Vehicle noise control method and device and vehicle

Abstract

The invention discloses a noise control method and device for a vehicle and the vehicle, and the method comprises the steps: obtaining the parameter value of a target state parameter group of a target vehicle, the target state parameter group comprises a plurality of state parameters, and each state parameter is related to the current noise generated by the vibration of the target vehicle; determining an equipment sound wave signal based on the parameter value of the target state parameter group; and based on the equipment sound wave signal, controlling sound playing equipment of the target vehicle to play sound, so that the current noise sound wave signal interferes with an equipment sound wave signal of the sound playing equipment when being transmitted to a cabin area of the target vehicle, and the noise loudness in the cabin area is smaller than a preset loudness threshold value. According to the invention, the technical problem of large loudness of vehicle noise is solved.

Description

Technical Field

[0001] The present invention belongs to the field of sound wave technology, and in particular relates to a vehicle noise control method, device and vehicle. Background Art

[0002] Vehicle noise includes road noise, transmission system noise, exhaust noise, etc. These noises can affect the driver. If the noise is too loud, it may cause the driver to be distracted while driving, and may even cause a safety accident. Therefore, the loudness of vehicle noise is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present invention provide a vehicle noise control method, device and vehicle, which solve the technical problem of high loudness of vehicle noise.

[0004] In a first aspect, an embodiment of the present invention provides a vehicle noise control method, comprising: obtaining a parameter value 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; determining a device sound wave signal based on the parameter value 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 when the current noise sound wave signal propagates to a 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.

[0005] In combination with the first aspect of the present invention, in some embodiments, before obtaining the parameter value of the target state parameter group of the target vehicle, it also includes: obtaining the current road type of the road on which the target vehicle is traveling; based on the current road type, determining the parameter type of the target state parameter group.

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

[0007] In combination with the first aspect of the present invention, in some embodiments, the state parameter is the acceleration of a chassis component of the vehicle, M three-axis acceleration sensors are provided at different chassis positions of the test vehicle, N sound collection devices are provided at different cockpit 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 traveling on the road type, the acceleration data of the M three-axis acceleration sensors and the 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. sensor, wherein the correlation between the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals is greater than the correlation between the 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 are divided into a first group of accelerations and a second group of accelerations, wherein the correlation between the first group of accelerations and the N test noise signals is greater than the correlation between the second group of accelerations and the N test noise signals; based on the first group of accelerations, the 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.

[0008] In combination with the first aspect of the present invention, in some embodiments, 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 based on the acceleration data of the M three-axis acceleration sensors and the N test noise signals, including: 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; taking each sensor combination in the plurality of sensor combinations as a target sensor combination in turn; taking the acceleration data of the target sensor combination as an independent variable and the N test noise signals as a dependent variable to perform multiple A re-correlation analysis is performed to obtain a multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination; an average value of the multiple correlation coefficients of each acceleration in the acceleration data of the target sensor combination is used as a correlation score between the acceleration data of the target sensor combination and the N test noise signals; among the correlation scores corresponding to each sensor combination in the multiple sensor combinations, the sensor combination corresponding to the largest correlation score is used as the first group of three-axis acceleration sensors, and the sensors of the M three-axis acceleration sensors except the first group of three-axis acceleration sensors are used as the second group of three-axis acceleration sensors.

[0009] In combination with the first aspect of the present invention, in some embodiments, the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals are divided into a first group of accelerations and a second group of accelerations, including: 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 multiple acceleration combinations; taking each acceleration combination in the multiple acceleration combinations in turn as a target acceleration combination; taking the target acceleration combination as an independent variable and the N test noise signals as dependent variables to perform multiple correlation analysis and obtain a multiple correlation coefficient of each acceleration in the target acceleration combination; taking the average value of the multiple correlation coefficients of each acceleration in the target acceleration combination as a correlation score between the target acceleration combination and the N test noise signals; and taking the acceleration combination corresponding to the largest correlation score in the correlation scores of each acceleration combination in the multiple acceleration combinations as the first group of accelerations, and taking the accelerations other than the first group of accelerations in the acceleration data of the first group of three-axis acceleration sensors as the second group of accelerations.

