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

Abstract

This application relates to the technical field of characterization of non-acoustic parameters of sound absorption and porous materials, and discloses a measurement method for inverting non-acoustic parameters of porous materials based on surface acoustic impedance data. The above method works with a measurement system, and the above system includes an execution module, an inversion model construction module, and a parameter inversion module. The execution module is used to measure the acoustic parameters of the hard-skeleton porous material in the full frequency band by using the standard transfer function method measurement system. The inversion model construction module is used to combine the semi-phenomenological Wilson model and 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 the acoustic parameters. The execution module includes a sound source device, a measurement tube, a microphone, a signal acquisition and processing device, an impedance tube, and a signal processing module. This application has the characteristic of being able to obtain the global optimal solution.

Description

Technical Field

[0001] This application relates to the technical field of acoustic absorption and non - acoustic parameter characterization of porous materials, and specifically to a measurement method for inverting non - acoustic parameters of porous materials based on surface acoustic impedance data. Background Technique

[0002] The acoustic absorption behavior of porous media is usually described by models containing structural parameters (such as porosity, flow resistance, and tortuosity) and other micro - structural parameters. As an emerging non - destructive testing method, the parameter inversion method has received extensive attention in the academic and industrial fields in recent years. Existing inversion techniques minimize the difference between experimental measurement values and theoretical prediction values through optimization algorithms, and invert the micro - parameters of materials through the measurement of the acoustic absorption coefficient.

[0003] Existing inversion techniques are mainly based on the Johnson - Champoux - Allard (JCA) model and the Wilson model. By using the SQP (Sequential Quadratic Programming) algorithm for parameter inversion calculation, the difference between experimental measurement values and theoretical prediction values is minimized, so as to inversely solve the model parameters. The Wilson model is mainly used to describe the relationship between the sound speed, acoustic absorption, non - linear characteristics of sound propagation of substances and medium properties (such as density, viscosity, temperature, etc.). The JCA model provides the relationship between the acoustic parameters (such as sound speed, attenuation coefficient, acoustic absorption coefficient, etc.) of porous materials and their physical properties (such as porosity, fluid viscosity, etc.).

[0004] However, due to the multi - starting - point and multi - path characteristics of the algorithm itself, the inversion results of existing inversion methods often have multiple solutions and uncertainties when no initial value is set. Therefore, it is necessary to manually input the initial value. If the manual initial value is input improperly, it is very 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 range. Therefore, it is necessary to design a measurement method for inverting the micro - non - acoustic parameters of hard - skeleton porous materials based on surface acoustic impedance data that can find the global optimal solution. Summary of the Invention

[0005] The purpose of this application is to provide a measurement method for inverting non - acoustic parameters of porous materials based on surface acoustic impedance data, so as to solve the problems proposed in the above background technique.

[0006] In a first aspect, the present application provides a measurement method for inverting non-acoustic parameters of a porous material based on surface acoustic impedance data. The method operates using a measurement system, which includes an execution module, an inversion model construction module, and a parameter inversion module. The execution module is used to measure the acoustic parameters of a hard-skeleton porous material in the full frequency band 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 invert the global optimal solution of the acoustic parameters using a hybrid optimization strategy that combines grid search and sequential quadratic programming (SQP) algorithm.

[0007] Optionally, in a possible implementation manner of the first aspect, the execution module includes a sound source device, a measurement 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 excite the porous material for acoustic characteristic measurement. The measurement tube is used to provide a fluid channel to control the propagation characteristics of the measurement sound wave 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 for data conversion and preliminary analysis. The impedance tube is used to measure the acoustic impedance and related acoustic characteristics.

[0008] 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 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.

[0009] 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 both 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 through the 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 adopt a strategy of gradually refining to optimize the way of constructing grid points.

[0010] In a second aspect, the present application provides a measurement method for inverting non-acoustic parameters of porous materials based on surface acoustic impedance data, including the following steps:

[0011] S1. Construct a measurement system: Configure a variety of measurement instruments and standard impedance tubes of various specifications to measure hard-skeleton porous materials in different frequency bands. Control the measurement temperature within a first preset range and the measurement humidity within a second preset range, and select a cylindrical sample to be measured with a specified size;

[0012] S2. Measure the normal incidence absorption coefficient: Select a corresponding measurement frequency range and a standard impedance tube matching the actual size according to the actual size of the sample to be measured, and measure the measured value of the normal incidence absorption coefficient in the frequency band matching the standard impedance tube;

[0013] S3. Construct an inversion model: Based on the measured value of the normal incidence absorption coefficient, combine the semi-phenomenological Wilson model and JCA model to construct a complete five-parameter inversion model framework;

[0014] S4. Realize and evaluate parameter inversion: Based on the five-parameter inversion model framework, adopt a hybrid optimization strategy combining the grid search method and the SQP algorithm to optimize the process of parameter inversion, and optimize the way of constructing grid points.

