A method, system, device, and medium for characterizing the intrinsic features of spatial field intensity distribution.

Abstract

This invention discloses a method, system, device, and medium for characterizing the intrinsic features of spatial field strength distribution. It verifies the existence of intrinsic features by constructing characteristic basis functions of the field strength distribution, then establishes an eigenvalue set through eigenmode decomposition, and iteratively solves the corresponding constraint functions. The final eigenvalue dataset is used to characterize the intrinsic features of the spatial field strength distribution, improving the feature analysis capability of electromagnetic information in the spatial domain. Based on a reasonable characterization of the intrinsic features of the field strength distribution, it uses sparse space construction as a breakthrough to reduce the sampling rate and the non-correlation of the sampling matrix and sparse basis as constraints to construct sparse basis and sampling methods. This combines basic electromagnetic field theory, signal processing methods, and mathematical theory, filling the gap in the theoretical research on the construction of characteristic functions in the spatial domain for electromagnetic signal time-frequency analysis.

Description

Technical Field

[0001] This invention belongs to the field of field strength characterization technology, specifically relating to a method, system, device and medium for characterizing the intrinsic characteristics of spatial field strength distribution. Background Technology

[0002] The electromagnetic space has become one of the main activity spaces for human society in the information and intelligent age. With the explosive growth in the number of various electromagnetic frequency-using devices and systems, the electromagnetic space is becoming increasingly complex, evolving into a complex system of multiple subjects, factors, and variables interacting and influencing each other. Changes in the electromagnetic field contain implicit information about target behavior; without a deep understanding of the distribution of electromagnetic field strength, it will be difficult to effectively improve information acquisition capabilities.

[0003] The distribution of electromagnetic field strength contains rich information about electromagnetic space, reflecting physical phenomena such as attenuation, reflection, transmission, and diffraction during the propagation of electromagnetic waves. The distribution of field strength is directly related to the radiation source and the propagation environment. Therefore, the characteristics of field strength distribution under different scenarios have both commonalities and differences. The commonalities reflect the global characteristics of the field strength distribution, while the differences reflect the local characteristics of a specific scenario. The combination of the two constitutes the intrinsic characteristics of the field strength distribution. Exploring these intrinsic characteristics helps to understand electromagnetic space from a new perspective.

[0004] Since traditional signal time-frequency analysis methods cannot effectively characterize the features of field strength distribution, there is an urgent need to study new methods to understand electromagnetic space from a new perspective, explore the implicit information of electromagnetic space, and meet the practical needs of high-resolution electromagnetic field distribution based on limited sampling resources. This research aims to study the intrinsic feature extraction method of field strength distribution and carry out intrinsic feature extraction and sparse reconstruction of field strength distribution. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device and medium for characterizing the intrinsic characteristics of spatial field strength distribution, so as to solve the technical problem that existing signal time-frequency analysis methods cannot effectively characterize the field strength distribution characteristics.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for characterizing the intrinsic features of spatial field intensity distribution includes the following steps:

[0008] Based on the conditions of the radiation source and the propagation environment, the field strength distribution is calculated using the ray tracing method to obtain the local characteristics of the field strength distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field strength distribution based on the distribution range of the independent variable.

[0009] Based on the local and global characteristics of the field strength distribution, a basis function for the field strength distribution is constructed.

[0010] Using the variational mode decomposition method, the multi-component field strength distribution is non-recursively decomposed into multiple eigenmode components with finite bandwidth based on the constructed basis functions. Based on the multiple eigenmode components with finite bandwidth, an intrinsic dataset and a variationally constrained objective function are constructed.

[0011] The objective function of the variational constraint is solved iteratively, and the relationship between the original field strength, eigenmode components and remainder terms is analyzed.

[0012] The intrinsic mode components or remainders with the highest correlation to the original field strength distribution are selected as components of the intrinsic dataset to characterize the intrinsic features of the spatial field strength distribution.

[0013] Furthermore, the independent variables include the reflection coefficient, diffraction coefficient, and transmission coefficient.

[0014] Furthermore, the step of obtaining the global characteristics of the field strength distribution based on the distribution range of the independent variable is as follows:

[0015] The Monte Carlo estimation method is used to generate a large number of uniformly distributed random points based on the distribution range of the independent variables;

[0016] The electromagnetic field of random numbers under different variable conditions was calculated using the ray tracing method. The distribution of electromagnetic field under different variable conditions was statistically analyzed, and a statistical mathematical model of Monte Carlo field strength distribution was established.

