Vehicle interior noise evaluation method and device, vehicle and storage medium

Abstract

The invention relates to the technical field of vehicles, in particular to an in-vehicle noise evaluation method and device for a vehicle, the vehicle and a storage medium, and the method comprises the steps: collecting ear pressure feeling data, noisy feeling data, quiet feeling data and subjective score data of a to-be-tested vehicle under a plurality of preset working conditions; based on the subjective scoring data, processing and calculating the pressure ear feeling data, the noisy feeling data and the quiet feeling data to obtain a pressure ear feeling LBS, a noisy feeling MNS and a quiet feeling HS; and inputting the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS into a pre-established noise evaluation model, so as to determine an in-vehicle noise evaluation result of the to-be-tested vehicle according to an output result of the noise evaluation model. Therefore, by collecting and analyzing the noise data of the vehicle under different working conditions and establishing the comprehensive evaluation model, the problems that in-vehicle noise evaluation is inaccurate and rough pavement noise is difficult to quickly recognize are solved, the in-vehicle noise problem recognition capability of the vehicle is improved, and the working efficiency is optimized.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method and device for evaluating interior noise of a vehicle, a vehicle, and a storage medium. Background Art

[0002] With the improvement of living standards and the development of the automobile industry, users have higher and higher requirements on the quality of noise inside cars.

[0003] In related technologies, the noise performance inside a car varies significantly when it is driving on different roads. Accurately evaluating the noise level inside a car is of guiding significance for quickly identifying the noise problems inside the car and evaluating the comprehensive NVH performance (Noise, Vibration, Harshness). The commonly used method is based on A-weighted indicators such as the total sound pressure level of the interior noise, speech clarity, loudness and sharpness.

[0004] However, these methods involve too many indicators and dimensions, making it difficult to accurately and effectively decompose the problem, which is not conducive to rapid problem identification and cannot comprehensively evaluate the vehicle road noise level, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a vehicle interior noise evaluation method, device, vehicle and storage medium to solve the problems of inaccurate interior noise evaluation and difficulty in quickly identifying the direction of interior noise rectification, thereby improving the vehicle's interior noise problem identification capability and optimizing work efficiency.

[0006] The first embodiment of the present application provides a method for evaluating the interior noise of a vehicle, comprising the following steps:

[0007] Collect the ear pressure data, noise data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions;

[0008] Based on the subjective scoring data, frequency processing is performed on the ear pressure feeling data, the noisy feeling data and the quiet feeling data to obtain ear pressure feeling LBS, noisy feeling MNS and quiet feeling HS respectively;

[0009] The ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS are input into a pre-established noise evaluation model to determine the in-vehicle noise evaluation result of the vehicle to be tested according to the output result of the noise evaluation model.

[0010] Optionally, the ear pressure sensation data includes: impact road noise time domain data and first rough road noise time domain data, the noise sensation data includes first asphalt road noise time domain data and second rough road noise time domain data, and the quiet sensation data includes second asphalt road noise time domain data and third rough road noise time domain data.

[0011] Optionally, based on the subjective scoring data, frequency processing is performed on the ear pressure data, the noisy feeling data and the quiet feeling data to obtain ear pressure feeling LBS, noisy feeling MNS and quiet feeling HS, respectively, including:

[0012] Based on the subjective scoring data, whole vehicle impact noise data is obtained according to the impact road noise time domain data, and whole vehicle low-frequency noise data is obtained according to the first rough road noise time domain data, and the ear pressure feeling LBS is obtained according to the whole vehicle impact noise data and the whole vehicle low-frequency noise data;

[0013] Based on the subjective scoring data, asphalt road medium frequency noise data and rough road medium frequency noise data are obtained according to the first asphalt road noise time domain data and the second rough road noise time domain data, and the noisy feeling MNS is obtained according to the asphalt road medium frequency noise data and the rough road medium frequency noise data;

[0014] Based on the subjective scoring data, mid-frequency noise data, asphalt pavement high-frequency noise data and rough pavement high-frequency data are obtained according to the second asphalt pavement noise time domain data and the third rough pavement noise time domain data, and the sense of quietness HS is obtained according to the mid-frequency noise data, the asphalt pavement high-frequency noise data and the rough pavement high-frequency data.

[0015] Optionally, the obtaining of vehicle impact noise data based on the subjective scoring data and the impact road noise time domain data, and the obtaining of vehicle low-frequency noise data based on the first rough road noise time domain data, include:

[0016] The method of obtaining the whole vehicle impact noise data based on the subjective scoring data and the whole vehicle impact noise time domain data, and obtaining the whole vehicle low-frequency noise data based on the first rough road noise time domain data, includes:

[0017] Based on the subjective scoring data, time domain tracking processing is performed on the time domain data of the impact road noise to obtain color mapping data, and frequency slicing processing and time slicing processing are performed on the color mapping data, and the impact noise peak value is obtained according to the frequency slicing processing result, and a first linear total sound pressure level and a second linear total sound pressure level are obtained according to the time slicing processing result, wherein the frequency range of the first linear total sound pressure level is lower than the frequency range of the second linear total sound pressure level, and the whole vehicle impact noise data is obtained according to the impact noise peak value, the first linear total sound pressure level and the second linear total sound pressure level;

[0018] Based on the subjective scoring data, the first rough road noise time domain data is subjected to linear autopower spectrum processing, and the front row noise data, the rear row noise data and the B-weighted frequency domain noise data are obtained based on the first processing result and the first preset processing requirement, and the whole vehicle low-frequency noise data is obtained based on the front row noise data, the rear row noise data and the B-weighted frequency domain noise data.

