Thermal energy recycling system in nuclear power station

Abstract

Disclosed is a thermal energy recycling system in a nuclear power station. The system comprises: a generator; a heat exchanger which is used for transferring heat to water in a secondary loop to generate steam; a steam generator which is used for transferring the steam to a steam turbine; the steam turbine which is used for driving the generator to rotate so as to generate electric energy; and a condenser which is used for cooling the steam so that the steam returns to the secondary loop. In this way, the flow speed of the water in the secondary loop can be monitored and adjusted in real time, thereby improving the working state of the heat exchanger.

Description

A heat energy recovery and reuse system in a nuclear power plant Technical Field

[0001] The present application relates to the field of heat recovery, and more specifically, to a heat recovery and reuse system in a nuclear power plant. Background Art

[0002] The heat recovery and reuse system in a nuclear power plant is a technology that uses the heat generated by the nuclear reactor to drive a steam turbine to generate electricity. It can improve the thermal efficiency of the nuclear power plant, reduce the generation of nuclear waste, and reduce the impact on the environment. However, the operation of traditional heat recovery and reuse systems is usually based on preset parameters and fixed control strategies, and lacks the ability to monitor and optimize real-time data in the system. Specifically, traditional systems are unable to control and optimize the parameters of heat exchange according to the actual conditions of high-temperature, high-pressure water and secondary loop water, resulting in low heat exchange efficiency. In addition, the control strategies in traditional systems are usually based on experience and static rules. These strategies may not be able to adapt to different working conditions and changing operating conditions, and lack flexibility and adaptability, resulting in low heat recovery efficiency and inability to fully utilize nuclear energy resources.

[0003] Therefore, an optimized heat recovery and reuse system in nuclear power plants is desired. Technical issues

[0004] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a heat recovery and reuse system in a nuclear power plant, which can monitor and adjust the flow rate of secondary loop water in real time and improve the working state of the heat exchanger. Technical Solutions

[0005] According to one aspect of the present application, a heat recovery and reuse system in a nuclear power plant is provided, comprising:

[0006] dynamo;

[0007] a heat exchanger to transfer heat to water in a secondary loop to generate steam;

[0008] a steam generator for transferring the steam to a steam turbine;

[0009] a steam turbine for driving the generator to rotate and generate electrical energy; and

[0010] A condenser is used to cool the steam and return the steam to the secondary circuit. Beneficial effects

[0011] Compared to existing technologies, the heat recovery and reuse system for a nuclear power plant provided in this application includes: a generator; a heat exchanger for transferring heat to water in a secondary circuit to generate steam; a steam generator for transferring the steam to a steam turbine; the steam turbine for driving the generator to generate electricity; and a condenser for cooling the steam and returning it to the secondary circuit. This allows for real-time monitoring and adjustment of the secondary circuit water flow rate, improving the operating condition of the heat exchanger. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.

[0013] FIG1 is a block diagram of a heat exchanger in a heat recovery and reuse system in a nuclear power plant according to an embodiment of the present application.

[0014] Figure 2 is a block diagram of the local timing characteristics interactive analysis module of the high-temperature and high-pressure water-secondary loop water in the heat recovery and reuse system of the nuclear power plant according to an embodiment of the present application.

[0015] FIG3 is a block diagram of the secondary loop water flow rate real-time control module in the heat recovery and reuse system in a nuclear power plant according to an embodiment of the present application.

[0016] FIG4 is a block diagram of the characteristic optimization unit in the heat recovery and reuse system in a nuclear power plant according to an embodiment of the present application.

[0017] FIG5 is a flow chart of a method for recovering and reusing heat energy in a nuclear power plant according to an embodiment of the present application.

[0018] FIG6 is a schematic diagram of a system architecture of a method for recovering and reusing heat energy in a nuclear power plant according to an embodiment of the present application.

[0019] FIG7 is a diagram showing an application scenario of a heat recovery and reuse system in a nuclear power plant according to an embodiment of the present application. Modes for Carrying Out the Invention

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts also fall within the scope of protection of this application.

[0021] As used in this application and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0022] Although the present application makes various references to certain modules in the system according to 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 illustrative only, and different aspects of the system and method can use different modules.

[0023] Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0024] 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 herein.

[0025] In response to the above technical problems, the technical solution of the present application proposes a heat recovery and reuse system in a nuclear power plant, which includes: a generator; a heat exchanger for transferring heat to water in a secondary loop to generate steam; a steam generator for transferring the steam to a steam turbine; a steam turbine for driving the generator to rotate and generate electricity; and a condenser for cooling the steam and returning the steam to the secondary loop.

[0026] Accordingly, considering that heat exchangers are crucial components of the heat recovery and reuse system in nuclear power plants, they facilitate heat exchange between the nuclear reactor and the secondary circuit. By exchanging heat between the high-temperature, high-pressure water in the nuclear reactor and the water in the secondary circuit, they transfer heat energy to the water in the secondary circuit, raising its temperature and converting it into steam. Therefore, to optimize the operating efficiency of this system, the flow rate of the secondary circuit water must be monitored and adjusted in real time to improve the operating status of the heat exchanger.

