Macro model construction method and device of electromagnetic system

Abstract

The invention discloses a macro model construction method and device for an electromagnetic system, and relates to the technical field of computational electromagnetis.The method comprises the steps that an initial macro model is constructed in a rational function mode in a frequency domain by means of S parameter data of a target system, physical constraint conditions are applied to model parameters based on the initial macro model, and a macro model is constructed; obtaining a time domain real number causal stability symmetry model; and optimizing a rational function coefficient of the time domain real number causal stability symmetry model by using an optimization algorithm by taking the minimum fitting error between the response of the time domain real number causal stability symmetry model and the response of the target system as a target, so as to realize the construction of a macro model with physical characteristics. The method ensures that the established model has time domain real number performance, causality, stability and symmetry, and ensures physical realizability. The problem that a traditional model construction method often faces insufficient precision or too high calculation complexity is solved, and the macro model construction efficiency and precision of the electromagnetic system are improved.

Description

Technical Field

[0001] The present application relates to the field of computational electromagnetics, and more specifically, to a method and device for constructing a macro model of an electromagnetic system. Background Art

[0002] In the design of modern electromagnetic systems, accurate electromagnetic characteristics analysis is the key to ensuring system performance. S parameters (scattering parameters) are the main parameters that describe the high-frequency characteristics of circuits or devices. S parameters can fully and accurately describe the reflection and transmission characteristics of the network at different frequencies. Through S parameters, we can understand the response of the target system at different frequencies. However, there are many difficulties in directly using S parameters for system design. For example, the processing time and memory requirements required to directly simulate complex systems such as chip-package-circuit board are not feasible for any computer. The macro model is a simplified behavioral description that ignores some aspects that are not important to the system behavior.

[0003] The macromodeling task is to extract the model by fitting the model parameters without using the internal structure information of the actual system so that it reproduces the behavior in terms of input and output characteristics. In the modeling process, accuracy is not the only criterion for evaluating the quality of the model. From a practical point of view, what is really important is the physical consistency of the final model, that is, any model must have the same basic physical properties as the corresponding system, including time domain real number, causality, stability and symmetry. When one or more of these properties are missing, inconsistent results may occur, leading to the failure of system design evaluation.

[0004] In the design of electromagnetic systems, in order to reflect the macroscopic physical characteristics of the system, it is usually necessary to establish an accurate mathematical model to fit these data. Traditional model building methods often face the problem of insufficient accuracy or high computational complexity, so a more efficient and accurate method is needed to achieve this goal. Summary of the invention

[0005] In response to at least one defect or improvement need in the prior art, the present invention provides a method and device for constructing a macro model of an electromagnetic system, so as to address the defects of the prior art, ensure that the macro model established for the electromagnetic system has time-domain real number properties, causality, stability and symmetry, and realize accurate and physically feasible macro model construction of the electromagnetic system.

[0006] To achieve the above object, according to a first aspect of the present invention, a method for constructing a macro model of an electromagnetic system is provided, comprising:

[0007] Acquire S parameter data of the target system and construct an initial macro model using a rational function form in the frequency domain, wherein the rational function form is a partial expansion;

[0008] Applying physical constraints to model parameters based on the initial macro model to obtain a time-domain real number causal stable symmetric model;

[0009] With the goal of minimizing the fitting error between the time-domain real-number causal stable symmetric model response and the target system response, the rational function coefficients of the time-domain real-number causal stable symmetric model are optimized using an optimization algorithm to achieve the construction of a macro model with physical characteristics.

[0010] As described in the macro model construction method of the electromagnetic system, the initial macro model can be expressed as:

[0011]

[0012] Among them, s is the complex frequency point; R n is the residue matrix of rational functions; D is the constant matrix; p n is the pole of a rational function.

