An acoustic metamaterial performance testing method, system, device and medium

Abstract

The present invention discloses a method, system, device and medium for testing the performance of acoustic metamaterials. The method includes: obtaining the key attribute parameters of the acoustic metamaterials and the requirement parameters of a preset simulation scenario; constructing a multi-objective optimization function according to the key attribute parameters, and inputting the objective solution output by the multi-objective optimization function and the requirement parameters into a simulation software for simulation; during the simulation process, using a two-stage screening mechanism to screen out a key feature subset from the objective solutions; inputting the key feature subset into a pre-trained performance prediction neural network model to output a test result reflecting the performance of the acoustic metamaterials; adjusting the parameters in the objective solution with the test result and performing iterative simulation to output a performance index value of the acoustic metamaterials; and determining the performance utility of the acoustic metamaterials according to the performance index value.

Description

Technical Field

[0001] The present invention relates to the field of acoustic material testing, and particularly to a method, system, device and medium for testing the performance of acoustic metamaterials. Background Art

[0002] Acoustic metamaterials achieve acoustic characteristics that traditional materials cannot achieve, such as low-frequency sound absorption, negative refractive index, and acoustic wave directional control, through artificially designed microstructures such as periodically arranged resonance units, porous structures, etc. Due to these excellent characteristics, they are widely used in the manufacturing of high-efficiency noise reduction and sound insulation materials in the production links of fields such as power equipment, intelligent vehicles, medical devices, and electronic devices. To meet the actual application effects of acoustic metamaterials and save costs and manufacturing time, it is necessary to test or simulate the performance of acoustic metamaterials.

[0003] Existing testing technologies often rely on a large number of physical experiments in a laboratory environment. However, the structure of acoustic metamaterials is highly complex, resulting in low direct experimental efficiency. Moreover, since the laboratory environment cannot reproduce real working conditions, the accuracy of the test results is poor, making it difficult to confirm the true performance of acoustic materials, and thus unable to effectively optimize and adjust acoustic materials to meet the requirements of production / application in related fields.

[0004] Therefore, how to effectively test the performance of acoustic metamaterials has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, system, device and medium for testing the performance of acoustic metamaterials, aiming to solve the problem of how to process data reflecting the performance of acoustic materials by constructing a multi-objective optimization function and a neural network model to improve the test accuracy and efficiency.

[0006] To solve the above technical problem, an embodiment of the present invention provides a method for testing the performance of acoustic metamaterials, including:

[0007] Obtaining the key attribute parameters of the acoustic metamaterial and the requirement parameters of the preset simulation scenario;

[0008] Constructing a multi-objective optimization function according to the key attribute parameters, and inputting the target solution output by the multi-objective optimization function and the requirement parameters into a simulation software for simulation;

[0009] During the simulation process, using a two-stage screening mechanism to screen out the key feature subset from the target solutions;

[0010] Inputting the key feature subset into a pre-trained performance prediction neural network model to output a test result reflecting the performance of the acoustic metamaterial;

[0011] Adjust the parameters in the target solution according to the test results and perform iterative simulation to output the performance index value of the acoustic metamaterial;

[0012] Determine the performance utility of the acoustic metamaterial according to the performance index value.

[0013] Further, the construction of the multi-objective optimization function according to the key attribute parameters, and the input of the target solution output by the multi-objective optimization function and the demand parameters into the simulation software for simulation includes:

[0014] Construct the multi-objective optimization function considering the environmental temperature constraint with the key attribute parameters as decision variables;

[0015] Use the preset genetic algorithm to search the multi-objective optimization function, and output the target solution including the microscopic pore structure, frequency response characteristics and material thickness of the acoustic metamaterial.

[0016] Create a simulation environment in the simulation software with the target solution and the demand parameters to simulate the performance of the acoustic metamaterial.

[0017] Further, the multi-objective optimization function includes:

[0018] The first sub-function with the maximum sound insulation of the acoustic metamaterial as the goal, the second sub-function with the maximum sound energy absorption efficiency of the acoustic metamaterial as the goal, and the third sub-function with the minimum density of the acoustic metamaterial as the goal.

