Method, device and storage medium for analyzing whistling noise in vehicle

Abstract

The invention discloses a method, a device and a storage medium for analyzing in-vehicle howling noise, wherein the method part comprises: obtaining in-vehicle noise data of a vehicle, processing the in-vehicle noise data to extract a howling order noise peak envelope curve of a target rotating component on the vehicle and a background order noise peak envelope curve of the vehicle, subtracting the howling order noise peak envelope curve from the background order noise peak envelope curve to obtain a noise difference curve, determining an objective evaluation index according to the noise difference curve, establishing a linear regression model of subjective and objective fitting scores according to the objective evaluation index and subjective scoring data of in-vehicle occupants on the in-vehicle noise data, and analyzing the howling noise in the vehicle according to the linear regression model; in the invention, by establishing a linear regression model of subjective and objective fitting scores, the howling noise in the vehicle is analyzed, so that the scoring result takes into account both subjective and objective feelings, and is more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle vibration noise analysis, and in particular to a method, device and storage medium for analyzing in-vehicle howling noise. Background Art

[0002] During vehicle operation, the rotation of the vehicle's rotating parts (including motors, transmission gears, superchargers, etc.) will cause vibration and / or noise responses, resulting in in-vehicle whistling noise that is felt by the passengers. The in-vehicle whistling noise has a significant impact on vehicle comfort. In-vehicle whistling noise is generally expressed in sound pressure level, but with the continuous improvement of the vehicle's vibration and noise performance, the sound pressure level of the car's in-vehicle noise can no longer reflect the quality of the vehicle's comfort.

[0003] At present, the evaluation of in-vehicle howling noise usually uses subjective evaluation to qualitatively determine the severity of gear howling noise. However, everyone's perception of sound is different. The same level of howling level will have very different subjective feelings under different background noises. Therefore, the use of subjective evaluation to evaluate in-vehicle howling noise is not objective enough, resulting in the evaluation results of vehicle howling noise being not unified and accurate. Summary of the invention

[0004] The present invention provides a method, device and storage medium for analyzing in-vehicle howling noise, so as to solve the problem that in the prior art, in-vehicle howling noise is evaluated by subjective evaluation, resulting in inconsistency and inaccuracy of the evaluation results.

[0005] A method for analyzing howling noise in a vehicle, comprising:

[0006] Acquire vehicle interior noise data;

[0007] Processing the in-vehicle noise data to extract a howling order noise peak envelope curve of a target rotating component on the vehicle and a background order noise peak envelope curve of the vehicle;

[0008] Subtracting the peak envelope curve of the howling order noise from the peak envelope curve of the background order noise to obtain a noise difference curve, and determining the objective evaluation index of the in-vehicle noise data according to the noise difference curve;

[0009] Establishing a linear regression model of subjective and objective fitting scores based on the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants;

[0010] The howling noise in the vehicle is analyzed according to the linear regression model and the objective evaluation index.

[0011] Furthermore, the step of establishing a linear regression model of subjective and objective fitting scores based on the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants includes:

[0012] A linear regression relationship between the objective evaluation index and the subjective scoring data is established to determine a linear regression model of the subjective and objective fitting score. The linear regression model is expressed by the following formula:

[0013] X = (AY) / B;

[0014] Wherein, X is the subjective and objective fitting score, Y is the objective evaluation index, and A and B are constants.

[0015] Furthermore, determining the objective evaluation index of the in-vehicle noise data according to the noise difference curve includes:

[0016] Determine a sensitive parameter according to the type of the target rotating component, wherein the sensitive parameter is a predetermined analysis accuracy when analyzing the noise difference curve, and the sensitive parameter is represented by a preset rotation speed;

[0017] Dividing the noise difference curve into a plurality of sub-noise difference curves according to the sensitive parameter, and determining the maximum noise difference in each of the sub-noise difference curves;

[0018] The objective evaluation index is determined according to the maximum noise difference in each of the sub-noise difference curves.

[0019] Furthermore, determining the objective evaluation index according to the maximum noise difference in each of the sub-noise difference curves includes:

[0020] Determine a ratio of the sensitive parameter to a rotation speed collection interval, wherein the rotation speed collection interval is a collection interval for collecting the rotation speed of the target rotating component;

[0021] Taking the smallest positive integer greater than or equal to the ratio as the target value;

[0022] The objective evaluation index is determined according to the target value and the maximum noise difference in each of the sub-noise difference curves, and the objective evaluation index needs to satisfy: in the noise difference curve, there is only a preset number of the maximum noise difference values ​​that are greater than or equal to the objective evaluation index, and the preset number is the sum of the target value and the preset value.

[0023] Further, the processing of the in-vehicle noise data to extract a howling order noise peak envelope curve of a target rotating component on the vehicle and a background order noise peak envelope curve of the vehicle includes:

[0024] Processing the in-vehicle noise data to convert the in-vehicle noise data into a three-dimensional in-vehicle noise graph showing how the noise sound pressure level varies with the rotation speed and noise frequency of the target rotating component;

[0025] Processing the three-dimensional map of in-vehicle noise to obtain a howling order noise curve of the target rotating component and a background order noise curve of the vehicle;

[0026] A howling order noise peak envelope curve is determined according to the howling order noise curve, and a background order noise peak envelope curve is determined according to the background order noise curve.

[0027] Furthermore, the processing of the three-dimensional map of the in-vehicle noise to obtain the howling order noise curve of the target rotating component and the background order noise curve of the vehicle includes:

[0028] Determine a howling order in the three-dimensional map of the in-vehicle noise, and translate the howling order by a preset frequency in a direction where the noise frequency increases, so as to obtain a background order;

[0029] Performing noise slicing processing of a preset bandwidth on the noise data of the howling order to obtain the howling order noise curve, wherein the noise sound pressure level of the howling order noise curve changes with the rotation speed;

[0030] The noise data in the background order is subjected to noise slicing processing of the preset bandwidth to obtain the background order noise curve, wherein the noise sound pressure level of the background order noise curve varies with the rotation speed.

