Soundproof parameter optimization method and device, equipment, medium and program product

Abstract

The invention relates to a sound insulation parameter optimization method and device, equipment, a medium and a program product, and relates to the technical field of data processing. The method at least solves the technical problems that in the prior art, the optimization effect on sound insulation parameters of a vehicle is poor, and the efficiency of determining the sound insulation parameters of the vehicle is low, and comprises the steps that multiple sets of first parameter values of the sound insulation material are determined based on at least one type of parameter of the sound insulation material of the vehicle and the first parameter range of each type of parameter; determining a whole vehicle acoustic performance parameter corresponding to each group of first parameter values in the plurality of groups of first parameter values, wherein the whole vehicle acoustic performance parameter is used for indicating the sound insulation performance of the vehicle; on the basis of the multiple sets of first parameter values and the whole vehicle acoustic performance parameters corresponding to each set of first parameter values, first optimization parameter values of at least one type of parameters are determined, the first optimization parameter values are parameter values which are determined on the basis of an agent model and enable the sound insulation performance of the vehicle to be optimal, and the agent model is obtained on the basis of the multiple sets of first parameter values.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and specifically to a method, device, equipment, medium and program product for optimizing sound insulation parameters. Background Art

[0002] With the continuous development of vehicle technology, users have higher and higher requirements for vehicle performance and comfort, and an important factor affecting comfort is the vehicle's sound insulation performance. In particular, the vehicle's sound insulation performance against high-frequency noise determines the quality positioning of the vehicle. When the vehicle's sound insulation performance is poor, users will feel high-frequency noise, making the user's driving experience poor.

[0003] The vehicle's sound insulation system is composed of sheet metal, glass and acoustic package, which have a large number of parameters and there are correlations between different parameters. Therefore, optimizing the parameters of sheet metal, glass and acoustic package, while controlling the weight and cost of the vehicle, is a particularly complex and important technical issue. In addition, different vehicles have different design requirements for sound insulation performance according to their own quality positioning. Therefore, the effect of optimizing the vehicle's sound insulation parameters is poor and the efficiency of determining the vehicle's sound insulation parameters is low. Summary of the invention

[0004] The present application provides a method, device, equipment, medium and program product for optimizing sound insulation parameters, so as to at least solve the technical problems in the related art that the optimization effect of the sound insulation parameters of the vehicle is poor and the efficiency of determining the sound insulation parameters of the vehicle is low. The technical solution of the present application is as follows:

[0005] According to a first aspect of the present application, there is provided a method for optimizing sound insulation parameters, the method comprising: determining multiple groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the vehicle's sound insulation material and a first parameter range of each type parameter; a group of first parameter values ​​comprises parameter values ​​of each type parameter of at least one type parameter; parameter values ​​of at least one type parameter in different groups of parameter values ​​are different; determining whole vehicle acoustic performance parameters corresponding to each group of first parameter values ​​in the multiple groups of first parameter values, the whole vehicle acoustic performance parameters being used to indicate the sound insulation performance of the vehicle; determining a first optimized parameter value of at least one type parameter based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, the first optimized parameter value being a parameter value that optimizes the sound insulation performance of the vehicle determined based on a proxy model, the proxy model being obtained based on the multiple groups of first parameter values.

[0006] According to the above technical means, the present application can first determine multiple groups of first parameter values ​​corresponding to at least one type of parameter of the vehicle's sound insulation material, and then determine the whole vehicle acoustic performance parameters indicating the sound insulation performance of the vehicle corresponding to each group of first parameter values ​​through the multiple groups of first parameter values. Based on this, the corresponding sound insulation performance can be determined when the parameter values ​​of at least one type of parameter of the vehicle's sound insulation material are different. In this way, based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, an agent model can be constructed to determine the corresponding whole vehicle acoustic performance parameters when the sound insulation performance of the vehicle is optimal. And further determine the parameter value corresponding to the whole vehicle acoustic performance parameter as the first optimized parameter value corresponding to at least one type of parameter of the sound insulation material. Thereby, the effect of optimizing the sound insulation parameters of the vehicle and the efficiency of determining the sound insulation parameters of the vehicle can be improved.

[0007] In a possible implementation, the above-mentioned determination of the whole vehicle acoustic performance parameters corresponding to each group of first parameter values ​​in the multiple groups of first parameter values ​​includes: determining the sound pressure corresponding to any group of first parameter values ​​in the multiple groups of first parameter values; based on the sound pressure corresponding to any group of first parameter values, determining the whole vehicle acoustic performance parameters corresponding to any group of first parameter values.

[0008] According to the above technical means, the present application can first determine the sound pressure corresponding to any group of first parameter values ​​when at least one type parameter of the vehicle's sound insulation material is any group of first parameter values ​​in multiple groups of first parameter values. Since sound pressure cannot intuitively represent the sound insulation performance of the vehicle, it is necessary to determine the whole vehicle acoustic performance parameters corresponding to any group of first parameter values ​​based on the sound pressure corresponding to any group of first parameter values, thereby indicating the sound insulation performance of the vehicle through the whole vehicle acoustic performance parameters. In this way, the sound insulation performance of the vehicle can be accurately indicated through the whole vehicle acoustic performance parameters, thereby improving the accuracy of determining the sound insulation parameters of the vehicle.

[0009] In a possible implementation, the above-mentioned determination of the first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values ​​includes: for any group of first parameter values ​​among the multiple groups of first parameter values, determining the weight cost parameter of the vehicle corresponding to any group of first parameter values, the weight cost parameter includes at least one of the following: whole vehicle weight, whole vehicle cost; based on multiple groups of first parameter values, the whole vehicle acoustic performance parameters corresponding to each group of first parameter values ​​and the weight cost parameter corresponding to each group of first parameter values, determining the first optimized parameter value of at least one type of parameter, the first optimized parameter value is any of the following: the parameter value that optimizes the sound insulation performance of the vehicle, the parameter value that minimizes the whole vehicle weight, and the parameter value that minimizes the whole vehicle cost of the vehicle.

[0010] According to the above technical means, the present application can also determine the vehicle weight and / or vehicle cost of the vehicle when at least one type parameter of the vehicle's sound insulation material is any one of a plurality of first parameter value groups. Thus, when determining the first optimized parameter value of at least one type parameter, the plurality of first parameter values, the vehicle acoustic performance parameters corresponding to each first parameter value group, and the vehicle weight and / or vehicle cost corresponding to each first parameter value group are comprehensively considered. In this way, according to the first optimized parameter value of at least one type parameter determined, the vehicle can achieve the requirements of optimal sound insulation performance, minimum vehicle weight, or minimum vehicle cost. Thus, the diversity of determining the vehicle's sound insulation parameters can be improved.

[0011] In a possible implementation, the above-mentioned determining the whole vehicle acoustic performance parameters corresponding to any group of first parameter values ​​based on the sound pressure corresponding to any group of first parameter values ​​includes: determining multiple operating states of the vehicle; for any group of first parameter values ​​among the multiple groups of first parameter values, based on the sound pressure corresponding to any group of first parameter values, determining the whole vehicle acoustic performance parameters of the vehicle under the target operating state corresponding to any group of first parameter values, wherein different operating states correspond to different vehicle acoustic performance parameters, and the target operating state is any operating state among the multiple operating states.

[0012] According to the above technical means, the present application can also comprehensively consider the running state of the vehicle, so that when the acoustic performance parameters of the whole vehicle corresponding to different running states are different, when at least one type parameter of the sound insulation material of the vehicle is any one of the multiple groups of first parameter values, the whole vehicle acoustic performance parameters corresponding to the vehicle under different running states are determined. In this way, the running state of the vehicle can be comprehensively considered to further improve the accuracy of determining the sound insulation parameters of the vehicle.

[0013] In a possible implementation, the above-mentioned determining the first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values ​​includes: determining the second optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values; determining the second parameter range corresponding to each type of parameter of at least one type of parameter based on the second optimized parameter value, when the error between the whole vehicle acoustic performance parameters and / or weight cost parameters corresponding to the second optimized parameter value and the preset parameter value is greater than the preset error, the second parameter range is included in the first parameter range; determining multiple groups of second parameter values ​​of the sound insulation material based on the second parameter range corresponding to each type of parameter; determining the first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and multiple groups of second parameter values, the accuracy of the first optimized parameter value is greater than the accuracy of the second optimized parameter value.

