Intelligent control system for heat energy recovery of circulating cooling water in nuclear power station

Abstract

The invention discloses an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power station. The system comprises a heat energy recovery device used for transferring heat energy of circulating cooling water to other media through a heat exchanger; the heat energy recovery device is used for receiving circulating cooling water, the cooling water pump is used for conveying the circulating cooling water to the position between the heat energy recovery device and a reactor, and the controller is used for adjusting working parameters of the heat energy recovery device. Therefore, the water consumption and the discharge amount of the nuclear power station can be effectively reduced, the discharge of greenhouse gases is reduced, and the reliability and the service life of the nuclear power station are improved.

Description

Technical Field

[0001] This application relates to the field of heat energy recovery, and more specifically, to an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant. Background Art

[0002] A nuclear power plant is a power generation method that uses the energy released by nuclear fission reactions and converts it into electrical energy. During the operation of a nuclear power plant, the power generation power device needs to be cooled to maintain a safe operating temperature. However, in traditional nuclear power plants, after the cooling water or coolant is cooled, the cooling water or coolant with a relatively high temperature is usually directly discharged to the outside, resulting in the waste of the heat energy carried therein. Therefore, it is necessary to recover the heat energy of the circulating cooling water in the nuclear power plant. However, the heat energy recovery control system in traditional nuclear power plants lacks intelligence and optimized regulation, and cannot make good use of the heat energy in the cooling water. This results in a low thermal efficiency of the nuclear power plant and cannot achieve better economy.

[0003] Therefore, an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant is desired. Summary of the Invention

[0004] In view of this, this application proposes an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant, which can effectively reduce the water consumption and emissions of the nuclear power plant, reduce greenhouse gas emissions, and improve the reliability and lifespan of the nuclear power plant.

[0005] According to one aspect of this application, there is provided an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant, including a heat energy recovery device for transferring the heat energy of the circulating cooling water to other media through a heat exchanger; a cooling water pump for conveying the circulating cooling water between the heat energy recovery device and the reactor, wherein the intelligent control system for heat energy recovery of circulating cooling water in the nuclear power plant further includes a controller for adjusting the operating parameters of the heat energy recovery device; wherein, the controller includes: a data acquisition module for acquiring the thermal load value, the temperature value and the flow rate value of the reactor at multiple predetermined time points within a predetermined time period; a data parameter time series arrangement module for arranging the thermal load value, the temperature value and the flow rate value of the reactor at the multiple predetermined time points into a thermal load time series input vector, a cooling water temperature time series input vector and a cooling water flow rate time series input vector respectively according to the time dimension; a cooling water heat exchange time series distribution module for calculating the cooling water heat exchange time series input vector by multiplying the cooling water temperature time series input vector and the cooling water flow rate time series input vector at corresponding positions; A data parameter local timing analysis module, configured to perform local timing analysis on the heat load timing input vector and the cooling water heat exchange timing input vector respectively to obtain a sequence of heat load local timing feature vectors and a sequence of cooling water heat exchange local timing feature vectors; A heat load - heat exchange timing interaction correlation encoding module, configured to perform parameter timing feature interaction correlation analysis on the sequence of heat load local timing feature vectors and the sequence of cooling water heat exchange local timing feature vectors to obtain heat load - heat exchange timing interaction features; and A cooling water flow control module, configured to determine whether the flow value of the cooling water at the current time point should be increased or decreased based on the heat load - heat exchange timing interaction features.

[0006] According to an embodiment of the present application, the system includes a heat energy recovery device, configured to transfer the heat energy of the circulating cooling water to other media through a heat exchanger; a cooling water pump, configured to transport the circulating cooling water between the heat energy recovery device and the reactor. Wherein, the intelligent control system for heat energy recovery of the circulating cooling water in the nuclear power plant further includes a controller, and the controller is configured to adjust the operating parameters of the heat energy recovery device. In this way, the water consumption and emissions of the nuclear power plant can be effectively reduced, greenhouse gas emissions can be reduced, and the reliability and lifespan of the nuclear power plant can be improved.

[0007] Other features and aspects of the present application will become clear from the following detailed description of the exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings included in and constituting a part of the specification, together with the specification, illustrate the exemplary embodiments, features, and aspects of the present application and are used to explain the principles of the present application.

[0009] Figure 1 A block diagram showing an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application.

[0010] Figure 2 A block diagram showing the controller in the intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application.

[0011] Figure 3 A block diagram showing the data parameter local timing analysis module in the intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application.

