An in-vehicle wind noise prediction method and device, electronic equipment and storage medium

Abstract

The application discloses an in-vehicle wind noise prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a side window surface wind noise time domain signal of a model vehicle at different wind speeds; processing the side window surface wind noise time domain signal to determine a side window surface wind noise average sound pressure level increment; using a predetermined wind noise correlation to determine an in-vehicle noise sound pressure level increment associated with the side window surface wind noise average sound pressure level increment; wherein the wind noise correlation is obtained by difference technology and curve estimation regression analysis according to the side window surface wind noise and the in-vehicle wind noise of a base vehicle; and determining the in-vehicle wind noise according to the in-vehicle noise sound pressure level increment. According to the technical scheme, the in-vehicle wind noise can be predicted through the wind noise correlation, the prediction reliability is high, the method is simple and easy to use, and the time consumption is short.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile wind noise test, in particular to an in-vehicle wind noise prediction method and device, electronic equipment and storage medium. BACKGROUND

[0002] Nowadays, people spend more and more time driving on the highway, and the requirements for automobile wind noise are also getting higher and higher. Due to the influence of the intersection area of windshield-A column-side window and outside rearview mirror, the front side window area is the main noise source of wind noise. To control this part of wind noise, it is necessary to start from the overall design and local design of the vehicle body, reduce the interaction between airflow and vehicle body, and thus reduce the pressure fluctuation of airflow.

[0003] The current common method is to use simulation analysis and model car wind tunnel test. The simulation analysis process is complex and time-consuming, the in-vehicle wind noise analysis result is quite different from the actual vehicle test result, the target is difficult to determine, and the wind noise target achievement situation cannot be directly evaluated. Model car wind tunnel test can accurately verify the side window field wind noise, but it lacks verification means for in-vehicle wind noise. The oil mud model with acoustic cabin can test the in-vehicle wind noise level, but it is quite different from the actual vehicle and cannot be used as a basis for evaluating the in-vehicle wind noise target achievement situation, and the model making cost is high, the cycle is long, and the technical difficulty is great.

[0004] Therefore, there is an urgent need for a method that can quickly, accurately and at low cost predict in-vehicle wind noise in the early stage of vehicle development. SUMMARY

[0005] The present application provides an in-vehicle wind noise prediction method, device, electronic equipment and storage medium to predict the in-vehicle wind noise of a model car.

[0006] According to one aspect of the present application, an in-vehicle wind noise prediction method is provided, which comprises:

[0007] Obtaining the side window surface wind noise time domain signal of the model car under different wind speeds;

[0008] Processing the side window surface wind noise time domain signal to determine the side window surface wind noise average sound pressure level increment value; wherein the side window surface wind noise average sound pressure level increment value is used to represent the increment value of the side window surface wind noise average sound pressure level under adjacent wind speeds; and the side window surface wind noise average sound pressure level is used to represent the average sound pressure of the side window surface of the model car;

[0009] Using a predetermined wind noise correlation to determine the in-vehicle noise sound pressure level increment value associated with the side window surface wind noise average sound pressure level increment value; wherein the wind noise correlation is obtained by difference technique and curve estimation regression analysis according to the side window surface wind noise and in-vehicle wind noise of a base vehicle.

[0010] determine the wind noise in the vehicle according to the wind noise level increment value.

[0011] According to another aspect of the present application, there is provided an in-vehicle wind noise prediction device, comprising:

[0012] a side window surface wind noise time domain signal acquisition module configured to acquire a side window surface wind noise time domain signal of a model vehicle at different wind speeds;

[0013] a side window surface wind noise average sound pressure level increment value determination module configured to process the side window surface wind noise time domain signal and determine a side window surface wind noise average sound pressure level increment value, wherein the side window surface wind noise average sound pressure level increment value is used to represent an increment value of the average sound pressure level of the side window surface wind noise at adjacent wind speeds, and the average sound pressure level of the side window surface wind noise is used to represent the average sound pressure of the side window surface of the model vehicle;

[0014] an in-vehicle noise sound pressure level increment value determination module configured to determine an in-vehicle noise sound pressure level increment value associated with the side window surface wind noise average sound pressure level increment value by using a pre-determined wind noise correlation, wherein the wind noise correlation is obtained by difference technique and curve estimation regression analysis based on the side window surface wind noise and the in-vehicle wind noise of a base vehicle;

[0015] an in-vehicle wind noise determination module configured to determine the wind noise in the vehicle according to the wind noise level increment value.

[0016] According to another aspect of the present application, there is provided an electronic device, comprising:

[0017] at least one processor; and

[0018] a memory connected to the at least one processor in communication; wherein,

[0019] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute an in-vehicle wind noise prediction method according to any one of the embodiments of the present application.

[0020] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to implement an in-vehicle wind noise prediction method according to any one of the embodiments of the present application when executed by the processor.