[0019] Optionally, the obtaining of asphalt pavement intermediate frequency noise data and rough pavement intermediate frequency noise data based on the subjective scoring data and the first asphalt pavement noise time domain data and the second rough pavement noise time domain data includes:

[0020] Based on the subjective scoring data, performing linear autopower spectrum processing on the first asphalt road noise time domain data and the second rough road noise time domain data to obtain a second processing result;

[0021] The asphalt road surface intermediate frequency noise data and the rough road surface intermediate frequency noise data are obtained based on the second processing result and the second preset processing requirement.

[0022] Optionally, the method of obtaining the intermediate frequency noise data, the asphalt road surface high frequency noise data and the rough road surface high frequency data based on the subjective scoring data and the second asphalt road surface noise time domain data and the third rough road surface noise time domain data includes:

[0023] Based on the subjective scoring data, performing linear autopower spectrum processing on the second asphalt road noise time domain data and the third rough road noise time domain data to obtain a third processing result;

[0024] The intermediate frequency noise data, the asphalt road surface high frequency noise data and the rough road surface high frequency data are obtained based on the third processing result and the third preset processing requirement.

[0025] Optionally, the noise evaluation model is:

[0026] F(t) = a×LBS+b×MNS+c×HS;

[0027] Wherein, F(t) is the output result of the noise evaluation model, and a, b and c are coefficients.

[0028] The second embodiment of the present application provides a vehicle interior noise evaluation device, comprising:

[0029] The acquisition module collects the ear pressure data, noise data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions;

[0030] A calculation module, based on the subjective scoring data, performs frequency processing on the ear pressure feeling data, the noisy feeling data and the quiet feeling data to obtain ear pressure feeling LBS, noisy feeling MNS and quiet feeling HS;

[0031] The determination module inputs the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS into a pre-established noise evaluation model to determine the in-vehicle noise evaluation result of the vehicle to be tested according to the output result of the noise evaluation model.

[0032] Optionally, the ear pressure sensation data includes: impact road noise time domain data and first rough road noise time domain data, the noise sensation data includes first asphalt road noise time domain data and second rough road noise time domain data, and the quiet sensation data includes second asphalt road noise time domain data and third rough road noise time domain data.

[0033] Optionally, the calculation module is specifically used to:

[0034] Based on the subjective scoring data, whole vehicle impact noise data is obtained according to the impact road noise time domain data, and whole vehicle low-frequency noise data is obtained according to the first rough road noise time domain data, and the ear pressure feeling LBS is obtained according to the whole vehicle impact noise data and the whole vehicle low-frequency noise data;

[0035] Based on the subjective scoring data, asphalt road medium frequency noise data and rough road medium frequency noise data are obtained according to the first asphalt road noise time domain data and the second rough road noise time domain data, and the noisy feeling MNS is obtained according to the asphalt road medium frequency noise data and the rough road medium frequency noise data;

[0036] Based on the subjective scoring data, mid-frequency noise data, asphalt pavement high-frequency noise data and rough pavement high-frequency data are obtained according to the second asphalt pavement noise time domain data and the third rough pavement noise time domain data, and the sense of quietness HS is obtained according to the mid-frequency noise data, the asphalt pavement high-frequency noise data and the rough pavement high-frequency data.

[0037] Optionally, the calculation module is specifically used to:

[0038] Based on the subjective scoring data, time domain tracking processing is performed on the time domain data of the impact road noise to obtain color mapping data, and frequency slicing processing and time slicing processing are performed on the color mapping data, and the impact noise peak value is obtained according to the frequency slicing processing result, and a first linear total sound pressure level and a second linear total sound pressure level are obtained according to the time slicing processing result, wherein the frequency range of the first linear total sound pressure level is lower than the frequency range of the second linear total sound pressure level, and the whole vehicle impact noise data is obtained according to the impact noise peak value, the first linear total sound pressure level and the second linear total sound pressure level;

[0039] Based on the subjective scoring data, the first rough road noise time domain data is subjected to linear autopower spectrum processing, and the front row noise data, the rear row noise data and the B-weighted frequency domain noise data are obtained based on the first processing result and the first preset processing requirement, and the whole vehicle low-frequency noise data is obtained based on the front row noise data, the rear row noise data and the B-weighted frequency domain noise data.

[0040] Optionally, the calculation module is specifically used to:

[0041] Based on the subjective scoring data, performing linear autopower spectrum processing on the first asphalt road noise time domain data and the second rough road noise time domain data to obtain a second processing result;

[0042] The asphalt road surface intermediate frequency noise data and the rough road surface intermediate frequency noise data are obtained based on the second processing result and the second preset processing requirement.

[0043] Optionally, the calculation module is specifically used to:

[0044] Based on the subjective scoring data, performing linear autopower spectrum processing on the second asphalt road noise time domain data and the third rough road noise time domain data to obtain a third processing result;

[0045] The intermediate frequency noise data, the asphalt road surface high frequency noise data and the rough road surface high frequency data are obtained based on the third processing result and the third preset processing requirement.

[0046] Optionally, the noise evaluation model is:

[0047] F(t) = a×LBS+b×MNS+c×HS;

[0048] Wherein, F(t) is the output result of the noise evaluation model, and a, b and c are coefficients.

[0049] The third aspect of the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are configured to execute the vehicle interior noise evaluation method as described in the above-mentioned implementation case.

[0050] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the vehicle interior noise evaluation method as described in the above-mentioned embodiment.

[0051] In summary, by collecting the ear pressure data, noisy data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions, and based on the subjective scoring data, the ear pressure data, noisy data and quiet data are frequency processed to obtain ear pressure LBS, noisy MNS and quiet HS; the processed data are input into the pre-established noise evaluation model, and the in-car noise evaluation result of the vehicle to be tested is determined according to the output result of the noise evaluation model. Therefore, by collecting and analyzing the noise data of the vehicle under different working conditions, a comprehensive evaluation model is established, which solves the problems of inaccurate in-car noise evaluation and difficulty in quickly identifying rough road noise, improves the vehicle's in-car noise problem identification ability, and optimizes work efficiency.