[0014] According to the above technical means, when determining the second optimized parameter value corresponding to the vehicle when the sound insulation performance of the vehicle is optimal based on multiple groups of first parameter values ​​and the vehicle acoustic performance parameters corresponding to each group of first parameter values, the present application can first determine whether the error between the vehicle acoustic performance parameters and / or weight cost parameters corresponding to the second optimized parameter value and the preset parameter value is greater than the preset error. Therefore, when the error is greater than the preset error, multiple groups of second parameter values ​​of the sound insulation material can be further determined, and based on the multiple groups of first parameter values ​​and the multiple groups of second parameter values, the first optimized parameter value of at least one type of parameter can be re-determined, thereby obtaining an optimized parameter value with better sound insulation performance. In this way, the effect of optimizing the sound insulation parameters of the vehicle can be further improved.

[0015] In a possible embodiment, before determining multiple groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the sound insulation material of the vehicle and the first parameter range of each type parameter, the method also includes: determining the identification of all type parameters of the sound insulation material deployed at different locations of the vehicle, the identification of all type parameters is used to indicate at least one of the following: deployment area, material name, material deployment level, parameter type; determining at least one type parameter from all type parameters of the sound insulation material.

[0016] According to the above technical means, the present application can first determine the identification of all type parameters of the sound insulation materials deployed at different positions of the vehicle, and determine at least one type parameter from all type parameters of the sound insulation materials. Thus, based on the information indicated by the identification of the type parameter, at least one type parameter can be accurately determined.

[0017] In a possible implementation, the above-mentioned determining at least one type parameter from all type parameters of the sound insulation material includes: determining a first type parameter from all type parameters of the sound insulation material; in the case where there are mutually related type parameters in the first type parameters, determining a second type parameter and a third type parameter from the first type parameters, the third type parameter being an associated parameter of the second type parameter; and determining the type parameters in the first type parameters except the third type parameter as at least one type parameter.

[0018] According to the above technical means, the present application can comprehensively consider the association relationship between type parameters and accurately determine at least one type parameter. Since the parameter value of a type parameter changes, the parameter values ​​of other associated parameters will change, so it is necessary to comprehensively consider the association relationship between type parameters so that the determined at least one type parameter does not include type parameters with associated relationships, thereby determining at least one type parameter more accurately.

[0019] In a possible implementation, the above-mentioned identification of all type parameters of the sound insulation materials deployed at different positions of the vehicle includes: parametric modeling of the sound insulation materials deployed at different positions of the vehicle to determine all type parameters of the sound insulation materials; and based on preset naming rules, determining the identification of all type parameters of the sound insulation materials.

[0020] According to the above technical means, the present application can perform parametric modeling on the sound insulation materials deployed at different locations of the vehicle and determine all types of parameters of the sound insulation materials. Then, based on the preset naming rules, the identification of all types of parameters of the sound insulation materials is determined. In this way, by unifying the identification of parameters, the efficiency of subsequent data processing can be improved.

[0021] According to a second aspect provided by the present application, a device for optimizing sound insulation parameters is provided, and the device for optimizing sound insulation parameters includes: a processing module; a processing module, used to determine multiple groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the vehicle's sound insulation material and a first parameter range of each type parameter; a group of first parameter values ​​includes a parameter value of each type parameter in at least one type parameter; the parameter value of at least one type parameter in different groups of parameter values ​​is different; the processing module is also used to determine a whole vehicle acoustic performance parameter corresponding to each group of first parameter values ​​in the multiple groups of first parameter values, and the whole vehicle acoustic performance parameter is used to indicate the sound insulation performance of the vehicle; the processing module is also used to determine a first optimized parameter value of at least one type parameter based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameter corresponding to each group of first parameter values, the first optimized parameter value is a parameter value that optimizes the sound insulation performance of the vehicle determined based on a proxy model, and the proxy model is obtained based on the multiple groups of first parameter values.

[0022] In one possible implementation, the processing module is specifically used to determine the sound pressure corresponding to any one group of first parameter values ​​among multiple groups of first parameter values; the processing module is specifically used to determine the whole vehicle acoustic performance parameters corresponding to any one group of first parameter values ​​based on the sound pressure corresponding to any one group of first parameter values.

[0023] In one possible implementation, the processing module is specifically used to determine, for any one of the multiple groups of first parameter values, a weight cost parameter of the vehicle corresponding to any one of the multiple groups of first parameter values, the weight cost parameter including at least one of the following: whole vehicle weight, whole vehicle cost; the processing module is specifically used to determine a first optimized parameter value of at least one type of parameter based on the multiple groups of first parameter values, the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, and the weight cost parameter corresponding to each group of first parameter values, the first optimized parameter value being any of the following: a parameter value that optimizes the sound insulation performance of the vehicle, a parameter value that minimizes the whole vehicle weight, and a parameter value that minimizes the whole vehicle cost.

[0024] In one possible implementation, the processing module is specifically used to determine multiple operating states of the vehicle; the processing module is specifically used to determine, for any group of first parameter values ​​among the multiple groups of first parameter values, the whole vehicle acoustic performance parameters of the vehicle corresponding to any group of first parameter values ​​in the target operating state based on the sound pressure corresponding to any group of first parameter values, different operating states correspond to different whole vehicle acoustic performance parameters, and the target operating state is any operating state among the multiple operating states.

[0025] In one possible implementation, a processing module is specifically used to determine a second optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and vehicle acoustic performance parameters corresponding to each group of first parameter values; a processing module is specifically used to determine a second parameter range corresponding to each type of parameter of at least one type of parameter based on the second optimized parameter value when an error between the vehicle acoustic performance parameters and / or weight cost parameters corresponding to the second optimized parameter value and a preset parameter value is greater than a preset error, and the second parameter range is included in the first parameter range; a processing module is specifically used to determine multiple groups of second parameter values ​​of sound insulation materials based on the second parameter range corresponding to each type of parameter; a processing module is specifically used to determine a first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and multiple groups of second parameter values, and the accuracy of the first optimized parameter value is greater than the accuracy of the second optimized parameter value.

[0026] In one possible embodiment, the processing module is also used to determine the identification of all type parameters of the sound insulation materials deployed at different locations of the vehicle, and the identification of all type parameters is used to indicate at least one of the following: deployment area, material name, material deployment level, and parameter type; the processing module is also used to determine at least one type parameter from all type parameters of the sound insulation materials.

[0027] In one possible implementation, the processing module is specifically used to determine the first type parameter from all type parameters of the sound insulation material; the processing module is specifically used to determine the second type parameter and the third type parameter from the first type parameter when there are mutually related type parameters in the first type parameter, and the third type parameter is an associated parameter of the second type parameter; the processing module is specifically used to determine the type parameters in the first type parameter except the third type parameter as at least one type parameter.

[0028] In one possible implementation, the processing module is specifically used to perform parametric modeling on the sound insulation materials deployed at different locations of the vehicle to determine all type parameters of the sound insulation materials; the processing module is specifically used to determine the identification of all type parameters of the sound insulation materials based on preset naming rules.

[0029] According to the third aspect provided by the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory is used to store one or more programs, and the one or more programs include computer execution instructions. When the electronic device is running, the processor executes the computer execution instructions stored in the memory, and the electronic device executes the method of the above-mentioned first aspect and any possible implementation method thereof.

[0030] According to the fourth aspect provided by the present application, a computer-readable storage medium is provided. When the computer instructions stored in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation method thereof.

[0031] According to the fifth aspect provided by the present application, a computer program product is provided, the computer program product comprising computer instructions, when the computer instructions are executed on an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation manner thereof.

[0032] Therefore, the above technical features of the present application have the following beneficial effects:

[0033] (1) The present application can first determine multiple groups of first parameter values ​​corresponding to at least one type of parameter of the vehicle's sound insulation material, and then determine the whole vehicle acoustic performance parameters indicating the sound insulation performance of the vehicle corresponding to each group of first parameter values ​​through the multiple groups of first parameter values. Based on this, the corresponding sound insulation performance can be determined when the parameter values ​​of at least one type of parameter of the vehicle's sound insulation material are different. In this way, based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, an agent model can be constructed to determine the corresponding whole vehicle acoustic performance parameters when the sound insulation performance of the vehicle is optimal. And further determine the parameter value corresponding to the whole vehicle acoustic performance parameter as the first optimized parameter value corresponding to at least one type of parameter of the sound insulation material. Thereby, the effect of optimizing the sound insulation parameters of the vehicle and the efficiency of determining the sound insulation parameters of the vehicle can be improved.