[0015] Optionally, in a possible implementation manner of the second aspect, the above step S3 includes the following steps:

[0016] S3-1. Invert three basic microscopic parameters of the material using the Wilson model. The three basic microscopic parameters include porosity, flow resistivity, and tortuosity;

[0017] S3-2. When inverting the JCA model parameters, define the transfer function H(f) 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) respectively represent the sound pressure values at different distances from the surface of the sample to be measured, and f is the measurement frequency; Based on the transfer function H(f), calculate the theoretical value of the normal incidence absorption coefficient α(f), expressed as α(f)=1-|R(f)|², where R(f) is the reflection coefficient and is proportional to the transfer function H(f);

[0018] S3-3. Complete the complete inversion process of five basic microscopic parameters through the JCA model. The five basic microscopic parameters are porosity, flow resistivity, tortuosity, viscous characteristic length, and thermal characteristic length, and set physical constraint conditions in the JCA model. The physical constraint conditions include the range limitation of the open porosity and the range setting of the flow resistivity.

[0019] Optionally, in a possible implementation manner of the second aspect, in the above step S3-3, the complete inversion process of the five basic microscopic parameters is completed through the JCA model, including:

[0020] Minimize the mean square error between the measured value and the theoretical value as the optimization objective, that is, the minimum mean square error , where x represents the five parameters to be inverted , corresponding to the porosity , flow resistance , tortuosity , viscous characteristic length and thermal characteristic length , is the measured value of the normal incidence sound absorption coefficient in S2, is the theoretical value of the normal incidence sound absorption coefficient in S3-2.

[0021] Optionally, in a possible implementation manner of the second aspect, the above step S4 includes the following steps:

[0022] S4-1. Presume the five basic microscopic parameters to be inverted in advance. According to the five basic microscopic parameters to be inverted, calculate the theoretical value of the normal incidence sound absorption coefficient in the JCA model; perform a global search within the feasible region of each basic microscopic parameter through the grid search method. Use the grid division strategy to divide the feasible region into several grids. The values corresponding to the lines in each grid are the size values of the basic microscopic parameters presumed in advance. Refine the search grid within the preset range of computing resources, locate the global optimal solution region, and select the candidate solution with the smallest error in the global optimal solution region;

[0023] 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; adjust the convergence speed of the global optimal solution of the parameters of the sample to be measured of the same material during subsequent measurements.

[0024] Optionally, in a possible implementation manner of the second aspect, in the above step S4-2, using the SQP algorithm to finely optimize the selected candidate solutions includes the following steps:

[0025] According to the feasible region of any parameter, divide the grid lines of the sample to be measured of each material at the initial grid line density during the first detection to obtain the set of assumed values of any parameter , where is the number of grid lines; substitute the set of assumed values of each parameter into the JCA model to determine the parameter corresponding to the minimum mean square error , and ; mark the parameter is a candidate solution for a local extreme point, and the two adjacent parameters and The region enclosed by the corresponding grid lines is the potential excellent region;

[0026] In the potential excellent region, refine the grid line density Redivide the grid lines, and find The corresponding parameters to redefine the potential excellent region, and repeat the above steps until the refined grid line density is less than the convergence threshold , after obtaining several candidate solutions for local extreme points, select the parameter corresponding to the minimum as the global optimal solution of this parameter.

[0027] 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 sample to be measured with the same material includes the following steps:

[0028] Record the global optimal solutions of the five basic microscopic parameters of the same material measured each time respectively;

[0029] 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;

[0030] According to the calculated variance X, adjust the initial grid line density of the next sample to be measured with the same material:

[0031]

[0032] Among them, is the initial grid line density of the previous measured sample, is the influence coefficient of the variance X and the initial grid line density.