[0052] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the implementation examples in conjunction with the accompanying drawings, in which:

[0054] Figure 1 A flowchart of a method for evaluating vehicle interior noise provided according to an implementation case of the present application;

[0055] Figure 2 A flow chart of a method for evaluating interior noise of a vehicle provided according to an implementation example of the present application;

[0056] Figure 3 A flowchart of test data collection for a method for evaluating vehicle interior noise according to an implementation example of the present application;

[0057] Figure 4 A schematic diagram of a time domain signal acquisition method for evaluating vehicle interior noise provided according to an implementation example of the present application;

[0058] Figure 5A rough road surface data processing evaluation curve diagram of a vehicle interior noise evaluation method provided according to an implementation case of the present application;

[0059] Figure 6 A rough road surface data processing flow chart of a vehicle interior noise evaluation method provided according to an implementation case of the present application;

[0060] Figure 7 A flowchart of noisy data processing of a method for evaluating interior noise of a vehicle provided according to an implementation case of the present application;

[0061] Figure 8 A data processing flow chart of quietness data processing of a vehicle interior noise evaluation method provided according to an implementation case of the present application;

[0062] Fig. 9 A curve diagram of the subjective and objective linear relationship of ear pressure sensation LBS of a vehicle interior noise evaluation method provided according to an implementation case of the present application;

[0063] Fig.10 A curve diagram showing the subjective and objective linear relationship of noise perception MNS of a vehicle interior noise evaluation method provided according to an implementation case of the present application;

[0064] Fig.11 A curve diagram showing the subjective and objective linear relationship of quietness HS of a vehicle interior noise evaluation method provided according to an implementation case of the present application;

[0065] Fig.12 A linear evaluation model curve diagram established for ear pressure, noise and quietness parameters of a vehicle interior noise evaluation method provided in an implementation case of the present application;

[0066] Fig.13 A schematic diagram of a vehicle interior noise evaluation device provided according to an implementation example of the present application;

[0067] Fig.14 A schematic diagram of the structure of a vehicle provided according to an implementation case of the present application. DETAILED DESCRIPTION

[0068] The implementation cases of the present application are described in detail below, and examples of the implementation cases are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The implementation cases described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0069] The following describes the vehicle interior noise evaluation method, device, vehicle and storage medium of the implementation case of the present application with reference to the accompanying drawings. In view of the problems of inaccurate interior noise evaluation and difficulty in quickly identifying rough road noise mentioned in the above background technology, the present application provides a vehicle interior noise evaluation method, in which the ear pressure data, noisy data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions are collected, and based on the subjective scoring data, the ear pressure data, noisy data and quiet data are processed and calculated to obtain the ear pressure LBS, noisy MNS and quiet HS; the processed data is input into a pre-established noise evaluation model, and the interior noise evaluation result of the vehicle to be tested is determined according to the output result of the noise evaluation model. Therefore, by collecting and analyzing the noise data of the vehicle under different working conditions, a comprehensive evaluation model is established, which solves the problems of inaccurate interior noise evaluation and difficulty in quickly identifying rough road noise, improves the vehicle's interior noise problem identification ability, and optimizes work efficiency.

[0070] Specifically, Figure 1 A flowchart of a method for evaluating interior noise of a vehicle provided for an implementation case of the present application.

[0071] In this implementation case, the main steps of the vehicle interior noise evaluation method of the implementation case of this application can be as follows: Figure 2 and Figure 3 As shown, Figure 2 This is a flow chart of a method for evaluating in-vehicle noise in an implementation case of this application. The process mainly includes four parts: test data collection, test data processing, parameter calculation and test data evaluation (based on the evaluation model). Figure 3 This is a flow chart of experimental data collection for a specific implementation case of the present application. The experimental data collection for the implementation case of the present application may include ear pressure sensation data collection, noise sensation data collection, and quiet sensation data collection. The specific strategy will be elaborated in detail later.

[0072] like Figure 1 As shown, the vehicle interior noise evaluation method comprises the following steps:

[0073] In step S101 , the ear pressure perception data, noise perception data, quiet perception data and subjective scoring data of the vehicle to be tested under a plurality of preset working conditions are collected.

[0074] Among them, the ear pressure data refers to the feeling of oppression on the ears caused by low-frequency noise when the vehicle is driving, especially on impact or rough roads; the noisy feeling data refers to the noisy feeling caused by medium-frequency noise when the vehicle is driving on asphalt roads or roads with medium roughness; the quietness data refers to the impact of high-frequency noise on the interior environment of the vehicle when driving at high speed on ordinary asphalt roads or rough roads, especially the degree of quietness; the subjective scoring data is evaluated and scored by the test participants based on their own feelings on the noise in the car. These scores reflect the passengers' direct experience and acceptance of the noise in the car, and are an important basis for linking objective noise data with people's actual perception.