[0034] (2) The present application can first determine the sound pressure corresponding to any one of the multiple groups of first parameter values ​​when at least one type parameter of the vehicle's sound insulation material is any one of the multiple groups of first parameter values. Since sound pressure cannot intuitively represent the sound insulation performance of the vehicle, it is necessary to determine the whole vehicle acoustic performance parameters corresponding to any one of the first parameter values ​​based on the sound pressure corresponding to any one of the first parameter values, thereby indicating the sound insulation performance of the vehicle through the whole vehicle acoustic performance parameters. In this way, the sound insulation performance of the vehicle can be accurately indicated through the whole vehicle acoustic performance parameters, thereby improving the accuracy of determining the sound insulation parameters of the vehicle.

[0035] (3) The present application can also determine the vehicle weight and / or vehicle cost of the vehicle when at least one type parameter of the vehicle's sound insulation material is any one of a plurality of first parameter value groups. Thus, when determining the first optimized parameter value of at least one type parameter, the plurality of first parameter values, the vehicle acoustic performance parameters corresponding to each first parameter value group, and the vehicle weight and / or vehicle cost corresponding to each first parameter value group are comprehensively considered. In this way, the vehicle can achieve the requirements of optimal sound insulation performance, minimum vehicle weight, or minimum vehicle cost based on the first optimized parameter value of at least one type parameter determined. Thus, the diversity of determining the vehicle's sound insulation parameters can be improved.

[0036] (4) The present application can also comprehensively consider the running state of the vehicle, so that when the acoustic performance parameters of the whole vehicle corresponding to different running states are different, when at least one type parameter of the sound insulation material of the vehicle is any one of the multiple groups of first parameter values, the acoustic performance parameters of the whole vehicle corresponding to the vehicle under different running states are determined. In this way, the running state of the vehicle can be comprehensively considered to further improve the accuracy of determining the sound insulation parameters of the vehicle.

[0037] (5) The present application can determine the second optimized parameter value corresponding to the vehicle when the sound insulation performance of the vehicle is optimal based on multiple groups of first parameter values ​​and the vehicle acoustic performance parameters corresponding to each group of first parameter values, and first determine whether the error between the vehicle acoustic performance parameters and / or weight cost parameters corresponding to the second optimized parameter value and the preset parameter value is greater than the preset error. Therefore, when the error is greater than the preset error, multiple groups of second parameter values ​​of the sound insulation material can be further determined, and based on the multiple groups of first parameter values ​​and the multiple groups of second parameter values, the first optimized parameter value of at least one type of parameter can be re-determined, so as to obtain an optimized parameter value with better sound insulation performance. In this way, the effect of optimizing the sound insulation parameters of the vehicle can be further improved.

[0038] (6) The present application can first determine the identifiers of all type parameters of the sound insulation materials deployed at different locations of the vehicle, and determine at least one type parameter from all type parameters of the sound insulation materials. Thus, based on the information indicated by the identifier of the type parameter, at least one type parameter can be accurately determined.

[0039] (7) The present application can comprehensively consider the association relationship between type parameters and accurately determine at least one type parameter. Since a change in the parameter value of a type parameter will cause a change in the parameter values ​​of other associated parameters, it is necessary to comprehensively consider the association relationship between type parameters so that the determined at least one type parameter does not include type parameters with associated relationships, thereby determining at least one type parameter more accurately.

[0040] (8) The present application can perform parametric modeling on the sound insulation materials deployed at different locations of the vehicle to determine all types of parameters of the sound insulation materials. Then, based on the preset naming rules, the identification of all types of parameters of the sound insulation materials is determined. In this way, by unifying the identification of parameters, the efficiency of subsequent data processing can be improved.

[0041] It should be noted that the technical effects brought about by any implementation method in the second to fifth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0042] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0044] Figure 1 is a structural schematic diagram of a sound insulation parameter optimization system according to an exemplary embodiment;

[0045] Figure 2 is a flow chart of a method for optimizing sound insulation parameters according to an exemplary embodiment;

[0046] Figure 3 is a schematic diagram showing the distribution area of ​​a sound insulation material in a vehicle according to an exemplary embodiment;

[0047] Figure 4 is a schematic diagram of a user interface according to an exemplary embodiment;

[0048] Figure 5 is another schematic diagram of a user interface according to an exemplary embodiment;

[0049] Figure 6 is another schematic diagram of a user interface according to an exemplary embodiment;

[0050] Figure 7 is a flow chart of another method for optimizing sound insulation parameters according to an exemplary embodiment;

[0051] Figure 8 is a flow chart of another method for optimizing sound insulation parameters according to an exemplary embodiment;

[0052] Fig. 9 is a schematic diagram of a vehicle structure according to an exemplary embodiment;

[0053] Fig.10 is a flow chart of another method for optimizing sound insulation parameters according to an exemplary embodiment;

[0054] Fig.11 is a flow chart of another method for optimizing sound insulation parameters according to an exemplary embodiment;

[0055] Fig.12 is a block diagram of a device for optimizing sound insulation parameters according to an exemplary embodiment;

[0056] Fig.13 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0057] In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0058] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0059] The sound insulation parameter optimization method provided in the embodiment of the present application can be applied to the sound insulation parameter optimization system. Figure 1 FIG. 1 shows a schematic diagram of the structure of the optimization system of the sound insulation parameters. Figure 1 As shown, the sound insulation parameter optimization system 10 includes: a host computer 11 and a vehicle 12, and the host computer 11 can be an electronic device.

[0060] The host computer 11 is used to determine multiple groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the sound insulation material of the vehicle 12 and the first parameter range of each type parameter; a group of first parameter values ​​includes parameter values ​​of each type parameter in at least one type parameter; and parameter values ​​of at least one type parameter in different groups of parameter values ​​are different.

[0061] The host computer 11 is used to determine the whole vehicle acoustic performance parameters corresponding to each group of first parameter values ​​in the multiple groups of first parameter values, and the whole vehicle acoustic performance parameters are used to indicate the sound insulation performance of the vehicle 12.

[0062] The host computer 11 is used to determine a first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values. The first optimized parameter value is a parameter value that optimizes the sound insulation performance of the vehicle 12 based on the proxy model. The proxy model is obtained based on multiple groups of first parameter values.

[0063] For ease of understanding, the optimization method of the sound insulation parameters provided in this application is specifically introduced below with reference to the accompanying drawings.

[0064] Figure 2 is a flow chart of a method for optimizing sound insulation parameters according to an exemplary embodiment, which is applied to electronic equipment, such as Figure 2 As shown, the optimization method of the sound insulation parameters includes the following S201-S203:

[0065] S201. Determine a plurality of groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the sound insulation material of the vehicle and a first parameter range of each type parameter.

[0066] A group of first parameter values ​​includes parameter values ​​of each type of parameter in at least one type of parameter, and parameter values ​​of at least one type of parameter in different groups of parameter values ​​are different.

[0067] It should be noted that a type parameter may correspond to multiple parameter values, so a group of parameter values ​​may include multiple parameter values. The different parameter values ​​of at least one type parameter in the above different groups of parameter values ​​can be understood as: at least one parameter value in the different groups of parameter values ​​is different.

[0068] Optionally, the sound insulation material of the vehicle can be the following materials: foam material, felt material, sheet metal and other isotropic materials. Among them, at least one type of parameter of the sound insulation material can include parameters of the following parameters: parameters of foam materials include: density, flow resistance, porosity, tortuosity, viscosity characteristic length, temperature characteristic length, damping, Young's modulus, Poisson's ratio, etc., parameters of felt materials include: density, flow resistance, porosity, tortuosity, viscosity characteristic length, temperature characteristic length, etc., parameters of isotropic materials include: density, Young's modulus, shear modulus, etc., NCT related parameters include: foam thickness, felt thickness, air layer thickness, air layer damping, sheet metal layer thickness, sheet metal layer damping, mass layer density, etc., parameters of sheet metal include: thickness, material type, and acoustic package coverage, etc.

[0069] Optionally, the parameter value of each type of parameter in at least one type of parameter may be adjusted respectively to obtain multiple sets of first parameter values ​​of the sound insulation material. Specifically, the parameter value of each type of parameter in at least one type of parameter may be adjusted respectively by a parameter change module.

