Energy storage frequency modulation instruction prediction correction method and system based on quantum entanglement entropy

Abstract

The invention provides an energy storage frequency modulation instruction prediction correction method and system based on quantum entanglement entropy, and belongs to the technical field of new energy and energy conservation. Through combination of a quantum information processing technology and deep learning, the precision and adaptability of energy storage system frequency modulation instruction prediction are significantly improved. The complexity of the frequency modulation instruction sequence is quantitatively evaluated through the quantum entanglement entropy, and intelligent judgment of the prediction difficulty is achieved; and the designed quantum entanglement dynamic correction factor effectively captures non-linear characteristics which are difficult to identify by the traditional GRU network, so that the prediction result can be adaptively adjusted and corrected, and the accuracy of energy storage system frequency modulation instruction prediction is improved. The method integrating quantum calculation and deep learning not only improves the generalization ability of the prediction model, but also provides more accurate decision support for the energy storage system to participate in power grid frequency modulation, and finally improves the stability and economy of power grid frequency modulation.

Description

Technical Field

[0001] This invention belongs to the field of new energy and energy conservation technology, and specifically relates to a method and system for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy. Background Technology

[0002] Traditional methods for frequency regulation of thermal power units using hybrid energy storage (extracapacitor + lithium battery) transmit the difference between the frequency regulation command and the actual operating status of the thermal power unit to the hybrid energy storage system. In this allocation mechanism, the low-frequency portion is handled by the lithium battery, while the high-frequency portion is handled by the extracapacitor. However, this process suffers from a time delay. It takes time for the signal to travel from the frequency regulation command to the extracapacitor and lithium battery, and there is also a time lag in the response of the extracapacitor or lithium battery itself. This time lag further affects the frequency regulation effect, reduces revenue, and ultimately has an adverse impact on the economic benefits of the power plant.

[0003] To address this issue, Chinese patent application CN120165407A proposes a method and system for predicting energy storage frequency regulation commands. This method decomposes the frequency regulation command sequence into multiple subsequences, identifies the subsequence with the largest frequency change as the target subsequence, generates a target virtual sequence, and uses a GRU network to predict the target virtual sequence and the remaining subsequences. Finally, all prediction results are superimposed to obtain the predicted frequency regulation command. By predicting the magnitude of the frequency regulation command in advance, overcapacity and batteries can act ahead of time, thereby increasing profitability. The original frequency regulation command sequence has strong nonlinearity, which greatly increases the difficulty of neural network prediction. The specific reasons are as follows: Nonlinear time series often contain complex patterns such as abrupt changes, multimodal fluctuations, and chaotic behavior (e.g., Lorentz systems). Traditional linear models are simply inadequate in modeling these complex patterns. While neural networks can theoretically approximate these patterns, achieving this requires constructing models with significantly higher complexity, which undoubtedly increases the difficulty and cost of modeling.

[0004] Complex nonlinear relationships require neural networks to have deeper network structures or more parameters. However, with small sample data, models are prone to overfitting, leading to poor performance on new data. Moreover, the non-convexity of the loss function makes the optimization process extremely difficult, and it is easy to get stuck in local optima during training. For example, when training a GRU, careful selection of the learning rate and initialization strategy is required, as even a slight mistake can affect model performance.

[0005] Furthermore, learning nonlinear patterns typically requires a large amount of data. For example, predicting chaotic systems may require tens of thousands of time steps to cover different states; otherwise, the model struggles to accurately learn the underlying patterns. Chaotic behavior is highly sensitive; even if the model fits historical data well, small errors in initial conditions can be amplified exponentially in long-term predictions, rendering the predictions invalid.

[0006] Given the above background, there is an urgent need for a method to improve the accuracy of frequency modulation prediction. Summary of the Invention

[0007] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a method and system for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy, comprising the following steps: Obtain the frequency regulation command sequence of the energy storage system; The acquired frequency modulation command sequence is converted into a quantum state sequence through differential phase quantum encoding; A many-body quantum system is constructed based on quantum state sequences. The quantum entanglement entropy is calculated by decomposing the constructed many-body quantum system. The prediction difficulty of the frequency modulation command sequence is determined based on the calculated quantum entanglement entropy. The frequency modulation command sequence of the energy storage system is input into the GRU network for prediction to obtain the original prediction value. A quantum entanglement dynamic correction factor is designed to dynamically adjust the prediction result of the GRU network.

