Method and apparatus for calculating high frequency electric field distribution considering permittivity time characteristics

Abstract

The application provides a high-frequency electric field distribution calculation method and device considering dielectric constant time characteristics, and belongs to the technical field of high-frequency electric field distribution calculation. The method comprises the following steps: processing an excitation signal by using continuous wavelet variation to obtain a wavelet quantity diagram of the excitation signal; determining an energy distribution concentrated area of the excitation signal and a VMD decomposition layer number; decomposing the excitation signal by using VMD to obtain different IMF components; extracting the instantaneous frequency h(t) of each IMF component by using an HHT algorithm; extracting the instantaneous frequency peak value distribution curve of each obtained IMF component; calculating the instantaneous frequency distribution in the window function by using a window function; calculating the synthetic instantaneous frequency by weighting the amplitude of each IMF component according to the calculation method of the calculation center; coupling the time-instantaneous frequency relationship with the dielectric constant-frequency variation relationship curve to obtain a dielectric constant-time variation relationship curve; and performing high-frequency electric field distribution calculation based on the dielectric constant-time variation relationship curve.

Description

Technical Field

[0001] This invention relates to the field of high-frequency electric field distribution calculation technology, and in particular to a method and apparatus for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant. Background Technology

[0002] When a charged material in a dielectric undergoes strain under the influence of an electric field, it generates charges of opposite polarity to those of the electrodes at its end faces. This phenomenon is called dielectric polarization. The dielectric constant describes the ability of a material to polarize under an electric field, i.e., the relationship between the intensity of dielectric polarization and the electric field. The dielectric constant values ​​of different dielectrics vary, and their relationships with temperature and power supply frequency also differ. This is because different materials exhibit different types of microscopic polarization. Common types of polarization include electronic polarization, ionic polarization, and dipole polarization, as well as sandwich polarization and spatial polarization, etc. Among these, dipole polarization is inelastic and requires a relatively long time. Therefore, the relative dielectric constant of a polar dielectric is highly dependent on the power supply frequency. At higher frequencies, the dipoles do not have enough time to rotate, resulting in a decrease in the relative dielectric constant.

[0003] In power system operation, equipment is subjected to various voltage surges, including lightning impulse voltages and fast transient overvoltages. The equivalent frequencies of these voltage waveforms can reach several megahertz or even tens of megahertz. Within this frequency range, it is necessary to consider the change of dielectric constant over time. Generally speaking, the dielectric constant decreases as the power supply frequency increases. Traditional electromagnetic simulations often neglect the polarization settling time within the material and calculate based on a constant dielectric constant. However, under lightning impulse voltages or VFTO voltages, the polarization settling time of the material cannot be directly ignored, which can easily lead to the calculated electric field failing to reflect the true electric field strength and distribution. At the same time, for non-periodic power signals, there is a lack of effective decomposition methods, and Fourier approximations are often used to replace the actual frequency, which also leads to calculation errors in electric field simulations and fails to accurately reflect the actual situation. Summary of the Invention

[0004] In view of this, the present invention provides a method and apparatus for calculating high-frequency electric field distribution that considers the time characteristics of dielectric constant, providing effective dielectric constant variation data for electric field calculation and improving the accuracy of electric field calculation.

[0005] The technical solution adopted by the embodiments of the present invention to solve its technical problem is as follows:

[0006] A method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant, comprising:

[0007] Step S1: The excitation signal is processed by continuous wavelet transform to obtain the wavelet quantity map of the excitation signal;

[0008] Step S2: Based on the wavelet graph of the excitation signal, determine the concentrated region of energy distribution of the excitation signal and the VMD decomposition layer number I;

[0009] Step S3: Based on the obtained decomposition level I, the excitation signal is decomposed using VMD to obtain different IMF components;

[0010] Step S4: Use the HHT algorithm to extract the instantaneous frequency h(t) of each IMF component;

[0011] Step S5: Extract the instantaneous frequency peak distribution curves of each of the obtained IMF components;

[0012] Step S6: Calculate the instantaneous frequency distribution within the window function using a window function;

[0013] Step S7: According to the calculation method of the centroid, the amplitudes of each IMF component are weighted and then the composite instantaneous frequency is calculated.