[0070] In some embodiments, in a method for optimizing sound insulation parameters provided by an embodiment of the present application, before the above step S201, the method may also include: determining the identification of all type parameters of sound insulation materials deployed at different locations of the vehicle, and determining at least one type parameter from all type parameters of the sound insulation materials.

[0071] Among them, the identifiers of all type parameters are used to indicate at least one of the following: deployment area, material name, material deployment level, and parameter type.

[0072] In the embodiment of the present application, the present application can first determine the identifiers of all type parameters of the sound insulation materials deployed at different locations of the vehicle, and determine at least one type parameter from all type parameters of the sound insulation materials. Thus, based on the information indicated by the identifier of the type parameter, at least one type parameter can be accurately determined.

[0073] In some embodiments, the identification of all type parameters of sound insulation materials deployed at different locations of the vehicle is determined, including: parametric modeling of the sound insulation materials deployed at different locations of the vehicle to determine all type parameters of the sound insulation materials; based on preset naming rules, determining the identification of all type parameters of the sound insulation materials.

[0074] Optionally, the preset analysis model can be a statistical energy analysis (SEA) model, which can be based on the vehicle SEA model to perform parameterized modeling on all types of parameters of the sound insulation material and define the initial state (i.e., all types of parameters) for subsequent parameter optimization.

[0075] It should be noted that based on the whole vehicle SEA model, since the key transmission paths of the vehicle body have different acoustic propagation characteristics, different parts partitions can be used, that is, to determine the area range to which each sound insulation material belongs.

[0076] For example, Figure 3 As shown in the figure, based on the vehicle SEA model, the distribution area of ​​the sound insulation material in the vehicle can be divided into: front hood sound insulation pad, roof lining, front carpet, front wall panel sound insulation pad, engine compartment sound insulation pad, trunk carpet, rear motor sound insulation pad, rear wheel cover, etc. It can also include: rear floor, front wall panel, front end of side trim, rear wheel hub sound insulation pad, rear wheel hub, storage board, cotton felt on the left and right sides of the front wall, etc.

[0077] Optionally, when different sound insulation materials are deployed in different areas, the corresponding component levels and specific parameter values ​​are different. The "parameterized list" can indicate the corresponding component levels and parameter initial values ​​(such as thickness) of each sound insulation material when deployed in different areas, as well as the parameter ranges (i.e., the upper and lower limits of the parameter values) of each type of parameter. The parameter types of various types of parameters can also be identified by the color of the text.

[0078] Specifically, the parameter types may be: optimizable parameters, non-optimizable parameters, independent optimization parameters within the same region, shared optimization parameters within the same region, shared optimization parameters across regions, etc.

[0079] Optionally, the whole vehicle SEA model can be parametrically modeled based on the preset naming rules and the "parametric list" to determine the identification of all type parameters of the sound insulation material. The preset naming rules can distinguish each type parameter for easy indexing and identification.

[0080] Exemplarily, the naming rule of isotropic material property names can be: Opt1_Steel 0.6mm_Case1, where Opt1 represents the material thickness identifier (the same material and thickness share one), Steel 0.6mm represents the material thickness name, and Case1 represents the area range where the sound insulation material is deployed (such as the front wall panel, floor, etc.). The naming rule of isotropic material name can be: Opt2_Stee2_Case2, where Opt2 represents the material identification, corresponding to the attribute, Stee2 represents the material name, Case2 represents the regional scope of sound insulation material deployment, corresponding to the attribute name (except when shared); the naming rule of noise control treatment (NCT) attribute name can be: Opt3_Dash_Case3, where Opt3 represents the identification of the regional scope of sound insulation material deployment (such as front wall panel sound insulation pad, front carpet, etc.), Dash represents the regional scope of sound insulation material deployment, and Case3 represents the position within the regional scope of sound insulation material deployment (such as the front, middle and rear of the front carpet, etc.); the naming rule of fiber and foam can be: Opt4_Felt_80kg / m 3 (dash)_Layer2_Case4, where Opt4 indicates the area where the sound insulation material is deployed, and corresponds to Felt_80kg / m 3 (dash) indicates sound insulation and corresponds to NCT (except when used together).

[0081] Based on this, an XML file containing the identification of all type parameters of sound insulation materials corresponding to each working condition can be exported from the whole vehicle SEA model.

[0082] In the embodiment of the present application, the present application can perform parametric modeling on the sound insulation materials deployed at different locations of the vehicle to determine all types of parameters of the sound insulation materials. Then, based on the preset naming rules, the identification of all types of parameters of the sound insulation materials is determined. In this way, by unifying the identification of parameters, the efficiency of subsequent data processing can be improved.

[0083] In some embodiments, at least one type parameter is determined from all type parameters of the sound insulation material, including: determining a first type parameter from all type parameters of the sound insulation material; in the case where there are mutually related type parameters in the first type parameters, determining a second type parameter and a third type parameter from the first type parameters, the third type parameter being an associated parameter of the second type parameter; and determining the type parameters in the first type parameters except the third type parameter as at least one type parameter.

[0084] Optionally, after determining the first type of parameters from all the type parameters of the sound insulation material, taking the variation law between density and acoustic parameters (such as porosity, flow resistance, tortuosity, viscosity characteristic length, temperature characteristic length) as an example, the second type of parameters and the third type of parameters can be determined from the first type of parameters, for example, the correlation between density and acoustic material parameters is determined to achieve the correlation between density and acoustic material parameters. Specifically, the conversion can be completed through Python coding to form an "acoustic parameter conversion code".

[0085] Specifically, the acoustic parameter conversion formula is shown in Formula 1:

[0086]

[0087] Where n is the density ratio, ρ is the density after change, ρ0 is the initial density, σ is the flow resistance after change, σ0 is the initial flow resistance, φ is the porosity after change, φ0 is the initial porosity, α ∞ is the distortion after the change, α ∞0 is the initial distortion, Λ is the viscosity characteristic length after the change, Λ0 is the initial viscosity characteristic length, Λ, is the temperature characteristic length after the change, and Λ,0 is the initial temperature characteristic length.

[0088] In this way, the correlation between density and acoustic material parameters can be determined based on Formula 1 to determine at least one type parameter.

[0089] The present application can comprehensively consider the association relationship between type parameters and accurately determine at least one type parameter. Since a change in the parameter value of a type parameter will cause a change in the parameter values ​​of other associated parameters, it is necessary to comprehensively consider the association relationship between type parameters so that the determined at least one type parameter does not include type parameters with associated relationships, thereby determining at least one type parameter more accurately.

[0090] Optionally, a "Design Parameter Extraction" program can be written based on Python and VA One file data structure. The program input file is the xml file obtained from the parameterized vehicle SEA model.

[0091] For example, Figure 4 As shown, a graphical user interface (GUI) can also be designed and developed to complete the check input of the optimization parameter type (i.e., the identification of at least one first type parameter), so as to output the parameterized temporary file tpl file and the design parameter list file through the program.

[0092] It should be noted that the temporary file tpl file replaces the corresponding type parameters in the original xml file with temporary variables in the format of "$(parameter name)$". The identification of the type parameters follows the above naming rules for easy indexing and identification. The design parameter list file is a csv file and supporting txt file classified by the type parameters of the sound insulation material, NCT related thickness and surface density parameters, coverage, isotropic material thickness, isotropic material name, etc.

[0093] Optionally, based on the identification of at least one first type parameter, at least one first type parameter is preprocessed. Specifically, a "Design Parameter Classification" program can be written based on Python data processing. The program will automatically generate a "Parameter Definition.xlsx" configuration file. The user inputs the corresponding parameters (constraints) according to the actual engineering needs and process requirements. The program will sort, filter, reorganize, associate variables, reset the upper and lower limits of variables, classify text and other data processing based on the input content to generate a new parameter list file, and classify it into different folders.

[0094] It should be noted that the "Parameter Definition.xlsx" configuration file is filled in based on the parameterized list and is the input interface of the data processing program. The "Parameter Definition.xlsx" configuration file contains: density, NCT thickness or surface density, MAT thickness, NCT related items, NCT non-optimized parameter items, etc.

[0095] Optionally, the "Design Parameter Classification" program determines the required boundary input files, calculation configuration file groups, output path folders, etc. based on the initial files (i.e. temporary files tpl files, csv files and supporting txt files) and the parameters entered by the user, and completes all pre-processing related work and checks before optimization.