[0031] Further, determining a howling order noise peak envelope curve according to the howling order noise curve includes:

[0032] Divide the howling order noise curve into N sections of howling sub-curves according to the acquisition speed range of the target rotating component, each of the howling sub-curves corresponds to a preset speed range;

[0033] Determine the maximum value of the order noise sound pressure level of each of the howling sub-curves to obtain N howling order noise peaks, and determine the rotation speed corresponding to each of the howling order noise peaks to obtain N groups of noise peak data;

[0034] The N groups of noise peak data are subjected to difference calculation to obtain a curve showing a change in the rotation speed with the howling order noise peak value, which is used as the howling order noise peak value envelope curve.

[0035] A vehicle whistling noise analysis device, comprising:

[0036] An acquisition module, used for acquiring vehicle interior noise data;

[0037] An extraction module, used for processing the in-vehicle noise data to extract a howling order noise peak envelope curve of a target rotating component on the vehicle and a background order noise peak envelope curve of the vehicle;

[0038] A determination module, configured to obtain a noise difference curve by subtracting the peak envelope curve of the howling order noise from the peak envelope curve of the background order noise, and determine an objective evaluation index of the in-vehicle noise data according to the noise difference curve;

[0039] An establishment module is used to establish a linear regression model of subjective and objective fitting scores according to the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants;

[0040] An analysis module is used to analyze the howling noise in the vehicle according to the linear regression model and the objective evaluation index.

[0041] A vehicle interior howling noise analysis device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the vehicle interior howling noise analysis method are implemented.

[0042] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned in-vehicle howling noise analysis method are implemented.

[0043] In one solution provided by the above-mentioned in-vehicle howling noise analysis method, device and storage medium, the in-vehicle noise data of the vehicle is obtained, and then the in-vehicle noise data is processed to extract the howling order noise peak envelope curve of the target rotating component on the vehicle and the background order noise peak envelope curve of the vehicle, and then the howling order noise peak envelope curve is subtracted from the background order noise peak envelope curve to obtain a noise difference curve, and an objective evaluation index is determined based on the noise difference curve, and finally a subjective and objective fitting score is established based on the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants. A linear regression model is provided, and the howling noise in the vehicle is analyzed according to the linear regression model; in the present invention, the howling order noise peak envelope curve and the background order noise peak envelope curve are obtained by calculation, and then the objective scoring index is determined according to the noise difference curve between the two, so as to obtain a linear regression model of subjective and objective fitting scoring, and finally the howling noise in the vehicle is analyzed according to the linear regression model and the objective scoring index, so that the scoring result takes into account both subjective and objective feelings, is more accurate and reliable, and solves the problem that the subjective evaluation of the howling noise in the vehicle leads to the evaluation result being not unified and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0045] Figure 1 is a structural schematic diagram of a vehicle interior howling noise analysis system according to an embodiment of the present invention;

[0046] Figure 2 is a flow chart of a method for analyzing in-vehicle howling noise in one embodiment of the present invention;

[0047] Figure 3 yes Figure 2 A schematic diagram of an implementation process of step S20;

[0048] Figure 4 yes Figure 2 A schematic diagram of an implementation process of step S30;

[0049] Figure 5 is a schematic diagram of a noise difference curve in an embodiment of the present invention;

[0050] Figure 6 is a schematic diagram of the structure of a vehicle howling noise analysis device according to an embodiment of the present invention;

[0051] Figure 7 1 is another structural schematic diagram of a vehicle interior howling noise analysis device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] The vehicle interior howling noise analysis method provided by the embodiment of the present invention can be applied in the following aspects: Figure 1In the in-vehicle howling noise analysis system, the in-vehicle howling noise analysis system includes a vehicle 101 and an in-vehicle howling noise analysis device 102, wherein the vehicle 101 is communicatively connected with the in-vehicle howling noise analysis device 102. The in-vehicle howling noise analysis device obtains the in-vehicle noise data of the vehicle, and then processes the in-vehicle noise data to extract the howling order noise peak envelope curve of the target rotating component on the vehicle and the background order noise peak envelope curve of the vehicle, and then subtracts the howling order noise peak envelope curve from the background order noise peak envelope curve to obtain a noise difference curve, and determines an objective evaluation index according to the noise difference curve, and finally establishes a linear regression model of subjective and objective fitting scores according to the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants, and analyzes the howling noise in the vehicle according to the linear regression model; obtains the howling order noise peak envelope curve and the background order noise peak envelope curve by calculation, and then determines the objective scoring index according to the noise difference curve of the two to obtain the linear regression model of subjective and objective fitting scores, and finally analyzes the howling noise in the vehicle according to the linear regression model and the objective scoring index, so that the scoring result takes into account both subjective and objective feelings and is more accurate and reliable.

[0054] In this embodiment, the in-vehicle howling noise analysis system includes a vehicle and an in-vehicle howling noise analysis device for exemplary purposes only. In other embodiments, the in-vehicle howling noise analysis system also includes other parts, which will not be described in detail here.

[0055] The in-vehicle howling noise analysis proposed in this embodiment is mainly applicable to analyzing the howling noise of rotating parts driven by the vehicle internal combustion engine, transmission gears, superchargers and other rotating parts.

[0056] In one embodiment, if Figure 2 As shown, a method for analyzing the howling noise in a vehicle is provided, and the method is applied in Figure 1 The vehicle interior howling noise analysis device in the example is described as follows, including the following steps:

[0057] S10: Acquire vehicle interior noise data.

[0058] In this embodiment, the in-vehicle whistling noise analysis system includes a sound sensor for collecting in-vehicle noise signals, and the sound sensor is set at the inner ear position of the in-vehicle occupants (including the driver and occupants in other positions). When the vehicle is driving, the in-vehicle noise signal collected by the sound sensor is obtained, and then the in-vehicle noise signal is converted into a digital signal and A-weighted, so as to obtain the in-vehicle noise data. The in-vehicle noise data is the noise data whose A-weighted sound pressure level changes with the noise frequency and the rotation speed of the rotating parts. The in-vehicle noise data processed by A-weighting is closer to the subjective feeling of the human ear and can better reflect the impact of in-vehicle noise on human ear damage.