[0075] Specifically, the microphones are arranged at the inner and outer ears of the driver and the inner and outer ears of the right rear passenger, and the test noise signal analysis frequency is set to 12800Hz. When collecting ear pressure data and impact road data, the vehicle drives at a constant speed of no more than 40km / h (recommended speed 20km / h) through a single pulse characteristic road surface. The recommended size of the impact characteristic road surface is a rectangular steel material with a length of 3000mm, a width of 25mm, and a height of 10mm; 5 groups of test data are collected; time domain signals such as Figure 4 (a) As shown. When collecting data on rough road surface A, the vehicle travels at a constant speed of no more than 40 km / h (recommended speed 30 km / h) through rough road surface A. The recommended specification of rough road surface A is gravel paving, with gravel particles of 10mm to 15mm and a road surface structure depth of 5mm±1.0mm. Five groups of test data are collected; the time domain signals collected are as follows Figure 4 (b) As shown. When collecting data on rough road surface B, the vehicle travels at a constant speed of 50km / h-80km / h (recommended speed 60km / h) through rough road surface B. The recommended specification of rough road surface B is gravel paving, with gravel particles of 6mm to 10mm and a road surface structure depth of 3.5mm±0.5mm. Five groups of test data are collected; the time domain signals collected are as follows Figure 4 (c) As shown. When collecting data on ordinary asphalt pavement, the vehicle travels at a constant speed of 50km / h-80km / h (recommended speed 60km / h) through ordinary asphalt pavement. The recommended specifications of ordinary asphalt pavement are gravel pavement, with gravel particles of 6mm to 10mm and a pavement structure depth of 0.5mm±0.25mm. Five groups of test data are collected; the time domain signals are collected as follows Figure 4 (d) is shown. The quietness data is collected. When collecting data on rough road surface B, the vehicle drives at a constant speed of not less than 100 km / h (recommended speed 100 km / h) through rough road surface B; 5 sets of test data are collected; the time domain signal is collected as follows Figure 4When collecting data on ordinary asphalt pavement, the vehicle travels at a constant speed of not less than 100 km / h (recommended speed 100 km / h) through the ordinary asphalt pavement. The recommended specifications of ordinary asphalt pavement are gravel pavement, with gravel particles of 6mm to 10mm and a pavement structure depth of 0.5mm±0.25mm. Collect 5 sets of test data; collect time domain signals such as Figure 4 (f) As shown in the figure, the vehicle interior noise under various road conditions is subjectively evaluated, and at least ten people are required to make subjective evaluations and record subjective scores.

[0076] Optionally, in some implementation cases, the ear pressure sensation data includes: impact road noise time domain data and first rough road noise time domain data, the noise sensation data includes first asphalt road noise time domain data and second rough road noise time domain data, and the quiet sensation data includes second asphalt road noise time domain data and third rough road noise time domain data.

[0077] It can be understood that the ear pressure data includes the time domain data of impact road noise, that is, simulating the situation that the vehicle encounters obstacles or uneven roads, and the first rough road noise time domain data, that is, simulating low-speed driving on a relatively rough road surface; these data are used to analyze the noise characteristics in the low-frequency range and evaluate the feeling of oppression of the ears caused by low-frequency noise. The noisy feeling data includes the first asphalt road noise time domain data, that is, driving on ordinary asphalt roads at medium speed, and the second rough road noise time domain data, that is, driving on a rougher road surface at medium speed; these data are used to analyze the noise characteristics in the medium frequency range and evaluate the noisy feeling caused by medium-frequency noise. The quiet feeling data includes the second asphalt road noise time domain data, that is, driving on ordinary asphalt roads at high speed, and the third rough road noise time domain data, that is, driving on a rougher road surface at high speed; these data are used to analyze the noise characteristics in the high frequency range and evaluate the degree of influence of high-frequency noise on the in-vehicle environment, especially the quietness when driving at high speed. Through the collection, processing and comprehensive analysis of the above data, the present invention can quickly determine the problems and rectification directions of the noise in the vehicle when driving on the road, and effectively improve the accuracy and reliability of noise problem identification.

[0078] In step S102, based on the subjective scoring data, frequency processing is performed on the ear pressure sensation data, the noisy sensation data, and the quiet sensation data to obtain the ear pressure sensation LBS, the noisy sensation MNS, and the quiet sensation HS.

[0079] Among them, the ear pressure feeling LBS (Low-frequency Boominess Sensation) mainly reflects the intensity and characteristics of low-frequency noise, which may make people feel uncomfortable, especially when the vehicle passes through uneven roads or encounters obstacles. The noise feeling MNS (Mid-frequency Noisiness Sensation) mainly reflects the intensity and characteristics of medium-frequency noise, which may make people feel irritated and uncomfortable, especially when driving on urban roads or highways. The quiet feeling HS (High-frequency Silence) mainly reflects the intensity and characteristics of high-frequency noise, which may affect the quietness of the car, especially when driving at high speeds.

[0080] Specifically, the rough road surface data processing evaluation curve diagram is as follows: Figure 5 As shown; the rough road surface data processing flow chart is as follows Figure 6 As shown; the noisy data processing flow chart is as follows Figure 7 As shown; the data processing flow chart of quiet feeling data processing is as follows Figure 8 As shown in the figure. LBS is a comprehensive index calculated by using a specific formula after frequency processing of the impact road noise and the first rough road noise, combined with subjective scoring data; MNS is a comprehensive index calculated by using a specific formula after frequency processing of the first asphalt road noise and the second rough road noise, combined with subjective scoring data; HS is a comprehensive index calculated by using a specific formula after frequency processing of the second asphalt road noise and the third rough road noise, combined with subjective scoring data. These three parameters represent the noise characteristics in different frequency ranges, and together they constitute a comprehensive in-vehicle noise evaluation system; the calculation of these parameters is not only based on objective noise data, but also combined with subjective scoring data, so that the evaluation results are more in line with people's actual feelings. Subjective scoring data is usually scored by multiple participants based on their own feelings, reflecting the actual experience of passengers with in-vehicle noise. Through this method, the in-vehicle noise performance of the vehicle under different working conditions can be evaluated more comprehensively and accurately, thereby helping automobile manufacturers to improve the design and enhance the NVH (Noise, Vibration, and Harshness) performance of the vehicle.