[0017] Based on the statistical mathematical model of the Monte Carlo field strength distribution, the correlation between the propagation conditions of electromagnetic waves and the global and individual characteristics of the field strength distribution is obtained, thus acquiring the global characteristics of the field strength distribution.

[0018] Furthermore, the basis functions of the field strength distribution are constructed based on the vector sum of the incident field and the reflected field of the same energy of a single radiating antenna.

[0019] Furthermore, the basis function formula for the field strength distribution is:

[0020]

[0021] In the formula, Let r be the basis function and r be the distance to the center of the radiation source.

[0022] Furthermore, the objective function of the variational constraint is:

[0023]

[0024] In the formula It is a variational function, and K is the decomposition scale, i.e., the number of decomposition modes. These are the decomposed intrinsic mode components. The center frequencies corresponding to different modal components, Let be the basis function of the k-th radiation source. Let be the total electric field strength.

[0025] Furthermore, the steps for iteratively solving the objective function of the variational constraints and analyzing the relationship between the original field strength, eigenmode components, and remainder terms are as follows:

[0026] By introducing a quadratic penalty factor and a Lagrange operator, and then using an alternating direction algorithm to find the minimum solution, the steepest descent method is used for iteration to update the variables and complete the variational minimization solution.

[0027] Effective modes are screened by the cross-correlation coefficient between each modal component and the original signal, and the relationship between the original field strength, intrinsic modal components, and remainder terms is analyzed by using the Pearson product-moment correlation coefficient.

[0028] A system for characterizing the intrinsic features of spatial field intensity distribution, characterized by comprising an acquisition module, an establishment module, a decomposition module, a solution module, and a characterization module, wherein:

[0029] Acquisition module: Used to calculate the field strength distribution using the ray tracing method based on the conditions of the radiation source and the propagation environment, acquire the local characteristics of the field strength distribution, determine the distribution range of the independent variable, and acquire the global characteristics of the field strength distribution based on the distribution range of the independent variable;

[0030] Establishment module: Based on the local and global features of the field strength distribution, construct the basis functions of the field strength distribution;

[0031] The solution module is used to non-recursively decompose the multi-component field strength distribution into multiple finite-bandwidth eigenmode components based on the constructed basis functions using the variational mode decomposition method, and construct the eigendata set and the objective function of variational constraints based on the multiple finite-bandwidth eigenmode components.

[0032] The solution module is used to iteratively solve the objective function of the variational constraints and analyze the relationship between the original field strength, eigenmode components, and remainder terms.

[0033] The characterization module is used to select the intrinsic mode components or remainders with the highest correlation to the original field strength distribution as components of the intrinsic dataset, and to characterize the intrinsic features of the spatial field strength distribution.

[0034] A terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0035] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0036] Compared with the prior art, the present invention has the following beneficial technical effects:

[0037] This invention discloses a method for characterizing the intrinsic features of spatial field strength distribution. It verifies the existence of intrinsic features by constructing characteristic basis functions of the field strength distribution, then establishes an eigenvalue set through eigenmode decomposition, and iteratively solves the corresponding constraint functions. The resulting eigenvalue dataset characterizes the intrinsic features of the spatial field strength distribution. This fills a gap in the theoretical research on the construction of characteristic functions in the spatial domain for electromagnetic signal time-frequency analysis, improves the feature analysis capability of electromagnetic information in the spatial domain, provides a theoretical basis for mining implicit information in electromagnetic space, provides prior knowledge for subsequent sparse representation and sampling methods, and studies the theory of characterizing the intrinsic features of field strength distribution.

[0038] Preferably, the global characteristics of the field strength distribution are obtained based on the statistical mathematical model of the Monte Carlo field strength distribution. This method has wide applicability and high accuracy in obtaining the global characteristics of the field strength distribution. It can intuitively display the global characteristics of the field strength distribution and provide decision-makers with more intuitive and clear information support.