[0059] Among them, in the process of the sound sensor collecting the noise signal inside the car, the sampling frequency of the noise signal inside the car needs to satisfy the sampling theorem (the sampling frequency can be 12800Hz); the speed change of the rotating part is collected at a preset speed collection interval R, where the range of R can be set to: 15rpm≤R≤25rpm; when collecting noise from different rotating parts, different frequency resolutions are set. For example, for the howling data collection of the rotating parts driven by the internal combustion engine, the frequency resolution is 2Hz, and for the howling data collection of other parts such as the motor, the frequency resolution is 4Hz.

[0060] The sound sensor may be a microphone sensor. In other embodiments, the sound sensor may be other sensors, which will not be described in detail here.

[0061] S20: Processing the in-vehicle noise data to extract a howling order noise peak envelope curve of a target rotating component on the vehicle and a background order noise peak envelope curve of the vehicle.

[0062] After obtaining the in-vehicle noise data, the in-vehicle noise data is processed. First, the howling order noise curve of the target rotating part on the vehicle is obtained, and the background order noise curve of the vehicle is obtained. Then, mathematical calculations are performed (Excel, matlab, subjective and objective evaluation software for vehicle howling, etc. can be used), and the howling order noise peak envelope curve is extracted according to the howling order noise curve, and the background order noise peak envelope curve is extracted according to the background order noise curve.

[0063] S30: subtracting the peak envelope curve of the howling order noise from the peak envelope curve of the background order noise to obtain a noise difference curve, and determining an objective evaluation index of the in-vehicle noise data according to the noise difference curve.

[0064] After obtaining the howling order noise peak envelope curve of the target rotating component and the background order noise peak envelope curve of the vehicle, the howling order noise peak envelope curve is subtracted from the background order noise peak envelope curve to obtain the noise difference curve, and then the objective evaluation index of the in-vehicle noise data is determined according to the noise difference curve.

[0065] Among them, the objective evaluation index of the in-vehicle noise data is determined according to the noise difference curve. The maximum value of the noise difference in the noise difference curve can be determined, and the maximum value of the noise difference in the noise difference curve is used as the objective evaluation index, which is simple and quick; or a value slightly smaller than the maximum value of the noise difference can be taken as the objective evaluation index. The objective evaluation index can also be determined by other methods, which will not be repeated here.

[0066] S40: Establishing a linear regression model of subjective and objective fitting scores based on the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants.

[0067] After determining the objective evaluation index of the interior noise data, a linear regression relationship between the objective evaluation index and the subjective scoring data of the interior noise data by the vehicle occupants is established to determine a linear regression model of the subjective and objective fitting scores.

[0068] Specifically, based on the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants, a linear regression model of subjective and objective fitting scoring is established, including:

[0069] A linear regression relationship between the objective evaluation index and the subjective scoring data is established to determine a linear regression model for subjective and objective fitting scores. Specifically, the linear regression model in this embodiment is expressed by the following formula:

[0070] X = (AY) / B;

[0071] Among them, X is the subjective and objective fitting score, Y is the objective evaluation index, and A and B are constants.

[0072] After obtaining the objective evaluation index of the in-car noise data and the subjective scoring data of the in-car occupants on the in-car noise data, the objective evaluation index and the subjective scoring data are fitted (the objective evaluation index and the subjective scoring data can be fitted by fitting software), and then the linear regression relationship between the objective evaluation index and the subjective scoring data is established according to the fitting of the objective evaluation index and the subjective scoring data, so as to obtain the linear regression model of the subjective and objective fitting score:

[0073] X = (AY) / B;

[0074] Among them, X is the subjective and objective fitting score, Y is the objective evaluation index, and A and B are constants.

[0075] Among them, when the linear regression analysis was performed on the objective evaluation index and the subjective rating data of the in-car noise data by the in-car occupants, R-squared (R 2 ) is larger, indicating that the correlation between the objective evaluation index and the fitting value of the subjective scoring data is better. 2 When it is greater than 0.8, it is considered that the correlation between the objective evaluation index and the subjective scoring data meets the requirements.

[0076] Among them, the subjective scoring data of the in-vehicle noise data is the subjective score obtained by relevant professionals and non-professionals jointly evaluating the in-vehicle noise when conducting a vehicle in-vehicle whistling test under specified working conditions at the test site. The specific subjective evaluation method is shown in Table 1:

[0077] Table 1

[0078]

[0079] S50: Analyze the howling noise in the vehicle according to the linear regression model and the objective evaluation index.

[0080] After obtaining the linear regression model, the objective evaluation index of the interior noise data is input into the linear regression model, and the whistling noise in the vehicle can be analyzed through the subjective and objective fitting scores to determine the impact of the interior noise data on the occupants, so as to optimize the vehicle according to the subjective and objective fitting scores to improve the NHV performance of the vehicle.

[0081] As shown in Table 2, taking the subjective evaluation and subjective and objective fitting scores of 11 vehicles by the passengers in the car as an example, the subjective evaluation and subjective and objective fitting scores of 11 different vehicles are compared. The specific contents are shown in Table 2:

[0082] Table 2

[0083]

[0084] It can be seen from Table 2 that the subjective score has a small error with the subjective and objective fitting score calculated according to the linear regression model. The subjective and objective fitting score obtained in this embodiment, on the basis of combining the objective evaluation index, has a high correlation with the subjective score and a small error. The subjective and objective fitting score can accurately and reliably describe the impact of the whistling noise of different rotating parts on the occupants. The linear regression model is used to analyze the whistling noise in the car, which is suitable for the analysis of the whistling problem caused by rotating parts such as motors, transmission gears, and superchargers. It does not require a large number of on-site subjective evaluation activities to obtain more accurate whistling noise analysis results, saving a lot of manpower and material resources, and avoiding the problem of arbitrary subjective evaluation. At the same time, it is conducive to quickly judging the whistling problem and the severity of the whistling of rotating parts on the vehicle during the development of various projects.