[0021] The technical scheme of the embodiment of the present application comprises the following steps: obtaining the side window surface wind noise time domain signal of a model vehicle under different wind speeds, then processing the side window surface wind noise time domain signal to obtain the side window surface wind noise average sound pressure level increment; determining the vehicle interior noise sound pressure level increment associated with the side window surface wind noise average sound pressure level increment by using the pre-determined wind noise correlation relationship; and determining the vehicle interior wind noise according to the vehicle interior noise sound pressure level increment. By using the wind noise correlation relationship, the vehicle interior wind noise can be predicted, the prediction reliability is high, and the method is simple and easy to use, and the time consumption is short.

[0022] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0024] Figure 1 is a flow chart of a vehicle interior wind noise prediction method provided by the first embodiment of the present application;

[0025] Figure 2 is a flow chart of a wind noise correlation relationship determination process provided by the second embodiment of the present application;

[0026] Figure 3 is a calculation result graph of the average sound pressure level of the base vehicle under each vehicle speed provided by the second embodiment of the present application;

[0027] Figure 4 is a function curve graph of the vehicle interior wind noise average sound pressure level increment of the base vehicle varying with the side window surface wind noise average sound pressure level increment of the base vehicle provided by the second embodiment of the present application;

[0028] Figure 5 is a flow chart of another vehicle interior wind noise prediction method provided by the third embodiment of the present application;

[0029] Figure 6 is a calculation result graph of the side window surface wind noise average sound pressure level of the model vehicle under each wind speed provided by the third embodiment of the present application;

[0030] Figure 7 is a comparison graph of the calculated value and the measured value of the vehicle interior wind noise sound pressure level of the model vehicle under each vehicle speed provided by the third embodiment of the present application;

[0031] Figure 8 is a flow chart of another in-vehicle wind noise prediction method provided by an embodiment of the present application;

[0032] Figure 9 is a structural schematic diagram of an in-vehicle wind noise prediction device provided by the fourth embodiment of the present application;

[0033] Figure 10 is a structural schematic diagram of an electronic device for implementing the in-vehicle wind noise prediction method of the present application. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the scope of the present application.

[0035] It should be noted that the terms in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0036] Embodiment one

[0037] Figure 1 A flow chart of an in-vehicle wind noise prediction method provided by the first embodiment of the present application, the present embodiment can be applicable to the case of predicting the in-vehicle wind noise level in the early stage of vehicle development. The method can be executed by an in-vehicle wind noise prediction device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device for in-vehicle wind noise prediction. As shown in the figure, the method comprises: Figure 1

[0038] S110, acquiring a side window surface wind noise time domain signal of a model vehicle at different wind speeds.

[0039] The model vehicle can be a hard resin model vehicle, which is a vehicle in the development stage. ​

[0040] In the embodiment, the wind speed can be set according to the model vehicle wind noise prediction requirements in the model vehicle. For example, the wind speed can be set as 60 km / h, 80 km / h, 100 km / h and 120 km / h. That is, the model vehicle is tested in the acoustic wind tunnel at a wind speed of 60 km / h, 80 km / h, 100 km / h and 120 km / h. Among them, the acoustic wind tunnel test environment can be set as: the temperature is in the range of 25℃±5℃, the humidity is in the range of 10%~85%, the maximum wind speed of the measurement section is greater than 160km / h, the wind tunnel low frequency vibration index is less than 3.0%, the measurement section is a semi-anechoic chamber structure, the free field space radius is greater than 6m, and the cutoff frequency is less than 80Hz.

[0041] In the scheme, the signal acquisition device can be used to obtain the side window surface wind noise of the model vehicle at different wind speeds, and the side window surface wind noise time domain signal at each wind speed is obtained.

[0042] S120, processing the side window surface wind noise time domain signal to determine the side window surface wind noise average sound pressure level increment value; wherein the side window surface wind noise average sound pressure level increment value is used to represent the increment value of the side window surface wind noise average sound pressure level at adjacent wind speed; and the side window surface wind noise average sound pressure level is used to represent the average sound pressure of the side window surface of the model vehicle.

[0043] In the scheme, the side window surface wind noise average sound pressure level is used to represent the average sound pressure of the side window surface of the model vehicle, and the side window surface wind noise average sound pressure level at each wind speed can be obtained by processing the side window surface wind noise time domain signal.

[0044] In the embodiment, after obtaining the side window surface wind noise average sound pressure level at each wind speed, the increment value of the side window surface wind noise average sound pressure level at adjacent wind speed can be calculated based on the relationship between the side window surface wind noise average sound pressure level and the wind speed.

[0045] S130, using the pre-determined wind noise correlation to determine the vehicle interior noise sound pressure level increment value associated with the side window surface wind noise average sound pressure level increment value; wherein the wind noise correlation is obtained by difference technique and curve estimation regression analysis according to the side window surface wind noise of the base vehicle and the vehicle interior wind noise.

[0046] Among them, the base vehicle can refer to a vehicle that has been put into use.

[0047] In the scheme, the side window surface wind noise and the in-vehicle wind noise of the base vehicle at the speeds of 40km / h, 60km / h, 80km / h, 100km / h and 120km / h can be acquired in advance. The side window surface wind noise and the in-vehicle wind noise of the base vehicle at different speeds are processed to obtain the wind noise correlation relationship of the side window surface wind noise average sound pressure level increment and the in-vehicle noise sound pressure level increment.

