Vehicle sound insulation performance optimization method, device, equipment, medium and program product

Abstract

The application relates to the technical field of vehicles, in particular to a vehicle sound insulation performance optimization method, device, equipment, medium and program product, wherein the method comprises the following steps: identifying at least one acoustic component of a target area of a vehicle; acquiring a reference coverage rate and a reference thickness distribution of the acoustic component; keeping one of the reference coverage rate and the reference thickness distribution unchanged, and adjusting the other one of the reference coverage rate and the reference thickness distribution to generate a plurality of candidate coverage rates and a plurality of candidate thickness distributions; performing sound insulation amount simulation under the candidate coverage rates and the plurality of candidate thickness distributions by using a statistical energy analysis model, and respectively obtaining sound insulation amount curves corresponding to the plurality of candidate coverage rates and the plurality of candidate thickness distributions; and analyzing the sensitivity of the sound insulation amount according to the sound insulation amount curves, and optimizing the sound insulation performance of the target area of the vehicle according to the sensitivity analysis result. Therefore, the problems of long cycle and low efficiency of vehicle sound insulation performance optimization in the related art are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, equipment, medium, and program product for optimizing vehicle sound insulation performance. Background Technology

[0002] As automotive performance requirements continue to increase, optimizing vehicle sound insulation performance has become a crucial aspect of vehicle development. How to efficiently and accurately optimize sound insulation performance is a common concern within the industry.

[0003] The optimization of sound insulation performance in related technologies mainly relies on simulation analysis and experimental attempts. This involves adjusting parameters and repeatedly verifying them to analyze and optimize the factors affecting the sound insulation performance of vehicles. However, this process is lengthy and inefficient. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, medium, and program product for optimizing vehicle sound insulation performance, in order to solve the problems of long cycle and low efficiency in the related art for optimizing vehicle sound insulation performance.

[0005] The first aspect of this application provides a method for optimizing the sound insulation performance of a vehicle, comprising the following steps: identifying at least one acoustic component in a target area of ​​the vehicle; obtaining a reference coverage rate and a reference thickness distribution of the acoustic component in the target area, keeping one of the reference coverage rate and the reference thickness distribution unchanged, and adjusting the other of the reference coverage rate and the reference thickness distribution to generate multiple candidate coverage rates and multiple candidate thickness distributions; using a statistical energy analysis model corresponding to the target area to perform sound insulation simulation under the candidate coverage rates and multiple candidate thickness distributions, obtaining multiple first sound insulation curves corresponding to the multiple candidate coverage rates and multiple second sound insulation curves corresponding to the candidate thickness distributions; analyzing the sensitivity of the coverage rate to the sound insulation and the sensitivity of the thickness distribution to the sound insulation based on the multiple first sound insulation curves and multiple second sound insulation curves, and optimizing the sound insulation performance of the target area of ​​the vehicle based on the sensitivity analysis results.

[0006] Optionally, before identifying at least one acoustic component in the target area of ​​the vehicle, the method further includes: identifying the outer envelope of the vehicle's passenger compartment; obtaining the noise transmission path and the location of the acoustic component; and dividing the outer envelope of the passenger compartment into multiple regions based on the noise transmission path and / or the location of the acoustic component.

[0007] Optionally, obtaining the reference coverage and reference thickness distribution of the acoustic components in the target area includes: dividing the thickness of the acoustic components into multiple thickness intervals according to preset rules; obtaining the area ratio of the acoustic components in each thickness interval; and generating a reference thickness distribution based on the area ratio of the multiple thickness intervals.

[0008] Optionally, keeping one of the reference coverage and reference thickness distribution unchanged, and adjusting the other of the reference coverage and reference thickness distribution to generate multiple candidate coverage and multiple candidate thickness distributions, including: keeping the reference thickness distribution unchanged, increasing or decreasing the coverage within a first preset range based on the reference coverage to generate multiple candidate coverage; keeping the reference coverage unchanged, increasing or decreasing the area proportion of the corresponding thickness interval within a second preset range based on the reference area proportion of each thickness interval, and adjusting the area proportion of other thickness intervals according to the increase or decrease ratio to generate multiple candidate thickness distributions.

[0009] Optionally, sound insulation simulation is performed using a statistical energy analysis model corresponding to the target area under candidate coverage and multiple candidate thickness distributions, including: acquiring multiple test sound frequencies of the target area; performing a first sound insulation simulation at each candidate coverage using the statistical energy analysis model corresponding to the target area at each test sound frequency, wherein the thickness distribution of the acoustic components in the first sound insulation simulation is the corresponding reference thickness distribution; and performing a second sound insulation simulation at each candidate thickness distribution using the statistical energy analysis model corresponding to the target area at each test sound frequency, wherein the coverage of the acoustic components in the second sound insulation simulation is the reference coverage.

[0010] Optionally, the sensitivity of coverage to sound insulation and the sensitivity of thickness distribution to sound insulation are analyzed based on multiple first sound insulation curves and multiple second sound insulation curves, including: calculating a first influence value of coverage at each test sound frequency and a first average influence value at multiple test sound frequencies based on multiple first sound insulation curves; calculating a second influence value of thickness distribution at each test sound frequency and a second average influence value at multiple test sound frequencies based on multiple second sound insulation curves; determining the sensitivity of coverage to sound insulation based on the first influence value and the first average influence value; and determining the sensitivity of thickness distribution to sound insulation based on the second influence value and the second influence value.