[0012] Figure 4 A flowchart showing an intelligent control method for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application.

[0013] Figure 5Shows a schematic architecture diagram of sub-step S130 in the intelligent control method for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application.

[0014] Figure 6 Shows an application scenario diagram of the intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application. Detailed implementation manners

[0015] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts also belong to the scope of protection of the present application.

[0016] As shown in the present application and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Hereinafter, various exemplary embodiments, features, and aspects of the present application will be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements with the same or similar functions. Although various aspects of the embodiments are shown in the drawings, unless otherwise specified, the drawings do not have to be drawn to scale.

[0018] In addition, for a better description of the present application, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present application can also be implemented without some specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail to highlight the gist of the present application.

[0019] To address the above technical problems, in the technical solution of this application, an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant is proposed. It is a technology that utilizes the heat energy of circulating cooling water to improve the thermal efficiency and economy of a nuclear power plant. The system consists of a heat energy recovery device, an intelligent controller, and a cooling water pump. The heat energy recovery device transfers the heat energy of the circulating cooling water to other media, such as air, water, or steam, through a heat exchanger, thereby realizing the utilization of heat energy. The intelligent controller automatically adjusts the working parameters of the heat energy recovery device, such as heat transfer amount, flow rate, and temperature, according to the operating state of the nuclear power plant to ensure the safe and stable operation of the nuclear power plant. The cooling water pump is responsible for transporting the circulating cooling water between the heat energy recovery device and the reactor to maintain the pressure and flow rate of the circulating cooling water. This system can effectively reduce the water consumption and emissions of the nuclear power plant, reduce greenhouse gas emissions, and improve the reliability and lifespan of the nuclear power plant.

[0020] Figure 1 FIG. shows a block diagram of an intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application. As Figure 1 shown, the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application includes: a heat energy recovery device 110 for transferring the heat energy of the circulating cooling water to other media through a heat exchanger; a cooling water pump 120 for transporting the circulating cooling water between the heat energy recovery device 110 and the reactor. Among them, the intelligent control system 100 for heat energy recovery of circulating cooling water in the nuclear power plant further includes a controller 130, and the controller 130 is used to adjust the working parameters of the heat energy recovery device 110.

[0021] Correspondingly, in the intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant, the intelligent controller can monitor and analyze the operating state of the nuclear power plant in real time, including the thermal load of the reactor, the temperature and flow rate of the cooling water, etc. Based on these data, the intelligent controller can judge the current working state and make corresponding adjustments according to the preset control strategy.

[0022] Specifically, when the load of the nuclear power plant is relatively low, the intelligent controller can reduce the heat exchange amount and flow rate of the heat recovery device to avoid overheating and energy waste. When the load is relatively high, the intelligent controller can increase the heat exchange amount and flow rate to meet the heat demand of the nuclear power plant. By precisely controlling and adjusting the operating parameters of the heat recovery device, the intelligent controller can maximize the efficiency of the system and ensure that the system operates within a safe and stable range. Moreover, the intelligent controller can also automatically adjust the temperature control of the heat recovery device according to different operating modes to adapt to different working conditions. For example, when the cooling water temperature is relatively low, the intelligent controller can increase the temperature of the heat recovery device to improve the heat exchange efficiency. When the cooling water temperature is relatively high, the intelligent controller can reduce the temperature of the heat recovery device to prevent overheating and equipment damage.

[0023] Figure 2 A block diagram schematic of the controller 130 in the intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application is shown. As Figure 2 shown, for the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application, the controller 130 includes: a data acquisition module 131, configured to obtain the thermal load value of the reactor, the temperature value and the flow rate value of the cooling water at a plurality of predetermined time points within a predetermined time period; a data parameter time series arrangement module 132, configured to arrange the thermal load value of the reactor, the temperature value and the flow rate value of the cooling water at the plurality of predetermined time points in the time dimension respectively into a thermal load time series input vector, a cooling water temperature time series input vector and a cooling water flow rate time series input vector; a cooling water heat exchange time series distribution module 133, configured to calculate the product of the cooling water temperature time series input vector and the cooling water flow rate time series input vector at each position point to obtain a cooling water heat exchange time series input vector; a data parameter local time series analysis module 134, configured to perform local time series analysis on the thermal load time series input vector and the cooling water heat exchange time series input vector respectively to obtain a sequence of thermal load local time series feature vectors and a sequence of cooling water heat exchange local time series feature vectors; a thermal load-heat exchange time series interaction correlation coding module 135, configured to perform parameter time series feature interaction correlation analysis on the sequence of thermal load local time series feature vectors and the sequence of cooling water heat exchange local time series feature vectors to obtain a thermal load-heat exchange time series interaction feature; and a cooling water flow rate control module 136, configured to determine whether the flow rate value of the cooling water at the current time point should be increased or decreased based on the thermal load-heat exchange time series interaction feature.