[0010] In combination with the first aspect of the present invention, in some embodiments, the M three-axis acceleration sensors include: a first three-axis acceleration sensor arranged on the top of the left front shock absorber tower of the test vehicle, a second three-axis acceleration sensor arranged on the top of the left rear shock absorber tower, a third three-axis acceleration sensor arranged on the top of the right front shock absorber tower, a fourth three-axis acceleration sensor arranged on the top of the right rear shock absorber tower, a fifth three-axis acceleration sensor arranged on the left side cross bar of the front subframe, and a sixth three-axis acceleration sensor arranged on the right side cross bar of the front subframe.

[0011] In combination with the first aspect of the present invention, in some embodiments, determining the equipment sound wave signal based on the parameter value of the target state parameter group includes: determining the chassis vibration information of the target vehicle based on the parameter value of the target state parameter group; determining the equipment sound wave signal based on the chassis vibration information.

[0012] In a second aspect, an embodiment of the present invention provides a vehicle noise control device, comprising: a parameter value acquisition unit, used to acquire a parameter value 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, used to determine a device sound wave signal based on the parameter value of the target state parameter group; and a playback control unit, used to control a sound playback 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, it interferes with the device sound wave signal of the sound playback device, so that the noise loudness in the cabin area is less than a preset loudness threshold.

[0013] In a third aspect, an embodiment of the present invention provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods described in the first aspect when executing the computer program.

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

[0015] The embodiment of the present invention obtains the parameter value of the target state parameter group of the target vehicle, the target state parameter group includes multiple state parameters, each state parameter is related to the current noise generated by the vibration of the target vehicle; based on the parameter value of the target state parameter group, the device sound wave signal is determined; based on the device sound wave signal, the sound playback device of the target vehicle is controlled to play sound, so that when the current noise sound wave signal is transmitted to the cabin area of ​​the target vehicle, it interferes with the device sound wave signal of the sound playback device, so that the noise loudness in the cabin area is less than the preset loudness threshold. The embodiment of the present invention cancels the vehicle noise by sound attenuation. Since the current noise sound wave signal interferes with the device sound wave signal of the sound playback device, the noise loudness in the cabin area is less than the preset loudness threshold, thereby reducing the loudness of the vehicle noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 is a flow chart of a vehicle noise control method according to an embodiment of the present invention;

[0018] Figure 2 Schematic diagram of the arrangement position of the three-axis acceleration sensor in an embodiment of the present invention;

[0019] Figure 3 A flowchart of determining a reference signal in an embodiment of the present invention;

[0020] Figure 4 is a functional module diagram of a noise control device for a vehicle in an embodiment of the present invention;

[0021] Figure 5 Schematic diagram of the structure of a vehicle in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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 only 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.

[0023] In the present invention, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0024] The embodiment of the present invention provides a vehicle noise control method, referring to Figure 1 As shown, the method includes the following steps S101 to S103:

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

[0026] In some implementations, before step S101, the process may further include: obtaining a current road type of the road on which the target vehicle is traveling; and 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 permeable asphalt pavement, gravel pavement, stone pavement, rain gutter pavement or speed bump, etc.

[0028] In some embodiments, determining the parameter type of the target state parameter group based on the current road type may include: inputting the current road type into a preset correspondence to obtain the parameter type of the target state parameter group, where the preset correspondence is a correspondence between the road type and the parameter type.

[0029] In some embodiments, the preset corresponding relationship may include the following multiple sub-relationships: when the road type is a speed bump, the parameter type of the target state parameter group may include: the z-axis acceleration of the left front shock absorber tower top, the z-axis acceleration of the left rear shock absorber tower top, the z-axis acceleration of the right rear shock absorber tower top, and the z-axis acceleration of the right side crossbar of the front subframe; when the road type is a rain gutter pavement, the parameter type of the target state parameter group may include: the x-axis acceleration and y-axis acceleration of the left front shock absorber tower top, the x-axis acceleration and y-axis acceleration of the left rear shock absorber tower top, and the x-axis acceleration and y-axis acceleration of the right side crossbar of the front subframe; when the road type is a permeable asphalt pavement, the parameter type of the target state parameter group may include : the x-axis acceleration, y-axis acceleration and z-axis acceleration of the left side crossbar of the front subframe, and the y-axis acceleration and z-axis acceleration of the left front shock absorber tower top; when the road type is a gravel road, the parameter types of the target state parameter group may include: the x-axis acceleration, y-axis acceleration and z-axis acceleration of the right side crossbar of the front subframe, the z-axis acceleration of the right front shock absorber tower top, and the x-axis acceleration of the left front shock absorber tower top; when the road type is a stone road, the parameter types of the target state parameter group may include: the x-axis acceleration and z-axis acceleration of the right side crossbar of the front subframe, the x-axis acceleration and z-axis acceleration of the left side crossbar of the front subframe, the z-axis acceleration of the right front shock absorber tower top, and the 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 change according to the road type. Under different road types, the vibration of the vehicle is different, and because of the vibration of different parts and vibrations in different directions, the amplitude, frequency and phase of the noise will be affected. Therefore, the embodiment of the present invention limits the parameter type of the target state parameter group to be determined based on the current road type. For example, 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 the z-axis acceleration; for example, when the road type is a rain trough pavement, the vehicle is slipping, so the parameter type of the target state parameter group mainly includes the x-axis acceleration and the y-axis acceleration. Therefore, determining the corresponding parameter type under different road types can better determine the vehicle vibration situation, and then determine the relevant attributes of the noise, so as to improve the accuracy of noise silencing in the vehicle cabin.