[0081] Optionally, in some implementation cases, based on the subjective scoring data, the ear pressure data, the noise data and the quietness data are frequency processed to obtain the ear pressure LBS, the noise MNS and the quietness HS, including: based on the subjective scoring data, the impact noise data of the whole vehicle is obtained according to the impact road noise time domain data, and the low-frequency noise data of the whole vehicle is obtained according to the first rough road noise time domain data, and the ear pressure LBS is obtained according to the impact noise data of the whole vehicle and the low-frequency noise data of the whole vehicle; based on the subjective scoring data, the asphalt road medium-frequency noise data and the rough road medium-frequency noise data are obtained according to the first asphalt road noise time domain data and the second rough road noise time domain data, and the noise MNS is obtained according to the asphalt road medium-frequency noise data and the rough road medium-frequency noise data; based on the subjective scoring data, the medium-frequency noise data, the asphalt road high-frequency noise data and the rough road high-frequency data are obtained according to the second asphalt road noise time domain data and the third rough road noise time domain data, and the quietness HS is obtained according to the medium-frequency noise data, the asphalt road high-frequency noise data and the rough road high-frequency data.

[0082] It can be understood that based on the subjective scoring data and the noise time domain data under different road conditions, the three scoring indicators of ear pressure LBS, noisy feeling MNS and quiet feeling HS were calculated respectively; by extracting the low-frequency noise characteristics of the impact road surface and the first rough road surface, the impact noise and low-frequency noise data of the whole vehicle were obtained, and the ear pressure LBS was calculated accordingly. Similarly, the medium-frequency noise data of the first and second rough roads were used to analyze the relationship between medium-frequency noise and noisy feeling, and the noisy feeling MNS was calculated. For the quiet feeling HS, the medium-frequency noisy feeling data and high-frequency noise data of the second and third rough roads, as well as other low-frequency noise characteristics that may affect the quiet feeling, were combined to obtain the score through a complex comprehensive evaluation method; these scoring indicators not only reflect the noise characteristics of the vehicle under different road conditions, but also provide a scientific basis for vehicle manufacturers to improve noise performance, and also provide consumers with an important reference when buying a car.

[0083] Optionally, in some implementation cases, based on the subjective scoring data, the impact noise data of the whole vehicle is obtained according to the time domain data of the impact road noise, and the low-frequency noise data of the whole vehicle is obtained according to the time domain data of the first rough road noise, including: based on the subjective scoring data, the time domain data of the impact road noise is subjected to time domain tracking processing to obtain color mapping data, and the color mapping data is subjected to frequency slicing processing and time slicing processing, and the impact noise peak is obtained according to the frequency slicing processing result, and the first linear total sound pressure level and the second linear total sound pressure level are obtained according to the time slicing processing result, wherein the frequency range of the first linear total sound pressure level is lower than the frequency range of the second linear total sound pressure level, and the impact noise data of the whole vehicle is obtained according to the impact noise peak, the first linear total sound pressure level and the second linear total sound pressure level; based on the subjective scoring data, the first rough road noise time domain data is subjected to linear autopower spectrum processing, and the front row noise data, the rear row noise data and the B-weighted frequency domain noise data are obtained based on the first processing result and the first preset processing requirement, and the low-frequency noise data of the whole vehicle is obtained according to the front row noise data, the rear row noise data and the B-weighted frequency domain noise data.

[0084] It can be understood that by combining subjective scoring data with noise time domain data, the impact road and first rough road noise are systematically analyzed in depth; for impact road noise, time domain tracking, color mapping, frequency slicing and time slicing processing are used to accurately capture key information such as the peak value and total sound pressure level of impact noise, thereby obtaining a comprehensive quantitative evaluation of the impact noise data of the whole vehicle; this process not only improves the accuracy of noise source identification, but also provides strong support for the optimization design of vehicle noise control. At the same time, for the first rough road noise, linear autopower spectrum processing is performed, and based on the processing results and preset requirements, the front row, rear row and B-weighted frequency domain noise data are subdivided and analyzed, realizing a comprehensive evaluation of the low-frequency noise level of the whole vehicle; this process not only reveals the frequency domain distribution characteristics and regional differences of low-frequency noise, but also provides targeted low-frequency noise control strategies for automobile manufacturers, which helps to improve the ride comfort and overall quality of the vehicle.

[0085] Optionally, in some implementation cases, based on the subjective scoring data, asphalt pavement intermediate frequency noise data and rough pavement intermediate frequency noise data are obtained according to the first asphalt pavement noise time domain data and the second rough pavement noise time domain data, including: based on the subjective scoring data, performing linear autopower spectrum processing on the first asphalt pavement noise time domain data and the second rough pavement noise time domain data to obtain a second processing result; based on the second processing result and the second preset processing requirement, obtaining the asphalt pavement intermediate frequency noise data and the rough pavement intermediate frequency noise data.

[0086] It can be understood that based on the subjective scoring data, the first asphalt road noise time domain data and the second rough road noise time domain data were subjected to in-depth linear auto-power spectrum processing. The pre-processed noise time domain data is subjected to linear auto-power spectrum processing, which is usually implemented through algorithms such as Fast Fourier Transform (FFT) to convert the time domain data into frequency domain data; in the frequency domain, the power spectral density (PSD) of each frequency point can be calculated to obtain the power spectrum distribution of the noise signal; by performing auto-power spectrum processing on the noise time domain data, the energy distribution of the noise signal in different frequency bands can be revealed, especially the noise characteristics of the mid-frequency band; by extracting the mid-frequency noise data, we can more accurately evaluate the impact of different road types on the noise inside the vehicle, and provide a scientific basis for the design and optimization of the chassis system and body system.

