Measurement method for inverting non-acoustic parameters of porous material based on surface acoustic impedance data

Abstract

The invention relates to the technical field of sound absorption and porous material non-acoustic parameter characterization, and discloses a measurement method for inverting porous material non-acoustic parameters based on surface acoustic impedance data, the method adopts a measurement system to work, and the system comprises an execution module, an inversion model construction module and a parameter inversion module. The execution module is used for carrying out full-band acoustic parameter measurement on a hard skeleton porous material by utilizing a standard transfer function method measurement system, and the inversion model construction module is used for combining a semi-aesthetic Wilson model with a JCA model to construct a complete five-parameter inversion model framework. The parameter inversion module is used for performing inversion on a globally optimal solution of acoustic parameters by adopting a hybrid optimization strategy combining grid search and a sequential quadratic programming (SQP) algorithm, and the execution module comprises a sound source device, a measuring tube, a microphone, a signal acquisition and processing device, an impedance tube and a signal processing module. The method has the characteristic of globally optimal solution.

Description

Technical Field

[0001] The present application relates to the technical field of sound absorption and non-acoustic parameter characterization of porous materials, and specifically to a method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion. Background Art

[0002] The sound absorption behavior of porous media is usually described by a model that includes structural parameters (such as porosity, flow resistance and tortuosity) and other microstructural parameters. As an emerging non-destructive testing method, parameter inversion has attracted extensive attention from academia and industry in recent years. Existing inversion techniques minimize the difference between experimental measurements and theoretical predictions through optimization algorithms, and invert the microscopic parameters of the material through the measurement of the sound absorption coefficient.

[0003] Existing inversion technologies are mainly based on the Johnson-Champoux-Allard (JCA) model and the Wilson model. By using the SQP (sequential quadratic programming) algorithm to perform parameter inversion calculations, the difference between experimental measurements and theoretical predictions is minimized, thereby reversing the model parameters. The Wilson model is mainly used to describe the nonlinear characteristics of the sound velocity, sound absorption, and sound propagation of materials and the relationship between them and medium properties (such as density, viscosity, temperature, etc.). The JCA model provides the relationship between the acoustic parameters of porous materials (such as sound velocity, attenuation coefficient, sound absorption coefficient, etc.) and their physical properties (such as porosity, fluid viscosity, etc.).

[0004] However, the existing inversion methods have the characteristics of multiple starting points and multiple paths in the algorithm itself. When the initial value is not set, the inversion results often have multiple solutions and uncertainties. Therefore, the initial value needs to be manually input. If the manual initial value is not input properly, it is easy to fall into the problem of local optimal solution. Under the given initial conditions, the solution found by the optimization algorithm is optimal in the local area, but not the best solution in the global scope. Therefore, it is necessary to design a measurement method based on surface acoustic impedance data to invert the microscopic non-acoustic parameters of hard skeleton porous materials, which can achieve the global optimal solution. Summary of the invention

[0005] The purpose of the present application is to provide a method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion, so as to solve the problems raised in the above background technology.

[0006] In the first aspect, the present application provides a measurement method for non-acoustic parameters of porous materials based on inversion of surface acoustic impedance data. The method adopts a measurement system to work, and the system includes an execution module, an inversion model construction module, and a parameter inversion module. The execution module is used to use a standard transfer function method measurement system to perform full-band acoustic parameter measurements on hard skeleton porous materials. The inversion model construction module is used to combine the semi-phenomenological Wilson model with the JCA model to construct a complete five-parameter inversion model framework. The parameter inversion module is used to use a hybrid optimization strategy that combines grid search and sequential quadratic programming (SQP) algorithm to invert the global optimal solution of acoustic parameters.