[0027] Based on this, the technical concept of this application is to automatically control the flow rate of the secondary loop water in real time by monitoring and collecting the flow rate and temperature values ​​of the high-temperature, high-pressure water and the secondary loop water in real time, and introducing data processing and analysis algorithms at the back end to perform time-series collaborative analysis of these data. In this way, the flow rate of the secondary loop water can be automatically and adaptively adjusted at the current time point based on the temperature and flow rate time-series data of the high-temperature, high-pressure water and the secondary loop water, thereby improving the heat exchange rate and heat recovery efficiency of the heat recovery and reuse system in the nuclear power plant, reducing the generation of nuclear waste, and reducing the impact on the environment.

[0028] FIG1 is a block diagram of a heat recovery and reuse system in a nuclear power plant according to an embodiment of the present application. As shown in FIG1 , according to the heat recovery and reuse system in a nuclear power plant according to an embodiment of the present application, the heat exchanger 100 includes: a high-temperature and high-pressure water data acquisition module 110 for acquiring the flow rate value and temperature value of the high-temperature and high-pressure water at a plurality of predetermined time points within a predetermined time period; a secondary loop water data acquisition module 120 for acquiring the flow rate value and temperature value of the secondary loop water at the plurality of predetermined time points; a high-temperature and high-pressure water data timing arrangement module 130 for arranging the flow rate value and temperature value of the high-temperature and high-pressure water at a plurality of predetermined time points within a predetermined time period according to a time dimension to obtain a high-temperature and high-pressure water temperature timing input vector and a high-temperature and high-pressure water flow rate timing input vector; a secondary loop water data timing arrangement module 140 for arranging the flow rate value and temperature value of the secondary loop water at the plurality of predetermined time points according to a time dimension to obtain a secondary loop water temperature timing input vector and a secondary loop water flow rate timing input vector; The pressurized water data timing correlation analysis module 150 is used to perform timing correlation analysis on the high-temperature and high-pressure water temperature timing input vector and the high-temperature and high-pressure water flow rate timing input vector to obtain a high-temperature and high-pressure water temperature-flow rate timing correlation feature; the secondary loop water data timing correlation analysis module 160 is used to perform timing correlation analysis on the secondary loop water temperature timing input vector and the secondary loop water flow rate timing input vector to obtain a secondary loop water temperature-flow rate timing correlation feature; the high-temperature and high-pressure water-secondary loop water local timing feature interactive analysis module 170 is used to perform interactive fusion analysis between local timing features on the high-temperature and high-pressure water temperature-flow rate timing correlation feature and the secondary loop water temperature-flow rate timing correlation feature to obtain an interactive fusion feature; and the secondary loop water flow rate real-time control module 180 is used to determine, based on the interactive fusion feature, whether the flow rate value of the secondary loop water at the current time point should increase, decrease, or remain unchanged.

[0029] Specifically, in the technical solution of the present application, first, the flow rate and temperature values ​​of the high-temperature, high-pressure water at multiple predetermined time points within a predetermined time period are obtained, and the flow rate and temperature values ​​of the secondary circuit water at these multiple predetermined time points are also obtained. Next, considering that the flow rate and temperature values ​​of the high-temperature, high-pressure water, as well as the flow rate and temperature values ​​of the secondary circuit water, not only exhibit a temporal dynamic change pattern in the time dimension, but also have a temporal synergistic relationship between these data, especially the temporal relationship between the flow rate and temperature values ​​of the high-temperature, high-pressure water, and the flow rate and temperature values ​​of the secondary circuit water, this relationship is particularly important for heat exchange and efficiency improvement. Therefore, in the technical solution of the present application, it is necessary to arrange the flow rate and temperature values ​​of the high-temperature, high-pressure water at multiple predetermined time points within the predetermined time period according to the time dimension to obtain a high-temperature, high-pressure water temperature time series input vector and a high-temperature, high-pressure water flow rate time series input vector, thereby integrating the temporal distribution information of the flow rate and temperature values ​​of the high-temperature, high-pressure water. In addition, the flow rate values ​​and temperature values ​​of the secondary loop water at the multiple predetermined time points are arranged according to the time dimension to obtain a secondary loop water temperature timing input vector and a secondary loop water flow rate timing input vector, so as to respectively integrate the timing distribution information of the flow rate value and temperature value of the secondary loop water.