[0024] Specifically, in the technical solution of the present application, first, the thermal load values, the temperature values and the flow rate values of the cooling water of the reactor at multiple predetermined time points within a predetermined time period are obtained. Then, considering that the thermal load values, the temperature values and the flow rate values of the cooling water all have a dynamic change law of time series in the time dimension, in order to be able to comprehensively control the parameters of the heat energy recovery device according to the time series change patterns and trends of these data parameters, in the technical solution of the present application, it is necessary to arrange the thermal load values, the temperature values and the flow rate values of the cooling water of the reactor at the multiple predetermined time points in the time dimension as a thermal load time series input vector, a cooling water temperature time series input vector and a cooling water flow rate time series input vector respectively, so as to respectively integrate the distribution information of the thermal load values, the temperature values and the flow rate values of the cooling water in the time series.

[0025] It should be understood that the heat exchange of the cooling water refers to the process in which the cooling water exchanges heat with other media in the heat energy recovery device. The temperature and flow rate of the cooling water are important factors affecting the heat exchange effect. Therefore, it is necessary to perform correlation coding on these two to more fully analyze the heat exchange characteristics and time series change patterns of the cooling water. Specifically, in the technical solution of the present application, the cooling water heat exchange time series input vector is further calculated by multiplying the cooling water temperature time series input vector and the cooling water flow rate time series input vector at the corresponding positions. In particular, here, the cooling water heat exchange time series input vector reflects the comprehensive influence of the cooling water temperature and flow rate.

[0026] Then, considering that the thermal load time series input vector is a vector describing the thermal load change of the reactor, and the cooling water heat exchange time series input vector is a vector describing the heat exchange situation of the cooling water in the heat energy recovery device. And, since the thermal load change of the reactor and the heat exchange situation of the cooling water in the heat energy recovery device will show different change patterns and trends under different time period spans in the time dimension. Therefore, in order to be able to more fully analyze the heat load and the heat exchange situation and process of the cooling water, in the technical solution of the present application, the thermal load time series input vector and the cooling water heat exchange time series input vector are further vector sliced respectively to obtain a sequence of thermal load local time series input vectors and a sequence of cooling water heat exchange local time series input vectors.

[0027] Subsequently, the sequence of the thermal load local time series input vectors and the sequence of the cooling water heat exchange local time series input vectors are respectively subjected to feature mining in a time series feature extractor based on a one-dimensional convolutional layer to respectively extract the local time series feature information of the thermal load and the cooling water heat exchange in the time dimension, so as to obtain a sequence of thermal load local time series feature vectors and a sequence of cooling water heat exchange local time series feature vectors.

[0028] Correspondingly, asFigure 3 As shown, the data parameter local time series analysis module 134 includes: a vector segmentation unit 1341 for respectively segmenting the heat load time series input vector and the cooling water heat exchange time series input vector to obtain a sequence of heat load local time series input vectors and a sequence of cooling water heat exchange local time series input vectors; and a parameter local time series feature extraction unit 1342 for respectively passing the sequence of heat load local time series input vectors and the sequence of cooling water heat exchange local time series input vectors through a time series feature extractor based on a one-dimensional convolutional layer to obtain a sequence of heat load local time series feature vectors and a sequence of cooling water heat exchange local time series feature vectors.

[0029] It should be understood that the data parameter local time series analysis module 134 includes two main units: a vector segmentation unit 1341 and a parameter local time series feature extraction unit 1342. The vector segmentation unit 1341 segments the input heat load time series input vector and the cooling water heat exchange time series input vector, dividing them into sequences of multiple local time series input vectors, which can decompose the original input vectors into multiple smaller local time series vectors for subsequent processing. The parameter local time series feature extraction unit 1342 processes the sequence of heat load local time series input vectors and the sequence of cooling water heat exchange local time series input vectors through a time series feature extractor based on a one-dimensional convolutional layer, which can extract the corresponding time series feature vector sequences from each local time series vector for subsequent data analysis and processing. Generally speaking, the vector segmentation unit is used to segment the input vectors into sequences of local time series vectors, while the parameter local time series feature extraction unit is used to extract the feature vector sequences from these local time series vectors. The combination of these two units enables the data parameter local time series analysis module to analyze and process the input time series data.

