WO2025081689A1 - Feedwater system for evaporator of reactor nuclear power unit - Google Patents

Abstract

Disclosed in the present application is a feedwater system for an evaporator of a reactor nuclear power unit. By means of sensors, the feedwater system monitors in real time the operating state of an evaporator, such as water level, temperature and pressure parameters, and data processing and analysis algorithms are introduced to a backend to perform time-series collaborative correlation analysis on these operating parameters of the evaporator, so as to monitor in real time the operating state of the evaporator. In this way, an opening value of a regulation valve can be adaptively controlled in real time on the basis of the operating state of the evaporator, such that automatic control over the feedwater system for the evaporator of the reactor nuclear power unit is implemented, thereby meeting the requirements of the flow and pressure of feedwater, ensuring the stability and safety of the feedwater system, and improving the safety and efficiency of nuclear power stations.

Description

A water supply system for evaporator of reactor nuclear power unit Technical Field

The present application relates to the field of intelligent control, and more specifically, to an evaporator water feed system for a reactor nuclear power unit.

Background Art

The evaporator feedwater system of the reactor nuclear power unit is an important component of the nuclear power plant, which is used to condense steam into water and send it back to the reactor core to maintain the stable operation of the reactor. The stability of the feedwater system is crucial to the safety and efficiency of the nuclear power plant.

Traditional water supply systems are usually controlled by manual operation to control the water flow and pressure, but this approach has some problems. For example, manual operation is easily affected by the operator's subjective factors and experience, which may lead to inaccurate operation or delayed response, thus affecting the stability and safety of the system. In addition, since the operation of the water supply system requires manual intervention, there is a risk of human error. In other words, the operator may cause the system to malfunction or abnormality due to negligence, misoperation or other reasons, which in turn affects the normal operation of the nuclear power plant.

In addition, the working state of the water supply system may be affected by multiple parameters, such as water level, temperature and pressure, etc. Traditional water supply systems can usually only monitor and control a single data in real time, and it is often difficult to monitor and adjust these parameters at the same time, resulting in untimely system response or failure to meet actual needs.

Therefore, an optimized evaporator feedwater system for a reactor nuclear power unit is desired.

Technical issues

In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a reactor nuclear power unit evaporator water supply system, which uses sensors to monitor the working state of the evaporator in real time, such as water level, temperature and pressure parameters, and introduces data processing and analysis algorithms at the back end to perform time series collaborative correlation analysis of these evaporator working parameters, so as to monitor the working state of the evaporator in real time. In this way, the opening value of the regulating valve can be adaptively controlled in real time based on the working state of the evaporator, thereby realizing automatic control of the reactor nuclear power unit evaporator water supply system to meet the needs of water supply flow and pressure, ensure the stability and safety of the water supply system, and improve the safety and efficiency of the nuclear power plant.

Technical Solutions

According to one aspect of the present application, a reactor nuclear power unit evaporator water supply system is provided, comprising:

An evaporator data acquisition module is used to obtain the water level value, pressure value and temperature value of the evaporator at multiple predetermined time points within a predetermined time period;

an evaporator data parameter time series arrangement module, for arranging the water level values, pressure values and temperature values of the evaporator at the plurality of predetermined time points into a water level value time series input vector, a pressure value time series input vector and a temperature value time series input vector according to the time dimension;

an evaporator parameter timing characteristic analysis module, used for performing timing analysis on the water level value timing input vector, the pressure value timing input vector and the temperature value timing input vector respectively to obtain a water level value timing characteristic vector, a pressure value timing characteristic vector and a temperature value timing characteristic vector;

An evaporator multi-parameter time series feature fusion module, used to fuse the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector to obtain an evaporator parameter time series collaborative fusion feature;

The regulating valve opening control module is used to determine whether the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained based on the evaporator parameter time series coordinated fusion characteristics.