[0031] In some embodiments, the state parameter is the acceleration of the chassis components of the vehicle, M triaxial acceleration sensors are provided at different chassis positions of the test vehicle, and N sound collection devices are provided at different cabin positions of the test vehicle, where M and N are both integers greater than 1, and the preset corresponding relationship can be determined by the following steps, including steps S1011 to S1015:

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

[0033] In some embodiments, the M three-axis acceleration sensors may include: a first three-axis acceleration sensor disposed on the top of a left front shock absorber tower of the test vehicle, a second three-axis acceleration sensor disposed on the top of a left rear shock absorber tower, a third three-axis acceleration sensor disposed on the top of a right front shock absorber tower, a fourth three-axis acceleration sensor disposed on the top of a right rear shock absorber tower, a fifth three-axis acceleration sensor disposed on the left side cross bar of the front subframe, and a sixth three-axis acceleration sensor disposed on the right side cross bar of the front subframe.

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

[0035] It should be noted that, in the case that the M three-axis acceleration sensors include a first three-axis acceleration sensor, a second three-axis acceleration sensor, a third three-axis acceleration sensor, a fourth three-axis acceleration sensor, a fifth three-axis acceleration sensor and a sixth three-axis acceleration sensor, the acceleration data of the M three-axis acceleration sensors may include the x-axis acceleration, y-axis acceleration and z-axis acceleration of the first three-axis acceleration sensor, the x-axis acceleration, y-axis acceleration and z-axis acceleration of the second three-axis acceleration sensor, the x-axis acceleration, y-axis acceleration and z-axis acceleration of the third three-axis acceleration sensor, the x-axis acceleration, y-axis acceleration and z-axis acceleration of the fourth three-axis acceleration sensor, the x-axis acceleration, y-axis acceleration and z-axis acceleration of the fifth three-axis acceleration sensor, and the x-axis acceleration, y-axis acceleration and z-axis acceleration of the sixth three-axis acceleration sensor.

[0036] S1012: Based on acceleration data of M three-axis acceleration sensors and 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 between the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals is greater than a correlation between the acceleration data of the second group of three-axis acceleration sensors and the N test noise signals.

[0037] In some embodiments, based on the acceleration data of M three-axis acceleration sensors and N test noise signals, 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 can include: selecting a preset number of sensors from the M three-axis acceleration sensors for combination, and enumerating all combinations to obtain multiple sensor combinations; taking each sensor combination in the multiple sensor combinations as a target sensor combination in turn; taking the acceleration data of the target sensor combination as an independent variable and the N test noise signals as dependent variables to perform multiple correlation analysis and obtain a multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination; taking the average value of the multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination as a correlation score between the acceleration data of the target sensor combination and the N test noise signals; and taking the sensor combination corresponding to the largest correlation score among the correlation scores corresponding to each sensor combination in the multiple sensor combinations as the first group of three-axis acceleration sensors, and taking the sensors of the M three-axis acceleration sensors except the first group of three-axis acceleration sensors as the second group of three-axis acceleration sensors.