[0030] Furthermore, considering that the flow rate and temperature of the high-temperature, high-pressure water have a time-series synergistic correlation feature, in order to capture and characterize the correlation degree and characteristic information between the temperature and flow rate of the high-temperature, high-pressure water, in the technical solution of the present application, the high-temperature, high-pressure water temperature time-series input vector and the high-temperature, high-pressure water flow rate time-series input vector are further associated and encoded into a high-temperature, high-pressure water temperature-flow rate time-series correlation matrix, and then the high-temperature, high-pressure water temperature-flow rate time-series correlation matrix is ​​subjected to feature mining in a high-temperature, high-pressure water temperature-flow rate time-series correlation feature extractor based on a convolutional neural network model to extract the time-series correlation feature distribution information between the temperature and flow rate of the high-temperature, high-pressure water, thereby obtaining a high-temperature, high-pressure water temperature-flow rate time-series correlation feature map. In this way, the time-series synergistic correlation relationship between the temperature and flow rate of the high-temperature, high-pressure water can be captured, which is helpful for the subsequent heat exchange state time-series analysis and flow rate control.

[0031] Correspondingly, the high-temperature and high-pressure water data timing association analysis module 150 is used to: after the high-temperature and high-pressure water temperature timing input vector and the high-temperature and high-pressure water flow rate timing input vector are associated and encoded into a high-temperature and high-pressure water temperature-flow rate timing association matrix, the high-temperature and high-pressure water temperature-flow rate timing association matrix is ​​passed through a high-temperature and high-pressure water temperature-flow rate timing association feature extractor based on a convolutional neural network model to obtain a high-temperature and high-pressure water temperature-flow rate timing association feature graph as the high-temperature and high-pressure water temperature-flow rate timing association feature.

[0032] It's worth noting that the Convolutional Neural Network (CNN) is a deep learning model primarily designed for processing data with grid structures, such as images and time series data. In the high-temperature, high-pressure water data time series correlation analysis module, a high-temperature, high-pressure water temperature-flow velocity time series correlation feature extractor based on a convolutional neural network model is used to extract features from the high-temperature, high-pressure water temperature-flow velocity time series correlation matrix. These features capture the temporal correlation between the temperature and flow velocity of high-temperature, high-pressure water, facilitating the understanding and analysis of its behavioral patterns, trends, and anomalies. The convolutional neural network model automatically learns the spatial and temporal features in the high-temperature, high-pressure water temperature-flow velocity time series correlation matrix. Convolutional layers effectively capture local patterns in time series data, while pooling layers reduce data dimensionality while retaining key features. By combining multiple convolutional and pooling layers, abstract features can be gradually extracted from the high-temperature, high-pressure water temperature-flow velocity time series correlation matrix. These features can be used for subsequent analysis, prediction, or decision-making tasks. In summary, the high-temperature and high-pressure water temperature-flow rate time series correlation feature extractor based on the convolutional neural network model can automatically learn the correlation features in the high-temperature and high-pressure water time series data, providing useful information for further analysis and application.

[0033] Similarly, the temperature and flow rate of the secondary loop water also have a temporal synergistic correlation relationship. In order to capture the degree of correlation and characteristics between the temperature and flow rate of the secondary loop water, in the technical solution of the present application, the secondary loop water temperature temporal input vector and the secondary loop water flow rate temporal input vector are further correlated and encoded into a secondary loop water temperature-flow rate temporal correlation matrix. The temperature-flow rate temporal correlation matrix is ​​then subjected to feature mining in a secondary loop water temperature-flow rate temporal correlation feature extractor based on a convolutional neural network model to extract the temporal synergistic correlation feature information between the temperature and flow rate of the secondary loop water, thereby obtaining a secondary loop water temperature-flow rate temporal correlation feature diagram. In this way, the relationship between the temperature and flow rate of the secondary loop water can be better understood and utilized, thereby optimizing the operation of the heat recovery and reuse system.

[0034] Correspondingly, the secondary loop water data timing association analysis module 160 is used to: after associating and encoding the secondary loop water temperature timing input vector and the secondary loop water flow rate timing input vector into a secondary loop water temperature-flow rate timing association matrix, the temperature-flow rate timing association matrix is ​​passed through a secondary loop water temperature-flow rate timing association feature extractor based on a convolutional neural network model to obtain a secondary loop water temperature-flow rate timing association feature diagram as the secondary loop water temperature-flow rate timing association feature.

[0035] Considering the complex correlation between the temperature and flow rate of high-temperature, high-pressure water and the secondary loop water in a heat recovery and reuse system, the degree of correlation between the temperature and flow rate of the high-temperature, high-pressure water and the secondary loop water varies within different local time periods. Therefore, to more fully analyze the heat exchange state within a nuclear power plant's heat recovery and reuse system, localized temporal interaction encoding of the heat exchange state between the high-temperature, high-pressure water and the secondary loop water is necessary. Based on this, in the technical solution of the present application, the high-temperature and high-pressure water temperature-flow velocity time series correlation feature map and the secondary circuit water temperature-flow velocity time series correlation feature map are further reconstructed in feature dimensions to obtain a sequence of high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors and a sequence of secondary circuit water temperature-flow velocity local time series correlation feature vectors, and then a granular interaction module between local features is used to process the sequence of high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors and the sequence of secondary circuit water temperature-flow velocity local time series correlation feature vectors to obtain an interactive fusion feature vector. It should be understood that by using the granular interaction module between local features to process the feature vector sequences of both, the temperature-flow velocity local time series correlation feature vector sequences of high-temperature and high-pressure water and secondary circuit water can be interacted and fused, so as to better understand and utilize the time series correlation characteristics between high-temperature and high-pressure water and secondary circuit water, and obtain a more global and fine-grained heat exchange interactive correlation time series feature representation.