Beneficial Effects

Compared with the prior art, the present application provides a reactor nuclear power unit evaporator water feed system, which uses sensors to monitor the working status of the evaporator in real time, such as water level, temperature and pressure parameters, and introduces data processing and analysis algorithms at the back end to perform time-series collaborative correlation analysis of these evaporator working parameters, so as to monitor the working status of the evaporator in real time. In this way, the opening value of the regulating valve can be adaptively controlled in real time based on the working status of the evaporator, thereby realizing automatic control of the reactor nuclear power unit evaporator water feed system to adapt to the requirements of water feed flow and pressure, ensure the stability and safety of the water feed system, and improve the safety and efficiency of the nuclear power plant.

BRIEF DESCRIPTION OF THE DRAWINGS

By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

FIG1 is a block diagram of an evaporator water supply system for a reactor nuclear power unit according to an embodiment of the present application;

FIG2 is a system architecture diagram of an evaporator water supply system for a reactor nuclear power unit according to an embodiment of the present application;

FIG3 is a block diagram of a training phase of an evaporator feedwater system for a reactor nuclear power unit according to an embodiment of the present application;

4 is a block diagram of an evaporator parameter timing characteristic analysis module in an evaporator water feed system of a reactor nuclear power unit according to an embodiment of the present application.

Embodiments of the present invention

Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and/or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and/or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

Specifically, in the technical solution of the present application, a evaporator water supply system for a reactor nuclear power unit is proposed, which can provide water to a steam generator to maintain the stable operation of the nuclear reactor. Among them, the evaporator water supply system for the reactor nuclear power unit includes: Steam generator: The steam generator is a key component in a nuclear power plant, which is used to convert the heat energy generated in the nuclear reactor into steam. There is a group of tube bundles inside the steam generator, and the nuclear reactor coolant (usually water) flows through these tube bundles and performs heat exchange at the tube wall and the water/steam interface inside the tube. Evaporator: The steam in the steam generator is condensed through the evaporator, transferring heat energy to the water around the evaporator. The evaporator is usually a large heat exchanger composed of a series of parallel tube bundles. The steam condenses outside the tube bundle, releasing heat, and evaporating the water inside the tube bundle. Feed water system: The main function of the feed water system is to supply water to the evaporator to replenish the water lost during the evaporation process. In particular, the water supply system generally includes the following main components: a. Feed water pump: The feed water pump is used to draw water from the water treatment system or water storage tank and provide sufficient pressure to deliver water to the evaporator; b. Deaerator: The deaerator is used to remove oxygen from the water to prevent corrosion and bubble formation in the evaporator; c. Desalter: The desalter is used to remove salt from the water to prevent scaling and corrosion in the evaporator; d. Regulating valve: The regulating valve is used to control the feed water flow and pressure to meet the needs of the evaporator; e. Water level control system: The water level control system is used to monitor the water level in the evaporator and automatically adjust the operation of the water supply system as needed to maintain a suitable water level.

Working principle: The working principle of the water supply system is to pump water to the evaporator through the water supply pump, and control the flow and pressure of the water supply through the regulating valve and water level control system. In the evaporator, the water is subjected to the heat of steam, part of the water evaporates into steam, and the remaining water cools the steam and returns to the water supply system. By circulating the water supply, the water volume in the evaporator is maintained to meet the needs of the steam generator.

In the technical solution of the present application, a reactor nuclear power unit evaporator water feed system is proposed. FIG. 1 is a block diagram of a reactor nuclear power unit evaporator water feed system according to an embodiment of the present application. FIG. 2 is a system architecture diagram of a reactor nuclear power unit evaporator water feed system according to an embodiment of the present application. As shown in FIG. 1 and FIG. 2, the reactor nuclear power unit evaporator water feed system 300 according to an embodiment of the present application includes: an evaporator data acquisition module 310, which is used to obtain the water level value, pressure value and temperature value of the evaporator at multiple predetermined time points within a predetermined time period; an evaporator data parameter timing arrangement module 320, which is used to arrange the water level value, pressure value and temperature value of the evaporator at the multiple predetermined time points according to the time dimension into a water level value timing input vector, a pressure value timing input vector and a temperature value timing input vector; an evaporator parameter timing feature analysis module 330, which is used to analyze the water level value timing input vectors respectively. The input vector, the pressure value timing input vector and the temperature value timing input vector are subjected to timing analysis to obtain the water level value timing feature vector, the pressure value timing feature vector and the temperature value timing feature vector; the evaporator multi-parameter timing feature fusion module 340 is used to fuse the water level value timing feature vector, the pressure value timing feature vector and the temperature value timing feature vector to obtain the evaporator parameter timing collaborative fusion feature; the regulating valve valve opening control module 350 is used to determine whether the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained based on the evaporator parameter timing collaborative fusion feature.

