Method, system and equipment for representing intrinsic characteristics of spatial field intensity distribution and medium

Abstract

The invention discloses a spatial field intensity distribution intrinsic characteristic characterization method, system and device and a medium, existence of intrinsic characteristics is confirmed through construction of a field intensity distribution characteristic basis function, an intrinsic characteristic set is established through intrinsic mode decomposition, a corresponding constraint function is iteratively solved, and a spatial field intensity distribution characteristic is obtained. The characteristic analysis capability of an electromagnetic information spatial domain is improved by representing the intrinsic characteristics of spatial field intensity distribution by using a finally obtained intrinsic data set, and on the basis of reasonably representing the intrinsic characteristics of the field intensity distribution, a sparse space structure is used as a breakthrough opening for reducing the sampling rate, and the non-correlation of a sampling matrix and a sparse base is used as a constraint condition, so that the characteristic analysis capability of the electromagnetic information spatial domain is improved. A sparse base and a sampling method are constructed, an electromagnetic field basic theory, a signal processing method and a mathematical theory are combined, and the blank of research of electromagnetic signal time-frequency analysis in a spatial domain characteristic function construction theory is filled.

Description

Technical Field

[0001] The present invention belongs to the technical field of field intensity characterization, and in particular relates to a method, system, device and medium for characterizing intrinsic characteristics of spatial field intensity distribution. Background Art

[0002] Electromagnetic space has become one of the main activity spaces of human society in the information age and the intelligent age. With the explosive growth in the number of various electromagnetic frequency-using equipment and systems, the electromagnetic space has become increasingly complex and has evolved into a complex system of input and output composed of multiple subjects, multiple factors, and multiple variables. The changes in the electromagnetic field contain implicit information about the target's behavior. If there is a lack of in-depth understanding of the distribution of electromagnetic field strength, it will be difficult to effectively improve the ability to obtain information in the complex situation where new interference, electromagnetic control and other tactics are widely used.

[0003] The field intensity distribution contains rich information about the electromagnetic space, reflecting the physical phenomena such as attenuation, reflection, transmission, and diffraction in the process of electromagnetic wave propagation. The field intensity distribution is directly related to the radiation source and the propagation environment. Therefore, the field intensity distribution characteristics in different scenarios have both similarities and differences. The similarities reflect the global characteristics of the field intensity distribution, and the differences reflect the local characteristics of specific scenarios. The combination of the two is the intrinsic characteristics of the field intensity distribution. Exploring the intrinsic characteristics will help us understand the electromagnetic space from a new perspective.

[0004] Since traditional signal time-frequency analysis methods cannot effectively characterize the characteristics of field strength distribution, it is urgent to study new methods to understand electromagnetic space from a new perspective and explore the implicit information of electromagnetic space. Based on limited sampling resources, we should meet the actual needs of high-resolution electromagnetic field distribution, study the method of extracting the intrinsic features 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 the present invention is to provide a method, system, device and medium for characterizing the intrinsic characteristics of spatial field intensity distribution, so as to solve the technical problem that the existing signal time-frequency analysis method cannot effectively characterize the field intensity distribution characteristics.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: A method for characterizing intrinsic characteristics of spatial field intensity distribution comprises the following steps: According to the conditions of the radiation source and the propagation environment, the field intensity distribution is calculated using the ray tracing method to obtain the local characteristics of the field intensity distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field intensity distribution based on the distribution range of the independent variable; Based on the local and global characteristics of the field intensity distribution, a basis function of the field intensity distribution is constructed; Using the variational mode decomposition method, the multi-component field intensity distribution is non-recursively decomposed into multiple finite bandwidth eigenmode components according to the constructed basis functions, and the eigendata set and the objective function of the variational constraint are constructed based on the multiple finite bandwidth eigenmode components. Iteratively solve the objective function of the variational constraint and analyze the relationship between the original field strength, eigenmode components and the residual terms; The eigenmodal components or remainders with the highest correlation with the original field intensity distribution are selected as components of the eigendata set to characterize the eigenfeatures of the spatial field intensity distribution.

