Method for controlling rod position of reactor control rod and related device

Abstract

The invention discloses a reactor control rod position control method and a related device, and the method comprises the steps: obtaining an original rod position sequence of a reactor control rod, decomposing the original rod position sequence, and obtaining M IMF components; inputting the M IMF components into a trained long short-term memory network model to obtain predicted values X1, X2,..., XM of the M IMF components, establishing an error evaluation index D-SMAPE according to the predicted values X1, X2,..., XM of the M IMF components and actual values thereof, and obtaining error coefficients delta Y1, delta Y2,..., delta YM corresponding to SWT-LMD subsequences; inputting the error coefficients delta Y1, delta Y2,..., and delta YM into a trained ResNet prediction model to obtain final IMF component prediction values Y1, Y2,..., and YM of the original rod position sequence; and controlling the control rod to act according to the final IMF component predicted values Y1, Y2,..., YM of the original rod position sequence. According to the method and the related device, the rod position of the reactor control rod can be accurately controlled.

Description

Technical Field

[0001] The invention belongs to the technical field of nuclear power engineering, and relates to a method for controlling the position of a reactor control rod and a related device. Background Art

[0002] Control rods, as a core component of a nuclear power plant, are crucial to its safe and stable operation. Their primary function is to de-energize the control rod drive mechanism when a reactor protection emergency shutdown is triggered, allowing the control rods to fall rapidly into the reactor core under the influence of gravity. This rapid descent effectively absorbs neutrons within the reactor, rapidly suppressing the rate of nuclear fission reactions and thus preventing dangerous conditions such as overpower in the reactor, providing a solid safety barrier for the reactor. During normal operation, the control rod drive mechanism's claws must accurately and precisely transmit magnetic pole movement commands according to the correct timing, thereby providing precise control rod position. This process requires extremely high precision and stability to prevent control rod misalignment from causing a significant power drop or even an unexpected reactor shutdown.

[0003] As the total installed capacity of nuclear power plants continues to increase, the demand for nuclear power plants to participate in peak and frequency regulation in response to grid load will increase. During nuclear power plant operation, the positions of reactor control rods will be continuously adjusted in response to unit load, placing higher demands on control rod position control. The development of more accurate control rod position prediction methods is crucial for the safe and stable operation of nuclear power plants. Currently developed reactor control rod prediction algorithms suffer from poor prediction accuracy. For example, the particle swarm optimization (PSO) algorithm is prone to falling into local optima and converges quickly, resulting in low convergence accuracy. The wavelet energy method (WEM) lacks adaptability, and the wavelet transform is sensitive to noise, especially when processing complex signals, which may cause edge blurring or loss. Local mean decomposition (LMD) also has some limitations. For example, when processing extremely complex or noisy signals, the decomposition results may be unstable or inaccurate. Summary of the Invention

[0004] The object of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide a method for controlling the position of a reactor control rod and a related device, which can accurately control the position of the reactor control rod.

[0005] To achieve the above-mentioned object, the present invention discloses a method for controlling the position of a reactor control rod, comprising:

[0006] Obtaining an original rod position sequence of a reactor control rod, and decomposing the original rod position sequence to obtain M IMF components;

[0007] The M IMF components are input into the trained long short-term memory network model to obtain the predicted values X1, X2, ..., X M , according to the predicted values X1, X2, ..., X of M IMF components M The error evaluation index D-SMAPE is established with its actual value, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ;

[0008] The error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., Y M ;

[0009] According to the final IMF component prediction values Y1, Y2, ..., Y M Control the control stick movements.

[0010] The further improvement of the method for controlling the position of reactor control rods of the present invention is as follows:

[0011] Furthermore, the process of decomposing the original bit sequence to obtain M IMF components is as follows:

[0012] Perform SWT-LMD modal decomposition on the original stick position sequence to obtain M IMF components.

[0013] Furthermore, the loss function of the ResNet prediction model during training is:

[0014]

[0015] Where N is the number of samples, is the revised predicted value of the control rod position.

