A wave-absorbing component parameter processing method, device, equipment and storage medium

Abstract

This application provides a method, apparatus, device, and storage medium for processing parameters of a microwave absorbing component. The microwave absorbing component includes at least two layers of absorbing material. The method includes: upon receiving a simulation command for the microwave absorbing component, acquiring the component's size data, the identity vectors of the absorbing materials, and the material identifiers corresponding to the identity vectors; constructing an initialization variable matrix based on the size data; determining target row vectors by matching the row vectors in the initialization variable matrix with the identity vectors; updating the target row vectors based on the material identifiers to generate a target variable matrix; converting the target variable matrix into a combination identifier for the absorbing materials; and outputting the combination identifier for the absorbing materials. This method achieves dimensionality reduction of the initialization variable matrix, improving the optimization efficiency of the microwave absorbing component combination design.

Description

Technical Field

[0001] This application relates to the field of microwave absorbing materials technology, and in particular to a method, apparatus, device and storage medium for processing parameters of microwave absorbing components. Background Technology

[0002] Multilayer microwave absorbing components typically employ a combination of multiple candidate materials in their design. As the types of candidate materials and the number of layers increase, the number of design variables increases dramatically, leading to low optimization computation efficiency. Therefore, how to improve the optimization computation efficiency of microwave absorbing components while ensuring design effectiveness is a pressing technical problem to be solved in this field. Summary of the Invention

[0003] In view of the above problems, embodiments of this application are proposed to provide a method for processing parameters of an absorbing component, an apparatus for processing parameters of an absorbing component, an electronic device, and a computer-readable storage medium to overcome or at least partially solve the above problems.

[0004] To address the aforementioned problems, in a first aspect of this application, an embodiment discloses a method for processing parameters of a microwave absorbing component, wherein the microwave absorbing component comprises at least two layers of absorbing material, including: Upon receiving a simulation command for the absorbing component, the system acquires the size data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector. Construct an initialization variable matrix based on the stated size data; The target row vector is determined by matching the row vectors in the initialization variable matrix with the identity vector. Update the target row vector based on the material identifier to generate a target variable matrix; Convert the target variable matrix into a microwave absorbing material combination identifier; Output the identifier of the microwave absorbing material combination.

[0005] Optionally, the size data includes the total number of layers and the number of material types, and the step of constructing an initialization variable matrix based on the size data includes: The total number of layers is determined as the number of rows in the matrix; The number of columns in the matrix is ​​determined by rounding up the binary logarithm of the number of material types. The matrix size is determined by combining the number of rows and columns of the matrix; Construct an empty matrix corresponding to the stated matrix size; Assign values ​​to the empty matrix to determine the initialization variable matrix.

[0006] Optionally, the step of determining the target row vector by matching the row vectors in the initialization variable matrix based on the identity vector includes: Compare the identity vector with the row vector bit by bit; The row vector that matches the identity vector is determined as the target row vector.

[0007] Optionally, the material identifier corresponds to electromagnetic data, and the step of updating the target row vector based on the material identifier to generate the target variable matrix includes: The reflectivity of the absorbing component is calculated based on the material identification. The target row vector is updated based on the reflectivity to generate the target variable matrix.

[0008] Optionally, the material identifier corresponds to electromagnetic data and material thickness, and the step of calculating the reflectivity of the absorbing component based on the material identifier includes: Based on a preset transmission line theory model, the electromagnetic data and the material thickness are converted into the input impedance of the outer absorbing material; The reflectivity of the absorbing component is determined based on the input impedance of the outer absorbing material and the preset free space impedance value.

[0009] Optionally, the step of determining the reflectivity of the absorbing component based on the input impedance of the outer absorbing material and a preset free-space impedance value includes: Determine the difference between the input impedance of the outer absorbing material and the preset free space impedance value; Determine the sum between the input impedance of the outer absorbing material and the preset free space impedance value; Determine the quotient between the difference and the sum; The absolute value of the quotient is taken as the decimal logarithm to determine the logarithm value; The product between the logarithmic value and the preset reflectivity coefficient is determined to be the reflectivity of the absorbing component.

[0010] Optionally, the step of updating the target row vector based on the reflectivity to generate the target variable matrix includes: When the reflectivity is less than a preset reflectivity benchmark value, the elements in the target row vector are cross-crossed and mutated to obtain an intermediate row vector; The intermediate row vectors are combined to obtain the target variable matrix.

