Material parameter optimization method and device of sound insulation material, equipment and medium

Abstract

The invention relates to the technical field of vehicle manufacturing, in particular to a material parameter optimization method and device of a sound insulation material, equipment and a medium, and the method comprises the steps: obtaining a target parameter of each material layer in the sound insulation material to be optimized, so as to predict a prediction result of sound absorption and insulation performance of the sound insulation material to be optimized; determining to-be-optimized parameters from the target parameters, adjusting the to-be-optimized parameters of each material layer when a prediction result does not meet a preset performance condition, taking the adjusted sound insulation material as a to-be-optimized sound insulation material, and obtaining a prediction result corresponding to the sound absorption and insulation performance again until the prediction result is detected to meet the preset performance condition. And determining that the corresponding adjusted sound insulation material is the optimized sound insulation material, and when the sound absorption and insulation performance of the sound insulation material does not meet the requirements, adjusting the parameters of each material layer of the sound insulation material, so that the sound absorption and insulation performance of the acoustic package material can meet the requirements of the scheme design in the early stage of development, thereby reducing the change setting probability in the later stage.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle manufacturing, and in particular to a material parameter optimization method, device, equipment and medium for sound insulation materials. Background Art

[0002] As an important automotive component, the acoustic package plays a very important role in the noise, vibration and harshness (NVH) of the entire vehicle. Acoustic package materials with different material formulas have a significant impact on the sound absorption and insulation performance of components and the entire vehicle. Compared with single-layer materials, multi-layer composite materials have better acoustic performance, so acoustic package materials usually use multi-layer composite materials.

[0003] At present, for the sound absorption and insulation performance of multi-layer composite materials, the sound absorption and insulation performance of single-layer materials is usually tested according to the material parameters of single-layer materials, and then based on the test results of each layer of materials, the sound absorption and insulation performance of the multi-layer composite materials is predicted according to manual experience. However, due to the different material parameters of each layer of materials and the different bonding methods between the layers, the sound absorption and insulation performance of the multi-layer composite materials will be affected, which will lead to inaccurate prediction accuracy of the sound absorption and insulation performance of the multi-layer composite materials, so that the NVH performance of the vehicle cannot meet the design requirements.

[0004] Therefore, how to optimize the material parameters of multi-layer composite sound insulation materials to ensure that the NVH performance of the vehicle meets the design requirements has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, the embodiments of the present invention provide a material parameter optimization method, device, equipment and medium for sound insulation materials to solve the problem of how to optimize the material parameters of multi-layer composite sound insulation materials to ensure that the NVH performance of the vehicle meets the design requirements.

[0006] In a first aspect, an embodiment of the present invention provides a method for optimizing material parameters of a sound insulation material, the method comprising:

[0007] Obtaining target parameters of each material layer in the sound insulation material to be optimized, wherein the target parameters include at least two parameters, and predicting the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers;

[0008] Determine the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized, and determine the parameter to be optimized from the target parameters according to the influence sensitivity of each parameter;

[0009] When the prediction result does not meet the preset performance condition, adjusting the parameters to be optimized of each material layer in the sound insulation material to be optimized to obtain an adjusted sound insulation material;

[0010] The adjusted sound insulation material is used as the sound insulation material to be optimized, and the step of obtaining the target parameter of each material layer in the sound insulation material to be optimized is returned to execute until it is detected that the prediction result meets the preset performance condition, and the corresponding adjusted sound insulation material is confirmed to be the optimized sound insulation material.

[0011] In a second aspect, an embodiment of the present invention provides a material parameter optimization device for a sound insulation material, the material parameter optimization device comprising:

[0012] A performance prediction module, used to obtain target parameters of each material layer in the sound insulation material to be optimized, wherein the target parameters include at least two parameters, and predict the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers;

[0013] A parameter adjustment determination module, used to determine the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized, and determine the parameter to be optimized from the target parameters according to the influence sensitivity of each parameter;

[0014] A material updating module, configured to adjust the parameters to be optimized of each material layer in the sound insulation material to be optimized to obtain an adjusted sound insulation material when the prediction result does not meet the preset performance condition;

[0015] an optimization confirmation module, configured to use the adjusted sound insulation material as the sound insulation material to be optimized, return to execute the step of obtaining the target parameter of each material layer in the sound insulation material to be optimized, until it is detected that the prediction result meets the preset performance condition, and confirm that the corresponding adjusted sound insulation material is the optimized sound insulation material.

[0016] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the material parameter optimization method as described in the first aspect when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the material parameter optimization method as described in the first aspect is implemented.

