Vehicle noise reduction method, device, equipment, medium, program product and vehicle

Abstract

The invention relates to a vehicle noise reduction method, device and equipment, a medium, a program product and a vehicle, the vehicle is controlled to carry out noise reduction processing according to target iteration step length data, and the target iteration step length data is determined based on iteration step length data under at least two secondary channels. The invention aims to improve the noise reduction effect of the vehicle.

Description

Technical Field

[0001] The present application relates to the field of noise reduction technology, and in particular to a vehicle noise reduction method, device, equipment, medium, program product, and vehicle. Background Art

[0002] With the advancement of automotive technology, many vehicles have added active noise reduction (ANC) features to enhance the passenger experience, reducing engine noise inside the vehicle. Currently, the adaptive filtering algorithm used in ANC is typically a least squares (LMS) algorithm with a gradient search. One of the key parameters in the LMS algorithm is the iteration step size. Related technologies use an experimentally calibrated iteration step size for ANC, but even with this calibrated iteration step size, the noise reduction effect is still poor. Summary of the Invention

[0003] The embodiments of the present application provide a vehicle noise reduction method, apparatus, device, medium, program product, and vehicle, which improve the vehicle noise reduction effect and at least partially solve the above-mentioned technical problems.

[0004] In order to achieve the above-mentioned object, according to a first aspect of the present application, a vehicle noise reduction method is provided, the vehicle noise reduction method comprising:

[0005] The vehicle is controlled to perform noise reduction processing according to target iteration step data, wherein the target iteration step data is determined based on iteration step data under at least two secondary channels.

[0006] According to a second aspect of the present application, there is provided an electronic device, comprising:

[0007] The noise reduction module is used to control the vehicle to perform noise reduction processing according to target iteration step data, wherein the target iteration step data is determined based on iteration step data under at least two secondary channels.

[0008] According to a third aspect of the present application, an electronic device is further provided, comprising a processor connected to a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program in the memory to execute any of the above-mentioned vehicle noise reduction methods.

[0009] According to a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above-mentioned vehicle noise reduction methods is implemented.

[0010] According to a fifth aspect of the present application, a computer program product is provided, which includes a computer program, and the computer program is executed by a processor to implement any of the above-mentioned vehicle noise reduction methods.

[0011] According to a sixth aspect of the present application, a vehicle is provided, which executes the vehicle noise reduction method as described above, or includes the electronic device or electronic equipment as described above.

[0012] The vehicle noise reduction method provided in the embodiments of the present application controls the vehicle to perform noise reduction processing based on target iteration step data, which is determined based on iteration step data from at least two secondary channels. This target iteration step data is determined based on the iteration step data from at least two secondary channels, and is used to control the vehicle to perform noise reduction processing. The target iteration step data is compatible with a variety of transmission function disturbances, enabling the noise reduction process to operate stably with a high convergence speed, thereby improving the vehicle noise reduction effect.

[0013] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0015] In order to more completely understand the present application and its beneficial effects, the following description will be given in conjunction with the accompanying drawings, wherein the same drawing numbers represent the same parts in the following description.

[0016] Figure 1 1 is a flow chart of an embodiment of a vehicle noise reduction method provided in an embodiment of the present invention;

[0017] Figure 2 Schematic diagram of the noise reduction algorithm provided in an embodiment of the present invention;

[0018] Figure 3 is a schematic diagram of an iterative step length curve provided in an embodiment of the present invention;

[0019] Figure 4 1 is a schematic diagram of a target iteration step data calculation process with transfer function perturbation compatibility provided in an embodiment of the present invention;

[0020] Figure 5 This is a flow chart of modeling and analysis of a multi-channel narrowband FxLMS convergence statistical analysis model provided in an embodiment of the present invention;

[0021] Figure 6 is a flow chart of constructing a secondary channel transfer function database provided in an embodiment of the present invention;

[0022] Figure 7This is a schematic diagram of the secondary channel transfer function corresponding to a certain working condition provided in an embodiment of the present invention.

[0023] Figure 8 is a schematic diagram of a delay change of a secondary channel transfer function provided in an embodiment of the present invention;

[0024] Figure 9 1 is a schematic diagram of the change in the order of the secondary channel transfer function provided in an embodiment of the present invention;

[0025] Figure 10 This is a flow chart of the noise reduction calibration compatible with the transfer function disturbance provided in an embodiment of the present invention;

[0026] Figure 11 is a simulation correction flow chart provided in an embodiment of the present invention;

[0027] Figure 12 is a test correction flow chart provided in an embodiment of the present invention;

[0028] Figure 13 1 is a structural diagram of a noise reduction calibration system compatible with a transfer function disturbance provided in an embodiment of the present invention;

[0029] Figure 14 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention;

[0030] Figure 15 2 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0032] With the advancement of automotive technology, many vehicles have added active noise cancellation (ANC) features to reduce engine noise in order to improve the overall NVH (Noise, Vibration, and Harshness) level and enhance the riding experience. Currently, the adaptive filtering algorithm used in ANC is typically a gradient-based least squares (LMS) algorithm. A key factor in the LMS algorithm is the iteration step size. If the iteration step size is too small, the algorithm converges slowly, resulting in poor noise reduction performance under dynamic conditions. If the iteration step size is too large, the algorithm becomes unstable and prone to divergence, which can cause unusual noise.

[0033] During active noise cancellation (ANC) operation, the engine speed fluctuates, and therefore the frequency of the noise signal controlled by ANC also varies. The LMS algorithm's iteration step size limits differ when controlling noise of different frequencies. Related technologies often use experimentally calibrated iteration step sizes for ANC, but even with these calibrated iteration step sizes, vehicle noise reduction effectiveness remains poor.

[0034] The inventors discovered that when the active noise reduction function is installed on mass-produced vehicles, factors such as the vehicle configuration, changes in body structure, changes in vehicle load, and changes in vehicle software versions need to be further considered. These factors will cause disturbances to the secondary channel of vehicle noise reduction, resulting in significant changes in the frequency response of the secondary channel. The original calibration method did not consider the transmission function disturbance problem, and thus could not address related issues, resulting in poor noise reduction effect.

[0035] Secondly, relying solely on experiments to calibrate these parameters incurs significant human and material costs. Changes in the secondary channels require adjusting the iteration step size and recalibrating the corresponding iteration step size data for the new secondary channels. Therefore, if transfer function perturbations are considered, it is necessary to calibrate the iteration step size data for multiple secondary channels, which incurs even greater costs through experimental calibration.

[0036] Related technologies can also use theoretical analysis to determine the iteration step size corresponding to the secondary channel, construct a symmetric matrix using the secondary channel transfer function, and then calculate the eigenvalues of the matrix at different frequencies to confirm the iteration step size data. However, when the secondary channel transfer function used by the active noise cancellation function is inconsistent with the actual secondary channel transfer function, it is impossible to construct a symmetric matrix, making this theoretical method ineffective for analysis and unable to consider transfer function disturbances.

[0037] Finally, the theoretical analysis that may exist at present may be to use the frequency domain method for calculation, but it cannot consider the influence of the transfer function order on the iteration step boundary, and the iteration step data obtained only by relying on the above theoretical calculation results has low reliability.