[0009] The acquired frequency regulation command sequence for the energy storage system is N represents the sequence length.

[0010] The specific method for converting the acquired frequency modulation command sequence into a quantum state sequence through differential phase quantum encoding is as follows: Calculate the normalization coefficient of the amplitude of the frequency modulation command sequence

[0011] Each sample value is determined by the difference between adjacent sample values. The specific formula for converting to a quantum state is as follows:

[0012]

[0013] in, The quantum state phase angle is determined by the difference between adjacent sampled values; It is a tiny constant. .

[0014] The specific method for the step of constructing a many-body quantum system based on quantum state sequences, calculating the quantum entanglement entropy by decomposing the constructed many-body quantum system, and determining the prediction difficulty of the frequency modulation command sequence based on the calculated quantum entanglement entropy is as follows: Constructing a multi-body quantum system based on quantum state sequences to form a joint density matrix of a two-body system; The constructed many-body quantum system is divided into subsystem A at the current moment and subsystem B at the next moment; Taking a partial trace of subsystem B at the next time step yields the reduced density matrix of subsystem A at the current time step; The quantum entanglement entropy, which characterizes the strength of quantum correlation, is calculated based on the reduced density matrix of subsystem A at the current moment. The difficulty of predicting frequency modulation command sequences is determined based on quantum entanglement entropy.

[0015] The joint density matrix of the two-body system The formula is expressed as follows:

[0016] in, This is the tensor product operator, used to represent a joint system of two quantum states; Let be the pure state density matrix of the k-th quantum state; Taking a partial trace of subsystem B at the next time step yields the reduced density matrix of subsystem A at the current time step, as expressed in the following formula;

[0017] in, Let A be the reduced density matrix of subsystem A at the current time. This represents the Hilbert space trace operation of subsystem B at the next time step, which preserves the quantum state information of subsystem A at the current time step; Denote the basis vectors of B; The quantum entanglement entropy, which characterizes the strength of quantum correlation, is calculated based on the reduced density matrix of subsystem A at the current moment.

[0018] in, This refers to the quantum entanglement entropy.

[0019] The specific method for the step of inputting the frequency modulation command sequence of the energy storage system into the GRU network for prediction to obtain the original prediction value, and designing a quantum entanglement dynamic correction factor to dynamically adjust the prediction result of the GRU network is as follows: Original sequence Input the GRU network and output the sequence of raw predictions for the next S steps. ; Based on quantum entanglement entropy and historical prediction bias, a quantum entanglement dynamic correction factor is constructed to obtain the correction amount; The correction is superimposed on the original prediction result sequence of GRU to obtain the final prediction result after quantum correction.

[0020] Based on quantum entanglement entropy and historical prediction bias, a dynamic correction factor for quantum entanglement is constructed, and the formula for the correction amount is expressed as follows:

[0021] in, This is an adjustment factor used to control the overall correction strength; The time decay coefficient is used to satisfy... With prediction step size s Exponential decay; It is a direction function; Historical bias sensitivity factor, used to assess prediction bias at recent times. It exhibits a nonlinear response; The correction is superimposed onto the original GRU prediction result sequence to obtain the final prediction result after quantum correction, as shown in the following formula:

[0022] in, This is the corrected predicted value. is the original prediction value of the GRU network at step s.

[0023] Secondly, the present invention provides an energy storage frequency modulation command prediction and correction system based on quantum entanglement entropy, comprising: The acquisition module is used to acquire the frequency regulation command sequence of the energy storage system; The quantum state sequence construction module is used to convert the acquired frequency modulation command sequence into a quantum state sequence through differential phase quantum encoding; The computation module is used to construct a many-body quantum system based on a quantum state sequence, calculate the quantum entanglement entropy by decomposing the constructed many-body quantum system, and determine the prediction difficulty of the frequency modulation command sequence based on the calculated quantum entanglement entropy. The prediction correction module is used to input the frequency modulation command sequence of the energy storage system into the GRU network for prediction, obtain the original prediction value, and design a quantum entanglement dynamic correction factor to dynamically adjust the prediction result of the GRU network.

[0024] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy.

[0025] Fourthly, the present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of a method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy.