[0036] Correspondingly, as shown in Figure 2, the high-temperature and high-pressure water-secondary loop water local time series feature interaction analysis module 170 includes: a dimension reconstruction unit 171, which is used to reconstruct the feature dimension of the high-temperature and high-pressure water temperature-flow velocity time series correlation feature diagram and the secondary loop water temperature-flow velocity time series correlation feature diagram to obtain a sequence of high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors and a sequence of secondary loop water temperature-flow velocity local time series correlation feature vectors; and a sequence interaction fusion analysis unit 172, which is used to use the local feature granularity interaction module to process the sequence of high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors and the sequence of secondary loop water temperature-flow velocity local time series correlation feature vectors to obtain an interaction fusion feature vector as the interaction fusion feature.

[0037] It should be understood that the HTHP water-secondary loop water local temporal feature interaction analysis module includes two units: a dimension reconstruction unit 171 and a sequence interaction fusion analysis unit 172. The dimensional reconstruction unit 171 reconstructs the feature dimensions of the HTHP water temperature-flow velocity temporal correlation feature graph and the secondary loop water temperature-flow velocity temporal correlation feature graph. The sequence interaction fusion analysis unit 172 uses a granular interaction module between local features to process the sequence of HTHP water temperature-flow velocity local temporal correlation feature vectors and the sequence of secondary loop water temperature-flow velocity local temporal correlation feature vectors to obtain an interaction fusion feature vector. Through the granular interaction module, the local temporal correlation feature vectors of HTHP water and secondary loop water are interacted to capture the correlation information between them. This results in an interaction fusion feature vector that contains the temporal correlation features between HTHP water and secondary loop water. In summary, the dimensionality reconstruction unit converts the feature map into a sequence of local temporal correlation feature vectors, while the sequence interaction fusion analysis unit processes the feature vector sequence through granular interaction modules to generate interactive fusion feature vectors. The purpose of these modules is to extract and fuse the temporal correlation features between high-temperature, high-pressure water and secondary circuit water to support subsequent analysis and application.

[0038] Specifically, the sequence interaction fusion analysis unit 172 includes: an attention enhancement subunit, which is used to perform attention enhancement based on the correlation between the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series association feature vectors and the sequence of the secondary circuit water temperature-flow velocity local time series association feature vectors to obtain a sequence of attention-enhanced high-temperature and high-pressure water temperature-flow velocity local time series association feature vectors and a sequence of attention-enhanced secondary circuit water temperature-flow velocity local time series association feature vectors; a first fusion subunit, which is used to fuse the feature vectors of the corresponding positions in the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series association feature vectors and the sequence of the attention-enhanced high-temperature and high-pressure water temperature-flow velocity local time series association feature vectors to obtain a sequence of high-temperature and high-pressure water temperature-flow velocity local fusion feature vectors, and fuse the secondary circuit water temperature-flow velocity local time series association feature vectors. A sequence of feature vectors and the feature vectors at corresponding positions in the sequence of the attention-enhanced secondary loop water temperature-flow velocity local time series correlation feature vectors to obtain a sequence of secondary loop water temperature-flow velocity local fusion feature vectors; a maximum pooling subunit, used to perform maximum pooling processing on the sequence of high-temperature and high-pressure water temperature-flow velocity local fusion feature vectors to obtain a high-temperature and high-pressure water temperature-flow velocity local fusion maximum pooling feature vector, and perform maximum pooling processing on the sequence of secondary loop water temperature-flow velocity local fusion feature vectors to obtain a secondary loop water temperature-flow velocity local fusion maximum pooling feature vector; and a second fusion subunit, used to fuse the high-temperature and high-pressure water temperature-flow velocity local fusion maximum pooling feature vector and the secondary loop water temperature-flow velocity local fusion maximum pooling feature vector to obtain the interactive fusion feature vector.

[0039] Subsequently, the interactive fusion feature vector is passed through a classifier to obtain a classification result, which is used to indicate whether the flow rate value of the secondary loop water at the current time point should increase, decrease, or remain unchanged. In other words, the local temporal semantic interactive fusion feature information between the temporal synergistic correlation features of the flow rate and temperature of the high-temperature and high-pressure water and the temporal synergistic correlation features of the flow rate and temperature of the secondary loop water is used for classification processing, thereby automatically performing real-time control of the flow rate value of the secondary loop water. In this way, the heat exchange rate and heat recovery efficiency of the heat recovery and reuse system in the nuclear power plant can be improved, the generation of nuclear waste can be reduced, and the impact on the environment can be reduced.