In particular, the evaporator data acquisition module 310 is used to obtain the water level value, pressure value and temperature value of the evaporator at multiple predetermined time points within a predetermined time period. In one example, the water level value of the evaporator at multiple predetermined time points within a predetermined time period can be obtained through a water level sensor, the pressure value of the evaporator at multiple predetermined time points within a predetermined time period can be obtained through a pressure sensor, and the temperature value of the evaporator at multiple predetermined time points within a predetermined time period can be obtained through a temperature sensor.

It is worth noting that the water level sensor is a device used to measure the height of the liquid water level. The water level sensor is usually connected to a data acquisition system or a control system to transmit the measured water level information to a monitoring device or an actuator. In this way, the water level changes can be monitored in real time and corresponding control measures can be taken. The pressure sensor is a device used to measure pressure. It can convert physical quantities into electrical signals in order to monitor and measure changes in pressure. The pressure sensor usually converts the measured pressure signal into a standard electrical signal output, such as an analog voltage signal or a digital signal. In this way, the pressure data can be easily transmitted to the monitoring device, control system or data acquisition system for processing and analysis. The temperature sensor is a device used to measure temperature. It can convert physical quantities into electrical signals in order to monitor and measure changes in temperature. The temperature sensor usually converts the measured temperature signal into a standard electrical signal output, such as an analog voltage signal or a digital signal. In this way, the temperature data can be easily transmitted to the monitoring device, control system or data acquisition system for processing and analysis.

In particular, the evaporator data parameter timing arrangement module 320 is used to arrange the water level value, pressure value and temperature value of the evaporator at the multiple predetermined time points into a water level value timing input vector, a pressure value timing input vector and a temperature value timing input vector according to the time dimension. Considering that the water level value, pressure value and temperature value of the evaporator have a dynamic change law of timing in the time dimension, that is, the water level value, pressure value and temperature value of the evaporator at the multiple predetermined time points have a timing correlation relationship in the sample dimension. Therefore, in the technical solution of the present application, the water level value, pressure value and temperature value of the evaporator at the multiple predetermined time points are further arranged into a water level value timing input vector, a pressure value timing input vector and a temperature value timing input vector according to the time dimension, so as to respectively integrate the distribution information of the water level value, pressure value and temperature value of the evaporator in timing.

In particular, the evaporator parameter timing characteristic analysis module 330 is used to perform timing analysis on the water level value timing input vector, the pressure value timing input vector and the temperature value timing input vector to obtain a water level value timing characteristic vector, a pressure value timing characteristic vector and a temperature value timing characteristic vector. In particular, in a specific example of the present application, as shown in Figure 4, the evaporator parameter timing feature analysis module 330 includes: an evaporator parameter timing data preprocessing unit 331, which is used to pass the water level value timing input vector, the pressure value timing input vector and the temperature value timing input vector through a preprocessor including an upsampling layer and a vector-image converter to obtain a water level value timing image, a pressure value timing image and a temperature value timing image; an evaporator multi-parameter timing feature extraction unit 332, which is used to extract features from the water level value timing image, the pressure value timing image and the temperature value timing image through a timing feature extractor based on a deep neural network model to obtain the water level value timing feature vector, the pressure value timing feature vector and the temperature value timing feature vector.