[0016] Furthermore, the final IMF component prediction values Y1, Y2, ..., Y M The process of controlling the control rod movement is:

[0017] The final IMF component prediction values Y1, Y2, ..., Y M It is converted into an electrical signal, and the control rod drive mechanism drives the control rod to lift or lower according to the electrical signal.

[0018] Furthermore, based on the dynamic resolution adjustment-adaptive digital-to-analog conversion algorithm, the final IMF component prediction values Y1, Y2, ..., Y M Converted into electrical signals.

[0019] The present invention discloses a control system for controlling the position of a reactor control rod, comprising:

[0020] an acquisition module, configured to acquire an original rod position sequence of the reactor control rods, and decompose the original rod position sequence to obtain M IMF components;

[0021] The first prediction module is used to input the M IMF components into the trained long short-term memory network model to obtain the predicted values X1, X2, ..., X M , according to the predicted values X1, X2, ..., X of M IMF components M The error evaluation index D-SMAPE is established with its actual value, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ;

[0022] The second prediction module is used to convert the error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., YM of the original stick position sequence;

[0023] A control module is used to predict the final IMF component values Y1, Y2, ..., Y according to the original stick position sequence. M Control the control stick movements.

[0024] The control system for the position of the reactor control rods of the present invention is further improved in that:

[0025] Furthermore, the process of decomposing the original bit sequence to obtain M IMF components is as follows:

[0026] Perform SWT-LMD modal decomposition on the original stick position sequence to obtain M IMF components.

[0027] Furthermore, the final IMF component prediction values Y1, Y2, ..., Y M The process of controlling the control rod movement is:

[0028] The final IMF component prediction values Y1, Y2, ..., Y M It is converted into an electrical signal, and the control rod drive mechanism drives the control rod to lift or lower according to the electrical signal.

[0029] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for controlling the position of a reactor control rod are implemented.

[0030] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method for controlling the position of a reactor control rod are implemented.

[0031] The present invention has the following beneficial effects:

[0032] The control method and related device for the position of the reactor control rods of the present invention use a stationary wavelet transform and LMD data preprocessing technology based on complementary advantages to decompose the original rod position sequence during specific operation. A hierarchical progressive decomposition method is used to couple the SWT and LMD technologies to overcome the shortcomings of the two decomposition methods, avoid the problems of modal aliasing and insufficient frequency resolution in traditional methods, improve prediction accuracy, and then use the long short-term memory network model and the ResNet prediction model to predict the final IMF component prediction values Y1, Y2, ..., Y of the original rod position sequence. M , and thereby control the position of the reactor control rods with higher accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0037] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0038] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0039] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0040] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0043] Example 1

[0044] refer to Figure 1 The method for controlling the position of a reactor control rod according to the present invention comprises the following steps:

[0045] 1) Obtaining an original rod position sequence of the reactor control rods, performing SWT-LMD modal decomposition on the original rod position sequence, and obtaining M IMF components;

[0046] By selecting the wavelet basis and the number of decomposition layers, the original stick position sequence is subjected to SWT decomposition to obtain sub-signals of different scales, such as approximate components and detail components; each sub-signal is subjected to LMD decomposition, and the result of the LMD decomposition is reconstructed to obtain M IMF components;

[0047] 2) Input the M IMF components into the trained long short-term memory network model (LSTM) to obtain the predicted values X1, X2, ..., X of the M IMF components. M , where the actual value of the IMF component corresponding to each predicted value is M1, M2, ..., M M , based on the predicted value and actual value of each IMF component, the error evaluation index D-SMA.PE is established, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ;

[0048] 3) The error coefficients ΔY1, ΔY2, ..., AY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., Y of the original stick position sequence M ;

[0049] 4) Based on the dynamic resolution adjustment-adaptive digital-to-analog converter algorithm (Dynamic Resolution Adaptive Digital-to-Analog Converter, hereinafter referred to as D-ADC), the final IMF component prediction values Y1, Y2, ..., Y MIt is converted into an electrical signal, and the control rod drive mechanism drives the control rod to lift or lower according to the electrical signal.