[0087] Optionally, in some implementation cases, based on the subjective scoring data, mid-frequency noisy data, asphalt pavement high-frequency noise data and rough pavement high-frequency data are obtained according to the second asphalt pavement noise time domain data and the third rough pavement noise time domain data, including: based on the subjective scoring data, performing linear autopower spectrum processing on the second asphalt pavement noise time domain data and the third rough pavement noise time domain data to obtain a third processing result; based on the third processing result and a third preset processing requirement, obtaining mid-frequency noisy data, asphalt pavement high-frequency noise data and rough pavement high-frequency data.

[0088] It is understandable that the second asphalt road noise time domain data and the third rough road noise time domain data are respectively subjected to fast Fourier transform (FFT) or similar frequency domain conversion methods to calculate their respective auto-power spectrum density, that is, the distribution of noise energy in frequency, and convert the time domain noise data into frequency domain representation, so that the spectral characteristics of the noise can be quantified; combined with subjective scoring data, the analysis results are more comprehensive and closer to reality; by extracting data in a specific frequency range, the noise performance of different road types in a specific frequency band can be clarified, providing a basis for the subsequent interior noise design of different models; the third preset processing requirements may include some specific data processing rules, threshold settings or correction factors for adjusting or verifying the extracted data. According to these requirements, necessary adjustments or verifications are made to the mid-frequency noisy data, the asphalt road high-frequency noise data, and the rough road high-frequency data, which helps to ensure the consistency and comparability of data processing, so that data from different sources or at different times can be effectively compared and analyzed.

[0089] In step S103, the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS are input into a pre-established noise evaluation model to determine the in-vehicle noise evaluation result of the vehicle to be tested according to the output result of the noise evaluation model.

[0090] Among them, the noise evaluation model is a method for comprehensively evaluating the vehicle's interior noise level. It is based on objective measurement data and subjective scoring data. It combines the noise characteristics in different frequency ranges (such as ear pressure LBS, noisy feeling MNS and quiet feeling HS) through mathematical formulas to provide a comprehensive interior noise evaluation result.

[0091] Specifically, the quantized values ​​of LBS, MNS and HS are input into the noise evaluation model. The model calculates the evaluation result of the in-car noise of the vehicle to be tested based on the input parameters and its internal algorithms and rules. This evaluation result may be a comprehensive score or may include multiple sub-item scores, such as loudness score, sharpness score, comfort score, etc. The comprehensive score output by the model can intuitively reflect the in-car noise performance of the vehicle to be tested. A higher score usually indicates better noise control performance, that is, lower ear pressure, noisy feeling and higher quietness. If the model outputs multiple sub-item scores, these scores can be further analyzed to understand the specific performance of the in-car noise. For example, a lower loudness score may indicate that the sound in the car is quieter, while a lower sharpness score may indicate that the sound is softer.

[0092] It should be noted that the accuracy and reliability of the model depend on the scientific nature of its algorithm, the comprehensiveness and representativeness of the data, and the subsequent calibration and verification work. At the same time, in practical applications, other factors that affect the noise evaluation results must also be considered, such as vehicle type, driving conditions, and individual differences among passengers. In summary, by using the noise evaluation model, we can evaluate the in-vehicle noise more comprehensively and objectively, and contribute to improving driving and riding comfort.

[0093] Optionally, in some implementation cases, the noise evaluation model is: F(t)=a×LBS+b×MNS+c×HS; wherein F(t) is the output result of the noise evaluation model, and the coefficients a, b, and c will change accordingly according to the increase in customer questionnaires and subjective and objective database samples of professionals. The approximate values ​​of the existing sample database are a=0.25, b=0.29, and c=0.46, and the linear fitting accuracy is good.

[0094] It can be understood that the model considers three factors: low-frequency sound source (LBS), medium-frequency sound source (MNS) and high-frequency sound source (HS), and weights them through coefficients a, b, and c to reflect their different importance in the overall noise impact. The coefficients a, b, and c can be adjusted according to customer needs and the subjective and objective database samples of professionals, which enables the model to adapt to different application scenarios and evaluation needs; within a given range, the relationship between noise impact and noise source can be approximated as linear, so under the existing sample database, the model shows good linear fitting accuracy. The curve of the subjective and objective linear relationship of ear pressure LBS is shown in the figure below: Fig. 9As shown; the linear relationship curve of the noisy MNS subjective and objective is as follows Fig.10 As shown; the curve of the subjective and objective linear relationship of the sense of tranquility HS is as follows Fig.11 As shown in the figure; the linear evaluation model curve established based on the ear pressure, noise and quietness parameters is shown in the figure Fig.12 shown.

[0095] According to the vehicle interior noise evaluation method proposed in the implementation case of this application, the ear pressure data, noisy data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions are collected. Based on the subjective scoring data, the ear pressure data, noisy data and quiet data are processed and calculated to obtain the ear pressure LBS, noisy MNS and quiet HS; the processed data is input into the pre-established noise evaluation model, and the interior noise evaluation result of the vehicle to be tested is determined according to the output result of the noise evaluation model. Therefore, by collecting and analyzing the noise data of the vehicle under different working conditions and establishing a comprehensive evaluation model, the problems of inaccurate interior noise evaluation and difficulty in quickly identifying rough road noise are solved, the vehicle's interior noise problem identification ability is improved, and work efficiency is optimized.

[0096] Next, the in-vehicle noise evaluation device for a vehicle proposed according to an implementation example of the present application will be described with reference to the accompanying drawings.

[0097] Fig.13 It is a block diagram of a vehicle interior noise evaluation device according to an implementation example of the present application.

[0098] like Fig.13 As shown, the vehicle interior noise evaluation device 10 includes: a collection module 100 , a calculation module 200 , and a determination module 300 .