[0039] Preferably, the variational mode decomposition method non-recursively decomposes the multi-component field strength distribution into multiple eigenmode components with finite bandwidth based on the constructed basis functions. It can determine the number of mode decompositions according to the actual situation of the signal, and adaptively match the optimal center frequency and finite bandwidth of each mode during the search and solution process, resulting in stable decomposition results. Attached Figure Description

[0040] Figure 1 This is a flowchart of a method for characterizing the intrinsic features of spatial field intensity distribution according to the present invention;

[0041] Figure 2 This is a detailed flowchart illustrating a method for characterizing the intrinsic features of spatial field strength distribution in an embodiment of the present invention. Detailed Implementation

[0042] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0043] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0044] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0045] The present invention will now be described in further detail with reference to the accompanying drawings:

[0046] like Figure 1 As shown, a method for characterizing the intrinsic features of spatial field intensity distribution includes the following steps:

[0047] Step 1: Based on the conditions of the radiation source and the propagation environment, use the ray tracing method to calculate the field strength distribution, obtain the local characteristics of the field strength distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field strength distribution based on the distribution range of the independent variable.

[0048] Based on the conditions of the radiation source and the propagation environment, the field strength E distribution is calculated using the ray tracing method to obtain the local characteristics of the field strength distribution under specific conditions, and then the reflection coefficient is determined. diffraction coefficient and transmission coefficient Equal distribution range;

[0049] Based on the distribution range of the independent variable, a statistical mathematical model of the Monte Carlo field strength distribution is established to obtain the global characteristics of the field strength distribution.

[0050] The independent variables include the reflection coefficient, diffraction coefficient, and transmission coefficient.

[0051] Step 2: Based on the local and global characteristics of the field strength distribution, construct the basis functions for the field strength distribution;

[0052] In large-scale environments, the main energy of the total electric field comes from direct waves and reflected waves from the ground. The basis functions of the field strength distribution are constructed based on the vector sum of the incident field and the reflected field of the same energy from a single radiating antenna. .

[0053] The basis function formula for the electric field distribution is:

[0054]

[0055] In the formula, Let r be the basis function and r be the distance to the center of the radiation source.

[0056] Step 3: Using the variational mode decomposition method, the multi-component field strength distribution is non-recursively decomposed into multiple eigenmode components with finite bandwidth based on the constructed basis functions. Based on the multiple eigenmode components with finite bandwidth, an intrinsic dataset and a variationally constrained objective function are constructed.

[0057] Specifically, the variational mode decomposition (VMD) method is used to non-recursively decompose the multi-component field strength distribution into multiple eigenmode components with finite bandwidth based on the constructed basis functions, thereby transforming a multi-component field strength distribution into multiple single-component sets and constructing an eigenvalue set.

[0058] The objective function of the variational constraint is:

[0059]

[0060] In the formula It is a variational function, and K is the decomposition scale, i.e., the number of decomposition modes. These are the decomposed intrinsic mode components. The center frequencies corresponding to different modal components, Let be the basis function of the k-th radiation source. Let be the total electric field strength.

[0061] Step four: Iteratively solve the objective function of the variational constraint and analyze the relationship between the original field strength, eigenmode components and remainder terms;

[0062] The steps for iteratively solving the objective function of the variational constraints and analyzing the relationship between the original field strength, eigenmode components, and remainder terms are as follows:

[0063] By introducing a quadratic penalty factor and a Lagrange operator, and then using an alternating direction algorithm to find the minimum solution, the steepest descent method is used for iteration to update the variables and complete the variational minimization solution.

[0064] Effective modes are screened by the cross-correlation coefficient between each modal component and the original signal, and the relationship between the original field strength, intrinsic modal components, and remainder terms is analyzed by using the Pearson product-moment correlation coefficient.

[0065] Step 5: Select the intrinsic mode components or remainders with the highest correlation to the original field strength distribution as the components of the intrinsic dataset to characterize the intrinsic features of the spatial field strength distribution.

[0066] Example

[0067] This invention provides a method for characterizing the intrinsic features of spatial field intensity distribution, comprising the following steps:

[0068] Step S1: Based on the number of radiating antennas, frequency, radiation direction, radiation intensity and polarization, as well as the propagation environment from simple to complex, namely free space, half space, undulating terrain and environment with vegetation cover, the field strength distribution in space is calculated using the ray tracing method.