[0019] Further, the use of the two-stage screening mechanism to screen out the key feature subset from the target solution includes:

[0020] In the first stage, perform feature screening and optimization on each parameter data in the target solution by the information criterion mechanism to obtain the initial feature set;

[0021] In the second stage, determine the contribution degree of each feature to the performance of the acoustic metamaterial by quantifying the relative importance and category difference of each feature in the initial feature set;

[0022] Screen out the key feature subset from the initial feature set according to the contribution degree.

[0023] Further, the performing feature screening and optimization on each parameter data in the target solution by the information criterion mechanism to obtain the initial feature set includes:

[0024] Construct a multi-distribution function with the information criterion mechanism, and calculate the transition probability of each parameter data according to the multi-distribution function;

[0025] Determine the characteristic frequency of each parameter data according to the transition probability;

[0026] Under the preset frequency threshold condition, feature screening is performed in the target solution with the characteristic frequency to obtain the initial feature set.

[0027] Further, adjusting the parameters in the target solution with the test result and performing iterative simulation to output the performance index value of the acoustic metamaterial, including:

[0028] Matching and analyzing the test result with the optimization objective of the multi-objective optimization function, and when the matching error exceeds the preset error threshold, adjusting the corresponding parameters in the target solution;

[0029] Iterating the simulation process with the adjusted parameters until convergence to obtain the performance index value.

[0030] Further, adjusting the parameters in the target solution with the test result and performing iterative simulation to output the performance index value of the acoustic metamaterial, further includes:

[0031] When the matching error exceeds the preset error threshold, introducing a temperature correction factor into the multi-objective optimization function for updating, and correcting the constraint conditions of the multi-objective optimization function;

[0032] Iterating the simulation process with the updated multi-objective optimization function to obtain the performance index value.

[0033] Another embodiment of the present invention provides an acoustic metamaterial performance test system, including:

[0034] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the acoustic metamaterial performance test method described above is implemented.

[0035] A parameter acquisition module, configured to acquire the key attribute parameters of the acoustic metamaterial and the requirement parameters of the preset simulation scenario;

[0036] A simulation module, configured to construct a multi-objective optimization function according to the key attribute parameters, and input the target solution output by the multi-objective optimization function and the requirement parameters into a simulation software for simulation;

[0037] A feature screening module, configured to use a two-stage screening mechanism to screen out a key feature subset from the target solution during the simulation process;

[0038] A performance prediction module, configured to input the key feature subset into a pre-trained performance prediction neural network model to output a test result reflecting the performance of the acoustic metamaterial;

[0039] An iterative module, configured to adjust parameters in the target solution according to the test results and perform iterative simulation, and output the performance index value of the acoustic metamaterial;

[0040] A performance analysis module, configured to determine the performance utility of the acoustic metamaterial according to the performance index value.

[0041] Another embodiment of the present invention provides a computer-readable storage medium storing a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the above-mentioned method for testing the performance of the acoustic metamaterial is implemented.

[0042] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0043] In the embodiments of the present invention, by constructing a multi-objective optimization function to determine the optimal parameter group of the acoustic metamaterial, and cooperating with the information criterion and contribution degree quantization mechanism to screen out the key features that fully reflect the acoustic performance from the acoustic metamaterial data, the influence of redundant features on the model performance can be avoided, the computational complexity can be reduced, and the interpretability of the model can be enhanced; by integrating the simulation tool of the deep neural network model to test the performance characteristics of the acoustic material, the complex characteristics of the acoustic material can be accurately captured, and the multi-objective genetic algorithm is used to call the simulation tool to dynamically adjust and optimize the scene parameters of the acoustic material, so that the performance of the acoustic material can be more accurately simulated. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic flowchart of the method for testing the performance of the acoustic metamaterial in one embodiment of the present invention;

[0045] Figure 2 is a schematic structural diagram of the system for testing the performance of the acoustic metamaterial in one embodiment of the present invention;

[0046] Figure 3 is a structural block diagram of a preferred embodiment of a computer device provided by the present invention;

[0047] Reference numerals: M1, parameter acquisition module; M2, simulation module; M3, feature screening module; M4, performance prediction module; M5, iterative module; M6, performance analysis module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0049] In the description of the present application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0050] In the description of the present application, it should be noted that, unless otherwise clearly defined and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two elements. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0051] In the description of the present application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which this technology belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0052] An embodiment of the present invention provides a method for testing the performance of acoustic metamaterials. Specifically, please refer to Figure 1 , Figure 1 which shows a schematic flow chart of the method for testing the performance of acoustic metamaterials in one of the embodiments of the present invention, including the following steps:

[0053] S1. Obtain the key property parameters of the acoustic metamaterial and the required parameters of the preset simulation scenario.