[0018] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0019] The present invention obtains target parameters of each material layer in the sound insulation material to be optimized, wherein the target parameters include at least two parameters. According to the target parameters of all material layers, a prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is predicted; according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized, the influence sensitivity of each parameter is determined, and according to the influence sensitivity of each parameter, the parameter to be optimized is determined from the target parameters; when the prediction result does not meet the preset performance condition, the parameter to be optimized of each material layer in the sound insulation material to be optimized is adjusted to obtain an adjusted sound insulation material; the adjusted sound insulation material is used as the sound insulation material to be optimized, and the step of obtaining the target parameters of each material layer in the sound insulation material to be optimized is returned to execute until it is detected that the prediction result meets the preset performance condition, and it is confirmed that the corresponding adjusted sound insulation material is the optimized sound insulation material. Among them, firstly, the sound absorption and insulation performance of the sound insulation material to be optimized is predicted according to the target parameters of each material layer in the sound insulation material to be optimized, and then, the influence sensitivity of each parameter in the target parameters is determined by adjusting the target parameters of each material layer. The greater the influence sensitivity, the greater the influence of the change of the corresponding parameter on the sound absorption and insulation performance of the sound insulation material to be optimized. Therefore, the parameters to be optimized in the target parameters are determined according to the influence sensitivity. When the prediction result does not meet the requirements, the parameters to be optimized of each material layer in the sound insulation material to be optimized are adjusted to ensure that the sound absorption and insulation performance of the adjusted sound insulation material meets the requirements, which can effectively improve the accuracy of the selection of acoustic package materials in the early stage design of vehicle development, so that the sound absorption and insulation performance of the acoustic package materials can meet the requirements of the early stage design of development, thereby ensuring that the NVH performance of the vehicle meets the design requirements and reducing the probability of later design changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 This is a schematic diagram of an application environment of a material parameter optimization method for a sound insulation material provided in Embodiment 1 of the present invention;

[0022] Figure 2 It is a schematic flow chart of a method for optimizing material parameters of a sound insulation material provided in the first embodiment of the present invention;

[0023] Figure 3It is a schematic flow chart of a method for optimizing material parameters of a sound insulation material provided in the second embodiment of the present invention;

[0024] Figure 4 It is a schematic flow chart of a method for optimizing material parameters of a sound insulation material provided in Embodiment 3 of the present invention;

[0025] Figure 5 It is a schematic flow chart of a material parameter optimization method of a sound insulation material provided by a fourth embodiment of the present invention;

[0026] Figure 6 It is a schematic flow chart of a method for optimizing material parameters of a sound insulation material provided in Embodiment 5 of the present invention;

[0027] Figure 7 It is a structural schematic diagram of a material parameter optimization device for a sound insulation material provided by Embodiment 6 of the present invention;

[0028] Figure 8 It is a structural diagram of a computer device provided in Embodiment 7 of the present invention. DETAILED DESCRIPTION

[0029] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0030] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0031] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0032] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0033] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0034] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0035] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0036] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0037] A material parameter optimization method for a sound insulation material provided in the first embodiment of the present invention can be applied in the following aspects: Figure 1 In an application environment, the client communicates with the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud terminal devices, personal digital assistants (PDAs) and other computer devices. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0038] See also Figure 2 , is a schematic diagram of a flow chart of a material parameter optimization method for a sound insulation material provided in Embodiment 1 of the present invention. The material parameter optimization method can be applied to Figure 1The client in the client, the computer device corresponding to the client connects to the server to obtain the corresponding background data. For example, the target parameters of each material layer in the sound insulation material to be optimized, in order to predict the sound absorption and insulation performance of the sound insulation material to be optimized, and based on the predicted sound absorption and insulation performance, the target parameters of each material layer are optimized.

[0039] like Figure 2 As shown, the material parameter optimization method may include the following steps:

[0040] Step S201, obtaining target parameters of each material layer in the sound insulation material to be optimized, the target parameters include at least two parameters, and predicting the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers.

[0041] Among them, the sound insulation material to be optimized adopts a multi-layer composite material, which is composed of multiple material layers bonded together. The target parameters refer to the material parameters related to the sound absorption and insulation performance in the material layer, including but not limited to thickness, density, flow resistance, porosity, tortuosity, loss factor, Young's modulus, Poisson's ratio, viscous characteristic length and thermal characteristic length. Density, flow resistance, porosity, tortuosity, loss factor, Young's modulus and Poisson's ratio can be obtained through instrument testing, and viscous characteristic length and thermal characteristic length can be obtained through engineering application software analysis.