[0038] In order to solve the above problems, the embodiments of the present application propose a vehicle noise reduction method, device, equipment, medium, program product and vehicle. The embodiments of the present application use iterative step data under at least two secondary channels to comprehensively determine the target iterative step data for controlling the vehicle to perform noise reduction processing. It can, to a certain extent, cope with the transmission function disturbance problem caused by the changes in the secondary channels, and can improve the vehicle noise reduction effect.

[0039] Specifically, the vehicle noise reduction method in the present application can be applied to an electronic device, and the execution subject of the vehicle noise reduction method can be an electronic device, which can be a vehicle, such as a car, an electric car, a hybrid car, etc.

[0040] The following describes various embodiments in detail by taking a vehicle as an example in which the vehicle noise reduction method is performed by a vehicle.

[0041] Correspondingly, if Figure 1 As shown, the vehicle noise reduction method may include the following steps:

[0042] S10. Controlling the vehicle to perform noise reduction processing according to target iteration step data, wherein the target iteration step data is determined based on iteration step data under at least two secondary channels.

[0043] In this embodiment, the noise reduction process primarily utilizes adaptive filtering, such as the LMS (Least Mean Squares) algorithm. The iteration step size is a crucial parameter in adaptive filtering. It controls the amplitude of weight coefficient updates in the adaptive filter and directly impacts the convergence speed and stability of the adaptive filter. The secondary channel is the path for noise reduction, and changes in the secondary channel can affect the noise reduction effect. Therefore, the optimal iteration step size for different secondary channels varies.

[0044] In this embodiment, iterative step data under at least two secondary channels are predetermined. The iterative step data is data related to an iterative step that can achieve fast and stable convergence speed under the corresponding secondary channel. The iterative step data may include an iterative step that can achieve fast and stable convergence speed. The iterative step data under all secondary channels are integrated to determine data that can include a target iterative step that can achieve fast and stable convergence speed under different secondary channels. The target iterative step data is used as the target iterative step data. The target iterative step data can be compatible with the transfer function disturbance of the secondary channel. The vehicle is controlled to perform noise reduction based on the target iterative step data, so that the vehicle can use a suitable iterative step for noise reduction in the noise reduction environment corresponding to different secondary channels, and can achieve stable noise reduction.

[0045] In the technical solution disclosed in this embodiment, a vehicle is controlled to perform noise reduction processing based on target iteration step data, which is determined based on iteration step data from at least two secondary channels. This target iteration step data is determined based on the iteration step data from at least two secondary channels, and is used to control the vehicle's noise reduction processing. This target iteration step data is compatible with a variety of transmission function disturbances, enabling the noise reduction process to operate stably with a high convergence speed, thereby improving the vehicle's noise reduction effect.

[0046] In one embodiment, controlling the vehicle to perform noise reduction processing according to the target iteration step data includes:

[0047] obtaining a reference signal of the vehicle noise;

[0048] generating an excitation signal based on the reference signal and the target iteration step data;

[0049] The speaker of the vehicle is controlled to emit sound based on the excitation signal to perform noise reduction processing.

[0050] In this embodiment, the reference signal includes information about the noise to be reduced. The reference signal of the vehicle noise can be obtained by using information such as the engine speed during actual vehicle driving. The noise reduction process can be active noise reduction. The adaptive filtering algorithm for the active noise reduction process can be set based on the target iteration step data, so that the adaptive filtering algorithm can perform iterative processing based on the target iteration step data. Based on the reference signal and the adaptive filtering algorithm using the target iteration step data, an excitation signal is generated, and the excitation signal is used to control the sound of the speaker, thereby reducing the noise.

[0051] Specifically, refer to Figure 2 The reference signal can include a sine reference signal and a cosine reference signal. The reference signal is filtered using the transfer function built into the active noise reduction algorithm to obtain a filtered reference signal. A filter with fixed parameters, such as an FIR filter, can be used. The filtered reference signal and the acquired error signal are input into an adaptive filtering algorithm set based on the target iteration step size data. The adaptive filtering algorithm can be, for example, LMS, to obtain updated adaptive filtering weight coefficients.

[0052] It can be understood that the iterative step size of the adaptive filtering weight coefficient is determined by the target iterative step size data. The adaptive filtering weight coefficient includes the weight coefficient of the sine reference signal and the cosine reference signal at a certain moment. The reference signal is processed based on the updated adaptive filtering weight coefficient to obtain an excitation signal. The vehicle's speaker is controlled to sound through the excitation signal. The sound emitted by the speaker is in the secondary channel of the real environment. When the sound emitted by the speaker reaches the microphone, it is in the opposite state to the order noise in the real vehicle noise, and can offset the real vehicle noise to achieve noise reduction processing.

[0053] The secondary channels in the real environment will experience transmission function disturbances due to factors such as vehicle configuration, changes in body structure, changes in vehicle load, and changes in vehicle software version. However, the target iteration step data determined by the iteration step data under at least two secondary channels can be compatible with the transmission function disturbances in the secondary channels in the real environment, and can also converge quickly and achieve stable noise reduction under different disturbances.

[0054] Furthermore, the secondary channel refers to the transmission path between the excitation signal and the microphone measurement signal, including circuit, sound and other paths. Figure 2As shown in the figure, the vehicle includes five speakers and four microphones. The excitation signal played by the speakers reaches the microphones through the secondary channel of the real environment. The microphones then measure the noise-reduced error signal, which represents the residual noise. This error signal is then fed back into the adaptive filtering algorithm to update the adaptive filtering weight coefficients.

[0055] In this way, the target iterative step data determined by the iterative step data under at least two secondary channels can be compatible with the transfer function disturbance in the secondary channels of the real environment, thereby improving the vehicle noise reduction effect.

[0056] In one embodiment, the iteration step data includes an iteration step boundary corresponding to a secondary channel and a frequency corresponding to the iteration step boundary. Determining target iteration step data based on the iteration step data of at least two secondary channels includes:

[0057] Obtaining an iteration step boundary corresponding to each frequency in the iteration step length data of the at least two secondary channels;

[0058] Determining a target iteration step size boundary corresponding to the frequency according to the iteration step size boundary corresponding to the frequency;

[0059] The target iteration step size data is determined according to the target iteration step size boundary corresponding to the frequency.

[0060] In this embodiment, each iteration step size data includes an iteration step size boundary under the corresponding secondary channel, and the frequency corresponding to the boundary. Specifically, the iteration step size data may include iteration step size boundaries corresponding to one or more frequencies. Frequency may refer to the vehicle noise frequency. When the engine speed changes, the noise frequency that needs to be controlled will also change. When controlling noise of different frequencies, the iteration step size boundaries that can achieve a good noise reduction effect are different. The iteration step size boundary is the maximum convergence iteration step size that can converge when performing noise reduction processing on a certain frequency noise under the secondary channel. If the iteration step size boundary is exceeded, the algorithm will no longer converge and the noise reduction effect will be poor. Under the current secondary channel, when the iteration step size within the iteration step size boundary corresponding to the frequency is selected for active noise reduction, the active noise reduction algorithm can converge and achieve a good noise reduction effect on the noise of the frequency in the secondary channel. Each frequency involved in the iterative step data of at least two secondary channels corresponds to at least one iterative step boundary. An iterative step boundary compatible with the transfer function disturbance is selected from at least one iterative step boundary corresponding to each frequency, which can be used as the target iterative step boundary corresponding to the frequency, and then the target iterative step boundary corresponding to at least one frequency is set as the target iterative step data.