[0011] A second aspect of this application provides a vehicle sound insulation performance optimization device, comprising: an identification module for identifying at least one acoustic component in a target area of ​​the vehicle; a generation module for acquiring a reference coverage rate and a reference thickness distribution of the acoustic component in the target area, keeping one of the reference coverage rate and the reference thickness distribution unchanged, and adjusting the other of the reference coverage rate and the reference thickness distribution to generate multiple candidate coverage rates and multiple candidate thickness distributions; a simulation module for performing sound insulation simulation under the candidate coverage rates and multiple candidate thickness distributions using a statistical energy analysis model corresponding to the target area, obtaining multiple first sound insulation curves corresponding to the multiple candidate coverage rates and multiple second sound insulation curves corresponding to the candidate thickness distributions; and an optimization module for analyzing the sensitivity of the coverage rate to the sound insulation and the sensitivity of the thickness distribution to the sound insulation based on the multiple first sound insulation curves and the multiple second sound insulation curves, and optimizing the sound insulation performance of the target area of ​​the vehicle based on the sensitivity analysis results.

[0012] Optionally, it further includes: a segmentation module for identifying the outer envelope of the vehicle's passenger compartment before identifying at least one acoustic component in the target area of ​​the vehicle; obtaining the vehicle's noise transmission path and the location of the acoustic component; and dividing the outer envelope of the passenger compartment into multiple regions based on the noise transmission path and / or the location of the acoustic component.

[0013] Optionally, the generation module is further used to: divide the thickness of the acoustic component into multiple thickness intervals according to preset rules; obtain the area ratio of the acoustic component in each thickness interval, and generate a reference thickness distribution based on the area ratio of the multiple thickness intervals.

[0014] Optionally, the generation module is further configured to: keep the reference thickness distribution unchanged, increase or decrease the coverage rate within a first preset range based on the reference coverage rate, so as to generate multiple candidate coverage rates; keep the reference coverage rate unchanged, increase or decrease the area ratio of the corresponding thickness interval within a second preset range based on the reference area ratio of each thickness interval, and adjust the area ratio of other thickness intervals according to the increase or decrease ratio, so as to generate multiple candidate thickness distributions.

[0015] Optionally, the simulation module is further configured to: acquire multiple test sound frequencies of the target area; at each test sound frequency, perform a first sound insulation simulation at each candidate coverage using the statistical energy analysis model corresponding to the target area, wherein the thickness distribution of the acoustic components during the first sound insulation simulation is the corresponding reference thickness distribution; and at each test sound frequency, perform a second sound insulation simulation at each candidate thickness distribution using the statistical energy analysis model corresponding to the target area, wherein the coverage of the acoustic components during the second sound insulation simulation is the reference coverage.

[0016] Optionally, the optimization module is further configured to: analyze the sensitivity of coverage to sound insulation and the sensitivity of thickness distribution to sound insulation based on multiple first sound insulation curves and multiple second sound insulation curves, including: calculating a first influence value of coverage at each test sound frequency and a first average influence value at multiple test sound frequencies based on multiple first sound insulation curves; calculating a second influence value of thickness distribution at each test sound frequency and a second average influence value at multiple test sound frequencies based on multiple second sound insulation curves; determining the sensitivity of coverage to sound insulation based on the first influence value and the first average influence value; and determining the sensitivity of thickness distribution to sound insulation based on the second influence value and the second influence value.

[0017] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to perform the vehicle sound insulation performance optimization method as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which is executed by a processor to perform the vehicle sound insulation performance optimization method as described above.

[0019] The fifth aspect of this application provides a computer program product, including a computer program or instructions, which, when executed, implement the vehicle sound insulation performance optimization method as described in the above embodiments.

[0020] Therefore, this application has at least the following beneficial effects: This application's embodiments can identify acoustic components in the target area of ​​a vehicle and obtain reference values ​​for coverage and thickness distribution. Using a univariate perturbation rule that keeps one parameter constant while adjusting another, multiple candidate coverage and thickness distributions are generated. A statistical energy analysis model specific to the target area is then used to conduct sound insulation simulations under all candidate parameters, determining the first sound insulation curve corresponding to coverage and the second sound insulation curve corresponding to thickness distribution. Based on the sound insulation curves, the sensitivity of coverage and thickness distribution to sound insulation is analyzed, enabling quantitative identification of key parameters significantly affecting sound insulation. This avoids blind trial and error, and the vehicle's sound insulation performance is optimized based on the sensitivity analysis results. This allows for precise positioning of the optimization direction, prioritizing adjustments to high-impact parameters. While ensuring improved sound insulation performance, this also increases optimization efficiency, reduces development costs, and shortens the development cycle. Therefore, it solves the technical problems of long cycles and low efficiency in vehicle sound insulation performance optimization in related technologies.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a vehicle sound insulation performance optimization method according to an embodiment of this application; Figure 2 This is a schematic diagram of vehicle area division according to an embodiment of this application; Figure 3 This is a schematic diagram of the statistical energy analysis model of the front system provided according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the sensitivity analysis of sound insulation based on the thickness distribution of the front enclosure system according to an embodiment of this application. Figure 5 This is a flowchart illustrating the overall implementation of the vehicle sound insulation performance optimization method provided in the embodiments of this application. Figure 6 This is an example diagram of a vehicle sound insulation performance optimization device provided according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] A method for determining the sound insulation contribution of a vehicle is disclosed in related technologies. This method aims to quickly and accurately determine the contribution of each component of the vehicle to sound insulation when it travels through different transmission paths. This is beneficial for quickly identifying weak points in sound insulation and optimizing them. However, after identifying the weak areas, no solution is provided for how to formulate effective plans to improve the sound insulation at these weak points and optimize sound insulation performance. Furthermore, in actual development, analysis is mainly conducted through simulation and experimentation, lacking direct and effective analytical methods. Simulation and experimentation analysis are time-consuming and inefficient.