[0030] Furthermore, considering that the sequence of heat load local time series feature vectors and the sequence of cooling water heat exchange local time series feature vectors respectively represent the local time series features of the heat load and the local time series features of the cooling water heat exchange, in order to be able to integrate the heat load local time series and the cooling water heat exchange local time series for comprehensive parameter control of the heat energy recovery device, in the technical solution of this application, a parameter feature sequence time series interaction unit is further used to process the sequence of heat load local time series feature vectors and the sequence of cooling water heat exchange local time series feature vectors to obtain heat load-heat exchange time series interaction feature vectors. It should be understood that by using the parameter feature sequence time series interaction unit, the sequence of heat load local time series feature vectors and the sequence of cooling water heat exchange local time series feature vectors can be interacted and fused to better capture and represent the association and mutual influence between the heat load and the heat exchange.

[0031] Accordingly, the heat load - heat exchange time - series interaction correlation encoding module 135 is configured to: use the parameter feature sequence time - series interaction unit to process the sequence of the heat load local time - series feature vectors and the sequence of the cooling water heat exchange local time - series feature vectors to obtain the heat load - heat exchange time - series interaction feature vector as the heat load - heat exchange time - series interaction feature.

[0032] Specifically, the heat load - heat exchange time - series interaction correlation encoding module 135 is further configured to: calculate the correlation between each heat load local time - series feature vector in the sequence of the heat load local time - series feature vectors and each cooling water heat exchange local time - series feature vector in the sequence of the cooling water heat exchange local time - series feature vectors according to the following correlation formula, where the correlation formula is: where, represents the correlation between the -th heat load local time - series feature vector in the sequence of the heat load local time - series feature vectors and the -th cooling water heat exchange local time - series feature vector in the sequence of the cooling water heat exchange local time - series feature vectors, represents the -th heat load local time - series feature vector in the sequence of the heat load local time - series feature vectors, and represents the -th cooling water heat exchange local time - series feature vector in the sequence of the cooling water heat exchange local time - series feature vectors. denotes a transpose operation; based on the correlation degrees between each local temporal feature vector of the sequence of local temporal feature vectors of heat load and all local temporal feature vectors of the sequence of local temporal feature vectors of cooling water heat exchange, and all local temporal feature vectors of the sequence of local temporal feature vectors of cooling water heat exchange, perform interactive update on each local temporal feature vector of the sequence of local temporal feature vectors of heat load to obtain a sequence of updated local temporal feature vectors of heat load; based on the correlation degrees between each local temporal feature vector of the sequence of local temporal feature vectors of cooling water heat exchange and all local temporal feature vectors of the sequence of local temporal feature vectors of heat load, and all local temporal feature vectors of the sequence of local temporal feature vectors of heat load, perform interactive update on each local temporal feature vector of the sequence of local temporal feature vectors of cooling water heat exchange to obtain a sequence of updated local temporal feature vectors of cooling water heat exchange; fuse the sequence of local temporal feature vectors of heat load and the sequence of updated local temporal feature vectors of heat load to obtain a sequence of interactively fused local temporal feature vectors of heat load; fuse the sequence of local temporal feature vectors of cooling water heat exchange and the sequence of updated local temporal feature vectors of cooling water heat exchange to obtain a sequence of interactively fused local temporal feature vectors of cooling water heat exchange; and splice the sequence of interactively fused local temporal feature vectors of heat load and the sequence of interactively fused local temporal feature vectors of cooling water heat exchange to obtain the ground state multi-scale feature vector.

[0033] Furthermore, pass the heat load - heat exchange temporal interaction feature vector through a classifier to obtain a classification result, where the classification result is used to indicate whether the flow rate value of the cooling water at the current time point should be increased or decreased. That is, use the interactive correlation feature information between the local temporal feature of the heat load and the local temporal feature of the cooling water heat exchange to perform classification processing, so as to perform real-time adaptive control on the parameters of the heat energy recovery device.

[0034] Correspondingly, the cooling water flow control module 136 is configured to: pass the heat load - heat exchange temporal interaction feature vector through a classifier to obtain a classification result, where the classification result is used to indicate whether the flow rate value of the cooling water at the current time point should be increased or decreased.

[0035] More specifically, the cooling water flow control module 136 is further configured to: perform fully connected encoding on the heat load - heat exchange temporal interaction feature vector using the fully connected layer of the classifier to obtain an encoded classification feature vector; and input the encoded classification feature vector into the Softmax classification function of the classifier to obtain the classification result.

