection model, it is first dimensionally transformed, exchanging the second and third dimensions, and the data becomes a tensor of n×k×m. After passing through each residual block, the data becomes a tensor of n×k'×m, where k' is the number of output channels corresponding to each residual block. Since the time-domain convolutional neural network has the property of data information sequence transmission, it is only necessary to take the information of the last time step and pass it through the ReLU activation function to enhance the network's expression ability and then enter the fully connected layer. After processing, a tensor of n×r is obtained, where r is the number of output layers set by the fully connected layer. Finally, a normalized exponential layer is used to perform mathematical calculations to convert it into a tensor representing the probability distribution, and finally the output layer outputs the prediction result according to the label corresponding to the maximum probability.

[0091] Among them, the residual block is a component unit of the time-domain convolutional neural network, which is composed of algorithms such as dilated causal convolution and residual connection. A residual block contains two dilated causal convolutions, instance regularization, ReLU activation function and Dropout structure. Among them, instance regularization ensures that the data after the dilated causal convolution in each residual block still obeys the normal distribution, which solves the gradient vanishing problem and increases the generalization ability of the model. The Dropout structure makes it possible for some parameters to randomly not participate in the weight update in each round of iteration during the model training process, which effectively solves the overfitting problem caused by the complex model architecture. Finally, the features obtained through the above processing are residually connected to the original data to obtain the output of the residual block. In addition, batch training is performed during model training, that is, the batch size is set. The batch size refers to the amount of data input into the model at one time for each iteration, and its value is usually set to 2. n After the model training is completed, the model performance is evaluated using the test sample set, and the evaluation indicators are selected as accuracy, confusion matrix, and macro-average. In this way, the training of the initial fault detection model can be completed and the target fault detection model can be obtained.

[0092] Step 7, when performing fault detection on the steam pressure sensor, the target fault detection model is deployed on the server side, and the signal corresponding to the first thermal parameter group is packaged and transmitted to the server side in bytes through the network communication protocol. The server side unpacks the bytes into character arrays according to the protocol and integrates them into a suitable data format, and then makes a judgment to select whether to send them to the target fault detection model for fault detection. When the signal corresponding to the first thermal parameter group is selected to be sent to the target fault detection model, the detection result can be obtained through the target fault detection model. In addition, new data and feedback results in the real-time detection process can be collected to update the target fault detection model and improve the accuracy and generalization ability of the target fault detection model.

[0093] Step 8, when a constant gain fault or a constant deviation fault occurs, the first steam pressure can be corrected to near the actual steam pressure (i.e., the target steam pressure) according to the diagnosed gain amplitude k or deviation amplitude b, so that the nuclear power system can operate with a slight performance reduction near the original state.

[0094] Step 9. In addition, a generalized predictive control algorithm is used to design a generalized predictive control structure based on real-time detection results, and the opening of the actuator is feedforward controlled according to the first fault degree obtained by real-time detection to offset the additional action of the actuator caused by the fault.

[0095] The nuclear power system control method provided in the embodiment of the present application can quickly and accurately diagnose the constant deviation fault or constant gain fault of the steam pressure sensor, reduce the impact of the constant deviation fault or constant gain fault of the steam pressure sensor on the normal operation of the nuclear power system, reduce the safety risks caused by the steam pressure sensor failure, and improve the operational efficiency and economic benefits by reducing downtime and reducing maintenance requirements. It provides a direction for the fault diagnosis and fault-tolerant control scheme of sensor failures, which can be extended to various types of reactor control systems. In addition, the embodiment of the present application adopts an active fault-tolerant control method based on the detection results, which can realize the timely correction of the output of the steam pressure sensor. At the same time, the operation stability of the nuclear power system can be further improved by eliminating the extra action of the actuator caused by the fault through the generalized predictive control algorithm.

[0096] Figure 2 This is a schematic diagram of the structure of the nuclear power system control device provided in the embodiment of the present application. Figure 2 The embodiment of the present application further provides a nuclear power system control device 200, which is applied to a nuclear power system. The nuclear power system includes a nuclear power plant, a steam pressure sensor and an actuator. The steam pressure sensor is used to detect the steam pressure of the nuclear power plant. The actuator is used to control the steam discharge amount entering the nuclear power plant. The nuclear power system control device 200 can implement the above-mentioned nuclear power system control method. The device 200 includes:

[0097] A parameter acquisition module 201 is used to acquire a first thermal parameter group of the nuclear power system under a current operating condition, wherein the first thermal parameter group includes at least one parameter, and the at least one parameter includes a first steam pressure collected by the steam pressure sensor;