[0025] Therefore, this application provides a method for optimizing the sound insulation performance of a vehicle. In this method, the sensitivity analysis of the thickness and coverage of multiple components in a certain area on the sound insulation can be realized. It can directly and effectively determine the impact of a certain component's change on the sound insulation of the area. Then, the thickness and coverage of the components in the area can be adjusted to optimize the sound insulation of the vehicle and its sound insulation performance.

[0026] Specifically,Figure 1 This is a flowchart illustrating a method for optimizing vehicle sound insulation performance provided in an embodiment of this application.

[0027] The vehicle sound insulation performance optimization method of this application embodiment can be applied to the vehicle research and development design stage. Optimizing sound insulation performance before the placement location and space are locked can reduce manpower and development costs.

[0028] like Figure 1 As shown, the method for optimizing the sound insulation performance of a vehicle includes the following steps: In step S101, at least one acoustic component of the target area of ​​the vehicle is identified.

[0029] The target area is the vehicle body acoustic analysis area, which is the specific object for optimizing the vehicle's sound insulation performance; the acoustic components are the parts that achieve sound insulation function within the target area, such as the front bulkhead sound insulation pad and the front bulkhead baffle pad in the front bulkhead system area.

[0030] In one embodiment of this application, before identifying at least one acoustic component in the target area of ​​the vehicle, the method further includes: identifying the outer envelope of the passenger compartment of the vehicle; obtaining the noise transmission path of the vehicle and the location of the acoustic component; and dividing the outer envelope of the passenger compartment into multiple regions according to the noise transmission path and / or the location of the acoustic component.

[0031] Among them, the outer envelope of the passenger compartment is the outer contour structure of the passenger compartment driving space, which is the last core structural boundary that noise must penetrate before entering the passenger compartment; the noise transmission path is the structural path of external noise from the sound source to the passenger compartment.

[0032] It is understood that, according to the embodiments of this application, the outer envelope of the passenger compartment can be divided into multiple regions based on the noise transmission path of the vehicle and / or the location of acoustic components, so as to subsequently achieve the sound insulation assessment of a single region of the vehicle.

[0033] For example, Figure 2 As shown in the embodiment of this application, the outer envelope of the passenger compartment of the vehicle can be divided into 8 areas, including the front enclosure system, the front door system, the rear door system, the side enclosure system, the front floor system, the rear floor system, the roof system, and the rear enclosure system.

[0034] In step S102, the reference coverage and reference thickness distribution of the acoustic component in the target area are obtained. One of the reference coverage and reference thickness distribution is kept unchanged, while the other is adjusted to generate multiple candidate coverage and multiple candidate thickness distributions.

[0035] Among them, the reference coverage rate is the actual coverage ratio of acoustic components in the target area; the reference thickness distribution is the combination of the area ratio of each thickness range.

[0036] It is understood that the embodiments of this application can obtain the reference coverage and reference thickness distribution of acoustic components in the target area, keep one of the reference coverage and reference thickness distribution unchanged, and adjust the other to generate multiple candidate coverage and multiple candidate thickness distributions. By using a single-variable perturbation rule, the interference of adjusting multiple variables at the same time is eliminated, and the influence of coverage and thickness distribution on sound insulation is accurately located, which facilitates the subsequent optimization of the sound insulation performance of the target area.

[0037] In one embodiment of this application, obtaining the reference coverage and reference thickness distribution of acoustic components in a target area includes: dividing the thickness of the acoustic components into multiple thickness intervals according to preset rules; obtaining the area ratio of the acoustic components in each thickness interval; and generating a reference thickness distribution based on the area ratio of the multiple thickness intervals.

[0038] Among them, the preset rule is the rule for dividing the thickness range, which can be set according to the specific situation; the thickness range is the thickness segment into which the physical thickness of the acoustic component is divided, such as 5mm, 10mm; the area ratio is the area ratio of the acoustic material of a certain thickness range in the acoustic component of the target area, and the sum of the area ratios of all thickness ranges is 100%.

[0039] Since the acoustic components of a vehicle are usually composed of materials of various thicknesses, the embodiments of the application can divide the thickness of the acoustic components into multiple thickness intervals according to preset rules, obtain the area ratio of the acoustic components in each thickness interval, and generate a reference thickness distribution based on the area ratio of multiple thickness intervals, so as to achieve a refined analysis of the thickness distribution and better reflect the actual situation.

[0040] In one embodiment of this application, keeping one of the reference coverage and reference thickness distribution unchanged, and adjusting the other of the reference coverage and reference thickness distribution to generate multiple candidate coverage and multiple candidate thickness distributions, includes: keeping the reference thickness distribution unchanged, increasing or decreasing the coverage within a first preset range based on the reference coverage to generate multiple candidate coverage; keeping the reference coverage unchanged, increasing or decreasing the area proportion of the corresponding thickness interval within a second preset range based on the reference area proportion of each thickness interval, and simultaneously adjusting the area proportion of other thickness intervals according to the increase or decrease ratio to generate multiple candidate thickness distributions.

[0041] The first and second preset ranges can be set according to specific circumstances, without any specific limitations. For example, they can be set to 10% above or below the reference thickness distribution or 10% above or below the reference coverage.