[0011] In a second aspect, this application discloses a parameter processing device for an absorbing component, wherein the absorbing component comprises at least two layers of absorbing material, and the device comprises: The acquisition module is used to acquire, upon receiving a simulation command for the absorbing component, the size data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector; A construction module is used to construct an initialization variable matrix based on the size data; The determination module is used to determine the target row vector by matching the row vectors in the initialization variable matrix based on the identity vector; The generation module is used to update the target row vector based on the material identifier and generate a target variable matrix; The conversion module is used to convert the target variable matrix into a microwave absorbing material combination identifier; The output module is used to output the identifier of the microwave absorbing material combination.

[0012] In a third aspect of this application, an electronic device is disclosed, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the microwave absorbing component parameter processing method as described above.

[0013] In a fourth aspect, embodiments of this application disclose a computer-readable storage medium storing a computer program that, when executed by a processor, implements the wave-absorbing component parameter processing method as described above.

[0014] The embodiments of this application have the following advantages: This application embodiment of the microwave absorbing component includes at least two layers of microwave absorbing material, comprising: upon receiving a simulation command for the microwave absorbing component, acquiring the size data of the microwave absorbing component, the identity vector of the microwave absorbing material, and the material identifier corresponding to the identity vector; constructing an initialization variable matrix based on the size data; determining a target row vector by matching the row vectors in the initialization variable matrix based on the identity vector; updating the target row vector based on the material identifier to generate a target variable matrix; converting the target variable matrix into a microwave absorbing material combination identifier; and outputting the microwave absorbing material combination identifier. By acquiring the size data of the absorbing components, the identity vectors of the absorbing materials, and their corresponding material identifiers, a data foundation is provided for subsequent optimization. An initialization variable matrix is ​​constructed based on the size data to achieve dimensionality reduction of the initialization variable matrix. Based on the matching of the identity vectors with the row vectors in the initialization variable matrix, each layer must select one and only one material, eliminating illegal solutions in the optimization process and improving optimization efficiency. The target row vectors are updated based on the material identifiers, enabling the search for the optimal solution in the dimensionality-reduced initialization variable matrix space. Finally, the target variable matrix is ​​converted into absorbing material combination identifiers and output, realizing the output from the initialization variable matrix to the absorbing material combination identifiers, thus improving the optimization efficiency of the absorbing component combination design. Attached Figure Description

[0015] Figure 1This is a flowchart illustrating the steps of an embodiment of a method for processing parameters of a wave-absorbing component according to this application; Figure 2 This is a flowchart illustrating the steps of another embodiment of the method for processing parameters of a microwave absorbing component in this application; Figure 3 This is a flowchart illustrating the steps involved in determining the target variable matrix in this application; Figure 4 This is a schematic diagram of the reflectivity optimization results of this application; Figure 5 This is a structural block diagram of an embodiment of a wave-absorbing component parameter processing device according to this application; Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of this application; Figure 7 This is a structural block diagram of a storage medium provided in an embodiment of this application. Detailed Implementation

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Figure 1 This illustration shows a flowchart of an embodiment of a method for processing parameters of a microwave absorbing component according to this application. The microwave absorbing component includes at least two layers of absorbing material, and the parameter processing of the microwave absorbing component may specifically include the following steps: Step 101: Upon receiving a simulation command for the absorbing component, acquire the size data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector; Upon receiving simulation commands for the absorbing material assembly, the system can acquire relevant data for both the assembly and the material. It can read the dimensional data of the absorbing assembly, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector from memory. The dimensional data characterizes the structural scale of the absorbing assembly; the identity vector represents the unique code of each absorbing material; and the material identifier corresponding to the identity vector represents the material index that corresponds one-to-one with the identity vector. This information can be used to retrieve relevant data on the absorbing material from the database, thus providing an accurate data foundation for subsequent optimization.

[0018] Step 102: Construct an initialization variable matrix based on the size data; After obtaining the size data, the number of rows and columns of the matrix can be determined based on the size data. After obtaining the number of rows and columns, an initial variable matrix can be randomly constructed, which lays the foundation for determining the selection of each layer of absorbing material through identity vector matching.

[0019] Step 103: Based on the identity vector, match the row vectors in the initialization variable matrix to determine the target row vector; After obtaining the initialization variable matrix, we can combine the row vectors in the initialization variable matrix to determine the identity vectors with consistent values ​​from the identity vectors of the candidate absorbing materials. We can then compare the values ​​of the row vectors with the corresponding digit values ​​of the identity vectors, and determine the matching identity vectors as the target row vectors. This establishes the correspondence between each row vector in the initialization variable matrix and the absorbing materials, providing a basis for subsequent optimization.