[0036] It should be understood that the role of the classifier is to use the given categories and known training data to learn classification rules and classifiers, and then classify (or predict) unknown data. Logistic regression, SVM, etc. are often used to solve binary classification problems. For multi-class classification problems, logistic regression or SVM can also be used, but it requires multiple binary classifications to form a multi-class classification, which is prone to errors and inefficient. The commonly used multi-class classification method is the Softmax classification function.

[0037] Furthermore, in the technical solution of the present application, the intelligent control system for recovering the thermal energy of the circulating cooling water in the nuclear power plant further includes a training module for training the one-dimensional convolutional layer-based time series feature extractor, the parameter feature sequence time series interaction device, and the classifier.

[0038] Among them, in one example, the training module includes: a training data acquisition unit for obtaining training data, where the training data includes the training heat load values of the reactor at multiple predetermined time points within a predetermined time period, the training temperature values of the cooling water, and the training flow rate values, and a true value indicating whether the flow rate value of the cooling water at the current time point should be increased or decreased; a training data parameter time series arrangement unit for arranging the training heat load values of the reactor, the training temperature values of the cooling water, and the training flow rate values at the multiple predetermined time points into a training heat load time series input vector, a training cooling water temperature time series input vector, and a training cooling water flow rate time series input vector respectively according to the time dimension; a training cooling water heat exchange time series distribution unit for calculating the product of the training cooling water temperature time series input vector and the training cooling water flow rate time series input vector at each position point to obtain a training cooling water heat exchange time series input vector; a training data parameter local time series analysis unit for respectively performing local time series analysis on the training heat load time series input vector and the training cooling water heat exchange time series input vector to obtain a sequence of training heat load local time series feature vectors and a sequence of training cooling water heat exchange local time series feature vectors; a training heat load-heat exchange time series interaction correlation coding unit for using a parameter feature sequence time series interaction device to process the sequence of training heat load local time series feature vectors and the sequence of training cooling water heat exchange local time series feature vectors to obtain a training heat load-heat exchange time series interaction feature vector; a training correction unit for correcting the training heat load-heat exchange time series interaction feature vector to obtain an optimized training heat load-heat exchange time series interaction feature vector; a training classification unit for passing the optimized training heat load-heat exchange time series interaction feature vector through a classifier to obtain a classification loss function value; and a loss training unit for training the one-dimensional convolutional layer-based time series feature extractor, the parameter feature sequence time series interaction device, and the classifier based on the classification loss function value.

[0039] Specifically, in the technical solution of this application, the sequence of training heat load local time series feature vectors and the sequence of training cooling water heat exchange local time series feature vectors respectively represent the time series correlation features within the local time domain determined from the global time domain through vector segmentation of the heat load value and the heat exchange value of the cooling water. In this way, after using the parameter feature sequence time series interaction device to process the sequence of training heat load local time series feature vectors and the sequence of training cooling water heat exchange local time series feature vectors, the training heat load-heat exchange time series interaction feature vector contains, in addition to the time series correlation features within the local time domain of the heat load value and the heat exchange value of the cooling water, time series interaction features based on the local time domain sequence distribution. That is, the training heat load-heat exchange time series interaction feature vector has a multi-dimensional time series feature representation with multiple time domain scales.

[0040] However, considering that the distribution differences of the temporal feature representation under multi-temporal scales and multi-dimensional features will lead to the sparsification of the local feature distribution relative to the overall feature representation of the training heat load-heat exchange temporal interaction feature vector, that is, the out-of-distribution sparse sub-manifold relative to the overall high-dimensional feature manifold, this will result in poor convergence of the training heat load-heat exchange temporal interaction feature vector to the predetermined class probability category representation in the probability space when mapping through a classifier, affecting the accuracy of the classification result. Therefore, preferably, the training heat load-heat exchange temporal interaction feature vector is corrected.

[0041] Correspondingly, in one example, the training correction unit is configured to: correct the training heat load-heat exchange temporal interaction feature vector with the following correction formula to obtain the optimized training heat load-heat exchange temporal interaction feature vector; where the correction formula is: Where, is the training heat load-heat exchange temporal interaction feature vector, is the training heat load-heat exchange temporal interaction feature vector at the -th position of the eigenvalue, represents the exponential operation of a numerical value, and the exponential operation of the numerical value represents calculating the value of the natural exponential function with the numerical value as the power, is the eigenvalue at the -th position of the optimized training heat load-heat exchange temporal interaction feature vector.