[0042] It is understood that, in this embodiment of the application, the reference thickness distribution is kept unchanged, and the coverage rate is increased or decreased within a first preset range based on the reference coverage rate to generate multiple candidate coverage rates; while keeping the reference coverage rate unchanged, the area ratio of the corresponding thickness interval is increased or decreased within a second preset range based on the reference area ratio of each thickness interval, and the area ratio of other thickness intervals is adjusted according to the increase or decrease ratio to generate multiple candidate thickness distributions. The adjustment of the thickness distribution adopts single interval adjustment and normalization of the remaining intervals, which not only ensures the core requirement of single variable disturbance, but also avoids the problem of imbalance of the total area ratio of thickness intervals, ensuring the rationality of parameter adjustment.

[0043] Taking the front enclosure system as an example, this application embodiment identifies that the acoustic components of the front enclosure system include front enclosure sound insulation pads and front enclosure baffle pads, and statistically analyzes their coverage and thickness distribution (i.e., reference coverage and reference thickness distribution), using them as a benchmark method for acoustic components in the target area, as shown in Table 1 and Table 2 respectively. Table 1 is a coverage statistics table, and Table 2 is a thickness distribution statistics table.

[0044]

[0045]

[0046] Based on this baseline scheme, by increasing and decreasing the coverage by 10% respectively while keeping the thickness distribution ratio unchanged, the trend of the sound insulation of the front enclosure system with the coverage can be observed. Considering that the sound insulation pad is usually composed of a combination of materials of various thicknesses, this application treats eight thicknesses as independent perturbation variables, adjusting their area proportions by ±10% for each. During the perturbation process, in order to ensure that the sum of the proportions of each thickness remains 100%, a normalization method is used to scale the other thicknesses proportionally. This avoids the problem of total non-conservation while keeping the relative relationships between other thicknesses unchanged. When there are multiple layers of sound insulation pads stacked, if only the sound insulation of one layer is analyzed, only the thickness distribution of the target layer is perturbed, while the parameters of other layers remain unchanged, to ensure that the analysis results can truly reflect the independent contribution of that layer.

[0047] In step S103, the sound insulation is simulated under candidate coverage and multiple candidate thickness distributions using the statistical energy analysis model corresponding to the target area, and multiple first sound insulation curves corresponding to multiple candidate coverage and multiple second sound insulation curves corresponding to multiple candidate thickness distributions are obtained.

[0048] The SEA (Statistical Energy Analysis) model is a system-level acoustic simulation model for automotive noise. It calculates sound insulation through energy transfer in subsystems. For example, the SEA model for the front bulkhead system is as follows: Figure 3As shown, the sound insulation curve reflects the sound insulation performance of the target area under different frequencies and coverage rates, as well as different frequency and thickness distributions.

[0049] It is understood that the embodiments of this application can use the statistical energy analysis model corresponding to the target area to perform sound insulation simulation under candidate coverage and multiple candidate thickness distributions, and obtain multiple first sound insulation curves corresponding to multiple candidate coverage and multiple second sound insulation curves corresponding to multiple candidate thickness distributions, so as to be used for subsequent analysis of the sound insulation performance of the target area, avoiding the use of the trial and error method in related technologies, shortening the optimization cycle and reducing R&D costs.

[0050] The construction process of the SEA model of the front-end system in this application embodiment is as follows: 1. Mesh generation: The front bulkhead system sheet metal, front bulkhead sound insulation pad, and front bulkhead baffle pad are meshed using finite element software.

[0051] 2. Build the SEA model: Using SEA software, import the front bulkhead sheet metal to build the sheet metal model, and generate MNCT based on the front bulkhead sound insulation pad and front bulkhead baffle pad grid, and set the material parameters; build the sound source side and the receiving side sound cavity, and set the sound cavity parameters.

[0052] After the SEA model was built, load analysis and simulation were performed on the acoustic cavity on the sound source side to obtain the sound insulation curve.

[0053] In one embodiment of this application, sound insulation simulation is performed using a statistical energy analysis model corresponding to the target area under candidate coverage and multiple candidate thickness distributions. This includes: acquiring multiple test sound frequencies of the target area; performing a first sound insulation simulation at each candidate coverage using the statistical energy analysis model corresponding to the target area at each test sound frequency, wherein the thickness distribution of the acoustic components during the first sound insulation simulation is the corresponding reference thickness distribution; and performing a second sound insulation simulation at each candidate thickness distribution using the statistical energy analysis model corresponding to the target area at each test sound frequency, wherein the coverage of the acoustic components during the second sound insulation simulation is the reference coverage.

[0054] The test sound frequency can be the standard acoustic frequency point of 1 / 3 octave band from 200Hz to 8000Hz; the first sound insulation curve is a simulation with a fixed thickness distribution as the reference thickness distribution and only the coverage is changed, which is used to analyze the independent influence of the coverage on the sound insulation; the second sound insulation simulation is a simulation with a fixed coverage as the reference coverage and only the thickness distribution is changed, which is used to analyze the independent influence of the thickness distribution on the sound insulation.

[0055] It is understood that the embodiments of this application can obtain multiple test sound frequencies of the target area, and at each test frequency, perform a first sound insulation simulation for each candidate coverage, and at each test sound frequency, perform a second sound insulation simulation for each candidate thickness distribution. The simulation rule of single frequency and single candidate parameter is adopted to ensure that the sound insulation of each frequency and each parameter can be accurately traced, laying a data foundation for subsequent frequency-based sensitivity analysis. The simulation logic of single variable fixation is strictly followed to ensure that the results of the first and second sound insulation simulations only reflect the influence of a single parameter, eliminate parameter coupling interference, and improve the accuracy of the analysis results.

