Noise control method and device for vehicle and vehicle

Abstract

The application discloses a noise control method and device of a vehicle and the vehicle, and relates to the technical field of vehicle noise control. The method comprises the following steps: acquiring parameter values of a target state parameter group of a target vehicle, wherein the target state parameter group comprises multiple state parameters, each state parameter is related to current noise generated by vibration of the target vehicle; determining an equipment sound wave signal based on the parameter values of the target state parameter group; and controlling a sound playing equipment of the target vehicle to play sound based on the equipment sound wave signal, so that the current noise sound wave signal interferes with the equipment sound wave signal of the sound playing equipment when the current noise sound wave signal propagates to a cabin area of the target vehicle, and the noise loudness in the cabin area is less than a preset loudness threshold. The application solves the technical problem of large noise loudness of a vehicle.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of sound waves, and particularly relates to a noise control method and device for a vehicle and the vehicle. BACKGROUND

[0002] The noise of a vehicle includes road noise, transmission system noise, exhaust noise and the like, which can affect a driver, and if the loudness of the noise is too large, the driver can be distracted during driving, and even a safety accident can be caused. Therefore, the loudness of the vehicle noise is a technical problem to be solved. SUMMARY

[0003] The present application provides a noise control method and device for a vehicle and the vehicle, which solves the technical problem of the loudness of the vehicle noise.

[0004] In a first aspect, the present application provides a noise control method for a vehicle, comprising: obtaining parameter values of a target state parameter group of a target vehicle, the target state parameter group comprising a plurality of state parameters, each state parameter being related to current noise generated by vibration of the target vehicle; determining a device sound wave signal based on the parameter values of the target state parameter group; and controlling a sound playing device of the target vehicle to play sound based on the device sound wave signal, so that the current noise sound wave signal interferes with the device sound wave signal when propagating to a cabin area of the target vehicle, so that the noise loudness in the cabin area is less than a preset loudness threshold.

[0005] In combination with the first aspect of the present application, in some embodiments, before the obtaining of the parameter values of the target state parameter group of the target vehicle, the method further comprises: obtaining a current road type of a driving road on which the target vehicle is located; and determining a parameter type of the target state parameter group based on the current road type.

[0006] In combination with the first aspect of the present application, in some embodiments, the determining of the parameter type of the target state parameter group based on the current road type comprises: inputting the current road type into a preset corresponding relationship to obtain the parameter type of the target state parameter group, the preset corresponding relationship being a corresponding relationship between road types and parameter types.

[0007] In some embodiments of the first aspect of the application, the state parameter is acceleration of a chassis component of the vehicle, M triaxial acceleration sensors are arranged at different chassis positions of the test vehicle, N sound collection devices are arranged at different cabin positions of the test vehicle, M and N are integers greater than 1, and the preset correspondence is determined by the following steps: for each road type, when the test vehicle is driving on the road type, acceleration data of the M triaxial acceleration sensors and N test noise signals collected by the N sound collection devices are obtained; based on the acceleration data of the M triaxial acceleration sensors and the N test noise signals, the M triaxial acceleration sensors are divided into a first group of triaxial acceleration sensors and a second group of triaxial acceleration sensors, wherein the correlation degree of the acceleration data of the first group of triaxial acceleration sensors and the N test noise signals is greater than the correlation degree of the acceleration data of the second group of triaxial acceleration sensors and the N test noise signals; based on the acceleration data of the first group of triaxial acceleration sensors and the N test noise signals, the acceleration data of the first group of triaxial acceleration sensors is divided into a first group of accelerations and a second group of accelerations, wherein the correlation degree of the first group of accelerations and the N test noise signals is greater than the correlation degree of the second group of accelerations and the N test noise signals; based on the first group of accelerations, a parameter type required for the road type is determined; and based on the parameter type required for each road type, the preset correspondence is established.

[0008] In some embodiments of the first aspect of the application, based on the acceleration data of the M triaxial acceleration sensors and the N test noise signals, the M triaxial acceleration sensors are divided into a first group of triaxial acceleration sensors and a second group of triaxial acceleration sensors, which includes: selecting a preset number of sensors from the M triaxial acceleration sensors for combination, and enumerating all combinations to obtain a plurality of sensor combinations; each sensor combination in the plurality of sensor combinations is sequentially taken as a target sensor combination; the acceleration data of the target sensor combination is taken as the independent variable, and the N test noise signals are taken as the dependent variable to perform multiple correlation analysis and obtain a multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination; the average value of the multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination is taken as the correlation score of the acceleration data of the target sensor combination and the N test noise signals; in the correlation score corresponding to each sensor combination in the plurality of sensor combinations, the sensor combination corresponding to the maximum correlation score is taken as the first group of triaxial acceleration sensors, and the sensors in the M triaxial acceleration sensors other than the first group of triaxial acceleration sensors are taken as the second group of triaxial acceleration sensors.

[0009] In some embodiments of the first aspect of the present application, the dividing the acceleration data of the first set of three-axis acceleration sensors into a first set of accelerations and a second set of accelerations based on the acceleration data of the first set of three-axis acceleration sensors and the N test noise signals comprises: selecting a preset number of accelerations from the acceleration data of the first set of three-axis acceleration sensors for combination, and enumerating all combinations to obtain a plurality of acceleration combinations; sequentially taking each acceleration combination in the plurality of acceleration combinations as a target acceleration combination; taking the target acceleration combination as an independent variable and the N test noise signals as a dependent variable to perform multiple correlation analysis and obtain a multiple correlation coefficient of each acceleration in the target acceleration combination; taking the average of the multiple correlation coefficients of each acceleration in the target acceleration combination as a correlation score of the target acceleration combination and the N test noise signals; and taking the acceleration combination corresponding to the maximum correlation score in the correlation scores of each acceleration combination in the plurality of acceleration combinations as the first set of accelerations, and taking the accelerations in the acceleration data of the first set of three-axis acceleration sensors other than the first set of accelerations as the second set of accelerations.