[0061] In this way, the target iteration step size boundary compatible with the transfer function disturbance is determined by using the iteration step size boundary corresponding to each frequency in the iteration step size data of at least two secondary channels. This allows the target iteration step size data to converge under different secondary channels, thereby improving the vehicle noise reduction effect.

[0062] In one embodiment, determining the target iteration step size boundary corresponding to the frequency according to the iteration step size boundary corresponding to the frequency includes:

[0063] The maximum iteration step size boundary among the iteration step size boundaries corresponding to the frequency is set as the target iteration step size boundary corresponding to the frequency.

[0064] In this embodiment, the maximum iteration step boundary among the iteration step boundaries corresponding to the frequency can be specifically set as the target iteration step boundary corresponding to the frequency, and the frequency and the target iteration step boundary corresponding to the frequency can be set as the target iteration step data. This ensures that the iteration step within the target iteration step boundaries corresponding to different frequencies is less than or equal to the iteration step boundary for the frequency in each secondary channel, thereby achieving good noise reduction effects and rapid convergence under different transfer function disturbance conditions.

[0065] In some embodiments, the iteration step data can be represented by an iteration step curve, referring to Figure 3 Each iteration step curve corresponds to a secondary channel. The iteration step curve includes the correspondence between different frequencies and different iteration step boundaries. The iteration step boundary refers to the maximum convergent iteration step. If this iteration step boundary is exceeded, the algorithm will not converge. Based on the iteration step curves corresponding to multiple secondary channels, the corresponding target iteration step boundaries are taken at different frequencies and connected to form the target iteration step boundaries corresponding to different frequencies. The target iteration step curve is obtained as the target iteration step data.

[0066] In this way, the maximum iteration step size boundary is used as the target iteration step size data, which can be compatible with a variety of transfer function disturbances while retaining the selectable iteration step size to a greater extent, and can reduce noise more stably, thereby further improving the noise reduction effect.

[0067] In one embodiment, obtaining a secondary channel transfer function corresponding to the secondary channel;

[0068] The secondary channel transfer function is input into a convergence statistical analysis model to obtain iterative step length data under the secondary channel.

[0069] In this embodiment, the secondary channel transfer functions of at least two secondary channels are pre-acquired. The differences in the secondary channel transfer functions of different secondary channels indicate different transfer function perturbations. Using a pre-established convergence statistical analysis model, theoretical analysis can be used to determine the iteration step size boundary for each secondary channel at at least one frequency that stabilizes the convergence algorithm. This allows for the generation of iteration step size data corresponding to each secondary channel, thereby avoiding the need for experimental calibration of the iteration step size data and limiting the labor and material costs of this calibration experiment.

[0070] Specifically, in the convergence statistical analysis model, each secondary channel needs to satisfy the following formula:

[0071]

[0072] Among them, ρ represents the spectral radius of the iterative matrix, A represents the amplitude, is a constant, I represents the unit matrix, S(f) is the matrix composed of the frequency response of the secondary channel transfer function and its reference signal, which needs to be determined according to the theoretical reference signal corresponding to the frequency f, the theoretical secondary channel transfer function and the algorithm built-in transfer function in the noise reduction algorithm. The algorithm built-in transfer function is the transfer function configured in the noise reduction algorithm used when the vehicle performs noise reduction. It is believed in the relevant technology that the algorithm built-in transfer function can characterize the secondary channel in the real noise reduction environment, but this embodiment believes that due to the existence of transfer function disturbance, the two are actually not equivalent. Based on the above formula, μ max (f), μ max (f) represents the theoretical iteration step boundary when the frequency is f. Currently, the theoretical iteration step boundary is a formula and does not have a specific value. The specific value needs to be determined by substituting the actual secondary channel transfer function.

[0073] By using the above-mentioned convergence statistical analysis model and substituting the secondary channel transfer function corresponding to each secondary channel into the model, the iterative step size boundary at the frequency f for each secondary channel can be quickly calculated based on the theoretical iterative step size boundary at the frequency f in the convergence statistical analysis model.

[0074] Based on this, the target iteration step size boundary μ that is compatible with the transfer function perturbation at this frequency f can be com , which can be expressed as:

[0075] μ com (f) = min{μ max (f)1,μ max (f)2,…μ max (f) n}

[0076] Among them, μ max (f)1,μ max (f)2,…μ max (f)n They represent the iteration step boundaries of the 1st, 2nd, ...nth secondary channels respectively.

[0077] In one embodiment, the process of constructing the convergence statistical analysis model includes the following steps:

[0078] Based on the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function and the theoretical secondary channel transfer function, the theoretical iteration step size boundary corresponding to the target frequency is calculated;

[0079] The convergence statistical analysis model is constructed based on the theoretical iteration step size boundary corresponding to the target frequency.

[0080] In this embodiment, the process of constructing a convergence statistical analysis model includes the following steps: constructing a theoretical reference signal using the target frequency to be denoised, and simultaneously obtaining the algorithm-built-in transfer function and the theoretical secondary channel transfer function. The theoretical reference signal and the theoretical secondary channel function are represented by substitutional equations, and the algorithm-built-in transfer function can be calculated using the algorithm-built-in transfer function built into the noise reduction algorithm, or it can be represented by substitutional equations. Subsequently, when calculating the iteration boundary, the actual algorithm-built-in transfer function can be substituted for the actual algorithm-built-in transfer function for calculation, without limitation here.

[0081] The noise reduction process is simulated using a theoretical reference signal, the algorithm's built-in transfer function, and the theoretical secondary channel transfer function to obtain the theoretical iteration step size boundary corresponding to the target frequency. Based on this target frequency corresponding theoretical iteration step size boundary, a convergence statistical analysis model is constructed. By inputting the actual secondary channel transfer function into this convergence statistical analysis model, the iteration step size boundary of the actual secondary channel transfer function at the target frequency can be quickly obtained.

[0082] In this way, through the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function and the theoretical secondary channel transfer function, the theoretical iterative step boundary calculated is used to construct a convergence statistical analysis model, which can quickly obtain the iterative step boundary under different secondary channels and save calibration costs.

[0083] In one embodiment, the step of calculating the theoretical iteration step size boundary corresponding to the target frequency based on the theoretical reference signal corresponding to the target frequency, the algorithm built-in transfer function, and the theoretical secondary channel transfer function includes:

[0084] Based on the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function and the theoretical secondary channel transfer function, an error iteration matrix of the adaptive filter corresponding to the target frequency is constructed;

[0085] Obtaining a convergence condition equation based on the error iteration matrix and a preset convergence condition;

[0086] According to the convergence condition equation, the theoretical iteration step size boundary corresponding to the target frequency is solved.

[0087] In this embodiment, for the currently selected target frequency f of the noise to be controlled, a theoretical reference signal is generated. Based on this theoretical reference signal, the algorithm's built-in transfer function and the theoretical channel transfer function are used to simulate the iterative process of adaptive filtering when the vehicle is performing noise reduction, and construct an iterative matrix of adaptive filtering errors:

[0088]

[0089] Among them, E ab (n) and E ab (n+1) represents the adaptive filtering weight coefficient error vector at the nth and n+1th moments, respectively, I represents the identity matrix, S represents the coefficient matrix related to the theoretical secondary channel transfer function, the algorithm built-in transfer function, and the theoretical reference signal, and μ represents the iteration step size.