[0014] Step S8: Couple the obtained relationship between time and instantaneous frequency with the relationship curve of dielectric constant as a function of frequency to obtain the relationship curve of dielectric constant as a function of time.

[0015] Step S9: Calculate the high-frequency electric field distribution based on the relationship curve of dielectric constant versus time.

[0016] Preferably, in step S1, the excitation signal f(t) is decomposed using Marr wavelet, and the expression for the continuous wavelet transform is:

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, Ψ(t) is the time-domain expression of the basic wavelet function. WT is the frequency domain expression for the basic wavelet function. f (a,b) represents the wavelet transformation coefficients, where a is the scaling parameter, b is the translation parameter, and t is the time.

[0021] According to WT f The wavelet graph of the excitation signal f(t) is obtained by decomposing the expression f(t) into (a,b).

[0022] Preferably, step S2 includes:

[0023] The number of energy concentration regions in the wavelet graph of the excitation signal f(t) is identified as the number of decomposition layers I required for VMD decomposition.

[0024] Preferably, step S3 includes:

[0025] Step S31: Use Hilbert transform on each modal component of the excitation signal f(t) to obtain the single-sided spectrum corresponding to each mode;

[0026] Step S32, using the translation factor e -jωit Adjust the single-sided spectrum of each mode to the estimated center frequency ω. i Position, forming position signals for each mode;

[0027] Step S33: Calculate the square of the gradient norm of the position signal to estimate the bandwidth of the frequency-shifted component modes. The constrained variational model expression is as follows:

[0028] ;

[0029] In the formula, {s i} and {ω i} represent the decomposed modal component functions and their corresponding center frequencies, respectively; i is the number of decomposition layers;

[0030] Step S34: Introduce the quadratic penalty factor α and the Lagrange multiplier ζ to transform the constrained problem into an unconstrained problem.

[0031] ;

[0032] Where L(.) is the augmented Lagrangian function;

[0033] Step S35: Solve the unconstrained problem function from step S34 using the Alternating Direction Multiplier Algorithm (ADMM). Specific steps include:

[0034] Step S351, Initialization The initial value is set to zero, ^ represents the frequency domain form corresponding to the Fourier transform of the signal, and n is the number of iterations;

[0035] Step S352, according to Iterative formula, solve sequentially :

[0036] ;

[0037] In the formula, F -1 (.) represents the inverse Fourier operation;

[0038] Step S353, iterative calculation The iterative formula is:

[0039]

[0040] Step S354, iterative update :

[0041]

[0042] Step S355: Repeat the above steps until the cutoff condition is met. The expression for the cutoff condition is:

[0043]

[0044] In the formula, η is the convergence tolerance;

[0045] The s obtained through iteration i Y(t) is the time series of the IMF component.

[0046] Preferably, step S4, extracting the instantaneous frequency of a single layer of the IMF, includes:

[0047] Step S41, transform the IMF time series Y(t) into an expression in terms of X(t):

[0048]

[0049] Further transform the Y(t) signal into an analytic signal Z(t):

[0050]

[0051] Based on the expression for Z(t), the instantaneous frequency h(t) can be expressed as:

[0052]

[0053] Preferably, step S7, which calculates the composite instantaneous frequency by weighting the amplitudes of each IMF component according to the method for calculating the centroid, includes:

[0054] After decomposition of each IMF component, the relationship between time, energy density, and frequency is obtained. Energy has different distributions at different times and frequencies. Based on the principle of centroid calculation in physics, the instantaneous frequency of superposition is calculated using the following formula:

[0055]

[0056] in, IMF I The instantaneous frequency; IMF I The instantaneous energy and amplitude of the time are given by Fre(t), which represents the synthesized instantaneous frequency, and the relationship between time and instantaneous frequency is given by .