[0096] Among them, the calculation configuration file group includes: "0_code", "1_input_file", "2_output_file", "3_xml_tpl", and "4_cost". The "0_code" folder is used to store the development program for analysis, the "1_input_file" folder is used to store the independent variable parameter list, the "2_output_file" folder is used to store the process variable list, the "3_xml_tpl" folder is used to store the xml file, temporary file tpl file, hdf result file, target file, and the "4_cost" folder is used to store the area file, unit price file, etc.

[0097] For example, Figure 5 As shown, select the input folder in the interface, and then use the controls "Folder Input", "Start Classification", "Check", and "Generate Variable File" to process the input folder and obtain the corresponding output file.

[0098] Optionally, the "Design Parameter Classification" program also provides a check module for checking the data integrity and format normativeness of the input information, and pops up information input reminders, such as the sound pressure level of the sound source of the vehicle sound insulation performance, the name of the sound cavity to be examined, the target input, etc. After the check is completed, an optimization boundary input file is generated, including the optimization parameter name, the upper and lower limits of the optimization parameter, the optimization target name, the optimization value, the constraint parameter name, the upper and lower limits of the constraint parameter, etc., for subsequent calls.

[0099] S202: Determine the whole vehicle acoustic performance parameter corresponding to each group of first parameter values ​​in the plurality of groups of first parameter values.

[0100] Among them, the whole vehicle acoustic performance parameters are used to indicate the sound insulation performance of the vehicle. The whole vehicle acoustic performance parameters include at least one of the following: sound insulation, target achievement, language clarity, total sound pressure level, average sound pressure level, loudness, etc. The target achievement can be understood as: whether the sound insulation corresponding to the sound pressure of different frequencies meets the preset conditions.

[0101] In some embodiments, in a method for optimizing sound insulation parameters provided in an embodiment of the present application, the above-mentioned step S202 may specifically include: determining the sound pressure corresponding to any group of first parameter values ​​among multiple groups of first parameter values; based on the sound pressure corresponding to any group of first parameter values, determining the acoustic performance parameters of the whole vehicle corresponding to any group of first parameter values.

[0102] In some embodiments, based on the sound pressure corresponding to any group of first parameter values, determining the whole vehicle acoustic performance parameters corresponding to any group of first parameter values, specifically may include: determining multiple operating states of the vehicle; for any group of first parameter values ​​among the multiple groups of first parameter values, based on the sound pressure corresponding to any group of first parameter values, determining the whole vehicle acoustic performance parameters of the vehicle under the target operating state corresponding to any group of first parameter values, different operating states correspond to different vehicle acoustic performance parameters, and the target operating state is any operating state among the multiple operating states.

[0103] Specifically, the multiple operating states may include: a front motor operating state, a rear motor operating state, a front wheel road noise operating state, a rear wheel road noise operating state, etc.

[0104] It can be understood that the front motor operating state is the state when the front motor of the vehicle is running, the rear motor operating state is the state when the rear motor of the vehicle is running, the front wheel road noise operating state is the operating state when the front wheels of the vehicle have road noise, and the rear wheel road noise operating state is the operating state when the rear wheels of the vehicle have road noise.

[0105] It should be noted that when the vehicle is in different operating states, the same set of first parameter values ​​corresponds to different vehicle acoustic performance parameters. In this way, the sound insulation performance corresponding to the same set of first parameter values ​​when the vehicle is in different operating states can be comprehensively considered.

[0106] In the embodiment of the present application, the present application can also comprehensively consider the running state of the vehicle, so that when the acoustic performance parameters of the whole vehicle corresponding to different running states are different, when at least one type parameter of the sound insulation material of the vehicle is any one of the multiple groups of first parameter values, the acoustic performance parameters of the whole vehicle corresponding to the vehicle under different running states are determined. In this way, the running state of the vehicle can be comprehensively considered to further improve the accuracy of determining the sound insulation parameters of the vehicle.

[0107] Specifically, based on the sound pressure results of each frequency calculated by VA One, the sound insulation of each frequency, target achievement, language clarity AI, total sound pressure level, average sound pressure level, loudness and other acoustic engineering indicators can be calculated to achieve customized and optimized target change selection and form a "sound pressure to engineering indicator conversion code" program.

[0108] Specifically, the conversion formula from sound pressure to engineering indicators is shown in Formula 2:

[0109] NR i =SPL si -SPL ci Formula 2

[0110] Among them, NR i is the noise attenuation (i.e. the acoustic performance parameter of the whole vehicle), SPL siis the sound pressure level at the source side, SPL ci is the sound pressure level at the receiving side.

[0111] In an embodiment of the present application, when at least one type parameter of the vehicle's sound insulation material is any one of a plurality of first parameter values, the present application can first determine the sound pressure corresponding to any one of a plurality of first parameter values. Since the sound pressure cannot intuitively represent the sound insulation performance of the vehicle, it is necessary to determine the whole vehicle acoustic performance parameters corresponding to any one of a plurality of first parameter values ​​based on the sound pressure corresponding to any one of a plurality of first parameter values, thereby indicating the sound insulation performance of the vehicle through the whole vehicle acoustic performance parameters. In this way, the sound insulation performance of the vehicle can be accurately indicated through the whole vehicle acoustic performance parameters, thereby improving the accuracy of determining the sound insulation parameters of the vehicle.

[0112] S203: Determine a first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values.

[0113] The first optimized parameter value is a parameter value that optimizes the sound insulation performance of the vehicle and is determined based on the proxy model, and the proxy model is obtained based on multiple groups of first parameter values.

[0114] Optionally, a corresponding proxy model can be constructed based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, so as to determine the parameter value that optimizes the sound insulation performance of the vehicle from the determined proxy model as the first optimized parameter value.

[0115] Optionally, based on the boundary input file, calculation configuration file group, output path folder, etc. generated above, the "Optimization Algorithm Platform" program can be called to fill in the algorithm parameters, complete dynamic optimization, and determine the first optimization parameter value of at least one type of parameter.

[0116] It should be noted that the "Optimization Algorithm Platform" program is an optimization platform that integrates many self-developed algorithms, and integrates the secondary development programs required for the high-frequency sound insulation performance analysis of the acoustic package, such as "multi-condition high-frequency sound insulation performance analysis process", "acoustic parameter conversion code", "vehicle weight and cost calculation code", "sound pressure to engineering indicator conversion code, etc.", "dynamic optimization process", etc., for customized optimization of high-frequency sound insulation performance.

[0117] Optionally, since determining the first optimization parameter value of at least one type of parameter is a typical optimization problem with many decision variables and high target latitude, the traditional genetic algorithm has low precision and the solution set is difficult to converge. The self-developed proxy model and optimization algorithm can solve this problem well. Therefore, the self-written design of experiment (DOE) algorithm (or Latin hypercube sampling (LHS) algorithm) can be called for large sample calculation, the self-written proxy model (eXtreme gradient boosting (XGBoost) algorithm, kernel partial least squares (KPLS) algorithm) can be used for performance prediction, and the self-written optimization algorithm (fuzzy design variables genetic algorithm (FDVGA) algorithm) can be used for parameter optimization.

[0118] For example, Figure 6 As shown in the figure, in the interface, you can select DOE algorithm, proxy model algorithm, optimization algorithm, and select input: existing samples, boundary input (i.e. parameter range), calculation configuration, solution software, output path, etc. After triggering the run, the corresponding results can be output in the form of charts.

[0119] It should be noted that the XGBoost algorithm is used to predict the sound pressure level index, and the KPLS algorithm is used to predict the vehicle cost and weight index. The LHS algorithm is a Latin hypercube sampling method that implements a high-dimensional uniform sampling method with an indefinite number of samples. The XGBoost algorithm is an ensemble learning algorithm for boosting decision trees, and the KPLS proxy model is a Kriging model that uses partial least squares. While maintaining good accuracy, it effectively reduces the number of hyperparameters of the prediction model and is suitable for high-dimensional model prediction. This algorithm builds a proxy model based on DOE large sample data.

[0120] Furthermore, the FDVGA optimization algorithm is a genetic algorithm based on fuzzy decision variables, which can handle high-dimensional nonlinear problems with large-scale decision variables. FDVGA can search for the optimal solution set. Through the standard test function, the FDVGA algorithm solution set is evenly distributed on the optimal surface.