[0056] In step S104, the sensitivity of coverage to sound insulation and the sensitivity of thickness distribution to sound insulation are analyzed based on multiple first sound insulation curves and multiple second sound insulation curves. The sound insulation performance of the target area of ​​the vehicle is optimized based on the sensitivity analysis results.

[0057] Among them, the first sound insulation curve is the frequency-sound insulation curve obtained by simulation after changing the coverage rate with the thickness distribution unchanged; the second sound insulation curve is the frequency-sound insulation curve obtained by simulation after changing the thickness distribution with the coverage rate unchanged; sensitivity is used to measure the influence of parameters (thickness distribution and coverage rate) on sound insulation.

[0058] It is understood that the embodiments of this application can analyze the sensitivity of coverage to sound insulation and the sensitivity of thickness distribution to sound insulation based on multiple first sound insulation curves and multiple second sound insulation curves. Based on the sensitivity analysis results, the sound insulation performance of the target area of ​​the vehicle can be optimized, the optimization direction can be accurately located, and high-impact parameters can be adjusted first. While ensuring the improvement of sound insulation performance, the optimization efficiency can be improved, the development cost can be reduced, and the development cycle can be shortened.

[0059] This application's embodiments can identify acoustic components in the target area of ​​a vehicle and obtain reference values ​​for coverage and thickness distribution. Using a univariate perturbation rule that keeps one parameter constant while adjusting another, multiple candidate parameters for coverage and thickness distribution are generated. A statistical energy analysis model specific to the target area is then used to conduct sound insulation simulations under all candidate parameters, determining the first sound insulation curve corresponding to coverage and the second sound insulation curve corresponding to thickness distribution. Based on the sound insulation curves, the sensitivity of coverage and thickness distribution to sound insulation is analyzed, enabling quantitative identification of key parameters that significantly affect sound insulation. This avoids blind trial and error, and the vehicle's sound insulation performance is optimized based on the sensitivity analysis results. This allows for precise positioning of the optimization direction, prioritizing adjustments to high-impact parameters, improving optimization efficiency, reducing development costs, and shortening the development cycle while ensuring improved sound insulation performance.

[0060] In one embodiment of this application, analyzing the sensitivity of coverage to sound insulation and the sensitivity of thickness distribution to sound insulation based on multiple first sound insulation curves and multiple second sound insulation curves includes: calculating a first influence value of coverage at each test sound frequency and a first average influence value at multiple test sound frequencies based on the multiple first sound insulation curves; calculating a second influence value of thickness distribution at each test sound frequency and a second average influence value at multiple test sound frequencies based on the multiple second sound insulation curves; determining the sensitivity of coverage to sound insulation based on the first influence value and the first average influence value; and determining the sensitivity of thickness distribution to sound insulation based on the second influence value and the second influence value.

[0061] Among them, IV (Impact Value) is the sound insulation after adjusting the parameters (thickness distribution and coverage) upwards minus the sound insulation after adjusting downwards, quantifying the influence of a single parameter on the sound insulation at a single test frequency. The larger the value, the higher the sensitivity of the parameter at that frequency. MIV (Mean Impact Value) is the arithmetic mean of the influence values ​​at all test frequencies, quantifying the comprehensive influence of a single parameter on the sound insulation across the entire frequency range. The larger the value, the higher the overall sensitivity of the parameter. IV and MIV values ​​are indicators used to measure the sensitivity of the parameters to the sound insulation.

[0062] It is understood that, based on the first sound insulation curve, the first influence value of coverage at each test frequency and the first average influence value across all frequencies can be calculated. Based on the second sound insulation curve, the second influence value of thickness distribution at each test frequency and the second average influence value across all frequencies can be calculated. Based on the first influence value and the first average influence value, the sensitivity of coverage to sound insulation can be determined. Based on the second influence value and the second average influence value, the sensitivity of thickness distribution to sound insulation can be determined, so as to accurately locate the direction of vehicle sound insulation performance optimization and achieve high optimization efficiency.

[0063] The MIV theory described in this application is a method for identifying and assessing the importance of influencing factors in complex systems. It primarily uses sensitivity analysis to help analyze the degree of influence of different variables (factors) on the system's output. MIV theory is widely used in noise and vibration control, engineering optimization design, and is particularly important in vehicle design. The core of MIV theory lies in sensitivity analysis. By varying various variables of the system, the degree of influence of these changes on system outcomes (such as noise and vibration) is assessed. Typically, the sensitivity of each variable to the output is obtained through numerical simulation or experimental data analysis. These sensitivity values ​​reflect the degree of influence of variable changes on the system output. Through sensitivity analysis, MIV theory helps engineers identify which variables have a significant impact on the target output (e.g., in-vehicle noise and vibration). By comparing all variables, the most important variables can be determined. These important variables are usually the factors that need to be prioritized in the system design, optimization, and control process. MIV theory not only focuses on the influence of individual variables but also analyzes the interactions between different variables and how these interactions affect the final result. In the control of vehicle noise and vibration, the interactions between variables may affect the in-vehicle noise intensity through vibration propagation, resonance, and other means. Therefore, MIV theory helps to reveal the key pathways of these interactions.