[0033] Compared with the prior art, the beneficial effects achieved by the present application are as follows: when measuring the non-acoustic parameters of porous materials, the grid search method is used to determine the initial value of the optimization algorithm, rather than relying on the initial parameters set artificially. Grid points are constructed within the physically feasible region of the parameters, and by evaluating the objective function values at these grid points, the optimal grid point is selected as the initial value of the SQP algorithm, effectively avoiding the problem of falling into the local optimal solution due to improper selection of the initial value. Grid search ensures the rationality of the initial value, while 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

[0034] The accompanying drawings are used to provide a further understanding of the present application and form 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 to the present application. In the accompanying drawings:

[0035] Figure 1 is a schematic diagram of the overall module structure provided by an embodiment of the present application;

[0036] Figure 2 is a schematic flowchart provided by an embodiment of the present application. Detailed implementation manners

[0037] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0038] Please refer to Figure 1 and Figure 2 The present application provides a technical solution: a measurement method for inverting non-acoustic parameters of porous materials based on surface acoustic impedance data. The above method works using a measurement system. The above system includes an execution module, an inversion model construction module, and a parameter inversion module. The execution module is used to measure the acoustic parameters of the hard-skeleton porous material in the full frequency band using the standard transfer function method for the measurement system. The inversion model construction module is used to combine the semi-phenomenological Wilson model and 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 the acoustic parameters;

[0039] The execution module includes a sound source device, a measurement 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 excite the porous material for acoustic characteristic measurement. The measurement tube is used to provide a fluid channel to control the propagation characteristics of the measured sound wave in the porous material in the environment. The microphone captures the acoustic 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. The impedance tube is used to measure the acoustic impedance and related acoustic characteristics;

[0040] 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.

[0041] 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.

[0042] In one embodiment, a method for measuring non-acoustic parameters of porous materials based on surface acoustic impedance data inversion includes the following steps:

[0043] 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;

[0044] 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;

[0045] S3. Inversion model construction: Combine the semi-phenomenological Wilson model and the JCA model to build a complete five-parameter inversion model framework;

[0046] 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;

[0047] In S3, the construction method of the five-parameter inversion model framework is:

[0048] S3-1. First, use the Wilson model to invert the three basic microscopic parameters of the material, including porosity, flow resistance, and tortuosity;

[0049] S3-2. When inverting the JCA model parameters, the transfer function H(f) is defined as the ratio of the sound pressures at two measurement positions, i.e., 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, f is the measurement frequency. Based on the measured transfer function H(f), calculate the theoretical calculated value α(f) of the normal incidence sound absorption coefficient, which is calculated by the formula α(f) = 1 - |R(f)|², where R(f) is the reflection coefficient and is proportional to the transfer function H(f);

[0050] S3-3. Further complete the complete inversion process of the five microscopic parameters through the JCA model. The other two basic microscopic parameters are the viscous characteristic length and the thermal characteristic length, and set physical constraint conditions in the model, including the range limitation of the open porosity and the range setting of the flow resistance;

[0051] In S3-3, the complete inversion process of the five microscopic parameters is: minimize the mean square error between the measured values and the theoretical calculated values as the optimization goal, i.e., the minimum mean square error , where x represents the five parameters to be inverted , corresponding to porosity , flow resistance , tortuosity , viscous characteristic length and thermal characteristic length , is the normal incidence sound absorption coefficient measured at the frequency ;

[0052] In S4, the specific process of solving the multi-solution problem in parameter inversion is:

[0053] S4-1. The five parameters 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, perform a global search within the parameter feasible region through the grid search method. Use the grid division strategy to divide the feasible region into several grids. The values corresponding to the lines in the grid are the sizes of the parameters assumed in advance. Refine the search grid within the range allowed by the computing resources to locate the possible global optimal solution region;

[0054] S4-2. Use the SQP algorithm to finely optimize the selected candidate solutions. By setting a reasonable convergence threshold, ensure that the algorithm can converge to the global optimal solution quickly and stably, and adjust the convergence speed of the global optimal solution of the parameters of the sample to be measured of the same material during subsequent measurements;

[0055] In S4-2, the specific process of the SQP algorithm for finely optimizing the selected candidate solutions is as follows:

[0056] According to the feasible region of a certain parameter, for the samples to be measured of each material, at the first detection, the grid lines are divided with the initial grid line density to obtain the assumed value of a certain parameter , where n is the number of grid lines. After substituting each parameter, the minimum mean square error is found for the corresponding parameter , and , this parameter is marked as a candidate solution for the local extreme point. The region enclosed by the grid lines corresponding to the two parameters and adjacent to this parameter is the potential excellent region;

[0057] In the potential excellent region, the grid lines are re-divided with the refined grid line density to find the corresponding parameter, and the potential excellent region is redefined. This process is repeated until the refined grid line density is less than the convergence threshold . After obtaining several candidate solutions for the local extreme points, the parameter corresponding to the minimum is selected as the global optimal solution for this parameter;

[0058] In S4-2, the specific method for adjusting the convergence speed of the global optimal solution of the parameters of the samples to be measured of the same material is as follows: Record the global optimal solutions of the five parameters of the same material measured each time, and calculate the variance of the global optimal solutions of the historical parameters . When setting the initial grid line density for the next sample to be measured of the same material, as becomes larger, the uncertainty of the global optimal solution is higher, and a larger initial grid line density is required, that is, , where is the influence coefficient of the variance on the initial grid line density.