[0042] In the embodiment of the present invention, for any material layer in the sound insulation material to be optimized, the target parameters of the material layer are obtained, so that the target parameters of all material layers can be obtained. Then, according to the target parameters of all material layers, the sound absorption and insulation performance of the sound insulation material to be optimized is predicted to obtain the corresponding prediction results, so as to clarify the sound absorption and insulation performance of the sound insulation material to be optimized.

[0043] It should be noted that if the sound absorption and insulation performance of the sound insulation material to be optimized under different noise frequency bands is to be predicted, the sound absorption and insulation performance of the sound insulation material to be optimized under the target noise frequency band can be predicted based on the target parameters of all material layers and the target noise frequency band. For details, please refer to the subsequent steps S301 and S302. Among them, the target noise frequency band can be 200Hz-300Hz, 300Hz-400Hz and other frequency bands, without limitation.

[0044] Step S202, determining the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized, and determining the parameter to be optimized from the target parameters according to the influence sensitivity of each parameter.

[0045] Among them, the influence sensitivity refers to the difficulty of influencing the sound absorption and insulation performance of the sound insulation material to be optimized. The greater the influence sensitivity of the parameter, the greater the influence of the change of the parameter on the sound absorption and insulation performance of the sound insulation material to be optimized, and the easier it is to change the sound absorption and insulation performance of the sound insulation material to be optimized. The parameter to be optimized refers to the parameter that needs to be optimized among the target parameters.

[0046] In the embodiment of the present invention, for any parameter in the target parameters, the parameter is adjusted to detect the degree of influence of the change of the parameter on the sound absorption and insulation performance of the sound insulation material to be optimized, so as to determine the influence sensitivity of the parameter. Similarly, the influence sensitivity of each parameter in the target parameters is obtained, the influence sensitivity of each parameter is analyzed, and the parameter to be optimized is determined in the target parameters according to the analysis result.

[0047] Step S203: when the prediction result does not meet the preset performance condition, the parameters to be optimized of each material layer in the sound insulation material to be optimized are adjusted to obtain an adjusted sound insulation material.

[0048] The preset performance condition refers to the sound absorption and insulation performance requirement that the sound insulation material is required to achieve, that is, the target sound absorption and insulation performance. Therefore, in the embodiment of the present invention, after obtaining the prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized, it is detected whether the prediction result is equal to the target sound absorption and insulation performance. When it is detected that the prediction result is not equal to the target sound absorption and insulation performance, for any material layer in the sound insulation material to be optimized, the parameter to be optimized in the target parameter of the material layer is adjusted. The specific adjustment method is: obtaining the value range of the parameter to be optimized, using the evolutionary algorithm to select the target value in the value range of the parameter to be optimized, and using the target value to update the parameter to be optimized to complete the adjustment of the parameter to be optimized of the material layer.

[0049] Similarly, the above adjustment method is used to adjust the parameters to be optimized of each material layer in the sound insulation material to be optimized. After the parameters to be optimized of all material layers are adjusted, the adjusted sound insulation material is obtained.

[0050] Step S204, taking the adjusted sound insulation material as the sound insulation material to be optimized, and returning to the step of obtaining the target parameters of each material layer in the sound insulation material to be optimized, until it is detected that the prediction result meets the preset performance condition, and confirming that the corresponding adjusted sound insulation material is the optimized sound insulation material.

[0051] The optimized sound insulation material refers to a sound insulation material whose sound absorption and insulation performance meets the preset performance conditions. Therefore, in the embodiment of the present invention, after obtaining the adjusted sound insulation material, the adjusted sound insulation material is used as the sound insulation material to be optimized. Then, the predicted result of the sound absorption and insulation performance of the sound insulation material to be optimized is re-predicted by using the above step S201, and it is judged whether the re-obtained predicted result meets the preset performance conditions. If not, the parameters to be optimized of each material layer in the sound insulation material to be optimized are adjusted by using steps S202 and S203 until the predicted result meets the preset performance conditions. Then, the adjustment of the parameters to be optimized of the sound insulation material to be optimized is stopped, and the adjusted sound insulation material corresponding to the predicted result meeting the preset performance conditions is used as the optimized sound insulation material.