Specifically, the evaporator parameter time series data preprocessing unit 331 is used to pass the water level value time series input vector, the pressure value time series input vector and the temperature value time series input vector through a preprocessor including an upsampling layer and a vector-to-image converter to obtain a water level value time series image, a pressure value time series image and a temperature value time series image. It should be understood that in the technical solution of the present application, in order to improve the ability to capture subtle changes in the water level value, pressure value and temperature value of the evaporator in time series, in the technical solution of the present application, the water level value time series input vector, the pressure value time series input vector and the temperature value time series input vector are further upsampled to increase the density and smoothness of the data parameters in time series, so as to facilitate the subsequent better representation of the working state characteristics of the evaporator. In addition, considering that compared with simple vector representation, the time series image can provide more information, including the time series relationship, fluctuation and trend change of the data. Therefore, after upsampling the water level value time series input vector, the pressure value time series input vector, and the temperature value time series input vector, respectively, the upsampled time series input vector is further subjected to vector-to-image conversion, and such conversion can better capture the time series dynamic changes of the water level, pressure, and temperature in the water supply system. Based on this, in the technical solution of the present application, the water level value time series input vector, the pressure value time series input vector, and the temperature value time series input vector are respectively passed through a preprocessor including an upsampling layer and a vector-to-image converter to obtain a water level value time series image, a pressure value time series image, and a temperature value time series image.

It is worth noting that upsampling refers to increasing the sampling rate of a signal, that is, increasing the density of sampling points. In digital signal processing, upsampling is usually used to convert a low sampling rate signal into a high sampling rate signal for more sophisticated analysis, processing or restoration. Upsampling can be used in a variety of applications, such as resampling of audio signals, amplification of images, interpolation of signals, etc.

Specifically, the evaporator multi-parameter time series feature extraction unit 332 is used to perform feature extraction on the water level value time series image, the pressure value time series image and the temperature value time series image respectively through a time series feature extractor based on a deep neural network model to obtain the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector. In the technical solution of the application, a time series feature extractor based on a convolutional neural network model with excellent performance in implicit feature extraction of images is used to perform feature mining on the water level value time series image, the pressure value time series image and the temperature value time series image respectively, so as to extract the time series implicit distribution feature information of the water level value, pressure value and temperature value of the evaporator in the time dimension respectively, thereby obtaining the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector. More specifically, each layer of the time series feature extractor based on the convolutional neural network model is used to perform the following operations on the input data in the forward pass of the layer: convolution processing is performed on the input data to obtain a convolution feature map; global mean pooling is performed on the convolution feature map based on a feature matrix to obtain a pooled feature map; and nonlinear activation is performed on the pooled feature map to obtain an activation feature map; wherein the output of the last layer of the time series feature extractor based on the convolutional neural network model is the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector, and the input of the first layer of the time series feature extractor based on the convolutional neural network model is the water level value time series image, the pressure value time series image and the temperature value time series image.

It is worth noting that Convolutional Neural Network (CNN) is a deep learning model that is mainly used to process data with grid structures, such as images and time series data. CNN has made significant breakthroughs in the field of computer vision and has also been widely used in other fields, such as natural language processing and medical image analysis. The core idea of CNN is to use convolutional layers and pooling layers to automatically extract the features of input data, and perform tasks such as classification or regression through fully connected layers. The following are the main components of CNN: Convolutional layer: The convolutional layer is the core component of CNN. It performs convolution operations on the input data by applying a series of convolution kernels (also called filters). The convolution operation can extract local features of the input data and perform calculations on the input data by sliding windows. Each convolution kernel learns different features, such as edges, textures, etc. The output of the convolution layer is called a feature map; Activation function: A nonlinear activation function, such as ReLU, is applied to the output of the convolution layer to introduce nonlinear transformations and increase the expressive power of the network; Pooling layer: The pooling layer is used to reduce the size of the feature map and reduce the spatial sensitivity of the network to the input data. Common pooling operations include maximum pooling and average pooling, which represent the features of a local area by selecting the maximum or average value of the local area; Fully connected layer: The fully connected layer connects the output of the pooling layer to one or more fully connected layers to map the extracted features to the final output category. The neurons in the fully connected layer are connected to all neurons in the previous layer. Each connection has a weight, which is learned through back propagation during training; Dropout: In order to reduce overfitting, Dropout technology is often used in CNN. Dropout randomly discards a part of neurons with a certain probability during training, thereby reducing the dependency between neurons and improving the generalization ability of the model. The training process of CNN usually uses backpropagation algorithm and gradient descent optimization to update network parameters to minimize the loss function. During training, CNN learns the feature representation of input data by continuously adjusting the weights and biases of the convolution kernel.