[0020] Step 104: Update the target row vector based on the material identifier to generate the target variable matrix; Electromagnetic data and material thickness can be determined based on material identification. Reflectivity can be calculated based on electromagnetic data and material thickness. The target variable matrix can be determined based on the comparison results of reflectivity data and preset reflectivity benchmark values, thereby realizing the update from material identification to target variable matrix. This enables the absorbing components corresponding to the target variable matrix to achieve a lower reflectivity, and finally obtains an optimized absorbing component combination scheme.

[0021] Step 105: Convert the target variable matrix into a microwave absorbing material combination identifier; By comparing the row vectors and identity vectors in the target variable matrix bit by bit, the identity vectors that are completely consistent with the values ​​of each row vector can be determined. Based on the correspondence between the identity vectors and the material identifiers, the material identifiers corresponding to each layer can be determined, thereby quickly obtaining the microwave absorbing materials of each layer that meet the reflectivity requirements.

[0022] Step 106: Output the identifier of the microwave absorbing material combination.

[0023] It can output the identifier of the determined absorbing material combination. The identifier of the absorbing material combination is composed of the material identifiers corresponding to each layer of absorbing material in the order of the layers. By outputting the identifier of the absorbing material combination, the optimal absorbing component combination scheme can be obtained.

[0024] This application embodiment of the microwave absorbing component includes at least two layers of microwave absorbing material, comprising: upon receiving a simulation command for the microwave absorbing component, acquiring the size data of the microwave absorbing component, the identity vector of the microwave absorbing material, and the material identifier corresponding to the identity vector; constructing an initialization variable matrix based on the size data; determining a target row vector by matching the row vectors in the initialization variable matrix based on the identity vector; updating the target row vector based on the material identifier to generate a target variable matrix; converting the target variable matrix into a microwave absorbing material combination identifier; and outputting the microwave absorbing material combination identifier. By acquiring the size data of the absorbing components, the identity vectors of the absorbing materials, and their corresponding material identifiers, a data foundation is provided for subsequent optimization. An initialization variable matrix is ​​constructed based on the size data to achieve dimensionality reduction of the initialization variable matrix. Based on the matching of the identity vectors with the row vectors in the initialization variable matrix, each layer must select one and only one material, eliminating illegal solutions in the optimization process and improving optimization efficiency. The target row vectors are updated based on the material identifiers, enabling the search for the optimal solution in the dimensionality-reduced initialization variable matrix space. Finally, the target variable matrix is ​​converted into absorbing material combination identifiers and output, realizing the output from the initialization variable matrix to the absorbing material combination identifiers, thus improving the optimization efficiency of the absorbing component combination design.

[0025] Figure 2 The flowchart illustrates another embodiment of the microwave absorbing component parameter processing method of this application. The microwave absorbing component includes at least two layers of absorbing material, and the microwave absorbing component parameter processing may specifically include the following steps: Step 201: Upon receiving a simulation command for the absorbing component, acquire the size data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector; Optionally, in response to simulation commands for the absorbing component, the dimensional data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector can be obtained. The dimensional data may include the total number of layers and the number of material types. For example, the total number of layers can be 40, and the number of material types can be 16. Each layer of the absorbing component can select from the 16 absorbing materials, and the selection relationship is usually described by classic 0-1 discrete variables.

[0026] The specific options are as follows: (1) in The material used in the g-th layer; The initial variables for the absorbing material; For the first Materials selected for layered absorbing materials .on the contrary, For the first Materials not selected for layered absorbing materials .

[0027] Furthermore, since it involves multiple layers of absorbing materials, the absorbing material selected for a particular layer is constrained for each layer. The constraints are expressed as follows: (2) in, For each layer of absorbing material, the initial variables for all 16 types of absorbing materials are: The sum must strictly equal 1, which is a strong constraint and is not conducive to solving optimization problems.

[0028] The dimensional data includes the total number of layers and the number of material types.

[0029] Step 202: Determine the total number of layers as the number of rows in the matrix; Optionally, in order to construct the initial variable matrix, the matrix size can be determined first. The number of matrix rows can be determined based on the total number of layers in the size data. For example, if the total number of layers is 40, the number of matrix rows can also be determined to be 40.

[0030] Step 203: Round up the binary logarithm of the number of material types to determine the number of columns in the matrix; Optionally, after determining the number of rows in the matrix, the number of columns can be determined based on the number of material types in the dimensional data. This can be achieved by rounding up the binary logarithm of the material type number to the nearest integer. For example, if the number of material types is 16, the binary logarithm of 16 is 4, so the number of columns is 4; if the number of material types is 14, the binary logarithm of 14 is rounded up to the nearest integer, so the number of columns is 4.

[0031] Step 204: Determine the matrix size by combining the number of rows and columns of the matrix; Optionally, after determining the number of rows and columns of the matrix, the matrix size can be determined based on the number of rows and columns, providing basic data for subsequent data processing.