[0054] In this embodiment, the key property parameters of the acoustic metamaterial for performance testing will be collected. Exemplarily, in this embodiment, specific data acquisition devices such as impedance tube test devices and reverberation chamber devices will be used. The key parameters of the acoustic metamaterial include, but are not limited to, aspects such as the physical properties of the material, acoustic response characteristics, and microstructure parameters. These parameters can all reflect to a certain extent the sound insulation characteristics of the material, the ability to block the propagation of sound waves, and the ability to absorb sound energy. Exemplarily, the physical properties include material density, thermal conductivity, etc., as well as the thickness of the material; the microstructure parameters include porosity, pore size distribution, flow resistivity, pore connectivity, etc.; the acoustic response characteristics such as frequency response characteristics, such as resonance frequency, frequency response curve, etc. Further, the collected parameter data needs to be preprocessed, including removing outliers, missing values, removing incorrect data, data conversion, and data normalization.

[0055] In the embodiment of the present invention, regarding the creation of the simulation environment, FOAM-X, COMSOL simulation software can be selected to create the simulation environment. For example, in this embodiment, the simulation of the noise control scenario of power equipment is preferably carried out. It should be understood that the required parameters of the simulation environment include, but are not limited to, the temperature, humidity, noise frequency range, and air flow velocity of the power equipment. Exemplarily, they can be collected inside the target power equipment through relevant sensor devices, anemometers, etc., or directly obtained from the equipment operation logs and real-time monitoring data.

[0056] S2. Construct a multi-objective optimization function according to the key property parameters, and input the target solution output by the multi-objective optimization function and the required parameters into the simulation software for simulation.

[0057] This step is the simulation process. To ensure the accuracy of the test results, in this embodiment, a multi-objective optimization function is introduced to solve the multi-objective optimization problem. It should be understood that the performance of the acoustic metamaterial is reflected in the sound insulation amount, acoustic impedance, sound absorption coefficient, sound velocity, and refractive index. For example, the acoustic impedance represents the ability of the material to block the propagation of sound waves, and it is affected by the fluctuations of parameters such as porosity; the sound insulation amount is expressed in decibels to represent the ability of the material to block the propagation of sound waves, and it is affected by parameters such as the thickness and density of the material, and the density itself also affects the lightweight requirement of the material; the sound absorption coefficient represents the proportion of sound energy absorbed by the material.

[0058] Based on the key performance of the composite acoustic material and the mapping relationship between the performance and the attribute parameters, in some embodiments of the present invention, a multi-objective optimization function considering the environmental temperature constraint is constructed with the key attribute parameters as decision variables. The multi-objective optimization function includes multiple sub-functions: a first sub-function with the maximum sound insulation of the acoustic metamaterial as the objective, a second sub-function with the maximum sound energy absorption efficiency of the acoustic metamaterial as the objective, and a third sub-function with the minimum density of the acoustic metamaterial as the objective. Preferably, the decision variables include the microscopic pore structure parameters, material density, and frequency response characteristic parameters of the acoustic metamaterial.

[0059] It should be noted that in this embodiment, the influence of the simulation environment is considered, and corresponding environmental constraint conditions are set. For example, the temperature is set to ≥150 °C, and the high temperature resistance of the material is introduced into the test content. In some embodiments of the present invention, geometric constraints can also be set to limit the installation space of the material, as well as physical constraints, such as ensuring that the pore sound absorption of the material is not less than 0.3.