[0010] In some embodiments of the first aspect of the present application, the M three-axis acceleration sensors comprise: a first three-axis acceleration sensor arranged on the top of the left front shock absorber of the test vehicle, a second three-axis acceleration sensor arranged on the top of the left rear shock absorber, a third three-axis acceleration sensor arranged on the top of the right front shock absorber, a fourth three-axis acceleration sensor arranged on the top of the right rear shock absorber, a fifth three-axis acceleration sensor arranged on the left side crossbar of the front subframe, and a sixth three-axis acceleration sensor arranged on the right side crossbar of the front subframe.

[0011] In some embodiments of the first aspect of the present application, the determining the device sound wave signal based on the parameter values of the target state parameter group comprises: determining chassis vibration information of the target vehicle based on the parameter values of the target state parameter group; and determining the device sound wave signal based on the chassis vibration information.

[0012] In a second aspect, an embodiment of the present application provides a noise control device for a vehicle, comprising: a parameter value acquisition unit configured to acquire parameter values of a target state parameter group of a target vehicle, the target state parameter group comprising a plurality of state parameters, each of the state parameters being related to a current noise generated by vibration of the target vehicle; a signal determination unit configured to determine a device sound wave signal based on the parameter values of the target state parameter group; and a playing control unit configured to control a sound playing device of the target vehicle to play sound based on the device sound wave signal, so that the current noise sound wave signal interferes with the device sound wave signal of the sound playing device when the current noise sound wave signal propagates to a cabin area of the target vehicle, and the noise loudness in the cabin area is less than a preset loudness threshold.

[0013] In a third aspect, an embodiment of the present application provides a vehicle, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the method of any one of the first aspect when executing the computer program.

[0014] The one or more technical solutions provided by the embodiments of the present application at least achieve the following technical effects or advantages:

[0015] The embodiments of the present application acquire the parameter values of a target state parameter group of a target vehicle, the target state parameter group comprising a plurality of state parameters, each of the state parameters being related to a current noise generated by vibration of the target vehicle; determine a device sound wave signal based on the parameter values of the target state parameter group; and control a sound playing device of the target vehicle to play sound based on the device sound wave signal, so that the current noise sound wave signal interferes with the device sound wave signal of the sound playing device when the current noise sound wave signal propagates to a cabin area of the target vehicle, and the noise loudness in the cabin area is less than a preset loudness threshold. The embodiments of the present application offset the vehicle noise in the form of sound cancellation, and since the current noise sound wave signal interferes with the device sound wave signal of the sound playing device, the noise loudness in the cabin area is less than the preset loudness threshold, thereby achieving the reduction of the loudness of the vehicle noise. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0017] Figure 1 The flowchart of the noise control method for the vehicle in the embodiments of the present application;

[0018] Figure 2 The schematic diagram of the arrangement position of the three-axis acceleration sensor in the embodiments of the present application;

[0019] Figure 3 Flow chart for determining reference signal in embodiments of the present application;

[0020] Figure 4 Functional module chart for noise control device of vehicle in embodiments of the present application;

[0021] Figure 5 Structural schematic diagram of vehicle in embodiments of the present application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0023] In the present application, the description such as "first", "second" and the like is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can realize it. When the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope required by the present application.

[0024] The embodiments of the present application provide a noise control method of a vehicle, as shown in the figure, the method comprises the following steps S101-S103: Figure 1

[0025] S101: obtaining parameter values of a target state parameter group of a target vehicle, the target state parameter group comprising a plurality of state parameters, each state parameter being related to current noise generated by vibration of the target vehicle.

[0026] In some embodiments, before step S101, it can also include: obtaining a current road type of a driving road where the target vehicle is located; determining a parameter type of the target state parameter group based on the current road type.

[0027] It should be noted that the road type can be a water-permeable asphalt pavement, a gravel pavement, a stone pavement, a rain groove pavement or a speed reduction zone, etc.

[0028] ​In some embodiments, determining the parameter type of the target state parameter group based on the current road type can include: inputting the current road type into a preset correspondence relationship to obtain the parameter type of the target state parameter group, the preset correspondence relationship being a correspondence relationship between road types and parameter types.

[0029] In some embodiments, the preset correspondence relationship can include a plurality of sub-relationships as follows: when the road type is a speed bump, the parameter type of the target state parameter group can include: z-axis acceleration of a left front shock absorber tower top, z-axis acceleration of a left rear shock absorber tower top, z-axis acceleration of a right rear shock absorber tower top, and z-axis acceleration of a right side cross bar of a front subframe; when the road type is a rain groove pavement, the parameter type of the target state parameter group can include: x-axis acceleration and y-axis acceleration of the left front shock absorber tower top, x-axis acceleration and y-axis acceleration of the left rear shock absorber tower top, and x-axis acceleration and y-axis acceleration of the right side cross bar of the front subframe; when the road type is a permeable asphalt pavement, the parameter type of the target state parameter group can include: x-axis acceleration, y-axis acceleration and z-axis acceleration of a left side cross bar of the front subframe, and y-axis acceleration and z-axis acceleration of the left front shock absorber tower top; when the road type is a gravel pavement, the parameter type of the target state parameter group can include: x-axis acceleration, y-axis acceleration and z-axis acceleration of the right side cross bar of the front subframe, z-axis acceleration of a right front shock absorber tower top, and x-axis acceleration of the left front shock absorber tower top; and when the road type is a stone pavement, the parameter type of the target state parameter group can include: x-axis acceleration and z-axis acceleration of the right side cross bar of the front subframe, x-axis acceleration and z-axis acceleration of the left side cross bar of the front subframe, z-axis acceleration of the right front shock absorber tower top, and x-axis acceleration and y-axis acceleration of the left front shock absorber tower top.