[0085] In this embodiment, by acquiring the in-vehicle noise data of the vehicle, and then processing the in-vehicle noise data to extract the howling order noise peak envelope curve of the target rotating component on the vehicle and the background order noise peak envelope curve of the vehicle, then subtracting the howling order noise peak envelope curve from the background order noise peak envelope curve to obtain a noise difference curve, and determining an objective evaluation index according to the noise difference curve, and finally establishing a linear regression model of subjective and objective fitting scoring according to the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants, and analyzing the howling noise in the vehicle according to the linear regression model; in the present invention, the howling order noise peak envelope curve and the background order noise peak envelope curve are obtained by calculation, and then determining the objective scoring index according to the noise difference curve of the two, so as to obtain a linear regression model of subjective and objective fitting scoring, and finally analyzing the howling noise in the vehicle according to the linear regression model and the objective scoring index, so that the scoring result takes into account both subjective and objective feelings, is more accurate and reliable, and solves the problem that the subjective evaluation of the in-vehicle howling noise leads to the evaluation result being not uniform and accurate.

[0086] In one embodiment, if Figure 3 As shown, in step S20, the vehicle interior noise data is processed to extract the howling order noise peak envelope curve of the target rotating component on the vehicle and the background order noise peak envelope curve of the vehicle, which specifically includes the following steps:

[0087] S21: Processing the in-vehicle noise data to convert the in-vehicle noise data into a three-dimensional in-vehicle noise graph showing how the noise sound pressure level varies with the rotation speed and noise frequency of the target rotating component.

[0088] After acquiring the in-vehicle noise data, the in-vehicle noise data is spectrally processed to convert the in-vehicle noise data into a three-dimensional in-vehicle noise color map showing the noise sound pressure level changing with the rotation speed and noise frequency of the target rotating parts. The horizontal axis of the in-vehicle noise three-dimensional color map represents the in-vehicle noise frequency (in Hz), and the vertical axis of the in-vehicle noise three-dimensional color map represents the speed of the collected target rotating parts (in rpm). Then the noise sound pressure level is represented by different colors. The noise sound pressure level changes with the rotation speed and noise frequency of the target rotating parts. The higher the decibel, the brighter the color.

[0089] S22: Processing the three-dimensional map of the vehicle interior noise to obtain a howling order noise curve of the target rotating component and a background order noise curve of the vehicle.

[0090] After obtaining the three-dimensional map of the interior noise of the vehicle, it is necessary to process the three-dimensional map of the interior noise of the vehicle to obtain the howling order noise curve of the target rotating component and the background order noise curve of the vehicle.

[0091] S23: determining a howling order noise peak envelope curve according to the howling order noise curve, and determining a background order noise peak envelope curve according to the background order noise curve.

[0092] After obtaining the howling order noise curve, the howling order noise curve is divided into N howling sub-curves according to the collected speed range of the target rotating part, each howling sub-curve corresponds to a preset speed range, and then the maximum value of the order noise sound pressure level of each howling sub-curve is determined to obtain N howling order noise peaks, and the speed corresponding to each howling order noise peak is determined to obtain N groups of noise peak data, and then the N groups of noise peak data are used to draw a howling order noise peak envelope curve according to the corresponding speed.

[0093] Similarly, after obtaining the background order noise curve, the background order noise curve is divided into N background sub-curves according to the collection speed range of the target rotating component, and each background sub-curve corresponds to a preset speed range. Then, the maximum value of the order noise sound pressure level of each background sub-curve is determined to obtain N background order noise peaks, and the speed corresponding to each background order noise peak is determined, and N groups of noise peak data are also obtained. Then, the N groups of noise peak data are used to draw a background order noise peak envelope curve according to the corresponding speed.

[0094] The collected speed range of the target rotating component is divided by the preset speed range to obtain N. The preset speed range may be 100 rpm, and N = collected speed range (of the motor or internal combustion engine) / 100).

[0095] In this embodiment, by processing the in-vehicle noise data, the in-vehicle noise data is converted into a three-dimensional in-vehicle noise graph in which the noise sound pressure level varies with the rotational speed and noise frequency of the target rotating component, and then the three-dimensional in-vehicle noise graph is processed to obtain the howling order noise curve of the target rotating component and the background order noise curve of the vehicle, and finally the howling order noise peak envelope curve is determined according to the howling order noise curve, and the background order noise peak envelope curve is determined according to the background order noise curve. The steps of processing the in-vehicle noise data to extract the howling order noise peak envelope curve of the target rotating component on the vehicle and the background order noise peak envelope curve of the vehicle are refined, providing a basis for the subsequent calculation of objective evaluation indicators.

[0096] In one embodiment, step S22, that is, processing the three-dimensional map of the vehicle interior noise to obtain the howling order noise curve of the target rotating component and the background order noise curve of the vehicle, specifically includes the following steps:

[0097] S221: Determine a howling order in the three-dimensional map of the in-vehicle noise, and translate the howling order by a preset frequency in a direction of increasing noise frequency to obtain a background order.

[0098] S222: performing noise slicing processing of a preset bandwidth on the noise data of the howling order to obtain a howling order noise curve, wherein the noise sound pressure level of the howling order noise curve varies with the rotation speed.

[0099] S223: performing noise slicing processing of a preset bandwidth on the noise data in the background order to obtain a background order noise curve, wherein the noise sound pressure level of the background order noise curve varies with the rotation speed.

[0100] After converting the in-vehicle noise data into a three-dimensional in-vehicle noise color map showing the noise sound pressure level changing with the rotation speed and noise frequency of the target rotating component, a brighter dividing line is determined in the three-dimensional in-vehicle noise map, i.e., the howling order, and the howling order is translated by a preset frequency (Δf can be 30Hz to 80Hz) in the direction of increasing noise frequency to obtain the background order. The background order obtained by translating the howling order by a preset frequency in the direction of increasing noise frequency makes the objective evaluation index obtained subsequently not affected by the noise of adjacent orders, and can be applied to a variety of rotating components.