[0060] Furthermore, the preset genetic algorithm is used to solve the constructed multi-objective optimization function. In this embodiment, the non-dominated sorting genetic algorithm (NSGA-II) is preferably used to search the multi-objective optimization function, and the Pareto solution set, that is, the target solution, is output. Exemplarily, 50 groups of candidate parameter combinations are output, including the optimal parameter groups composed of these decision variables such as the microscopic pore structure, frequency response characteristics, and material thickness.

[0061] A simulation environment is created in the simulation software with the target solution and the required parameters to realize the simulation of the performance of the acoustic metamaterial and verify the actual performance of the parameters. For example, by setting the values of the required parameters, the internal environment of the power equipment is simulated in the simulation software, such as simulating the noise reduction control of the power transformer room, and the sound insulation performance of the acoustic material in this scenario is tested.

[0062] S3. During the simulation process, a two-stage screening mechanism is used to screen out the key feature subset from the target solution.

[0063] It can be understood that the target solution contains multiple optimized key attribute parameter combinations, and these parameter combinations can be first analyzed and structured. They affect the feature screening direction. For example, if the low-frequency performance becomes the focus after optimization, the two-stage screening mechanism will be used to screen out the features related to low frequency from them, such as pore connectivity.

[0064] In the first stage, considering the role of the information criterion mechanism in model optimization, the information criterion mechanism is used to optimize the features of each parameter data in the target solution to obtain an initial feature set.

[0065] Specifically, a multi-distribution function is constructed with the information criterion mechanism, the transition probability of each parameter data is calculated according to the multi-distribution function, and the characteristic frequency of each parameter data is determined according to the transition probability.

[0066] Exemplarily, this embodiment constructs probability distribution functions through the AIC criterion and the BIC criterion respectively, that is, calculates the conditional probability of each feature being selected in the solution set. Combining the two criteria can avoid the deviation of a single criterion and improve the robustness of feature selection.

[0067] Among them, the function constructed based on the AIC criterion is expressed as:

[0068]

[0069] By weighting the AIC value, it is possible to balance the goodness of fit and complexity of the model with the current features.

[0070] Where, Indicates the probability of feature s being selected based on the AIC criterion;

[0071] Indicates the distribution probability of AIC; Indicates whether the sth feature is included in the feature subset, Represents the state combination of all features except the sth feature.

[0072] The function constructed with the BIC criterion is expressed as:

[0073]

[0074] The BIC criterion imposes a heavier penalty on model complexity, where Indicates the probability of feature s being selected based on the BIC criterion; Represents the distribution probability of BIC.

[0075] Based on the calculated transition probabilities, we perform cyclic sampling to determine the number of times each feature is selected. The characteristic frequency is calculated by calculating the ratio of this number to the preset number of repeated samplings. For example, if the characteristic porosity is selected 600 times in a sampling cycle of 1000, the characteristic frequency is 0.6.

[0076] Under the preset frequency threshold condition, features are screened in the target solution using the characteristic frequency to obtain an initial feature set. For example, the threshold can be set to 0.5, and features with a characteristic frequency greater than the threshold are retained during the screening process and included in the initial feature subset. In some embodiments of the present invention, a significant error control is also introduced during the sampling process, which is expressed as follows:

[0077]

[0078] Where, The standard deviation of the characteristic frequency; q is the correlation significance of the variable; t is the number of samples.

[0079] In this embodiment, an efficient and robust feature selection is achieved by constructing a transition probability function based on the information criterion and combining it with sampling, providing high-quality input for the subsequent neural network model.

[0080] Next is the second stage. In this stage, by quantifying the relative importance and class differences of each feature in the initial feature set, the contribution degree of each feature to the performance of acoustic metamaterials is determined, that is, this stage is used to quantify the contribution degree of different features to the material performance (such as sound insulation quantity, sound absorption coefficient), so as to screen out key features. Specifically, the screening formula constructed in this embodiment is expressed as follows:

[0081]

[0082] Among them, is the matching importance of feature f, that is, the contribution degree; U is the feature set; is the representation of the i-th feature in U; is the information gain function; c is the number of features; is the gain deviation; is the weight coefficient; is the number of times of randomly extracting acoustic features; is the r-th randomly selected acoustic feature set; is the feature set in the r-th sampling 's nearest neighbor feature; is the prior probability of class g; The prior probability of the set ; is the difference function between feature f and the nearest neighbor feature in .