[0069] If the radiating antenna reaches the receiving point along a line-of-sight path, then the i-th (i=1,2,…I) radiating antenna is in… r The electric field generated at that location is:

[0070]

[0071]

[0072]

[0073] In the formula, It can also be called the incident field, where k is the wave number. and These are pitch angle and azimuth angle, respectively. It is radiated power. and These represent the antenna in and Gain in direction, and They represent the electric field at and The phase on.

[0074] If it is a non-line-of-sight propagation path Then, starting from the transmitting antenna, the calculation proceeds along the ray path until the observation point is reached. The mathematical expression of this process is:

[0075]

[0076] In the formula, n, m, and l represent the total number of reflections, diffractions, and transmissions, respectively. , and point

[0077] These are the reflection coefficient, diffraction coefficient, and transmission coefficient, respectively. The diffusion factor after reflection, transmission, or diffraction. Let be the distance from the q-th ray node to the (q+1)-th node. Total electric field. This involves calculating the sum of the contributions of all rays reaching the receiving point; the total electric field of the I radiating antennas is then given by...

[0078]

[0079] By calculating the above formula, the local characteristics of the field strength distribution under a specific environment are obtained, and the reflection coefficient is determined. diffraction coefficient and transmission coefficient Equal distribution range.

[0080] Step S2: Using the Monte Carlo estimation method, a large number of uniformly distributed random points are generated based on the distribution range of the independent variables. (i=1,2,⋯M).

[0081] Then, the electromagnetic field of random numbers under different variable conditions is calculated using the ray tracing method. The distribution of electromagnetic field under different variable conditions is statistically analyzed, and a statistical mathematical model of Monte Carlo field strength distribution is established. The correlation between the propagation conditions of electromagnetic waves and the global and individual characteristics of field strength distribution is obtained, and the global characteristics of field strength distribution are acquired.

[0082] Step S3: The total electric field in space is the result of the vector superposition of electromagnetic waves emitted by each radiating antenna and reflected, diffracted, and transmitted waves. In large-scale environments, the main energy of the total electric field comes from direct waves and reflected waves from the ground. Therefore, the vector sum of the incident field and the primary reflection field of a single radiating antenna becomes the basic component of the field strength distribution in any scenario. The basis functions of the field strength distribution are constructed based on the vector sum of the incident field and the reflection field of the same energy from a single radiating antenna. Discard parts that have a smaller impact on the features:

[0083]

[0084]

[0085]

[0086] In the formula, Let r be the basis function and r be the distance to the center of the radiation source.

[0087] Step S4: Using the Variational Mode Decomposition (VMD) method, the multi-component field strength distribution is non-recursively decomposed into multiple eigenmode components with finite bandwidth based on the constructed basis functions through variational optimization. This transforms a multi-component field strength distribution into multiple sets of single components, thereby constructing an eigenvalue set. The objective function for constructing the variational constraints is as follows:

[0088]

[0089] In the formula It is a variational function, and K is the decomposition scale, i.e., the number of decomposition modes. These are the decomposed intrinsic mode components. The center frequencies corresponding to different modal components, Let be the basis function of the k-th radiation source. Let be the total electric field strength.

[0090] The following is a detailed introduction to variational mode decomposition:

[0091] Variational mode decomposition (VMD): decomposes a time series f into k time series with fixed center frequencies. modal components This minimizes the sum of the frequency estimation bandwidths for each modal component. A Hilbert transform is performed on each modal component to obtain its one-sided spectrum. The center frequency is estimated through a mixture, and the one-sided spectra of each component are modulated onto the fundamental frequency band. Then, Gaussian smoothing is applied to the L2 regularization of the demodulated signal gradient to obtain the bandwidth of the power component. The corresponding constrained variational model is expressed as:

[0092]

[0093] Where ∂t denotes partial derivatives, and δ(t) denotes the Dirac distribution function.

[0094] Solving variational problems:

[0095] By introducing Lagrange multipliers and a quadratic penalty function, the constrained variational problem described above is transformed into an unconstrained problem, i.e.

[0096]

[0097] By using the alternating direction method of multipliers to find the saddle point of the extended Lagrange expression, and obtaining the frequency domain update of each mode, the iterative expressions for the modal components and center frequencies are:

[0098]

[0099] in, Wiener filtering represents the components; Indicates the center frequency of the corresponding mode; for Perform an inverse Fourier transform and take the real part { The time-domain modal components are obtained; n represents the number of iterations; ω represents the frequency value. Finally, the sequence is transformed back to the time domain by inverse Fourier transform to obtain the k narrowband IMF components after sequence decomposition, thus completing the adaptive segmentation of the signal in the frequency domain.