[0007] Furthermore, the independent variables include reflection coefficient, diffraction coefficient and transmission coefficient.

[0008] Furthermore, the step of obtaining the global characteristics of the field intensity distribution according to the distribution range of the independent variable is: Monte Carlo estimation method is used to generate a large number of uniformly distributed random points according to the distribution range of independent variables; Use ray tracing method to calculate the electromagnetic field of random numbers under different variable conditions, statistically analyze the distribution of electromagnetic fields under different variable conditions, and establish a statistical mathematical model of Monte Carlo field strength distribution; Based on the statistical mathematical model of Monte Carlo field intensity distribution, the correlation between the propagation conditions of electromagnetic waves and the global characteristics and individual characteristics of field intensity distribution is obtained, and the global characteristics of field intensity distribution are obtained.

[0009] Furthermore, the basis function of the field intensity distribution is constructed according to the vector sum of the incident field of a single radiating antenna and the reflected field with the same energy.

[0010] Furthermore, the basis function formula of the field intensity distribution is:

[0011] In the formula, is the basis function, and r is the distance to the center of the radiation source.

[0012] Furthermore, the objective function of the variational constraint is:

[0013] In the formula is the variational function, K is the decomposition scale, i.e. the number of decomposition modes, is the decomposed eigenmode component, is the center frequency corresponding to different modal components, is the basis function of the kth radiation source, is the total electric field strength.

[0014] Furthermore, the steps of iteratively solving the objective function of the variational constraint and analyzing the relationship between the original field intensity, the eigenmode component and the remainder are: The quadratic penalty factor and Lagrangian operator are introduced, and then the alternating direction algorithm is used to find the minimization solution. The steepest descent method is used to iterate and update the variables to complete the solution of variational minimization. The effective modes are screened through the mutual correlation coefficient between each modal component and the original signal, and the relationship between the original field strength, the eigenmodal component and the remainder is analyzed with the help of the Pearson product-moment correlation coefficient.

[0015] A spatial field intensity distribution intrinsic characteristic characterization system, characterized by comprising an acquisition module, a building module, a decomposition module, a solution module and a characterization module, wherein: Acquisition module: used to calculate the field intensity distribution by ray tracing method according to the conditions of the radiation source and the propagation environment, obtain the local characteristics of the field intensity distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field intensity distribution according to the distribution range of the independent variable; Establishing module: constructing basis functions of field intensity distribution based on local and global characteristics of field intensity distribution; Solution module: used to use variational mode decomposition method to non-recursively decompose the multi-component field intensity distribution into multiple finite bandwidth eigenmode components based on the constructed basis functions, and construct an eigendata set and a variational constrained objective function based on multiple finite bandwidth eigenmode components; Solution module: used to iteratively solve the objective function of the variational constraint and analyze the relationship between the original field strength, eigenmode components and the remainder; The characterization module is used to select the eigenmodal component or remainder with the highest correlation with the original field intensity distribution as a component of the eigendata set to characterize the eigenfeatures of the spatial field intensity distribution.

[0016] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0017] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention discloses a method for characterizing the intrinsic characteristics of spatial field intensity distribution. The method confirms the existence of intrinsic characteristics by constructing a characteristic basis function of the field intensity distribution, then establishes an intrinsic characteristic set by eigenmode decomposition, and iteratively solves the corresponding constraint function. The intrinsic characteristics of the spatial field intensity distribution are characterized by using the eigendata set finally obtained, which fills the gap in the theoretical research on the construction of characteristic functions in the spatial domain of electromagnetic signal time-frequency analysis, improves the characteristic analysis capability of electromagnetic information in the spatial domain, provides a theoretical basis for mining implicit information in the electromagnetic space, provides prior knowledge for subsequent sparse expression and sampling methods, and studies the theory of characterizing the intrinsic characteristics of the field intensity distribution.