[0007] Optionally, in a possible implementation of the first aspect, the execution module includes a sound source device, a measuring tube, a microphone, a signal acquisition and processing device, an impedance tube, and a signal processing module. The signal acquisition and processing device is electrically connected to the signal processing module, and the signal acquisition and processing device is electrically connected to the microphone. The sound source device is used to generate a required acoustic signal to excite the porous material to measure acoustic properties. The measuring tube is used to provide a fluid channel to control the propagation characteristics of sound waves in the porous material in the environment. The microphone captures the sound wave signal and converts it into an electrical signal. The signal acquisition and processing device is used to receive and process the electrical signal from the microphone to perform data conversion and preliminary analysis. The impedance tube is used to measure the acoustic impedance and related acoustic properties. The inversion model construction module includes a Wilson model inversion module, a JCA model inversion module, and a physical constraint setting module. The signal processing module is electrically connected to the Wilson model inversion module. The physical constraint setting module is electrically connected to the Wilson model inversion module and the JCA model inversion module. The Wilson model inversion module is used to invert the three basic microscopic parameters of the material, namely, porosity, flow resistance, and tortuosity. The JCA model inversion module is used to complete the complete inversion process of the five basic microscopic parameters after obtaining the three basic microscopic parameters. The physical constraint setting module is used to set physical constraint conditions to ensure that the parameters obtained during the inversion process are within the physically feasible domain. The parameter inversion module includes a grid search module, an SQP algorithm module, a convergence determination module, and a grid optimization module. The grid search module and the SQP algorithm module are electrically connected to the Wilson model inversion module and the JCA model inversion module. The SQP algorithm module is electrically connected to the grid search module. The grid search module is used to perform a global search within the parameter feasible domain by a grid search method to obtain candidate solutions. The SQP algorithm module is used to finely optimize the selected candidate solutions. The convergence determination module is used to set a reasonable convergence threshold and adopt an adaptive step size strategy. The grid optimization module is used to optimize the way of constructing grid points by adopting a gradually refined strategy.

[0008] In a second aspect, the present application provides a method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion, comprising the following steps: S1. Constructing a measurement system: configuring a variety of measuring instruments and standard impedance tubes of various specifications to measure hard skeleton porous materials in different frequency bands, controlling the measuring temperature within a first preset range and the measuring humidity within a second preset range, and selecting a cylindrical sample to be tested of a specified size; S2. Normal incidence sound absorption coefficient measurement: According to the actual size of the sample to be tested, select the corresponding measurement frequency range and the standard impedance tube that matches the actual size, and measure the normal incidence sound absorption coefficient measurement value of the frequency band matching the standard impedance tube; S3. Inversion model construction: Based on the measured values ​​of normal incidence sound absorption coefficient, the semi-phenomenological Wilson model and JCA model are combined to construct a complete five-parameter inversion model framework; S4. Parameter inversion implementation and evaluation: Based on the five-parameter inversion model framework, a hybrid optimization strategy combining the grid search method and the SQP algorithm is used to optimize the parameter inversion process and optimize the way of constructing grid points.

[0009] Optionally, in a possible implementation manner of the second aspect, step S3 includes the following steps: S3-1. Using the Wilson model, the three basic microscopic parameters of the material are inverted. The three basic microscopic parameters include porosity, flow resistance and tortuosity. S3-2. When inverting the parameters of the JCA model, the transfer function H(f) is defined as the ratio of the sound pressures at two measurement positions, expressed as H(f)=P2(f) / P1(f), where P1(f) and P2(f) represent the sound pressure values ​​at different distances from the surface of the sample to be tested, and f is the measurement frequency. Based on the transfer function H(f), the theoretical value of the normal incidence sound absorption coefficient α(f) is calculated, expressed as α(f)=1-|R(f)|², where R(f) is the reflection coefficient, which is proportional to the transfer function H(f); S3-3. The complete inversion process of five basic microscopic parameters is completed through the JCA model. The five basic microscopic parameters are porosity, flow resistance, tortuosity, viscous characteristic length and thermal characteristic length. Physical constraints are set in the JCA model. The physical constraints include the range limitation of open porosity and the range setting of flow resistance.

[0010] Optionally, in a possible implementation of the second aspect, in the above step S3-3, a complete inversion process of five basic microscopic parameters is completed by using the JCA model, including: The optimization goal is to minimize the mean square error between the measured value and the theoretical value, that is, the minimum mean square error , where x represents the five parameters that need to be inverted , corresponding to the porosity , flow resistance , tortuosity , viscosity characteristic length and thermal characteristic length , is the normal incidence sound absorption coefficient measurement in S2, is the theoretical value of the normal incidence sound absorption coefficient in S3-2.