[0052] In an embodiment of the present invention, a target parameter of each material layer in a sound insulation material to be optimized is obtained, wherein the target parameter includes at least two parameters; a prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is predicted according to the target parameters of all material layers; an influence sensitivity of each parameter is determined according to the influence of a change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized; and a parameter to be optimized is determined from the target parameters according to the influence sensitivity of each parameter; when the prediction result does not meet a preset performance condition, the parameter to be optimized of each material layer in the sound insulation material to be optimized is adjusted to obtain an adjusted sound insulation material; the adjusted sound insulation material is used as the sound insulation material to be optimized, and the step of obtaining the target parameter of each material layer in the sound insulation material to be optimized is returned to execute until it is detected that the prediction result meets the preset performance condition, and it is confirmed that the corresponding adjusted sound insulation material is the optimized sound insulation material. Among them, firstly, the sound absorption and insulation performance of the sound insulation material to be optimized is predicted according to the target parameters of each material layer in the sound insulation material to be optimized, and then, the influence sensitivity of each parameter in the target parameters is determined by adjusting the target parameters of each material layer. The greater the influence sensitivity, the greater the influence of the change of the corresponding parameter on the sound absorption and insulation performance of the sound insulation material to be optimized. Therefore, the parameters to be optimized in the target parameters are determined according to the influence sensitivity. When the prediction result does not meet the requirements, the parameters to be optimized of each material layer in the sound insulation material to be optimized are adjusted to ensure that the sound absorption and insulation performance of the adjusted sound insulation material meets the requirements, which can effectively improve the accuracy of the selection of acoustic package materials in the early stage design of vehicle development, so that the sound absorption and insulation performance of the acoustic package materials can meet the requirements of the early stage design of development, thereby reducing the probability of later design changes.

[0053] In one embodiment, with respect to step S201, before predicting the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers, the following steps are included:

[0054] Obtaining bonding parameters of a bonding layer between two adjacent material layers in the sound insulation material to be optimized;

[0055] The method of predicting the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers includes:

[0056] According to the target parameters of all material layers and the bonding parameters of all bonding layers, a prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is predicted.

[0057] In the embodiment of the present invention, the bonding parameter is used to characterize the bonding method between two material layers. Among them, the bonding method of the material mainly considers the two material properties required for bonding, for example: the material itself is sticky and can be bonded by heating itself; inserting a molten composite film layer between two material layers, and forming an air layer by local bonding with low bonding strength requirements, etc. Different bonding methods are classified and identified using preset bonding parameters, and the bonding parameters corresponding to each bonding method can be obtained. Therefore, when obtaining the target parameters of all material layers of the sound insulation material to be optimized, the bonding parameters of the bonding layer between two adjacent material layers in the sound insulation material to be optimized are obtained at the same time, and the bonding parameters of all bonding layers contained in the sound insulation material to be optimized can be obtained, and then according to the target parameters of all material layers and the bonding parameters of all bonding layers, the sound absorption and insulation performance of the sound insulation material to be optimized is predicted, and the corresponding prediction results are obtained. Different bonding methods have different sound absorption and insulation performance of the sound insulation material. The sound absorption and insulation performance of the sound insulation material to be optimized is predicted in combination with the bonding method, which improves the accuracy of the prediction results.

[0058] In one embodiment, referring to Figure 3 , is a schematic diagram of a process for optimizing material parameters of a sound insulation material provided in the second embodiment of the present invention, such as Figure 3 As shown, in step S201, the prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is predicted according to the target parameters of all material layers and the bonding parameters of all bonding layers, including the following specific steps:

[0059] Step S301: for any material layer, the sound absorption and insulation performance of the material layer is obtained according to the target parameters of the material layer.

[0060] Step S302: predicting the sound absorption and insulation performance of the sound insulation material to be optimized based on the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers.

[0061] In the embodiment of the present invention, with respect to step S301 and step S302, first, with respect to any material layer of the sound insulation material to be optimized, after obtaining the target parameters of the material layer, the sound absorption and insulation performance of the material layer can be predicted according to the target parameters of the material layer, and the relationship between the parameter value of the target parameter in the material layer and the sound absorption and insulation performance of the material layer can be reflected, so that the sound absorption and insulation performance of all material layers can be obtained.

[0062] After obtaining the sound absorption and insulation performance of each material layer, the sound absorption and insulation performance of the sound insulation material to be optimized is predicted by combining the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers to obtain corresponding prediction results.

[0063] It should be noted that if one wants to predict the sound absorption and insulation performance of the sound insulation material to be optimized under different noise frequency bands, then for any material layer, according to the target parameters and target noise frequency band of the material layer, the sound absorption and insulation performance of the material layer under the target noise frequency band is obtained, and then according to the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers, the sound absorption and insulation performance of the sound insulation material to be optimized is predicted, that is, the sound absorption and insulation performance of the sound insulation material to be optimized under the target noise frequency band.