[0099] The acquisition module 100 collects the ear pressure data, noise data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions;

[0100] The calculation module 200 processes the ear pressure feeling data, the noisy feeling data and the quiet feeling data based on the subjective scoring data to calculate the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS;

[0101] The determination module 300 inputs the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS into a pre-established noise evaluation model to determine the in-vehicle noise evaluation result of the vehicle to be tested according to the output result of the noise evaluation model.

[0102] Optionally, in the acquisition module 100, the ear pressure sensation data includes: impact road noise time domain data and first rough road noise time domain data, the noise sensation data includes first asphalt road noise time domain data and second rough road noise time domain data, and the quiet sensation data includes second asphalt road noise time domain data and third rough road noise time domain data.

[0103] Optionally, the calculation module 200 is specifically used to: based on the subjective scoring data, obtain the impact noise data of the whole vehicle according to the impact road noise time domain data, and obtain the low-frequency noise data of the whole vehicle according to the first rough road noise time domain data, and obtain the ear pressure feeling LBS according to the impact noise data of the whole vehicle and the low-frequency noise data of the whole vehicle; based on the subjective scoring data, obtain the asphalt road medium-frequency noise data and the rough road medium-frequency noise data according to the first asphalt road noise time domain data and the second rough road noise time domain data, and obtain the noisy feeling MNS according to the asphalt road medium-frequency noise data and the rough road medium-frequency noise data; based on the subjective scoring data, obtain the medium-frequency noise feeling data, the asphalt road high-frequency noise data and the rough road high-frequency data according to the second asphalt road noise time domain data and the third rough road noise time domain data, and obtain the quiet feeling HS according to the medium-frequency noise feeling data, the asphalt road high-frequency noise data and the rough road high-frequency data.

[0104] Optionally, the calculation module 200 is specifically used to: based on the subjective scoring data, perform time domain tracking processing on the time domain data of the impact road noise to obtain color mapping data, and perform frequency slicing processing and time slicing processing on the color mapping data, and obtain the impact noise peak value according to the frequency slicing processing result, and obtain the first linear total sound pressure level and the second linear total sound pressure level according to the time slicing processing result, wherein the frequency range of the first linear total sound pressure level is lower than the frequency range of the second linear total sound pressure level, and obtain the whole vehicle impact noise data according to the impact noise peak value, the first linear total sound pressure level and the second linear total sound pressure level; based on the subjective scoring data, perform linear autopower spectrum processing on the first rough road noise time domain data, and obtain front row noise data, rear row noise data and B-weighted frequency domain noise data based on the first processing result and the first preset processing requirement, and obtain the whole vehicle low-frequency noise data according to the front row noise data, the rear row noise data and the B-weighted frequency domain noise data.

[0105] Optionally, the calculation module 200 is specifically used to: based on the subjective scoring data, perform linear autopower spectrum processing on the first asphalt pavement noise time domain data and the second rough pavement noise time domain data to obtain a second processing result; based on the second processing result and the second preset processing requirement, obtain the asphalt pavement intermediate frequency noise data and the rough pavement intermediate frequency noise data.

[0106] Optionally, the calculation module 200 is specifically used to: based on the subjective scoring data, perform linear autopower spectrum processing on the second asphalt pavement noise time domain data and the third rough pavement noise time domain data to obtain a third processing result; based on the third processing result and a third preset processing requirement, obtain mid-frequency noisy data, asphalt pavement high-frequency noise data and rough pavement high-frequency data.

[0107] Optionally, in the determination module 300, the noise evaluation model is: F(t)=a×LBS+b×MNS+c×HS; wherein the coefficients a, b, and c will change accordingly according to the increase in samples of the customer questionnaire and the subjective and objective database of professionals. The approximate values ​​of the existing sample database are a=0.25, b=0.29, and c=0.46, and the linear fitting accuracy is good.

[0108] It should be noted that the above explanation of the implementation case of the vehicle interior noise evaluation method is also applicable to the vehicle interior noise evaluation device of the implementation case, and will not be repeated here.

[0109] According to the in-car noise evaluation device of the vehicle proposed in the implementation case of this application, the ear pressure data, noisy data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions are collected. Based on the subjective scoring data, the ear pressure data, noisy data and quiet data are processed and calculated to obtain the ear pressure LBS, noisy MNS and quiet HS; the processed data is input into the pre-established noise evaluation model, and the in-car noise evaluation result of the vehicle to be tested is determined according to the output result of the noise evaluation model. Therefore, by collecting and analyzing the noise data of the vehicle under different working conditions and establishing a comprehensive evaluation model, the problems of inaccurate in-car noise evaluation and difficulty in quickly identifying rough road noise are solved, the vehicle's in-car noise problem identification ability is improved, and work efficiency is optimized.

[0110] Fig.14 A schematic diagram of the structure of an electronic device provided for the implementation case of this application. The electronic device may include:

[0111] A memory 1401 , a processor 1402 , and a computer program stored in the memory 1401 and executable on the processor 1402 .

[0112] When the processor 1402 executes the program, the vehicle interior noise evaluation method provided in the above implementation case is implemented.

[0113] Furthermore, the electronic device further comprises:

[0114] The communication interface 1403 is used for communication between the memory 1401 and the processor 1402 .

[0115] The memory 1401 is used to store computer programs that can be executed on the processor 1402 .

[0116] The memory 1401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0117] If the memory 1401, the processor 1402 and the communication interface 1403 are implemented independently, the communication interface 1403, the memory 1401 and the processor 1402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.14 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0118] Optionally, in a specific implementation, if the memory 1401, the processor 1402 and the communication interface 1403 are integrated on a chip, the memory 1401, the processor 1402 and the communication interface 1403 can communicate with each other through an internal interface.

[0119] The processor 1402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the implementation case of the present application.

[0120] The implementation case of the present application also provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, the above-mentioned vehicle interior noise evaluation method is implemented.