[0038] For example, assume that the M three-axis acceleration sensors include a first three-axis acceleration sensor a, a second three-axis acceleration sensor b, a third three-axis acceleration sensor c, a fourth three-axis acceleration sensor d, a fifth three-axis acceleration sensor e, and a sixth three-axis acceleration sensor f, and the preset number is 5. Then, the enumeration can be done by the permutation and combination formula, that is, It can be known that there are 6 sensor combinations, 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). Assuming that the correlation score of the combination (b,c,d,e,f) is 0.9, the correlation score of the combination (a,c,d,e,f) is 0.8, the correlation score of the combination (a,b,d,e,f) is 0.83, the correlation score of the combination (a,b,c,e,f) is 0.92, the correlation score of the combination (a,b,c,d,f) is 0.91, and the correlation score of the combination (a,b,c,d,e) is 0.9, then the combination (a,b,c,e,f) is used as the first group of three-axis acceleration sensors, and d is used 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 coefficient of each acceleration in the acceleration data of the target sensor combination, the multiple correlation coefficient may be the multiple correlation coefficient of the acceleration corresponding to the noise in the frequency range of 30 to 500 Hz.

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

[0041] In some embodiments, based on the acceleration data of the first group of three-axis acceleration sensors and N test noise signals, 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 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 multiple acceleration combinations; taking each acceleration combination in the multiple acceleration combinations in turn as a target acceleration combination; taking the target acceleration combination as an independent variable and the N test noise signals as dependent variables to perform multiple correlation analysis and obtain a multiple correlation coefficient of each acceleration in the target acceleration combination; taking the average value of the multiple correlation coefficients of each acceleration in the target acceleration combination as a correlation score between the target acceleration combination and the N test noise signals; and taking the acceleration combination corresponding to the largest correlation score in the correlation scores of each acceleration combination in the multiple acceleration combinations as the first group of accelerations, and taking the accelerations other than the first group of accelerations in the acceleration data of the first group of three-axis acceleration sensors 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 the x-axis acceleration (x1), y-axis acceleration (y1), and z-axis acceleration (z1) of the first three-axis acceleration sensor, the x-axis acceleration (x2), y-axis acceleration (y2), and z-axis acceleration (z2) of the second three-axis acceleration sensor, and the x-axis acceleration (x3), y-axis acceleration (y3), and z-axis acceleration (z3) of the third three-axis acceleration sensor. There are a total of 9 accelerations, which can be enumerated by the formula of permutation and combination, that is, It can be seen that there are 70 acceleration combinations. Some of the combinations are illustrated below: (x1, y1, z3, y3), (y1, x1, z1, x2), (z1, x2, y2, z3) and (x1, y1, z2, z3) and so on.

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

[0044] It should be noted that M three-axis acceleration sensors are provided at different chassis positions of the test vehicle, and each three-axis acceleration sensor can detect the acceleration of the x-axis, the acceleration of the y-axis, and the acceleration of the z-axis. Although each state parameter is correlated with the vibration of the vehicle chassis, if all the data are selected, the amount of calculation will increase and the efficiency of program operation will be reduced. Therefore, the embodiment of the present invention only selects part of the data to reduce the amount of calculation. Further, the part of the data selected by the embodiment of the present invention is not selected at random. Specifically, the degree of correlation between the vibration and noise at different positions of the chassis is different. Therefore, it is necessary to first select a position with a greater degree of correlation, that is, through the first multiple correlation analysis, select the first group of three-axis acceleration sensors with a greater degree of correlation with noise. Then, the degree of correlation between the vibration and noise in different directions of the same chassis position is also different. For example, if a certain position of the chassis moves upward, a collision will occur and generate noise, and if it moves left and right, no collision will occur and no noise will be generated. Therefore, a second multiple correlation analysis is performed to select the first group of accelerations with a greater degree of correlation with noise. In summary, the embodiment of the present invention not only considers the correlation between vibration and noise at different chassis positions, but also considers the correlation between vibration and noise in different directions, so that the first group of accelerations can accurately reflect the chassis vibration conditions, and then accurately determine the noise-related properties, thereby improving the reliability of the data used to analyze noise.

[0045] S1014: Based on the first set of accelerations, determine the parameter type required for the road type.

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

[0047] S1015: Establishing a preset correspondence based on the parameter types required for each road type.

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

[0049] In some embodiments, determining the equipment sound wave signal based on the parameter values ​​of the target state parameter group may include: determining chassis vibration information of the target vehicle based on the parameter values ​​of the target state parameter group; and determining the equipment sound wave signal based on the chassis vibration information.

[0050] S103: Based on the device sound wave signal, control the sound playing device of the target vehicle to play sound, 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 if the noise loudness in the cockpit area is less than the preset loudness threshold, it means that the noise loudness in the cockpit area is low enough and will not distract the driver while driving. The preset loudness threshold can be 20dB, 30dB, 40dB, 50dB or 60dB, etc.