[0013] The macro model construction method of the electromagnetic system, wherein the physical constraints are applied to the model parameters based on the initial macro model to obtain a physically realizable model, comprises:

[0014] Determine the poles of rational functions as real numbers or conjugate complex pairs to ensure the time domain realness of the model;

[0015] Make sure the real part of the pole of the rational function is less than 0 to ensure the causality and stability of the model;

[0016] Determine that the residue matrix and constant matrix of the rational function are symmetric square matrices to ensure the symmetry of the model. As in the macro model construction method of the electromagnetic system, the rational function coefficients of the time domain real number causal stable symmetric model are optimized by using an optimization algorithm with the goal of minimizing the fitting error between the time domain real number causal stable symmetric model response and the target system response, including:

[0017] Determine the optimal order of the rational function based on the S-parameter data of the target system;

[0018] Determine the number of sampling frequency points based on the S parameter data of the target system;

[0019] A numerical method is used to estimate model optimization parameters based on the optimal order and the number of sampling frequencies; wherein the model optimization parameters are used to measure the degree of fit between the time domain real number causal stable symmetric model response and the S parameter data;

[0020] The optimization algorithm is used to update the optimization parameters of the model to minimize the fitting error between the time-domain real number causal stable symmetric model response and the target system response.

[0021] In the macro model construction method of the electromagnetic system, determining the optimal order of the rational function based on the S parameter data of the target system includes:

[0022] Determine the initial order based on the frequency domain characteristics of the S parameters of the target system;

[0023] Based on the initial order, a grid search or ergodic test is performed according to the fitting error and the physical characteristics of the model to gradually increase the order to obtain an optimal order that meets the application conditions of the target system.

[0024] The macro model construction method of the electromagnetic system, after obtaining the S parameters, further includes:

[0025] The S parameters are subjected to data preprocessing, wherein the data preprocessing includes denoising processing, error processing, and smoothing processing.

[0026] As described in the macro model construction method of the electromagnetic system, the optimization algorithm includes the least squares method, the gradient descent method or the genetic algorithm.

[0027] According to the second aspect of the present invention, there is also provided a macro model construction device for an electromagnetic system, comprising: a construction unit, used to obtain S parameter data of a target system and construct an initial macro model in the frequency domain using a rational function form, wherein the rational function form is a partial expansion; a processing unit, used to apply physical constraints to model parameters based on the initial macro model to obtain a time domain real number causal stable symmetric model; an optimization unit, used to optimize the rational function coefficients of the time domain real number causal stable symmetric model using an optimization algorithm with the goal of minimizing the fitting error between the response of the time domain real number causal stable symmetric model and the response of the target system, so as to realize the construction of a macro model with physical characteristics.

[0028] According to a third aspect of the present invention, there is further provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned macro model building method of the electromagnetic system when running.

[0029] According to a fourth aspect of the present invention, there is also provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the macro model construction method of the electromagnetic system through the computer program.

[0030] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0031] (1) The present invention provides a method for constructing a macro model of an electromagnetic system. The method uses the S parameter data of the target system in the frequency domain in the form of rational functions to construct an initial macro model. Based on the initial macro model, physical constraints are applied to the model parameters to obtain a time-domain real number causal stable symmetric model. Then, with the goal of minimizing the fitting error between the time-domain real number causal stable symmetric model response and the target system response, the rational function coefficients of the time-domain real number causal stable symmetric model are optimized using an optimization algorithm to achieve the construction of a macro model with physical characteristics. It ensures that the established model has time-domain real number, causality, stability and symmetry, ensuring physical feasibility. It solves the problem that traditional model construction methods often face insufficient precision or high computational complexity, and improves the efficiency and accuracy of macro model construction of electromagnetic systems.

[0032] (2) The present invention selects representative S parameter sample data and combines the optimal order and the number of sampling frequencies to improve the model fitting accuracy while ensuring the calculation efficiency. By using the optimization algorithm to fine-tune the model parameters, it is ensured that the time domain real number causal stable symmetric model can accurately reflect the physical characteristics of the target system, has good prediction ability and generalization performance, and can be widely used in system analysis and design in the fields of electronic engineering, microwave engineering, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A schematic flow chart of an optional method for constructing a macro model of an electromagnetic system provided in an embodiment of the present application;

[0035] Figure 2 A schematic diagram of the structure of an optional macro model building device for an electromagnetic system provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of the structure of an optional electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0038] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. 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 limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0039] According to one aspect of the embodiments of the present application, a method for constructing a macro model of an electromagnetic system is provided. Figure 1 The macro model construction method of the electromagnetic system provided in the embodiment of the present application is described.