[0064] In one embodiment, referring to Figure 4 , is a schematic diagram of a process for optimizing material parameters of a sound insulation material provided in Embodiment 3 of the present invention, such as Figure 4 As shown, in step S301, obtaining the sound absorption and insulation performance of the material layer according to the target parameters of the material layer includes:

[0065] Step S401, obtaining a trained first sound absorption and insulation performance prediction model.

[0066] The trained first sound absorption and insulation performance prediction model refers to the first sound absorption and insulation performance prediction model that has been trained, which is used to predict the sound absorption and insulation performance of a single material layer. It has strong prediction ability and can accurately predict the sound absorption and insulation performance of the material layer when faced with new data of a single material layer.

[0067] Before obtaining the trained first sound absorption and insulation performance prediction model, it is necessary to construct the first sound absorption and insulation performance prediction model and train the constructed first sound absorption and insulation performance prediction model. Therefore, in the embodiment of the present invention, the first sound absorption and insulation performance prediction model adopts a one-dimensional convolutional neural network (1D Convolutional Neural Network, 1D-CNN), and uses the target parameters of the single-layer sound insulation material as the input data of the first sound absorption and insulation performance prediction model, and the sound absorption and insulation performance is used as the output data. A single-layer sound insulation material with known sound absorption and insulation performance is obtained, and the sound absorption and insulation performance is used as the label of the corresponding single-layer sound insulation material. The target parameters of the single-layer sound insulation material are obtained as training samples, so that multiple training samples constitute a sample data set, and 80% of the sample data set is used as a training set, and the remaining 20% ​​is used as a test set.

[0068] Set the model parameters in the first sound absorption and insulation performance prediction model, for example: the convolution kernel size is set to 2*2, the step size is set to 2, the learning rate is set to 0.1, and the number of iterations is set to 50. After the model parameters are set, each training sample in the training set is input into the first sound absorption and insulation performance prediction model, and the prediction results of the sound absorption and insulation performance are output accordingly. The loss between the prediction results and the corresponding labels is calculated, and the model parameters of the first sound absorption and insulation performance prediction model are reversely corrected using the stochastic gradient descent method until the loss converges to obtain the trained first sound absorption and insulation performance prediction model.

[0069] Step S402: input the target parameters of the material layer as input data into the trained first sound absorption and insulation performance prediction model, and output the sound absorption and insulation performance of the material layer accordingly.

[0070] In the embodiment of the present invention, after obtaining the trained first sound absorption and insulation performance prediction model in step S401, the target parameters of the material layer are input into the trained first sound absorption and insulation performance prediction model, and the sound absorption and insulation performance of the material layer is output accordingly.

[0071] In one embodiment, referring to Figure 5 , is a schematic diagram of a process for optimizing material parameters of a sound insulation material provided in Embodiment 4 of the present invention, such as Figure 5 As shown, in step S302, the prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is obtained according to the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers, including:

[0072] Step S501, obtaining a trained second sound absorption and insulation performance prediction model.

[0073] Among them, the trained second sound absorption and insulation performance prediction model refers to the second sound absorption and insulation performance prediction model that has been trained, which is used to predict the sound absorption and insulation performance of the sound insulation material using multi-layer composite materials. It has strong prediction ability and can accurately predict the sound absorption and insulation performance of the sound insulation material when facing new data of the sound insulation material.

[0074] Before obtaining the trained second sound absorption and insulation performance prediction model, it is necessary to construct the second sound absorption and insulation performance prediction model and train the constructed second sound absorption and insulation performance prediction model. Therefore, in the embodiment of the present invention, the second sound absorption and insulation performance prediction model adopts a one-dimensional convolutional neural network (1D Convolutional Neural Network, 1D-CNN), and the sound absorption and insulation performance of all material layers in the sound insulation material and the bonding parameters of all bonding layers are used as input data of the second sound absorption and insulation performance prediction model, and the sound absorption and insulation performance is used as output data. Acquire a sound insulation material with known sound absorption and insulation performance, use the sound absorption and insulation performance as the label of the corresponding sound insulation material, obtain the sound absorption and insulation performance of all material layers in the sound insulation material and the bonding parameters of all bonding layers as training samples, so that multiple training samples constitute a sample data set, and 80% of the sample data set is used as a training set, and the remaining 20% ​​is used as a test set.

[0075] The model parameters in the first sound absorption and insulation performance prediction model are set, for example: the convolution kernel size is set to 2*2, the step size is set to 2, the learning rate is set to 0.1, and the number of iterations is set to 50. After the model parameters are set, each training sample in the training set is input into the second sound absorption and insulation performance prediction model, and the prediction results of the sound absorption and insulation performance are output accordingly. The loss between the prediction results and the corresponding labels is calculated, and the model parameters of the second sound absorption and insulation performance prediction model are reversely corrected using the stochastic gradient descent method until the loss converges to obtain the trained second sound absorption and insulation performance prediction model.