[0121] In the description of this specification, the description with reference to the terms "an implementation case", "some implementation cases", "examples", "specific examples", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the implementation case or example are included in at least one implementation case or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same implementation case or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N implementation cases or examples in a suitable manner. In addition, those skilled in the art may combine and combine different implementation cases or examples described in this specification and the features of different implementation cases or examples, without contradicting each other.

[0122] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0123] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the implementation cases of the present application belong.

[0124] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] A person of ordinary skill in the art may understand that all or part of the steps in the above-mentioned implementation method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps in the implementation method.

Claims

1. A method for evaluating vehicle interior noise, characterized in that: The following steps are involved: Collect the ear pressure data, noise data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions; Based on the subjective scoring data, the ear pressure feeling data, the noisy feeling data and the quiet feeling data are processed and calculated to obtain the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS; The ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS are input into a pre-established noise evaluation model to determine the in-vehicle noise evaluation result of the vehicle to be tested according to the output result of the noise evaluation model.

2. The method according to claim 1, characterized in that The ear pressure sensation data includes: impact road noise time domain data and first rough road noise time domain data, the noisy sensation data includes first asphalt road noise time domain data and second rough road noise time domain data, and the quiet sensation data includes second asphalt road noise time domain data and third rough road noise time domain data.

3. The method according to claim 2, characterized in that Based on the subjective scoring data, frequency processing is performed on the ear pressure data, the noisy feeling data and the quiet feeling data to obtain ear pressure feeling LBS, noisy feeling MNS and quiet feeling HS, including: Based on the subjective scoring data, whole vehicle impact noise data is obtained according to the impact road noise time domain data, and whole vehicle low-frequency noise data is obtained according to the first rough road noise time domain data, and the ear pressure feeling LBS is obtained according to the whole vehicle impact noise data and the whole vehicle low-frequency noise data; Based on the subjective scoring data, asphalt road medium frequency noise data and rough road medium frequency noise data are obtained according to the first asphalt road noise time domain data and the second rough road noise time domain data, and the noisy feeling MNS is obtained according to the asphalt road medium frequency noise data and the rough road medium frequency noise data; Based on the subjective scoring data, mid-frequency noise data, asphalt pavement high-frequency noise data and rough pavement high-frequency data are obtained according to the second asphalt pavement noise time domain data and the third rough pavement noise time domain data, and the sense of quietness HS is obtained according to the mid-frequency noise data, the asphalt pavement high-frequency noise data and the rough pavement high-frequency data.

4. The method according to claim 3, characterized in that The method of obtaining the whole vehicle impact noise data based on the subjective scoring data and the whole vehicle impact noise time domain data, and obtaining the whole vehicle low-frequency noise data based on the first rough road noise time domain data, includes: Based on the subjective scoring data, time domain tracking processing is performed on the time domain data of the impact road noise to obtain color mapping data, and frequency slicing processing and time slicing processing are performed on the color mapping data, and the impact noise peak value is obtained according to the frequency slicing processing result, and a first linear total sound pressure level and a second linear total sound pressure level are obtained according to the time slicing processing result, wherein the frequency range of the first linear total sound pressure level is lower than the frequency range of the second linear total sound pressure level, and the whole vehicle impact noise data is obtained according to the impact noise peak value, the first linear total sound pressure level and the second linear total sound pressure level; Based on the subjective scoring data, the first rough road noise time domain data is subjected to linear autopower spectrum processing, and the front row noise data, the rear row noise data and the B-weighted frequency domain noise data are obtained based on the first processing result and the first preset processing requirement, and the whole vehicle low-frequency noise data is obtained based on the front row noise data, the rear row noise data and the B-weighted frequency domain noise data.

5. The method according to claim 3, characterized in that: The method of obtaining asphalt pavement intermediate frequency noise data and rough pavement intermediate frequency noise data based on the subjective scoring data and the first asphalt pavement noise time domain data and the second rough pavement noise time domain data comprises: Based on the subjective scoring data, performing linear autopower spectrum processing on the first asphalt road noise time domain data and the second rough road noise time domain data to obtain a second processing result; The asphalt road surface intermediate frequency noise data and the rough road surface intermediate frequency noise data are obtained based on the second processing result and the second preset processing requirement.

6. The method according to claim 3, characterized in that The method of obtaining the intermediate frequency noise data, the asphalt road surface high frequency noise data and the rough road surface high frequency data based on the subjective scoring data and the second asphalt road surface noise time domain data and the third rough road surface noise time domain data includes: Based on the subjective scoring data, performing linear autopower spectrum processing on the second asphalt road noise time domain data and the third rough road noise time domain data to obtain a third processing result; The intermediate frequency noise data, the asphalt road surface high frequency noise data and the rough road surface high frequency data are obtained based on the third processing result and the third preset processing requirement.

7. The method according to any one of claims 1 to 6, characterized in that The noise evaluation model is: F(t) = a×ear pressure LBS+b×noisy MNS+c×quiet HS; Wherein, F(t) is the output result of the noise evaluation model, and a, b and c are coefficients.

8. A vehicle interior noise evaluation device, characterized in that: The following steps are involved: The acquisition module collects the ear pressure data, noise data, quiet data and subjective scoring data of the vehicle to be tested under multiple preset working conditions; A calculation module, based on the subjective scoring data, processes the ear pressure feeling data, the noisy feeling data and the quiet feeling data to calculate the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS respectively; The determination module inputs the ear pressure feeling LBS, the noisy feeling MNS and the quiet feeling HS into a pre-established noise evaluation model to determine the in-vehicle noise evaluation result of the vehicle to be tested according to the output result of the noise evaluation model.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle interior noise evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the vehicle interior noise evaluation method according to any one of claims 1 to 7.

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