[0100] VMD transforms a sequence from the time domain to the frequency domain for decomposition. For this type of nonlinear data, it can not only preserve the original information well, but also avoid the overlap of variable information, and the decomposition process has strong robustness.

[0101] The number of decompositions K is selected based on the center frequency:

[0102] The effectiveness of VMD decomposition is mainly affected by the selected number of modes. When the selected number of modes is small, since the VMD algorithm is equivalent to an adaptive filter bank, some important information in the original signal will be filtered out, affecting the accuracy of subsequent predictions. Conversely, when the selected number of modes is large, the center frequencies of adjacent mode components will be close together, leading to mode repetition or additional noise. The main difference between different modes lies in their center frequencies. Therefore, by observing the distribution of center frequencies under different number of modes, an appropriate number of mode values ​​can be selected.

[0103] K is further determined based on the correlation coefficient:

[0104] To avoid overlapping passbands and modes after decomposition, which would lead to over-decomposition of the wind power signal, the correlation between adjacent modal components was analyzed to determine the appropriate value K for the number of modes, and the Pearson correlation coefficient was calculated.

[0105] Step S5 introduces a quadratic penalty factor and a Lagrange operator. The quadratic penalty term is used to ensure that the sum of the decomposed intrinsic mode components is as similar as possible to the original field strength. The Lagrange operator transforms the equation into an unconstrained optimization problem. Then, the alternating direction algorithm is used to find the minimization solution. The steepest descent method is used for iteration to update the variables and complete the solution of the complex variational minimization.

[0106] Step S6: Valid modes are screened by the cross-correlation coefficient between each modal component and the original signal, and the relationship between the original field strength, intrinsic modal components, and remainder terms is analyzed by using the Pearson product-moment correlation coefficient.

[0107] Step S7: Select the intrinsic mode components or remainders with the highest correlation to the original field strength distribution as the components of the feature set, and reasonably select the preset decomposition scale K.

[0108] This invention addresses the practical need for handling high-resolution electromagnetic field distributions with limited sampling resources by researching the intrinsic characteristics of field strength distribution and sparse reconstruction methods. Firstly, facing the complex and variable spatial field strength distribution, it proposes the hypothesis that "the electromagnetic spatial field strength distribution possesses intrinsic characteristics." Then, based on a reasonable characterization of these intrinsic characteristics, it uses sparse spatial construction as a breakthrough to reduce the sampling rate, and employs the non-correlation of the sampling matrix and sparse basis as constraints to construct a sparse basis and sampling method, combining fundamental electromagnetic field theory, signal processing methods, and mathematical theory.

[0109] This invention also provides a system for characterizing the intrinsic features of spatial field intensity distribution, comprising an acquisition module, an establishment module, a decomposition module, a solution module, and a characterization module, wherein:

[0110] Acquisition module: Used to calculate the field strength distribution using the ray tracing method based on the conditions of the radiation source and the propagation environment, acquire the local characteristics of the field strength distribution, determine the distribution range of the independent variable, and acquire the global characteristics of the field strength distribution based on the distribution range of the independent variable;

[0111] Establishment module: Based on the local and global features of the field strength distribution, construct the basis functions of the field strength distribution;

[0112] The solution module is used to non-recursively decompose the multi-component field strength distribution into multiple finite-bandwidth eigenmode components based on the constructed basis functions using the variational mode decomposition method, and construct the eigendata set and the objective function of variational constraints based on the multiple finite-bandwidth eigenmode components.

[0113] The solution module is used to iteratively solve the objective function of the variational constraints and analyze the relationship between the original field strength, eigenmode components, and remainder terms.