[0076] Step S502: input the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers as input data to the trained second sound absorption and insulation performance prediction model, and output the prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized accordingly.

[0077] In the embodiment of the present invention, after the trained second sound absorption and insulation performance prediction model is obtained through step S501, the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers are input into the trained second sound absorption and insulation performance prediction model, and the prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is output accordingly.

[0078] In one embodiment, referring to Figure 6 , is a schematic diagram of a process for optimizing material parameters of a sound insulation material provided in Embodiment 5 of the present invention, such as Figure 6As shown, in step S202, determining the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized includes:

[0079] Step S601: for any parameter among the target parameters, the parameter of each material layer in the sound insulation material to be optimized is adjusted according to a preset ratio to obtain a corresponding target sound insulation material.

[0080] In the embodiment of the present invention, for any parameter in the target parameter, the parameter value of each material layer is adjusted by 5% based on the parameter, which may be increased by 5% or decreased by 5%, which is not required by the present invention, so as to obtain the adjusted sound insulation material to be optimized, and use it as the target sound insulation material. For example, assuming that the target parameter includes density, since each material layer of the sound insulation material to be optimized has obtained the corresponding density, therefore, for any material layer, the density of the material layer is adjusted by 5% to update the density of the material layer. Similarly, updating the density of all material layers is equivalent to updating the sound insulation material to be optimized, so that the sound insulation material to be optimized with the updated density is used as the target sound insulation material.

[0081] Step S602, respectively obtaining the sound absorption and insulation performance of each material layer in the target sound insulation material as the new sound absorption and insulation performance, and for any material layer, obtaining a first influence sensitivity characterizing the influence of the parameter on the sound absorption and insulation performance of the material layer according to the difference between the sound absorption and insulation performance of the material layer and the new sound absorption and insulation performance.

[0082] In the embodiment of the present invention, for any material layer in the target sound insulation material, the sound absorption and insulation performance of the material layer is obtained as the new sound absorption and insulation performance by using the above-mentioned trained first sound absorption and insulation performance prediction model, and then the ratio between the new sound absorption and insulation performance and the sound absorption and insulation performance of the material layer after adjustment is calculated, and the ratio is normalized, and the normalized ratio is used as the first influence sensitivity characterizing the influence of the parameter on the sound absorption and insulation performance of the material layer.

[0083] Similarly, the first influence sensitivity of the parameter on the sound absorption and insulation performance of each material layer of the target sound insulation material can be obtained.

[0084] Step S603: predicting the sound absorption and insulation performance of the target sound insulation material as a new prediction result, and obtaining a second influence sensitivity characterizing the influence of the material layer on the sound absorption and insulation performance of the sound insulation material to be optimized according to the difference between the new prediction result and the prediction result.

[0085] In the embodiment of the present invention, according to step S602, the sound absorption and insulation performance of each material layer of the target sound insulation material can be obtained, and then the sound absorption and insulation performance of the target sound insulation material is predicted using the trained second sound absorption and insulation performance prediction model, and the corresponding prediction result is obtained as a new prediction result.

[0086] The ratio between the new prediction result and the prediction result of the sound insulation material to be optimized before the parameter adjustment is calculated, and the ratio is normalized. The normalized ratio is used as the second influence sensitivity characterizing the influence of the material layer on the sound absorption and insulation performance of the sound insulation material to be optimized.

[0087] Step S604: determining the influence sensitivity of the parameter according to all the first influence sensitivities and the second influence sensitivities.

[0088] In the embodiment of the present invention, for the first influence sensitivity and the second influence sensitivity under the parameter, the product of all the first influence sensitivities and the second influence sensitivities is obtained, and the product is used as the influence sensitivity of the parameter.

[0089] In one embodiment, in step S202, determining the parameter to be optimized from the target parameter according to the influence sensitivity of each parameter includes:

[0090] According to the influence sensitivity of each parameter, the maximum influence sensitivity is obtained, and the parameter corresponding to the maximum influence sensitivity is used as the parameter to be optimized.

[0091] In the embodiment of the present invention, after obtaining the influence sensitivity of each parameter in the target parameter, the influence sensitivities of all parameters are compared to obtain the maximum influence sensitivity, and the parameter corresponding to the maximum influence sensitivity is used as the parameter to be optimized.