[0042] That is, by dealing with the sparse distribution in the high-dimensional feature space through re-probability-based regularization to activate the training heat load-heat exchange temporal interaction feature vector in the natural distribution transfer of the geometric manifold in the high-dimensional feature space to the probability space, so as to improve the class convergence of the complex high-dimensional feature manifold with high spatial sparsity under the predetermined class probability by means of re-probability-based smoothing regularization of the distribution sparse sub-manifold of the high-dimensional feature manifold of the training heat load-heat exchange temporal interaction feature vector , thereby enhancing the accuracy of the classification result obtained by the training heat load-heat exchange temporal interaction feature vector through the classifier. In this way, the parameter adaptive control of the thermal energy recovery device can be carried out based on the temporal variation of the reactor's heat load and the temperature and flow rate of the cooling water, so as to improve the efficiency of the thermal energy recovery device, ensure that the thermal energy recovery device can work within the safe and stable operating range, thereby improving the thermal efficiency and economy of the nuclear power plant and reducing greenhouse gas emissions.

[0043] Further, the loss training unit is configured to: use the classifier to process the optimized training heat load - heat exchange time - series interaction feature vector with the following training classification formula to obtain a training classification result; where the training classification formula is: ; where to is a weight matrix, to is a bias vector, is the optimized training heat load - heat exchange time - series interaction feature vector; and calculate the cross - entropy value between the training classification result and the true value as the classification loss function value.

[0044] In summary, the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant according to the embodiments of the present application is elucidated. It can effectively reduce the water consumption and emissions of the nuclear power plant, reduce greenhouse gas emissions, and improve the reliability and lifespan of the nuclear power plant.

[0045] As described above, the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant according to the embodiments of the present application can be implemented in various terminal devices, such as a server with an intelligent control algorithm for heat energy recovery of circulating cooling water in a nuclear power plant. In one example, the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant can be integrated into the terminal device as a software module and / or a hardware module. For example, the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant can be a software module in the operating system of the terminal device, or can be an application program developed for the terminal device; of course, the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant can also be one of the many hardware modules of the terminal device.

[0046] Alternatively, in another example, the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant and the terminal device can also be separate devices, and the intelligent control system 100 for heat energy recovery of circulating cooling water in a nuclear power plant can be connected to the terminal device through a wired and / or wireless network and transmit interaction information according to a predefined data format.

[0047] Figure 4 Shows a flowchart of an intelligent control method for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application. As Figure 4 shown, the intelligent control method for heat energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application includes: S110, transferring the heat energy of the circulating cooling water to other media through a heat exchanger of the heat energy recovery device; S120, transporting the circulating cooling water between the heat energy recovery device and the reactor through a cooling water pump; and S130, adjusting the working parameters of the heat energy recovery device.

[0048] Figure 5 A schematic diagram showing the system architecture of sub-step S130 in the intelligent control method for thermal energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application. As Figure 5 shown, in a possible implementation, adjusting the operating parameters of the thermal energy recovery device includes: obtaining the thermal load values, the temperature values and the flow rate values of the cooling water of the reactor at a plurality of predetermined time points within a predetermined time period; arranging the thermal load values, the temperature values and the flow rate values of the cooling water of the reactor at the plurality of predetermined time points in a time dimension to form a thermal load time series input vector, a cooling water temperature time series input vector and a cooling water flow rate time series input vector respectively; calculating a cooling water heat exchange time series input vector by multiplying the cooling water temperature time series input vector and the cooling water flow rate time series input vector at corresponding positions; respectively performing local time series analysis on the thermal load time series input vector and the cooling water heat exchange time series input vector to obtain a sequence of thermal load local time series feature vectors and a sequence of cooling water heat exchange local time series feature vectors; performing parameter time series feature interaction correlation analysis on the sequence of thermal load local time series feature vectors and the sequence of cooling water heat exchange local time series feature vectors to obtain a thermal load - heat exchange time series interaction feature; and determining whether the flow rate value of the cooling water at the current time point should be increased or decreased based on the thermal load - heat exchange time series interaction feature.

[0049] Here, those skilled in the art can understand that the specific operations of each step in the above intelligent control method for thermal energy recovery of circulating cooling water in a nuclear power plant have been introduced in detail in the description of the intelligent control system for thermal energy recovery of circulating cooling water in the nuclear power plant above with reference to Figures 1 to 3 and thus, the repeated description thereof will be omitted.