[0090] The preset convergence condition is related to the error iteration matrix of the adaptive filter. The preset convergence condition is a necessary condition for the convergence of the error iteration matrix of the adaptive filter and also a necessary condition for the convergence of the algorithm. Specifically, the spectral radius of the error iteration matrix is less than 1. Therefore, the convergence condition equation can be constructed based on the error iteration matrix and the preset convergence condition, which can be expressed as:

[0091]

[0092] After constructing the convergence condition equation above, solving it yields the theoretical iteration step size bounds that satisfy the preset convergence conditions at the selected frequency. The theoretical iteration step size bounds are a formula that includes variables such as the theoretical secondary transfer function. Substituting the actual secondary channel transfer function into this formula allows for rapid calculation of the iteration step size bounds corresponding to the actual secondary channel transfer function at the target frequency.

[0093] In one embodiment, the step of constructing an error iteration matrix of the adaptive filter corresponding to the target frequency based on a theoretical reference signal corresponding to the target frequency, an algorithm built-in transfer function, and the theoretical secondary channel transfer function includes:

[0094] Calculating a theoretical excitation signal based on the algorithm's built-in transfer function and the theoretical reference signal;

[0095] Obtaining a theoretical error signal according to the theoretical excitation signal, the theoretical secondary channel transfer function, and the theoretical original noise signal;

[0096] Obtaining an adaptive filtering error corresponding to the target frequency according to the theoretical error signal;

[0097] The error iteration matrix is constructed according to the adaptive filtering error corresponding to the target frequency.

[0098] In this embodiment, specific examples are given on how to construct an error iteration matrix for adaptive filtering and how to calculate a theoretical iteration step size boundary at a target frequency.

[0099] First, for the theoretical secondary channel to be calibrated, the theoretical secondary channel transfer function is used to represent it, which is recorded as s ij , for the algorithm's built-in transfer function, it is recorded as The frequency to be denoised is selected as f. At frequency f, the process of constructing the error iteration matrix of adaptive filtering corresponding to the secondary channel is as follows:

[0100] (1) Generate a theoretical reference signal corresponding to frequency f, which can be written as:

[0101] x a (n)=A cos(ω s nΔt)

[0102] x b (n) = A sin (ω s nΔt)

[0103] Where A represents the amplitude of the theoretical reference signal, ω s represents the angular frequency of the reference signal;

[0104] (2) After filtering the theoretical reference signal using the algorithm's built-in transfer function, it can be written as:

[0105]

[0106] in, and It is related to the algorithm's built-in transfer function and can be written as:

[0107]

[0108] (3) Based on the above signal and the following equation, the initial updated adaptive filtering weight coefficients can be obtained, including the weight coefficients corresponding to the sine reference signal and cosine reference signal in the above theoretical reference signal, which are expressed as:

[0109]

[0110] Where a(n) and b(n) represent the weight coefficients of the sine and cosine reference signals, respectively, in the theoretical reference signal at time n, μ represents the iteration step size, and e(n) represents the error signal. It should be noted that during the theoretical calculation, these parameters are replaced with expressions or symbols without affecting the subsequent solution process.

[0111] (4) After processing the theoretical reference signal using the adaptive filtering weight coefficients obtained in step (3), the noise reduction process is simulated based on the theoretical secondary channel transfer function to obtain the theoretical error signal.

[0112] Specifically, according to the adaptive filtering weight coefficient, the theoretical excitation signal of the speaker is generated. The theoretical excitation signal of the j-th speaker can be written as:

[0113]

[0114] After the theoretical excitation signal passes through the theoretical secondary channel corresponding to the theoretical secondary channel transfer function, it reaches the i-th microphone corresponding to the theoretical secondary channel to obtain the theoretical noise reduction sound signal. The theoretical noise reduction sound signal can be expressed as:

[0115]

[0116] Among them, s ij represents the theoretical secondary channel function and can be written as:

[0117]

[0118] The theoretical noise reduction sound signal obtained by the i-th microphone can cancel the original noise signal at the i-th microphone. The original noise signal does not need to obtain specific parameters and is represented in the form of Fourier series:

[0119]

[0120] Among them, ω m represents the angular frequency of the m-th order component, and Δt represents the sampling time interval.

[0121] When analog noise reduction is used, the excitation signal from the speaker reduces the original noise signal at the microphone. The signal collected by the microphone is the theoretical error signal that has not been canceled, which is expressed as:

[0122]

[0123] The theoretical error signal contains a large number of different frequency components. To simplify the analysis process, we can select the component signal corresponding to the target frequency that needs to be denoised as the research object and calculate the adaptive filtering error of the component corresponding to the target frequency. This adaptive filtering error can be expressed as:

[0124]

[0125] in:

[0126]

[0127] Among them, a i,opt and b i,opt Respectively represent the optimal weight coefficients of the sine and cosine reference signals included in the optimal coefficients. The optimal coefficients here are still expressed in substitutions, which does not affect the subsequent solution calculations. The adaptive filtering error between the optimal filter coefficients and the actual adaptive filter weight coefficients can be written as:

[0128]

[0129] By combining the above formulas, we can calculate the error iteration formula of the adaptive filtering weight coefficient, re-express the error signal, and then obtain the error expectation of the adaptive filtering weight coefficient. Rewrite it into a matrix vector form to obtain the iterative error of the adaptive filter, which should be:

[0130]

[0131] Among them, E ab (n) and E ab (n+1) represents the adaptive filter weight coefficient error vector at time n and time n+1, respectively. I represents the identity matrix. S represents the coefficient matrix associated with the theoretical secondary channel transfer function, the algorithm's built-in transfer function, and other equations. μ represents the iteration step size. The theoretical iteration step size bounds for the target frequency will be solved by referring to the process for deriving the convergence condition equations described above. This will not be repeated here.

[0132] refer to Figure 4 The overall process of this embodiment is to perform theoretical analysis on the theoretical secondary channel function, the algorithm's built-in transfer function, and the theoretical reference signal corresponding to the target frequency at each selected target frequency where noise needs to be controlled, and obtain the theoretical iteration step boundary corresponding to the target frequency to characterize the maximum convergence iteration step of the theoretical secondary channel at the selected target frequency, and then construct a convergence statistical analysis model. Then, the actual secondary channel transfer function corresponding to different secondary channels is used as input, and the iteration step boundary corresponding to each secondary channel at the target frequency is obtained through the convergence statistical analysis model, thereby quickly obtaining the iteration step data corresponding to different secondary channels.

[0133] In this way, through the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function, and the theoretical channel transfer function, the theoretical iteration step boundary is theoretically analyzed and calculated, and a convergence statistical analysis model is constructed. The secondary channel transfer functions of different secondary channels are input into the convergence statistical analysis model, and the iteration step boundary at the target frequency can be obtained, and then the iteration step data of different secondary channels can be quickly obtained from a theoretical level. The more secondary channels used in this embodiment, the better the effect of compatible transfer function disturbance. When faced with a large number of secondary channels, the convergence statistical analysis model analyzed theoretically above can be used to quickly obtain the iteration step data of a large number of secondary channels, which can reduce manpower and material costs and improve the efficiency of obtaining the target iteration step data.