[0040] Figure 1 is a flow chart of an optional method for constructing a macro model of an electromagnetic system provided in an embodiment of the present application, such as Figure 1 As shown, the process of the method may include the following steps (STEP, referred to as S):

[0041] S102, obtaining S parameter data of the target system and constructing an initial macro model using a rational function form in the frequency domain, wherein the rational function form is a partial expansion;

[0042] S104, applying physical constraints to model parameters based on the initial macro model to obtain a time-domain real number causal stable symmetric model;

[0043] S106, with the goal of minimizing the fitting error between the time-domain real-number causal stable symmetric model response and the target system response, the rational function coefficients of the time-domain real-number causal stable symmetric model are optimized using an optimization algorithm to achieve the construction of a macro model with physical characteristics.

[0044] Optionally, the initial macro model can be expressed as:

[0045]

[0046] Among them, s is the complex frequency point; R n is the residue matrix of rational functions; D is the constant matrix; p n is the pole of a rational function.

[0047] An optional macro model construction method for an electromagnetic system provided in the present application can be used in constructing a macro model of a target system. It should be noted that the target system here can be a complex system such as a chip, a circuit board, or a device.

[0048] In the macro model construction of the embodiment of the present application, taking a 2-port circuit as an example, electromagnetic simulation software (such as EMX, e Wave, etc.) is used to calculate the S parameters of a 2-port circuit at different frequencies, covering a frequency range of 0 GHz to 10 GHz, with a frequency interval of 0.1 GHz.

[0049] The initial macro model is constructed using a rational function form in the frequency domain (Laplace domain), and the initial macro model can be expressed as:

[0050]

[0051] Among them, s is the complex frequency point, corresponding to the complex number expression of 100 frequency point data from 0 GHz to 10 GHz, and the residue matrix Rn and constant matrix D are respectively:

[0052]

[0053] In the present invention, a specific expression form is used to construct the initial macro model, and this expression form is a partially expanded rational function. This form is selected based on its close connection with the physical characteristics of the system and its convenience in the circuit simulation process.

[0054] Specifically, there are three main forms of expression for rational functions: polynomial type, pole-zero type, and partial expansion type. Among them, although the polynomial type is simple, it cannot directly express the poles of rational functions, which is not conducive to understanding and analyzing the physical characteristics of the system. On the other hand, although the pole-zero type can directly express the poles of rational functions, when performing circuit simulation, it uses multiplication to process each fraction, which makes the operation relatively complicated.

[0055] The use of partially expanded rational functions to add each fraction makes the operation more intuitive and flexible when performing circuit simulation. In addition, this form also makes it easy to convert the macro model into an equivalent circuit for further simulation after it is established.

[0056] Therefore, the present invention adopts a partial expansion type to construct an expression form of a rational function, so as to facilitate circuit simulation and understanding of the physical characteristics of the system.

[0057] In electromagnetic system design, depending on the application requirements, the macromodel also needs to comply with certain constraints related to the “physical” properties of the real-world system, including time domain realness, causality, stability, and symmetry.

[0058] In this embodiment, physical constraints are applied to model parameters based on the initial macro model to obtain a time domain real number causal stable symmetric model, including:

[0059] Make sure that all poles of rational functions are real numbers or conjugate complex pairs to ensure the time domain real number property of the model; make sure that the real parts of all poles of rational functions are less than 0 to ensure the causality and stability of the model; make sure that the residue matrix Rn and constant matrix D of rational functions are symmetric square matrices to ensure the symmetry of the model. The details are as follows:

[0060] (1) Real number property in time domain

[0061] The macro model is used to describe the frequency behavior of the system. In subsequent circuit simulation, it is usually necessary to convert it into a time domain signal through an inverse Fourier transform. Due to the complex nature of frequency, the converted time domain signal may have an imaginary part in terms of value, and the imaginary part of the time domain signal has no actual physical meaning, so it needs to be eliminated. The following method can be used to ensure the time domain real number of the signal, that is, the converted signal has only a real part but no imaginary part in terms of value. The specific operation is:

[0062] If the pole pn is a real number, then pn=c(c∈);

[0063] If the poles pn and pn+1 are a pair of complex numbers, then they are a pair of conjugate complex numbers, requiring pn=a+bi, pn+1=a-bi(a,b∈).