[0052] It should be noted that in the prior art, the noise generated by chassis vibration is mainly absorbed by sound-absorbing materials, but the effect is not ideal, and the noise inside the cabin is still relatively large. The embodiment of the present invention controls the sound playing device of the target vehicle to play sound, and reduces the loudness of the noise through the principle of sound wave signal interference, avoiding the sound absorption only through sound-absorbing materials, and then combining the vehicle vibration to determine the relevant properties of the noise, so as to improve the accuracy of noise silencing in the vehicle cabin.

[0053] It should be noted that the active road noise control technology adopted in the embodiment of the present invention adopts electronic active control technology, which has the advantages of short development cycle, good control effect and no improvement of the body chassis structure. It has received extensive attention from domestic and foreign manufacturers in recent years. The active road noise control technology uses an additional sound source to artificially and specifically generate a sound signal with the same amplitude and opposite phase as the control noise in a specified area, thereby offsetting the noise signal and achieving a noise reduction effect. Usually, a control algorithm combining feedforward or feedforward with a reference signal is used for control, and the quality of the control effect is largely determined by whether the selection of the reference signal is reasonable. The target state parameter group of the embodiment of the present invention can be regarded as a reference signal in the active road noise control technology. The reference signal must be strongly correlated with the controlled noise signal in the vehicle. How to find the best combination among many possible reference points is a key step in the development process of the active road noise control system for automobiles. In order to solve the problem that the quality of reference signal selection has a significant impact on the effect of the active road noise control system, the embodiment of the present invention uses a multiple coherence method to optimize a reference signal combination that is strongly correlated with the target noise, and improves the computational efficiency based on the traversal method. The optimization method can screen out reference signal channels with better coherence, thereby improving the noise reduction performance of the active road noise control system of the vehicle.

[0054] It should be noted that the active road noise control technology mainly uses the method of sound attenuation to arrange acceleration sensors on the chassis system to obtain relevant road noise information, and then uses the controller to control the in-vehicle speakers to generate anti-phase signals that can eliminate road noise according to the vehicle speed, road noise signal and predefined calibration information, thereby realizing real-time control of road noise. Due to the large number of chassis systems and components of the whole vehicle, selecting the reference signal position is a complex project. The principle of reference signal optimization is to divide the frequency domain signals of multi-input and multi-output signals into different combinations, calculate their coherence coefficients, and perform data processing and sorting screening, and then re-combine the signals based on the screened reference points until the target effect is achieved. The coherence of the signal directly affects the quality of the active noise control effect. The reference signal optimization method provided in the embodiment of the present invention is a reference sensor screening method for active road noise control based on the traversal method, which can effectively improve the screening efficiency of the reference sensor, improve the noise reduction effect of the screened reference sensor on the vehicle interior noise, 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 for the reference signal optimization system of the active noise reduction system may include a microphone array, which consists of several microphones arranged evenly and linearly, and the microphone array can collect noise signals from the front and rear rows; for the data acquisition and processing system, it is recommended to use mature noise and vibration industry software for testing: such as the vibration and noise test systems of LMS, HEAD, and BK. The reference point signal sensor is generally a vibration sensor, which is used to output the chassis vibration signal. Microphones are arranged in areas where noise control is performed in the car, including but not limited to seat headrests, ceilings, and windows. The number of microphones is y; three-axis acceleration sensors are arranged at locations where the chassis of the prototype vehicle may have a strong correlation with the noise inside the car, such as the front and rear wheel centers, front and rear suspension links, and subframes. The number of vibration sensors is x, and the three-axis is 3x acceleration signals, reference Figure 2 As shown, Figure 2 The figure is a schematic diagram of the layout of the three-axis acceleration sensor in the embodiment of the present invention. Use wires to connect these devices, including the power cord between the data acquisition system and the power supply, the connection between the microphone and the data acquisition system, and the connection between the vibration sensor and the data collector. Debugging the system and entering the data acquisition module must be carried out in a professional test site. Close the windows, air conditioner and other equipment that may affect the in-car sound field environment, and keep the test personnel in the car quiet. Drive the test vehicle under different working conditions, such as permeable asphalt roads, gravel roads, stone roads, rain trough roads, speed bumps, etc., which can cover the user's usage scenario. Analyze the collected in-car noise environment data and clarify the active road noise control target. The multiple correlation analysis method is used to optimize the reference signal. The calculation of the multiple correlation coefficient can refer to formula (1), where S XY (f) is the cross power spectrum of the input reference acceleration vibration signal and the output error microphone noise signal, S + XX (f) Autopower spectrum matrix of the input reference acceleration vibration signal, S YY (f) The auto-power spectrum matrix of the output error microphone noise signal, is the multi-correlation coefficient, S H XY (f) is the matrix conjugate transpose. The coherence of the signal directly affects the effectiveness of active noise control. The calculation of the theoretical maximum noise reduction that the system can achieve can refer to formula (2), where NR is the theoretical maximum noise reduction, The value range of is from 0 to 1. When the multi-coherence coefficient is above 0.9, the reference signal has a strong correlation with the expected signal. The multi-coherence analysis method based on the ergodic method has the following specific identification process: x three-axis acceleration sensors are arranged on the chassis of the test vehicle, with a total of 3x acceleration signals. The target noise reduction area is the noise signal of y positions of the passengers in the car. It is necessary to select a combination of b acceleration signals on a acceleration sensor with good coherence with the microphone signal from these x acceleration sensors as the reference signal of the system. Select a acceleration sensor from the candidate x acceleration sensors in a permutation and combination manner, with a total of The invention discloses a method for selecting a plurality of acceleration sensors; respectively calculating the multiple coherence coefficients of the 3a acceleration signals of a acceleration sensor in each selection method to the microphone signals at y positions of the occupants in the vehicle; calculating the mean value of the multiple coherence coefficients corresponding to the noise in the frequency range of 30 to 500 Hz, and arranging the acceleration sensor combinations in descending order according to the mean value; obtaining the acceleration sensor combinations with coherence from strong to weak, so as to obtain a acceleration sensor combination with the best coherence; selecting b acceleration signals from the 3a acceleration signals of the selected a triaxial acceleration sensors in a permutation and combination manner, for a total of combinations; calculate the multiple coherence coefficients of the acceleration signals of b paths for the microphone signals at y positions of the occupants in the vehicle respectively; calculate the mean of the multiple coherence coefficients corresponding to the noise in the frequency range of 30 to 500 Hz, and arrange the direction combinations of the acceleration sensors in descending order according to the mean; obtain the direction combinations with coherence from strong to weak, and refer to the flowchart of the optimization method for reference Figure 3 As shown, Figure 3 This is a flow chart for determining a reference signal in an embodiment of the present invention; the coherence coefficient of the reference signal combination for y position microphones near the frequency to be optimized is basically above 0.9, which can meet the requirements of the road noise active control system for the reference signal.