[0057] A high-frequency electric field distribution calculation device considering the time characteristics of dielectric constant includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned method.

[0058] As can be seen from the above technical solution, the high-frequency electric field distribution calculation method and device considering the time characteristics of dielectric constant provided by the embodiments of the present invention firstly uses continuous wavelet transformation to process the excitation signal to obtain the wavelet quantity map of the excitation signal; determines the concentrated area of ​​energy distribution of the excitation signal and the number of VMD decomposition layers; performs VMD decomposition on the excitation signal to obtain different IMF components; extracts the instantaneous frequency h(t) of each IMF component using the HHT algorithm; extracts the instantaneous frequency peak distribution curve of each IMF component; calculates the instantaneous frequency distribution within the window function using a window function; calculates the composite instantaneous frequency by weighting the amplitude of each IMF component according to the calculation method of calculating the centroid; couples the relationship between time and instantaneous frequency with the relationship curve of dielectric constant changing with frequency to obtain the relationship curve of dielectric constant changing with time; and calculates the high-frequency electric field distribution based on the relationship curve of dielectric constant changing with time. This invention provides effective dielectric constant variation data for electric field calculation, which can improve the accuracy of electric field calculation. In addition, it can avoid the mode aliasing phenomenon that may occur in traditional calculation methods, solve the problem of inability to decompose similar frequencies, and improve calculation efficiency and frequency accuracy. Attached Figure Description

[0059] Figure 1 This is a flowchart of the high-frequency electric field distribution calculation method considering the time characteristics of dielectric constant according to the present invention.

[0060] Figure 2 This is a block diagram of an apparatus for implementing the high-frequency electric field distribution calculation method considering the time characteristics of dielectric constant in the embodiments disclosed in this invention. Detailed Implementation

[0061] The technical solution and effects of the present invention will be further described in detail below with reference to the accompanying drawings.

[0062] This invention extracts the instantaneous frequency of aperiodic voltage waveforms encountered by a power system (such as lightning impulse voltage) using the energy centroid algorithm, and accurately calculates the electric field distribution of different insulating materials as the power supply frequency changes, based on the relationship between the relative permittivity of the coupling insulating medium and the frequency. This invention is applicable to the maintenance and normal operation of power equipment, the assessment of power insulation performance, and the calculation of electric field distribution under different power supply excitations.

[0063] refer to Figure 1 As shown, this invention provides a method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant. The specific steps are as follows:

[0064] A method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant, comprising:

[0065] Step S1: The excitation signal is processed by continuous wavelet transform to obtain the wavelet quantity map of the excitation signal;

[0066] Step S2: Based on the wavelet graph of the excitation signal, determine the concentrated energy distribution region of the excitation signal and the VMD decomposition layer I; the number of concentrated energy regions in the wavelet graph of the excitation signal f(t) is used as the number of decomposition layers I required for VMD decomposition. The number of concentrated energy regions can be determined manually or automatically.

[0067] Step S3: Based on the obtained decomposition level I, the excitation signal is decomposed into VMD to obtain different IMF components;

[0068] Step S4: Use the HHT algorithm to extract the instantaneous frequency h(t) of each IMF component;

[0069] Step S5: Extract the instantaneous frequency peak distribution curves of each IMF component.

[0070] Step S6: Calculate the instantaneous frequency distribution within the window function using a window function;

[0071] Step S7: According to the calculation method of the centroid, the amplitudes of each IMF component are weighted and then the composite instantaneous frequency is calculated.

[0072] Step S8: Couple the obtained relationship between time and instantaneous frequency with the relationship curve of dielectric constant as a function of frequency to obtain the relationship curve of dielectric constant as a function of time.

[0073] Step S9: Calculate the high-frequency electric field distribution based on the relationship curve of dielectric constant versus time.