[0050] Figure 6 A diagram showing an application scenario of the intelligent control system for thermal energy recovery of circulating cooling water in a nuclear power plant according to an embodiment of the present application. As Figure 6 shown, in this application scenario, first, obtain the thermal load values (e.g., Figure 6 D1 shown in the figure), the temperature values and the flow rate values of the cooling water (e.g., Figure 6 D2 shown in the figure) of the reactor at a plurality of predetermined time points within a predetermined time period, and then input the thermal load values, the temperature values and the flow rate values of the cooling water of the reactor at the plurality of predetermined time points to a server (e.g., Figure 6In the S) illustrated, the server can use the intelligent control algorithm for heat energy recovery of the circulating cooling water in the nuclear power plant to process the thermal load value of the reactor, the temperature value and the flow value of the cooling water at the multiple predetermined time points to obtain a classification result indicating whether the flow value of the cooling water at the current time point should be increased or decreased.

[0051] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of an instruction, and the module, the segment of the program, or the part of the instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0052] The embodiments of the present application have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skilled persons in the technical field to understand the embodiments disclosed herein.

Claims

1. An intelligent control system for recovering thermal energy of circulating cooling water in a nuclear power plant, comprising: Heat recovery device, used to transfer the heat energy of circulating cooling water to other media through a heat exchanger; A cooling water pump, used to transport the circulating cooling water between the heat energy recovery device and the reactor, characterized in that the heat energy recovery intelligent control system for circulating cooling water in the nuclear power plant also includes a controller, and the controller is used to adjust the working parameters of the heat energy recovery device; Wherein, the controller comprises: A data acquisition module, used to obtain the heat load value of the reactor, the temperature value and the flow value of the cooling water at a plurality of predetermined time points within a predetermined time period; A data parameter time series arrangement module, used for arranging the heat load values, cooling water temperature values and flow values of the reactor at the plurality of predetermined time points into a heat load time series input vector, a cooling water temperature time series input vector and a cooling water flow time series input vector according to the time dimension; A cooling water heat exchange timing distribution module, used for calculating the cooling water heat exchange timing input vector obtained by multiplying the cooling water temperature timing input vector and the cooling water flow timing input vector by a position point; A data parameter local time series analysis module, used for performing local time series analysis on the heat load time series input vector and the cooling water heat exchange time series input vector respectively to obtain a sequence of heat load local time series characteristic vectors and a sequence of cooling water heat exchange local time series characteristic vectors; A heat load-heat exchange time series interaction coding module, used for performing parameter time series feature interaction analysis on the sequence of the heat load local time series feature vectors and the sequence of the cooling water heat exchange local time series feature vectors to obtain a heat load-heat exchange time series interaction feature; and The cooling water flow control module is used to determine whether the flow value of the cooling water at the current time point should be increased or decreased based on the heat load-heat exchange timing interaction characteristics.

2. The intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to claim 1, wherein The data parameter local timing analysis module includes: a vector segmentation unit, configured to segment the heat load time series input vector and the cooling water heat exchange time series input vector respectively to obtain a sequence of heat load local time series input vectors and a sequence of cooling water heat exchange local time series input vectors; and A parameter local time series feature extraction unit is used to pass the sequence of the local time series input vector of the heat load and the sequence of the local time series input vector of the cooling water heat exchange through a time series feature extractor based on a one-dimensional convolutional layer to obtain a sequence of the local time series feature vectors of the heat load and a sequence of the local time series feature vectors of the cooling water heat exchange.

3. The intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to claim 2, wherein The heat load-heat exchange timing interaction correlation coding module is used to: A parameter feature sequence timing interactor is used to process the sequence of the heat load local timing feature vectors and the sequence of the cooling water heat exchange local timing feature vectors to obtain a heat load-heat exchange timing interaction feature vector as the heat load-heat exchange timing interaction feature.