[0026] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for predicting and correcting frequency modulation commands for energy storage based on quantum entanglement entropy, comprising the following steps: acquiring a frequency modulation command sequence of an energy storage system; converting the acquired frequency modulation command sequence into a quantum state sequence through differential phase quantum encoding; constructing a many-body quantum system based on the quantum state sequence, calculating the quantum entanglement entropy through the constructed many-body quantum system decomposition, and determining the prediction difficulty of the frequency modulation command sequence based on the calculated quantum entanglement entropy; inputting the frequency modulation command sequence of the energy storage system into a GRU network for prediction, obtaining the original prediction value, and designing a quantum entanglement dynamic correction factor to dynamically adjust the prediction result of the GRU network. By combining quantum information processing technology with deep learning, the accuracy and adaptability of frequency modulation command prediction for energy storage systems are significantly improved. The quantitative evaluation of the complexity of the frequency modulation command sequence through quantum entanglement entropy enables intelligent determination of prediction difficulty; the designed quantum entanglement dynamic correction factor effectively captures nonlinear characteristics that are difficult for traditional GRU networks to identify, enabling the prediction result to adaptively adjust and correct, thereby improving the accuracy of frequency modulation command prediction for energy storage systems. This approach, which integrates quantum computing and deep learning, not only improves the generalization ability of prediction models but also provides more accurate decision support for energy storage systems to participate in grid frequency regulation, ultimately enhancing the stability and economy of grid frequency regulation.

[0027] Furthermore, quantum entanglement entropy is directly used as a correction weight, quantifying the prediction difficulty and embedding it into the correction process. The greater the quantum entanglement entropy, the greater the prediction difficulty and the stronger the correction force. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system diagram of Embodiment 4 of the present invention. Detailed Implementation

[0029] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0030] Example 1 like Figure 1 As shown, a method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy includes the following steps: S1: Obtain the frequency regulation command sequence of the energy storage system; S2: The acquired frequency modulation command sequence is converted into a quantum state sequence through differential phase quantum encoding; S3: Construct a many-body quantum system based on quantum state sequences, calculate the quantum entanglement entropy by decomposing the constructed many-body quantum system, and determine the prediction difficulty of the frequency modulation command sequence based on the calculated quantum entanglement entropy; S4: Input the frequency modulation command sequence of the energy storage system into the GRU network for prediction to obtain the original prediction value. Design a quantum entanglement dynamic correction factor to dynamically adjust the prediction result of the GRU network.

[0031] Specifically, in S1, the acquired frequency regulation command sequence for the energy storage system is as follows: N represents the sequence length.

[0032] Specifically, in S2, the acquired frequency modulation command is mapped to a quantum state sequence, and the potential modes of the quantum state sequence are characterized using quantum superposition. The specific method is as follows: Calculate the normalization coefficient of the amplitude of the frequency modulation command sequence ; Each sample value is determined by the difference between adjacent sample values. The specific formula for converting to a quantum state is as follows:

[0033]

[0034] in, The quantum state phase angle is determined by the difference between adjacent sampled values; It is a tiny constant. ; Zhongruo =0, then =0.

[0035] Specifically, in S3, a many-body quantum system is constructed based on the quantum state sequence, forming a joint density matrix of a two-body system. The specific formula is as follows:

[0036] in, This is the tensor product operator, used to represent a joint system of two quantum states; Let be the pure state density matrix of the k-th quantum state.

[0037] The quantum entanglement entropy is calculated by decomposing a constructed many-body quantum system, as follows: The constructed many-body quantum system is divided into subsystem A at the current moment and subsystem B at the next moment; Taking a partial trace of subsystem B at the next time step yields the reduced density matrix of subsystem A at the current time step;

[0038] in, Let A be the reduced density matrix of subsystem A at the current time. This represents the Hilbert space trace operation of subsystem B at the next time step, which preserves the quantum state information of subsystem A at the current time step; Let B be the basis vector. The quantum statistical properties of subsystem A are extracted by trace operation, thus eliminating the influence of the degrees of freedom of B.

[0039] Calculate the quantum correlation strength based on the reduced density matrix of subsystem A at the current time. The quantum entanglement entropy is calculated using the following formula:

[0040] in, This refers to the quantum entanglement entropy.

[0041] The prediction difficulty of the frequency modulation command sequence is determined based on quantum entanglement entropy, as follows:

[0042] like The frequency modulation command sequence approximates a classical periodic signal and is easy to predict; if The frequency modulation command sequence exhibits strong quantum chaotic characteristics, making it extremely difficult to predict.