[0074] In step S1, the principle of wavelet quantity map extraction is as follows: This is called a basic wavelet:

[0075] (1)

[0076] In the formula, Ψ(t) is the time-domain expression of the basic wavelet function, a is the scaling parameter, b is the translation parameter, and t is time;

[0077] The excitation signal f(t) is decomposed using Marr wavelets, and the expression for the continuous wavelet transform is as follows:

[0078] (2)

[0079] (3)

[0080] (4)

[0081] In the formula, WT f (a,b) are wavelet transform coefficients; WT f (a,b) is characterized by exponential decay, non-tight support, and very good time-frequency localization characteristics.

[0082] According to WT f The wavelet graph of the excitation signal f(t) is obtained by decomposing the expression f(t) into (a,b).

[0083] Step S2 involves performing continuous wavelet decomposition on the signal to be analyzed to obtain the distribution of its wavelet quantity map. Generally, different functions have different energy distributions; by analyzing the regions where these distributions are concentrated, the number of decomposition layers required for VMD decomposition can be determined.

[0084] Virtual Mode Decomposition (VMD) is a novel adaptive signal decomposition technique. Its main function is to decompose a multi-component signal into multiple component modes with specific sparsity characteristics. It requires that the center frequencies of each component signal do not coincide and that each component signal has the closest bandwidth around its respective center frequency. VMD separates the component signals using signal analysis techniques such as Hilbert transform and frequency mixing, under the constraint that the sum of the decomposed component signals equals the original signal. Step S3 includes:

[0085] Step S31: Use Hilbert transform on each modal component of the excitation signal f(t) to obtain the single-sided spectrum corresponding to each mode;

[0086] Step S32, using the translation factor e -jωit Adjust the single-sided spectrum of each mode to the estimated center frequency ω. i Position, forming position signals for each mode;

[0087] Step S33: Calculate the square of the gradient norm of the position signal to estimate the bandwidth of the component modes after the frequency shift. The constrained variational model expression is as follows:

[0088] (5)

[0089] In the formula, {s i} and {ω i} represent the decomposed modal component functions and their corresponding center frequencies, respectively; i is the number of decomposition layers;

[0090] Step S34 introduces a quadratic penalty factor α and a Lagrange multiplier ζ to solve equation (5), thus transforming the constrained problem into an unconstrained problem:

[0091] (6)

[0092] Where L(.) is the augmented Lagrangian function;

[0093] Step S35: Solve the unconstrained problem function from step S34 using the Alternating Direction Multiplier Algorithm (ADMM). Specific steps include:

[0094] Step S351, Initialization The initial value is set to zero, ^ represents the frequency domain form of the Fourier transform of the signal, n is the number of iterations, and the initial value is 1;

[0095] Step S352: Iterate according to equation (7) and solve sequentially. :

[0096] (7)

[0097] In the formula, F -1 (.) represents the inverse Fourier operation;

[0098] Step S353, according to formula (8):

[0099] (8)

[0100] Step S354, iterative update :

[0101] (9)

[0102] Step S355: Repeat the above steps until the cutoff condition is met. The cutoff condition expression is:

[0103] (10)

[0104] In the formula, η is the convergence tolerance;

[0105] The s obtained through iteration i Y(t) is the time series of the IMF component.

[0106] Step S4, extracting the instantaneous frequencies of a single layer of the IMF, includes:

[0107] Step S41, transform the IMF time series Y(t) into an expression in terms of X(t):

[0108] (11)

[0109] Further transform the Y(t) signal into an analytic signal Z(t):

[0110] (12)

[0111] Based on the expression for Z(t), the instantaneous frequency h(t) can be expressed as:

[0112] (13)

[0113] Step S7, following the method for calculating the centroid, weights the amplitudes of each IMF component and then calculates the composite instantaneous frequency, including:

[0114] By decomposing different IMFs, the relationship between time, energy density, and frequency is obtained, showing that energy has different distributions at different times and frequencies. Following the method for calculating the center of mass in physics, the relationship between time and frequency is analyzed. The principle formula for calculating the center of mass is:

[0115] (14)

[0116] x and y are the corresponding horizontal and vertical coordinates.