[0019] Preferably, the global characteristics of the field intensity distribution are obtained based on the statistical mathematical model of the Monte Carlo field intensity distribution. This method has wide applicability and high accuracy in obtaining the global characteristics of the field intensity distribution. It can intuitively display the global characteristics of the field intensity distribution and provide decision makers with more intuitive and clear information support.

[0020] Preferably, the variational modal decomposition method non-recursively decomposes the multi-component field intensity distribution into multiple finite bandwidth intrinsic modal components according to the constructed basis functions, can determine the number of modal 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, and the decomposition result is stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for characterizing intrinsic characteristics of spatial field intensity distribution according to the present invention; Figure 2 The figure is a detailed flow chart of a method for characterizing intrinsic characteristics of spatial field intensity distribution in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] The present invention is further described in detail below in conjunction with the accompanying drawings: like Figure 1 As shown, a method for characterizing the intrinsic characteristics of spatial field intensity distribution includes the following steps: Step 1: According to the conditions of the radiation source and the propagation environment, the field intensity distribution is calculated using the ray tracing method to obtain the local characteristics of the field intensity distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field intensity distribution according to the distribution range of the independent variable; According to the conditions of the radiation source and the propagation environment, the field intensity E distribution is calculated using the ray tracing method to obtain the local characteristics of the field intensity distribution under a specific environment, and then determine the reflection coefficient , diffraction coefficient and transmission coefficient Equal distribution range; According to the distribution range of the independent variable, a statistical mathematical model of the Monte Carlo field intensity distribution is established to obtain the global characteristics of the field intensity distribution.

[0026] The independent variables include reflection coefficient, diffraction coefficient and transmission coefficient.

[0027] Step 2: constructing a basis function of the field intensity distribution based on the local and global characteristics of the field intensity distribution; In a large-scale environment, the main energy of the total electric field comes from direct waves and reflected waves from the ground. The basis function of the field intensity distribution is constructed based on the vector sum of the incident field of a single radiating antenna and the reflected field of the same energy. .

[0028] The basis function formula of field intensity distribution is:

[0029] In the formula, is the basis function, and r is the distance to the center of the radiation source.

[0030] Step 3, using the variational mode decomposition method, the multi-component field intensity distribution is non-recursively decomposed into multiple finite bandwidth intrinsic mode components according to the constructed basis function, and an eigendata set and a variational constrained objective function are constructed based on the multiple finite bandwidth intrinsic mode components; Specifically, the variational mode decomposition (VMD) method is used to non-recursively decompose the multi-component field intensity distribution into multiple finite bandwidth eigenmode components based on the constructed basis functions, so as to convert a multi-component field intensity distribution into multiple single-component sets, thereby constructing an eigenfeature set; The objective function of the variational constraint is:

[0031] In the formula is the variational function, K is the decomposition scale, i.e. the number of decomposition modes, is the decomposed eigenmode component, is the center frequency corresponding to different modal components, is the basis function of the kth radiation source, is the total electric field strength.

[0032] Step 4, iteratively solve the objective function of the variational constraint and analyze the relationship between the original field strength, the eigenmode component and the residual term; The steps of iteratively solving the objective function of the variational constraint and analyzing the relationship between the original field strength, the eigenmode component and the residual term are as follows: The quadratic penalty factor and Lagrangian operator are introduced, and then the alternating direction algorithm is used to find the minimization solution. The steepest descent method is used to iterate and update the variables to complete the solution of variational minimization. The effective modes are screened through the mutual correlation coefficient between each modal component and the original signal, and the relationship between the original field strength, the eigenmodal component and the remainder is analyzed with the help of the Pearson product-moment correlation coefficient.

[0033] Step 5, selecting the eigenmode component or the remainder with the highest correlation with the original field intensity distribution as a component of the eigendata set to characterize the eigenfeatures of the spatial field intensity distribution; Example The present invention provides a method for characterizing intrinsic characteristics of spatial field intensity distribution, comprising the following steps: Step S1, according to the number of radiating antennas, frequency, radiation direction, radiation intensity and polarization mode, as well as the propagation environments from simple to complex, namely free space, half space, undulating terrain and environment covered with vegetation, the field strength distribution in space is calculated using ray tracing method. If the radiating antenna reaches the receiving point in the line of sight, the i-th (i=1,2,…I) radiating antenna is r The electric field generated at the position is:

[0034]

[0035]

[0036] In the formula, It can also be called the incident field, k is the wave number, and are the elevation angle and azimuth angle, is the radiated power, and Represents the antenna and The gain in direction, and Represent the electric field in and The phase on.