[0048] In the embodiment, the in-vehicle noise sound pressure level increment can be calculated by substituting the side window surface wind noise average sound pressure level increment into the wind noise correlation relationship.

[0049] In the technical scheme, the in-vehicle noise sound pressure level increment associated with the side window surface wind noise average sound pressure level increment can be determined by using the predetermined wind noise correlation relationship, and the determination comprises:

[0050] According to the side window surface wind noise average sound pressure level increment, the in-vehicle noise sound pressure level increment corresponding to the side window surface wind noise average sound pressure level increment in the predetermined wind noise correlation relationship is searched.

[0051] In the scheme, the side window surface wind noise average sound pressure level increment and the in-vehicle noise sound pressure level increment correlation relationship is calculated in advance according to the side window surface wind noise and the in-vehicle wind noise of the base vehicle. After the side window surface wind noise average sound pressure level increment is determined, the side window surface wind noise average sound pressure level increment can be substituted into the wind noise correlation relationship to calculate the in-vehicle noise sound pressure level increment.

[0052] By determining the in-vehicle noise sound pressure level increment, the in-vehicle wind noise at the ear of the driver at different speeds can be determined based on the in-vehicle noise sound pressure level increment, and the prediction reliability can be improved.

[0053] S140, according to the in-vehicle noise sound pressure level increment, the in-vehicle wind noise is determined.

[0054] The in-vehicle noise sound pressure level increment can be the increment of the wind noise sound pressure level at the ear of the driver at the adjacent speed on the road.

[0055] In the technical scheme, the in-vehicle wind noise can be determined according to the in-vehicle noise sound pressure level increment, and the determination comprises:

[0056] The in-vehicle noise sound pressure level increment is processed by using the reverse difference technology to obtain the in-vehicle wind noise at the ear of the driver.

[0057] Specifically, the in-vehicle noise pressure level at the ear of the driver of the base vehicle at the minimum vehicle speed can be taken as a reference, and the inverse difference technique is used to solve the in-vehicle wind noise at the ear of the driver of the model vehicle at other speeds. For example, the in-vehicle noise pressure level of the base vehicle at a speed of 40 km / h can be taken as a reference to solve the in-vehicle wind noise of the model vehicle at speeds of 60 km / h, 80 km / h, 100 km / h and 120 km / h, respectively.

[0058] By predicting the in-vehicle wind noise, the in-vehicle wind noise prediction has high reliability, and the test accuracy and the wind noise correlation calculation accuracy are controllable. The in-vehicle wind noise target of the model vehicle in the modeling stage can be intuitively evaluated.

[0059] The technical scheme of the embodiment of the present application obtains the side window surface wind noise time domain signal of the model vehicle at different wind speeds, then processes the side window surface wind noise time domain signal to obtain the side window surface wind noise average sound pressure level increment, and determines the in-vehicle noise pressure level increment associated with the side window surface wind noise average sound pressure level increment by using the pre-determined wind noise correlation. According to the in-vehicle noise pressure level increment, the in-vehicle wind noise is determined. By executing the technical scheme, the in-vehicle wind noise of the model vehicle on the road can be predicted by the wind noise correlation, the prediction has high reliability, and the method is simple and easy to use, and time-consuming is short.

[0060] Embodiment two

[0061] Figure 2 The flowchart of the wind noise correlation determination process provided by the second embodiment of the present application, the relationship between the present embodiment and the above-mentioned embodiment is described in detail. As shown in Figure 2 The method comprises:

[0062] S210, obtaining the in-vehicle wind noise time domain signal and the side window surface wind noise time domain signal of the base vehicle at different speeds;

[0063] Specifically, the side window of the base vehicle is divided into a plurality of test regions, and the area S of each test region and the total area S of the side window are recorded after measurement and calculation. Wherein, the test region can be divided to be finer near the rearview mirror and the A-pillar, and to be coarser away from the rearview mirror and the A-pillar. The number of test regions can be determined according to specific requirements and equipment resources, generally not less than 5, the more the number, the higher the test accuracy, generally not more than 15. For example, the side window of the base vehicle can be divided into 10 test regions. i

[0064] ​A plurality of surface microphones are arranged outside the side window of the base vehicle, and a microphone is arranged at the headrest of the seat corresponding to the side window; the surface microphones are arranged one-to-one corresponding to the test areas divided above, and are arranged at the center of each test area, and the microphone is arranged at the position of the driver's outer ear. If the number of test areas is large, a batch testing method can be used to reduce the number of surface microphones pasted at a time, and the total number of test areas will not be reduced, which can improve the accuracy and reduce the interference of the surface microphones on the flow field of the side window surface. For example, 10 calibrated surface microphones are arranged outside the side window of the base vehicle, and are arranged at the center of 10 test areas, 5 at a time, and are arranged in 2 times; and a calibrated microphone is arranged at the position of the driver's outer ear of the base vehicle. Tesa-4657 tape is used to seal the door, window and other surface gaps of the base vehicle.