[0030] It should be noted that the parameter type of the target state parameter group of the target vehicle can be fixed or can vary according to the road type. Under different road types, the vibration of the vehicle is different, and the vibration of different components and the vibration in different directions will affect the amplitude, frequency and phase of the noise. Therefore, the embodiments of the present application limit the determination of the parameter type of the target state parameter group based on the current road type, such as when the road type is a speed bump, the vibration direction of the vehicle chassis is mainly the z-axis, so the parameter type of the target state parameter group mainly includes z-axis acceleration; and such as when the road type is a rain groove pavement, the vehicle is slippery, so the parameter type of the target state parameter group mainly includes x-axis acceleration and y-axis acceleration. Therefore, determining the corresponding parameter type under different road types can better determine the vibration of the vehicle and further determine the related properties of the noise, so as to improve the accuracy of noise cancellation in the vehicle cabin.

[0031] In some embodiments, the state parameter is acceleration of a chassis component of the vehicle, different chassis positions of the test vehicle are provided with M triaxial acceleration sensors, different cabin positions of the test vehicle are provided with N sound collection devices, M and N are integers greater than 1, and the preset correspondence can be determined by the following steps S1011-S1015.

[0032] S1011: For each road type, when the test vehicle is driving on the road type, acceleration data of the M triaxial acceleration sensors and N test noise signals collected by the N sound collection devices are obtained.

[0033] In some embodiments, the M triaxial acceleration sensors can include: a first triaxial acceleration sensor arranged on the top of the left front shock absorber tower of the test vehicle, a second triaxial acceleration sensor arranged on the top of the left rear shock absorber tower, a third triaxial acceleration sensor arranged on the top of the right front shock absorber tower, a fourth triaxial acceleration sensor arranged on the top of the right rear shock absorber tower, a fifth triaxial acceleration sensor arranged on the left side crossbar of the front subframe, and a sixth triaxial acceleration sensor arranged on the right side crossbar of the front subframe.

[0034] It should be noted that the cabin position can refer to a two-dimensional plane where the seat is located, or a three-dimensional space inside the cabin. In some embodiments, the N sound collection devices can include: a first sound collection device arranged at the headrest of the seat of the test vehicle, a second sound collection device arranged at the roof of the test vehicle, and a third sound collection device arranged at the door of the test vehicle.

[0035] It should be noted that when the M triaxial acceleration sensors include the first triaxial acceleration sensor, the second triaxial acceleration sensor, the third triaxial acceleration sensor, the fourth triaxial acceleration sensor, the fifth triaxial acceleration sensor, and the sixth triaxial acceleration sensor, the acceleration data of the M triaxial acceleration sensors can include the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the first triaxial acceleration sensor, the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the second triaxial acceleration sensor, the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the third triaxial acceleration sensor, the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the fourth triaxial acceleration sensor, the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the fifth triaxial acceleration sensor, and the x-axis acceleration, the y-axis acceleration, and the z-axis acceleration of the sixth triaxial acceleration sensor.

[0036] S1012: Based on the acceleration data of the M triaxial acceleration sensors and the N test noise signals, the M triaxial acceleration sensors are divided into a first group of triaxial acceleration sensors and a second group of triaxial acceleration sensors, wherein the correlation degree of the acceleration data of the first group of triaxial acceleration sensors and the N test noise signals is greater than the correlation degree of the acceleration data of the second group of triaxial acceleration sensors and the N test noise signals.

[0037] In some embodiments, based on the acceleration data of the M triaxial acceleration sensors and the N test noise signals, the M triaxial acceleration sensors can be divided into a first group of triaxial acceleration sensors and a second group of triaxial acceleration sensors, which can include: selecting a preset number of sensors from the M triaxial acceleration sensors for combination, and enumerating all combinations to obtain a plurality of sensor combinations; each sensor combination in the plurality of sensor combinations is sequentially taken as a target sensor combination; the acceleration data of the target sensor combination is taken as the independent variable, and the N test noise signals are taken as the dependent variable to perform multiple correlation analysis and obtain the multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination; the average value of the multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination is taken as the correlation score of the acceleration data of the target sensor combination and the N test noise signals; in the correlation score corresponding to each sensor combination in the plurality of sensor combinations, the sensor combination corresponding to the maximum correlation score is taken as the first group of triaxial acceleration sensors, and the sensors other than the first group of triaxial acceleration sensors in the M triaxial acceleration sensors are taken as the second group of triaxial acceleration sensors.

[0038] For example, assuming that the M triaxial acceleration sensors include a first triaxial acceleration sensor a, a second triaxial acceleration sensor b, a third triaxial acceleration sensor c, a fourth triaxial acceleration sensor d, a fifth triaxial acceleration sensor e, and a sixth triaxial acceleration sensor f, and the preset number is 5. Then, the enumeration can be done through the formula of permutation combination, that is, It can be known that the plurality of sensor combinations include 6 kinds, including (b, c, d, e, f), (a, c, d, e, f), (a, b, d, e, f), (a, b, c, e, f), (a, b, c, d, f) and (a, b, c, d, e) in turn. Assuming that the correlation score of the (b, c, d, e, f) combination is 0.9, the correlation score of the (a, c, d, e, f) combination is 0.8, the correlation score of the (a, b, d, e, f) combination is 0.83, the correlation score of the (a, b, c, e, f) combination is 0.92, the correlation score of the (a, b, c, d, f) combination is 0.91, and the correlation score of the (a, b, c, d, e) combination is 0.9, then the (a, b, c, e, f) combination is taken as the first group of three-axis acceleration sensors, and d is taken as the second group of three-axis acceleration sensors.