[0032] Step 205: Construct an empty matrix corresponding to the matrix size; Optionally, based on the determined number of rows and columns of the matrix, an empty matrix corresponding to the given number of rows and columns can be constructed. By constructing an empty matrix, it can be ensured that the matrix is ​​initialized with null values, providing a structural basis for subsequent data writing.

[0033] Step 206: Assign values ​​to the empty matrix and determine the initialization variable matrix.

[0034] Optionally, to obtain the initial variable matrix, each element in the empty matrix can be randomly assigned a value, for example, by randomly assigning an initial value of -1 or 1 to each element. The assignment process can be performed row-by-row or column-by-column, such as starting from the first row and first column, sequentially writing randomly generated -1s or 1s until the entire empty matrix is ​​filled. After the assignment is complete, the initial variable matrix is ​​obtained. This provides the basic variable space for subsequent determination of the target row vector based on identity vector matching.

[0035] In this embodiment, the total number of layers is determined as the number of rows in the matrix; the number of columns in the matrix is ​​determined by rounding up the binary logarithm of the number of material types; the matrix size is determined by combining the number of rows and columns; an empty matrix corresponding to the matrix size is constructed; and values ​​are assigned to the empty matrix to determine the initialization variable matrix. By transforming the total number of layers and the number of material types into the initialization variable matrix, the dimension of the initialization variable matrix is ​​reduced from the product of the total number of layers and the number of material types to the product of the number of rows and columns, thus achieving dimensionality reduction of the initialization variable matrix. The initialization variable matrix is ​​constructed through random assignment, providing a structural foundation for subsequent determination of target row vectors based on identity vector matching.

[0036] Step 207: Compare the identity vector with the row vector bit by bit; Optionally, an encoding vector can be obtained based on the absorbing material index. For example, if the absorbing materials are numbered 1 to 16, the absorbing material index can be converted into an encoding vector, as shown below: (3) in, This refers to the serial number of the absorbing material; Number of columns in a matrix; The first of the binary values ​​of the absorbing material's serial number The value of a bit is either 0 or 1; The number is a digit sequence number.

[0037] Each element can be determined based on the absorber material serial number. The value is padded to the matrix column number to obtain the encoded vector. The encoded vector can be converted into an identity vector, as follows: (4) in For the identity vector The value of the bit is either 1 or -1; The first binary value The numerical value of a bit.

[0038] After obtaining the initial variable matrix, the row vector can be determined. Each value of this row vector is compared with the identity vectors of all absorbing materials. For example, the first value of the row vector is compared with the first value of the identity vector, the second value with the second value, and so on, until all comparisons are completed. The row vector is considered to have successfully matched the identity vector if and only if the values ​​of all its bits are completely identical to the corresponding values ​​of the identity vectors.

[0039] Interpolation functions can be used to determine whether the selection criteria are met.

[0040] The interpolation function formula is as follows: (5) in, The initial variables for the absorbing material; For the identity vector The value of the digit; The number of columns in the matrix; For the design variable at position s in layer g, when and When they are exactly the same, =1, when at least one bit is different. =0.

[0041] For example, taking 16 types of absorbing materials as an example, the total number of layers is Any of the layers The initial variables of the absorbing layer and the absorbing material can be expressed as follows: (6) in, The initial variables for the absorbing material; This is the transpose operator.

[0042] Table 1. Correspondence between design variables, interpolation functions, and absorbing materials

[0043] Step 208: Determine the row vector that matches the identity vector as the target row vector.

[0044] Optionally, after completing the bit-by-bit comparison, the row vector that matches the identity vector is determined as the target row vector. The target row vector represents the microwave absorbing material selected in the current layer, thereby realizing the mapping from the row vector in the initial variable matrix to the specific microwave absorbing material.

[0045] This application compares the identity vector and the row vector bit by bit; the row vector that matches the identity vector is determined as the target row vector. A mapping relationship between each row vector in the initialization variable matrix and the absorbing material is established, which allows each row vector to correspond to a unique and valid absorbing material, fundamentally eliminating the problem of illegal solutions caused by strong constraints, thereby improving optimization efficiency.

[0046] Step 209: Calculate the reflectivity of the absorbing component based on the material identification; Optionally, after determining the target row vector, the material identifier corresponding to the target row vector can be determined, and then electromagnetic data can be determined from a preset material database based on the material identifier. The reflectivity of the absorbing component can be calculated based on the electromagnetic data.