[0101] S222: performing noise slicing processing of a preset bandwidth on the noise data of the howling order to obtain a howling order noise curve, wherein the noise sound pressure level of the howling order noise curve varies with the rotation speed.

[0102] After determining the howling order, the noise data of the howling order is subjected to noise slicing processing of a preset bandwidth to obtain a howling order noise curve, wherein the noise sound pressure level of the howling order noise curve varies with the rotation speed. The preset bandwidth may be 0.5 order.

[0103] S223: performing noise slicing processing of a preset bandwidth on the noise data in the background order to obtain a background order noise curve, wherein the noise sound pressure level of the background order noise curve varies with the rotation speed.

[0104] After the background order is determined, the noise data of the background order is processed by noise slicing with the same preset bandwidth to obtain the background order noise curve, and the noise sound pressure level of the background order noise curve changes with the speed. The extraction bandwidth of the howling order noise and the background order noise is the preset bandwidth.

[0105] In this embodiment, the howling order is determined in the three-dimensional map of the in-vehicle noise, and the howling order is shifted by a preset frequency in the direction of increasing noise frequency to obtain the background order, and then the noise data of the howling order is subjected to noise slicing processing of a preset bandwidth to obtain a howling order noise curve, and the noise sound pressure level of the howling order noise curve varies with the rotational speed; the noise data in the background order is subjected to noise slicing processing of a preset bandwidth to obtain a background order noise curve, and the noise sound pressure level of the background order noise curve varies with the rotational speed. The process of processing the three-dimensional map of the in-vehicle noise to obtain the howling order noise curve of the target rotating part and the background order noise curve of the vehicle is clarified, which provides a basis for obtaining the howling order noise curve and the background order noise curve.

[0106] In one embodiment, in step S23, determining the howling order noise peak envelope curve according to the howling order noise curve specifically includes the following steps:

[0107] S231: Divide the howling order noise curve into N sections of howling sub-curves according to the acquisition speed range of the target rotating component, and each howling sub-curve corresponds to a preset speed range.

[0108] S232: Determine the maximum value of the order noise sound pressure level of each howling sub-curve to obtain N howling order noise peaks, and determine the rotation speed corresponding to each howling order noise peak to obtain N groups of noise peak data.

[0109] S233: performing difference calculation on the N groups of noise peak data to obtain a curve showing the rotation speed changing with the howling order noise peak value as a howling order noise peak value envelope curve.

[0110] After obtaining the howling order noise curve, divide the howling order noise curve into N howling sub-curves according to the collected speed range of the target rotating component. Each howling sub-curve corresponds to a preset speed range. Determine the maximum value of the order noise sound pressure level of each howling sub-curve to obtain N howling order noise peaks, and determine the speed corresponding to each howling order noise peak to obtain N groups of noise peak data. Perform difference calculation on the N groups of noise peak data to obtain a curve showing the change of speed with the howling order noise peak as the howling order noise peak envelope curve. Similarly, the background order noise peak envelope curve can be obtained.

[0111] In this embodiment, the relationship between the sound pressure level of the order noise (including the howling order noise and the background order noise) and the rotation speed of the target rotating part can be expressed as:

[0112] L(i)=f(x i );

[0113] Where i = 1, 2, 3…n, L(i) is the order noise sound pressure level, x iis the speed of the target rotating component collected at each speed collection interval, and n is the total amount of in-vehicle noise data.

[0114] The order noise curve (including the howling order noise curve and the background order noise curve) is divided into N sub-curves according to the acquisition speed range of the target rotating part, and the maximum value of the order noise sound pressure level corresponding to each sub-curve is extracted to obtain N groups of noise peak data. The noise sound pressure level and the corresponding speed of the noise peak data are recorded as f(x kmax ) and x kmax , where k = 1, 2, 3…N, and the difference operation is performed on N groups of noise peak data by interpolation operation method to obtain the processed speed x i (i=1,2,3…n corresponding to the noise sound pressure level L E (x i ), then the order noise peak envelope curve can be expressed as:

[0115] L E (x i )=f E (x i ); i=1,2,3…n.

[0116] That is, both the howling order noise peak envelope curve and the background order noise peak envelope curve can be expressed by the above formula at the same time.

[0117] Among them, the interpolation methods include linear interpolation, Lagrange interpolation, and spline interpolation methods.

[0118] For example, the linear interpolation method is used to perform difference calculation on N groups of noise peak data:

[0119] When x kmax ≤x i ≥x k+1max (k=1,2,3…N-1),

[0120] When x1≤x i ≥x 1max hour,

[0121] When x Nmax ≤x i ≥x n hour,

[0122] In this embodiment, the howling order noise curve is divided into N howling sub-curves according to the collected speed range of the target rotating part, each howling sub-curve corresponds to a preset speed range, and the maximum value of the order noise sound pressure level of each howling sub-curve is determined to obtain N howling order noise peaks, and the speed corresponding to each howling order noise peak is determined to obtain N groups of noise peak data. The N groups of noise peak data are differenced to obtain a curve of the speed varying with the howling order noise peak as the howling order noise peak envelope curve, which is used as the howling order noise peak envelope curve. The specific process of determining the howling order noise peak envelope curve based on the howling order noise curve is clarified, which provides a basis for the subsequent determination of the noise difference curve.

[0123] In one embodiment, if Figure 4 As shown, in step S30, the objective evaluation index of the vehicle interior noise data is determined according to the noise difference curve, which specifically includes the following steps:

[0124] S31: Determine a sensitive parameter according to the type of the target rotating component, the sensitive parameter being the analysis accuracy when the noise difference curve is analyzed in advance, and the sensitive parameter is represented by a preset rotation speed.