[0040] Accordingly, as shown in Figure 3, the secondary loop water flow rate real-time control module 180 includes: a feature optimization unit 181, which is used to perform feature optimization on the interactive fusion feature vector to obtain an optimized interactive fusion feature vector; and a secondary loop water flow rate control unit 182, which is used to pass the optimized interactive fusion feature vector through a classifier to obtain a classification result, and the classification result is used to indicate whether the flow rate value of the secondary loop water at the current time point should increase, decrease or remain unchanged.

[0041] It should be understood that the secondary loop water flow rate real-time control module includes two units: a feature optimization unit 181 and a secondary loop water flow rate control unit 182. The feature optimization unit 181 performs feature optimization on the interactive fusion feature vector to obtain an optimized interactive fusion feature vector. Specifically, the feature optimization unit can further process the interactive fusion feature vector, such as removing noise, adjusting feature weights, or enhancing key features. This can improve the accuracy and reliability of the features, thereby better representing the temporal correlation characteristics between high-temperature, high-pressure water and the secondary loop water. The secondary loop water flow rate control unit 182 passes the optimized interactive fusion feature vector through a classifier to obtain a classification result, indicating whether the secondary loop water flow rate value at the current time point should increase, decrease, or remain unchanged. Specifically, the secondary loop water flow rate control unit uses the optimized interactive fusion feature vector as input and classifies it through a classifier. The classifier can be a machine learning model, such as a support vector machine (SVM) or random forest, or other applicable classification algorithms. The classification results represent control recommendations for the secondary loop water flow rate, indicating whether the secondary loop water flow rate needs to be adjusted to meet specific requirements or conditions. In summary, the feature optimization unit is used to optimize the interactive fusion feature vector to improve feature accuracy and reliability. The secondary loop water flow rate control unit uses the optimized feature vector through a classifier to obtain control recommendations for the secondary loop water flow rate at the current time point. The purpose of these modules is to achieve real-time control of the secondary loop water flow rate to meet system requirements and optimize operational performance.

[0042] As shown in Figure 4, the feature optimization unit 181 includes: a sequence optimization subunit 1811, which is used to optimize and fuse the sequence of the high-temperature and high-pressure water temperature-flow rate local time series correlation feature vectors and the sequence of the secondary loop water temperature-flow rate local time series correlation feature vectors to obtain an optimized feature vector; and an optimized feature fusion subunit 1812, which is used to fuse the optimized feature vector with the interactive fusion feature vector to obtain the optimized interactive fusion feature vector.

[0043] In particular, in the above technical solution, in the above technical solution, the sequence of the local time series correlation feature vectors of the temperature and flow rate of high temperature and high pressure water and the sequence of the local time series correlation feature vectors of the temperature and flow rate of the secondary circuit are respectively used to express the time series correlation features of the flow rate value and the temperature value of the high temperature and high pressure water in the local time domain space and the time series correlation features of the flow rate value and the temperature value of the secondary circuit water in the local time domain space. Therefore, when the sequence of the local time series correlation feature vectors of the temperature and flow rate of high temperature and high pressure water and the sequence of the local time series correlation feature vectors of the temperature and flow rate of the secondary circuit are passed through the granularity interaction module between local features, the local time series correlation feature vectors of the temperature and flow rate of high temperature and high pressure water are taken into consideration. The difference in the distribution of source domain data representation values ​​between the sequence of linked feature vectors and the sequence of the secondary loop water temperature-flow velocity local time series associated feature vectors may lead to the sparsity of semantic interactions between feature sequences, thereby affecting the expression effect of the interactive fusion feature vector. Therefore, in order to improve the granularity-by-granularity interactive fusion expression effect between the local features of the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series associated feature vectors and the sequence of the secondary loop water temperature-flow velocity local time series associated feature vectors, the present application preferably optimizes the fusion of the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series associated feature vectors and the sequence of the secondary loop water temperature-flow velocity local time series associated feature vectors.

[0044] Accordingly, the sequence optimization subunit 1811 is further configured to optimize and fuse the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors and the sequence of the secondary circuit water temperature-flow velocity local time series correlation feature vectors using the following optimization formula to obtain the optimized feature vector; wherein the optimization formula is: ;in, and are the first eigenvectors obtained by sequentially concatenating the local temporal correlation eigenvectors of the high-temperature and high-pressure water temperature and velocity. The second eigenvector obtained by cascading the sequence of the secondary circuit water temperature-flow rate local time series correlation eigenvector The eigenvalues ​​of and Respectively represent the square of the first norm of the eigenvector and the square root of the second norm of the eigenvector, the first eigenvector and the second eigenvector have the same length , represents vector addition, represents vector subtraction, and is the weight hyperparameter, represents the eigenvalue of the optimized eigenvector.