[0134] In some embodiments, after constructing the convergence statistical analysis model based on the theoretical iteration step size boundary corresponding to the target frequency, the method further includes:

[0135] The target frequency is updated, and based on the new target frequency, the theoretical reference signal corresponding to the target frequency, the algorithm built-in transfer function and the theoretical secondary channel transfer function are re-executed to calculate the theoretical iteration step corresponding to the target frequency.

[0136] The above embodiment uses a selected target frequency f to calculate the theoretical iteration step boundary of the secondary channel at the target frequency f. In the face of a real noise reduction scenario, there is more than one target frequency that needs to be denoised. Therefore, after calculating the theoretical iteration step boundary of a target frequency, it is necessary to update the target frequency and re-execute the above steps based on the new target frequency to obtain the theoretical iteration step boundary corresponding to the new target frequency. Therefore, a convergence statistical analysis model can be constructed based on the theoretical iteration step boundaries corresponding to multiple target frequencies f. After using the real secondary channel transfer function corresponding to the secondary channel to input the convergence statistical analysis model, the iteration step boundaries corresponding to multiple frequencies under the secondary channel can be obtained, thereby increasing the application scenarios of the iteration step data of the secondary channel.

[0137] Specifically, at least two frequencies are selected as target frequencies that require noise reduction. Figure 5After constructing a theoretical reference signal using the target frequency, the theoretical reference signal is filtered using the algorithm's built-in transfer function. The filtered theoretical parameter signal is input into the adaptive filtering algorithm to obtain updated adaptive filtering weight coefficients. The theoretical reference signal is processed based on the adaptive filtering weight coefficients to generate a theoretical excitation signal. The theoretical excitation signal is filtered using the theoretical secondary channel transfer function to simulate the transmission process of the theoretical excitation signal in the theoretical secondary channel. This produces a theoretical noise-reduced sound signal when the excitation signal reaches the microphone. This theoretical noise-reduced sound signal cancels out the original noise signal at the microphone, resulting in a theoretical error signal. The component corresponding to the target frequency in the theoretical error signal is analyzed, and the adaptive filtering error between the adaptive filtering weight coefficients and the optimal coefficients is calculated. This constructs the adaptive filtering error iteration matrix. The convergence condition equation is constructed using the convergence condition that the spectral radius of the error iteration matrix is less than 1. The theoretical iteration step size boundary at the target frequency can be solved. The above calculation is then determined to determine whether all selected target frequencies have completed the above calculation. If not, a new target frequency is determined from the remaining target frequencies and the above calculation process is repeated.

[0138] Based on the above process, the theoretical iteration step boundaries at at least two target frequencies can be calculated, and a convergence statistical analysis model can be constructed. This model can also be called a multi-channel narrowband FxLMS convergence statistical analysis model. After the model is constructed, the secondary channel transfer functions of at least two secondary channels can be selected and input into the convergence statistical analysis model to obtain the iteration step boundaries corresponding to each secondary channel at different frequencies as the iteration step data corresponding to the secondary channel. Based on the iteration step data of the secondary channels, the target iteration step boundary compatible with the transfer function disturbance at each frequency can be calculated and set as the target iteration step data compatible with the transfer function disturbance. The target iteration step data is used to set the iteration step of the adaptive filtering algorithm used in active noise reduction in the actual noise reduction process. This is compatible with the transfer function disturbance and can stably converge the adaptive filtering algorithm under different transfer function disturbance environments, thereby achieving a more stable and better noise reduction effect.

[0139] In one embodiment, before obtaining the secondary channel transfer function corresponding to the secondary channel, the method includes:

[0140] Obtain the first secondary channel transfer function measured under actual vehicle operating conditions;

[0141] adjusting the delay and / or order of the first secondary channel transfer function to obtain a second secondary channel transfer function;

[0142] Secondary channel transfer functions corresponding to at least two secondary channels are set based on the first transfer function and the second transfer function.

[0143] In this embodiment, referring to Figure 6 The secondary channel transfer function (SCTF) can be measured under various vehicle configurations, body structures, internal loads, and other real-world operating conditions. This SCTF characterizes the transmission process of sound signals through the vehicle's actual secondary channel under these conditions. Specific real-world operating conditions include, but are not limited to, unladen, fully laden, with the front seats positioned at the frontmost position, with the front seats positioned at the rearmost position, and with some optional configurations. This allows the SCTF to be derived for a variety of real-world secondary channels.

[0144] The secondary channel refers to the transmission path between the vehicle's speaker excitation signal and the microphone measurement signal, including circuit, sound and other paths. The secondary channel transfer function corresponding to the secondary channel characterizes the changes in the excitation signal in the path. A vehicle can have multiple speakers and multiple microphones, so there can be multiple secondary channels in the same vehicle. For example, there are 20 secondary channels in a vehicle with 4 microphones and 5 speakers. Sij represents the secondary channel transfer function from the jth speaker to the i-th microphone. In other words, under a certain actual vehicle working condition, there are multiple secondary channels and a set of multiple secondary channel transfer functions. Refer to Figure 7 The variable factors of the secondary channel are more than those of the actual vehicle working conditions, but this does not affect the theoretical calibration of the iteration step size boundary corresponding to each secondary channel in this embodiment.

[0145] The inventors discovered that the order of the secondary channel transfer function can also affect the iteration step size boundary. This means that related technologies may not have considered the impact of the transfer function order on algorithm convergence. This results in low reliability of the iteration step size boundary obtained based on theoretical calculations.

[0146] Based on this, this embodiment also modifies the first secondary channel transfer function of the real secondary channel measured under actual vehicle working conditions by delay and order, and forms a new secondary channel transfer function as the second secondary channel transfer function of the virtual secondary channel. Figure 8 ,by Figure 8 As an example, the first secondary channel transfer function in (a) is used to adjust the delay to obtain Figure 8 (b) and Figure 8 (c) The second secondary channel transfer function. Figure 9 ,by Figure 9 As an example, the first secondary channel transfer function in (a) is reduced to Figure 9(b) The second secondary channel transfer function. The first secondary channel transfer function corresponds to a real secondary channel transfer function, and the second secondary channel transfer function corresponds to a virtual secondary channel transfer function, both of which can characterize different secondary channels. Therefore, at least two secondary channel transfer functions can be selected from the first and second secondary channel transfer functions to simulate transfer function disturbances. Each secondary channel transfer function corresponds to a real secondary channel or a virtual secondary channel, and each secondary channel transfer function can reflect the frequency response between the excitation signals of different speakers in the vehicle's secondary channel and the detection signals of different microphones.

[0147] In one embodiment, a least squares regression method can be performed on the first secondary channel transfer function measured under at least two actual vehicle operating conditions to fit a set of multiple secondary channel transfer functions with the smallest difference from the transfer functions under various actual vehicle operating conditions, corresponding to multiple secondary channels between each speaker and each microphone on the vehicle. This set of secondary channel transfer functions will be used as the algorithm-built-in transfer function in the noise reduction algorithm.