[0059] This application requires the selection of a cylindrical sample in one of two different sizes: the large sample has a diameter requirement of 98 ± 0.5 mm and is suitable for the measurement of the normal incidence sound absorption coefficient in the frequency band of 50 - 1600 Hz; the small sample has a diameter requirement of 29 ± 0.5 mm and is suitable for the measurement of the normal incidence sound absorption coefficient in the frequency band of 500 - 6400 Hz. In the specific embodiments of this application, the standard thickness of the selected samples is 24 ± 0.5 mm, and this thickness is the optimal choice verified by a large number of experiments, which can ensure the measurement accuracy while ensuring that the sample has sufficient mechanical strength. This application requires that the surface of the sample must be kept flat, and the surface flatness deviation does not exceed 0.1 mm, which is 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. To eliminate the influence of environmental factors, all samples need to be stored in standard environmental conditions (temperature 20 ± 2 °C, relative humidity 65 ± 5%) for at least 24 hours before testing to ensure that the samples are in a stable state. To ensure the physical rationality of the inversion results, strict constraint conditions are set for each parameter: the porosity φ ranges from 0.1 to 0.99, and this range covers the vast majority of actual porous materials; the flow resistance σ ranges from 100 to 500000 N·m-4s, and the parameter range is determined based on a large amount of experimental data; The tortuosity is limited between 1 and 4, which is in line with the physical characteristics of actual porous materials; the ranges of the viscous characteristic length Λ and the thermal characteristic length Λ' are both 1 - 1000 μm, and it is required that Λ ≤ Λ', and these constraint conditions ensure that the inversion results have clear physical meanings.

[0060] The measurement system adopted in this application strictly follows the GB / T18696.2 - 2002 standard, and the system is configured with two different specifications of standard impedance tubes, with inner diameters of 98 mm and 29 mm respectively. The impedance tubes are made of high-quality stainless steel materials, and the wall thickness is not less than 10 mm, which can effectively prevent the influence of tube wall vibration on the measurement. The inner wall of the pipeline has been precisely processed, and the surface roughness Ra does not exceed 0.2 microns. Such a 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 ≤ 0.2 dB), a B&K signal generator and a collector (sampling rate ≥ 48 kHz), and an experimental power amplifier (distortion ≤ 0.1%). The configuration of these high-precision devices ensures the accuracy and reliability of the entire measurement system.

[0061] The method of the present application has the following remarkable advantages: First, the measurement process is convenient and efficient. Only a test sample that meets the standard size is required, and all the required parameters can be obtained through a single measurement. Moreover, the non-contact measurement method is adopted, which will not cause any damage to the sample. Second, the algorithm has excellent performance. The grid search effectively avoids the problem of improper selection of the initial value. The SQP algorithm has fast convergence characteristics, and strict physical constraints ensure the rationality of the results. Finally, it has a wide range of applications and is applicable to the characterization of various hard-skeleton porous materials, providing a reliable parameter basis for material design and performance optimization.

