Nuclear power operation parameter prediction method and device

Abstract

The invention belongs to the technical field of nuclear power, and particularly relates to a nuclear power operation parameter prediction method and device. According to the nuclear power operation parameter prediction method provided by the invention, population initialization is carried out by adopting Tent chaotic mapping, so that the uniformity of the population in a search space is effectively improved, and the quality of an initial solution is improved; an eagle optimization algorithm is introduced to be fused into a local exploration strategy, and the ergodicity of the algorithm in a solution space search process is enhanced; in addition, a self-adaptive inertia weight factor is introduced, so that the algorithm maintains good balance between exploration and development, and the self-adaptive capability of the algorithm is enhanced. According to the method, the convergence speed is increased, and a better solution is obtained more easily in the search process. Accurate data support and scientific decision basis can be provided for an operator to formulate an operation and maintenance strategy in advance, the operator is guided to accurately increase and decrease power, overrun of thermal power, nuclear power and the like of the unit is avoided, load loss in a high-temperature period in summer is reduced, and the efficiency of the nuclear power unit is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of nuclear power, and particularly relates to a method and device for predicting nuclear power operation parameters. Background Art

[0002] In the related art, when a coastal nuclear power unit operates in summer, the tide causes fluctuations in the temperature of the seawater at the cold-end inlet, resulting in a sudden inflow of high-temperature seawater into the water inlet, causing a sudden rise in the temperature of the circulating water inlet in a short period of time, and further causing a sharp increase in the back pressure. If the unit maintains the electric power, the nuclear power will increase. To ensure that the nuclear power and thermal power do not exceed the operation threshold during the daily parameter fluctuations of the unit, the electric power of the unit needs to be restricted. If the set power of the unit is too high, there is a risk of nuclear power exceeding the limit. If the set power of the unit is too low, the economy of the unit will decline. Therefore, it is urgent to solve the problem that the output of the nuclear power plant unit is limited due to the over-limitation of the thermal power, nuclear power, etc. of the unit. Summary of the Invention

[0003] To overcome the problems existing in the related art, a method and device for predicting nuclear power operation parameters are provided.

[0004] According to one aspect of the embodiments of the present disclosure, a method for predicting nuclear power operation parameters is provided, and the method includes: Step 1, obtaining historical operation data and preprocessing the historical operation data to generate a sample set; Step 2, decomposing the sample set by using a VMD model to obtain an IMF sequence; Step 3, correcting each IMF in the IMF sequence to obtain a plurality of corrected IMFs, Step 4, superimposing the predicted values output by the plurality of corrected IMFs to obtain a final prediction result.

[0005] In a possible implementation manner, Step 3 includes Step 31 to Step 33; Step 31, in the first stage, globally searching the exploration space composed of the IMF sequence, determining the optimal region and updating the positions of the elements of each IMF, as shown in Equations Five to Seven: Equation Five Equation Six Equation Seven In the formula, represents the i-th IMF, is the initial value of the i-th IMF in the j-th dimension, i ∈ [1, N], j ∈ [1, m], N is the number of IMFs in the IMF sequence, and m is the number of dimensions of each IMF; is the target position randomly selected by the i-th IMF; is the objective function value of the i-th IMF; is the value of the j-th dimension of the randomly selected target position of the i-th IMF; is the new value of the i-th IMF in the j-th dimension in the first stage; is a random integer within the range; is the objective function value of the target position of the i-th IMF; is the objective function value of the i-th IMF after the update in the first stage; is the IMF after the update in the first stage, is a random natural number; is a random number; Step 32. In the second stage, perform a global search on the exploration space, determine the optimal region, and update the positions of the elements in each IMF again, as shown in Equation (8) and Equation (9): Equation (8) Equation (9) In the formula: is the j-th element in, is the IMF after the update in the second stage, is the new value of the i-th IMF in the j-th dimension in the second stage, represents the objective function value of the i-th IMF in the second stage, ; is the current iteration number, is the preset total number of iterations, is the lower bound of the search range of, is the upper bound of the search range of; Step 33. In the case of repeat Step 31 to Step 32 until when, save and output the multiple corrected IMFs obtained by the current update.

[0006] In a possible implementation, Step 1 includes: Step 11. Apply the 3 criterion cleaning strategy based on the normal distribution to identify and eliminate abnormal data in the historical operation data; Step 12. Use the standardization method shown in Equation (1) to perform dimensionless processing on the cleaned data, thereby constructing a sample set; Equation (1) In the formula, represents the sample value after dimensionless processing; represents the original sample value; represents the mean of the original sample; represents the standard deviation of the original sample.