[0011] Optionally, in a possible implementation manner of the second aspect, step S4 includes the following steps: S4-1. Pre-assume five basic microscopic parameters to be inverted, and calculate the theoretical value of the normal-incidence sound absorption coefficient in the JCA model based on the five basic microscopic parameters to be inverted; perform a global search in the feasible domain of each basic microscopic parameter by means of a grid search method, and divide the feasible domain into several grids by using a grid division strategy. The value corresponding to the line in each grid is the pre-assumed value of the basic microscopic parameter. Refine the search grid within the preset range of computing resources, locate the global optimal solution region, and screen out the candidate solution with the smallest error in the global optimal solution region; S4-2. Use the SQP algorithm to finely optimize the selected candidate solutions; set a reasonable convergence threshold to ensure that the algorithm can converge to the global optimal solution; and adjust the convergence speed of the global optimal solution of the parameters of the samples to be tested of the same material in subsequent measurements.

[0012] Optionally, in a possible implementation manner of the second aspect, in the above step S4-2, finely optimizing the selected candidate solutions by using an SQP algorithm includes the following steps: According to the feasible domain of any parameter, the sample of each material to be tested is tested at the initial grid line density during the first test. Divide the grid lines to obtain a set of assumed values ​​for any parameter ,in is the number of grid lines; substitute the assumed value set of each parameter into the JCA model to determine the minimum mean square error The corresponding parameters ,and ; Mark parameter is a candidate solution for a local extreme point, and its two adjacent parameters and The area enclosed by the corresponding grid lines is a potential good area; In the potential good area, the mesh line density is refined Re-grid the lines to find The corresponding parameters are used to redefine the potential good area, and the above steps are repeated until the grid line density is refined. Less than the convergence threshold , after obtaining several candidate solutions for local extreme points, select The corresponding parameter at the minimum As the global optimal solution for this parameter.

[0013] Optionally, in a possible implementation manner of the second aspect, in the above step S4-2, adjusting the convergence speed of the global optimal solution of the parameters of the samples to be tested of the same material includes the following steps: The global optimal solution of the five basic microscopic parameters of the same material is recorded each time; For the recorded global optimal solution data, calculate the variance X of each parameter to reflect the uncertainty of the global optimal solution of each parameter; According to the calculated variance X, adjust the initial grid line density of the next sample of the same material to be tested:

[0014] in, is the initial grid line density of the last measured sample, is the influence coefficient of variance X and initial grid line density.