[0064] The corresponding residue matrix Rn is also constrained in the same way.

[0065] Take r11,n as an example, and the other ports are similar. If the pole pn is a real number, then the residue r11,n is also a real number, requiring r11,n=c'(c'∈);

[0066] If the poles pn and pn+1 are a pair of conjugate complex numbers, then the residues r11,n and r11,n+1 are also a pair of conjugate complex numbers, requiring pn=a'+b'i, pn+1=a'-b'i(a',b'∈).

[0067] (2) Causality and stability

[0068] The general partially expanded macro model will have a first-order term sE of complex frequency. The Kramers-Kronig relationship can prove that the first-order term sE may cause the macro model based on S parameters to violate causality. Therefore, directly eliminating the first-order term sE during modeling can effectively avoid the violation of causality. On this basis, the negative real part of the pole can be guaranteed to fully guarantee causality. At the same time, according to the principles of system theory, the negative real part of the pole can also ensure the stability of the model. The specific operation is: if the pole pn is a real number, c<0 is required; if the poles pn and pn+1 are a pair of conjugate complex numbers, a<0 ​​is required. There is no requirement for the residue.

[0069] (3) Symmetry

[0070] Usually, the passive system described by the macro model has multiple ports. If an excitation is given at port 1, a response will be obtained at port 2. Conversely, if the same excitation is given at port 2, the same response will be obtained at port 1. This is called the symmetry of the system. In the macro model, as long as the residue matrix Rn and the constant matrix D are symmetric square matrices, that is, r12,n=r21,n and d12=d21, the symmetry of the system can be guaranteed, and its physical characteristics can be constrained by mathematical conditions.

[0071] The present invention ensures that the established model has time domain real number, causality, stability and symmetry by applying physical constraints to model parameters, and can ensure that the constructed macro model of the target system is physically feasible.

[0072] In an exemplary embodiment, the optimization of the rational function coefficients of the time-domain real number causal stable symmetric model using an optimization algorithm with the goal of minimizing the fitting error between the time-domain real number causal stable symmetric model response and the target system response includes:

[0073] S31: determining the optimal order of the rational function based on the S parameter data of the target system;

[0074] S32: determining the number of sampling frequency points based on S parameter data of the target system;

[0075] S33: estimating model optimization parameters using a numerical method based on the optimal order and the number of sampling frequencies; wherein the model optimization parameters are used to measure the degree of fit between the time-domain real number causal stable symmetric model response and the S parameter data;

[0076] S34: using an optimization algorithm to update the model optimization parameters to minimize the fitting error between the time-domain real number causal stable symmetric model response and the target system response.

[0077] It should be noted that the number of sampling frequency points must be greater than or equal to the number of unknown parameters of the rational function to ensure the correct estimation of the model.

[0078] For S31, the optimal order of the rational function is determined based on the S-parameter data of the target system:

[0079] In this embodiment, the S parameters of the 2-port circuit have 4 curves, namely port 1-1, port 1-2, port 2-1, and port 2-2. Each curve has a peak, and a peak represents a resonant frequency, which is represented by a pair of complex poles, plus a real pole to adjust the response amplitude and attenuation speed. Therefore, the initial order of the model Nmin=2*1+1=3.

[0080] If the resonant frequency difference between any two curves is too large, a pair of complex poles needs to be added to distinguish them. Therefore, the order is gradually increased according to the fitting error and the physical characteristics of the model, such as grid search or ergodic test, to obtain the optimal order that meets the application conditions of the target system. For example, increase the order by 2 each time, that is, the order N increases in the manner of 3, 5, 7, ..., Nmax. The four curves require a maximum of four resonant frequencies, that is, 4 pairs of complex poles, plus one real pole, so Nmax = 2*4+1 = 9.