[0083] It can be seen from the above screening formula that is the information gain term, measuring the relative importance of the information gain of feature among all features. represents the information gain of feature , and the larger the value, the stronger the discrimination ability of this feature for the target variable (such as sound insulation quantity).

[0084] is the class difference term, used to evaluate the discrimination ability of features between different classes.

[0085] According to the value of , features with a large contribution to the performance of acoustic materials are screened out from the initial feature set and incorporated into the key feature subset. In this embodiment, by quantifying the information gain (reflecting the prediction ability of features for the target) and class differences (reflecting the contribution of features to class discrimination), the importance of features can be accurately evaluated, improving the reliability of the screening results.

[0086] S4. Input the key feature subset into the pre-trained performance prediction neural network model, and output the test results reflecting the performance of the acoustic metamaterial.

[0087] This step is the process of inputting the selected key feature subset into the pre-trained performance prediction neural network model for performance prediction.

[0088] In order to further improve the generalization ability of the model while reducing computational redundancy, the embodiments of the present invention use the total cost minimization algorithm to cluster the data. Exemplarily, algorithms such as the K-means clustering algorithm and the improved K-Medoids algorithm can be selected. Specifically, select the corresponding data in the pre-created sample dataset as the initial central data, obtain the central distance between the remaining data and the dataset center, assign the remaining data to the dataset center with the shortest central distance, randomly select non-central data, and calculate the total cost of the central data according to the non-central data, as shown below:

[0089]

[0090] where S is the total cost; the i-th central data, the i-th non-central data; m is the number of central data; n is the number of non-central data; is the central data and the non-central data distance.

[0091] It can be understood that if the total cost is less than or equal to zero, the non-central data replaces the central data to form new central data until no change occurs, otherwise the central data remains unchanged. According to the total cost minimum theory, cluster the key feature subset to obtain clustering clusters, and divide the training set and the test set. Preferably, divide the data of the clustering clusters into the training set and the test set according to 7:3.

[0092] Further, train the deep neural network model with the training set. Specifically, add a convolutional layer, a pooling layer, and a fully connected layer to the deep neural network, and introduce a loss function to optimize the parameters of the deep neural network. In some embodiments of the present invention, the Momentum algorithm can be selected as the optimizer of the model to update the hyperparameters of the deep neural network and learn the characteristics and laws of acoustic material simulation. Or introduce regularization techniques to prevent overfitting of the model, such as adding batch normalization and Dropout operators.

[0093] Finally, train the performance prediction neural network model, input the key feature subset into the performance prediction neural network model for analysis, and output the test results reflecting the performance of the acoustic metamaterial.

[0094] S5~S6: Adjust the parameters in the target solution based on the test results and perform iterative simulation to output the performance index values of the acoustic metamaterial.

[0095] This step is the parameter correction process. It is understood that the model outputs test results that reflect the performance of the acoustic metamaterial, and it should be understood that the multi-objective optimization function can reflect the relationship between the performance and attribute parameters of the determined acoustic material by analyzing the mapping relationship between decision variables and optimization objectives.

[0096] The test results are matched against the optimization objectives of the multi-objective optimization function. If the matching error exceeds a preset threshold, the corresponding parameters in the target solution are adjusted. For example, the optimization objectives set include: a sound insulation target of ≥ 35dB at 100Hz, a material density of ρ ≤ 800 kg / m³, and a sound absorption coefficient α of ≥ 0.8 at 300Hz. If the error between the calculated test results and these target values exceeds a threshold, such as 5%, the parameters are adjusted, such as by filtering out unnecessary features and reducing redundancy.

[0097] In addition to tuning parameters, embodiments of the present invention can also optimize the objective function. This involves introducing a temperature correction factor into the multi-objective optimization function for updating. Constraints within the multi-objective optimization function can also be modified, such as by tightening or loosening the material density constraint. Alternatively, genetic algorithm parameters can be adjusted, such as by increasing the population size or adjusting the crossover / mutation rate.