[0121] For example, Figure 7As shown in the figure, the VA One platform can be used to parametrically model the part parameters based on the vehicle SEA model, define the initial state, and use it for subsequent parameter optimization to obtain the corresponding xm1 file (SEA model). Then, based on the "Design Parameter Extraction" program, the type parameters of the variables to be parameterized are checked through the GUI, and the global type parameters of the vehicle acoustic package and sheet metal are quickly and automatically parameterized to obtain the parameterized temporary file tpl file and design parameter list file. Furthermore, the "Design Parameter Extraction" program can be used to convert the input in the previous step into a boundary input file, a calculation configuration file group, an output path folder, etc., to complete all pre-processing related work and inspections before optimization, and obtain the corresponding boundary input file, calculation configuration file group, and output path folder. Finally, the "Optimization Algorithm Platform" program integrates a large number of development programs (analysis, process, and algorithm types) to automatically complete the global customized optimization of high-frequency sound insulation performance and obtain the optimization result file.

[0122] In an embodiment of the present application, the present application can first determine a plurality of groups of first parameter values ​​corresponding to at least one type of parameter of the vehicle's sound insulation material, thereby determining the whole vehicle acoustic performance parameters indicating the sound insulation performance of the vehicle corresponding to each group of first parameter values ​​through the plurality of groups of first parameter values. Based on this, the corresponding sound insulation performance can be determined when the parameter values ​​of at least one type of parameter of the vehicle's sound insulation material are different. In this way, based on the plurality of groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, an agent model can be constructed to determine the corresponding whole vehicle acoustic performance parameters when the sound insulation performance of the vehicle is optimal. And further determine the parameter value corresponding to the whole vehicle acoustic performance parameter as the first optimized parameter value corresponding to at least one type of parameter of the sound insulation material. Thereby, the effect of optimizing the sound insulation parameters of the vehicle and the efficiency of determining the sound insulation parameters of the vehicle can be improved.

[0123] In some embodiments, Figure 8 As shown, in a method for optimizing sound insulation parameters provided in an embodiment of the present application, the above step S203 may specifically include S801-S802.

[0124] S801. For any group of first parameter values ​​among multiple groups of first parameter values, determine a weight cost parameter of a vehicle corresponding to any group of first parameter values.

[0125] The weight cost parameter includes at least one of the following: vehicle weight and vehicle cost.

[0126] It can be understood that after determining multiple groups of first parameter values ​​corresponding to at least one type of parameter of the vehicle's sound insulation material, the vehicle weight and / or vehicle cost corresponding to the vehicle produced based on these parameters can be specifically determined according to the parameters in each group of first parameter values.

[0127] Specifically, according to the corresponding calculation formulas for the vehicle weight and vehicle cost in the data structure of the VA One software, the vehicle weight and / or vehicle cost that changes with parameters can be counted, and information such as the weight and cost of various sound insulation materials can be counted at the same time (including: at least one type of parameter of sound insulation materials such as foam materials, felt materials, sheet metal, etc.), forming a "vehicle weight and vehicle cost calculation code" program.

[0128] Exemplarily, the calculation conversion formula for the vehicle weight and vehicle cost is shown in Formula 3:

[0129]

[0130] Among them, M total Indicates the total weight of sheet metal and acoustic package, C total represents the total cost of sheet metal and acoustic package, CR s represents the parts coverage, M s represents the weight of the part, S represents the area of ​​the part (i.e., the sound insulation material), T represents the material thickness, ρ represents the material density, and P s represents the unit price of materials, α represents the coefficient of material cost in total cost (usually 0.7), i represents the layer coefficient of the part, and j represents the part coefficient.

[0131] like Fig. 9 As shown, the vehicle structure includes A-pillars, front wall panels, hoods, cabins, front wheel covers, fenders, main carpets, ceilings, B-pillars, rear wheel covers, pressure relief valves, side trims, C-pillars, D-pillars and other structures. The vehicle weight and vehicle cost of each component can be determined based on any set of first parameter values, thereby determining the vehicle weight and vehicle cost of the vehicle.

[0132] S802. Determine a first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values, the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, and the weight cost parameters corresponding to each group of first parameter values.

[0133] Among them, the first optimized parameter value is any one of the following: a parameter value that optimizes the sound insulation performance of the vehicle, a parameter value that minimizes the vehicle's overall weight, and a parameter value that minimizes the vehicle's overall cost.

[0134] For example, Fig.10As shown, after parametric modeling of part parameters based on the vehicle SEA model and designing the variable definition of at least one type parameter (such as felt density, foam thickness, etc.), the type parameter conversion (such as the conversion of felt material density and other acoustic parameters) can be performed, and then the vehicle weight and vehicle cost are calculated, and the corresponding sound pressure results (vehicle acoustic performance parameters of the vehicle to noise) under different operating conditions (front motor, rear motor, front wheel road noise, rear wheel road noise) are determined. Then, based on the sound pressure conversion engineering indicators (sound insulation, AI, etc. for optimization of goals and constraint settings), the corresponding vehicle acoustic performance parameters are determined.

[0135] In an embodiment of the present application, the present application can also determine the vehicle weight and / or vehicle cost of the vehicle when at least one type parameter of the vehicle's sound insulation material is any one of a plurality of first parameter value groups. Thus, when determining the first optimized parameter value of at least one type parameter, the plurality of first parameter values, the vehicle acoustic performance parameters corresponding to each first parameter value group, and the vehicle weight and / or vehicle cost corresponding to each first parameter value group are comprehensively considered. In this way, the vehicle can achieve the requirements of optimal sound insulation performance, minimum vehicle weight, or minimum vehicle cost based on the first optimized parameter value of at least one type parameter determined. Thus, the diversity of determining the vehicle's sound insulation parameters can be improved.

[0136] In some embodiments, Fig.11 As shown, in a method for optimizing sound insulation parameters provided in an embodiment of the present application, the above step S203 may specifically include S1101-S1104.

[0137] S1101. Determine a second optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values.

[0138] Optionally, a "dynamic optimization process" may be further set to determine the accuracy of the last optimization result and decide whether to increase local sampling to further determine a more optimized first optimization parameter value to solve the problem of low model accuracy.

[0139] S1102. When the error between the whole vehicle acoustic performance parameter and / or weight cost parameter corresponding to the second optimized parameter value and the preset parameter value is greater than the preset error, determine a second parameter range corresponding to each type of parameter in at least one type of parameter based on the second optimized parameter value.

[0140] The second parameter range is included in the first parameter range.

[0141] In this way, after determining the whole vehicle acoustic performance parameters with the best sound insulation performance from multiple groups of first parameter values, it is possible to further determine whether the whole vehicle acoustic performance parameters and / or weight cost parameters corresponding to this group of parameter values ​​meet the preset conditions (i.e., whether the error between the preset parameter values ​​is less than the preset error). When the error is greater than the preset error, the second parameter range corresponding to each type of parameter can be further determined.

[0142] S1103. Determine multiple groups of second parameter values ​​of the sound insulation material based on the second parameter range corresponding to each type of parameter.

[0143] S1104. Determine a first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and multiple groups of second parameter values.

[0144] The precision of the first optimization parameter value is greater than the precision of the second optimization parameter value.

[0145] Furthermore, based on the determined second parameter range corresponding to each type of parameter, multiple groups of first optimized parameter values ​​of the sound insulation material are re-determined, so as to re-construct the corresponding proxy model based on the multiple groups of first parameter values ​​and the multiple groups of optimized parameters, thereby determining the parameter value that optimizes the sound insulation performance of the vehicle from the newly constructed proxy model as the first optimized parameter value.

[0146] It should be noted that, based on the second parameter range corresponding to each type of parameter, multiple groups of first optimization parameter values ​​of the sound insulation material are determined, and based on the multiple groups of first optimization parameter values, the first optimization parameter value of at least one type of parameter is determined. The specific process can refer to the above steps S201-S203 and will not be repeated here.

[0147] In the embodiment of the present application, the present application can determine the second optimized parameter value corresponding to the vehicle when the sound insulation performance of the vehicle is optimal based on multiple groups of first parameter values ​​and the vehicle acoustic performance parameters corresponding to each group of first parameter values, and first determine whether the error between the vehicle acoustic performance parameters and / or weight cost parameters corresponding to the second optimized parameter value and the preset parameter value is greater than the preset error. Therefore, when the error is greater than the preset error, multiple groups of second parameter values ​​of the sound insulation material can be further determined, and based on the multiple groups of first parameter values ​​and the multiple groups of second parameter values, the first optimized parameter value of at least one type of parameter can be re-determined, so as to obtain an optimized parameter value with better sound insulation performance. In this way, the effect of optimizing the sound insulation parameters of the vehicle can be further improved.