It is worth mentioning that in other specific examples of the present application, the water level value time series input vector, the pressure value time series input vector and the temperature value time series input vector can also be analyzed in other ways to obtain the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector, for example: the time series analysis step of the water level value time series input vector: collect the time series data of the water level sensor, including the water level value and the corresponding timestamp; pre-process the timestamp, such as converting it into an appropriate time format; pre-process the water level value, such as removing noise, filling missing values, etc.; calculate statistical features, such as mean, maximum, minimum, standard deviation, etc.; calculate time domain features, such as autocorrelation, difference, etc.; calculate frequency domain features, such as Fourier transform, wavelet transform, etc.; combine the calculated eigenvalues into a water level value time series feature vector; the time series analysis step of the pressure value time series input vector: collect the time series data of the pressure sensor, including Pressure value and corresponding timestamp; preprocess the timestamp, such as converting it into an appropriate time format; preprocess the pressure value, such as removing noise, filling missing values, etc.; calculate statistical features, such as mean, maximum, minimum, standard deviation, etc.; calculate time domain features, such as autocorrelation, difference, etc.; calculate frequency domain features, such as Fourier transform, wavelet transform, etc.; combine the calculated eigenvalues into a pressure value time series feature vector; timing analysis steps for temperature value time series input vector: collect time series data from temperature sensor, including temperature value and corresponding timestamp; preprocess the timestamp, such as converting it into an appropriate time format; preprocess the temperature value, such as removing noise, filling missing values, etc.; calculate statistical features, such as mean, maximum, minimum, standard deviation, etc.; calculate time domain features, such as autocorrelation, difference, etc.; calculate frequency domain features, such as Fourier transform, wavelet transform, etc.; combine the calculated eigenvalues into a temperature value time series feature vector.

In particular, the evaporator multi-parameter time series feature fusion module 340 is used to fuse the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector to obtain the evaporator parameter time series collaborative fusion feature. It should be understood that the water level value, the pressure value and the temperature value are important parameters in the water supply system, and their time series feature vectors can provide information about the state and change of the evaporator. Therefore, it is necessary to fuse the water level time series feature, the pressure time series feature and the temperature time series feature of the evaporator to comprehensively monitor the state of the evaporator and control the valve opening value of the regulating valve. Based on this, in the technical solution of the present application, a Bayesian probability model is further used to fuse the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector to obtain the regulating valve operation posterior feature vector. By using the Bayesian probability model, the characteristic information in the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector can be integrated to obtain a more comprehensive and accurate representation of the evaporator state. Furthermore, the Bayesian model can also take into account the correlation and weight between the time series characteristics of the water level value, pressure value and temperature value of the evaporator, so as to better reflect the actual situation of the evaporator.

It is worth noting that the Bayesian probability model is a probabilistic modeling method based on Bayes' theorem, which is used to infer and predict the probability of unknown events. It is based on Bayes' theorem and calculates the posterior probability through prior probability and observed data to update the probability estimate of unknown events. The Bayesian probability model focuses on the conditional dependencies between events. It includes two important components: Prior probability: Prior probability is an estimate of the probability of an event before considering any observed data. It is based on previous experience, domain knowledge or assumptions. Prior probability represents a subjective or objective estimate of the probability of an event; Posterior probability: The posterior probability is the probability of an event calculated according to Bayes' theorem after considering the observed data. It is obtained by combining the prior probability with the information of the observed data and updating it according to the conditional dependencies between events. Bayesian probability models are widely used in many fields, including statistics, machine learning, artificial intelligence, bioinformatics, etc.