[0148] Reference Figure 10 Based on the algorithm's built-in transfer function and the first and second secondary channel transfer functions mentioned above, a secondary channel transfer function database can be constructed. Subsequently, the algorithm's built-in transfer function can be called from the secondary channel transfer function database to simulate the processing of the active noise reduction algorithm. The secondary channel transfer functions of at least two secondary channels can be called from the first and second secondary channel transfer functions in the database to simulate transfer function disturbances. The secondary channel transfer functions of the at least two secondary channels are input into the multi-channel narrowband FxLMS convergence statistical analysis model. Based on the multi-channel narrowband FxLMS convergence statistical analysis model, iterative step data for the secondary channel transfer functions of the secondary channels can be obtained. Theoretical target iteration step data that is compatible with transfer function disturbances can then be selected based on the iteration step data for the secondary channel transfer functions of the secondary channels. The theoretical target iteration step data is then simulated and experimentally corrected using real noise data obtained from engine noise measurements, ultimately obtaining target iteration step data that can be used for noise reduction in real vehicles.

[0149] Optionally, the target iteration step length data can be corrected through simulation and / or experimental testing before being used for real-vehicle noise reduction. Using test equipment, measure the in-vehicle engine noise and speed under various operating conditions, including idle, 30 POT, 50 POT, kickdown, and WOT. Select any of these operating conditions as the first real-vehicle operating condition, preferably using the idle condition to improve test stability.

[0150] The simulation correction can be to use the first real noise data of the idle condition as input, simulate the idle condition to perform iterative step simulation calibration, debug the iterative step in the simulation test, obtain the iterative step limit value simulated under the idle condition, and use the simulated iterative step limit value of the idle condition to correct the target iterative step data, and then use the second real noise data under other second real vehicle conditions to perform dynamic simulation correction on the target iterative step data.

[0151] The experimental correction can be the measured iteration step limit value that allows the algorithm to converge under idle conditions. The target iteration step data is corrected using the iteration step limit value measured under the idle condition, and then the target iteration step data is dynamically simulated and corrected under other second actual vehicle conditions.

[0152] In one embodiment, the simulation correction includes the following steps:

[0153] Performing simulation noise reduction processing based on first real noise data of a first actual vehicle operating condition, the algorithm's built-in transfer function, and an initial iteration step size;

[0154] Adjusting the initial iteration step size according to the divergence of the simulation noise reduction process to obtain a simulation iteration step size limit value;

[0155] The target iteration step data is adjusted according to the simulation iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle working condition.

[0156] Specifically, refer to Figure 11 , take the idle condition as the first actual vehicle condition, the engine idle noise data and speed information measured in the idle condition as the first real noise data, and the engine idle noise data and speed information as the input of the simulation noise reduction process, and build a simulation noise reduction algorithm. Set a smaller initial iteration step in the simulation noise reduction algorithm, such as 0.05. In the simulation noise reduction algorithm, use the algorithm's built-in transfer function to filter the reference signal, and use the secondary channel transfer function corresponding to the idle condition to filter the excitation signal. Based on the simulation noise reduction algorithm set above, perform simulation noise reduction processing, extract the speed second-order component of the noise after noise reduction, calculate the noise reduction amount, and observe whether the simulation noise reduction processing diverges based on the noise reduction amount. If it does not diverge, increase the initial iteration step, modify the iteration step setting value in the simulation noise reduction algorithm, and re-execute the simulation noise reduction process. If it diverges, the iterative step setting value in the current simulation noise reduction algorithm is the simulation iterative step limit value of the idle condition. The simulation iterative step limit value of the idle condition is compared with the maximum convergence iterative step of the secondary channel corresponding to the idle condition obtained in the above theoretical analysis process, and the ratio of the two is obtained as the convergence step correction coefficient.

[0157] The target iteration step data is adjusted according to the convergence step correction coefficient: in the target iteration step data, the target iteration step corresponding to different frequencies will be multiplied by the simulation iteration step limit value to obtain the simulation-corrected target iteration step corresponding to different frequencies, which constitute the adjusted target iteration step data, and can realize the simulation correction of the target iteration step data.

[0158] In one embodiment, after the target iteration step length data is simulated and corrected using the first real noise data corresponding to the first actual vehicle operating condition, the simulation correction may further include:

[0159] Acquiring second real noise data measured by the vehicle in at least two second real vehicle operating conditions;

[0160] The adjusted target iteration step data is dynamically simulated and corrected according to at least two of the second real noise data.

[0161] Specifically, refer to Figure 11 , based on the adjusted target iteration step data, the target iteration step corresponding to multiple frequencies is obtained, and the simulation noise reduction algorithm is set. 30POT, 50POT, KickDown and WOT working conditions are used as the second working condition, and the noise and speed data of the 30POT, 50POT, KickDown and WOT working conditions are obtained by measurement as the second real noise data. Dynamic working condition simulation correction is carried out to observe whether the simulation noise reduction algorithm converges under the dynamic second real vehicle working condition. If it does not converge, the simulation iteration step limit value is lowered, and based on the updated simulation iteration step limit value, the target iteration step data before adjustment is readjusted to obtain the adjusted target iteration step data again. The second real noise data of the second real vehicle working condition is used again to perform dynamic simulation correction on it until the algorithm converges and the target iteration step data after simulation correction is output.

[0162] In some embodiments, the test modification comprises:

[0163] Based on the algorithm's built-in transfer function and initial iteration step size, test noise reduction was performed under the first real vehicle operating condition;

[0164] Adjusting the initial iteration step size according to the noise reduction condition of the experimental noise reduction process to obtain a limit value of the experimental iteration step size;

[0165] The target iteration step data is adjusted according to the test iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle operating condition.

[0166] Specifically, taking the idle condition as the first actual vehicle condition, the target iteration step data can be modified experimentally, referring to Figure 12, burn the noise reduction algorithm into the actual vehicle, use the algorithm's built-in transfer function, the actual vehicle's secondary channel and the microphone measurement signal to generate an excitation signal, control the speaker's sound, select an initial iteration step size, and carry out experimental noise reduction processing under idle conditions.

[0167] The vehicle noise measured by the microphone under test idling conditions is compared with the vehicle noise measured by the microphone without the experimental noise reduction process enabled. The noise reduction performance of the experimental noise reduction algorithm is calculated. If noise reduction is detected, the initial iteration step size is gradually increased until the noise reduction effect weakens. This weakening noise reduction effect indicates that the algorithm has diverged. The iteration step size of the experimental noise reduction process at this point is used as the test iteration step size limit under idling conditions. The idle iteration step size limit for the idling condition is compared with the maximum converged iteration step size of the corresponding secondary channel under idling conditions obtained in the theoretical analysis above. The ratio of the two is calculated as the convergence step size correction factor.

[0168] The target iteration step data is adjusted according to the convergence step correction coefficient: in the target iteration step data, the target iteration step corresponding to different frequencies will be multiplied by the convergence step correction coefficient to obtain the experimentally corrected target iteration step corresponding to different frequencies, forming the adjusted target iteration step data, which can realize the experimental correction of the target iteration step data.

[0169] In one embodiment, after the target iteration step length data is experimentally corrected using the first real noise data corresponding to the first actual vehicle operating condition, the experimental correction may further include:

[0170] Under at least two second actual vehicle operating conditions of the vehicle, dynamic test correction is performed on the adjusted target iteration step data.