[0037] If the path is non-line-of-sight , then starting from the transmitting antenna, the advancement calculation is performed along the ray path until it reaches the observation point. The mathematical expression of this process is:

[0038] Where n, m, and l are the total number of reflections, diffractions, and transmissions, respectively. , and point are the reflection coefficient, diffraction coefficient and transmission coefficient, is the diffusion factor after reflection, transmission or diffraction, is the distance from the qth ray node to the q+1th node. Total electric field That is, to find the sum of the contributions of all rays reaching the receiving point, then the total electric field of I radiating antennas is

[0039] By calculating the above formula, we can obtain the local characteristics of the field intensity distribution under a specific environment and determine the reflection coefficient. , diffraction coefficient and transmission coefficient Equal distribution range.

[0040] Step S2: Generate a large number of uniformly distributed random points based on the distribution range of the independent variable using the Monte Carlo estimation method (i=1,2,⋯M).

[0041] Then, the ray tracing method is used to calculate the electromagnetic field of random numbers under different variable conditions, statistically analyze the electromagnetic field distribution under different variable conditions, establish a statistical mathematical model of Monte Carlo field strength distribution, obtain the correlation between the propagation conditions of electromagnetic waves and the global and individual characteristics of field strength distribution, and obtain the global characteristics of field strength distribution.

[0042] Step S3, the total electric field in space is the result of the vector superposition of the electromagnetic waves emitted by each radiating antenna and the reflected waves, diffracted waves, and transmitted waves. In a large-scale environment, the main energy of the total electric field comes from the direct waves and the reflected waves from the ground. Therefore, the vector sum of the incident field of a single radiating antenna and the first reflected field will become the basic composition of the field intensity distribution in any scenario. The basis function of the field intensity distribution is constructed based on the vector sum of the incident field of a single radiating antenna and the reflected field of the same energy. , discard the part that has less influence on the feature:

[0043]

[0044]

[0045] In the formula, is the basis function, and r is the distance to the center of the radiation source.

[0046] Step S4, using the variational mode decomposition (VMD) method, through the variational optimization method, the multi-component field intensity distribution is non-recursively decomposed into multiple finite bandwidth intrinsic mode components based on the constructed basis function, so as to convert a multi-component field intensity distribution into multiple single component sets, thereby constructing an intrinsic feature set. The objective function of constructing the variational constraint is as follows:

[0047] In the formula is the variational function, K is the decomposition scale, i.e. the number of decomposition modes, is the decomposed eigenmode component, is the center frequency corresponding to different modal components, is the basis function of the kth radiation source, is the total electric field strength.

[0048] The following is a detailed introduction to variational mode decomposition: Variational mode decomposition (VMD): decomposes a time series f into k components with fixed center frequencies. The modal components , and minimize the sum of the frequency estimation bandwidth of each modal quantity. Perform Hilbert transform on each modal component to obtain its single-sided spectrum. By mixing and estimating the center frequency, the single-sided spectrum of each component is modulated to the baseband, and then Gaussian smoothing is performed on the L2 regularization of the demodulated signal gradient to obtain the bandwidth of the power component. The corresponding constrained variational model is expressed as:

[0049] Among them, ∂t represents partial derivative, and δ(t) represents Dirac distribution function.

[0050] Solution of variational problem: By introducing Lagrange multipliers and quadratic penalty functions, the above constrained variational problem is transformed into an unconstrained problem, that is,

[0051] The alternating direction method of multipliers is used to find the saddle point of the extended Lagrangian expression and obtain the frequency domain update of each mode. The iterative expressions of the modal components and the center frequency are:

[0052] in, Wiener filtering of the representation components; represents the center frequency of the corresponding mode; Perform inverse Fourier transform and take the real part { } to obtain the time domain modal components; n represents the number of iterations; ω represents the frequency value. Finally, the inverse Fourier transform is used to convert the sequence into the time domain to obtain the k narrowband IMF components after sequence decomposition, completing the adaptive segmentation of the signal in the frequency domain.