[0050] The process of performing SWT-LMD modal decomposition on the original stick position sequence in step 1) is as follows:

[0051] 11) Perform SWT decomposition on the original stick position sequence. The specific process is as follows:

[0052] 111) Wavelet transform;

[0053] The reactor rod position signal after denoising Continuous wavelet transform is performed, and according to the characteristics of the reactor rod position signal, the Daubechies wavelet basis function method is selected to obtain the wavelet coefficient W x (a, b) is:

[0054]

[0055] Among them, a is the scale parameter, b is the translation parameter, ψ(t) is the wavelet basis function, and ψ* represents the complex conjugate.

[0056] 112) For the wavelet coefficient W x (a, b) are synchronously compressed to obtain a high-resolution time-frequency representation T x (a, b) is:

[0057]

[0058] Among them, ω x (a, b) is the instantaneous frequency of the wavelet coefficients, and δ(·) is the Daubechies wavelet basis function.

[0059] 113) Modal extraction;

[0060] According to the time-frequency representation T x (a, b), Extracted modal component M of reactor rod control signal k (t) is:

[0061]

[0062] Among them, ω k and ω k+1 is the frequency range of the kth modal component.

[0063] 12) For the modal component M k (t) Perform LMD decomposition. The specific process is as follows:

[0064] 121) Calculate the local mean and envelope function:

[0065] For the modal component M of the reactor rod control signalk (t), initialize the residual signal r(t) as:

[0066] r(t)=M k (t)

[0067] Obtain all local extreme points of the initial residual control stick position signal r(t), and then obtain the upper envelope max(r(t)) and lower envelope min(r(t)) through linear interpolation between adjacent extreme points;

[0068] Calculate the average value m(t) of the upper and lower envelopes as:

[0069]

[0070] Calculate half of the difference between the upper and lower envelopes a(t) as:

[0071]

[0072] 122) Separation of AM and FM components;

[0073] Subtract the local mean function m(t) from the control rod position signal r(t), and then divide it by the envelope estimation function a(t) to obtain the pure frequency modulation function F(t):

[0074]

[0075] 123) Construct a product function;

[0076] By integrating the pure frequency modulation function F(t), we get the phase function, and by taking the derivative of the phase function, we get the instantaneous frequency and the product function PF. n (t) is:

[0077] PF n (t) = a n (t)·cos(∫F n (t)dt)

[0078] 124) Iterative decomposition;

[0079] Reactor rod position signal Subtract the first product function PF n (t), and obtain the residual control rod position signal r1(t); repeat the above steps for r1(t) to obtain the second product function PF2(t) and the residual signal r2(t), and so on until the residual control rod position signal r n (t) A termination condition is satisfied, where the amplitude of the residual signal is less than a preset amplitude or the preset number of decomposition layers is reached.

[0080] 13) Modal reconstruction combining SWT and LMD;

[0081] Perform inverse wavelet transform on each component after LMD decomposition and reconstruction to obtain the final control rod position reconstruction signal for:

[0082]

[0083] The algorithm provides high-resolution time-frequency representation through synchronized compressed wavelet transform (SWT), combined with the adaptive decomposition capability of local mean decomposition (LMD), and converts the signal from the time-frequency domain back to the time domain through inverse wavelet transform, completing the entire combination process. The resulting signal decomposition result has both the multi-resolution analysis characteristics of wavelet transform and the adaptive decomposition characteristics of LMD, which can more accurately extract the modal components of non-stationary signals.

[0084] The process of putting the M IMF components into the trained long short-term memory network model (LSTM) for prediction in step 2) is as follows:

[0085] 21) Initialize the network control stick position IMF parameters;

[0086] 211) Initialize the control stick IMF parameters of the LSTM network, including the weight matrix W f , W i , W O and W c and the bias term b f , b i ,b0,b C ;

[0087] 212) Initialize the hidden state h0 and the memory cell state C0.