[0047] In an optional embodiment of this application, the material identifier corresponds to electromagnetic data and material thickness, and the step of calculating the reflectivity of the absorbing component based on the material identifier includes: Sub-step S11: Based on a preset transmission line theory model, the electromagnetic data and the material thickness are converted into the input impedance of the outer absorbing material; After determining the material identifier, the corresponding electromagnetic data and material thickness can be obtained from a preset material database. Then, the input impedance of the outer absorbing material can be obtained according to a preset transmission line theory model. The electromagnetic parameters can include characteristic impedance, relative permeability, and relative permittivity. The specific method is as follows: (7) in Let g be the input impedance of the g-th layer of absorbing material; The characteristic impedance of the g-th layer; The input impedance of the g-1th layer of absorbing material; It is the transmission constant; Let g be the material thickness of the g-th layer.

[0048] The formula for calculating the characteristic impedance of the g-th layer is as follows: (8) in The characteristic impedance of the g-th layer; Let be the relative permeability of the g-th layer of absorbing material; Let g be the relative permittivity of the g-th layer of absorbing material; This is the preset free space impedance value.

[0049] The formula for calculating the transmission constant is as follows: (9) in It is the transmission constant; The imaginary unit; Pi; For frequency; The speed of light; Let be the relative permeability of the g-th layer of absorbing material; Let be the relative permittivity of the g-th layer of absorbing material.

[0050] Optionally, the input impedance of each layer can be calculated recursively starting from the first layer to determine the input impedance of the outer absorbing material. For example, the initial input impedance of the first layer can be set to 0, i.e., Z. =0. Therefore, after completing the recursion for all layers, the input impedance of the outer absorbing material can be determined for subsequent reflectivity calculations.

[0051] Sub-step S12: Determine the reflectivity of the absorbing component based on the input impedance of the outer absorbing material and the preset free space impedance value.

[0052] Optionally, after obtaining the input impedance of the outer absorbing material, the reflectivity of the absorbing component can be calculated by combining it with the preset free space impedance value. The reflectivity, combined with the preset reflectivity benchmark value, can determine whether the absorbing component meets the optimization target, thus enabling the screening of the target row vector.

[0053] This application embodiment is based on a preset transmission line theory model, converting electromagnetic data and material thickness into the input impedance of the outer absorbing material; the reflectivity of the absorbing component is determined based on the input impedance of the outer absorbing material and a preset free space impedance value. By substituting the electromagnetic parameters and material thickness of each layer of absorbing material into the preset transmission line theory model, the calculation is performed layer by layer starting from the innermost layer, ultimately obtaining the input impedance of the outer absorbing material. Based on this input impedance of the outer absorbing material and the preset free space impedance value, the reflectivity of the absorbing component at each frequency point within the target frequency band is calculated, providing a basis for subsequent optimization of the target row vector based on reflectivity.

[0054] In an optional embodiment of this application, the step of determining the reflectivity of the absorbing component based on the input impedance of the outer absorbing material and a preset free space impedance value includes: Sub-step S21: Determine the difference between the input impedance of the outer absorbing material and the preset free space impedance value; To calculate reflectivity, the difference between the input impedance of the outer absorbing material and the preset free-space impedance value can be determined, providing a data basis for subsequent calculations.

[0055] Sub-step S22: Determine the sum between the input impedance of the outer absorbing material and the preset free space impedance value; After obtaining the input impedance of the outer absorbing material, the sum of the input impedance of the outer absorbing material and the preset free space impedance value can be determined.

[0056] Sub-step S23: Determine the quotient between the difference and the sum; Optionally, after obtaining the difference and the sum, the quotient between the difference and the sum can be calculated.

[0057] Sub-step S24: Take the decimal logarithm of the absolute value of the quotient to determine the logarithm value; After obtaining the quotient, we can obtain its absolute value, and then take the decimal logarithm of the absolute value to determine the logarithmic value.

[0058] Sub-step S25: Determine the product between the logarithmic value and the preset reflectivity coefficient as the reflectivity of the absorbing component.

[0059] After obtaining the logarithmic value, the product between the logarithmic value and the preset reflectivity coefficient can be determined as the reflectivity of the absorbing component.

[0060] The formula for calculating reflectance is as follows: (10) in Reflectivity; The preset reflectivity coefficient; The input impedance of the outer absorbing material; This is the preset free space impedance value.

[0061] Optionally, after obtaining the input impedance of the outer absorbing material, the difference between the input impedance of the outer absorbing material and the preset free space impedance value can be calculated, as well as the sum between the input impedance of the outer absorbing material and the preset free space impedance value. The quotient between the difference and the sum is then determined. Next, the absolute value of the quotient is taken as the decimal logarithm to determine the logarithmic value. Finally, the product between the logarithmic value and the preset reflectivity coefficient is determined as the reflectivity of the absorbing component.