In particular, the regulating valve valve opening control module 350 is used to determine whether the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained based on the evaporator parameter time series collaborative fusion characteristics. In the technical solution of the present application, the regulating valve operation posterior feature vector is passed through a classifier to obtain a classification result, and the classification result is used to indicate whether the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained. That is, the actual working state feature information of the evaporator is used for classification processing, so as to accurately control the valve opening value of the regulating valve in real time. In this way, the opening value of the regulating valve can be adaptively controlled in real time based on the working state of the evaporator, thereby realizing automatic control of the evaporator feedwater system of the reactor nuclear power unit to meet the requirements of feedwater flow and pressure. Specifically, the regulating valve operation posterior feature vector is fully connected and encoded using multiple fully connected layers of the classifier to obtain an encoded classification feature vector; and the encoded classification feature vector is passed through the Softmax classification function of the classifier to obtain the classification result.

A classifier is a machine learning model or algorithm that is used to classify input data into different categories or labels. Classifiers are part of supervised learning and perform classification tasks by learning the mapping relationship from input data to output categories.

A fully connected layer is a common type of layer in neural networks. In a fully connected layer, each neuron is connected to all neurons in the previous layer, and each connection has a weight. This means that each neuron in the fully connected layer receives inputs from all neurons in the previous layer, and performs a weighted sum of these inputs through the weights, and then passes the result to the next layer.

The Softmax classification function is a commonly used activation function for multi-classification problems. It converts each element of the input vector into a probability value between 0 and 1, and the sum of these probability values is equal to 1. The Softmax function is often used in the output layer of a neural network, especially for multi-classification problems, because it can map the network output to the probability distribution of each category. During the training process, the output of the Softmax function can be used to calculate the loss function and update the network parameters through the back propagation algorithm. It is worth noting that the output of the Softmax function does not change the relative size relationship between the elements, but only normalizes them. Therefore, the Softmax function does not change the characteristics of the input vector, but only converts it into a probability distribution form.

It should be understood that before using the above neural network model for inference, it is necessary to train the preprocessor including the upsampling layer and the vector-image converter, the time series feature extractor based on the convolutional neural network model, the Bayesian probability model and the classifier. That is, the evaporator feed water system 300 of the reactor nuclear power unit according to the present application also includes a training stage 400 for training the preprocessor including the upsampling layer and the vector-image converter, the time series feature extractor based on the convolutional neural network model, the Bayesian probability model and the classifier.

FIG3 is a block diagram of the training phase of the evaporator water supply system of a reactor nuclear power unit according to an embodiment of the present application. As shown in FIG3, the evaporator water supply system 300 of the reactor nuclear power unit according to an embodiment of the present application includes: a training phase 400, including: a training data acquisition unit 410, for obtaining the training water level value, training pressure value and training temperature value of the evaporator at multiple predetermined time points within a predetermined time period, and the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained; a training data time series arrangement unit 420, for arranging the training water level value, training pressure value and training temperature value of the evaporator at the multiple predetermined time points into training water level values according to the time dimension respectively. a training data preprocessing unit 430, for respectively passing the training water level value time series input vector, the training pressure value time series input vector and the training temperature value time series input vector through the preprocessor comprising an upsampling layer and a vector-to-image converter to obtain a training water level value time series image, a training pressure value time series image and a training temperature value time series image; a training evaporator parameter time series feature extraction unit 440, for respectively passing the training water level value time series image, the training pressure value time series image and the training temperature value time series input vector through the preprocessor comprising an upsampling layer and a vector-to-image converter to obtain a training water level value time series image, a training pressure value time series image and a training temperature value time series image The training temperature value time series image is respectively passed through the time series feature extractor based on the convolutional neural network model to obtain the training water level value time series feature vector, the training pressure value time series feature vector and the training temperature value time series feature vector; the training evaporator multi-parameter time series feature fusion unit 450 is used to use the Bayesian probability model to fuse the training water level value time series feature vector, the training pressure value time series feature vector and the training temperature value time series feature vector to obtain the training control valve operation posterior feature vector; the feature distribution optimization unit 460 is used to perform feature accuracy alignment optimization based on dimensional representation and inverse recovery on the training control valve operation posterior feature vector to obtain the optimized training control valve operation posterior feature vector; the classification loss unit 470 is used to pass the optimized training control valve operation posterior feature vector through the classifier to obtain the classification loss function value; the model training unit 480 is used to train the preprocessor including the upsampling layer and the vector-image converter, the time series feature extractor based on the convolutional neural network model, the Bayesian probability model and the classifier based on the classification loss function value and through the direction propagation of gradient descent.