[0117] Following formula (14), the formula for calculating the superimposed instantaneous frequency is:

[0118] (15)

[0119] Among them, among them, IMF I The instantaneous frequency; IMF I The instantaneous energy and amplitude of , Fre(t) represents the synthesized instantaneous frequency, and represents the relationship between time and instantaneous frequency.

[0120] The instantaneous frequency obtained by superposition using this method meets engineering requirements in terms of both physical meaning and signal processing, and has good application value.

[0121] This invention proposes a high-frequency electric field distribution calculation method considering the time characteristics of the dielectric constant. It utilizes the energy centroid method to extract the instantaneous frequency of the voltage, coupling the dielectric constant with the frequency response curve to accurately calculate the electric field distribution of the insulating material under non-periodic voltage excitation. Its advantages include:

[0122] 1) It fills the gap in electric field simulation calculations regarding the variation of dielectric constant with frequency, providing a new calculation method and approach for high-frequency electric field simulation. This improves the accuracy of electric field calculations.

[0123] 2) It provides a feasible method for extracting the instantaneous frequency of non-periodic power supply signals, and the calculation results meet objective reality and are true and effective.

[0124] 3) It solves the problem of modal aliasing that may occur in traditional calculation methods, and solves the problem of inability to decompose similar frequencies, thereby improving calculation efficiency and frequency accuracy.

[0125] According to the embodiments disclosed herein, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0126] Figure 2 A schematic block diagram of an example electronic device 200 that can be used to implement embodiments of the invention disclosed herein is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention disclosed herein and / or claimed.

[0127] like Figure 2 As shown, device 200 includes a computing unit 201, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 202 or a computer program loaded from storage unit 208 into random access memory (RAM) 203. RAM 203 may also store various programs and data required for the operation of device 200. The computing unit 201, ROM 202, and RAM 203 are interconnected via bus 204. Input / output (I / O) interface 205 is also connected to bus 204.

[0128] Multiple components in device 200 are connected to I / O interface 205, including: input unit 206, such as keyboard, mouse, etc.; output unit 207, such as various types of monitors, speakers, etc.; storage unit 208, such as disk, optical disk, etc.; and communication unit 209, such as network card, modem, wireless transceiver, etc. Communication unit 209 allows device 200 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0129] The computing unit 201 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 201 performs the various methods and processes described above, such as the calculation of high-frequency electric field distribution considering the time characteristics of dielectric constant. For example, in some embodiments, the calculation of high-frequency electric field distribution considering the time characteristics of dielectric constant can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed on device 200 via ROM 202 and / or communication unit 209. When the computer program is loaded into RAM 203 and executed by the computing unit 201, one or more steps of the calculation of high-frequency electric field distribution considering the time characteristics of dielectric constant described above can be performed. Alternatively, in other embodiments, the computing unit 201 may be configured by any other suitable means (e.g., by means of firmware) to perform high-frequency electric field distribution calculations that take into account the time characteristics of the dielectric constant.

[0130] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] Program code for implementing the methods disclosed in this invention may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a standalone software package, or entirely on a remote machine or server.