[0065] Under the required working conditions, the wind noise test of the base vehicle is completed on the road to obtain the side window surface wind noise time domain signal and the in-vehicle wind noise time domain signal. Optionally, the base vehicle is tested for wind noise at speeds of 40km / h, 60km / h, 80km / h, 100km / h and 120km / h on the road, and the side window surface wind noise time domain signal and the in-vehicle wind noise time domain signal at the driver's ear are obtained in 2 rounds of testing. The test site is a closed road, the runway is a smooth asphalt road, and the environmental wind speed is not greater than 3m / s.

[0066] S220, processing the in-vehicle wind noise time domain signal and the side window surface wind noise time domain signal of the base vehicle to obtain the in-vehicle wind noise average sound pressure level increase value and the side window surface wind noise average sound pressure level increase value of the base vehicle;

[0067] In this scheme, the side window surface wind noise time domain signal of the base vehicle is converted into a frequency domain noise signal by Fourier transform, and A-weighting processing is performed; the area weighting method is used to obtain the average sound power level of the side window surface wind noise of the base vehicle at each speed, and the average sound pressure level of the side window surface wind noise of the base vehicle at each speed is obtained by logarithmic operation.

[0068] Specifically, the average sound pressure level of the side window surface wind noise is calculated by the following formula:

[0069]

[0070] wherein p Ext represents the average sound pressure level of the side window surface wind noise, n represents the number of test areas, S i represents the area of the test area, p i,Ext represents the sound pressure level of the side window surface wind noise of the test area.

[0071] After the average sound pressure level of the side window surface wind noise, the average sound pressure level increment of the side window surface wind noise of the base car at 60km / h, 80km / h, 100km / h and 120km / h speeds is solved respectively by using the difference technique, taking the average sound pressure level of the side window surface wind noise of the base car at 40km / h speed as the reference Ext(基础车) .

[0072] The time domain signal of the in-vehicle wind noise near the driver's ear of the base car is converted into a frequency domain signal by Fourier transform, and A-weighting processing is performed to obtain the average sound pressure level of the in-vehicle wind noise near the driver's ear of the base car at each speed Int(基础车) .

[0073] Taking the average sound pressure level of the in-vehicle wind noise near the driver's ear of the base car at 40km / h speed as the reference, the average sound pressure level increment of the in-vehicle wind noise near the driver's ear of the base car at 60km / h, 80km / h, 100km / h and 120km / h speeds is solved respectively by using the difference technique Int(基础车) .

[0074] Exemplarily, Figure 3 is a calculation result diagram of the average sound pressure level of the base car at each speed provided by the embodiment two of the present application, Figure 3 (a) is a calculation result diagram of the average sound pressure level of the side window surface wind noise of the base car at each speed; Figure 3 (b) is a calculation result diagram of the average sound pressure level of the in-vehicle wind noise of the base car at each speed, as Figure 3 indicated, the time domain signal of the in-vehicle wind noise near the driver's ear and the time domain signal of the side window surface wind noise of the base car can be converted into a frequency domain signal by Fourier transform, and A-weighting processing is performed to obtain the average sound pressure level of the side window surface wind noise of the base car and the average sound pressure level of the in-vehicle wind noise near the driver's ear of the base car at each speed.

[0075] S230, taking the average sound pressure level increment of the in-vehicle wind noise of the base car as the dependent variable, and taking the average sound pressure level increment of the side window surface wind noise of the base car as the independent variable, a wind noise correlation relationship is constructed.

[0076] Specifically, taking the ΔP Ext(基础车) at different speeds as the independent variable, and taking the ΔP Int(基础车) as the dependent variable, a function relationship curve of the dependent variable and the independent variable is established by curve estimation regression analysis, the number of function relationship curves is reduced by dividing the frequency interval to take the average value, the calculation process is simplified, and the wind noise correlation relationship of the corresponding function relationship of the dependent variable and the independent variable is determined. Preferably, the wind noise correlation relationship can be constructed by curve estimation regression analysis of SPSS software.

[0077] Exemplarily, Figure 4is a function curve diagram provided by the second embodiment of the present application, which shows the change of the average sound pressure level increment of the wind noise in the basic car with the average sound pressure level increment of the side window surface wind noise of the basic car. The noise below 200 Hz is mainly contributed by the power assembly and the road surface, and the wind noise energy above 8000 Hz is very small, so the function relationship between the dependent variable and the independent variable is established in the frequency band range of 200-8000 Hz. As shown in Figure 4 , the trends of the 17 corresponding function relationship curves at all frequencies are similar, and are distributed within a certain range. In order to reduce the number of wind noise correlation relationships and simplify the calculation process, 200-1250 Hz is taken as the first frequency interval, and 1600-8000 Hz is taken as the second frequency interval. The dependent variable and the independent variable in the two frequency intervals are averaged, and the average value is taken as the simplified replacement value of the dependent variable and the independent variable in the corresponding frequency interval, to obtain the simplified wind noise correlation relationship:

[0078] The wind noise correlation relationship of the corresponding function of the 200-1250 Hz frequency interval is:

[0079] ΔP Int(1) = 0.0085877138 x ΔP Ext(1) 2 + 0.2455137065 x ΔP Ext(1) + 1.1471873997;

[0080] The wind noise correlation relationship of the corresponding function of the 1600-8000 Hz frequency interval is:

[0081] ΔP Int(2) = 0.0064880446 x ΔP Ext(2) 2 + 0.1869146333 x ΔP Ext(2) + 0.5138751409;

[0082] The square values of the correlation coefficients of the two wind noise correlation relationships are 0.9999587556 and 0.9999907422 respectively, which shows that the regression analysis of the wind noise correlation relationship and the change trend of the dependent variable with the independent variable has very good correlation.