[0098] Then, similarly, the simulation process is iterated using the updated multi-objective optimization function to obtain performance index values, such as an output sound insulation value of 35dB and a sound absorption coefficient of 0.8.

[0099] The adjusted parameters are re-entered into the simulation software and iterated until convergence is achieved, resulting in the desired index value reflecting the true performance of the acoustic material. Based on this performance index value, the performance utility of the acoustic metamaterial can be determined. For example, if structural improvements to the material are necessary, an adjustment strategy can be developed before production to optimize material performance, such as increasing the material's sound insulation percentage, to ensure that the material meets the requirements of a specific application, saving time and cost.

[0100] In summary, the embodiments of the present invention achieve accurate testing of the performance of acoustic materials by creating a simulation environment and combining a trained neural network model; during the testing process, a multi-objective function with the performance parameters of the acoustic materials as the optimization problem is constructed, and various key attribute parameters affecting the performance of the acoustic materials are defined as decision variables, which can achieve efficient optimization of the acoustic performance parameters and improve the testing efficiency; a two-stage feature screening mechanism involving information criterion screening and feature contribution degree quantification is adopted to screen out the key feature set from the feature parameter group included in the optimal solution output by the objective function, which can balance the model complexity and fitting degree and ensure the strong correlation between the key features and the acoustic performance.

[0101] An embodiment of the present invention provides an acoustic metamaterial performance testing system. Specifically, please refer to Figure 2 , Figure 2 which shows a schematic structural diagram of the acoustic metamaterial performance testing system in one of the embodiments of the present invention, including:

[0102] A parameter acquisition module M1, configured to acquire the key attribute parameters of the acoustic metamaterial and the requirement parameters of the preset simulation scenario;

[0103] A simulation module M2, configured to construct a multi-objective optimization function according to the key attribute parameters, and input the objective solution output by the multi-objective optimization function and the requirement parameters into simulation software for simulation;

[0104] A feature screening module M3, configured to use a two-stage screening mechanism to screen out a key feature subset from the objective solution during the simulation process;

[0105] A performance prediction module M4, configured to input the key feature subset into a pre-trained performance prediction neural network model and output a test result reflecting the performance of the acoustic metamaterial;

[0106] An iteration module M5, configured to adjust the parameters in the objective solution with the test result and perform iterative simulation, and output the performance index value of the acoustic metamaterial;

[0107] A performance analysis module M6, configured to determine the performance utility of the acoustic metamaterial according to the performance index value.

[0108] As Figure 3 shown, the embodiments of the present invention also provide a computer device. Figure 3 This is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. The processor implements the method as described above when executing the computer program.

[0109] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ……), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0110] The processor can be a central processing unit (CPU), or can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The processor is the control center of the terminal device and connects various parts of the terminal device through various interfaces and circuits.

[0111] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory can be a high-speed random access memory, or can also be a non-volatile memory, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., or the memory can also be other volatile solid-state storage devices.

[0112] It should be noted that the above terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3The structural block diagram is only an example of the terminal device, which does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine some components, or different components. Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0113] Correspondingly, an embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the steps in the above-described embodiment methods, such as Figure 1 the steps S1 to S6 described therein.

[0114] The technical features and technical effects of the acoustic metamaterial performance testing system proposed in the embodiment of the present invention are the same as those of the acoustic metamaterial performance testing method proposed in the embodiment of the present invention, and will not be elaborated herein.

[0115] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the appended claims.

Claims

1. An acoustic metamaterial performance testing method, characterized in that Including: Obtaining the key attribute parameters of the acoustic metamaterial and the required parameters of the preset simulation scenario; Constructing a multi-objective optimization function according to the key attribute parameters, and inputting the objective solution output by the multi-objective optimization function and the required parameters into a simulation software for simulation; During the simulation process, using a two-stage screening mechanism to screen out the key feature subset from the objective solutions; Specifically: In the first stage, perform feature optimization on each parameter data in the objective solution by means of an information criterion mechanism to obtain an initial feature set. Specifically: construct a multi-distribution function with the information criterion mechanism, calculate the transition probability of each parameter data according to the multi-distribution function; determine the characteristic frequency of each parameter data according to the transition probability; Under the condition of a preset frequency threshold, perform feature screening in the objective solution with the characteristic frequency to obtain the initial feature set; In the second stage, determine the contribution degree of each feature to the performance of the acoustic metamaterial by quantifying the relative importance and category difference of each feature in the initial feature set; Screen out the key feature subset from the initial feature set with the contribution degree; Input the key feature subset into a pre-trained performance prediction neural network model, and output a test result reflecting the performance of the acoustic metamaterial; Adjust the parameters in the objective solution with the test result and perform iterative simulation, and output the performance index value of the acoustic metamaterial; Determine the performance utility of the acoustic metamaterial according to the performance index value.