[0053] VMD converts the sequence from the time domain to the frequency domain for decomposition. For this type of nonlinear data, it can not only retain the original information well, but also avoid the overlap of variable information, and the decomposition process has strong robustness.

[0054] Select the number of decompositions K according to the center frequency: The effect of VMD decomposition is mainly affected by the selected value of the modal number. When the selected value of the modal is small, some important information in the original signal will be filtered out because the VMD algorithm is equivalent to an adaptive filter bank, affecting the accuracy of subsequent predictions; and when the selected value of the modal is large, the center frequencies of adjacent modal components will be close to each other, resulting in modal repetition or additional noise. The main difference between different modes lies in the difference in center frequency, so the appropriate modal value is selected by observing the distribution of center frequency under different modal numbers.

[0055] K is further determined based on the correlation coefficient: In order to avoid the phenomenon of passband overlap and mode overlap after decomposition due to the close center frequencies, thus causing over-decomposition of the wind power signal, in order to further determine the selected value K of the modal number, the correlation of the decomposed adjacent modal components is analyzed and the Pearson correlation coefficient is solved.

[0056] Step S5, introducing a quadratic penalty factor and a Lagrangian 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 Lagrangian operator converts the equation into an unconstrained optimization problem, and then uses the alternating direction algorithm to find the minimization solution. The steepest descent method is used to iterate and update the variables to complete the solution of the complex variational minimization.

[0057] Step S6, screening effective modes through the mutual correlation coefficient index between each modal component and the original signal, and analyzing the relationship between the original field strength, the intrinsic modal component and the remainder by means of the Pearson product-moment correlation coefficient.

[0058] Step S7, selecting the eigenmode component or the remainder with the highest correlation with the original field intensity distribution as a component of the feature set, and reasonably selecting a preset decomposition scale K.

[0059] This invention is based on the actual needs of limited sampling resources to cope with high-resolution electromagnetic field distribution, and conducts research on the intrinsic characteristics of field intensity distribution and sparse reconstruction methods. First, facing the complex and changeable spatial field intensity distribution, the hypothesis of "there are intrinsic characteristics of electromagnetic space field intensity distribution" is proposed, and then on the basis of reasonably characterizing the intrinsic characteristics of field intensity distribution, sparse space construction is used as a breakthrough to reduce the sampling rate, and the non-correlation of sampling matrix and sparse basis is used as a constraint condition to construct sparse basis and sampling method, combining the basic theory of electromagnetic field, signal processing method and mathematical theory.

[0060] The present invention also provides a spatial field intensity distribution intrinsic feature characterization system, comprising an acquisition module, an establishment module, a decomposition module, a solution module and a characterization module, wherein: Acquisition module: used to calculate the field intensity distribution by ray tracing method according to the conditions of the radiation source and the propagation environment, obtain the local characteristics of the field intensity distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field intensity distribution according to the distribution range of the independent variable; Establishing module: constructing basis functions of field intensity distribution based on local and global characteristics of field intensity distribution; Solution module: used to use variational mode decomposition method to non-recursively decompose the multi-component field intensity distribution into multiple finite bandwidth eigenmode components based on the constructed basis functions, and construct an eigendata set and a variational constrained objective function based on multiple finite bandwidth eigenmode components; Solution module: used to iteratively solve the objective function of the variational constraint and analyze the relationship between the original field strength, eigenmode components and the remainder; The characterization module is used to select the eigenmodal component or remainder with the highest correlation with the original field intensity distribution as a component of the eigendata set to characterize the eigenfeatures of the spatial field intensity distribution.

[0061] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0062] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims of the invention.