[0098] a detection module 202, configured to perform fault detection according to the first thermal parameter group to obtain a detection result, wherein the detection result is used to indicate that the steam pressure sensor is in a normal state or a fault state, and when the detection result indicates a fault state, the detection result includes a first fault type and a first fault degree of the steam pressure sensor;

[0099] A correction module 203, configured to correct the first steam pressure according to the first fault type and the first fault degree to obtain a target steam pressure if the detection result indicates that the steam pressure sensor is in a fault state;

[0100] The control module 204 is used to control the opening of the actuator according to the target steam pressure to control the steam discharge amount entering the nuclear power plant.

[0101] The specific implementation of the nuclear power system control device 200 is basically the same as the specific implementation of the above-mentioned nuclear power system control method, and will not be repeated here.

[0102] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned nuclear power system control method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0103] Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. Figure 3 . Electronic equipment includes:

[0104] The processor 301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0105] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 302, and the processor 301 calls and executes the nuclear power system control method of the embodiment of this application;

[0106] Input / output interface 303, used to implement information input and output;

[0107] The communication interface 304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0108] A bus 305 that transmits information between the various components of the device (e.g., the processor 301, the memory 302, the input / output interface 303, and the communication interface 304);

[0109] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0110] The embodiment of the present application also provides a nuclear power system, including:

[0111] Nuclear power plants;

[0112] Steam pressure sensor;

[0113] Actuators; and

[0114] In the above-mentioned electronic equipment, the steam pressure sensor is used to detect the steam pressure of the nuclear power device, and the actuator is used to control the steam discharge amount entering the nuclear power device.

[0115] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned nuclear power system control method is implemented.

[0116] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0117] The nuclear power system control method, device, electronic device, nuclear power system and medium proposed in the present application obtain the first thermal parameter group of the nuclear power system under the current operating condition, and perform fault detection according to the first thermal parameter group to obtain the detection result. If the detection result indicates that the steam pressure sensor is in a faulty state, the first steam pressure is corrected according to the first fault type and the first fault degree contained in the detection result to obtain the target steam pressure, and then the opening of the actuator is controlled according to the target steam pressure to control the steam discharge amount entering the nuclear power device. In the above manner, the steam pressure sensor can be fault-detected, and it can be determined whether the first steam pressure detected by the steam pressure sensor needs to be corrected according to the obtained detection result. When correction is required, the opening of the actuator is controlled according to the corrected target steam pressure, thereby controlling the steam discharge amount entering the nuclear power device. In this way, the possibility of the nuclear power system making an erroneous judgment due to the failure of the steam pressure sensor can be reduced, and the operating stability of the nuclear power system can be improved.

[0118] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0119] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0120] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0122] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0123] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0125] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0128] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A nuclear power system control method, applied to a nuclear power system, the nuclear power system comprising a nuclear power plant, a steam pressure sensor and an actuator, the steam pressure sensor is used to detect the steam pressure of the nuclear power plant, the actuator is used to control the steam discharge amount entering the nuclear power plant, characterized in that: The method comprises: Acquire a first thermal parameter group of the nuclear power system under a current operating condition, wherein the first thermal parameter group includes at least one parameter, and the at least one parameter includes a first steam pressure collected by the steam pressure sensor; Perform fault detection according to the first thermal parameter group to obtain a detection result, wherein the detection result is used to indicate that the steam pressure sensor is in a normal state or a fault state, and when the detection result indicates a fault state, the detection result includes a first fault type and a first fault degree of the steam pressure sensor; If the detection result indicates that the steam pressure sensor is in a fault state, the first steam pressure is corrected according to the first fault type and the first fault degree to obtain a target steam pressure; The opening of the actuator is controlled according to the target steam pressure to control the steam discharge amount entering the nuclear power plant.

2. The method according to claim 1, characterized in that The first fault type includes a constant gain fault or a constant deviation fault. When the first fault type is a constant gain fault, the first fault degree is used to describe the gain amplitude. When the first fault type is a constant deviation fault, the first fault degree is used to describe the deviation amplitude. According to the first fault type and the first fault degree, the first steam pressure is corrected to obtain a target steam pressure, including: If the first fault type is a constant gain fault, adjusting the first steam pressure according to the gain amplitude to obtain the target steam pressure; If the first fault type is a constant deviation fault, the deviation amplitude is subtracted from the first steam pressure to obtain the target steam pressure.