[0007] In a possible implementation, in step 31, an initialized IMF sequence is formed based on the Tent chaotic map, and the chaotic Tent map is shown in Equation (3): Equation (3) In the formula: represents the th chaotic value, represents the th chaotic value, The domain of is ; Equation (4) In the formula: is the chaotic element generated by the Tent map.

[0008] In a possible implementation, the adaptive inertia weight factor shown in Equation (10) is introduced into Equation (6) to obtain Equation (11): Equation (10) Equation (11) Introduce into Equation (8) to obtain Equation (12): Equation (12) In the formula: is the initial inertia weight, is the inertia weight when the new iteration reaches the maximum number of iterations.

[0009] In a possible implementation, the method further includes step 5 of splitting the sample set into a training set and a test set, decomposing the training set using the VMD model to obtain an IMF sequence, and based on the test set, using the coefficient of determination to evaluate the performance of multiple trained models, as shown in Equation (13): Equation (13) In the formula: is the actual output value, is the predicted output value, represents the actual output value after averaging.

[0010] According to another aspect of the embodiments of the present disclosure, a nuclear power operation parameter prediction device is provided, and the device includes: A preprocessing module, configured to obtain historical operation data and preprocess the historical operation data to generate a sample set; A decomposition module, configured to decompose a sample set by using a VMD model to obtain an IMF sequence; A correction module, configured to correct each IMF in the IMF sequence to obtain a plurality of corrected IMFs; A prediction module, configured to superimpose predicted values output by the plurality of corrected IMFs to obtain a final prediction result.

[0011] According to another aspect of the embodiments of the present disclosure, there is provided a nuclear power operation parameter prediction device, including: A processor; A memory for storing instructions executable by the processor; Wherein the processor is configured to execute the above-mentioned method.

[0012] According to another aspect of the embodiments of the present disclosure, there is provided a non-volatile computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned method is implemented.

[0013] The beneficial effects of the present disclosure are as follows: The nuclear power operation parameter prediction method provided by the present disclosure uses Tent chaotic mapping for population initialization, effectively improving the uniformity of the population in the search space and enhancing the quality of the initial solution; introducing the osprey optimization algorithm and integrating a local exploration strategy to enhance the traversability of the algorithm in the solution space search process; in addition, introducing an adaptive inertia weight factor to maintain a good balance between exploration and development of the algorithm and enhance its adaptive ability. The convergence speed of the method of the present disclosure is improved, and it is easier to obtain a better solution during the search process. In view of the current situation that the cold-end inlet seawater temperature fluctuates due to tides, resulting in fluctuations in the unit operation parameters and further causing the unit output to be reduced in summer to avoid exceeding the unit operation parameter limits, a seawater temperature and tide level prediction model is effectively constructed, which can provide accurate data support and scientific decision-making basis for operators to formulate operation and maintenance strategies in advance, guide the operator to accurately increase or decrease the power, avoid exceeding the limits of the unit thermal power, nuclear power, etc., reduce the load loss during high-temperature periods in summer, and thus improve the efficiency of the nuclear power unit. Description of the Drawings

[0014] Figure 1 is a schematic diagram of a nuclear power operation parameter prediction method shown in an embodiment of the present disclosure.

[0015] Figure 2 is a schematic diagram of an IMF sequence obtained by VMD decomposition shown in an application example of the present disclosure.

[0016] Figures 3a to 3g is a schematic diagram of each trained IMF shown in an application example of the present disclosure.

[0017] Figure 4It is a block diagram of a nuclear power operation parameter prediction device shown in an embodiment of the present disclosure. Detailed implementation manners

[0018] The present disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Unless otherwise defined, the technical and scientific terms used in the present disclosure have the same meanings as those commonly understood by those skilled in the technical field to which the present disclosure belongs; the terms used in the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure; the term "including" and any variations thereof in the text of the present disclosure are intended to cover non-exclusive inclusion. Obviously, the embodiments described in the present disclosure are only a part of the embodiments of the present disclosure, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.