[0088] 22) Adaptive gating mechanism;

[0089] 221) Forget Gate:

[0090]

[0091] Among them, f t is the output of the forget gate at time t, σ is the Sigmoid function, and its output value is between 0 and 1; W f is the weight matrix of the forget gate; Indicates that the hidden state h of the previous moment t-1 and the current input x t Splice together; b f is the bias term of the forget gate; g(t) is a time-dependent adaptive function used to dynamically adjust the weight of the forget gate; α f is the adaptive coefficient.

[0092] 222) Input Gate:

[0093] The output i of the input gate at time t t for:

[0094]

[0095] Among them, W i is the weight matrix of the input gate, b i is the corresponding bias term, α i is the adaptive coefficient.

[0096] 223) memory unit status;

[0097] Calculate the state value of the candidate memory unit for:

[0098]

[0099] Calculate the candidate memory unit state value C t for:

[0100]

[0101] Among them, the tanh function maps the output value to between -1 and 1, W c is the weight matrix of the candidate memory unit state, b c is the corresponding bias term.

[0102] 224) Output Gate:

[0103] The output o of the output gate at time t t for

[0104]

[0105] Among them, W o is the weight matrix of the output gate, b o is the bias term, α o is the adaptive coefficient.

[0106] 225) Update hidden state:

[0107] h t =o t tanh(C t )

[0108] Among them, the tanh function maps the output value to between -1 and 1.

[0109] 23) Multi-level memory unit;

[0110] For each level 1, update the memory cell state

[0111]

[0112] in, and They are the outputs of the forget gate and input gate of the first layer respectively; is the candidate memory unit state of the first layer.

[0113] Calculate multi-level hidden states for:

[0114]

[0115] in, It is the output gate of the first layer.

[0116] Determine the final hidden state h t for:

[0117]

[0118] Among them, β l is the weight coefficient of the first layer.

[0119] 24) Output control rod position prediction results;

[0120] The final hidden state h is converted through the fully connected layer t Mapped to the helium flow output space, the control rod position prediction result is obtained for:

[0121]

[0122] Among them, w y and b y are the weight matrix and bias term of the fully connected layer respectively.

[0123] The process of establishing the error evaluation index D-SMAPE based on the predicted value of each IMF component and the actual value of each IMF component in step 2) is as follows:

[0124] 2a1) Calculate the dynamic error of the control rod position e i for:

[0125]

[0126] 2a2) Error aggregation;

[0127] Average all errors in the control rod position sequence:

[0128]

[0129] Segment aggregation:

[0130]

[0131] 2a3) Output error coefficient;

[0132] Multiply the average value obtained in step 2a2) by 100% to obtain the final SMAPE value, and then express the control rod position error coefficients ΔY1, ΔY2, ..., ΔY predicted by the model in the form of percentages. M .

[0133] In step 3), the error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ...Y of the original stick position sequence M The process is:

[0134] 31) Residual block construction;

[0135] Introduce dynamic residual connection to control the rod position error coefficient ΔY according to the input i The dynamic characteristics of the residual connection weight coefficient α(Δy i ):

[0136] y i =F(ΔY i ,{w i})+α(ΔY i )·ΔY i

[0137] Among them, y i is the predicted value of the control rod position, F(ΔY i ,{w i}) is the residual function; σ is the Sigmoid activation function, and α(ΔY i ) is limited to the range [0, 1].

[0138] α(ΔY i )for:

[0139] α(ΔY i )=σ(W α ΔY i +b α )

[0140] 32) Construct a residual network;

[0141] A residual network for controlling the stick position is constructed by stacking multiple residual blocks;

[0142] Assume that the residual network has I control stick bit residual blocks, and the input of the first control stick bit residual block is X1-1 , the output is X1, then:

[0143] x l =ResBlock(x 1-1 )

[0144] Among them, ResBlock represents the operation of the residual block.

[0145] Assume that the output of the last residual block is X1, and through a fully connected layer, the control stick position prediction value y is obtained i for:

[0146] y i =w out x l +b out

[0147] Among them, w out is the weight matrix of the output layer, b out is the bias vector.