2. The acoustic metamaterial performance testing method according to claim 1, characterized in that The constructing a multi-objective optimization function according to the key attribute parameters, and inputting the objective solution output by the multi-objective optimization function and the required parameters into a simulation software for simulation includes: Constructing the multi-objective optimization function considering environmental temperature constraints with the key attribute parameters as decision variables; Using a preset genetic algorithm to search for the multi-objective optimization function, and outputting the objective solution including the microscopic pore structure, frequency response characteristics and material thickness of the acoustic metamaterial; Creating a simulation environment in the simulation software with the objective solution and the required parameters to simulate the performance of the acoustic metamaterial.

3. The acoustic metamaterial performance testing method according to claim 1, characterized in that, The multi-objective optimization function includes: A first sub-function with the maximum sound insulation of the acoustic metamaterial as the objective, a second sub-function with the maximum sound energy absorption efficiency of the acoustic metamaterial as the objective, and a third sub-function with the minimum density of the acoustic metamaterial as the objective.

4. The acoustic metamaterial performance testing method according to claim 1, characterized in that The adjusting the parameters in the objective solution with the test result and performing iterative simulation, and outputting the performance index value of the acoustic metamaterial includes: Performing matching analysis on the test result and the optimization objective of the multi-objective optimization function, and when the matching error exceeds a preset error threshold, adjusting the corresponding parameters in the objective solution; Iterating the simulation process with the adjusted parameters until convergence to obtain the performance index value.

5. The acoustic metamaterial performance testing method according to claim 4, characterized in that The adjusting the parameters in the objective solution with the test result and performing iterative simulation, and outputting the performance index value of the acoustic metamaterial further includes: When the matching error exceeds a preset error threshold, a temperature correction factor is introduced into the multi-objective optimization function for updating, and the constraint conditions of the multi-objective optimization function are corrected; The simulation process is iterated with the updated multi-objective optimization function to obtain the performance index value.

6. An acoustic metamaterial performance testing system, characterized in that, It includes: A parameter acquisition module for acquiring the key attribute parameters of the acoustic metamaterial and the required parameters of the preset simulation scenario; A simulation module for constructing a multi-objective optimization function according to the key attribute parameters, and inputting the target solution output by the multi-objective optimization function and the required parameters into simulation software for simulation; A feature screening module for screening out a key feature subset from the target solutions by using a two-stage screening mechanism during the simulation process; Specifically: in the first stage, the information criterion mechanism is used to optimize the features of each parameter data in the target solution to obtain an initial feature set. Specifically: a multi-distribution function is constructed by the information criterion mechanism, and the transition probability of each parameter data is calculated according to the multi-distribution function; according to the transition probability, the feature frequency of each parameter data is determined; Under the preset frequency threshold condition, feature screening is performed on the target solution with the feature frequency to obtain the initial feature set; In the second stage, by quantifying the relative importance and category differences of each feature in the initial feature set, the contribution degree of each feature to the performance of the acoustic metamaterial is determined; The key feature subset is screened out from the initial feature set with the contribution degree; A performance prediction module for inputting the key feature subset into a pre-trained performance prediction neural network model and outputting a test result reflecting the performance of the acoustic metamaterial; An iteration module for adjusting the parameters in the target solution with the test result and performing iterative simulation, and outputting the performance index value of the acoustic metamaterial; A performance analysis module for determining the performance utility of the acoustic metamaterial according to the performance index value.

7. A computer device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the acoustic metamaterial performance test method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, it implements the acoustic metamaterial performance test method according to any one of claims 1 to 5.

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