[0056]

[0057] The embodiment of the present invention obtains the parameter value of the target state parameter group of the target vehicle, the target state parameter group includes multiple state parameters, each state parameter is related to the current noise generated by the vibration of the target vehicle; based on the parameter value of the target state parameter group, the device sound wave signal is determined; based on the device sound wave signal, the sound playback device of the target vehicle is controlled to play sound, so that when the current noise sound wave signal is transmitted to the cabin area of ​​the target vehicle, it interferes with the device sound wave signal of the sound playback device, so that the noise loudness in the cabin area is less than the preset loudness threshold. The embodiment of the present invention cancels the vehicle noise by sound attenuation. Since the current noise sound wave signal interferes with the device sound wave signal of the sound playback device, the noise loudness in the cabin area is less than the preset loudness threshold, thereby reducing the loudness of the vehicle noise.

[0058] Based on the same inventive concept, Figure 4 As shown, an embodiment of the present invention provides a vehicle noise control device 10, including: a parameter value acquisition unit 110, used to acquire a parameter value of a target state parameter group of a target vehicle, the target state parameter group including a plurality of state parameters, each state parameter being related to a current noise generated by vibration of the target vehicle; a signal determination unit 120, used to determine a device sound wave signal based on the parameter value of the target state parameter group; a playback control unit 130, used to control a sound playback 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, it interferes with the device sound wave signal of the sound playback device, so that the noise loudness in the cabin area is less than a preset loudness threshold.

[0059] It is understandable that the vehicle noise control device 10 further includes: a type determination unit, which is used to obtain the current road type of the road where the target vehicle is traveling before obtaining the parameter value of the target state parameter group of the target vehicle; and determine the parameter type of the target state parameter group based on the current road type. Wherein, determining the parameter type of the target state parameter group based on the current road type includes: 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 the road type and the parameter type.