[0020] Referring to "embodiments" in the present disclosure means that specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present disclosure. The phrase appears at various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0021] Figure 1 It is a schematic diagram of a nuclear power operation parameter prediction method shown in an embodiment of the present disclosure. This method can be executed by a terminal device. Herein, the terminal device can be a server, a desktop computer, a laptop computer, etc. The present disclosure does not limit the type of the terminal device. As Figure 1 shown, this method includes: Step 1: Obtain historical operation data and preprocess the historical operation data to generate a sample set. As an example of this embodiment, in Step 1, it includes: Step 11: Apply a 3 -sigma criterion cleaning strategy to identify and eliminate abnormal data in the historical operation data to ensure the accuracy and reliability of the data.

[0022] Step 12: Use a standardization method as shown in Equation 1 to perform dimensionless processing on the cleaned data, thereby constructing a sample set that meets the research requirements.

[0023] Equation 1 In the formula, represents the sample value after dimensionless processing; represents the original sample value; represents the mean of the original sample; represents the standard deviation of the original sample. It should be noted that the same characters in the formulas of this disclosure have the same meaning.

[0024] Step 2: Use the VMD model to decompose the sample set to obtain the IMF sequence.

[0025] As an example of this embodiment, use the VMD model to decompose the sample set to form the IMF sequence shown in Equation 2.

[0026] Equation 2 In the formula: represents the i-th IMF, is the initial value of the i-th IMF in the j-th dimension, i ∈ [1, N], j ∈ [1, m], N is the number of IMFs in the IMF sequence, and m is the number of dimensions of each IMF.

[0027] In a possible implementation, an initialized IMF sequence is formed using Tent chaotic mapping. The chaotic Tent mapping is shown in Equation 3: Equation 3 In the formula: represents the th chaotic value, represents the th chaotic value, The domain of is ; is the chaotic parameter; . The chaotic sequence generated based on Tent chaotic mapping has better uniformity. The elements of each IMF sequence are shown in Equation 4: Equation 4 In the formula: is the chaotic element generated by the Tent mapping, is the lower bound of the search range of is the upper bound of the search range of

[0028] Step 3: Input each IMF in the IMF sequence into a preset model for training to obtain multiple trained IMFs. (As shown in Figure 1 , input IMF1 into Transformer1 to obtain the predicted value of IMF1) Step 3 includes Steps 31 to 33.

[0029] Step 31: In the first stage, perform a global search on the exploration space composed of the IMF sequence to determine the optimal region and update the positions of the elements of each IMF, as shown in Equations 5 to 7: Formula Five Formula Six Formula Seven In the formula: is the target position randomly selected for the i-th IMF; is the objective function value of the i-th IMF; is the value of the target position randomly selected for the i-th IMF in the j-th dimension; is the new value of the i-th IMF in the j-th dimension in the first stage; is a random integer within the range; is the objective function value of the target position of the i-th IMF; is the objective function value of the i-th IMF after the first-stage update; is the IMF after the first-stage update, is a random natural number within; is a random number with a value of 1 or 2; Step 32. In the second stage, perform a global search on the exploration space, determine the optimal region, and update the positions of the elements in each IMF again, as shown in Formula Eight and Formula Nine: Formula Eight Formula Nine In the formula: is the j-th element in, is the IMF after the second-stage update, is the new value of the i-th IMF in the j-th dimension in the second stage, represents the objective function value of the i-th IMF in the second stage, ; is the current iteration number, is the preset total number of iterations.

[0030] Step 33. In the case of , repeat Step 31 to Step 32 until , save and output the multiple trained IMFs obtained by the current update.

[0031] Step 4. Superimpose the predicted values output by the multiple trained IMFs to obtain the final predicted result.

[0032] In a possible implementation, an adaptive inertia weight factor as shown in Formula Ten is adopted in the first stage and the second stage , the method of the present disclosure can maintain a certain balance between exploration and development and enhance the adaptive ability.

[0033] Equation ten In the formula: is the initial inertia weight, is the inertia weight when the new iteration reaches the maximum number of iterations.

[0034] Introduce the weight factor into Equation six to obtain Equation eleven: Equation eleven Introduce the weight factor into Equation eight to obtain Equation twelve: Equation twelve The present disclosure introduces an adaptive inertia weight factor in the first stage and the second stage, enabling the algorithm to maintain a good balance between exploration and development and enhancing its adaptive ability. This improves the convergence speed of the overall model and makes it easier to obtain better solutions during the search process.

[0035] In a possible implementation, the method further includes: Step 5, splitting the sample set into a training set and a test set. For example, 80% of the sample set is divided into the training set and 20% is divided into the test set. Based on the training set, the VMD model is used to decompose it into IMF sequences. Based on the test set, the coefficient of determination is used to evaluate the performance of multiple trained models, as shown in Equation thirteen: Equation thirteen In the formula: is the actual output value, is the predicted output value, represents the actual output value after averaging.