[0148] Exemplarily, as shown in Table 1, the first optimized parameter value of at least one type parameter determined in the embodiment of the present application includes an initial value, a parameter value that optimizes the sound insulation performance of the vehicle, a parameter value that minimizes the vehicle weight, and a parameter value that minimizes the vehicle cost. The type parameters of the acoustic package and sheet metal corresponding to the vehicle (e.g., 101 related parameters) are globally customized and optimized, and the optimization results of these 101 parameters are listed in the initial state and the three customized schemes of lowest cost with unchanged performance, lowest weight with unchanged performance, and best performance with unchanged cost.

[0149] It should be noted that Table 1 only shows the first optimized parameter values ​​of some type parameters by way of example. Specifically, the type parameters corresponding to the front cover sound insulation pad may include: semi-cured felt thickness, semi-cured felt density, sheet metal thickness; the type parameters corresponding to the top cover lining may include: foam thickness, film surface density, sheet metal thickness; the type parameters corresponding to the front carpet may include: soft layer felt thickness (front footrest, front section of center console, front leg section, middle front section of center console, front lower front seat, middle rear section of center console, rear lower front seat, rear rear section of center console, rear footrest, rear leg section, rear seat), soft layer felt density, hard layer felt thickness, hard layer felt density, EVA surface density, sheet metal thickness (front, middle, rear); the type parameters corresponding to the trunk carpet may include: felt thickness (rear carpet Front front section, front middle section of rear carpet, front rear section of rear carpet, middle section of rear carpet, rear front section of rear carpet, rear middle section of rear carpet, both sides of rear carpet, front ends of both sides of rear carpet), felt density, EVA surface density; type parameters corresponding to rear motor sound insulation pad may include: semi-cured felt thickness (front front section, front middle section, front rear section, middle, rear front section, rear middle section), semi-cured felt density; type parameters corresponding to rear floor may include: sheet metal thickness (front front section, front middle section, front rear section, middle, rear front section, rear middle and rear sections, both sides, spring seat); type parameters corresponding to front wall panel sound insulation pad may include: soft layer felt thickness (upper outer side, Upper right inner side, middle right outer side, middle inner side, lower outer side, lower inner side, gutter part, wheel hub cover side, longitudinal beam side, upper left inner side, middle left outer side), soft layer felt density, EVA surface density, hard layer felt thickness, hard layer felt density; Type parameters corresponding to engine compartment sound insulation pad may include: semi-cured felt thickness (upper outer side, upper inner side, middle outer side, middle inner side, lower outer side, lower inner side), semi-cured felt density; Type parameters corresponding to front wall panel may include: sheet metal thickness (gutter part, upper part, lower part, longitudinal beam side); Type parameters corresponding to front end of side trim may include: felt thickness, felt density; Type parameters corresponding to rear wheel cover may include: soft The type parameters corresponding to the rear wheel hub cover sound insulation pad may include: soft layer felt thickness (lower side, upper side), soft layer felt density, EVA surface density, hard layer felt thickness, hard layer felt density; the type parameters corresponding to the rear wheel hub cover may include: sheet metal thickness (outer side, upper inner side, lower inner side); the type parameters corresponding to the storage plate may include: soft layer felt thickness, soft layer felt density, EVA surface density, hard layer felt thickness, hard layer felt density; the type parameters corresponding to the cotton felt on the left and right sides of the front wall panel may include: soft layer felt thickness, soft layer felt density, hard layer felt thickness, hard layer felt density and sheet metal thickness.

[0150] Table 1

[0151]

[0152] It can be understood that the embodiments of the present application address the deficiencies in the prior art and provide a global customized optimization method for the sound insulation performance of the entire vehicle based on an autonomous agent model and an optimization algorithm, which is used for customized optimization of high-frequency noise and realizes global customized optimization of high-frequency noise-related global parameters including acoustic bag thickness, density, sound-absorbing cotton coverage, and sheet metal panel thickness.

[0153] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, the device or electronic device for optimizing sound insulation parameters includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware 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 exceed the scope of this application.

[0154] The embodiments of the present application can exemplarily divide the functional modules of the device for optimizing sound insulation parameters or the electronic device according to the above method. For example, the device for optimizing sound insulation parameters or the electronic device may include various functional modules corresponding to the various functional divisions, or two or more functions may be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0155] Fig.12 is a block diagram of a device for optimizing sound insulation parameters according to an exemplary embodiment. Fig.12 The sound insulation parameter optimization device 1200 includes: a processing module 1201 and a storage module 1202.

[0156] The processing module 1201 is used to determine multiple groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the vehicle's sound insulation material and a first parameter range of each type parameter; a group of first parameter values ​​includes parameter values ​​of each type parameter in at least one type parameter; the parameter values ​​of at least one type parameter in different groups of parameter values ​​are different; the processing module 1201 is also used to determine the whole vehicle acoustic performance parameters corresponding to each group of first parameter values ​​in the multiple groups of first parameter values, and the whole vehicle acoustic performance parameters are used to indicate the sound insulation performance of the vehicle; the processing module 1201 is also used to determine the first optimized parameter value of at least one type parameter based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, the first optimized parameter value is a parameter value that optimizes the sound insulation performance of the vehicle determined based on a proxy model, and the proxy model is obtained based on the multiple groups of first parameter values.

[0157] In one possible implementation, the processing module 1201 is specifically used to determine the sound pressure corresponding to any one group of first parameter values ​​among multiple groups of first parameter values; the processing module 1201 is specifically used to determine the whole vehicle acoustic performance parameters corresponding to any one group of first parameter values ​​based on the sound pressure corresponding to any one group of first parameter values.

[0158] In one possible implementation, the processing module 1201 is specifically used to determine, for any one of the multiple groups of first parameter values, a weight cost parameter of the vehicle corresponding to any one of the multiple groups of first parameter values, the weight cost parameter including at least one of the following: vehicle weight and vehicle cost; the processing module 1201 is specifically used to determine a first optimized parameter value of at least one type of parameter based on the multiple groups of first parameter values, the vehicle acoustic performance parameters corresponding to each group of first parameter values, and the weight cost parameter corresponding to each group of first parameter values, the first optimized parameter value being any one of the following: a parameter value that optimizes the sound insulation performance of the vehicle, a parameter value that minimizes the vehicle weight, and a parameter value that minimizes the vehicle cost.

[0159] In one possible implementation, the processing module 1201 is specifically used to determine multiple operating states of the vehicle; the processing module 1201 is specifically used to determine, for any group of first parameter values ​​among the multiple groups of first parameter values, the whole vehicle acoustic performance parameters of the vehicle corresponding to any group of first parameter values ​​in the target operating state based on the sound pressure corresponding to any group of first parameter values, different operating states correspond to different whole vehicle acoustic performance parameters, and the target operating state is any operating state among the multiple operating states.

[0160] In one possible implementation, the processing module 1201 is specifically used to determine a second optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and vehicle acoustic performance parameters corresponding to each group of first parameter values; the processing module 1201 is specifically used to determine a second parameter range corresponding to each type of parameter in at least one type of parameter based on the second optimized parameter value when an error between the vehicle acoustic performance parameter and / or weight cost parameter corresponding to the second optimized parameter value and a preset parameter value is greater than a preset error, and the second parameter range is included in the first parameter range; the processing module 1201 is specifically used to determine multiple groups of second parameter values ​​of the sound insulation material based on the second parameter range corresponding to each type of parameter; the processing module 1201 is specifically used to determine a first optimized parameter value of at least one type of parameter based on multiple groups of first parameter values ​​and multiple groups of second parameter values, and the accuracy of the first optimized parameter value is greater than the accuracy of the second optimized parameter value.

[0161] In one possible implementation, the processing module 1201 is further used to determine the identification of all type parameters of the sound insulation materials deployed at different locations of the vehicle, and the identification of all type parameters is used to indicate at least one of the following: deployment area, material name, material deployment level, and parameter type; the processing module 1201 is further used to determine at least one type parameter from all type parameters of the sound insulation materials.

[0162] In one possible implementation, the processing module 1201 is specifically used to determine a first type parameter from all type parameters of the sound insulation material; the processing module 1201 is specifically used to determine a second type parameter and a third type parameter from the first type parameter when there are mutually related type parameters in the first type parameter, the third type parameter being an associated parameter of the second type parameter; the processing module 1201 is specifically used to determine the type parameters in the first type parameter except the third type parameter as at least one type parameter.