4. The intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to claim 3, characterized in that The heat load-heat exchange timing interaction correlation coding module is used to: Calculate the correlation degree between each local time series feature vector of the heat load in the sequence of local time series feature vectors of the heat load and each local time series feature vector of the cooling water heat exchange in the sequence of local time series feature vectors of the cooling water heat exchange using the following correlation degree formula, where the correlation degree formula is: Wherein, represents the correlation degree between the th local time series feature vector of the heat load in the sequence of local time series feature vectors of the heat load and the th local time series feature vector of the cooling water heat exchange in the sequence of local time series feature vectors of the cooling water heat exchange, represents the th local time series feature vector of the heat load in the sequence of local time series feature vectors of the heat load, and represents the th local time series feature vector of the cooling water heat exchange in the sequence of local time series feature vectors of the cooling water heat exchange, represents the transpose operation; Based on the correlation between each local temporal feature vector of the sequence of local temporal feature vectors of the heat load and all local temporal feature vectors of the sequence of local temporal feature vectors of the cooling water heat exchange, and all local temporal feature vectors of the sequence of local temporal feature vectors of the cooling water heat exchange, perform interactive update on each local temporal feature vector of the sequence of local temporal feature vectors of the heat load to obtain a sequence of updated local temporal feature vectors of the heat load; Based on the correlation between each local temporal feature vector of the sequence of local temporal feature vectors of the cooling water heat exchange and all local temporal feature vectors of the sequence of local temporal feature vectors of the heat load, and all local temporal feature vectors of the sequence of local temporal feature vectors of the heat load, perform interactive update on each local temporal feature vector of the sequence of local temporal feature vectors of the cooling water heat exchange to obtain a sequence of updated local temporal feature vectors of the cooling water heat exchange; Fuse the sequence of local temporal feature vectors of the heat load and the sequence of updated local temporal feature vectors of the heat load to obtain a sequence of interactively fused local temporal feature vectors of the heat load; Fuse the sequence of local temporal feature vectors of the cooling water heat exchange and the sequence of updated local temporal feature vectors of the cooling water heat exchange to obtain a sequence of interactively fused local temporal feature vectors of the cooling water heat exchange; and Concatenate the sequence of interactively fused local temporal feature vectors of the heat load and the sequence of interactively fused local temporal feature vectors of the cooling water heat exchange to obtain the multi-scale feature vector of the ground state.

5. The intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to claim 4, characterized in that, The cooling water flow control module is used for: Pass the heat load - heat exchange temporal interaction feature vector through a classifier to obtain a classification result, and the classification result is used to indicate whether the flow value of the cooling water at the current time point should be increased or decreased.

6. The intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to claim 5, characterized in that, It further includes a training module for training the temporal feature extractor based on the one-dimensional convolutional layer, the parameter feature sequence temporal interaction unit, and the classifier.

7. The intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to claim 6, characterized in that, The training module includes: A training data acquisition unit for acquiring training data, where the training data includes the training heat load values of the reactor, the training temperature values of the cooling water, and the training flow values at multiple predetermined time points within a predetermined time period, and the true value indicating whether the flow value of the cooling water at the current time point should be increased or decreased; A training data parameter temporal arrangement unit for arranging the training heat load values, the training temperature values of the cooling water, and the training flow values of the reactor at the multiple predetermined time points into a training heat load temporal input vector, a training cooling water temperature temporal input vector, and a training cooling water flow temporal input vector respectively according to the time dimension; A training cooling water heat exchange temporal distribution unit for calculating the product of the training cooling water temperature temporal input vector and the training cooling water flow temporal input vector at each position point to obtain a training cooling water heat exchange temporal input vector; A training data parameter local time series analysis unit, which is used to perform local time series analysis on the training heat load time series input vector and the training cooling water heat exchange time series input vector respectively to obtain a sequence of training heat load local time series feature vectors and a sequence of training cooling water heat exchange local time series feature vectors; A training heat load - heat exchange time series interaction correlation encoding unit, which is used to use a parameter feature sequence time series interaction device to process the sequence of training heat load local time series feature vectors and the sequence of training cooling water heat exchange local time series feature vectors to obtain a training heat load - heat exchange time series interaction feature vector; A training correction unit, which is used to correct the training heat load - heat exchange time series interaction feature vector to obtain an optimized training heat load - heat exchange time series interaction feature vector; A training classification unit, which is used to pass the optimized training heat load - heat exchange time series interaction feature vector through a classifier to obtain a classification loss function value; and A loss training unit, which is used to train the time series feature extractor based on the one - dimensional convolutional layer, the parameter feature sequence time series interaction device, and the classifier based on the classification loss function value.

8. The intelligent control system for heat energy recovery of circulating cooling water in a nuclear power plant according to claim 7, characterized in that, The loss training unit is used for: Use the classifier to process the optimized training heat load - heat exchange time - series interaction feature vector with the following training classification formula to obtain a training classification result; wherein, the training classification formula is: Wherein, to is a weight matrix, to is a bias vector, is the optimized training heat load - heat exchange time - series interaction feature vector; and Calculating the cross - entropy value between the training classification result and the true value as the classification loss function value.

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