[0081] For S32, the number of sampling frequencies is determined based on the S parameter data of the target system:

[0082] In this embodiment, the unknown parameters of the rational function include N poles pn, 4N residues r11,n, r12,n, r21,n and r22,n, and 4 constants d11, d12, d21 and d22. The number of sampling frequency points Ns≥5N+4, and is less than the total number of frequency points 100. The initial number of sampling frequency points takes the minimum value, and the points are evenly selected; if the fitting error requirement cannot be met, the frequency points with the largest error can be gradually added as sampling frequency points to improve the fitting accuracy.

[0083] Furthermore, a set of Ns sample data is selected from the S parameter data According to the optimal order and the number of sampling frequencies in combination with the data points, the rational function coefficients of the time-domain real-number causal stable symmetric model are optimized by using an optimization algorithm with the goal of minimizing the fitting error between the response of the time-domain real-number causal stable symmetric model and the response of the target system, so as to realize the construction of a macro model with physical characteristics.

[0084] Select Ns sample data from the S parameter data set Among them, each sample contains a frequency point sk and its corresponding model value After obtaining the sample data, the model is constructed, and the rational function form H(s) of the real causal stable symmetric model in the time domain is defined. Based on the optimal order and the number of sampling frequency points, the model optimization parameter x is estimated by numerical method, that is, the unknown parameter of the rational function in the initialization model, and then the residual vector is calculated. That is, the difference between the actual value of the model and the calculated value, and defines the cumulative square error function E(x), which is used to measure the degree of fit between the model response and the actual S-parameter data.

[0085] Perform model optimization and use optimization algorithms (such as least squares method, gradient descent method, etc.) to adjust the model optimization parameter x so that the cumulative square error E(x) is minimized. It should be noted that during the optimization process, the model order N can be dynamically adjusted according to the fitting accuracy requirements until a satisfactory fitting effect is achieved.

[0086] Furthermore, model validation was performed and the constructed macro model was verified using S parameter data that did not participate in the optimization to evaluate the predictive ability and generalization performance of the model.

[0087] The present invention selects representative S parameter sample data, combines the optimal order and the number of sampling frequencies, and can improve the fitting accuracy of the model while ensuring the calculation efficiency. By using the optimization algorithm to fine-tune the model parameters, it is ensured that the time-domain real number causal stable symmetric model can accurately reflect the physical characteristics of the target system. The macro model constructed by the present invention has good predictive ability and generalization performance, and can be widely used in system analysis and design in the fields of electronic engineering, microwave engineering, etc.

[0088] In an exemplary embodiment, the S parameter data of a high-frequency circuit is selected as a data set. According to the circuit characteristics and prior knowledge, the optimal order of the model is determined to be 4 and the number of sampling frequency points is 20. 20 frequency points and their corresponding S parameter values ​​are randomly selected from the data set as sample data. The rational function form of the time-domain real causal stable symmetric model is defined, and the model parameters are initialized. The residual vector and the cumulative square error are calculated, and the model parameters are optimized using the least squares method. After multiple rounds of iterations, a macro model that meets the fitting accuracy requirements is obtained. The model is verified using S parameter data that is not involved in the optimization, and the results show that the model has good predictive ability and generalization performance.

[0089] Optionally, the steps of the above embodiment may be repeated for S parameter data of another microwave device, and the optimal order of the model and the number of sampling frequencies may be adjusted according to the characteristics of the device, a new macro model may be obtained through an optimization algorithm, and its performance may be verified.

[0090] In summary, the present invention provides a macro model construction method for an electromagnetic system, which is suitable for realizing accurate and physically feasible macro modeling according to S parameter data. By applying an optimization algorithm, the present invention can improve the fitting accuracy and prediction ability of the model while ensuring computational efficiency.