[0163] In one possible implementation, the processing module 1201 is specifically used to perform parametric modeling on the sound insulation materials deployed at different locations of the vehicle to determine all type parameters of the sound insulation materials; the processing module 1201 is specifically used to determine the identification of all type parameters of the sound insulation materials based on a preset naming rule.

[0164] In a possible implementation, the storage module 1202 is used to store parameters such as multiple groups of first parameter values ​​of sound insulation materials, whole vehicle acoustic performance parameters corresponding to each group of first parameter values, and first optimized parameter values ​​of at least one type of parameters.

[0165] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0166] Fig.13 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Fig.13 As shown, the electronic device 1300 includes but is not limited to: a processor 1301 and a memory 1302 .

[0167] The memory 1302 is used to store executable instructions of the processor 1301. It can be understood that the processor 1301 is configured to execute instructions to implement the method for optimizing the sound insulation parameters in the above embodiment.

[0168] It should be noted that those skilled in the art can understand that Fig.13 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include Fig.13 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0169] The processor 1301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 1302, and calling data stored in the memory 1302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 1301 may include one or more processing units. Optionally, the processor 1301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 1301.

[0170] The memory 1302 may be used to store software programs and various data. The memory 1302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one functional module (such as a processing module, a storage module, etc.), etc. In addition, the memory 1302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0171] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 1302 including instructions, and the above instructions can be executed by the processor 1301 of the electronic device 1300 to implement the method for optimizing the sound insulation parameters in the above embodiment.

[0172] In actual implementation, Fig.12 The functions of the processing module 1201 in Fig.13The processor 1301 in the embodiment calls the computer program stored in the memory 1302. The specific execution process can refer to the description of the optimization method of the sound insulation parameters in the above embodiment, which will not be repeated here.

[0173] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0174] In an exemplary embodiment, the embodiment of the present application also provides a computer program product including one or more instructions, which can be executed by the processor 1301 of the electronic device 1300 to complete the method for optimizing the sound insulation parameters in the above embodiment.

[0175] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the embodiment of the above-mentioned sound insulation parameter optimization method are implemented, and the same technical effect as the above-mentioned sound insulation parameter optimization method can be achieved. In order to avoid repetition, they will not be repeated here.

[0176] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0177] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device 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 device, 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.

[0178] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0179] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0180] 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 readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or CD and other media that can store program code.

[0181] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for optimizing sound insulation parameters, characterized in that: The method comprises: Determine multiple groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the vehicle and a first parameter range of each type parameter; a group of first parameter values ​​includes a parameter value of each type parameter of the at least one type parameter; and parameter values ​​of at least one type parameter in different groups of parameter values ​​are different; Determine a whole vehicle acoustic performance parameter corresponding to each group of first parameter values ​​in the plurality of groups of first parameter values, wherein the whole vehicle acoustic performance parameter is used to indicate the sound insulation performance of the vehicle; Based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, a first optimized parameter value of at least one type of parameter is determined, wherein the first optimized parameter value is a parameter value that optimizes the sound insulation performance of the vehicle based on a proxy model, and the proxy model is obtained based on the multiple groups of first parameter values.

2. The method according to claim 1, characterized in that The determining the whole vehicle acoustic performance parameter corresponding to each group of first parameter values ​​in the plurality of groups of first parameter values ​​comprises: For any group of first parameter values ​​among the multiple groups of first parameter values, determining a sound pressure corresponding to the any group of first parameter values; Based on the sound pressure corresponding to any one group of first parameter values, the whole vehicle acoustic performance parameter corresponding to any one group of first parameter values ​​is determined.

3. The method according to claim 1, characterized in that The determining, based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, a first optimized parameter value of the at least one type of parameter comprises: For any one of the plurality of first parameter values, determine a weight cost parameter of the vehicle corresponding to the first parameter value, wherein the weight cost parameter includes at least one of the following: vehicle weight and vehicle cost; Based on the multiple groups of first parameter values, the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, and the weight cost parameters corresponding to each group of first parameter values, the first optimized parameter value of the at least one type of parameter is determined, and the first optimized parameter value is any one of the following: the parameter value that optimizes the sound insulation performance of the vehicle, the parameter value that minimizes the whole vehicle weight, and the parameter value that minimizes the whole vehicle cost of the vehicle.

4. The method according to claim 2, characterized in that: The determining, based on the sound pressure corresponding to any one of the first parameter values, the whole vehicle acoustic performance parameter corresponding to any one of the first parameter values, comprises: determining a plurality of operating states of the vehicle; For any one group of first parameter values ​​among the multiple groups of first parameter values, based on the sound pressure corresponding to the any one group of first parameter values, the whole vehicle acoustic performance parameters of the vehicle in the target operating state corresponding to the any one group of first parameter values ​​are determined, different operating states correspond to different whole vehicle acoustic performance parameters, and the target operating state is any one of the multiple operating states.

5. The method according to any one of claims 1 to 4, characterized in that The determining, based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, a first optimized parameter value of the at least one type of parameter comprises: Determining a second optimized parameter value of the at least one type of parameter based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values; In a case where the error between the whole vehicle acoustic performance parameter and / or weight cost parameter corresponding to the second optimized parameter value and the preset parameter value is greater than the preset error, determining a second parameter range corresponding to each type of parameter in the at least one type of parameter based on the second optimized parameter value, the second parameter range being included in the first parameter range; Determining multiple groups of second parameter values ​​of the sound insulation material based on the second parameter range corresponding to each type of parameter; The first optimized parameter value of the at least one type of parameter is determined based on the multiple groups of first parameter values ​​and the multiple groups of second parameter values, wherein the precision of the first optimized parameter value is greater than the precision of the second optimized parameter value.

6. The method according to any one of claims 1 to 4, characterized in that Before determining a plurality of sets of first parameter values ​​of the sound insulation material based on at least one type parameter of the sound insulation material of the vehicle and a first parameter range of each type parameter, the method further includes: Determine the identifiers of all types of parameters of the sound insulation material deployed at different locations of the vehicle, wherein the identifiers of all types of parameters are used to indicate at least one of the following: deployment area, material name, material deployment level, and parameter type; From among the total number of type parameters of the sound insulating material, the at least one type parameter is determined.

7. The method according to claim 6, characterized in that Determining the at least one type parameter from among all the type parameters of the sound insulation material comprises: Determining a first type parameter from among all the type parameters of the sound insulation material; In the case where there are mutually associated type parameters in the first type parameters, determining a second type parameter and a third type parameter from the first type parameters, the third type parameter being an associated parameter of the second type parameter; The type parameters other than the third type parameter in the first type parameters are determined as the at least one type parameter.

8. The method according to claim 6, characterized in that The identification of all type parameters of the sound insulation materials deployed at different locations of the vehicle includes: Performing parameterized modeling on the sound insulation materials deployed at different locations of the vehicle to determine all type parameters of the sound insulation materials; Based on a preset naming rule, identifications of all type parameters of the sound insulation material are determined.

9. A device for optimizing sound insulation parameters, characterized in that: The device for optimizing sound insulation parameters includes: a processing module; The processing module is used to determine multiple groups of first parameter values ​​of the sound insulation material based on at least one type parameter of the sound insulation material of the vehicle and a first parameter range of each type parameter; a group of first parameter values ​​includes a parameter value of each type parameter of the at least one type parameter; and parameter values ​​of at least one type parameter in different groups of parameter values ​​are different; The processing module is further used to determine the whole vehicle acoustic performance parameter corresponding to each group of first parameter values ​​in the multiple groups of first parameter values, and the whole vehicle acoustic performance parameter is used to indicate the sound insulation performance of the vehicle; The processing module is also used to determine a first optimized parameter value of at least one type of parameter based on the multiple groups of first parameter values ​​and the whole vehicle acoustic performance parameters corresponding to each group of first parameter values, wherein the first optimized parameter value is a parameter value that optimizes the sound insulation performance of the vehicle based on a proxy model, and the proxy model is obtained based on the multiple groups of first parameter values.

10. An electronic device, characterized in that: include: A processor and a memory; wherein the memory is used to store one or more programs, and the one or more programs include computer-executable instructions. When the electronic device is running, the processor executes the computer-executable instructions stored in the memory, and the electronic device executes the method described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that: When the computer instructions stored in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device executes the method as claimed in any one of claims 1 to 8.

12. A computer program product, characterized in that The computer program product comprises computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the method according to any one of claims 1 to 8.

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