[0062] This application determines the difference between the input impedance of the outer absorbing material and a preset free space impedance value; determines the sum of the input impedance of the outer absorbing material and the preset free space impedance value; determines the quotient between the difference and the sum; takes the decimal logarithm of the absolute value of the quotient to determine the logarithm; and determines the product of the logarithm and a preset reflectivity coefficient as the reflectivity of the absorbing component. The input impedance of the outer absorbing material can be converted into reflectivity, which is used to quantitatively evaluate the absorbing performance of the current absorbing component combination scheme.

[0063] Step 210: Update the target row vector based on the reflectivity to generate the target variable matrix.

[0064] After updating the target row vector based on reflectivity, the target variable matrix can be obtained, thus realizing iterative optimization of the initial variable matrix based on reflectivity performance feedback.

[0065] This application calculates the reflectivity of the absorbing component based on material identification; and updates the target row vector based on the reflectivity to generate a target variable matrix. Updating the target row vector based on reflectivity allows for the selection of superior target row vectors and the generation of a target variable matrix, ultimately yielding a absorbing component combination scheme that minimizes reflectivity.

[0066] In an optional embodiment of this application, the step of updating the target row vector based on the reflectivity to generate the target variable matrix includes: Sub-step S31: If the reflectivity is less than the preset reflectivity reference value, perform crossover and mutation on the elements in the target row vector to obtain the intermediate row vector; After calculating the reflectivity, it can be compared with a preset reflectivity benchmark value. If the reflectivity is less than the preset benchmark value, it can be determined that the absorption performance of the absorption component combination scheme corresponding to the current target row vector is superior. The reflectivity can also be updated to a new preset benchmark value, and crossover and mutation operations can be performed on the current target row vector to generate an intermediate row vector.

[0067] Sub-step S32: Combine the intermediate row vectors to obtain the target variable matrix.

[0068] After generating intermediate row vectors through crossover and mutation operations, all intermediate row vectors can be combined sequentially to obtain the target variable matrix. The number of rows after combination is consistent with the number of rows in the initial variable matrix, which is consistent with the total number of layers in the absorbing component.

[0069] This application, when the reflectivity is less than a preset reflectivity benchmark value, performs crossover and mutation on the elements in the target row vector to obtain an intermediate row vector; combining the intermediate row vectors yields the target variable matrix. By combining the intermediate row vectors to obtain the target variable matrix, the initial variable matrix is ​​updated, providing a new search space for the subsequent optimization process and laying the structural foundation for finally obtaining the optimal absorption component combination scheme.

[0070] Step 211: Convert the target variable matrix into a microwave absorbing material combination identifier; The identity vector that is completely consistent with the values ​​of each row vector can be determined as the identity vector corresponding to that row; the target variable matrix can be converted into the microwave absorbing material combination identifier according to the correspondence between the identity vector and the material identifier, so as to obtain the optimal microwave absorbing component combination scheme that meets the reflectivity requirements after optimization.

[0071] Step 212: Output the identifier of the microwave absorbing material combination.

[0072] After outputting the identification of the absorbing material combination, the optimal absorbing component combination scheme can be obtained, and the reflectivity of the absorbing component is also optimized.

[0073] Upon receiving a simulation command for an absorbing component, this application acquires the component's dimensional data, the absorbing material's identity vector, and the material identifier corresponding to the identity vector; determines the total number of layers as the number of matrix rows; rounds up the binary logarithm of the number of material types to determine the number of matrix columns; combines the matrix row and column numbers to determine the matrix size; constructs an empty matrix corresponding to the matrix size; assigns values ​​to the empty matrix to determine the initialization variable matrix; compares the identity vector with the row vector bit by bit; determines the row vector that matches the identity vector as the target row vector; calculates the absorbing component's reflectivity based on the material identifier; updates the target row vector based on the reflectivity to generate the target variable matrix; converts the target variable matrix into an absorbing material combination identifier; and outputs the absorbing material combination identifier. By acquiring the size data of the absorbing components, the identity vectors of the absorbing materials, and the material identifiers corresponding to the identity vectors, a data foundation was provided for the optimization process. The total number of layers was determined as the number of rows in the matrix, and the number of columns in the matrix was determined by rounding up the binary logarithm of the number of material types. An empty matrix was constructed, and values ​​were assigned to the empty matrix to obtain the initialization variable matrix. This reduced the dimension of the initialization variable matrix from the product of the total number of layers and the number of material types to the product of the number of rows and the number of columns in the matrix, thus achieving dimensionality reduction of the initialization variable matrix. The identity vector and the row vector were compared bit by bit, and the row vector that matched the identity vector was determined as the target row vector. This ensured that each layer could only select one material, eliminating illegal solutions in the optimization process. The reflectivity of the absorbing components was calculated based on the material identifiers. The reflectivity can be used to quantitatively evaluate the performance of the absorbing components. The target row vector was updated based on the reflectivity to generate the target variable matrix. Finally, the target variable matrix was converted into the absorbing material combination identifier and output, obtaining the optimal absorbing component combination scheme and improving the optimization efficiency of the absorbing component combination design.