3. The method according to claim 1, characterized in that Performing fault detection according to the first thermal parameter group to obtain a detection result includes: Fault detection is performed according to the pre-built target fault detection model and the first thermal parameter group to obtain the detection result.

4. The method according to claim 3, characterized in that The target fault detection model is obtained according to the following process: Constructing a sample data set, wherein the sample data set includes a training sample set and a test sample set; Training the initial fault detection model according to the training sample set until the loss function of the initial fault detection model converges to obtain an intermediate fault detection model; Testing the intermediate fault detection model according to the test sample set to obtain a test result, wherein the test result includes a detection accuracy rate of the intermediate fault detection model; If the detection accuracy is greater than a preset accuracy, the intermediate fault detection model is determined as the target fault detection model.

5. The method according to claim 4, characterized in that The constructing of the sample data set includes: Acquire a plurality of second thermal parameter groups of the nuclear power system under a plurality of operating conditions within a preset historical time period, wherein the plurality of operating conditions include steady-state conditions and transient conditions, and the parameters in each of the second thermal parameter groups are related to each of the operating conditions of the nuclear power system; According to the second thermal parameter group under each of the operating conditions and the preconfigured multiple second fault types and multiple second fault degrees, a fault simulation is performed on the nuclear power system under each of the operating conditions to obtain multiple third thermal parameter groups under each of the operating conditions and a fault label corresponding to each of the third thermal parameter groups, wherein the fault label is used to indicate the second fault type and the second fault degree corresponding to each of the third thermal parameter groups; Obtaining a plurality of fault sample data according to a plurality of third thermal parameter groups under a plurality of the operating conditions and the fault labels respectively corresponding to the plurality of third thermal parameter groups under a plurality of the operating conditions; configuring a normal label for each second thermal parameter group under the operating condition, and obtaining a plurality of normal sample data according to a plurality of second thermal parameter groups under the plurality of operating conditions and the normal label corresponding to each second thermal parameter group; Obtaining a plurality of sample data according to the plurality of fault sample data and the plurality of normal sample data; Dividing the plurality of sample data according to a preset ratio to obtain the training sample set and the test sample set; The sample data set is obtained according to the training sample set and the test sample set.

6. The method according to claim 4, characterized in that Before training the initial fault detection model according to the training sample set, the method further includes: In response to the selection instruction of the initial fault detection model, the following steps are performed: Setting the activation function of the initial fault detection model as a target activation function; Setting the loss function of the initial fault detection model as a target loss function; Setting the optimizer of the initial fault detection model to a target optimization algorithm; Setting the learning rate of the initial fault detection model to a target value; The selection instruction includes an identifier of the target activation function, an identifier of the target loss function, an identifier of the target optimization algorithm, and the target value; After the initial fault detection model is trained according to the training sample set, and before the loss function of the initial fault detection model converges, the method further includes: Determining a loss value of the initial fault detection model according to the loss function; When the loss value is less than or equal to a preset loss value, a learning rate of the initial fault detection model is reduced.

7. A nuclear power system control device, applied to a nuclear power system, the nuclear power system comprising a nuclear power plant, a steam pressure sensor and an actuator, the steam pressure sensor is used to detect the steam pressure of the nuclear power plant, the actuator is used to control the steam discharge amount entering the nuclear power plant, characterized in that: The device comprises: A parameter acquisition module, used for acquiring a first thermal parameter group of the nuclear power system under a current operating condition, wherein the first thermal parameter group includes at least one parameter, and the at least one parameter includes a first steam pressure collected by the steam pressure sensor; a detection module, configured to perform fault detection according to the first thermal parameter group to obtain a detection result, wherein the detection result is used to indicate that the steam pressure sensor is in a normal state or a fault state, and when the detection result indicates a fault state, the detection result includes a first fault type and a first fault degree of the steam pressure sensor; a correction module, configured to correct the first steam pressure according to the first fault type and the first fault degree to obtain a target steam pressure if the detection result indicates that the steam pressure sensor is in a fault state; A control module is used to control the opening of the actuator according to the target steam pressure to control the steam discharge amount entering the nuclear power plant.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the nuclear power system control method according to any one of claims 1 to 6 when executing the computer program.

9. A nuclear power system, characterized in that: include: Nuclear power plants; Steam pressure sensor; Actuator; as well as According to the electronic device according to claim 8, the steam pressure sensor is used to detect the steam pressure of the nuclear power device, and the actuator is used to control the steam discharge amount entering the nuclear power device.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the nuclear power system control method according to any one of claims 1 to 6 is implemented.

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