[0045] Here, the above-mentioned optimization fusion is based on the correspondence under the eigenvalue granularity to perform a serialized fusion representation of the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors and the sequence of the secondary loop water temperature-flow velocity local time series correlation feature vectors to perform a foreground manifold and a background manifold division based on the vector scale, so as to stack the dynamic eigenvalue channelization association of the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors and the sequence of the secondary loop water temperature-flow velocity local time series correlation feature vectors under the feature corresponding channel supermanifold aggregation mechanism, thereby marking the high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vectors. The characteristic semantic information of the change between the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vector and the sequence of the secondary circuit water temperature-flow velocity local time series correlation feature vector is realized according to the variability of the semantic content between the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vector and the sequence of the secondary circuit water temperature-flow velocity local time series correlation feature vector in different calculation dimensions, so as to enhance the interactive fusion effect of the sequence of the high-temperature and high-pressure water temperature-flow velocity local time series correlation feature vector and the sequence of the secondary circuit water temperature-flow velocity local time series correlation feature vector. In this way, the optimized feature vector By fusing the interactive fusion feature vector with the aforementioned vector, the accuracy of the classification results obtained by the classifier using the interactive fusion feature vector can be improved. This allows for real-time adaptive adjustment of the secondary loop water flow rate based on the changing states of the high-temperature, high-pressure water and secondary loop water in the nuclear power plant's heat recovery and reuse system, thereby improving the heat exchange rate and heat recovery efficiency of the nuclear power plant's heat recovery and reuse system, reducing the generation of nuclear waste, and minimizing the impact on the environment.

[0046] Furthermore, the secondary loop water flow rate control unit 182 includes: a fully connected encoding subunit, used to use the fully connected layer of the classifier to fully connect encode the optimized interactive fusion feature vector to obtain a coded classification feature vector; and a classification subunit, used to input the coded classification feature vector into the Softmax classification function of the classifier to obtain the classification result.

[0047] It's easy to understand that a classifier uses given categories and known training data to learn classification rules and classifiers, and then classify (or predict) unknown data. Logistic regression and SVM are commonly used to solve binary classification problems. For multi-class classification, logistic regression or SVM can also be used, but multiple binary classifications are required to form a multi-class classification. However, this approach is error-prone and inefficient. A commonly used multi-classification method is the Softmax classification function.

[0048] In summary, a heat recovery and reuse system in a nuclear power plant based on the embodiment of the present application is illustrated, which can monitor and adjust the flow rate of the secondary loop water in real time and improve the working condition of the heat exchanger.

[0049] As described above, the heat energy recovery and reuse system in a nuclear power plant based on the embodiment of the present application can be implemented in various terminal devices, such as a server having a heat energy recovery and reuse algorithm in a nuclear power plant based on the embodiment of the present application. In one example, the heat energy recovery and reuse system in a nuclear power plant based on the embodiment of the present application can be integrated into a terminal device as a software module and / or a hardware module. For example, the heat energy recovery and reuse system in a nuclear power plant based on the embodiment of the present application can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the heat energy recovery and reuse system in a nuclear power plant based on the embodiment of the present application can also be one of the many hardware modules of the terminal device.

[0050] Alternatively, in another example, the heat energy recovery and reuse system in the nuclear power plant based on the embodiment of the present application and the terminal device may also be separate devices, and the heat energy recovery and reuse system in the nuclear power plant may be connected to the terminal device through a wired and / or wireless network, and transmit interactive information in accordance with an agreed data format.

[0051] Figure 5 is a flow chart of a method for recovering and reusing heat energy in a nuclear power plant according to an embodiment of the present application. Figure 6 is a schematic diagram of a system architecture of a method for recovering and reusing heat energy in a nuclear power plant according to an embodiment of the present application. As shown in Figures 5 and 6, a method for recovering and reusing heat energy in a nuclear power plant according to an embodiment of the present application includes: S110, obtaining the flow rate value and temperature value of high-temperature and high-pressure water at a plurality of predetermined time points within a predetermined time period; S120, obtaining the flow rate value and temperature value of the secondary loop water at the plurality of predetermined time points; S130, arranging the flow rate value and temperature value of the high-temperature and high-pressure water at a plurality of predetermined time points within a predetermined time period according to the time dimension to obtain a high-temperature and high-pressure water temperature timing input vector and a high-temperature and high-pressure water flow rate timing input vector; S140, arranging the flow rate value and temperature value of the secondary loop water at the plurality of predetermined time points according to the time dimension to obtain a secondary loop water temperature timing input vector and a secondary loop water flow rate timing input vector. vector; S150, performing a timing correlation analysis on the high-temperature and high-pressure water temperature timing input vector and the high-temperature and high-pressure water flow rate timing input vector to obtain a high-temperature and high-pressure water temperature-flow rate timing correlation feature; S160, performing a timing correlation analysis on the secondary loop water temperature timing input vector and the secondary loop water flow rate timing input vector to obtain a secondary loop water temperature-flow rate timing correlation feature; S170, performing an interactive fusion analysis between local timing features on the high-temperature and high-pressure water temperature-flow rate timing correlation feature and the secondary loop water temperature-flow rate timing correlation feature to obtain an interactive fusion feature; and, S180, based on the interactive fusion feature, determining whether the flow rate value of the secondary loop water at the current time point should increase, decrease, or remain unchanged.