[0074] Figure 3 The flowchart illustrating the steps for determining the target variable matrix in this application is shown. After the system starts, users can input size data, identity vectors, and material identifiers. A preset reflectivity benchmark value is set, an initial variable matrix is ​​constructed based on the size data, the target row vector is determined, reflectivity is calculated, and it is determined whether preset conditions are met. For example, if the reflectivity is less than the preset reflectivity benchmark value, the target variable matrix is ​​output if the conditions are met; otherwise, a selection operation is performed, involving crossover and mutation operations to generate new intermediate row vectors for a new round of iteration. For example, a genetic algorithm can be used. The parameters of the genetic algorithm can be set as follows: population size of 500, tournament selection with a value of 3; crossover rate of 0.8; mutation rate of 0.05; reflectivity change rate less than 0.1 over 100 consecutive generations; and maximum number of iterations of 500.

[0075] Figure 4The diagram shows the reflectivity optimization results of this application. The optimization design is carried out using the parameter processing method of the absorbing component. There are 16 kinds of absorbing materials with a thickness of 1mm. The optimization target is to minimize the sum of reflectivity in the 7-10GHz frequency band. A multi-layer absorbing component with 40 layers is designed, and the reflectivity of each layer is less than the preset reflectivity reference value. For example, the preset reflectivity reference value is -10.

[0076] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0077] Figure 5 This diagram illustrates a structural block diagram of an embodiment of a microwave absorbing component parameter processing device according to this application. The microwave absorbing component includes at least two layers of absorbing material, and the microwave absorbing component parameter processing device may specifically include the following modules: The acquisition module 501 is used to acquire the size data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector when receiving a simulation command for the absorbing component. Construction module 502 is used to construct an initialization variable matrix based on the size data; The determination module 503 is used to determine the target row vector by matching the row vectors in the initialization variable matrix based on the identity vector; Generation module 504 is used to update the target row vector based on the material identifier and generate a target variable matrix; The conversion module 505 is used to convert the target variable matrix into a microwave absorbing material combination identifier; Output module 506 is used to output the identifier of the microwave absorbing material combination.

[0078] In an optional embodiment of this application, the construction module 502 includes: The first determining submodule is used to determine the total number of layers as the number of matrix rows; The second determining submodule is used to round up the binary logarithm of the number of material types to determine the number of matrix columns; The third determining submodule is used to determine the matrix size by combining the number of rows and the number of columns of the matrix; A submodule is constructed to build an empty matrix corresponding to the specified matrix size; The fourth determination submodule is used to assign values ​​to the empty matrix and determine the initialization variable matrix.

[0079] In an optional embodiment of this application, the determining module 503 includes: The comparison submodule is used to compare the identity vector with the row vector bit by bit; The fifth determination submodule is used to determine the row vector that is consistent with the identity vector as the target row vector.

[0080] In an optional embodiment of this application, the generation module 504 includes: A calculation submodule is used to calculate the reflectivity of the absorbing component based on the material identifier; The update submodule is used to update the target row vector based on the reflectivity to generate the target variable matrix.

[0081] In an optional embodiment of this application, the computing submodule includes: The conversion submodule is used to convert the electromagnetic data and the material thickness into the input impedance of the outer absorbing material based on a preset transmission line theory model. The sixth determining submodule is used to determine the reflectivity of the absorbing component based on the input impedance of the outer absorbing material and the preset free space impedance value.

[0082] In an optional embodiment of this application, the sixth determining submodule includes: The first determining unit is used to determine the difference between the input impedance of the outer absorbing material and the preset free space impedance value; The second determining unit is used to determine the sum between the input impedance of the outer absorbing material and the preset free space impedance value; The third determining unit is used to determine the quotient between the difference and the sum; The fourth determining unit is used to determine the logarithm by taking the decimal logarithm of the absolute value of the quotient; The fifth determining unit is used to determine the product between the logarithmic value and the preset reflectivity coefficient as the reflectivity of the absorbing component.

[0083] In an optional embodiment of this application, the update submodule includes: The conversion unit is used to perform crossover and mutation on the elements in the target row vector to obtain an intermediate row vector when the reflectivity is less than a preset reflectivity reference value. The combination unit is used to combine the intermediate row vectors to obtain the target variable matrix.