[0039] It should be noted that when performing multiple correlation analysis to obtain the multiple correlation coefficients of each acceleration of the target sensor combination, the multiple correlation coefficients can be the multiple correlation coefficients of the accelerations corresponding to the noise in the frequency range of 30-500 Hz.

[0040] S1013: Based on the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals, the acceleration data of the first group of three-axis acceleration sensors is divided into a first group of accelerations and a second group of accelerations, wherein the correlation degree of the first group of accelerations with the N test noise signals is greater than the correlation degree of the second group of accelerations with the N test noise signals.

[0041] In some embodiments, based on the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals, the acceleration data of the first group of three-axis acceleration sensors can be divided into a first group of accelerations and a second group of accelerations, which can include: selecting a preset number of accelerations from the acceleration data of the first group of three-axis acceleration sensors for combination, and enumerating all combinations to obtain a plurality of acceleration combinations; each acceleration combination in the plurality of acceleration combinations is sequentially taken as a target acceleration combination; the target acceleration combination is taken as the independent variable, and the N test noise signals are taken as the dependent variable to perform multiple correlation analysis and obtain the multiple correlation coefficients of each acceleration in the target acceleration combination; the average value of the multiple correlation coefficients of each acceleration in the target acceleration combination is taken as the correlation score of the target acceleration combination with the N test noise signals; in the correlation scores of each acceleration combination in the plurality of acceleration combinations, the acceleration combination corresponding to the maximum correlation score is taken as the first group of accelerations, and the accelerations in the acceleration data of the first group of three-axis acceleration sensors other than the first group of accelerations are taken as the second group of accelerations.

[0042] For example, assuming that the preset number is 4, the first group of three-axis acceleration sensors includes a first three-axis acceleration sensor, a second three-axis acceleration sensor, and a third three-axis acceleration sensor, and the acceleration data of the first group of three-axis acceleration sensors includes x-axis acceleration (x1), y-axis acceleration (y1), and z-axis acceleration (z1) of the first three-axis acceleration sensor, x-axis acceleration (x2), y-axis acceleration (y2), and z-axis acceleration (z2) of the second three-axis acceleration sensor, and x-axis acceleration (x3), y-axis acceleration (y3), and z-axis acceleration (z3) of the third three-axis acceleration sensor, a total of 9 accelerations, and the enumeration can be obtained by the permutation and combination formula, that is, It can be known that the multiple acceleration combinations include 70 kinds, and some combinations will be exemplified below: (x1, y1, z3, y3), (y1, x1, z1, x2), (z1, x2, y2, z3), and (x1, y1, z2, z3), and the like.

[0043] It should be noted that, when the multiple correlation analysis is performed to obtain the multiple correlation coefficient of each acceleration in the target acceleration combination, the multiple correlation coefficient can be the multiple correlation coefficient of the acceleration corresponding to the noise in the frequency range of 30-500 Hz.

[0044] It should be noted that the M three-axis acceleration sensors are arranged at different chassis positions of the test vehicle, and each three-axis acceleration sensor can detect x-axis acceleration, y-axis acceleration, and z-axis acceleration. Although each state parameter is correlated with the vibration of the vehicle chassis, if all the data are selected, the calculation amount will be increased, and the program running efficiency will be reduced. Therefore, only part of the data is selected in the embodiment of the present application to reduce the calculation amount. Further, the part of the data selected in the embodiment of the present application is not randomly selected. Specifically, the correlation degree of the vibration at different positions of the chassis with the noise is different. Therefore, first, a first group of three-axis acceleration sensors with a larger correlation degree with the noise is selected by performing a first multiple correlation analysis. Then, the correlation degree of the vibration in different directions at the same chassis position with the noise is also different. For example, upward movement of a certain position of the chassis will cause a collision and generate noise, and leftward and rightward movements will not cause a collision and will not generate noise. Therefore, a second multiple correlation analysis is performed to select a first group of accelerations with a larger correlation degree with the noise. In summary, the embodiment of the present application not only considers the correlation degree of the vibration at different positions of the chassis with the noise, but also considers the correlation degree of the vibration in different directions with the noise, so that the first group of accelerations can accurately reflect the vibration condition of the chassis, and the noise correlation attribute can be accurately determined, thereby improving the reliability of the data used for analyzing the noise.

[0045] S1014: Determine the parameter type required for the type of road based on the first group of accelerations.

[0046] For example, assuming that the information of the first group of accelerations includes the value of the x-axis acceleration of the first three-axis acceleration sensor arranged at the right side crossbar of the front subframe is A, the value of the y-axis acceleration is B, the value of the z-axis acceleration of the second three-axis acceleration sensor arranged at the top of the right front shock absorber is C, and the value of the x-axis acceleration of the third three-axis acceleration sensor arranged at the top of the left front shock absorber is D, it can be known that the parameter types required by this type of road include the x-axis acceleration and the y-axis acceleration of the right side crossbar of the front subframe, the z-axis acceleration of the top of the right front shock absorber, and the x-axis acceleration of the top of the left front shock absorber.

[0047] S1015: Establish a preset correspondence relationship based on the parameter types required by each type of road.

[0048] S102: Determine the device sound wave signal based on the parameter values of the target state parameter group.

[0049] In some embodiments, determining the device sound wave signal based on the parameter values of the target state parameter group can include determining chassis vibration information of the target vehicle based on the parameter values of the target state parameter group, and determining the device sound wave signal based on the chassis vibration information.

[0050] S103: Control the sound playing device of the target vehicle to play sound based on the device sound wave signal, so that when the current noise sound wave signal propagates to the cabin area of the target vehicle, it interferes with the device sound wave signal of the sound playing device, so that the noise loudness in the cabin area is less than a preset loudness threshold.