[0091] According to another aspect of an embodiment of the present application, a macro model building device for implementing the above-mentioned macro model building method of the electromagnetic system is also provided. Figure 2 is a schematic diagram of the structure of an optional macro model building device for an electromagnetic system according to an embodiment of the present application, such as Figure 2 As shown, the device may include:

[0092] A construction unit 202 is used to obtain S parameter data of the target system and construct an initial macro model using a rational function form in the frequency domain, wherein the rational function form is a partial expansion;

[0093] The processing unit 204 is used to apply the physical constraint conditions to the model parameters based on the initial macro model to obtain a time domain real number causal stable symmetric model;

[0094] The optimization unit 206 is used to optimize the rational function coefficients of the time-domain real-number causal stable symmetric model by using an optimization algorithm with the goal of minimizing the fitting error between the time-domain real-number causal stable symmetric model response and the target system response, so as to realize the construction of a macro model with physical characteristics.

[0095] It should be noted that the construction unit 202 in this embodiment can be used to execute the above step S102, the processing unit 204 in this embodiment can be used to execute the above step S104, and the optimization unit 206 in this embodiment can be used to execute the above step S106.

[0096] Through the above modules, by using the S parameter data of the target system in the frequency domain using the rational function form, the initial macro model is constructed, and the physical constraints are applied to the model parameters based on the initial macro model to obtain the time domain real number causal stability symmetry model; then, with the goal of minimizing the fitting error between the time domain real number causal stability symmetry model response and the target system response, the rational function coefficients of the time domain real number causal stability symmetry model are optimized using the optimization algorithm to achieve the construction of a macro model with physical characteristics. It ensures that the established model has time domain real number, causality, stability and symmetry, ensuring physical feasibility. It solves the problem of insufficient precision or high computational complexity often faced by traditional model construction methods, and improves the efficiency and accuracy of macro model construction of electromagnetic systems.

[0097] It should be noted here that the examples and scenarios implemented by the above-mentioned modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiments. It should be noted that the above-mentioned modules as part of the device can run in a hardware environment and can be implemented by software or hardware, wherein the hardware environment includes a network environment.

[0098] According to another aspect of the embodiments of the present application, a storage medium is further provided. Optionally, in this embodiment, the storage medium can be used to execute the program code of any of the macro model building methods of the electromagnetic system in the embodiments of the present application.

[0099] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps:

[0100] S1, obtaining S parameter data of the target system and constructing an initial macro model using a rational function form in the frequency domain, wherein the rational function form is a partial expansion;

[0101] S2, applying physical constraints to model parameters based on the initial macro model to obtain a time-domain real number causal stable symmetric model;

[0102] S3, with the goal of minimizing the fitting error between the time-domain real-number causal stable symmetric model response and the target system response, the rational function coefficients of the time-domain real-number causal stable symmetric model are optimized using an optimization algorithm to achieve the construction of a macro model with physical characteristics.

[0103] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, which will not be described in detail in this embodiment.

[0104] Among them, computer-readable storage media may include, but are not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0105] According to another aspect of the embodiments of the present application, an electronic device for implementing the above-mentioned macro model construction method of the electromagnetic system is also provided. The electronic device may be a server, a terminal, or a combination thereof.

[0106] Figure 3 is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application, such as Figure 3 As shown, it includes a processor 302, a communication interface 304, a memory 306 and a communication bus 308, wherein the processor 302, the communication interface 304, and the memory 306 communicate with each other through the communication bus 308, wherein:

[0107] Memory 306, used for storing computer programs;

[0108] The processor 302 is used to implement the following steps when executing the computer program stored in the memory 306:

[0109] S1, constructing a rotation basis function based on the unit pair information of the generated inhomogeneous medium target, wherein the unit pair information is the segmented volume unit data corresponding to the volume segmentation unit generated after geometric segmentation of the inhomogeneous medium target.

[0110] S2, remove the redundant degrees of freedom of the rotation basis function and obtain the target rotation basis function set.

[0111] S3, numerically discretize the electric field integral equation of the full current of the inhomogeneous medium target through the target rotation basis function set to obtain the target electric field integral equation, and test the target electric field integral equation through the test function set to obtain the global coupling matrix equation.

[0112] S4, iteratively solve the global coupling matrix equation to obtain the electromagnetic characteristics of the inhomogeneous medium target.