[0083] The technical scheme of the embodiment of the present application obtains the in-vehicle wind noise time domain signal and the side window surface wind noise time domain signal of the base vehicle at different vehicle speeds, processes the in-vehicle wind noise time domain signal and the side window surface wind noise time domain signal of the base vehicle, obtains the in-vehicle wind noise average sound pressure level increment of the base vehicle and the side window surface wind noise average sound pressure level increment of the base vehicle, then takes the in-vehicle wind noise average sound pressure level increment of the base vehicle as the dependent variable, takes the side window surface wind noise average sound pressure level increment of the base vehicle as the independent variable, and constructs the wind noise correlation. By executing the technical scheme, the wind noise correlation is not an actual transfer function, the wind noise generated by the windshield, chassis and other regions and the contribution of other sound sources such as the power assembly, tires and chassis are considered to some extent in the wind noise correlation, the in-vehicle wind noise level on the road can be predicted, which is closer to the customer's use condition, and is not limited to the road, and can be extended to the acoustic wind tunnel.

[0084] Embodiment three

[0085] Figure 5 The flowchart of another in-vehicle wind noise prediction method provided by the third embodiment of the present application, the relationship between the present embodiment and the above-mentioned embodiments is a further detailed description of the process of obtaining the side window surface wind noise time domain signal of the model vehicle at different wind speeds. As shown in Figure 5 The method comprises the following steps:

[0086] S510, the side window surface of the model vehicle is divided according to a pre-set division mode, and at least two test regions are obtained;

[0087] In the present scheme, the division mode can be set according to the in-vehicle wind noise prediction requirement of the model vehicle. Specifically, the division mode is consistent with the division mode of the base vehicle. For example, the side window surface of the model vehicle can be divided into 10 test regions.

[0088] S520, based on the surface microphone installed on the at least two test regions, the side window surface wind noise time domain signal of the model vehicle at different wind speeds is collected.

[0089] In the present embodiment, 10 calibrated surface microphones are arranged outside the driver's side window of the model vehicle, the positions of the surface microphones correspond to the positions of the side window surface microphones of the base vehicle one by one, 5 are arranged at a time, and the arrangement is divided into two times. The gap of the rearview mirror of the model vehicle is sealed using Tesa-4657 cloth-based adhesive tape.

[0090] The model vehicle is tested in an acoustic wind tunnel at wind speeds of 60km / h, 80km / h, 100km / h and 120km / h, and the side window surface wind noise time domain signals of the model vehicle are obtained in two rounds of testing. The side window surface wind noise is obtained by a surface microphone, is not affected by vibration excitation of tires, powertrains and the like, has good compatibility, has low requirements on the model of the wind noise correlation relationship, and can be met by resin materials and various oil clay models; a large number of steps such as internal cavity opening, hole opening, glass or aluminum plate arrangement, acoustic cabin arrangement and the like can be omitted, thereby effectively reducing the manufacturing cost and period of the model vehicle.

[0091] S530, processing the side window surface wind noise time domain signal to determine the side window surface wind noise average sound pressure level increment value; wherein the side window surface wind noise average sound pressure level increment value is used to represent the increment value of the side window surface wind noise average sound pressure level under different wind speeds; and the side window surface wind noise average sound pressure level is used to represent the average sound pressure of the side window surface of the model vehicle.

[0092] In the present scheme, the side window surface wind noise average sound pressure level is used to represent the average sound pressure of the side window surface of the model vehicle, the side window surface wind noise average sound pressure level under each wind speed can be obtained by processing the side window surface wind noise time domain signal, and the side window surface wind noise average sound pressure level increment value under adjacent wind speeds can be determined based on the relationship between the wind speed and the side window surface wind noise average sound pressure level under each wind speed.

[0093] In the present technical scheme, the side window surface wind noise time domain signal can be processed to determine the side window surface wind noise average sound pressure level increment value, which can include steps A1-A4:

[0094] Step A1, processing the side window surface wind noise time domain signal of the at least two test regions by Fourier transform to obtain the side window surface wind noise frequency domain signal of the at least two test regions;

[0095] Wherein, the Fourier transform means that a certain function meeting certain conditions can be represented as a linear combination of trigonometric functions or integrals thereof. The side window surface wind noise time domain signal can be converted into a frequency domain signal by Fourier transform.