[0092] Corresponding to the material parameter optimization method of the above embodiment, Figure 7 A structural block diagram of a material parameter optimization device for a sound insulation material provided in a sixth embodiment of the present invention is shown. For ease of description, only the part related to the embodiment of the present invention is shown.

[0093] See also Figure 7 , the material parameter optimization device comprises:

[0094] The performance prediction module 71 is used to obtain the target parameters of each material layer in the sound insulation material to be optimized, wherein the target parameters include at least two parameters, and predict the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers;

[0095] A parameter adjustment determination module 72 is used to determine the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized, and determine the parameter to be optimized from the target parameters according to the influence sensitivity of each parameter;

[0096] A material updating module 73, configured to adjust the parameters to be optimized of each material layer in the sound insulation material to be optimized to obtain an adjusted sound insulation material when the prediction result does not meet the preset performance condition;

[0097] The optimization confirmation module 74 is used to use the adjusted sound insulation material as the sound insulation material to be optimized, return to the step of obtaining the target parameter of each material layer in the sound insulation material to be optimized, until it is detected that the prediction result meets the preset performance condition, and confirm that the corresponding adjusted sound insulation material is the optimized sound insulation material.

[0098] Optionally, the performance prediction module 71 includes:

[0099] The bonding feature extraction submodule is used to obtain the bonding parameters of the bonding layer between two adjacent material layers in the sound insulation material to be optimized before predicting the sound absorption and insulation performance prediction result corresponding to the sound insulation material to be optimized according to the target parameters of all material layers;

[0100] The prediction result acquisition submodule is used to predict the sound absorption and insulation performance prediction results of the sound insulation material to be optimized according to the target parameters of all material layers and the bonding parameters of all bonding layers.

[0101] Optionally, the prediction result acquisition submodule includes:

[0102] A first prediction unit is used to obtain the sound absorption and insulation performance of any material layer according to the target parameters of the material layer;

[0103] The second prediction unit is used to predict the sound absorption and insulation performance of the sound insulation material to be optimized according to the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers.

[0104] Optionally, the first prediction unit includes:

[0105] A first model acquisition subunit is used to acquire a trained first sound absorption and insulation performance prediction model;

[0106] The first model prediction subunit is used to input the target parameters of the material layer as input data into the trained first sound absorption and insulation performance prediction model, and correspondingly output the sound absorption and insulation performance of the material layer.

[0107] Optionally, the second prediction unit includes:

[0108] A second model acquisition subunit is used to acquire a trained second sound absorption and insulation performance prediction model;

[0109] The second model prediction subunit is used to input the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers as input data into the trained second sound absorption and insulation performance prediction model, and correspondingly output the prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized.

[0110] Optionally, the parameter adjustment determination module 72 includes:

[0111] A material adjustment submodule, for adjusting the parameter of each material layer in the sound insulation material to be optimized according to a preset ratio for any parameter in the target parameters, so as to obtain a target sound insulation material;

[0112] A first impact analysis submodule is used to obtain the sound absorption and insulation performance of each material layer in the target sound insulation material as the new sound absorption and insulation performance, and for any material layer, according to the difference between the sound absorption and insulation performance of the material layer and the new sound absorption and insulation performance, obtain a first impact sensitivity characterizing the influence of the parameter on the sound absorption and insulation performance of the material layer;

[0113] A second impact analysis submodule is used to predict the prediction result of the sound absorption and insulation performance of the target sound insulation material as a new prediction result, and obtain a second impact sensitivity characterizing the influence of the material layer on the sound absorption and insulation performance of the sound insulation material to be optimized according to the difference between the new prediction result and the prediction result;

[0114] The total impact analysis submodule is used to determine the impact sensitivity of the parameter according to all the first impact sensitivities and the second impact sensitivities.

[0115] Optionally, the parameter adjustment determination module 72 includes:

[0116] The optimization confirmation submodule is used to obtain the maximum influence sensitivity according to the influence sensitivity of each parameter, and use the parameter corresponding to the maximum influence sensitivity as the parameter to be optimized.

[0117] It should be noted that the information interaction, execution process, etc. between the above-mentioned modules, sub-modules, units, and sub-units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0118] Figure 8 This is a schematic diagram of the structure of a computer device provided in Embodiment 7 of the present invention. Figure 8 As shown, the computer device of this embodiment includes: at least one processor ( Figure 8Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned material parameter optimization method embodiments are implemented.