[0148] 33) Model training;

[0149] 331) Define the loss function;

[0150] The loss function is:

[0151]

[0152] Where N is the number of samples, is the revised predicted value of the control rod position.

[0153] 332) Select optimizer;

[0154] Calculate the gradient: Let the parameter be θ, the gradient g of the loss function with respect to the parameter t for:

[0155]

[0156] Calculate the first-order moment estimate and the second-order moment estimate as:

[0157]

[0158] Where β1 and β2 are attenuation rates, which are usually set to 0.9 and 0.999 respectively.

[0159] Update parameters:

[0160]

[0161] Among them, α is the learning rate, ∈ is the factor constant.

[0162] 333) Circuit training;

[0163] Forward propagation: transform x i Input into the model, and through calculation of each layer, the predicted value of the control rod position is obtained

[0164] Calculate loss: Calculate the loss L of the current batch according to the loss function.

[0165] Backpropagation: Calculate the gradient g of the loss with respect to the model parameters t .

[0166] Parameter update: Use the optimizer to update the model’s parameters θ.

[0167] 334) output prediction results;

[0168] The final feature is mapped to the output space through the fully connected layer to obtain the final predicted value of the control stick position for:

[0169]

[0170] Among them, w yi and b yi are the weight matrix and bias term of the fully connected layer respectively.

[0171] The operation process of step 4) is:

[0172] 41) Adjust dynamic resolution;

[0173] Detect the frequency f of the input control rod position signal k ;

[0174] Dynamically adjust the control rod position resolution N according to the frequency k for:

[0175]

[0176] Requantize the control stick position signal using the adjusted resolution.

[0177] 42) The generated electrical signal is:

[0178]

[0179] Among them, v out(n) is the analog voltage signal of the control rod output at the nth sampling moment, f i，n is the conversion coefficient, N k，n It is the digital signal value of the control stick bit input at the nth sampling moment.

[0180] Example 2

[0181] The control system for controlling the position of a reactor control rod according to the present invention comprises:

[0182] an acquisition module, configured to acquire an original rod position sequence of the reactor control rods, and decompose the original rod position sequence to obtain M IMF components;

[0183] The first prediction module is used to input the M IMF components into the trained long short-term memory network model to obtain the predicted values X1, X2, ..., X M , according to the predicted values X1, X2, ..., X of M IMF components M The error evaluation index D-SMAPE is established with its actual value, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ;

[0184] The second prediction module is used to convert the error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., Y M ;

[0185] A control module is used to predict the final IMF component values Y1, Y2, ..., Y according to the original stick position sequence. M Control the control stick movements.

[0186] In this embodiment, the process of decomposing the original bit sequence to obtain M IMF components is as follows:

[0187] Perform SWT-LMD modal decomposition on the original stick position sequence to obtain M IMF components.

[0188] In this embodiment, the final IMF component prediction values Y1, Y2, ..., Y M The process of controlling the control rod movement is:

[0189] The final IMF component prediction values Y1, Y2, ..., Y M It is converted into an electrical signal, and the control rod drive mechanism drives the control rod to lift or lower according to the electrical signal.

[0190] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0191] Example 3

[0192] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for controlling the position of a reactor control rod are implemented. For example, the steps include: obtaining an original rod position sequence of the reactor control rods, decomposing the original rod position sequence to obtain M IMF components; inputting the M IMF components into a trained long short-term memory network model to obtain predicted values X1, X2, ..., X of the M IMF components. M , according to the predicted values X1, X2, ..., X of M IMF components M The error evaluation index D-SMAPE is established with its actual value, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ; The error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., Y M The control rod motion is controlled based on the final IMF component prediction values Y1, Y2, ..., YM of the original rod position sequence. The memory may include internal memory, such as a high-speed random access memory, or may also include non-volatile memory, such as at least one disk drive. The processor, network interface, and memory are interconnected via an internal bus, which may be an Industry Standard Architecture bus, a Peripheral Component Interconnect bus, an Extended Industry Standard Architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the programs may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0193] Example 4