[0064] MIV (Mean Influence Scale) is an indicator used to assess the degree of influence of an input independent variable on an output variable. The sign of the MIV indicates the direction of the influence, while its absolute value represents the degree of influence. MIV is generated by adding or subtracting 10% from the feature index values ​​of the independent variables to construct two new training samples. The change in influence on the model output when these new samples are used as input variables is calculated. The average of these changes yields the MIV value for each independent variable. This process is repeated for each independent variable to obtain its MIV value. The influence of each independent variable is then ranked by the intensity of color in a heatmap or line graph.

[0065] ; in, This represents the frequency range of the independent variable. The original value of the independent variable. Increase the prediction result by 10% for the independent variable. Predictions with 10% reduction in independent variables.

[0066] Specifically, the embodiments of this application can perform system-level simulation analysis on schemes with increased and decreased coverage and thickness distribution to obtain two complete sets of sound insulation curves. By comparing the differences between these two sets of curves and the baseline scheme, the corresponding IV can be calculated. Specifically, subtracting the decreased sound insulation from the increased sound insulation of a certain thickness yields the influence value of that thickness at different frequency points. Further, by averaging the influence value across the entire frequency range, the MIV of that thickness can be obtained, thereby quantifying its contribution to the overall sound insulation performance. Similarly, the sensitivity analysis of coverage can be performed by comparing the differences in sound insulation under conditions of increased and decreased coverage to obtain the sensitivity index of coverage to system performance.

[0067] During the analysis and processing phase, the MIV values ​​corresponding to different thicknesses can be compared and ranked to identify which thicknesses are the key factors affecting sound insulation. By comparing frequency domain curves, the effects of different thicknesses in different frequency bands can also be identified.

[0068] Taking the front enclosure system as an example, the sensitivity analysis of the sound insulation of the front enclosure system is as follows: Figure 4 As shown, the sensitivity analysis includes the average sound insulation value of the front enclosure system and the sensitivity analysis at various frequency points from 200Hz to 8000Hz. Specifically, this application only shows the sensitivity analysis at one frequency, which is... Figure 4 The diagram shown illustrates the sensitivity analysis at 200Hz.

[0069] Sensitivity analysis revealed significant differences in the impact of sound insulation pad coverage and thickness distribution on the system's sound insulation sensitivity across different frequency bands. Overall, coverage was the most critical factor affecting sound insulation, especially in the mid-to-low frequency range (200Hz-1000Hz), where the MIV value was highest, indicating that increasing coverage effectively improved front-end sound insulation and reduced noise transmission. In the high-frequency range (3150Hz-8000Hz), the sensitivity of specific thicknesses (such as thicker sound insulation pads) was more pronounced, demonstrating stronger control over high-frequency noise attenuation.

[0070] Coverage exhibits the highest average MIV value, indicating its greatest contribution to the sound insulation of the front bulkhead system, making it the primary factor. Overall, optimization of the front bulkhead system should prioritize improving coverage and adjusting key thicknesses at specific frequencies to achieve a balance in noise control. This provides quantitative guidance for the design of the vehicle's acoustic package, supports rapid iteration of solutions during the development phase, and improves the vehicle's mid-to-high frequency airborne acoustic performance.

[0071] Specifically, taking the front enclosure system as an example, the overall implementation process of the vehicle sound insulation performance optimization method in this application embodiment is as follows: Figure 5 As shown, it includes: 1. The area to be improved in terms of sound insulation is the front fascia system.

[0072] 2. Confirm the composition of the acoustic components of the front bulkhead system, including the front bulkhead sound insulation pad and the front bulkhead baffle liner.

[0073] 3. Confirm the basic scheme of thickness distribution and coverage of the front bulkhead system, and the current thickness distribution and sheet metal coverage of the front bulkhead sound insulation pad and front bulkhead baffle pad.

[0074] 4. Increase and decrease the coverage rate by 10% respectively, while keeping the thickness distribution ratio unchanged. Adjust the area ratio of each component by ±10% while keeping the coverage rate unchanged.

[0075] 5. Perform system-level simulation analysis on the adjusted and lowered schemes respectively, calculate the corresponding impact values, and obtain the impact values ​​of thickness distribution or coverage at different frequency points. By averaging the impact values ​​over the entire frequency range, the average impact value can be obtained.

[0076] 6. Establish the mapping relationship between coverage distribution and system sound insulation, and thickness distribution and system sound insulation, and optimize the sound insulation performance of the front enclosure system based on the mapping relationship.

[0077] In summary, the entire process establishes a mapping relationship between coverage and thickness distribution and system sound insulation, and clearly reveals the importance of different design parameters through sensitivity indicators. This process includes the establishment and verification of benchmark schemes, as well as perturbation design, sound insulation calculation, sensitivity extraction, and result interpretation, forming a complete and logically clear technical route. This method not only identifies the most sensitive thickness parameters and coverage configurations but also provides quantitative basis for subsequent acoustic package optimization, thereby achieving a more reasonable trade-off between cost, weight, and sound insulation performance.

[0078] The vehicle sound insulation performance optimization method proposed in this application can identify acoustic components in the target area of ​​the vehicle and obtain reference values ​​for coverage and thickness distribution. It uses a univariate perturbation rule that keeps one parameter constant while adjusting another to generate multiple candidate parameters for coverage and thickness distribution. Then, using a statistical energy analysis model specific to the target area, it conducts sound insulation simulations under all candidate parameters to determine the first sound insulation curve corresponding to the coverage and the second sound insulation curve corresponding to the thickness distribution. Based on the sound insulation curves, it analyzes the sensitivity of coverage and thickness distribution to sound insulation, quantitatively identifying key parameters that significantly affect sound insulation, avoiding blind trial and error. Based on the sensitivity analysis results, it optimizes the vehicle's sound insulation performance to accurately pinpoint the optimization direction, prioritizing adjustments to high-impact parameters. This ensures improved sound insulation performance while increasing optimization efficiency, reducing development costs, and shortening the development cycle.