[0036] The nuclear power operation parameter prediction method provided by the present disclosure uses Tent chaotic mapping for population initialization, effectively improving the uniformity of the population in the search space and enhancing the quality of the initial solution; introducing the osprey optimization algorithm and integrating local exploration strategies to enhance the traversability of the algorithm in the solution space search process; in addition, introducing an adaptive inertia weight factor to maintain a good balance between exploration and development of the algorithm and enhance its adaptability. The method of the present disclosure has an improved convergence speed, and it is easier to obtain better solutions during the search process. Aiming at the current situation that the cold-end inlet seawater temperature fluctuates due to tides, resulting in fluctuations in the unit operation parameters and further causing the unit output to be reduced in summer to avoid exceeding the limit of the unit operation parameters, a seawater temperature and tide level prediction model is effectively constructed, which can provide accurate data support and scientific decision-making basis for operators to formulate operation and maintenance strategies in advance, guide the operator to accurately increase or decrease the power, avoid exceeding the limit of the unit thermal power, nuclear power, etc., reduce the load loss during high-temperature periods in summer, and thus improve the efficiency of nuclear power units.

[0037] In an application example, historical operation data is obtained and preprocessed to generate a sample set.

[0038] The sample set is decomposed by VMD to obtain the IMF sequences shown in Table 1. In this embodiment, the central frequency of the sample set is used as the basis for judging whether the decomposition reaches a stable state. When the last component obtained by the decomposition tends to be stable, the corresponding K value at this time is the optimal K value. The penalty coefficient of VMD is set to 2000, and the convergence tolerance is set to , and the Lagrange multiplier replacement rate is set to 0.01. Table 1 presents the central frequency distribution of the example data under different K value settings. When the K values are 7 and 8 respectively, the central frequencies of the last-layer components have shown a tendency to be stable. It can be inferred therefrom that when K takes the value of 7, the variational mode decomposition (VMD) can achieve the optimal decomposition effect.

[0039] Table 1 The IMF components are obtained by VMD decomposition and the residual components are removed to obtain 7 IMF components, and the original data is compared with them, as Figure 2 shown.

[0040] Each IMF sequence is input into a preset model to obtain multiple trained models as shown in Figures 3a to 3g . Finally, the predicted values of each IMF sequence are superimposed to finally obtain the prediction result.

[0041] In the embodiments of the present disclosure, the change rules and trends of seawater temperature and the liquid level in front of the grid can be accurately predicted, which can provide precise data support and decision-making basis for operators to formulate electric power setting strategies in advance, guide the operator to accurately raise and lower the power, reduce the load loss during high-temperature periods in summer, and avoid exceeding the limits of unit thermal power and nuclear power, effectively contributing to the improvement of the unit output efficiency.

[0042] According to another aspect of the embodiments of the present disclosure, a nuclear power operation parameter prediction device is provided. The device includes: A preprocessing module, configured to obtain historical operation data and preprocess the historical operation data to generate a sample set; A decomposition module, configured to decompose the sample set by using a VMD model to obtain an IMF sequence; A correction module, configured to correct each IMF in the IMF sequence to obtain a plurality of corrected IMFs, A prediction module, configured to superimpose the predicted values output by the plurality of corrected IMFs to obtain a final prediction result.

[0043] The description of the above device has been elaborated in detail in the description of the above method and will not be repeated here.

[0044] Figure 4 FIG. is a block diagram of a nuclear power operation parameter prediction device shown in the embodiments of the present disclosure. For example, device 1900 may be provided as a server. Referring to Figure 4 , device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0045] Device 1900 may further include a power supply component 1926 configured to perform power management of device 1900, a wired or wireless network interface 1950 configured to connect device 1900 to a network, and an input / output (I / O) interface 1958. Device 1900 may operate based on an operating system stored in the memory 1932, such as Windows ServerTM, MacOS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

[0046] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, and the above computer program instructions can be executed by the processing component 1922 of device 1900 to complete the above method.

[0047] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.

[0048] The computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, — but is not limited to — an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as being a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0049] The computer-readable program instructions described herein may be downloaded to respective computing / processing devices from a computer-readable storage medium or may be downloaded to an external computer or an external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0050] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the present disclosure.