[0125] After obtaining the noise difference curve, it is necessary to determine the type of the target rotating component and the sensitive parameter. The target rotating component is the rotating component that needs to detect the whistling noise in the vehicle, that is, the rotating component corresponding to the noise difference curve. The sensitive parameter is the analysis accuracy when analyzing the noise difference curve in advance. The sensitive parameter is expressed by a preset speed in rpm.

[0126] Among them, the sensitive parameters are set according to actual needs. For example, when analyzing the howling noise generated by the direct drive gear of the internal combustion engine, the value range of the sensitive parameter is [100, 300], that is, the sensitive parameter needs to be greater than or equal to 100rpm and less than or equal to 300rpm; when analyzing the howling noise of the drive motor, the value range of the sensitive parameter is [300, 1000], that is, the sensitive parameter needs to be greater than or equal to 300rpm and less than or equal to 1000rpm; when analyzing the howling noise of other components, the value can also be determined according to the actual situation.

[0127] S32: Divide the noise difference curve into a plurality of sub-noise difference curves according to the sensitive parameters, and determine the maximum noise difference in each sub-noise difference curve.

[0128] After determining the sensitive parameter according to the type of the target rotating component, the noise difference curve is divided into a plurality of sub-noise difference curves according to the sensitive parameter, and the maximum noise difference in each sub-noise difference curve is determined.

[0129] S33: determining an objective evaluation index according to the maximum noise difference in each sub-noise difference curve.

[0130] After the maximum noise difference in each sub-noise difference curve is determined, an objective evaluation index may be determined according to the maximum noise difference in each sub-noise difference curve.

[0131] For example, the sub-noise difference curves are sorted in descending order according to the maximum noise difference in each sub-noise difference curve, and the m+1th maximum noise difference is selected from the maximum noise difference in each sub-noise difference curve as the objective evaluation index, where m can be a preset value or can be determined according to the sensitive parameter and the speed collection interval of the target rotating component; in addition, the objective evaluation index can also be determined according to other methods, which will not be repeated here.

[0132] For example, the noise difference curve of the target rotating part is shown in Figure 5, where the x-axis is the speed of the target rotating part in revolutions per minute (rpm). In Figure 4, the acquisition speed range of the target rotating part is 8000 rpm; the y-axis is the noise difference, which is expressed in sound pressure level in decibels (dB); the black curve in the figure is the noise difference curve, which indicates that the sound level noise difference changes with the speed of the target rotating part. The black straight line parallel to the x-axis in the figure is an objective evaluation indicator. Figure 4 As shown in , the fifth maximum noise difference in the noise difference curve is C. When m is 4, the fifth maximum noise difference in the noise difference curve can be determined, and the objective evaluation index is the noise difference at point C.

[0133] Since the number of sub-noise difference curves is determined by sensitive parameters, on the basis of a certain acquisition speed range of the target rotating component, the fewer sensitive parameters are, the more sub-noise difference curves there are, and the more maximum noise differences there are, so that the accuracy of the objective evaluation index obtained is higher.

[0134] For example, when the acquisition speed range of the target rotating component is 8000rpm and the sensitive parameter is 200rpm, 40 sub-noise difference curves can be obtained, that is, the maximum noise difference within the 40 sub-noise difference curves can be obtained. When the speed acquisition interval of the target rotating component is 25rpm, it can be determined that m is 8, then the 9 maximum noise differences are objective evaluation indicators, and the objective evaluation index can be accurate to 0.25.

[0135] In this embodiment, the acquisition speed range of the target rotating component is 8000 rpm and the sensitive parameter is 200 rpm, which is only an exemplary description. In other embodiments, the acquisition speed range and sensitive parameter of the target rotating component can also be other values, which will not be repeated here.

[0136] In this embodiment, a sensitive parameter is determined according to the type of the target rotating component. The sensitive parameter is the analysis accuracy when the noise difference curve is analyzed in advance. The sensitive parameter is represented by a preset speed. The noise difference curve is divided into a plurality of sub-noise difference curves according to the sensitive parameter, and the maximum noise difference in each sub-noise difference curve is determined. The objective evaluation index is determined according to the maximum noise difference in each sub-noise difference curve. The steps of determining the objective evaluation index of the in-vehicle noise data according to the noise difference curve are refined. The noise difference curve is divided into a plurality of noise difference curves according to the sensitive parameter, and then the objective evaluation index is determined. The accuracy of the objective evaluation index can be set according to the sensitive parameter. The smaller the sensitive parameter is, the smaller the speed range corresponding to the sub-noise difference curve is, the more the maximum noise difference is determined, and the more accurate the objective evaluation index is.

[0137] In one embodiment, in step S33, determining the objective evaluation index according to the maximum noise difference in each sub-noise difference curve specifically includes the following steps:

[0138] S331: Determine a ratio of a sensitive parameter to a rotation speed collection interval, where the rotation speed collection interval is a collection interval for collecting the rotation speed of a target rotating component.

[0139] S332: Take the smallest positive integer greater than or equal to the ratio as the target value.

[0140] S333: Determine an objective evaluation index according to the target value and the maximum noise difference in each sub-noise difference curve. The objective evaluation index must satisfy the following requirement: in the noise difference curve, there are only a preset number of maximum noise differences that are greater than or equal to the objective evaluation index, and the preset number is the sum of the target value and the preset value.

[0141] The objective evaluation index is H, the sensitive parameter is B, the speed collection interval is R, the target value is m, and the ratio of the sensitive parameter to the speed collection interval is determined as Then m satisfies The smallest positive integer of , then in the noise difference curve, there are only m+1 maximum noise differences greater than or equal to H.