[0096] Step A2, performing single-value evaluation index calculation on the side window surface wind noise frequency domain signal of the at least two test regions to obtain the side window surface wind noise sound pressure level of the at least two test regions;

[0097] In the present embodiment, the single-value evaluation index includes A weighting. By performing A weighting processing on the side window surface wind noise frequency domain signal, the side window surface wind noise frequency domain signal can be more consistent with the noise signal near the driver's ear.

[0098] Step A3, calculating the average sound pressure level of the side window surface wind noise according to the area of the at least two test areas, to obtain the average sound pressure level of the side window surface wind noise, according to a preset average sound pressure level calculation formula.

[0099] Optionally, the average sound pressure level of the side window surface wind noise is calculated by the following formula:

[0100]

[0101] wherein, p Ext represents the average sound pressure level of the side window surface wind noise, n represents the number of test areas, S i represents the area of the test area, p i,Ext represents the sound pressure level of the side window surface wind noise of the test area.

[0102] In the embodiment, the average sound power level of the side window surface wind noise of the model vehicle at each wind speed is obtained by using the area weighting method, and the average sound pressure level of the side window surface wind noise of the model vehicle at each wind speed is obtained by logarithmic operation.

[0103] Step A4, processing the average sound pressure level of the side window surface wind noise by using the difference value technology to determine the average sound pressure level increment of the side window surface wind noise.

[0104] Specifically, taking the average sound pressure level of the side window surface wind noise of the base vehicle at the speed of 40km / h as the reference, the difference value technology is used to solve the average sound pressure level increment of the side window surface wind noise of the model vehicle at the speeds of 60km / h, 80km / h, 100km / h and 120km / h respectively. Ext(模型车) .

[0105] Exemplarily, Figure 6 is a calculation result diagram of the average sound pressure level of the side window surface wind noise of the model vehicle at each wind speed provided by the embodiment three, as shown in Figure 6 the average sound pressure level of the side window surface wind noise is calculated according to the area of the test area, according to a preset average sound pressure level calculation formula.

[0106] S540, determining the interior noise sound pressure level increment associated with the average sound pressure level increment of the side window surface wind noise by using a predetermined wind noise correlation; wherein the wind noise correlation is obtained by difference value technology and curve estimation regression analysis according to the side window surface wind noise of the base vehicle and the interior wind noise;

[0107] Specifically, ΔP Ext(模型车) is brought into the wind noise correlation to solve the interior noise sound pressure level increment ΔP Int(模型车) at different speeds.

[0108] S550, determining the wind noise in the vehicle according to the increase of the sound pressure level of the vehicle interior noise.

[0109] Specifically, the sound pressure level of the wind noise in the vehicle of the development vehicle at the speeds of 60km / h, 80km / h, 100km / h and 120km / h can be obtained by inversely using the difference value technology based on the sound pressure level of the wind noise in the vehicle of the base vehicle at the speed of 40km / h. Int(模型车) .

[0110] Exemplarily, Figure 7 is a comparison diagram of the calculated values and the measured values of the sound pressure level of the wind noise in the vehicle of the model vehicle at various speeds provided by the embodiment three of the present application, the wind noise test of the model vehicle is carried out in the same environment and working condition as the base vehicle, and after the wind noise in the ear of the driver of the model vehicle at various speeds is processed according to the same requirements as the base vehicle, the comparison and analysis are carried out with the above calculated values, as shown in Figure 7 It can be seen that the noise level consistency is very high, and the error is very small, which indicates that the prediction method has high reliability.

[0111] The technical scheme of the embodiment of the present application divides the side window surface of the model vehicle to obtain at least two test regions, collects the time domain signals of the side window surface wind noise of the model vehicle in at least two test regions at different wind speeds based on the surface sound receivers installed on the at least two test regions, processes the time domain signals of the side window surface wind noise to obtain the average sound pressure level increase value of the side window surface wind noise, determines the sound pressure level increase value of the vehicle interior noise associated with the average sound pressure level increase value of the side window surface wind noise by using the pre-determined wind noise correlation, and determines the wind noise in the vehicle according to the sound pressure level increase value of the vehicle interior noise. By executing the technical scheme, the wind noise in the vehicle can be predicted by the wind noise correlation. The wind noise prediction has high reliability, and the test precision and the calculation precision of the wind noise correlation are controllable. The method is simple and easy to use, and time-consuming is short.

[0112] Exemplarily, Figure 8 is a flow chart of another wind noise prediction method provided by the embodiment of the present application, as shown in Figure 8 , the steps are as follows: step one, obtaining the wind noise in the vehicle and the side window surface wind noise of the base vehicle; step two, obtaining the wind noise correlation by difference value technology and curve estimation regression analysis; step three, obtaining the side window surface wind noise of the model vehicle; and step four, solving the wind noise in the vehicle of the model vehicle by using the wind noise correlation.

[0113] Embodiment four

[0114] Figure 9 is a structural schematic diagram of a wind noise prediction device provided by the embodiment four of the present application. As shown in Figure 9 , the device comprises:

[0115] The side window surface wind noise time domain signal acquisition module 910 is configured to acquire side window surface wind noise time domain signals of the model vehicle at different wind speeds.