[0060] It can be understood that the state parameter is the acceleration of the chassis components of the vehicle, M three-axis acceleration sensors are set at different chassis positions of the test vehicle, and N sound collection devices are set at different cabin positions of the test vehicle, M and N are both integers greater than 1, and the vehicle noise control device 10 also includes: a relationship determination unit, used to determine a preset corresponding relationship; the relationship determination unit includes: a data acquisition subunit, used to obtain the acceleration data of the M three-axis acceleration sensors and the N test noise signals collected by the N sound collection devices for each road type when the test vehicle is traveling on the road type; a first division subunit, used to divide the M three-axis acceleration sensors into a first group of three-axis acceleration sensors and a second group based on the acceleration data of the M three-axis acceleration sensors and the N test noise signals. A three-axis acceleration sensor, wherein the correlation between the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals is greater than the correlation between the acceleration data of the second group of three-axis acceleration sensors and the N test noise signals; a second division subunit, used to divide 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 between the first group of accelerations and the N test noise signals is greater than the correlation between the second group of accelerations and the N test noise signals; a parameter type determination subunit, used to determine the parameter type required for the road type based on the first group of accelerations; a relationship establishment subunit, used to establish a 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 used to: select a preset number of sensors from M three-axis acceleration sensors for combination, and enumerate all combinations to obtain multiple sensor combinations; take each sensor combination in the multiple sensor combinations as a target sensor combination in turn; take the acceleration data of the target sensor combination as an independent variable and the N test noise signals as dependent variables 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 between the acceleration data of the target sensor combination and the N test noise signals; among the correlation scores corresponding to each sensor combination in the multiple sensor combinations, take the sensor combination corresponding to the largest correlation score as the first group of three-axis acceleration sensors, and take the sensors of the M three-axis acceleration sensors except the first group of 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 used 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 multiple acceleration combinations; take each acceleration combination in the multiple acceleration combinations as the target acceleration combination in turn; take the target acceleration 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 target acceleration combination; take the average value of the multiple correlation coefficient of each acceleration in the target acceleration combination as the correlation score between the target acceleration combination and the N test noise signals; among the correlation scores of each acceleration combination in the multiple acceleration combinations, take the acceleration combination corresponding to the largest correlation score as the first group of accelerations, and take the accelerations other than the first group of accelerations in the acceleration data of the first group of three-axis acceleration sensors as the second group of accelerations.

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

[0064] It can be understood that the signal determination unit 120 is specifically used to: determine the chassis vibration information of the target vehicle based on the parameter value 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 vehicle noise control device 10 in the embodiment of the present invention are described in the aforementioned vehicle noise control method, and for the sake of brevity of the specification, they are not repeated here.

[0066] Based on the same inventive concept, an embodiment of the present invention further provides a vehicle, such as Figure 5 As shown, it includes a memory 504, a processor 502, and a computer program stored in the memory 504 and executable on the processor 502. The processor 502 executes the program to implement the steps described in any implementation of the vehicle noise control method embodiment.

[0067] Among them, Figure 5In the embodiment of the present invention, a bus architecture (represented by bus 500) is shown, which may include any number of interconnected buses and bridges, and bus 500 links various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 may be used to store data used by processor 502 when performing operations.

[0068] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on a computer-readable medium or transmitted via a computer-readable medium. Other examples and implementations are within the scope and spirit of the present invention and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hard wiring, or a combination of any of these. In addition, each functional unit may be integrated into a processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0070] The units described as separate components may or may not be physically separated, and the components of the control device may or may not be physical units, that is, they may be located in one place or distributed in multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0071] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0072] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the scope of the claims of the present invention.

Claims

1. A vehicle noise control method, characterized in that: include: Acquire parameter values ​​of a target state parameter group of a target vehicle, wherein the target state parameter group includes a plurality of state parameters, each of which is related to current noise generated by vibration of the target vehicle; Determining a device acoustic wave signal based on parameter values ​​of the target state parameter group; Based on the device sound wave signal, the sound playing device of the target vehicle is controlled to play sound, so that when the current noise sound wave signal is transmitted 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.

2. The vehicle noise control method according to claim 1, characterized in that: Before obtaining the parameter value of the target state parameter group of the target vehicle, the method further includes: Obtaining the current road type of the road on which the target vehicle is traveling; Based on the current road type, a parameter type of the target state parameter group is determined.