[0043] Specifically, in S4, the frequency modulation command sequence of the energy storage system is input into the GRU network for prediction to obtain the original predicted value. A quantum entanglement dynamic correction factor is then designed to dynamically adjust the prediction results of the GRU network. The details are as follows: Original sequence Input the GRU network and output the sequence of raw predictions for the next S steps. ; Based on quantum entanglement entropy and historical prediction bias, a quantum entanglement dynamic correction factor (QEDCF) is constructed to obtain the correction amount; specifically represented as follows:

[0044] in, This is an adjustment factor with a default value of 0.1, used to control the overall correction strength; This is the time decay factor, with a default value of 0.3, used to satisfy... With prediction step size s Exponential decay; It is a direction function; Historical bias sensitivity factor, used to assess prediction bias at recent times. It exhibits a nonlinear response.

[0045] The correction is superimposed onto the original GRU prediction result sequence to obtain the final prediction result after quantum correction, as shown in the following formula:

[0046] in, This is the corrected predicted value. is the original prediction value of the GRU network at step s.

[0047] Furthermore, Defined as:

[0048] Example 2 To further verify the advantages of the present invention, the present invention uses the method of the present invention and the GRU direct prediction method to predict the frequency modulation sequence, and the results are shown in Table 1 below.

[0049] Frequency modulation sequence 1 is derived from frequency modulation data from 0:00 to 20:00 on December 1, 2024, from a power plant in Hulunbuir.

[0050] Frequency modulation sequence 2 is derived from frequency modulation data from a power plant in Hulunbuir from 2:00 AM to 10:00 PM on December 2, 2024.

[0051] Table 1. Frequency Modulation Sequence Prediction Results

[0052] The experimental results show that all four evaluation indicators have decreased, indicating that the prediction method proposed in this invention can reduce the nonlinearity of the original sequence compared to the traditional GRU prediction method, further improving the prediction accuracy. This can help power plants improve their frequency regulation response capability and further increase their profitability.

[0053] Example 3 A storage frequency modulation command prediction and correction system based on quantum entanglement entropy includes: The acquisition module is used to acquire the frequency regulation command sequence of the energy storage system; The quantum state sequence construction module is used to convert the acquired frequency modulation command sequence into a quantum state sequence through differential phase quantum encoding; The computation module is used to construct a many-body quantum system based on a quantum state sequence, calculate the quantum entanglement entropy by decomposing the constructed many-body quantum system, and determine the prediction difficulty of the frequency modulation command sequence based on the calculated quantum entanglement entropy. The prediction correction module is used to input the frequency modulation command sequence of the energy storage system into the GRU network for prediction, obtain the original prediction value, and design a quantum entanglement dynamic correction factor to dynamically adjust the prediction result of the GRU network.

[0054] Example 4 like Figure 2 As shown, the present invention also provides an electronic device 100 based on a quantum entanglement entropy-based method for predicting and correcting energy storage frequency modulation commands; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0055] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the energy storage frequency modulation command prediction and correction method based on quantum entanglement entropy described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0056] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0057] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for predicting and correcting energy storage frequency modulation instructions based on quantum entanglement entropy, and the processor 102 can execute the multiple instructions to achieve the following: Obtain the frequency regulation command sequence of the energy storage system; The acquired frequency modulation command sequence is converted into a quantum state sequence through differential phase quantum encoding; A many-body quantum system is constructed based on quantum state sequences. The quantum entanglement entropy is calculated by decomposing the constructed many-body quantum system. The prediction difficulty of the frequency modulation command sequence is determined based on the calculated quantum entanglement entropy. The frequency modulation command sequence of the energy storage system is input into the GRU network for prediction to obtain the original prediction value. A quantum entanglement dynamic correction factor is designed to dynamically adjust the prediction result of the GRU network.

[0058] Example 5 If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy, characterized in that, Includes the following steps: Obtain the frequency regulation command sequence of the energy storage system; The acquired frequency modulation command sequence is converted into a quantum state sequence through differential phase quantum encoding; A many-body quantum system is constructed based on quantum state sequences. The quantum entanglement entropy is calculated by decomposing the constructed many-body quantum system. The prediction difficulty of the frequency modulation command sequence is determined based on the calculated quantum entanglement entropy. The frequency modulation command sequence of the energy storage system is input into the GRU network for prediction to obtain the original prediction value. A quantum entanglement dynamic correction factor is designed to dynamically adjust the prediction result of the GRU network.