[0084] Figure 6 This application shows a structural block diagram of an electronic device according to an embodiment of the present application. The present application also provides an electronic device comprising: The processor 601 and the storage medium 602 store a computer program executable by the processor 601. The processor 601 executes the computer program to implement the absorbing component parameter processing method as described in any of the embodiments of this application.

[0085] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0086] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0087] Reference Figure 7 This diagram illustrates a structural block diagram of a storage medium provided in an embodiment of this application. This application also provides a computer-readable storage medium 701, on which a computer program is stored. When the computer program is run by a processor, it executes the microwave absorbing component parameter processing method as described in any one of the embodiments of this application.

[0088] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0089] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products 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.

[0090] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should 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 terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate 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.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal 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.

[0093] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0094] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0095] The above provides a detailed description of a method for processing parameters of an absorbing component, a device for processing parameters of an absorbing component, an electronic device, and a computer-readable storage medium provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for processing parameters of a microwave absorbing component, characterized in that, The microwave absorbing assembly comprises at least two layers of microwave absorbing material, including: Upon receiving a simulation command for the absorbing component, the system acquires the size data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector. Construct an initialization variable matrix based on the stated size data; The target row vector is determined by matching the row vectors in the initialization variable matrix with the identity vector. Update the target row vector based on the material identifier to generate a target variable matrix; Convert the target variable matrix into a microwave absorbing material combination identifier; Output the identifier of the microwave absorbing material combination.

2. The method according to claim 1, characterized in that, The dimensional data includes the total number of layers and the number of material types. The step of constructing an initialization variable matrix based on the dimensional data includes: The total number of layers is determined as the number of rows in the matrix; The number of columns in the matrix is ​​determined by rounding up the binary logarithm of the number of material types. The matrix size is determined by combining the number of rows and columns of the matrix; Construct an empty matrix corresponding to the stated matrix size; Assign values ​​to the empty matrix to determine the initialization variable matrix.

3. The method according to claim 1, characterized in that, The step of determining the target row vector by matching the row vectors in the initialization variable matrix based on the identity vector includes: Compare the identity vector with the row vector bit by bit; The row vector that matches the identity vector is determined as the target row vector.

4. The method according to claim 1, characterized in that, The material identifier corresponds to electromagnetic data, and the step of updating the target row vector based on the material identifier to generate the target variable matrix includes: The reflectivity of the absorbing component is calculated based on the material identification. The target row vector is updated based on the reflectivity to generate the target variable matrix.

5. The method according to claim 4, characterized in that, The material identifier corresponds to electromagnetic data and material thickness. The step of calculating the reflectivity of the absorbing component based on the material identifier includes: Based on a preset transmission line theory model, the electromagnetic data and the material thickness are converted into the input impedance of the outer absorbing material; The reflectivity of the absorbing component is determined based on the input impedance of the outer absorbing material and the preset free space impedance value.

6. The method according to claim 5, characterized in that, The step of determining the reflectivity of the absorbing component based on the input impedance of the outer absorbing material and the preset free space impedance value includes: Determine the difference between the input impedance of the outer absorbing material and the preset free space impedance value; Determine the sum between the input impedance of the outer absorbing material and the preset free space impedance value; Determine the quotient between the difference and the sum; The absolute value of the quotient is taken as the decimal logarithm to determine the logarithm value; The product between the logarithmic value and the preset reflectivity coefficient is determined to be the reflectivity of the absorbing component.

7. The method according to claim 4, characterized in that, The step of updating the target row vector based on the reflectivity to generate the target variable matrix includes: When the reflectivity is less than a preset reflectivity benchmark value, the elements in the target row vector are cross-crossed and mutated to obtain an intermediate row vector; The intermediate row vectors are combined to obtain the target variable matrix.

8. A parameter processing device for a microwave absorbing component, characterized in that, The microwave absorbing assembly comprises at least two layers of microwave absorbing material, including: The acquisition module is used to acquire, upon receiving a simulation command for the absorbing component, the size data of the absorbing component, the identity vector of the absorbing material, and the material identifier corresponding to the identity vector; A construction module is used to construct an initialization variable matrix based on the size data; The determination module is used to determine the target row vector by matching the row vectors in the initialization variable matrix based on the identity vector; The generation module is used to update the target row vector based on the material identifier and generate a target variable matrix; The conversion module is used to convert the target variable matrix into a microwave absorbing material combination identifier; The output module is used to output the identifier of the microwave absorbing material combination.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the absorbing component parameter processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the absorbing component parameter processing method as described in any one of claims 1 to 7.

Images