[0051] It should be noted that the noise loudness in the cabin area is less than the preset loudness threshold, which means that the noise loudness in the cabin area is small enough and will not cause the driver to be distracted during driving. The preset loudness threshold can be 20 dB, 30 dB, 40 dB, 50 dB, or 60 dB, etc.

[0052] It should be noted that in the prior art, the noise generated by the chassis vibration is mainly absorbed by the sound-absorbing material, but the effect is not ideal, and the noise inside the cabin is still large. The embodiments of the present application control the sound playing device of the target vehicle to play sound, reduce the loudness of the noise through the principle of sound wave signal interference, avoid only absorbing noise through sound-absorbing materials, and further determine the relevant attributes of the noise in combination with the vibration condition of the vehicle, to improve the accuracy of noise cancellation in the vehicle cabin.

[0053] It should be noted that the road noise active control technology adopted in the embodiment of the present application adopts electronic active control technology, has the advantages of short development cycle, good control effect and no involvement of vehicle chassis structure improvement, and has been widely concerned by domestic and foreign manufacturers in recent years. The road noise active control technology artificially and selectively generates an acoustic signal with the same amplitude and opposite phase of the noise signal in a specified area to cancel the noise signal and achieve the noise reduction effect. Usually, a control algorithm combining feedforward or feedforward with reference signal is used for control, and the selection of the reference signal determines whether the control effect is good or not. The target state parameter group of the embodiment of the present application can be regarded as a reference signal in the road noise active control technology. The reference signal must be strongly correlated with the controlled noise signal in the vehicle. How to optimize the best combination from numerous possible reference points is a key step in the development process of the automobile road noise active control system. The embodiment of the present application aims at the problem that the selection quality of the reference signal has a great influence on the effect of the road noise active control system, optimizes the reference signal combination with strong correlation with the target noise by using the multiple coherence method, and improves the operation efficiency based on the traversal method. The optimization method can select the reference signal channel with better coherence, and further improve the noise reduction performance of the automobile road noise active control system.

[0054] It should be noted that the road noise active control technology mainly uses sound cancellation to arrange acceleration sensors on the chassis system to obtain road noise information, and then use a controller to control the in-vehicle loudspeaker to generate an anti-phase signal that can eliminate road noise according to the vehicle speed, road noise signal and predefined calibration information, so as to realize real-time control of road noise. Due to the large number of vehicle chassis systems and components, selecting a reference signal position is a complex engineering. The principle of reference signal optimization is to divide the frequency domain signals of multiple input and output signals into different combinations, calculate their coherence coefficients, and perform data processing and sorting selection. Based on the selected reference points, the signal combination is reselected until the target effect is achieved. The coherence of the signal directly affects the effect of active noise control. The reference signal optimization method provided by the embodiment of the present application is a reference sensor selection method based on the traversal method of active road noise control, which can effectively improve the selection efficiency of the reference sensor, improve the noise reduction effect of the selected reference sensor on the internal noise of the vehicle, improve the algorithm efficiency, improve the effect of active noise reduction, and reduce the use of sensor hardware.

[0055] It should be noted that the equipment required by the active noise reduction system reference signal optimization system can include a microphone array composed of a plurality of uniformly linearly arranged microphones, the microphone array being capable of collecting front and rear noise signals; a data acquisition and processing system, and it is suggested to use mature noise and vibration industrial software for testing, such as the vibration and noise test systems of LMS, HEAD and BK. A reference point signal sensor, generally a vibration sensor, is used to output chassis vibration signals. Microphones are arranged in the area of the vehicle interior for noise control, including but not limited to the seat headrest, ceiling and window positions, and the number of microphones is y; three-axis acceleration sensors are arranged at positions where the coherence between the chassis of the sample vehicle and the interior noise can be strong, such as the front and rear wheel hubs, front and rear suspension connecting rods and subframe positions, and the number of vibration sensors is x, and the three axes are 3x acceleration signals, as shown in the reference Figure 2 Figure 2 The figure is a schematic diagram of the arrangement position of the three-axis acceleration sensor in the embodiment of the application. Wires are used to connect these devices, including power lines for the data acquisition system and power supply, connections between the microphones and the data acquisition system, and connections between the vibration sensors and the data acquisition system. The system is debugged, and the data acquisition module is entered, which needs to be performed in a professional test site, with the vehicle windows, air conditioner and other devices that can affect the sound field environment in the vehicle interior being closed, and the test personnel in the vehicle interior keeping quiet. The test vehicle is driven to travel under different working conditions, such as water-permeable asphalt road, gravel road, stone road, rain groove road and speed bump, which can cover the road surfaces used by users. The collected interior noise environment data is analyzed to determine the road noise active control target. A multiple correlation analysis method is used for reference signal optimization, and the calculation of the multiple coherence coefficient can refer to formula (1), wherein S XY (f) is the cross-power spectrum of the input reference acceleration vibration signal and the output error microphone noise signal, + XX (f) is the cross-power spectrum of the input reference acceleration vibration signal and the output error microphone noise signal, YY (f) is the cross-power spectrum of the input reference acceleration vibration signal and the output error microphone noise signal, S is the multiple coherence coefficient, H XY (f) is the cross-power spectrum of the input reference acceleration vibration signal and the output error microphone noise signal, ​The value range of the multiple coherence coefficient is 0 to 1, and when the multiple coherence coefficient is greater than or equal to 0.9, the reference signal has strong coherence with the expected signal. The specific identification process of the multiple coherence analysis method based on the traversal method is as follows: x three-axis acceleration sensors are arranged on the chassis of the test vehicle, and there are 3x acceleration signals, the target noise reduction area is the noise signals of y positions of the vehicle occupants, and b acceleration signals with good coherence with the microphone signals are selected from the x acceleration sensors as the reference signals of the system. A selection method is provided; the multiple coherence coefficients of the 3a acceleration signals of the a acceleration sensors in each selection method to the microphone signals of the y positions of the vehicle occupants are calculated; the mean values of the multiple coherence coefficients corresponding to the noise in the frequency range of 30-500 Hz are calculated, and the acceleration sensor combinations are arranged in descending order according to the mean values; the acceleration sensor combinations from strong to weak coherence are obtained, so as to obtain the a acceleration sensor combinations with the best coherence; b acceleration signals are selected from the 3a acceleration signals of the selected a three-axis acceleration sensors in a permutation and combination manner, and there are A combination; the multiple coherence coefficients of the b acceleration signals to the microphone signals of the y positions of the vehicle occupants are calculated; the mean values of the multiple coherence coefficients corresponding to the noise in the frequency range of 30-500 Hz are calculated, and the direction combinations of the acceleration sensors are arranged in descending order according to the mean values; the direction combinations from strong to weak coherence are obtained, and the flow chart of the reference optimization method can be referred to as shown in Figure 3 , Figure 3 The flow chart for determining the reference signal in the embodiment of the application; the coherence coefficients of the reference signal combination to the y position microphones near the frequency to be optimized are basically greater than 0.9, which can meet the requirements of the reference signal of the road noise active control system.