[0079] Next, the vehicle sound insulation performance optimization device according to the embodiments of this application is described with reference to the accompanying drawings.

[0080] Figure 6 This is a block diagram of a vehicle sound insulation performance optimization device according to an embodiment of this application.

[0081] like Figure 6 As shown, the vehicle sound insulation performance optimization device 10 includes: an identification module 100, a generation module 200, a simulation module 300, and an optimization module 400.

[0082] The identification module 100 is used to identify at least one acoustic component in the target area of ​​the vehicle; the generation module 200 is used to obtain the reference coverage and reference thickness distribution of the acoustic component in the target area, keeping one of the reference coverage and reference thickness distribution unchanged, and adjusting the other to generate multiple candidate coverage and multiple candidate thickness distributions; the simulation module 300 is used to perform sound insulation simulation under the candidate coverage and multiple candidate thickness distributions using the statistical energy analysis model corresponding to the target area, and obtain multiple first sound insulation curves corresponding to multiple candidate coverage and multiple second sound insulation curves corresponding to multiple candidate thickness distributions; the optimization module 400 is used to analyze the sensitivity of coverage to sound insulation and the sensitivity of thickness distribution to sound insulation based on the multiple first sound insulation curves and multiple second sound insulation curves, and optimize the sound insulation performance of the target area of ​​the vehicle based on the sensitivity analysis results.

[0083] In one embodiment of this application, the vehicle sound insulation performance optimization device 10 of this application embodiment further includes: a division module.

[0084] The segmentation module is used to identify the outer envelope of the vehicle's passenger compartment before identifying at least one acoustic component in the target area of ​​the vehicle; obtain the noise transmission path and the location of the acoustic components; and divide the outer envelope of the passenger compartment into multiple regions based on the noise transmission path and / or the location of the acoustic components.

[0085] In one embodiment of this application, the generation module 200 is further configured to: divide the thickness of the acoustic component into multiple thickness intervals according to a preset rule; obtain the area ratio of the acoustic component in each thickness interval, and generate a reference thickness distribution based on the area ratio of the multiple thickness intervals.

[0086] In one embodiment of this application, the generation module 200 is further configured to: keep the reference thickness distribution unchanged, increase or decrease the coverage rate within a first preset range based on the reference coverage rate, so as to generate multiple candidate coverage rates; keep the reference coverage rate unchanged, increase or decrease the area ratio of the corresponding thickness interval within a second preset range based on the reference area ratio of each thickness interval, and adjust the area ratio of other thickness intervals according to the increase or decrease ratio, so as to generate multiple candidate thickness distributions.

[0087] In one embodiment of this application, the simulation module 300 is further configured to: acquire multiple test sound frequencies of the target area; at each test sound frequency, perform a first sound insulation simulation at each candidate coverage using the statistical energy analysis model corresponding to the target area, wherein the thickness distribution of the acoustic components during the first sound insulation simulation is the corresponding reference thickness distribution; and at each test sound frequency, perform a second sound insulation simulation at each candidate thickness distribution using the statistical energy analysis model corresponding to the target area, wherein the coverage of the acoustic components during the second sound insulation simulation is the reference coverage.

[0088] In one embodiment of this application, the optimization module 400 is further configured to: calculate a first influence value of coverage at each test sound frequency and a first average influence value at the multiple test sound frequencies based on multiple first sound insulation curves; calculate a second influence value of thickness distribution at each test sound frequency and a second average influence value at the multiple test sound frequencies based on multiple second sound insulation curves; determine the sensitivity of coverage to sound insulation based on the first influence value and the first average influence value; and determine the sensitivity of thickness distribution to sound insulation based on the second influence value and the second influence value.

[0089] It should be noted that the foregoing explanation of the vehicle sound insulation performance optimization method embodiment also applies to the vehicle sound insulation performance optimization device of this embodiment, and will not be repeated here.

[0090] According to the vehicle sound insulation performance optimization device proposed in this application, it can identify the acoustic components of the target area of ​​the vehicle and obtain reference benchmark values ​​for coverage and thickness distribution. Using a univariate perturbation rule that keeps one parameter constant while adjusting another, it generates multiple candidate parameters for coverage and thickness distribution. Then, using a statistical energy analysis model specific to the target area, it conducts sound insulation simulations under all candidate parameters to determine the first sound insulation curve corresponding to the coverage and the second sound insulation curve corresponding to the thickness distribution. Based on the sound insulation curves, it analyzes the sensitivity of coverage and thickness distribution to sound insulation, quantitatively identifying key parameters that significantly affect sound insulation, avoiding blind trial and error. Furthermore, it optimizes the vehicle's sound insulation performance based on the sensitivity analysis results, accurately positioning the optimization direction, prioritizing the adjustment of high-impact parameters, and improving optimization efficiency, reducing development costs, and shortening the development cycle while ensuring improved sound insulation performance.

[0091] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and capable of running on the processor 702.

[0092] When the processor 702 executes the program, it implements the vehicle sound insulation performance optimization method provided in the above embodiments.

[0093] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0094] The memory 701 is used to store computer programs that can run on the processor 702.