[0142] In this embodiment, by determining the ratio of the sensitive parameter to the speed collection interval, the speed collection interval is the collection interval for collecting the speed of the target rotating component, and taking the smallest positive integer greater than or equal to the ratio as the target value, the objective evaluation index is determined according to the target value and the maximum noise difference in each sub-noise difference curve. The objective evaluation index must meet the following requirements: in the noise difference curve, there is only a preset number of maximum noise differences that are greater than or equal to the objective evaluation index, and the preset number is the sum of the target value and the preset value. The specific process of determining the objective evaluation index according to the maximum noise difference in each sub-noise difference curve is clarified, and the objective evaluation index is determined according to the ratio of the sensitive parameter to the speed collection interval. The higher the accuracy of the sensitive parameter and the speed collection interval, the higher the accuracy of the objective evaluation index, which provides a basis for quantifying the objective evaluation index.

[0143] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0144] In one embodiment, a vehicle interior howling noise analysis device is provided, and the vehicle interior howling noise analysis device corresponds to the vehicle interior howling noise analysis method in the above embodiment. Figure 6 As shown, the vehicle interior howling noise analysis device includes an acquisition module 601, an extraction module 602, a determination module 603, a creation module 604 and an analysis module 605. The detailed description of each functional module is as follows:

[0145] An acquisition module 601 is used to acquire vehicle interior noise data;

[0146] An extraction module 602 is used to process the in-vehicle noise data to extract a peak envelope curve of a howling order noise of a target rotating component on the vehicle and a peak envelope curve of a background order noise of the vehicle;

[0147] A determination module 603 is used to obtain a noise difference curve by subtracting the peak envelope curve of the howling order noise from the peak envelope curve of the background order noise, and determine an objective evaluation index of the in-vehicle noise data according to the noise difference curve;

[0148] Establishing module 604, for establishing a linear regression model of subjective and objective fitting scores according to the objective evaluation index and the subjective scoring data of the in-vehicle occupants on the in-vehicle noise data;

[0149] The analysis module 605 is used to analyze the howling noise in the vehicle according to the linear regression model and the objective evaluation index.

[0150] Furthermore, the establishing module 604 is specifically used for:

[0151] A linear regression relationship between the objective evaluation index and the subjective scoring data is established to determine a linear regression model of the subjective and objective fitting score. The linear regression model is expressed by the following formula:

[0152] X = (AY) / B;

[0153] Wherein, X is the subjective and objective fitting score, Y is the objective evaluation index, and A and B are constants.

[0154] Furthermore, the determining module 603 is specifically configured to:

[0155] Determining the objective evaluation index of the in-vehicle noise data according to the noise difference curve includes:

[0156] Determine a sensitive parameter according to the type of the target rotating component, wherein the sensitive parameter is a predetermined analysis accuracy when analyzing the noise difference curve, and the sensitive parameter is represented by a preset rotation speed;

[0157] Dividing the noise difference curve into a plurality of sub-noise difference curves according to the sensitive parameter, and determining the maximum noise difference in each of the sub-noise difference curves;

[0158] The objective evaluation index is determined according to the maximum noise difference in each of the sub-noise difference curves.

[0159] Furthermore, the determining module 603 is further specifically configured to:

[0160] Determine a ratio of the sensitive parameter to a rotation speed collection interval, wherein the rotation speed collection interval is a collection interval for collecting the rotation speed of the target rotating component;

[0161] Taking the smallest positive integer greater than or equal to the ratio as the target value;

[0162] The objective evaluation index is determined according to the target value and the maximum noise difference in each of the sub-noise difference curves, and the objective evaluation index needs to satisfy: in the noise difference curve, there is only a preset number of the maximum noise difference values ​​that are greater than or equal to the objective evaluation index, and the preset number is the sum of the target value and the preset value.

[0163] Furthermore, the extraction module 602 is specifically used for:

[0164] Processing the in-vehicle noise data to convert the in-vehicle noise data into a three-dimensional in-vehicle noise graph showing how the noise sound pressure level varies with the rotation speed and noise frequency of the target rotating component;

[0165] Processing the three-dimensional map of in-vehicle noise to obtain a howling order noise curve of the target rotating component and a background order noise curve of the vehicle;

[0166] A howling order noise peak envelope curve is determined according to the howling order noise curve, and a background order noise peak envelope curve is determined according to the background order noise curve.

[0167] Furthermore, the extraction module 602 is further specifically used for:

[0168] Determine a howling order in the three-dimensional map of the in-vehicle noise, and translate the howling order by a preset frequency in a direction where the noise frequency increases, so as to obtain a background order;

[0169] Performing noise slicing processing of a preset bandwidth on the noise data of the howling order to obtain the howling order noise curve, wherein the noise sound pressure level of the howling order noise curve changes with the rotation speed;

[0170] The noise data in the background order is subjected to noise slicing processing of the preset bandwidth to obtain the background order noise curve, wherein the noise sound pressure level of the background order noise curve varies with the rotation speed.

[0171] Furthermore, the extraction module 602 is further specifically used for:

[0172] Divide the howling order noise curve into N sections of howling sub-curves according to the acquisition speed range of the target rotating component, each of the howling sub-curves corresponds to a preset speed range;

[0173] Determine the maximum value of the order noise sound pressure level of each of the howling sub-curves to obtain N howling order noise peaks, and determine the rotation speed corresponding to each of the howling order noise peaks to obtain N groups of noise peak data;

[0174] The N groups of noise peak data are subjected to difference calculation to obtain a curve showing a change in the rotation speed with the howling order noise peak value, which is used as the howling order noise peak value envelope curve.

[0175] For the specific definition of the in-vehicle whistling noise analysis device, please refer to the definition of the in-vehicle whistling noise analysis method mentioned above, which will not be repeated here. Each module in the above-mentioned in-vehicle whistling noise analysis device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0176] In one embodiment, a vehicle-mounted howling noise analysis device is provided, which can be a computer device. The vehicle-mounted howling noise analysis device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the vehicle-mounted howling noise analysis device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the vehicle-mounted howling noise analysis device is used to communicate with an external device via a network connection. When the computer program is executed by the processor, a vehicle-mounted howling noise analysis method is implemented.

[0177] In one embodiment, Figure 7 As shown, a vehicle interior howling noise analysis device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the vehicle interior howling noise analysis method are implemented.