[0015] Compared with the prior art, the beneficial effects achieved by the present application are: when measuring the non-acoustic parameters of porous materials, a grid search method is used to determine the initial values ​​of the optimization algorithm, rather than relying on artificially set initial parameters, and grid points are constructed within the physical feasible domain of the parameters. By evaluating the objective function values ​​on these grid points, the optimal grid points are selected as the initial values ​​of the SQP algorithm, which effectively avoids the problem of falling into a local optimal solution due to improper selection of initial values. The grid search ensures the rationality of the initial values, and the SQP algorithm ensures that it can quickly converge to the global optimal solution on this basis, significantly improving the reliability and accuracy of parameter inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings: Figure 1 It is a schematic diagram of the overall module structure provided by an embodiment of the present application; Figure 2 It is a flowchart provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] See also Figure 1 and Figure 2 The present application provides a technical solution: a method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion. The method adopts a measurement system to work. The system includes an execution module, an inversion model construction module, and a parameter inversion module. The execution module is used to measure the full-band acoustic parameters of hard skeleton porous materials using a standard transfer function method measurement system. The inversion model construction module is used to combine the semi-phenomenological Wilson model with the JCA model to construct a complete five-parameter inversion model framework. The parameter inversion module is used to adopt a hybrid optimization strategy combining grid search and sequential quadratic programming (SQP) algorithm to invert the global optimal solution of acoustic parameters. The execution module includes a sound source device, a measuring tube, a microphone, a signal acquisition and processing device, an impedance tube, and a signal processing module. The signal acquisition and processing device is electrically connected to the signal processing module, and the signal acquisition and processing device is electrically connected to the microphone. The sound source device is used to generate the required acoustic signal to stimulate the porous material to measure the acoustic characteristics. The measuring tube is used to provide a fluid channel to control the propagation characteristics of the sound wave in the porous material. The microphone captures the sound wave signal and converts it into an electrical signal. The signal acquisition and processing device is used to receive and process the electrical signal from the microphone, and perform data conversion and preliminary analysis. The impedance tube is used to measure the acoustic impedance and related acoustic characteristics. The inversion model construction module includes a Wilson model inversion module, a JCA model inversion module, and a physical constraint setting module. The signal processing module is electrically connected to the Wilson model inversion module, and the physical constraint setting module is electrically connected to the Wilson model inversion module and the JCA model inversion module. The Wilson model inversion module is used to invert the three basic microscopic parameters of the material, namely, porosity, flow resistance, and tortuosity. The JCA model inversion module is used to further complete the complete inversion process of five microscopic parameters after obtaining the three basic parameters. The physical constraint setting module is used to set relevant physical constraint conditions to ensure that the parameters obtained during the inversion process are within the physically feasible domain. The parameter inversion module includes a grid search module, an SQP algorithm module, a convergence determination module, and a grid optimization module. The grid search module and the SQP algorithm module are electrically connected to the Wilson model inversion module and the JCA model inversion module. The SQP algorithm module is electrically connected to the grid search module. The grid search module is used to perform a global search within the parameter feasible domain by a grid search method. The SQP algorithm module is used to perform fine optimization on the selected candidate solutions. The convergence determination module is used to set a reasonable convergence threshold and adopt an adaptive step size strategy. The grid optimization module is used to adopt a step-by-step refinement strategy to optimize the way of constructing grid points. In one embodiment, a method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion includes the following steps: S1. Construct the measurement system: configure various measuring instruments and standard impedance tubes of various specifications for measuring hard skeleton porous materials in different frequency bands, control the measuring temperature and humidity within a certain range, and select cylindrical samples of specified size to be tested; S2. Measurement of normal incidence sound absorption coefficient: First, select a suitable measurement frequency range according to the actual size of the sample to be tested, and a standard impedance tube that matches the actual size, measure the normal incidence sound absorption coefficient in the frequency band that matches the standard impedance tube, and collect and process the data in real time through acoustic software; S3. Inversion model construction: Combine the semi-phenomenological Wilson model and the JCA model to build a complete five-parameter inversion model framework; S4. Parameter inversion implementation and evaluation: A hybrid optimization strategy combining the grid search method and the SQP algorithm is used to solve the multi-solution problem in parameter inversion and optimize the way of constructing grid points; In S3, the construction method of the five-parameter inversion model framework is: S3-1. First, the Wilson model is used to invert the three basic microscopic parameters of the material, including porosity, flow resistance and tortuosity; S3-2. When inverting the parameters of the JCA model, the transfer function H(f) is defined as the ratio of the sound pressures at two measurement positions, that is, H(f)=P2(f) / P1(f), where P1(f) and P2(f) represent the sound pressure values ​​at different distances from the surface of the sample to be measured, respectively, and f is the measurement frequency. Based on the measured transfer function H(f), the theoretical calculated value of the normal incidence sound absorption coefficient α(f) is calculated using the formula α(f)=1-|R(f)|², where R(f) is the reflection coefficient, which is proportional to the transfer function H(f); S3-3. The complete inversion process of five microscopic parameters is further completed through the JCA model. The remaining two basic microscopic parameters are the viscous characteristic length and the thermal characteristic length. Physical constraints are set in the model, including the range of open porosity and the range of flow resistance. In S3-3, the complete inversion process of the five microscopic parameters is as follows: the minimization of the mean square error between the measured value and the theoretical calculated value is taken as the optimization target, that is, the minimum mean square error , where x represents the five parameters that need to be inverted , corresponding to the porosity , flow resistance , tortuosity , viscosity characteristic length and thermal characteristic length , is at frequency Normal incidence sound absorption coefficient measured at; In S4, the specific process of solving the multi-solution problem in parameter inversion is: S4-1. The five parameters that need to be inverted need to be assumed in advance to obtain the theoretical calculated value of the normal incidence sound absorption coefficient calculated based on the assumed parameters in the JCA model. First, a global search is performed in the feasible domain of the parameters using the grid search method. The feasible domain is divided into several grids using the grid division strategy. The values ​​corresponding to the lines in the grids are the values ​​of the parameters assumed in advance. The search grid is refined within the scope allowed by the computing resources to locate the possible global optimal solution area. S4-2. Use the SQP algorithm to finely optimize the selected candidate solutions. By setting a reasonable convergence threshold, ensure that the algorithm can quickly and stably converge to the global optimal solution. In subsequent measurements, adjust the convergence speed of the global optimal solution of the parameters of the samples to be tested of the same material; In S4-2, the specific process of the SQP algorithm to finely optimize the selected candidate solutions is as follows: According to the feasible domain of a certain parameter, the sample of each material to be tested is tested at the initial grid line density during the first test. Divide the grid lines to get the assumed value of a parameter , where n is the number of grid lines, and the minimum mean square error is found after substituting each parameter The corresponding parameters ,and , marking this parameter as a candidate solution for the local extreme point, the two parameters adjacent to this parameter and The area enclosed by the corresponding grid lines is a potential good area; In the potential good area, the mesh line density is refined Re-grid the lines to find The corresponding parameters are used to redefine the potential good areas, and this process is repeated until the grid line density is refined. Less than the convergence threshold , after obtaining several candidate solutions for local extreme points, select The corresponding parameter at the minimum As the global optimal solution for this parameter; In S4-2, the specific method for adjusting the convergence speed of the global optimal solution of the parameters of the samples to be tested of the same material is: record the global optimal solution of the five parameters of the same material each time, calculate the variance of the global optimal solution of the historical parameters , when setting the initial grid line density of the next sample of the same material to be tested When, with The larger the value, the higher the uncertainty of the global optimal solution, and the larger the initial grid line density is required. ,Right now ,in is the influence coefficient of variance and initial grid line density.