[0052] Here, those skilled in the art can understand that the specific operations of each step in the above-mentioned heat energy recovery and reuse method in a nuclear power plant have been introduced in detail in the description of the heat energy recovery and reuse system in a nuclear power plant with reference to Figures 1 to 4 above, and therefore, its repeated description will be omitted.

[0053] FIG7 is a diagram illustrating an application scenario of a heat recovery and reuse system in a nuclear power plant according to an embodiment of the present application. As shown in FIG7 , in this application scenario, first, the flow rate and temperature values ​​of high-temperature, high-pressure water at multiple predetermined time points within a predetermined time period (e.g., D1 as shown in FIG7 ) and the flow rate and temperature values ​​of secondary loop water at the multiple predetermined time points (e.g., D2 as shown in FIG7 ) are obtained. Then, the flow rate and temperature values ​​at the multiple predetermined time points and the flow rate and temperature values ​​of the secondary loop water at the multiple predetermined time points are input into a server (e.g., S as shown in FIG7 ) that is deployed with a heat recovery and reuse algorithm in a nuclear power plant. The server can use the heat recovery and reuse algorithm in the nuclear power plant to process the flow rate and temperature values ​​at the multiple predetermined time points and the flow rate and temperature values ​​of the secondary loop water at the multiple predetermined time points to obtain a classification result indicating whether the flow rate value of the secondary loop water at the current time point should be increased, decreased, or maintained unchanged.

[0054] This application uses specific terms to describe the embodiments of this application. For example, "first / second embodiment", "one embodiment", and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or multiple times in different places in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0055] In addition, it will be understood by those skilled in the art that various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0056] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or highly formal sense, unless explicitly defined as such herein.

[0057] The above is an explanation of the present application and should not be considered as limiting thereof. Although several exemplary embodiments of the present application are described, it will be readily understood by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present application. Therefore, all such modifications are intended to be included within the scope of the present application as defined by the claims. It should be understood that the above is an explanation of the present application and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present application is defined by the claims and their equivalents.

Claims

1. A thermal energy recovery and reuse system in a nuclear power plant, characterized in that, Comprising: A generator; A heat exchanger for transferring heat to water in a secondary circuit to generate steam; A steam generator for transferring the steam to a steam turbine; A steam turbine for driving the generator to rotate and generate electric energy; and A condenser for cooling the steam to return the steam to the secondary circuit.

2. The thermal energy recovery and reuse system in a nuclear power plant according to claim 1, characterized in that The heat exchanger includes: A high-temperature and high-pressure water data acquisition module for obtaining the flow velocity values and temperature values of high-temperature and high-pressure water at multiple predetermined time points within a predetermined time period; A secondary circuit water data acquisition module for obtaining the flow velocity values and temperature values of the secondary circuit water at the multiple predetermined time points; A high-temperature and high-pressure water data time series arrangement module for arranging the flow velocity values and temperature values of the high-temperature and high-pressure water at multiple predetermined time points within a predetermined time period respectively in the time dimension to obtain a high-temperature and high-pressure water temperature time series input vector and a high-temperature and high-pressure water flow velocity time series input vector; A secondary circuit water data time series arrangement module for arranging the flow velocity values and temperature values of the secondary circuit water at the multiple predetermined time points respectively in the time dimension to obtain a secondary circuit water temperature time series input vector and a secondary circuit water flow velocity time series input vector; A high-temperature and high-pressure water data time series correlation analysis module for performing time series correlation analysis on the high-temperature and high-pressure water temperature time series input vector and the high-temperature and high-pressure water flow velocity time series input vector to obtain a high-temperature and high-pressure water temperature-flow velocity time series correlation feature; A secondary circuit water data time series correlation analysis module for performing time series correlation analysis on the secondary circuit water temperature time series input vector and the secondary circuit water flow velocity time series input vector to obtain a secondary circuit water temperature-flow velocity time series correlation feature; A high-temperature and high-pressure water-secondary circuit water local time series feature interaction analysis module for performing local time series feature interaction fusion analysis on the high-temperature and high-pressure water temperature-flow velocity time series correlation feature and the secondary circuit water temperature-flow velocity time series correlation feature to obtain an interaction fusion feature; and A secondary circuit water flow velocity real-time control module for determining whether the flow velocity value of the secondary circuit water at the current time point should be increased, decreased or remain unchanged based on the interaction fusion feature.

3. The thermal energy recovery and reuse system in a nuclear power plant according to claim 2, characterized in that, The high-temperature and high-pressure water data time series correlation analysis module is used for: After correlatively encoding the high-temperature and high-pressure water temperature time series input vector and the high-temperature and high-pressure water flow velocity time series input vector into a high-temperature and high-pressure water temperature-flow velocity time series correlation matrix, passing the high-temperature and high-pressure water temperature-flow velocity time series correlation matrix through a high-temperature and high-pressure water temperature-flow velocity time series correlation feature extractor based on a convolutional neural network model to obtain a high-temperature and high-pressure water temperature-flow velocity time series correlation feature map as the high-temperature and high-pressure water temperature-flow velocity time series correlation feature.