[0056]

[0057] The embodiment of the application obtains the parameter values of the target state parameter group of the target vehicle, the target state parameter group includes a plurality of state parameters, each state parameter is related to the current noise generated by the vibration of the target vehicle; based on the parameter values of the target state parameter group, the device sound wave signal is determined; based on the device sound wave signal, the sound playing device of the target vehicle is controlled to play sound, so that the current noise sound wave signal propagates to the cabin area of the target vehicle and interferes with the device sound wave signal of the sound playing device, so that the noise loudness in the cabin area is less than the preset loudness threshold. The embodiment of the application offsets the vehicle noise in the form of sound cancellation, since the current noise sound wave signal interferes with the device sound wave signal of the sound playing device, the noise loudness in the cabin area is less than the preset loudness threshold, so that the loudness of the vehicle noise is reduced.

[0058] Based on the same inventive concept, referring to Figure 4 The embodiment of the present application provides a noise control device 10 of a vehicle, which comprises: a parameter value acquisition unit 110, which is used for acquiring parameter values of a target state parameter group of a target vehicle, the target state parameter group comprising a plurality of state parameters, and each state parameter being related to current noise generated by vibration of the target vehicle; a signal determination unit 120, which is used for determining a device sound wave signal based on the parameter values of the target state parameter group; and a playing control unit 130, which is used for controlling a sound playing device of the target vehicle to play sound based on the device sound wave signal, so that when the current noise sound wave signal propagates to a cabin area of the target vehicle, the current noise sound wave signal interferes with the device sound wave signal of the sound playing device, and the noise loudness in the cabin area is less than a preset loudness threshold.

[0059] It can be understood that the noise control device 10 of the vehicle further comprises: a type determination unit, which is used for acquiring a current road type of a driving road where the target vehicle is located before acquiring the parameter values of the target state parameter group of the target vehicle; and determining a parameter type of the target state parameter group based on the current road type. Wherein, the parameter type of the target state parameter group is determined based on the current road type, which comprises: inputting the current road type into a preset corresponding relationship to obtain the parameter type of the target state parameter group, and the preset corresponding relationship is a corresponding relationship between road types and parameter types.

[0060] It can be understood that the state parameter is the acceleration of the vehicle chassis parts, different chassis positions of the test vehicle are provided with M three-axis acceleration sensors, and different cabin positions of the test vehicle are provided with N sound collection devices, M and N are both integers greater than 1, the noise control device 10 of the vehicle further comprises: a relationship determining unit for determining a preset corresponding relationship; the relationship determining unit comprises: a data acquisition subunit for, for each road type, acquiring acceleration data of the M three-axis acceleration sensors and N test noise signals collected by the N sound collection devices when the test vehicle is driving on the road type; a first division subunit for dividing the M three-axis acceleration sensors into a first group of three-axis acceleration sensors and a second group of three-axis acceleration sensors based on the acceleration data of the M three-axis acceleration sensors and the N test noise signals, wherein the correlation degree of the acceleration data of the first group of three-axis acceleration sensors with the N test noise signals is greater than the correlation degree of the acceleration data of the second group of three-axis acceleration sensors with the N test noise signals; a second division subunit for dividing the acceleration data of the first group of three-axis acceleration sensors into a first group of accelerations and a second group of accelerations based on the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals, wherein the correlation degree of the first group of accelerations with the N test noise signals is greater than the correlation degree of the second group of accelerations with the N test noise signals; a parameter type determining subunit for determining the parameter type required for the road type based on the first group of accelerations; and a relationship establishing subunit for establishing the preset corresponding relationship based on the parameter type required for each road type.

[0061] It can be understood that the first division subunit is specifically configured to: select a preset number of sensors from the M three-axis acceleration sensors for combination, and enumerate all combinations to obtain a plurality of sensor combinations; sequentially take each sensor combination in the plurality of sensor combinations as a target sensor combination; take the acceleration data of the target sensor combination as the independent variable and the N test noise signals as the dependent variable to perform multiple correlation analysis and obtain the multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination; take the average value of the multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination as the correlation score of the acceleration data of the target sensor combination and the N test noise signals; and in the correlation score corresponding to each sensor combination in the plurality of sensor combinations, take the sensor combination corresponding to the maximum correlation score as the first group of three-axis acceleration sensors, and take the sensors other than the first group of three-axis acceleration sensors in the M three-axis acceleration sensors as the second group of three-axis acceleration sensors.