[0132] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0134] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0135] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0136] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0137] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above-described embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant, characterized in that, include: Step S1: The excitation signal is processed by continuous wavelet transform to obtain the wavelet quantity map of the excitation signal; Step S2: Based on the wavelet graph of the excitation signal, determine the concentrated region of energy distribution of the excitation signal and the VMD decomposition layer number I; Step S3: Based on the obtained decomposition level I, the excitation signal is decomposed using VMD to obtain different IMF components; Step S4: Use the HHT algorithm to extract the instantaneous frequency h(t) of each IMF component; Step S5: Extract the instantaneous frequency peak distribution curves of each of the obtained IMF components; Step S6: Calculate the instantaneous frequency distribution within the window function using a window function; Step S7: According to the calculation method of the centroid, the amplitudes of each IMF component are weighted and then the composite instantaneous frequency is calculated. Step S8: Couple the obtained relationship between time and instantaneous frequency with the relationship curve of dielectric constant as a function of frequency to obtain the relationship curve of dielectric constant as a function of time. Step S9: Calculate the high-frequency electric field distribution based on the relationship curve of dielectric constant versus time; Step S3 includes: Step S31: Use Hilbert transform on each modal component of the excitation signal f(t) to obtain the single-sided spectrum corresponding to each mode; Step S32, using the translation factor Adjust the single-sided spectrum of each mode to the estimated center frequency ω. i Position, forming position signals for each mode; Step S33: Calculate the square of the gradient norm of the position signal to estimate the bandwidth of the frequency-shifted component modes. The constrained variational model expression is as follows: ； In the formula, {s i } and {ω i } represent the decomposed modal component functions and their corresponding center frequencies, respectively; i is the number of decomposition layers; Step S34: Introduce the quadratic penalty factor α and the Lagrange multiplier ζ to transform the constrained problem into an unconstrained problem. ； Where L(.) is the augmented Lagrangian function; f(t) is the excitation signal; Step S35: Solve the unconstrained problem function from step S34 using the Alternating Direction Multiplier (ADMM) method. Specific steps include: Step S351, Initialization The initial value is set to zero, ^ represents the frequency domain form corresponding to the Fourier transform of the signal, and n is the number of iterations; Step S352, according to Iterative formula, solve sequentially : ； In the formula, F -1 (.) represents the inverse Fourier operation; Step S353, iterative calculation The iterative formula is: ； Step S354, iterative update : ； Step S355: Repeat the above steps until the cutoff condition is met. The expression for the cutoff condition is: ； In the formula, η is the convergence tolerance; The s obtained through iteration i Y(t) is the time series of the IMF component.

2. The method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant as described in claim 1, characterized in that, In step S1, the excitation signal f(t) is decomposed using Marr wavelets, and the expression for the continuous wavelet transformation is: ； ； ； In the formula, Ψ(t) is the time-domain expression of the basic wavelet function. WT is the frequency domain expression for the basic wavelet function. f (a,b) represents the wavelet transformation coefficients, where a is the scaling parameter, b is the translation parameter, and t is the time. According to WT f The wavelet graph of the excitation signal f(t) is obtained by decomposing the expression f(t) into (a,b).

3. The method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant as described in claim 2, characterized in that, Step S2 includes: The number of energy concentration regions in the wavelet graph of the excitation signal f(t) is identified as the number of decomposition layers I required for VMD decomposition.

4. The method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant as described in claim 3, characterized in that, Step S4, extracting the instantaneous frequency of a single layer of the IMF, includes: Step S41, transform the IMF time series Y(t) into an expression in terms of X(t): ； Further transform the Y(t) signal into an analytic signal Z(t): ； Based on the expression for Z(t), the instantaneous frequency h(t) can be expressed as: 。 5. The method for calculating high-frequency electric field distribution considering the time characteristics of dielectric constant as described in claim 4, characterized in that, Step S7, following the method for calculating the centroid, involves weighting the amplitudes of each IMF component and then calculating the composite instantaneous frequency, including: After decomposition of each IMF component, the relationship between time, energy density, and frequency is obtained. Energy has different distributions at different times and frequencies. Based on the principle of centroid calculation in physics, the instantaneous frequency of superposition is calculated using the following formula: ； in, IMF I The instantaneous frequency; IMF I The instantaneous energy and amplitude of the time are given by Fre(t), which represents the synthesized instantaneous frequency, and the relationship between time and instantaneous frequency is given by .

6. A high-frequency electric field distribution calculation device considering the time characteristics of dielectric constant, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

7. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

8. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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