[0194] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method for controlling the position of a reactor control rod are implemented. For example, the steps include: obtaining an original rod position sequence of a reactor control rod, decomposing the original rod position sequence to obtain M IMF components; inputting the M IMF components into a trained long short-term memory network model to obtain predicted values X1, X2, .., X of the M IMF components. M , according to the predicted values X1, X2, ..., X of M IMF components M The error evaluation index D-SMAPE is established with its actual value, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ; The error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., Y M According to the final IMF component prediction value Y1, Y2, ..., Y M Control rod movement. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory and / or cache memory, etc. The non-volatile memory may include read-only memory, hard disk, flash memory, optical disk, magnetic disk, etc.

[0195] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0196] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0197] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0199] Those skilled in the art will readily identify other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0200] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0201] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for controlling the position of a reactor control rod, characterized in that: include: Obtaining an original rod position sequence of a reactor control rod, and decomposing the original rod position sequence to obtain M IMF components; The M IMF components are input into the trained long short-term memory network model to obtain the predicted values X1, X2, ..., X M , according to the predicted values X1, X2, ..., X of M IMF components M The error evaluation index D-SMAPE is established with its actual value, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ; The error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., Y M ; According to the final IMF component prediction values Y1, Y2, ..., Y M Control the control stick movements.

2. The method for controlling the position of a reactor control rod according to claim 1, characterized in that: The process of decomposing the original bit sequence to obtain M IMF components is as follows: Perform SWT-LMD modal decomposition on the original stick position sequence to obtain M IMF components.

3. The method for controlling the position of a reactor control rod according to claim 1, characterized in that: The loss function of the ResNet prediction model during training is: Where N is the number of samples, is the revised predicted value of the control rod position.

4. The method for controlling the position of a reactor control rod according to claim 1, wherein: The final IMF component prediction values Y1, Y2, ..., Y based on the original stick position sequence M The process of controlling the control rod movement is: The final IMF component prediction values Y1, Y2, ..., Y M It is converted into an electrical signal, and the control rod drive mechanism drives the control rod to lift or lower according to the electrical signal.

5. The method for controlling the position of a reactor control rod according to claim 4, characterized in that: Based on the dynamic resolution adjustment-adaptive digital-to-analog conversion algorithm, the final IMF component prediction values Y1, Y2, ..., Y M Converted into electrical signals.

6. A control system for the position of a reactor control rod, characterized in that: include: an acquisition module, configured to acquire an original rod position sequence of the reactor control rods, and decompose the original rod position sequence to obtain M IMF components; The first prediction module is used to input the M IMF components into the trained long short-term memory network model to obtain the predicted values X1, X2, .., X M , according to the predicted values X1, X2, .., X of M IMF components M The error evaluation index D-SMAPE is established with its actual value, and the error coefficients ΔY1, ΔY2, ..., ΔY corresponding to the SWT-LMD subsequence are obtained. M ; The second prediction module is used to convert the error coefficients ΔY1, ΔY2, ..., ΔY M Input into the trained ResNet prediction model to obtain the final IMF component prediction values Y1, Y2, ..., Y M ; A control module is used to predict the final IMF component values Y1, Y2, ..., Y according to the original stick position sequence. M Control the control stick movements.

7. The control system for reactor control rod positions according to claim 6, characterized in that: The process of decomposing the original bit sequence to obtain M IMF components is as follows: Perform SWT-LMD modal decomposition on the original stick position sequence to obtain M IMF components.

8. The control system for reactor control rod positions according to claim 6, characterized in that: The final IMF component prediction values Y1, Y2, ..., Y based on the original stick position sequence M The process of controlling the control rod movement is: The final IMF component prediction values Y1, Y2, ..., Y M It is converted into an electrical signal, and the control rod drive mechanism drives the control rod to lift or lower according to the electrical signal.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for controlling the position of a reactor control rod are implemented as described in any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for controlling the position of a reactor control rod are implemented as claimed in any one of claims 1 to 5.

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