[0171] Specifically, refer to Figure 12 Based on the adjusted target iteration step data, the target iteration step corresponding to multiple frequencies is obtained and used to set the experimental noise reduction algorithm. 30POT, 50POT, KickDown and WOT working conditions are used as the second working conditions. The vehicle noise measured by the microphone when the experimental noise reduction algorithm is enabled and when the experimental noise reduction algorithm is not enabled is tested under the 30POT, 50POT, KickDown and WOT working conditions. The two are compared to calculate the noise reduction performance of the experimental noise reduction algorithm under dynamic working conditions. The noise reduction performance is used to observe whether the experimental noise reduction algorithm converges. If it does not converge, the experimental iteration step limit value is reduced. Based on the updated experimental iteration step limit value, the target iteration step data before adjustment is readjusted to obtain the adjusted target iteration step data. Dynamic simulation correction is performed again under other second real vehicle working conditions until the algorithm converges and the target iteration step data after experimental correction is output.

[0172] In some embodiments, the target iteration data can be subjected to simulation correction and / or iterative correction as needed, resulting in corresponding simulation-corrected target iteration step data and experimentally corrected target iteration step data. Simulation correction has low cost, while experimental correction has high accuracy. When both corrections are performed, the target iteration step data after simulation correction and experimental correction can be used to determine the comprehensive simulation-corrected target iteration step data. This can further improve the accuracy of the target iteration step data and enhance the noise reduction effect.

[0173] In some embodiments, controlling the vehicle to perform noise reduction processing according to the target iteration step data includes:

[0174] Adjusting the target iteration step data according to the reserved margin coefficient;

[0175] The vehicle is controlled to perform noise reduction processing based on the adjusted target iteration step data.

[0176] In this embodiment, a 70% margin factor is reserved to maintain the stability of the noise reduction algorithm. The target iteration step sizes corresponding to different frequencies of the target iteration step data are multiplied by this margin factor to adjust the target iteration step data. During noise reduction, the target iteration step size within the frequency range corresponding to the engine speed range in which noise reduction is required is selected from the adjusted target iteration step size data and used as the noise reduction parameter for the active noise reduction algorithm, completing the calibration of the active noise reduction algorithm with transfer function disturbance compatibility.

[0177] When changes to the vehicle's load, software, and hardware interfere with the vehicle's secondary channel, the calibrated target iteration step size effectively mitigates the impact of secondary channel interference on noise reduction stability. Furthermore, the calibration process primarily relies on theoretical calculations to determine the theoretical iteration step size boundaries. Only simulations and tests performed on idle conditions are required to adjust the theoretical boundaries, resulting in highly efficient and reliable calibration.

[0178] This embodiment also provides a noise reduction calibration system compatible with transmission function disturbance, referring to Figure 13 ,The system consists of 5 subsystems, and the detailed functions and descriptions are as follows:

[0179] The active engine noise control subsystem, which can be installed in a vehicle, includes an onboard microphone 1301, the FxLMS algorithm or other noise reduction algorithm and computing hardware 1302, a real secondary channel 1303 within the vehicle, and an onboard speaker 1304. This subsystem uses the onboard microphone 1301 to measure noise signals and control the onboard speaker 1304 to produce sound, thus achieving active noise reduction and reducing engine noise within the vehicle.

[0180] The noise reduction performance test subsystem consists of a noise measurement device 1305 and noise reduction performance calculation and hardware 1306. This subsystem can measure the engine noise signal inside the vehicle when the noise reduction algorithm is turned on or off, and then calculate the noise reduction performance of the noise reduction algorithm.

[0181] The transfer function perturbation compatibility parameter calculation subsystem consists of secondary channel transfer function identification and calculation hardware 1307, a secondary channel transfer function database 1308, and a multi-channel narrowband FxLMS statistical analysis model and its hardware 1309. This system can identify different secondary channels, measure their corresponding secondary channel transfer functions, and then derive transfer function perturbation compatibility parameters through theoretical calculation.

[0182] The noise reduction performance simulation and analysis subsystem is composed of a noise reduction performance simulation and analysis system 1310. This subsystem can use the engine noise performance and transmission function disturbance compatible parameters from actual vehicle testing to carry out noise reduction performance simulation analysis.

[0183] The correction subsystem consists of a first module 1311 and a second module 1312. This subsystem can use the noise reduction performance obtained from simulation and experiments to correct the parameters of the transfer function disturbance compatibility obtained by theoretical calculations, and output the corrected parameters for use by the noise reduction algorithm and computing hardware 1302, enabling it to have the ability to be compatible with the transfer function disturbance.

[0184] As can be seen from the above description, the system can use the in-vehicle microphone measurement signal, speaker excitation signal, and the in-vehicle secondary channel transfer function and its frequency response to calculate parameters with transfer function disturbance compatibility for use by noise reduction algorithms such as FxLMS, enabling them to resist secondary channel disturbances and maintain good noise reduction effect and stability.

[0185] This embodiment also provides an electronic device, which can be integrated into a vehicle, for example, Figure 14 As shown, the electronic device may include:

[0186] The noise reduction module 1001 is used to control the vehicle to perform noise reduction processing according to target iteration step data, where the target iteration step data is determined based on iteration step data under at least two secondary channels.

[0187] Optionally, the noise reduction module 1001 is further configured to obtain a reference signal of the vehicle noise;

[0188] generating an excitation signal based on the reference signal and the adaptive target iteration step size data;

[0189] The speaker of the vehicle is controlled to emit sound based on the excitation signal to perform noise reduction processing.

[0190] Optionally, the iteration step data includes an iteration step boundary corresponding to a secondary channel and a frequency corresponding to the iteration step boundary, and determining the target iteration step data based on the iteration step data under at least two secondary channels includes:

[0191] Obtaining an iteration step boundary corresponding to each frequency in the iteration step length data of the at least two secondary channels;

[0192] Determining a target iteration step size boundary corresponding to the frequency according to the iteration step size boundary corresponding to the frequency;

[0193] The target iteration step size data is determined according to the target iteration step size boundary corresponding to the frequency.

[0194] Optionally, determining a target iteration step size boundary corresponding to the frequency according to the iteration step size boundary corresponding to the frequency includes:

[0195] The maximum iteration step size boundary among the iteration step size boundaries corresponding to the frequency is set as the target iteration step size boundary corresponding to the frequency.

[0196] Optionally, the process of constructing the convergence statistical analysis model includes the following steps:

[0197] Based on the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function and the theoretical secondary channel transfer function, the theoretical iteration step size boundary corresponding to the target frequency is calculated;

[0198] The convergence statistical analysis model is constructed based on the theoretical iteration step size boundary corresponding to the target frequency.

[0199] Optionally, after constructing the convergence statistical analysis model based on the theoretical iteration step size boundary corresponding to the target frequency, the method further includes:

[0200] The target frequency is updated, and based on the new target frequency, the theoretical reference signal corresponding to the target frequency, the algorithm built-in transfer function and the theoretical secondary channel transfer function are re-executed to calculate the theoretical iteration step corresponding to the target frequency.

[0201] Optionally, the calculating of the theoretical iteration step size boundary corresponding to the target frequency based on the theoretical reference signal corresponding to the target frequency, the algorithm built-in transfer function and the theoretical secondary channel transfer function includes:

[0202] Based on the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function and the theoretical secondary channel transfer function, an error iteration matrix of the adaptive filter corresponding to the target frequency is constructed;

[0203] Obtaining a convergence condition equation based on the error iteration matrix and a preset convergence condition;

[0204] According to the convergence condition equation, the theoretical iteration step size boundary corresponding to the target frequency is solved.