[0019] This application requires the selection of one cylindrical sample of two different sizes: the large sample diameter is required to be 98±0.5mm, which is suitable for the measurement of normal incidence sound absorption coefficient in the frequency band of 50-1600Hz; the small sample diameter is required to be 29±0.5mm, which is suitable for the measurement of normal incidence sound absorption coefficient in the frequency band of 500-6400Hz. The standard thickness of the samples selected in the specific embodiments of this application is 24±0.5mm. This thickness is the optimal choice verified by a large number of experiments, which can ensure that the sample has sufficient mechanical strength while ensuring the measurement accuracy. This application requires that the sample surface must be kept flat, and the surface flatness deviation does not exceed 0.1mm, in order to ensure good contact between the sample and the end face of the impedance tube. At the same time, good sealing must be maintained between the edge of the sample and the inner wall of the impedance tube to avoid measurement errors caused by sound wave leakage. In order to eliminate the influence of environmental factors, all samples need to be stored under standard environmental conditions (temperature 20±2℃, relative humidity 65±5%) for at least 24 hours before testing to ensure that the sample is in a stable state. To ensure the physical rationality of the inversion results, strict constraints are set for each parameter: the porosity φ ranges from 0.1 to 0.99, which covers most of the actual porous materials; the flow resistance σ ranges from 100 to 500,000 N·m-4s, and the parameter range is determined based on a large amount of experimental data; The tortuosity is limited to between 1 and 4, which is consistent with the physical properties of actual porous materials. The ranges of the viscous characteristic length Λ and the thermal characteristic length Λ' are both 1-1000μm, and Λ ≤ Λ'. These constraints ensure that the inversion results have clear physical meanings.

[0020] The measurement system used in this application strictly follows the GB / T18696.2-2002 standard. The system is equipped with two standard impedance tubes of different specifications, with inner diameters of 98mm and 29mm. The impedance tube is made of high-quality stainless steel, and the wall thickness is not less than 10mm, which can effectively prevent the influence of wall vibration on the measurement. The inner wall of the pipeline is precisely machined, and the surface roughness Ra does not exceed 0.2 microns. This highly smooth inner wall can minimize the energy loss of sound waves during propagation. The acoustic measurement system uses high-precision measurement equipment, including a 1 / 4-inch condenser microphone (sensitivity error ≤ 0.2dB), a B&K signal generator and collector (sampling rate ≥ 48kHz), and an experimental power amplifier (distortion ≤ 0.1%). The configuration of these high-precision equipment ensures the accuracy and reliability of the entire measurement system.