[0114] The characterization module is used to select the intrinsic mode components or remainders with the highest correlation to the original field strength distribution as components of the intrinsic dataset, and to characterize the intrinsic features of the spatial field strength distribution.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A method for characterizing the intrinsic features of spatial field intensity distribution, characterized in that, Includes the following steps: Based on the conditions of the radiation source and the propagation environment, the field strength distribution is calculated using the ray tracing method to obtain the local characteristics of the field strength distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field strength distribution based on the distribution range of the independent variable. Based on the local and global characteristics of the field strength distribution, a basis function for the field strength distribution is constructed. Using the variational mode decomposition method, the multi-component field strength distribution is non-recursively decomposed into multiple eigenmode components with finite bandwidth based on the constructed basis functions. Based on the multiple eigenmode components with finite bandwidth, an intrinsic dataset and a variationally constrained objective function are constructed. The objective function of the variational constraint is solved iteratively, and the relationship between the original field strength, eigenmode components and remainder terms is analyzed. The intrinsic mode components or remainders with the highest correlation to the original field strength distribution are selected as components of the intrinsic dataset to characterize the intrinsic features of the spatial field strength distribution.

2. The method for characterizing the intrinsic features of spatial field intensity distribution according to claim 1, characterized in that, The independent variables include the reflection coefficient, diffraction coefficient, and transmission coefficient.

3. The method for characterizing the intrinsic features of spatial field intensity distribution according to claim 1, characterized in that, The step of obtaining the global characteristics of the field strength distribution based on the distribution range of the independent variable is as follows: The Monte Carlo estimation method is used to generate a large number of uniformly distributed random points based on the distribution range of the independent variables; The electromagnetic field of random numbers under different variable conditions was calculated using the ray tracing method. The distribution of electromagnetic field under different variable conditions was statistically analyzed, and a statistical mathematical model of Monte Carlo field strength distribution was established. Based on the statistical mathematical model of the Monte Carlo field strength distribution, the correlation between the propagation conditions of electromagnetic waves and the global and individual characteristics of the field strength distribution is obtained, thus acquiring the global characteristics of the field strength distribution.

4. The method for characterizing the intrinsic features of spatial field intensity distribution according to claim 1, characterized in that, The basis functions of the field strength distribution are constructed based on the vector sum of the incident field and the reflected field of the same energy from a single radiating antenna.

5. A method for characterizing the intrinsic features of spatial field intensity distribution according to claim 1 or 4, characterized in that, The basis function formula for the field strength distribution is: In the formula, Let r be the basis function and r be the distance to the center of the radiation source.

6. The method for characterizing the intrinsic features of spatial field intensity distribution according to claim 1, characterized in that, The objective function of the variational constraint is: In the formula It is a variational function, and K is the decomposition scale, i.e., the number of decomposition modes. These are the decomposed intrinsic mode components. The center frequencies corresponding to different modal components, Let be the basis function of the k-th radiation source. Let be the total electric field strength.

7. The method for characterizing the intrinsic features of spatial field intensity distribution according to claim 1, characterized in that, The steps for iteratively solving the objective function of the variational constraint and analyzing the relationship between the original field strength, eigenmode components, and remainder terms are as follows: By introducing a quadratic penalty factor and a Lagrange operator, and then using an alternating direction algorithm to find the minimum solution, the steepest descent method is used for iteration to update the variables and complete the variational minimization solution. Effective modes are screened by the cross-correlation coefficient between each modal component and the original signal, and the relationship between the original field strength, intrinsic modal components, and remainder terms is analyzed by using the Pearson product-moment correlation coefficient.

8. A system for characterizing the intrinsic features of spatial field intensity distribution, characterized in that, It includes an acquisition module, a construction module, a decomposition module, a solution module, and a representation module, among which: Acquisition module: Used to calculate the field strength distribution using the ray tracing method based on the conditions of the radiation source and the propagation environment, acquire the local characteristics of the field strength distribution, determine the distribution range of the independent variable, and acquire the global characteristics of the field strength distribution based on the distribution range of the independent variable; Establishment module: Based on the local and global features of the field strength distribution, construct the basis functions of the field strength distribution; The solution module is used to non-recursively decompose the multi-component field strength distribution into multiple finite-bandwidth eigenmode components based on the constructed basis functions using the variational mode decomposition method, and construct the eigendata set and the objective function of variational constraints based on the multiple finite-bandwidth eigenmode components. The solution module is used to iteratively solve the objective function of the variational constraints and analyze the relationship between the original field strength, eigenmode components, and remainder terms. The characterization module is used to select the intrinsic mode components or remainders with the highest correlation to the original field strength distribution as components of the intrinsic dataset, and to characterize the intrinsic features of the spatial field strength distribution.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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