In particular, in the technical solution of the present application, the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector respectively express the time series correlation characteristics of the water level value, the pressure value and the temperature value in the local time domain under the global time domain determined by vector-image conversion within the local time domain and between the local time domains. Therefore, since the Bayesian probability model is used to fuse the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector, the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector are point-by-point Bayesian calculations are performed on the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector. Therefore, the obtained posterior feature vector of the regulating valve operation also has a multi-scale time series correlation representation of the time series parameter characteristics within the local time domain and between the local time domains. Therefore, when the control valve operation posterior feature vector is classified and regressed through a classifier, the multi-scale temporal correlation representation of the control valve operation posterior feature vector in the local time domain and between local time domains will also affect the training effect of the control valve operation posterior feature vector when it is trained by the classifier due to the difference in correlation accuracy of the multi-time domain spatial scale correlation representation of the temporal correlation feature. Therefore, during the training process, the applicant of the present application records the control valve operation posterior feature vector as Perform feature accuracy alignment based on dimensional representation and inversion recovery, which is specifically expressed as: ; is the posterior characteristic vector of the control valve operation No. The eigenvalues at the positions, Represents the posterior feature vector of the control valve operation The zero norm of is the posterior characteristic vector of the control valve operation The length of is a weight hyperparameter. Here, in view of the accuracy contradiction between the high-dimensional feature space encoding of multi-dimensional time series parameter features based on multi-temporal spatial scales and the multi-temporal spatial scale time series feature association editing, the feature accuracy alignment based on dimensional representation and inverse recovery is generated by treating the multi-temporal spatial scale time series feature association editing as the inverse embedding generation of the high-dimensional feature space encoding with the time series parameter feature distribution, and by equipping the feature values represented as the encoding with sparse distribution balance of scale representation, and performing inverse recovery of the association details based on vector counting, so as to realize the adaptive alignment of the accuracy difference in the training process, and improve the training effect of the posterior feature vector of the control valve operation when the classification regression training is performed by the classifier. In this way, the opening value of the control valve can be adaptively controlled based on the working state of the evaporator, thereby realizing the real-time automatic control of the evaporator feed water system of the reactor nuclear power unit to meet the needs of feed water flow and pressure, ensure the stability and safety of the feed water system, and improve the safety and efficiency of the nuclear power plant.

As described above, the evaporator water feed system 300 of the reactor nuclear power unit according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with an evaporator water feed algorithm of the reactor nuclear power unit. In a possible implementation, the evaporator water feed system 300 of the reactor nuclear power unit according to the embodiment of the present application can be integrated into the wireless terminal as a software module and/or a hardware module. For example, the evaporator water feed system 300 of the reactor nuclear power unit can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the evaporator water feed system 300 of the reactor nuclear power unit can also be one of the many hardware modules of the wireless terminal.

Alternatively, in another example, the reactor nuclear power unit evaporator water feed system 300 and the wireless terminal may also be separate devices, and the reactor nuclear power unit evaporator water feed system 300 may be connected to the wireless terminal via a wired and/or wireless network, and transmit interactive information in accordance with an agreed data format.

The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (8)