[0021] The method of this application has the following significant advantages: first, the measurement process is convenient and efficient. Only a test sample that meets the standard size is required. All required parameters can be obtained through a single measurement, and a non-contact measurement method is used, which will not cause any damage to the sample. Secondly, the algorithm has excellent performance, and the grid search effectively avoids the problem of improper initial value selection. The SQP algorithm has a fast convergence characteristic, and strict physical constraints ensure the rationality of the results. Finally, it has a wide range of applications and is suitable for the characterization of various types of hard skeleton porous materials, providing a reliable parameter basis for material design and performance optimization.

[0022] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0023] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion, characterized in that The method adopts a measurement system to work, and the system includes an execution module, an inversion model construction module, and a parameter inversion module. The execution module is used to use a standard transfer function method measurement system to measure the full-band acoustic parameters of hard skeleton porous materials. The inversion model construction module is used to combine the semi-phenomenological Wilson model with the JCA model to construct a complete five-parameter inversion model framework. The parameter inversion module is used to adopt a hybrid optimization strategy combining grid search and sequential quadratic programming (SQP) algorithm to invert the global optimal solution of acoustic parameters.

2. The method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion according to claim 1 is characterized in that: The execution module includes a sound source device, a measuring tube, a microphone, a signal acquisition and processing device, an impedance tube, and a signal processing module. The signal acquisition and processing device is electrically connected to the signal processing module, and the signal acquisition and processing device is electrically connected to the microphone. The sound source device is used to generate the required acoustic signal to stimulate the porous material to measure the acoustic characteristics. The measuring tube is used to provide a fluid channel to control the environment to measure the propagation characteristics of the sound wave in the porous material. The microphone captures the sound wave signal and converts it into an electrical signal. The signal acquisition and processing device is used to receive and process the electrical signal from the microphone, perform data conversion and preliminary analysis, and the impedance tube is used to measure the acoustic impedance and related acoustic characteristics. The inversion model construction module includes a Wilson model inversion module, a JCA model inversion module, and a physical constraint setting module. The signal processing module is electrically connected to the Wilson model inversion module. The physical constraint setting module is electrically connected to the Wilson model inversion module and the JCA model inversion module. The Wilson model inversion module is used to invert the three basic microscopic parameters of the material, namely, porosity, flow resistance, and tortuosity. The JCA model inversion module is used to complete the complete inversion process of the five basic microscopic parameters after obtaining the three basic microscopic parameters. The physical constraint setting module is used to set physical constraint conditions to ensure that the parameters obtained during the inversion process are within the physically feasible domain. The parameter inversion module includes a grid search module, an SQP algorithm module, a convergence determination module, and a grid optimization module. The grid search module and the SQP algorithm module are electrically connected to the Wilson model inversion module and the JCA model inversion module. The SQP algorithm module is electrically connected to the grid search module. The grid search module is used to perform a global search in a parameter feasible domain by a grid search method to obtain candidate solutions. The SQP algorithm module is used to perform fine optimization on the selected candidate solutions. The convergence determination module is used to set a reasonable convergence threshold and adopt an adaptive step size strategy. The grid optimization module is used to optimize the way of constructing grid points by adopting a gradually refined strategy.

3. The method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion according to claim 1 is characterized in that: The following steps are involved: S1. Constructing a measurement system: configuring a variety of measuring instruments and standard impedance tubes of various specifications to measure hard skeleton porous materials in different frequency bands, controlling the measuring temperature within a first preset range and the measuring humidity within a second preset range, and selecting a cylindrical sample to be tested of a specified size; S2. Normal incidence sound absorption coefficient measurement: According to the actual size of the sample to be tested, select the corresponding measurement frequency range and the standard impedance tube that matches the actual size, and measure the normal incidence sound absorption coefficient measurement value of the frequency band matching the standard impedance tube; S3. Inversion model construction: Based on the measured values ​​of normal incidence sound absorption coefficient, the semi-phenomenological Wilson model and JCA model are combined to construct a complete five-parameter inversion model framework; S4. Parameter inversion implementation and evaluation: Based on the five-parameter inversion model framework, a hybrid optimization strategy combining the grid search method and the SQP algorithm is used to optimize the parameter inversion process and optimize the way of constructing grid points.