[0062] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0063] Finally, it should be noted that the above are only the preferred embodiments of the present application and are not used to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A measurement method for inverting non-acoustic parameters of porous materials based on surface acoustic impedance data, characterized in that, The method works by using a measurement system, which includes an execution module, an inversion model construction module, and a parameter inversion module. The execution module is used to measure the acoustic parameters of the hard-skeleton porous material in the full frequency band by using the standard transfer function method to measure the system. The inversion model construction module is used to combine the semi-phenomenological Wilson model and 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 the acoustic parameters; The execution module includes a sound source device, a measurement 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 excite the porous material for acoustic property measurement. The measurement tube is used to provide a fluid channel to control the propagation characteristics of the measured sound wave 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, 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, 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 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 both 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 through the 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 reasonable convergence thresholds and adopt an adaptive step size strategy. The grid optimization module is used to adopt a strategy of gradually refining to optimize the way of constructing grid points; The method includes the following steps: S1. Construct a measurement system: Configure a variety of measurement instruments and standard impedance tubes of various specifications to measure the hard-skeleton porous material in different frequency bands. Control the measurement temperature within the first preset range and the measurement humidity within the second preset range. Select a cylindrical test sample of a specified size; S2. Normal incidence sound absorption coefficient measurement: Select the corresponding measurement frequency range and a standard impedance tube that matches the actual size according to the actual size of the sample to be measured, and measure the normal incidence sound absorption coefficient measurement value in the frequency band that matches the standard impedance tube; S3. Inversion model construction: Based on the measured values of the normal incidence sound absorption coefficient, combine the semi-phenomenological Wilson model and JCA model to construct a complete five-parameter inversion model framework; S4. Parameter inversion implementation and evaluation: Based on the five-parameter inversion model framework, adopt a hybrid optimization strategy that combines the grid search method and the SQP algorithm to optimize the parameter inversion process, and optimize the method of constructing grid points; The step S3 includes the following steps: S3-1. Invert the three basic microscopic parameters of the material using the Wilson model. The three basic microscopic parameters include porosity, flow resistance, and tortuosity; S3-2. When inversing the JCA model parameters, define the transfer function H(f) 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) respectively represent the sound pressure values at different distances from the surface of the sample to be measured, and f is the measurement frequency; Based on the transfer function H(f), calculate the theoretical value of the normal incidence sound absorption coefficient α(f), expressed as α(f) = 1 - |R(f)|², where R(f) is the reflection coefficient and is proportional to the transfer function H(f); S3-3. Complete the complete inversion process of the five basic microscopic parameters through the JCA model. The five basic microscopic parameters are porosity, flow resistance, tortuosity, viscous characteristic length, and thermal characteristic length, and set physical constraint conditions in the JCA model. The physical constraint conditions include the range limitation of the open porosity and the range setting of the flow resistance.

2. The measurement method for inversely calculating non-acoustic parameters of porous materials based on surface acoustic impedance data according to claim 1, wherein In the step S3-3, completing the complete inversion process of the five basic microscopic parameters through the JCA model includes: Taking the minimization of the mean square error between the measured value and the theoretical value as the optimization objective, that is, the minimum value of the mean square error minF(x) = ∑[α m (f) - α(f, x)] 2 , where x represents the five parameters to be inverted corresponding to the porosity , flow resistance σ, tortuosity α ∞ , viscous characteristic length Λ, and thermal characteristic length Λ', α m (f) is the measured value of the normal incidence sound absorption coefficient in S2, and α(f, x) is the theoretical value of the normal incidence sound absorption coefficient in S3-2.

3. A measurement method for inverting non-acoustic parameters of porous materials based on surface acoustic impedance data according to claim 2, characterized in that, The step S4 includes the following steps: S4-1. Presume the five basic microscopic parameters to be inverted in advance. According to the five basic microscopic parameters to be inverted, calculate the theoretical value of the normal incidence sound absorption coefficient in the JCA model; Conduct a global search within the feasible region of each basic microscopic parameter through the grid search method. Adopt a grid division strategy to divide the feasible region into several grids. The values corresponding to the lines in each grid are the size values of the presumed basic microscopic parameters. Refine the search grid within the preset range of computing resources, locate the global optimal solution region, and select 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; Adjust the convergence speed of the global optimal solution of the parameters of the sample to be measured of the same material during subsequent measurements.

4. A measurement method for inverting non-acoustic parameters of porous materials based on surface acoustic impedance data according to claim 3, characterized in that In the step S4-2, using the SQP algorithm to finely optimize the selected candidate solutions includes the following steps: According to the feasible region of any parameter, the grid lines of the test samples of each material are divided with the initial grid line density k0 during the first detection, and a set of assumed values {j1, j2, …, j n} of any parameter is obtained, where n is the number of grid lines; substitute the set of assumed values of each parameter into the JCA model to determine the parameter j i corresponding to the minimum mean square error minF(x), and i ∈ n; mark the parameter j i as a candidate solution for the local extreme point, and the region enclosed by the grid lines corresponding to its two adjacent parameters j i-1 and j i+1 is the potential excellent region; Redivide the grid lines with a refined grid line density k1 in the potentially excellent area, find the parameters corresponding to minF(x) to redefine the potentially excellent area, and repeat the above steps until the refined grid line density k1 is less than the convergence threshold k s After obtaining the candidate solutions of several local extreme points, select the parameter j corresponding to the minimum value of minF(x) m as the global optimal solution of this parameter.

5. A measurement method for inverting non-acoustic parameters of porous materials based on surface acoustic impedance data according to claim 4, characterized in that, In the step S4-2, adjusting the convergence speed of the global optimal solution of the parameters of the sample to be measured of the same material includes the following steps: Record the global optimal solutions of the five basic microscopic parameters of the same material measured 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 to be measured of the same material: k 0d = k0 + μX Among them, k0 is the initial grid line density of the previous measured sample, and μ is the influence coefficient of the variance X and the initial grid line density.

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