[0116] The side window surface wind noise average sound pressure level increment determination module 920 is configured to process the side window surface wind noise time domain signals and determine a side window surface wind noise average sound pressure level increment.

[0117] The vehicle interior noise sound pressure level increment determination module 930 is configured to determine a vehicle interior noise sound pressure level increment associated with the side window surface wind noise average sound pressure level increment by using a predetermined wind noise correlation relationship.

[0118] The vehicle interior wind noise determination module 940 is configured to determine a vehicle interior wind noise according to the vehicle interior noise sound pressure level increment.

[0119] Optionally, the vehicle interior noise sound pressure level increment determination module 930 is specifically configured to:

[0120] According to the side window surface wind noise average sound pressure level increment, the vehicle interior noise sound pressure level increment corresponding to the side window surface wind noise average sound pressure level increment is searched in the predetermined wind noise correlation relationship.

[0121] Optionally, the device further includes:

[0122] The time domain signal acquisition module is configured to acquire vehicle interior wind noise time domain signals and side window surface wind noise time domain signals of the base vehicle at different vehicle speeds.

[0123] The average sound pressure level increment obtaining module is configured to process the vehicle interior wind noise time domain signals and the side window surface wind noise time domain signals of the base vehicle to obtain a base vehicle interior wind noise average sound pressure level increment and a base vehicle side window surface wind noise average sound pressure level increment.

[0124] The wind noise correlation relationship construction module is configured to construct a wind noise correlation relationship by taking the base vehicle interior wind noise average sound pressure level increment as a dependent variable and taking the base vehicle side window surface wind noise average sound pressure level increment as an independent variable.

[0125] Optionally, the side window surface wind noise time domain signal acquisition module 910 is specifically configured to:

[0126] The side window surface of the model vehicle is divided according to a preset division mode, and at least two test regions are obtained;

[0127] Based on the surface microphones installed on the at least two test regions, time domain signals of the side window surface wind noise of the model vehicle in the at least two test regions under different wind speeds are collected.

[0128] Optionally, the side window surface wind noise average sound pressure level increment determination module 920 comprises:

[0129] The side window surface wind noise frequency domain signal obtaining unit is configured to process the side window surface wind noise time domain signals of the at least two test regions by using Fourier transform, and obtain side window surface wind noise frequency domain signals of the at least two test regions.

[0130] The side window surface wind noise sound pressure level obtaining unit is configured to perform single-value evaluation index calculation on the side window surface wind noise frequency domain signals of the at least two test regions, and obtain side window surface wind noise sound pressure levels of the at least two test regions.

[0131] The side window surface wind noise average sound pressure level obtaining unit is configured to calculate the side window surface wind noise sound pressure levels of the at least two test regions according to the areas of the at least two test regions according to a preset average sound pressure level calculation formula, and obtain the side window surface wind noise average sound pressure level.

[0132] The side window surface wind noise average sound pressure level increment determination unit is configured to process the side window surface wind noise average sound pressure level by using a difference technique, and determine the side window surface wind noise average sound pressure level increment.

[0133] Optionally, the side window surface wind noise average sound pressure level obtaining unit is specifically configured to:

[0134] The side window surface wind noise average sound pressure level is calculated by using the following formula:

[0135]

[0136] wherein, p Ext represents the side window surface wind noise average sound pressure level, n represents the number of test regions, S i represents the area of the test region, and p i,Ext represents the side window surface wind noise sound pressure level of the test region.

[0137] Optionally, the vehicle interior wind noise determination module 940 is specifically configured to:

[0138] The vehicle interior noise sound pressure level increment is processed by using a reverse difference technique, and the vehicle interior wind noise near the ear of the driver is obtained.

[0139] The vehicle interior wind noise prediction device provided by the embodiment of the present application can execute the vehicle interior wind noise prediction method provided by any embodiment of the present application, and has the function modules and beneficial effects corresponding to the execution method.

[0140] Embodiment five

[0141] Figure 10 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the present application described and / or claimed in this document.

[0142] As Figure 10 shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0143] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0144] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, and the like. The processor 11 performs various methods and processes described above, such as an in-vehicle wind noise prediction method.

[0145] In some embodiments, an in-vehicle wind noise prediction method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded onto and / or installed in the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of an in-vehicle wind noise prediction method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform an in-vehicle wind noise prediction method by any other suitable means, such as by means of firmware.

[0146] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0147] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a machine or a remote machine or a server.

[0148] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0149] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0150] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0151] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0152] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, and this is not limited herein.