4. The method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion according to claim 3 is characterized in that: The step S3 comprises the following steps: S3-1. Using the Wilson model, the three basic microscopic parameters of the material are inverted. The three basic microscopic parameters include porosity, flow resistance and tortuosity. S3-2. When inverting the parameters of the JCA model, the transfer function H(f) is defined as the ratio of the sound pressures at two measurement positions, expressed as H(f)=P2(f) / P1(f), where P1(f) and P2(f) represent the sound pressure values ​​at different distances from the surface of the sample to be tested, and f is the measurement frequency. Based on the transfer function H(f), the theoretical value of the normal incidence sound absorption coefficient α(f) is calculated, expressed as α(f)=1-|R(f)|², where R(f) is the reflection coefficient, which is proportional to the transfer function H(f); S3-3. The complete inversion process of five basic microscopic parameters is completed through the JCA model. The five basic microscopic parameters are porosity, flow resistance, tortuosity, viscous characteristic length and thermal characteristic length. Physical constraints are set in the JCA model. The physical constraints include the range limitation of open porosity and the range setting of flow resistance.

5. The method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion according to claim 4, characterized in that: In step S3-3, the complete inversion process of five basic microscopic parameters is completed by the JCA model, including: The optimization goal is to minimize the mean square error between the measured value and the theoretical value, that is, the minimum mean square error , where x represents the five parameters that need to be inverted , corresponding to the porosity , flow resistance , tortuosity , viscosity characteristic length and thermal characteristic length , is the normal incidence sound absorption coefficient measurement value in S2, is the theoretical value of the normal incidence sound absorption coefficient in S3-2.

6. The method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion according to claim 5, characterized in that: The step S4 comprises the following steps: S4-1. Pre-assume five basic microscopic parameters to be inverted, and calculate the theoretical value of the normal-incidence sound absorption coefficient in the JCA model based on the five basic microscopic parameters to be inverted; perform a global search in the feasible domain of each basic microscopic parameter by means of a grid search method, and divide the feasible domain into several grids by using a grid division strategy. The value corresponding to the line in each grid is the pre-assumed value of the basic microscopic parameter. Refine the search grid within the preset range of computing resources, locate the global optimal solution region, and screen out the candidate solution with the smallest error in the global optimal solution region; S4-2. Use the SQP algorithm to finely optimize the selected candidate solutions; set a reasonable convergence threshold to ensure that the algorithm can converge to the global optimal solution; and adjust the convergence speed of the global optimal solution of the parameters of the samples to be tested of the same material in subsequent measurements.

7. The method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion according to claim 6, characterized in that: In step S4-2, the selected candidate solutions are finely optimized using the SQP algorithm, including the following steps: According to the feasible domain of any parameter, the sample of each material to be tested is tested at the initial grid line density during the first test. Divide the grid lines to obtain a set of assumed values ​​for any parameter ,in is the number of grid lines; substitute the assumed value set of each parameter into the JCA model to determine the minimum mean square error The corresponding parameters ,and ; Mark parameter is a candidate solution for a local extreme point, and its two adjacent parameters and The area enclosed by the corresponding grid lines is a potential good area; In the potential good area, the mesh line density is refined Re-grid the lines to find The corresponding parameters are used to redefine the potential good area, and the above steps are repeated until the grid line density is refined. Less than the convergence threshold , after obtaining several candidate solutions for local extreme points, select The corresponding parameter at the minimum As the global optimal solution for this parameter.

8. The method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion according to claim 7, characterized in that: In step S4-2, the convergence speed of the global optimal solution of the parameters of the samples to be tested of the same material is adjusted, including the following steps: The global optimal solution of the five basic microscopic parameters of the same material is recorded each time; For the recorded global optimal solution data, calculate the variance X of each parameter to reflect the uncertainty of the global optimal solution of each parameter; According to the calculated variance X, adjust the initial grid line density of the next sample of the same material to be tested: in, is the initial grid line density of the last measured sample, is the influence coefficient of variance X and initial grid line density.

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