[0062] It can be understood that the second division subunit is specifically configured to: select a preset number of accelerations from the acceleration data of the first group of three-axis acceleration sensors for combination, and enumerate all combinations to obtain a plurality of acceleration combinations; sequentially take each acceleration combination in the plurality of acceleration combinations as a target acceleration combination; take the target acceleration combination as an independent variable and the N test noise signals as a dependent variable to perform multiple correlation analysis and obtain a multiple correlation coefficient of each acceleration in the target acceleration combination; take the average value of the multiple correlation coefficient of each acceleration in the target acceleration combination as a correlation score of the target acceleration combination and the N test noise signals; and in the correlation scores of each acceleration combination in the plurality of acceleration combinations, take the acceleration combination corresponding to the maximum correlation score as the first group of accelerations, and take the accelerations in the acceleration data of the first group of three-axis acceleration sensors except the first group of accelerations as the second group of accelerations.

[0063] The M three-axis acceleration sensors include: a first three-axis acceleration sensor arranged on a top of a left front shock absorber of the test vehicle, a second three-axis acceleration sensor arranged on a top of a left rear shock absorber of the test vehicle, a third three-axis acceleration sensor arranged on a top of a right front shock absorber of the test vehicle, a fourth three-axis acceleration sensor arranged on a top of a right rear shock absorber of the test vehicle, a fifth three-axis acceleration sensor arranged on a left side crossbar of a front subframe of the test vehicle, and a sixth three-axis acceleration sensor arranged on a right side crossbar of the front subframe of the test vehicle.

[0064] It can be understood that the signal determination unit 120 is specifically configured to: determine chassis vibration information of the target vehicle based on the parameter values of the target state parameter group; and determine the equipment sound wave signal based on the chassis vibration information.

[0065] It should be understood that more implementation details of the noise control device 10 of the vehicle in the embodiment of the present application are described with reference to the aforementioned noise control method of the vehicle, and for the sake of brevity of the description, will not be repeated here.

[0066] Based on the same inventive concept, the embodiments of the present application also provide a vehicle, as shown in Figure 5 As shown, the vehicle comprises a memory 504, a processor 502, and a computer program stored in the memory 504 and executable on the processor 502, and the processor 502 executes the program to implement the steps of any embodiment of the noise control method of the vehicle.

[0067] In the embodiment of the present application, the noise control device 10 of the vehicle comprises a signal determination unit 120 and a noise control unit 130. Figure 5In particular embodiments, bus architecture (represented generally by the bus 500) can include any number of interconnecting buses and bridges, the bus 500 linking together various circuits such as one or more processors represented by the processor 502, and memory represented by the memory 504. The bus 500 can also link together various other circuits which can include, among other things, peripheral devices, voltage stabilizers and power management circuits, all of which are well known in the art, and therefore, not further described herein. The bus interface 505 provides an interface between the bus 500 and the receiver 501 and the transmitter 503. The receiver 501 and the transmitter 503 can be the same component, i.e., a transceiver, providing a unit for communicating with various other apparatuses over a transmission medium. The processor 502 is responsible for managing the bus 500 and general processing, while the memory 504 can be used for storing data used by the processor 502 in executing operational processes.

[0068] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored on or transferred over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions can also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. Also, as technology evolves, the "functionalities" described herein can be implemented by various combinations of digital and analog circuits.

[0069] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. Among them, the above-mentioned device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division mode, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, indirect coupling or communication connection between units or modules, which can be electrical or other forms.

[0070] The units described as separate components can or can not be physically separated, and the components of the control device can or can not be physical units, i.e., they can be located in one place, or they can be distributed on multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0071] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0072] The above only describes the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the scope of the claims of the present application.

Claims

1. A noise control method of a vehicle, characterized by, The method comprises the following steps: obtaining parameter values of a target state parameter group of a target vehicle, the target state parameter group comprising a plurality of state parameters, each of which is related to current noise generated by vibration of the target vehicle; wherein, before the step of obtaining parameter values of the target state parameter group of the target vehicle, the method further comprises the following steps: obtaining a current road type of a driving road on which the target vehicle is located; determining a parameter type of the target state parameter group based on the current road type; wherein, the current road type is input into a preset corresponding relationship to obtain the parameter type of the target state parameter group, the preset corresponding relationship being a corresponding relationship between road types and parameter types; the state parameters are accelerations of vehicle chassis components, a test vehicle is provided with M three-axis acceleration sensors at different chassis positions of the test vehicle, and the test vehicle is provided with N sound collection devices at different cabin positions of the test vehicle, M and N being integers greater than 1, and the preset corresponding relationship being determined through the following steps: for each road type, when the test vehicle is driving on the road type, acceleration data of the M three-axis acceleration sensors and N test noise signals collected by the N sound collection devices are obtained; based on the acceleration data of the M three-axis acceleration sensors and the N test noise signals, the M three-axis acceleration sensors are divided into a first group of three-axis acceleration sensors and a second group of three-axis acceleration sensors, wherein the correlation degree of acceleration data of the first group of three-axis acceleration sensors with the N test noise signals is greater than the correlation degree of acceleration data of the second group of three-axis acceleration sensors with the N test noise signals; based on the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals, the acceleration data of the first group of three-axis acceleration sensors is divided into a first group of accelerations and a second group of accelerations, wherein the correlation degree of the first group of accelerations with the N test noise signals is greater than the correlation degree of the second group of accelerations with the N test noise signals; based on the first group of accelerations, a parameter type required for the road type is determined; based on the parameter type required for each road type, the preset corresponding relationship is established; based on the parameter values of the target state parameter group, determining a device sound wave signal; based on the device sound wave signal, controlling a sound playing device of the target vehicle to play sound, so that when a current noise sound wave signal propagates to a cabin area of the target vehicle, the current noise sound wave signal interferes with the device sound wave signal of the sound playing device, so that the noise loudness in the cabin area is less than a preset loudness threshold.