2. The method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy according to claim 1, characterized in that, The acquired frequency regulation command sequence for the energy storage system is N represents the sequence length.

3. The method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy according to claim 2, characterized in that, The specific method for converting the acquired frequency modulation command sequence into a quantum state sequence through differential phase quantum encoding is as follows: Calculate the normalization coefficient of the amplitude of the frequency modulation command sequence Each sample value is determined by the difference between adjacent sample values. The specific formula for converting to a quantum state is as follows: in, The quantum state phase angle is determined by the difference between adjacent sampled values; It is a tiny constant. .

4. The method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy according to claim 3, characterized in that, The specific method for the step of constructing a many-body quantum system based on quantum state sequences, calculating the quantum entanglement entropy by decomposing the constructed many-body quantum system, and determining the prediction difficulty of the frequency modulation command sequence based on the calculated quantum entanglement entropy is as follows: Constructing a multi-body quantum system based on quantum state sequences, forming a joint density matrix of a two-body system; The constructed many-body quantum system is divided into subsystem A at the current moment and subsystem B at the next moment; Taking a partial trace of subsystem B at the next time step yields the reduced density matrix of subsystem A at the current time step; The quantum entanglement entropy, which characterizes the strength of quantum correlation, is calculated based on the reduced density matrix of subsystem A at the current moment. The difficulty of predicting frequency modulation command sequences is determined based on quantum entanglement entropy.

5. The method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy according to claim 4, characterized in that, The joint density matrix of the two-body system The formula is expressed as follows: in, This is the tensor product operator, used to represent a joint system of two quantum states; Let be the pure state density matrix of the k-th quantum state; Taking a partial trace of subsystem B at the next time step yields the reduced density matrix of subsystem A at the current time step, as expressed in the following formula; in, Let A be the reduced density matrix of subsystem A at the current time. This represents the Hilbert space trace operation of subsystem B at the next time step, which preserves the quantum state information of subsystem A at the current time step; Denote the basis vectors of B; The quantum entanglement entropy, which characterizes the strength of quantum correlation, is calculated based on the reduced density matrix of subsystem A at the current moment. in, This refers to the quantum entanglement entropy.

6. The method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy according to claim 5, characterized in that, The specific method for the step of inputting the frequency modulation command sequence of the energy storage system into the GRU network for prediction to obtain the original prediction value, and designing a quantum entanglement dynamic correction factor to dynamically adjust the prediction result of the GRU network is as follows: Original sequence Input the GRU network and output the sequence of raw predictions for the next S steps. ; Based on quantum entanglement entropy and historical prediction bias, a quantum entanglement dynamic correction factor is constructed to obtain the correction amount; The correction is superimposed on the original prediction result sequence of GRU to obtain the final prediction result after quantum correction.

7. The method for predicting and correcting energy storage frequency modulation commands based on quantum entanglement entropy according to claim 6, characterized in that, Based on quantum entanglement entropy and historical prediction bias, a dynamic correction factor for quantum entanglement is constructed, and the formula for the correction amount is expressed as follows: in, This is an adjustment factor used to control the overall correction strength; The time decay coefficient is used to satisfy... With prediction step size s Exponential decay; It is a direction function; Historical bias sensitivity factor, used to assess prediction bias at recent times. It exhibits a nonlinear response; The correction is superimposed onto the original GRU prediction result sequence to obtain the final prediction result after quantum correction, as shown in the following formula: in, This is the corrected predicted value. is the original prediction value of the GRU network at step s.

8. A storage frequency modulation command prediction and correction system based on quantum entanglement entropy, characterized in that, include: The acquisition module is used to acquire the frequency regulation command sequence of the energy storage system; The quantum state sequence construction module is used to convert the acquired frequency modulation command sequence into a quantum state sequence through differential phase quantum encoding; The computation module is used to construct a many-body quantum system based on a quantum state sequence, calculate the quantum entanglement entropy by decomposing the constructed many-body quantum system, and determine the prediction difficulty of the frequency modulation command sequence based on the calculated quantum entanglement entropy. The prediction correction module is used to input the frequency modulation command sequence of the energy storage system into the GRU network for prediction, obtain the original prediction value, and design a quantum entanglement dynamic correction factor to dynamically adjust the prediction result of the GRU network.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the energy storage frequency modulation command prediction and correction method based on quantum entanglement entropy as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy storage frequency modulation command prediction and correction method based on quantum entanglement entropy as described in any one of claims 1 to 7.

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