[0113] Optionally, the communication bus may be a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The communication interface is used for communication between the electronic device and other devices.

[0114] The memory may include RAM, or may include nonvolatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0115] As an example, the memory 306 may include, but is not limited to, the construction unit 202, the processing unit 204, and the optimization unit 206 in the macro model construction device of the electromagnetic system. In addition, it may also include, but is not limited to, other module units in the macro model construction device of the electromagnetic system, which will not be repeated in this example.

[0116] The above-mentioned processor can be a general-purpose processor, which can include but not be limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processing), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0117] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0118] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0119] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0123] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program code.

[0124] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0125] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0126] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A macro model construction method for an electromagnetic system, characterized in that: include: Acquiring S parameter data of the target system in a rational function form in the frequency domain to construct an initial macro model, wherein the rational function form is a partial expansion; Applying physical constraints to model parameters based on the initial macro model to obtain a time-domain real number causal stable symmetric model; With the goal of minimizing the fitting error between the time-domain real-number causal stable symmetric model response and the target system response, the rational function coefficients of the time-domain real-number causal stable symmetric model are optimized using an optimization algorithm to achieve the construction of a macro model with physical characteristics.

2. The method for constructing a macro model of an electromagnetic system according to claim 1, wherein: The initial macro model can be expressed as: Among them, s is the complex frequency point; R n is the residue matrix of rational functions; D is the constant matrix; p n is the pole of a rational function.

3. The method for constructing a macro model of an electromagnetic system according to claim 2, wherein: The applying physical constraints to model parameters based on the initial macro model includes: Determine the poles of rational functions as real numbers or conjugate complex pairs to ensure the time domain realness of the model; Make sure the real part of the pole of the rational function is less than 0 to ensure the causality and stability of the model; The residue matrix and constant matrix of the rational function are determined to be symmetric square matrices to ensure the symmetry of the model.

4. The method for constructing a macro model of an electromagnetic system according to claim 1, wherein: The method optimizes the rational function coefficients of the time-domain real number causal stable symmetric model by using an optimization algorithm with the goal of minimizing the fitting error between the time-domain real number causal stable symmetric model response and the target system response, including: Determine the optimal order of the rational function based on the S-parameter data of the target system; Determine the number of sampling frequency points based on the S parameter data of the target system; A numerical method is used to estimate model optimization parameters based on the optimal order and the number of sampling frequencies; wherein the model optimization parameters are used to measure the degree of fit between the time domain real number causal stable symmetric model response and the S parameter data; The optimization algorithm is used to update the optimization parameters of the model to minimize the fitting error between the time-domain real number causal stable symmetric model response and the target system response.

5. The method for constructing a macro model of an electromagnetic system according to claim 4, wherein: The step of determining the optimal order of the rational function based on the S parameter data of the target system comprises: Determine the initial order based on the frequency domain characteristics of the S parameters of the target system; Based on the initial order, a grid search or ergodic test is performed according to the fitting error and the physical characteristics of the model to gradually increase the order to obtain an optimal order that meets the application conditions of the target system.

6. The method for constructing a macro model of an electromagnetic system according to claim 1, wherein: After obtaining the S parameters, the method further includes: The S parameters are subjected to data preprocessing, wherein the data preprocessing includes denoising processing, error processing and smoothing processing.

7. The method for constructing a macro model of an electromagnetic system according to claim 1, wherein: The optimization algorithm includes least square method, gradient descent method or genetic algorithm.

8. A macro model construction device for an electromagnetic system, characterized in that: include: A construction unit, used to obtain S parameter data of the target system and construct an initial macro model using a rational function form in the frequency domain, wherein the rational function form is a partial expansion; A processing unit, configured to apply physical constraints to model parameters based on the initial macro model to obtain a time-domain real number causal stable symmetric model; The optimization unit is used to optimize the rational function coefficients of the time-domain real-number causal stable symmetric model by using an optimization algorithm with the goal of minimizing the fitting error between the time-domain real-number causal stable symmetric model response and the target system response, so as to realize the construction of a macro model with physical characteristics.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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