2. The noise control method of a vehicle according to claim 1, characterized by, The step of dividing the M three-axis acceleration sensors into a first group of three-axis acceleration sensors and a second group of three-axis acceleration sensors based on the acceleration data of the M three-axis acceleration sensors and the N test noise signals comprises the following steps: selecting a preset number of sensors from the M three-axis acceleration sensors for combination, and enumerating all combinations to obtain a plurality of sensor combinations; in turn, taking each sensor combination in the plurality of sensor combinations as a target sensor combination; taking the acceleration data of the target sensor combination as the independent variable and the N test noise signals as the dependent variable to perform multiple correlation analysis and obtaining a multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination; taking the average of the multiple correlation coefficients of each acceleration in the acceleration data of the target sensor combination as a correlation score of the acceleration data of the target sensor combination and the N test noise signals; in the correlation scores of each sensor combination in the plurality of sensor combinations, taking the sensor combination corresponding to the maximum correlation score as the first group of three-axis acceleration sensors and taking the sensors in the M three-axis acceleration sensors other than the first group of three-axis acceleration sensors as the second group of three-axis acceleration sensors.

3. The noise control method of a vehicle according to claim 1, characterized by, The dividing, based on the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals, of the acceleration data of the first group of three-axis acceleration sensors into a first group of accelerations and a second group of accelerations, comprises: selecting a preset number of accelerations from the acceleration data of the first group of three-axis acceleration sensors to form combinations and enumerating all combinations to obtain a plurality of acceleration combinations; taking each acceleration combination in the plurality of acceleration combinations as a target acceleration combination in turn; taking the target acceleration combination as the independent variable and the N test noise signals as the dependent variable to perform multiple correlation analysis and obtaining a multiple correlation coefficient of each acceleration in the target acceleration combination; taking the average of the multiple correlation coefficients of each acceleration in the target acceleration combination as a correlation score of the target acceleration combination and the N test noise signals; in the correlation scores of each acceleration combination in the plurality of acceleration combinations, taking the acceleration combination corresponding to the maximum correlation score as the first group of accelerations and taking the accelerations in the acceleration data of the first group of three-axis acceleration sensors other than the first group of accelerations as the second group of accelerations.

4. The noise control method of a vehicle according to claim 1, characterized by, The M three-axis acceleration sensors comprise a first three-axis acceleration sensor arranged on a top of a left front shock absorber, a second three-axis acceleration sensor arranged on a top of a left rear shock absorber, a third three-axis acceleration sensor arranged on a top of a right front shock absorber, a fourth three-axis acceleration sensor arranged on a top of a right rear shock absorber, a fifth three-axis acceleration sensor arranged on a left side crossbar of a front subframe, and a sixth three-axis acceleration sensor arranged on a right side crossbar of the front subframe.

5. The noise control method of a vehicle according to claim 1, characterized by, The determining, based on the parameter values of the target state parameter group, of the device sound wave signal comprises: determining chassis vibration information of the target vehicle based on the parameter values of the target state parameter group; determining the device sound wave signal based on the chassis vibration information.

6. A noise control device for a vehicle, characterized by comprises: The parameter value acquisition unit is configured to acquire parameter values of a target state parameter group of the target vehicle, the target state parameter group comprising a plurality of state parameters, each of the state parameters being related to a current noise generated by vibration of the target vehicle; wherein, before the parameter values of the target state parameter group of the target vehicle are acquired, the method further comprises: acquiring a current road type of a driving road on which the target vehicle is located; and determining a parameter type of the target state parameter group based on the current road type; wherein, the current road type is input into a preset corresponding relationship to obtain the parameter type of the target state parameter group, the preset corresponding relationship being a corresponding relationship between road types and parameter types; the state parameter is acceleration of a chassis component of the vehicle, M three-axis acceleration sensors are arranged at different chassis positions of a test vehicle, N sound collection devices are arranged at different cabin positions of the test vehicle, M and N are both integers greater than 1, and the preset corresponding relationship is determined by the following steps: for each road type, when the test vehicle is driving on the road type, acceleration data of the M three-axis acceleration sensors and N test noise signals collected by the N sound collection devices are acquired; based on the acceleration data of the M three-axis acceleration sensors and the N test noise signals, the M three-axis acceleration sensors are divided into a first group of three-axis acceleration sensors and a second group of three-axis acceleration sensors, wherein a correlation degree of acceleration data of the first group of three-axis acceleration sensors and the N test noise signals is greater than a correlation degree of acceleration data of the second group of three-axis acceleration sensors and the N test noise signals; based on the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals, the acceleration data of the first group of three-axis acceleration sensors is divided into a first group of acceleration and a second group of acceleration, wherein a correlation degree of the first group of acceleration and the N test noise signals is greater than a correlation degree of the second group of acceleration and the N test noise signals; based on the first group of acceleration, a parameter type required for the road type is determined; and based on the parameter type required for each road type, the preset corresponding relationship is established; The signal determination unit is configured to determine a device sound wave signal based on the parameter values of the target state parameter group. The playing control unit is configured to control a sound playing device of the target vehicle to play sound based on the device sound wave signal, so that when a current noise sound wave signal propagates to a cabin area of the target vehicle, the current noise sound wave signal interferes with the device sound wave signal of the sound playing device, and noise loudness in the cabin area is less than a preset loudness threshold.

7. A vehicle characterized by comprising: The computer program is stored in the memory and executable on the processor, and the processor executes the computer program to implement the method in any one of claims 1-5. The computer program is stored in the memory and executable on the processor, and the processor executes the computer program to implement the method in any one of claims 1-5.

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