[0051] Aspects of the present disclosure are described herein with reference to the flowchart and / or block diagram of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer - readable program instructions.

[0052] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine such that when the instructions are executed by the processor of the computer or other programmable data - processing apparatus, a device is created that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner. Thus, the computer - readable medium storing the instructions includes a manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0053] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0054] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions.

[0055] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the market, or to enable other ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting nuclear power operation parameters, characterized in that, The method includes: Step 1: Obtain historical operation data and preprocess the historical operation data to generate a sample set; Step 2: Decompose the sample set using the VMD model to obtain an IMF sequence; Step 3: Correct each IMF in the IMF sequence to obtain multiple corrected IMFs; Step 4: Superimpose the predicted values output by the multiple corrected IMFs to obtain a final prediction result.

2. The method according to claim 1, characterized in that, Step 3 includes Step 31 to Step 33; Step 31: In the first stage, globally search the exploration space composed of the IMF sequence, determine the optimal region, and update the positions of the elements of each IMF, as shown in Equations 5 to 7; Formula V Formula VI Formula VII In the formula, represents the i-th IMF, is the initial value of the i-th IMF in the j-th dimension, where i ∈ [1, N], j ∈ [1, m], N is the number of IMFs in the IMF sequence, and m is the number of dimensions of each IMF; is the target position randomly selected for the i-th IMF; is the objective function value of the i-th IMF; is the value of the randomly selected target position of the i-th IMF in the j-th dimension; is the new value of the i-th IMF in the j-th dimension in the first stage; is a random integer within the range; is the objective function value of the target position of the i-th IMF; is the objective function value of the i-th IMF after the first-stage update; is the IMF after the first-stage update, is a random natural number; is a random number; Step 32: In the second stage, globally search the exploration space, determine the optimal region, and update the positions of the elements in each IMF again, as shown in Equations 8 and 9; Formula VIII Formula IX In the formula: is the j-th element in the IMF updated in the second stage, is the new value of the i-th IMF in the second stage at the j-th dimension, represents the objective function value of the i-th IMF in the second stage, ; is the current iteration number, is the preset total number of iterations, is the lower bound of the search range of is the upper bound of the search range of; Step 33, in the case of , repeat Steps 31 to 32 until is reached, then save and output the multiple corrected IMFs obtained by the current update.

3. The method according to claim 1, characterized in that, In Step 1, it includes: Step 11, apply the 3 criterion cleaning strategy to identify and eliminate abnormal data in the historical operation data; Step 12: Use the normalization method shown in Equation 1 to perform dimensionless processing on the cleaned data, thereby constructing a sample set; Formula 1 In the formula, represents the sample value after dimensionless processing; represents the original sample value; represents the mean value of the original sample; represents the standard deviation of the original sample.

4. The method according to claim 1, wherein In Step 31, use an initialized IMF sequence formed based on the Tent chaotic map, and the chaotic Tent map is shown in Equation 3; Formula III Wherein: represents the th chaotic value, represents the th chaotic value, has a domain of ; is a chaotic parameter, and the elements of each IMF sequence are shown in Equation Four: Formula Four In the formula: is the chaotic element generated by the Tent mapping.

5. The method according to claim 1, wherein Introduce the adaptive inertia weight factor shown in Equation (X) into Equation (VI) to obtain Equation (XI): Formula X Formula XI Introduce formula VIII to obtain formula XII: Introduce formula VIII to obtain formula XII: Formula XII In the formula: is the initial inertia weight, is the inertia weight when the new iteration reaches the maximum number of iterations.

6. The method according to claim 1, characterized in that The method further includes step 5 of splitting the sample set into a training set and a test set, decomposing the training set using the VMD model to obtain an IMF sequence, and based on the test set, using the coefficient of determination to perform performance evaluation on multiple trained models, as shown in Equation XIII: Formula XIII Wherein: is the actual output value, is the predicted output value, represents the actual output value after averaging.

7. A device for predicting nuclear power operation parameters, characterized in that, The device includes: A preprocessing module for obtaining historical operation data and preprocessing the historical operation data to generate a sample set; A decomposition module for decomposing the sample set using the VMD model to obtain an IMF sequence; A correction module for correcting each IMF in the IMF sequence to obtain multiple corrected IMFs; A prediction module for superimposing the predicted values output by the multiple corrected IMFs to obtain a final prediction result.

8. A device for predicting nuclear power operation parameters, characterized in that, The device includes: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the method according to any one of claims 1 to 6.

9. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

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