4. The thermal energy recovery and reuse system in a nuclear power plant according to claim 3, characterized in that, The secondary circuit water data time series correlation analysis module is used for: After correlatively encoding the secondary loop water temperature time - series input vector and the secondary loop water flow velocity time - series input vector into a secondary loop water temperature - flow velocity time - series correlation matrix, the temperature - flow velocity time - series correlation matrix is passed through a secondary loop water temperature - flow velocity time - series correlation feature extractor based on a convolutional neural network model to obtain a secondary loop water temperature - flow velocity time - series correlation feature map as the secondary loop water temperature - flow velocity time - series correlation feature.

5. The thermal energy recovery and reuse system in a nuclear power plant according to claim 4, wherein, The high - temperature and high - pressure water - secondary loop water local time - series feature interaction analysis module includes: A dimension reconstruction unit for reconstructing the feature dimensions of the high - temperature and high - pressure water temperature - flow velocity time - series correlation feature map and the secondary loop water temperature - flow velocity time - series correlation feature map to obtain a sequence of high - temperature and high - pressure water temperature - flow velocity local time - series correlation feature vectors and a sequence of secondary loop water temperature - flow velocity local time - series correlation feature vectors; and A sequence interaction and fusion analysis unit for using a per - granularity interaction module between local features to process the sequence of high - temperature and high - pressure water temperature - flow velocity local time - series correlation feature vectors and the sequence of secondary loop water temperature - flow velocity local time - series correlation feature vectors to obtain an interaction and fusion feature vector as the interaction and fusion feature.

6. The thermal energy recovery and reuse system in a nuclear power plant according to claim 5, characterized in that, The sequence interaction and fusion analysis unit includes: An attention enhancement sub - unit for enhancing attention based on the correlation between the sequence of high - temperature and high - pressure water temperature - flow velocity local time - series correlation feature vectors and the sequence of secondary loop water temperature - flow velocity local time - series correlation feature vectors to obtain a sequence of attention - enhanced high - temperature and high - pressure water temperature - flow velocity local time - series correlation feature vectors and a sequence of attention - enhanced secondary loop water temperature - flow velocity local time - series correlation feature vectors; A first fusion sub - unit for fusing the feature vectors at corresponding positions in the sequence of high - temperature and high - pressure water temperature - flow velocity local time - series correlation feature vectors and the sequence of attention - enhanced high - temperature and high - pressure water temperature - flow velocity local time - series correlation feature vectors to obtain a sequence of high - temperature and high - pressure water temperature - flow velocity local fusion feature vectors, and fusing the feature vectors at corresponding positions in the sequence of secondary loop water temperature - flow velocity local time - series correlation feature vectors and the sequence of attention - enhanced secondary loop water temperature - flow velocity local time - series correlation feature vectors to obtain a sequence of secondary loop water temperature - flow velocity local fusion feature vectors; A maximum pooling sub - unit for performing maximum pooling processing on the sequence of high - temperature and high - pressure water temperature - flow velocity local fusion feature vectors to obtain a high - temperature and high - pressure water temperature - flow velocity local fusion maximum pooling feature vector, and performing maximum pooling processing on the sequence of secondary loop water temperature - flow velocity local fusion feature vectors to obtain a secondary loop water temperature - flow velocity local fusion maximum pooling feature vector; and A second fusion sub - unit for fusing the high - temperature and high - pressure water temperature - flow velocity local fusion maximum pooling feature vector and the secondary loop water temperature - flow velocity local fusion maximum pooling feature vector to obtain the interaction and fusion feature vector.

7. The thermal energy recovery and reuse system in a nuclear power plant according to claim 6, wherein The secondary loop water flow velocity real - time control module includes: A feature optimization unit for optimizing the interactive fusion feature vector to obtain an optimized interactive fusion feature vector; and A secondary loop water flow rate control unit for passing the optimized interactive fusion feature vector through a classifier to obtain a classification result, where the classification result is used to indicate whether the water flow rate value of the secondary loop at the current time point should increase, decrease, or remain unchanged.

8. The thermal energy recovery and reuse system in a nuclear power plant according to claim 7, characterized in that, The feature optimization unit includes: A sequence optimization subunit for optimizing and fusing the sequences of the high-temperature and high-pressure water temperature-flow rate local temporal correlation feature vectors and the secondary loop water temperature-flow rate local temporal correlation feature vectors to obtain an optimized feature vector; and An optimized feature fusion subunit for fusing the optimized feature vector with the interactive fusion feature vector to obtain the optimized interactive fusion feature vector.

9. The thermal energy recovery and reuse system in a nuclear power plant according to claim 8, wherein The secondary loop water flow rate control unit includes: A fully connected encoding subunit for performing fully connected encoding on the optimized interactive fusion feature vector using the fully connected layer of the classifier to obtain an encoded classification feature vector; and A classification subunit for inputting the encoded classification feature vector into the Softmax classification function of the classifier to obtain the classification result.

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