A reactor nuclear power unit evaporator water supply system, characterized by comprising: An evaporator data acquisition module is used to obtain the water level value, pressure value and temperature value of the evaporator at multiple predetermined time points within a predetermined time period; an evaporator data parameter time series arrangement module, for arranging the water level values, pressure values and temperature values of the evaporator at the plurality of predetermined time points into a water level value time series input vector, a pressure value time series input vector and a temperature value time series input vector according to the time dimension; an evaporator parameter timing characteristic analysis module, used for performing timing analysis on the water level value timing input vector, the pressure value timing input vector and the temperature value timing input vector respectively to obtain a water level value timing characteristic vector, a pressure value timing characteristic vector and a temperature value timing characteristic vector; An evaporator multi-parameter time series feature fusion module, used to fuse the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector to obtain an evaporator parameter time series collaborative fusion feature; The regulating valve opening control module is used to determine whether the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained based on the evaporator parameter time series coordinated fusion characteristics. The reactor nuclear power unit evaporator water supply system according to claim 1, characterized in that the evaporator parameter time series characteristic analysis module comprises: an evaporator parameter time series data preprocessing unit, used for respectively passing the water level value time series input vector, the pressure value time series input vector and the temperature value time series input vector through a preprocessor including an upsampling layer and a vector-to-image converter to obtain a water level value time series image, a pressure value time series image and a temperature value time series image; The evaporator multi-parameter time series feature extraction unit is used to perform feature extraction on the water level value time series image, the pressure value time series image and the temperature value time series image respectively through a time series feature extractor based on a deep neural network model to obtain the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector. The evaporator water feed system for a reactor nuclear power unit according to claim 2, characterized in that the deep neural network model is a convolutional neural network model. The evaporator water supply system for a reactor nuclear power unit according to claim 3 is characterized in that the evaporator multi-parameter time series feature fusion module is used to: use a Bayesian probability model to fuse the water level value time series feature vector, the pressure value time series feature vector and the temperature value time series feature vector to obtain a control valve operation posterior feature vector as the evaporator parameter time series collaborative fusion feature. The evaporator water feed system for a reactor nuclear power unit according to claim 4 is characterized in that the regulating valve valve opening control module is used to: pass the regulating valve operation posterior feature vector through a classifier to obtain a classification result, and the classification result is used to indicate whether the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained. The evaporator feed water system for a reactor nuclear power unit according to claim 5 is characterized in that it also includes a training module for training the preprocessor including the upsampling layer and the vector-to-image converter, the temporal feature extractor based on the convolutional neural network model, the Bayesian probability model and the classifier. The evaporator water supply system for a reactor nuclear power unit according to claim 6, characterized in that the training module comprises: A training data acquisition unit, used to obtain training water level values, training pressure values and training temperature values of the evaporator at multiple predetermined time points within a predetermined time period, and whether the valve opening value of the regulating valve at the current time point should be increased, decreased or maintained; A training data time series arrangement unit, used to arrange the training water level values, training pressure values and training temperature values of the evaporator at the plurality of predetermined time points into a training water level value time series input vector, a training pressure value time series input vector and a training temperature value time series input vector according to the time dimension respectively; A training data preprocessing unit, used for passing the training water level value time series input vector, the training pressure value time series input vector and the training temperature value time series input vector through the preprocessor including the upsampling layer and the vector-to-image converter to obtain a training water level value time series image, a training pressure value time series image and a training temperature value time series image; A training evaporator parameter time series feature extraction unit, used for respectively passing the training water level value time series image, the training pressure value time series image and the training temperature value time series image through the time series feature extractor based on the convolutional neural network model to obtain a training water level value time series feature vector, a training pressure value time series feature vector and a training temperature value time series feature vector; A training evaporator multi-parameter time series feature fusion unit is used to use the Bayesian probability model to fuse the training water level value time series feature vector, the training pressure value time series feature vector and the training temperature value time series feature vector to obtain a training regulating valve operation posterior feature vector; A feature distribution optimization unit, used for performing feature accuracy alignment optimization based on dimensional characterization and inverse recovery on the training control valve operation posterior feature vector to obtain an optimized training control valve operation posterior feature vector; A classification loss unit, used for passing the optimized training regulating valve operation posterior feature vector through the classifier to obtain a classification loss function value; A model training unit is used to train the preprocessor including the upsampling layer and the vector-to-image converter, the temporal feature extractor based on the convolutional neural network model, the Bayesian probability model and the classifier based on the classification loss function value and through the direction propagation of gradient descent. The evaporator water feed system for a reactor nuclear power unit according to claim 7, characterized in that the classification loss unit is used to: Using the classifier to process the optimized training regulating valve operation posterior feature vector to obtain a training classification result: and The cross entropy loss function value between the training classification result and the real value of the valve opening value of the regulating valve at the current time point that should be increased, decreased or maintained is calculated as the classification loss function value.