Claims

1. A method for characterizing intrinsic characteristics of spatial field intensity distribution, characterized in that: The following steps are involved: According to the conditions of the radiation source and the propagation environment, the field intensity distribution is calculated using the ray tracing method to obtain the local characteristics of the field intensity distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field intensity distribution based on the distribution range of the independent variable; Based on the local and global characteristics of the field intensity distribution, a basis function of the field intensity distribution is constructed; Using the variational mode decomposition method, the multi-component field intensity distribution is non-recursively decomposed into multiple finite bandwidth eigenmode components according to the constructed basis functions, and the eigendata set and the objective function of the variational constraint are constructed based on the multiple finite bandwidth eigenmode components. Iteratively solve the objective function of the variational constraint and analyze the relationship between the original field strength, eigenmode components and the residual terms; The eigenmodal components or remainders with the highest correlation with the original field intensity distribution are selected as components of the eigendata set to characterize the eigenfeatures of the spatial field intensity distribution.

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

3. A method for characterizing intrinsic characteristics of spatial field intensity distribution according to claim 1, characterized in that: The step of obtaining the global characteristics of the field intensity distribution according to the distribution range of the independent variable is: Monte Carlo estimation method is used to generate a large number of uniformly distributed random points according to the distribution range of independent variables; Use ray tracing method to calculate the electromagnetic field of random numbers under different variable conditions, statistically analyze the distribution of electromagnetic fields under different variable conditions, and establish a statistical mathematical model of Monte Carlo field strength distribution; Based on the statistical mathematical model of Monte Carlo field intensity distribution, the correlation between the propagation conditions of electromagnetic waves and the global characteristics and individual characteristics of field intensity distribution is obtained, and the global characteristics of field intensity distribution are obtained.

4. A method for characterizing intrinsic characteristics of spatial field intensity distribution according to claim 1, characterized in that: The basis function of the field intensity distribution is constructed according to the vector sum of the incident field of a single radiating antenna and the reflected field with the same energy.

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

6. A method for characterizing intrinsic characteristics of spatial field intensity distribution according to claim 1, characterized in that: The objective function of the variational constraint is: In the formula is the variational function, K is the decomposition scale, i.e. the number of decomposition modes, is the decomposed eigenmode component, is the center frequency corresponding to different modal components, is the basis function of the kth radiation source, is the total electric field strength.

7. A method for characterizing intrinsic characteristics of spatial field intensity distribution according to claim 1, characterized in that: The steps of iteratively solving the objective function of the variational constraint and analyzing the relationship between the original field intensity, the eigenmode component and the remainder are: The quadratic penalty factor and Lagrangian operator are introduced, and then the alternating direction algorithm is used to find the minimization solution. The steepest descent method is used to iterate and update the variables to complete the solution of variational minimization. The effective modes are screened through the mutual correlation coefficient between each modal component and the original signal, and the relationship between the original field strength, the eigenmodal component and the remainder is analyzed with the help of the Pearson product-moment correlation coefficient.

8. A spatial field intensity distribution intrinsic characteristic characterization system, characterized in that: It includes acquisition module, establishment module, decomposition module, solution module and characterization module, among which: Acquisition module: used to calculate the field intensity distribution by ray tracing method according to the conditions of the radiation source and the propagation environment, obtain the local characteristics of the field intensity distribution, determine the distribution range of the independent variable, and obtain the global characteristics of the field intensity distribution according to the distribution range of the independent variable; Establishing module: constructing basis functions of field intensity distribution based on local and global characteristics of field intensity distribution; Solution module: used to use variational mode decomposition method to non-recursively decompose the multi-component field intensity distribution into multiple finite bandwidth eigenmode components based on the constructed basis functions, and construct an eigendata set and a variational constrained objective function based on multiple finite bandwidth eigenmode components; Solution module: used to iteratively solve the objective function of the variational constraint and analyze the relationship between the original field strength, eigenmode components and the remainder; The characterization module is used to select the eigenmodal component or remainder with the highest correlation with the original field intensity distribution as a component of the eigendata set to characterize the eigenfeatures of the spatial field intensity 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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