[0095] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0096] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0097] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0098] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0099] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor, implements the above-described method for optimizing vehicle sound insulation performance.

[0100] This application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described method for optimizing vehicle sound insulation performance.

[0101] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0102] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0103] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0104] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0105] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

Claims

1. A method for optimizing the sound insulation performance of a vehicle, characterized in that, Includes the following steps: At least one acoustic component that identifies a target area of ​​the vehicle; The reference coverage and reference thickness distribution of the acoustic component in the target area are obtained. One of the reference coverage and the reference thickness distribution is kept unchanged, and the other of the reference coverage and the reference thickness distribution is adjusted to generate multiple candidate coverage and multiple candidate thickness distributions. Using the statistical energy analysis model corresponding to the target area, sound insulation simulation is performed under the candidate coverage rate and multiple candidate thickness distributions to obtain multiple first sound insulation curves corresponding to multiple candidate coverage rates and multiple second sound insulation curves corresponding to the candidate thickness distributions. Based on the multiple first sound insulation curves and the multiple second sound insulation curves, the sensitivity of the coverage rate to the sound insulation and the sensitivity of the thickness distribution to the sound insulation are analyzed, and the sound insulation performance of the target area of ​​the vehicle is optimized based on the sensitivity analysis results.

2. The method for optimizing vehicle sound insulation performance according to claim 1, characterized in that, Prior to identifying at least one acoustic component in the target area of ​​the vehicle, the method also includes: Identify the outer envelope of the passenger compartment of the vehicle; Obtain the noise transmission path and acoustic component placement locations of the vehicle; Based on the noise transmission path and / or the location of the acoustic components, the outer envelope of the occupant compartment is divided into multiple regions.

3. The method for optimizing vehicle sound insulation performance according to claim 1, characterized in that, The step of obtaining the reference coverage and reference thickness distribution of the acoustic component in the target area includes: The thickness of the acoustic component is divided into multiple thickness ranges according to a preset rule; The area ratio of the acoustic component in each thickness range is obtained, and the reference thickness distribution is generated based on the area ratio of the multiple thickness ranges.

4. The method for optimizing vehicle sound insulation performance according to claim 3, characterized in that, The step of keeping one of the reference coverage and the reference thickness distribution unchanged, and adjusting the other of the reference coverage and the reference thickness distribution to generate multiple candidate coverage and multiple candidate thickness distributions includes: Keeping the reference thickness distribution unchanged, the coverage rate is increased or decreased within a first preset range based on the reference coverage rate to generate multiple candidate coverage rates; Keeping the reference coverage unchanged, the area ratio of the corresponding thickness interval is increased or decreased within a second preset range based on the reference area ratio of each thickness interval. At the same time, the area ratio of other thickness intervals is adjusted according to the increase or decrease ratio to generate multiple candidate thickness distributions.

5. The method for optimizing vehicle sound insulation performance according to claim 3, characterized in that, The step of using the statistical energy analysis model corresponding to the target area to simulate sound insulation under the candidate coverage and multiple candidate thickness distributions includes: Acquire multiple test sound frequencies in the target area; At each test sound frequency, the statistical energy analysis model corresponding to the target area is used to perform a first sound insulation simulation at each candidate coverage rate, wherein the thickness distribution of the acoustic components during the first sound insulation simulation is the corresponding reference thickness distribution; At each test sound frequency, a second sound insulation simulation is performed using the statistical energy analysis model corresponding to the target area under each candidate thickness distribution, wherein the coverage rate of the acoustic component during the second sound insulation simulation is the reference coverage rate.

6. The method for optimizing vehicle sound insulation performance according to claim 5, characterized in that, The analysis of the sensitivity of the coverage rate to the sound insulation and the sensitivity of the thickness distribution to the sound insulation based on the plurality of first sound insulation curves and the plurality of second sound insulation curves includes: Calculate the first influence value of the coverage rate at each test sound frequency and the first average influence value at the plurality of test sound frequencies based on the plurality of first sound insulation curves; Calculate the second influence value of the thickness distribution at each test sound frequency and the second average influence value at the multiple test sound frequencies based on the multiple second sound insulation curves; The sensitivity of the coverage rate to the sound insulation is determined based on the first influence value and the first average influence, and the sensitivity of the thickness distribution to the sound insulation is determined based on the second influence value and the second influence value.

7. A device for optimizing vehicle sound insulation performance, characterized in that, include: A recognition module for recognizing at least one acoustic component in a target area of ​​a vehicle; A generation module is used to obtain the reference coverage and reference thickness distribution of the acoustic component in the target area, keep one of the reference coverage and the reference thickness distribution unchanged, and adjust the other of the reference coverage and the reference thickness distribution to generate multiple candidate coverage and multiple candidate thickness distributions. The simulation module is used to simulate the sound insulation under the candidate coverage and multiple candidate thickness distributions using the statistical energy analysis model corresponding to the target area, and to obtain multiple first sound insulation curves corresponding to multiple candidate coverage and multiple second sound insulation curves corresponding to the candidate thickness distribution. The optimization module is used to analyze the sensitivity of the coverage rate to the sound insulation and the sensitivity of the thickness distribution to the sound insulation based on the plurality of first sound insulation curves and the plurality of second sound insulation curves, and to optimize the sound insulation performance of the target area of ​​the vehicle based on the sensitivity analysis results.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle sound insulation performance optimization method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instructions are executed by a processor to implement the vehicle sound insulation performance optimization method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the vehicle sound insulation performance optimization method as described in any one of claims 1-6.

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