[0178] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned in-vehicle howling noise analysis method are implemented.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0180] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0181] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for analyzing in-vehicle howling noise, characterized in that: include: Acquire vehicle interior noise data; Processing the in-vehicle noise data to extract a howling order noise peak envelope curve of a target rotating component on the vehicle and a background order noise peak envelope curve of the vehicle; Subtracting the peak envelope curve of the howling order noise from the peak envelope curve of the background order noise to obtain a noise difference curve, and determining an objective evaluation index of the in-vehicle noise data according to the noise difference curve; Establishing a linear regression model of subjective and objective fitting scores according to the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants; Analyzing the howling noise in the vehicle according to the linear regression model and the objective evaluation index; The processing of the vehicle interior noise data to extract the howling order noise peak envelope curve of the target rotating component on the vehicle and the background order noise peak envelope curve of the vehicle includes: Processing the in-vehicle noise data to convert the in-vehicle noise data into a three-dimensional in-vehicle noise graph showing how the noise sound pressure level varies with the rotation speed and noise frequency of the target rotating component; Processing the three-dimensional map of in-vehicle noise to obtain a howling order noise curve of the target rotating component and a background order noise curve of the vehicle; A howling order noise peak envelope curve is determined according to the howling order noise curve, and a background order noise peak envelope curve is determined according to the background order noise curve.

2. The vehicle interior howling noise analysis method according to claim 1, characterized in that: The step of establishing a linear regression model of subjective and objective fitting scores based on the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants includes: A linear regression relationship between the objective evaluation index and the subjective scoring data is established to determine a linear regression model of the subjective and objective fitting score. The linear regression model is expressed by the following formula: ； in, Score the subjective and objective fit, is the objective evaluation index, and is a constant.

3. The method for analyzing in-vehicle squeal noise according to claim 1, characterized in that: Determining the objective evaluation index of the in-vehicle noise data according to the noise difference curve includes: Determine a sensitive parameter according to the type of the target rotating component, wherein the sensitive parameter is a predetermined analysis accuracy when analyzing the noise difference curve, and the sensitive parameter is represented by a preset rotation speed; Dividing the noise difference curve into a plurality of sub-noise difference curves according to the sensitive parameter, and determining the maximum noise difference in each of the sub-noise difference curves; The objective evaluation index is determined according to the maximum noise difference in each of the sub-noise difference curves.

4. The method for analyzing in-vehicle squeal noise according to claim 3, characterized in that: The determining the objective evaluation index according to the maximum noise difference in each of the sub-noise difference curves includes: Determine a ratio of the sensitive parameter to a rotation speed collection interval, wherein the rotation speed collection interval is a collection interval for collecting the rotation speed of the target rotating component; Taking the smallest positive integer greater than or equal to the ratio as the target value; The objective evaluation index is determined according to the target value and the maximum noise difference in each of the sub-noise difference curves, and the objective evaluation index needs to satisfy: in the noise difference curve, there is only a preset number of the maximum noise difference values ​​that are greater than or equal to the objective evaluation index, and the preset number is the sum of the target value and the preset value.

5. The method for analyzing in-vehicle squeal noise according to claim 1, characterized in that: The processing of the three-dimensional map of the vehicle interior noise to obtain the howling order noise curve of the target rotating component and the background order noise curve of the vehicle includes: Determine a howling order in the three-dimensional map of the in-vehicle noise, and translate the howling order by a preset frequency in a direction where the noise frequency increases, so as to obtain a background order; Performing noise slicing processing of a preset bandwidth on the noise data of the howling order to obtain the howling order noise curve, wherein the noise sound pressure level of the howling order noise curve changes with the rotation speed; The noise data in the background order is subjected to noise slicing processing of the preset bandwidth to obtain the background order noise curve, wherein the noise sound pressure level of the background order noise curve varies with the rotation speed.

6. The method for analyzing in-vehicle squeal noise according to claim 1, characterized in that: The step of determining a howling order noise peak envelope curve according to the howling order noise curve comprises: Divide the howling order noise curve into N sections of howling sub-curves according to the acquisition speed range of the target rotating component, each of the howling sub-curves corresponds to a preset speed range; Determine the maximum value of the order noise sound pressure level of each of the howling sub-curves to obtain N howling order noise peaks, and determine the rotation speed corresponding to each of the howling order noise peaks to obtain N groups of noise peak data; The N groups of noise peak data are subjected to difference calculation to obtain a curve showing a change in the rotation speed with the howling order noise peak value, which is used as the howling order noise peak value envelope curve.

7. A vehicle whistling noise analysis device, characterized in that: include: An acquisition module, used for acquiring vehicle interior noise data; An extraction module, used for processing the in-vehicle noise data to extract a howling order noise peak envelope curve of a target rotating component on the vehicle and a background order noise peak envelope curve of the vehicle; A determination module, configured to obtain a noise difference curve by subtracting the peak envelope curve of the howling order noise from the peak envelope curve of the background order noise, and determine an objective evaluation index of the in-vehicle noise data according to the noise difference curve; An establishment module is used to establish a linear regression model of subjective and objective fitting scores according to the objective evaluation index and the subjective scoring data of the in-vehicle noise data by the in-vehicle occupants; An analysis module, configured to analyze the howling noise in the vehicle according to the linear regression model and the objective evaluation index; Wherein, the extraction module is used for: Processing the in-vehicle noise data to convert the in-vehicle noise data into a three-dimensional in-vehicle noise graph showing how the noise sound pressure level varies with the rotation speed and noise frequency of the target rotating component; Processing the three-dimensional map of in-vehicle noise to obtain a howling order noise curve of the target rotating component and a background order noise curve of the vehicle; A howling order noise peak envelope curve is determined according to the howling order noise curve, and a background order noise peak envelope curve is determined according to the background order noise curve.

8. A vehicle interior howling noise analysis device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the vehicle interior howling noise analysis method according to any one of claims 1 to 6 are implemented.

9. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the vehicle interior howling noise analysis method according to any one of claims 1 to 6 are implemented.

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