3. The vehicle noise control method according to claim 2, characterized in that: The step of determining the parameter type of the target state parameter group based on the current road type includes: The current road type is input into a preset corresponding relationship to obtain a parameter type of the target state parameter group, wherein the preset corresponding relationship is a corresponding relationship between a road type and a parameter type.

4. The vehicle noise control method according to claim 3, characterized in that: The state parameter is the acceleration of the vehicle chassis components, M triaxial acceleration sensors are arranged at different chassis positions of the test vehicle, N sound collection devices are arranged at different cockpit 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 traveling on the road type, the test vehicle obtains acceleration data of the M three-axis acceleration sensors and N test noise signals collected by the N sound collection devices; 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 between the acceleration data of the first group of three-axis acceleration sensors and the N test noise signals is greater than the correlation between the 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, 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, wherein a correlation between the first group of accelerations and the N test noise signals is greater than a correlation between the second group of accelerations and the N test noise signals; Determining the type of parameters required for the road type based on the first set of accelerations; The preset corresponding relationship is established based on the parameter type required for each road type.

5. The vehicle noise control method according to claim 4, characterized in that: The method of dividing the M triaxial acceleration sensors into a first group of triaxial acceleration sensors and a second group of triaxial acceleration sensors based on the acceleration data of the M triaxial acceleration sensors and the N test noise signals comprises: 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; Taking each sensor combination in the plurality of sensor combinations in turn as a target sensor combination; Taking the acceleration data of the target sensor combination as an independent variable and the N test noise signals as dependent variables to perform a multiple correlation analysis, and obtaining a multiple correlation coefficient of each acceleration in the acceleration data of the target sensor combination; taking an average value of multiple correlation coefficients of each acceleration in the acceleration data of the target sensor combination as a correlation score between the acceleration data of the target sensor combination and the N test noise signals; Among the correlation scores corresponding to each sensor combination in the multiple sensor combinations, the sensor combination corresponding to the largest correlation score is used as the first group of three-axis acceleration sensors, and the sensors among the M three-axis acceleration sensors except the first group of three-axis acceleration sensors are used as the second group of three-axis acceleration sensors.

6. The vehicle noise control method according to claim 4, characterized in that: The step of 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 comprises: Selecting a preset number of accelerations from the acceleration data of the first group of three-axis acceleration sensors for combination, and enumerating all the 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 dependent variables to perform multiple correlation analysis, and obtain a multiple correlation coefficient of each acceleration in the target acceleration combination; taking an average value of the multiple correlation coefficients of each acceleration in the target acceleration combination as a correlation score between the target acceleration combination and the N test noise signals; Among the correlation scores of each acceleration combination in the multiple acceleration combinations, the acceleration combination corresponding to the largest correlation score is used as the first group of accelerations, and the accelerations other than the first group of accelerations in the acceleration data of the first group of three-axis acceleration sensors are used as the second group of accelerations.

7. The vehicle noise control method according to claim 4, characterized in that: The M three-axis acceleration sensors include: a first three-axis acceleration sensor arranged on the top of the left front shock absorber tower of the test vehicle, a second three-axis acceleration sensor arranged on the top of the left rear shock absorber tower, a third three-axis acceleration sensor arranged on the top of the right front shock absorber tower, a fourth three-axis acceleration sensor arranged on the top of the right rear shock absorber tower, a fifth three-axis acceleration sensor arranged on the left side cross bar of the front subframe, and a sixth three-axis acceleration sensor arranged on the right side cross bar of the front subframe.

8. The vehicle noise control method according to claim 1, characterized in that: The determining of the device sound wave signal based on the parameter value of the target state parameter group includes: Determining chassis vibration information of the target vehicle based on parameter values ​​of the target state parameter group; Based on the chassis vibration information, the equipment sound wave signal is determined.

9. A noise control device for a vehicle, characterized in that: include: A parameter value acquisition unit, used to acquire a parameter value of a target state parameter group of a target vehicle, wherein the target state parameter group includes a plurality of state parameters, each of which is related to a current noise generated by vibration of the target vehicle; a signal determination unit, configured to determine a device acoustic wave signal based on a parameter value of the target state parameter group; A playback control unit is used to control the sound playback device of the target vehicle to play sound based on the device sound wave signal, so that when the current noise sound wave signal is transmitted to the cabin area of ​​the target vehicle, it interferes with the device sound wave signal of the sound playback device, so that the noise loudness in the cabin area is less than a preset loudness threshold.

10. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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