[0119] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 8 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0120] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0121] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory may also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0122] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0123] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0124] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0125] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0126] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for optimizing material parameters of a sound insulation material, characterized in that: The material parameter optimization method comprises: Obtaining target parameters of each material layer in the sound insulation material to be optimized, wherein the target parameters include at least two parameters, and predicting the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers; Determine the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized, and determine the parameter to be optimized from the target parameters according to the influence sensitivity of each parameter; When the prediction result does not meet the preset performance condition, adjusting the parameters to be optimized of each material layer in the sound insulation material to be optimized to obtain an adjusted sound insulation material; The adjusted sound insulation material is used as the sound insulation material to be optimized, and the step of obtaining the target parameter of each material layer in the sound insulation material to be optimized is returned to execute until it is detected that the prediction result meets the preset performance condition, and the corresponding adjusted sound insulation material is confirmed to be the optimized sound insulation material.

2. The material parameter optimization method according to claim 1, characterized in that: Before predicting the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers, the method includes: Obtaining bonding parameters of a bonding layer between two adjacent material layers in the sound insulation material to be optimized; The method of predicting the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers includes: According to the target parameters of all material layers and the bonding parameters of all bonding layers, a prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is predicted.

3. The material parameter optimization method according to claim 2, characterized in that: The prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is obtained according to the target parameters of all material layers and the bonding parameters of all bonding layers, including: For any material layer, according to the target parameters of the material layer, the sound absorption and insulation performance of the material layer is obtained; According to the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers, a prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is predicted.

4. The material parameter optimization method according to claim 3, characterized in that: The step of obtaining the sound absorption and insulation performance of the material layer according to the target parameters of the material layer includes: Obtain the trained first sound absorption and insulation performance prediction model; The target parameters of the material layer are input as input data to the trained first sound absorption and insulation performance prediction model, and the sound absorption and insulation performance of the material layer is output accordingly.

5. The material parameter optimization method according to claim 3, characterized in that: The prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is obtained according to the sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers, including: Obtaining the trained second sound absorption and insulation performance prediction model; The sound absorption and insulation performance of all material layers and the bonding parameters of all bonding layers are input as input data to the trained second sound absorption and insulation performance prediction model, and the prediction result of the sound absorption and insulation performance of the sound insulation material to be optimized is output accordingly.

6. The material parameter optimization method according to claim 3, characterized in that: The step of determining the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized comprises: For any parameter among the target parameters, the parameter of each material layer in the sound insulation material to be optimized is adjusted according to a preset ratio to obtain a target sound insulation material; Acquire the sound absorption and insulation performance of each material layer in the target sound insulation material as the new sound absorption and insulation performance, and for any material layer, acquire a first influence sensitivity characterizing the influence of the parameter on the sound absorption and insulation performance of the material layer according to the difference between the sound absorption and insulation performance of the material layer and the new sound absorption and insulation performance; Predicting a prediction result of the sound absorption and insulation performance of the target sound insulation material as a new prediction result, and obtaining a second influence sensitivity characterizing the influence of the material layer on the sound absorption and insulation performance of the sound insulation material to be optimized according to a difference between the new prediction result and the prediction result; The influence sensitivity of the parameter is determined according to all the first influence sensitivities and the second influence sensitivities.

7. The material parameter optimization method according to claim 1, characterized in that: Determining the parameters to be optimized from the target parameters according to the influence sensitivity of each parameter includes: According to the influence sensitivity of each parameter, the maximum influence sensitivity is obtained, and the parameter corresponding to the maximum influence sensitivity is used as the parameter to be optimized.

8. A material parameter optimization device for sound insulation material, characterized in that: The material parameter optimization device comprises: A performance prediction module, used to obtain target parameters of each material layer in the sound insulation material to be optimized, wherein the target parameters include at least two parameters, and predict the sound absorption and insulation performance of the sound insulation material to be optimized according to the target parameters of all material layers; A parameter adjustment determination module, used to determine the influence sensitivity of each parameter according to the influence of the change of each parameter in the target parameters on the sound absorption and insulation performance of the sound insulation material to be optimized, and determine the parameter to be optimized from the target parameters according to the influence sensitivity of each parameter; A material updating module, configured to adjust the parameters to be optimized of each material layer in the sound insulation material to be optimized to obtain an adjusted sound insulation material when the prediction result does not meet the preset performance condition; an optimization confirmation module, configured to use the adjusted sound insulation material as the sound insulation material to be optimized, return to execute the step of obtaining the target parameter of each material layer in the sound insulation material to be optimized, until it is detected that the prediction result meets the preset performance condition, and confirm that the corresponding adjusted sound insulation material is the optimized sound insulation material.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the material parameter optimization method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the material parameter optimization method according to any one of claims 1 to 7 is implemented.

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