[0153] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of predicting wind noise in a vehicle, characterized by, The method comprises the following steps: acquiring a side window surface wind noise time domain signal of a model vehicle at different wind speeds; processing the side window surface wind noise time domain signal to determine a side window surface wind noise average sound pressure level increment value; wherein the side window surface wind noise average sound pressure level increment value is used to represent an increment value of the side window surface wind noise average sound pressure level at adjacent wind speeds; and the side window surface wind noise average sound pressure level is used to represent the average sound pressure of the side window surface of the model vehicle; determining a vehicle interior noise sound pressure level increment value associated with the side window surface wind noise average sound pressure level increment value by using a predetermined wind noise correlation relationship; wherein the wind noise correlation relationship is obtained by using a difference value technique and curve estimation regression analysis based on the side window surface wind noise and the vehicle interior wind noise of a base vehicle; determining the vehicle interior wind noise based on the vehicle interior noise sound pressure level increment value; wherein the determination process of the wind noise correlation relationship comprises the following steps: acquiring a vehicle interior wind noise time domain signal and a side window surface wind noise time domain signal of the base vehicle at different vehicle speeds; processing the vehicle interior wind noise time domain signal and the side window surface wind noise time domain signal of the base vehicle to obtain a base vehicle interior wind noise average sound pressure level increment value and a base vehicle side window surface wind noise average sound pressure level increment value; using the base vehicle interior wind noise average sound pressure level increment value as the dependent variable and using the base vehicle side window surface wind noise average sound pressure level increment value as the independent variable to construct the wind noise correlation relationship.

2. The method of claim 1, wherein, determining a vehicle interior noise sound pressure level increment value associated with the side window surface wind noise average sound pressure level increment value by using a predetermined wind noise correlation relationship, comprising: finding the vehicle interior noise sound pressure level increment value corresponding to the side window surface wind noise average sound pressure level increment value in the predetermined wind noise correlation relationship based on the side window surface wind noise average sound pressure level increment value.

3. The method of claim 1, wherein, acquiring a side window surface wind noise time domain signal of a model vehicle at different wind speeds, comprising: dividing the side window surface of the model vehicle according to a pre-set division mode to obtain at least two test regions; collecting the side window surface wind noise time domain signal of the at least two test regions of the model vehicle at different wind speeds based on the surface microphones installed on the at least two test regions.

4. The method of claim 1, wherein, processing the side window surface wind noise time domain signal to determine the side window surface wind noise average sound pressure level increment value, comprising: processing the side window surface wind noise time domain signal of the at least two test regions by using Fourier transform to obtain the side window surface wind noise frequency domain signal of the at least two test regions; performing single-value evaluation index calculation on the side window surface wind noise frequency domain signal of the at least two test regions to obtain the side window surface wind noise sound pressure level of the at least two test regions; calculating the side window surface wind noise sound pressure level of the at least two test regions according to the areas of the at least two test regions by using a pre-set average sound pressure level calculation formula to obtain the side window surface wind noise average sound pressure level; processing the side window surface wind noise average sound pressure level by using a difference value technique to determine the side window surface wind noise average sound pressure level increment value.

5. The method of claim 4, wherein, The method comprises the following steps: calculating the side window surface wind noise average sound pressure level by using the following formula: ； wherein, represents the average sound pressure level of the side window surface wind noise, represents the number of test areas, represents the area of the test area, represents the side window surface wind noise sound pressure level of the test area.

6. The method of claim 1, wherein, According to the vehicle interior noise sound pressure level increment, the vehicle interior wind noise is determined, comprising: The vehicle interior noise sound pressure level increment is processed by using the difference technique, and the vehicle interior wind noise near the driver's ear is obtained.

7. An in-vehicle wind noise prediction device characterized by comprising: Comprise: The side window surface wind noise time domain signal acquisition module is used for acquiring the side window surface wind noise time domain signal of the model vehicle under different wind speeds; The side window surface wind noise average sound pressure level increment determination module is used for processing the side window surface wind noise time domain signal, and determining the side window surface wind noise average sound pressure level increment; wherein the side window surface wind noise average sound pressure level increment is used for representing the increment value of the adjacent wind speed side window surface wind noise average sound pressure level; the side window surface wind noise average sound pressure level is used for representing the average sound pressure of the side window surface of the model vehicle; The vehicle interior noise sound pressure level increment determination module is used for determining the vehicle interior noise sound pressure level increment associated with the side window surface wind noise average sound pressure level increment by using the pre-determined wind noise correlation; wherein the wind noise correlation is obtained by difference technique and curve estimation regression analysis according to the side window surface wind noise and the vehicle interior wind noise of the base vehicle; The vehicle interior wind noise determination module is used for determining the vehicle interior wind noise according to the vehicle interior noise sound pressure level increment; The time domain signal acquisition module is used for acquiring the vehicle interior wind noise time domain signal and the side window surface wind noise time domain signal of the base vehicle under different vehicle speeds; The average sound pressure level increment obtaining module is used for processing the vehicle interior wind noise time domain signal and the side window surface wind noise time domain signal of the base vehicle, and obtaining the base vehicle interior wind noise average sound pressure level increment and the base vehicle side window surface wind noise average sound pressure level increment; The wind noise correlation construction module is used for constructing the wind noise correlation by taking the base vehicle interior wind noise average sound pressure level increment as the dependent variable and taking the base vehicle side window surface wind noise average sound pressure level increment as the independent variable.

8. An electronic device, comprising: The electronic device comprises: At least one processor; and The memory is connected in communication with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle interior wind noise prediction method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to execute the vehicle interior wind noise prediction method in any one of claims 1-6 when executed.

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