[0205] Optionally, constructing an error iteration matrix of the adaptive filtering corresponding to the target frequency based on a theoretical reference signal corresponding to the target frequency, an algorithm built-in transfer function, and the theoretical secondary channel transfer function includes:

[0206] Calculating a theoretical excitation signal based on the algorithm's built-in transfer function and the theoretical reference signal;

[0207] Obtaining a theoretical error signal according to the theoretical excitation signal, the theoretical secondary channel transfer function, and the theoretical original noise signal;

[0208] Obtaining an adaptive filtering error corresponding to the target frequency according to the theoretical error signal;

[0209] The error iteration matrix is constructed according to the adaptive filtering error corresponding to the target frequency.

[0210] Optionally, before obtaining the secondary channel transfer function corresponding to the secondary channel, the method includes:

[0211] Obtain the first secondary channel transfer function measured under actual vehicle operating conditions;

[0212] adjusting the delay and / or order of the first secondary channel transfer function to obtain a second secondary channel transfer function;

[0213] Secondary channel transfer functions corresponding to at least two secondary channels are set based on the first secondary channel transfer function and the second secondary channel transfer function.

[0214] Optionally, the algorithm built-in transfer function is obtained by regressing the first secondary channel transfer function measured under at least two actual vehicle operating conditions.

[0215] Optionally, before controlling the vehicle to perform noise reduction processing according to the target iteration step data, the method further includes:

[0216] Performing simulation noise reduction processing based on first real noise data of a first real vehicle operating condition, a built-in transfer function of the algorithm, and an initial iteration step size;

[0217] Adjusting the initial iteration step size according to the divergence of the simulation noise reduction process to obtain a simulation iteration step size limit value;

[0218] The target iteration step data is adjusted according to the simulation iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle working condition.

[0219] Optionally, after adjusting the target iteration step data according to the simulation iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle operating condition, the method further includes:

[0220] Acquiring second real noise data measured by the vehicle in at least two second real vehicle operating conditions;

[0221] The adjusted target iteration step data is dynamically simulated and corrected according to at least two of the second real noise data.

[0222] Optionally, before controlling the vehicle to perform noise reduction processing according to the target iteration step data, the method further includes:

[0223] Based on the algorithm's built-in transfer function and initial iteration step size, test noise reduction was performed under the first real vehicle operating condition;

[0224] Adjusting the initial iteration step size according to the noise reduction condition of the experimental noise reduction process to obtain a limit value of the experimental iteration step size;

[0225] The target iteration step data is adjusted according to the test iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle operating condition.

[0226] Optionally, after adjusting the target iteration step data according to the test iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle operating condition, the method further includes:

[0227] Under at least two second actual vehicle operating conditions of the vehicle, dynamic test correction is performed on the adjusted target iteration step data.

[0228] Optionally, controlling the vehicle to perform noise reduction processing according to the target iteration step data includes:

[0229] Adjusting the target iteration step data according to the reserved margin coefficient;

[0230] The vehicle is controlled to perform noise reduction processing based on the adjusted target iteration step data.

[0231] In this embodiment, the vehicle is controlled to perform noise reduction processing based on target iteration step data, which is determined based on iteration step data from at least two secondary channels. This target iteration step data is determined based on the iteration step data from at least two secondary channels, and is used to control the vehicle's noise reduction processing. This target iteration step data is compatible with a variety of transmission function disturbances, enabling the noise reduction process to run stably and at a high convergence speed, thereby improving the vehicle's noise reduction effect.

[0232] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0233] Accordingly, an embodiment of the present application further provides an electronic device, such as Figure 15 As shown, Figure 15 Schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 1100 also includes a processor 1101 having one or more processing cores, a memory 1102 having one or more computer-readable storage media, and a computer program stored on the memory 1102 and executable on the processor. The processor 1101 is electrically connected to the memory 1102. Those skilled in the art will understand that the electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0234] The processor 1101 is the control center of the electronic device 1100. It uses various interfaces and lines to connect the various parts of the entire electronic device 1100. By running or loading software programs and / or units stored in the memory 1102 and calling data stored in the memory 1102, it executes various functions of the electronic device 1100 and processes data, thereby monitoring the electronic device 1100 as a whole. The processor 1101 can be a processor (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU), a network processor (Network Processor, NP), etc., and can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of this application.

[0235] In the embodiment of the present application, the processor 1101 in the electronic device 1100 loads instructions corresponding to one or more application processes into the memory 1102 according to the following steps, and the processor 1101 runs the application stored in the memory 1102 to implement various functions, such as:

[0236] The vehicle is controlled to perform noise reduction processing according to target iteration step data, wherein the target iteration step data is determined based on iteration step data under at least two secondary channels.

[0237] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0238] Optional, such as Figure 15As shown, the electronic device 1100 further includes: a touch screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. Among them, the processor 1101 is electrically connected to the touch screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107 respectively. Those skilled in the art will understand that Figure 15 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0239] The touch display screen 1103 can be used to display a graphical user interface and receive operation instructions generated by the user acting on the graphical user interface. The touch display screen 1103 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user and various graphical user interfaces of the electronic device, and these graphical user interfaces can be composed of graphics, text, icons, videos and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. The touch panel can be used to collect the user's touch operation on or near it (such as the user uses any suitable object or accessory such as a finger, a stylus, etc. on the touch panel or near the touch panel) and generate corresponding operation instructions, and the operation instructions execute the corresponding program. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, detects the signal caused by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 1101, and can receive commands sent by the processor 1101 and execute them. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 1101 to determine the type of touch event. Then the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present invention, the touch panel and the display panel can be integrated into the touch display screen 1103 to realize input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to realize the input function.

[0240] The radio frequency circuit 1104 may be used to transmit and receive radio frequency signals, thereby establishing wireless communication with network medical devices or other electronic devices through wireless communication, and transmitting and receiving signals between network medical devices or other electronic devices.

[0241] The audio circuit 1105 can be used to provide an audio interface between the user and the electronic device through a speaker and a microphone. The audio circuit 1105 can convert the received audio data into an electrical signal and transmit it to the speaker, which then converts it into a sound signal for output. On the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1105 and converted into audio data. The audio data is then output to the processor 1101 for processing, and then sent to another electronic device through the radio frequency circuit 1104, or the audio data is output to the memory 1102 for further processing. The audio circuit 1105 may also include an earphone jack to provide communication between external headphones and the electronic device.

[0242] The input unit 1106 may be configured to receive input digital, character information, or user feature information (such as fingerprint, iris, or facial information), and to generate keyboard, mouse, joystick, optical, or trackball signal input related to user settings and function control.

[0243] Power supply 1107 is used to supply power to various components of electronic device 1100. Optionally, power supply 1107 can be logically connected to processor 1101 via a power management device, thereby enabling the power management device to manage charging, discharging, and power consumption. Power supply 1107 can also include one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0244] although Figure 15 Not shown, the electronic device 1100 may further include a camera, a sensor, a wireless fidelity module, a Bluetooth module, etc., which will not be described in detail here.

[0245] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0246] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0247] To this end, an embodiment of the present application provides a computer-readable storage medium storing a plurality of computer programs. The computer programs can be loaded by a processor to execute any of the vehicle noise reduction methods provided in the embodiments of the present application. The computer programs can execute the following steps of the vehicle noise reduction method:

[0248] The vehicle is controlled to perform noise reduction processing according to target iteration step data, wherein the target iteration step data is determined based on iteration step data under at least two secondary channels.

[0249] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0250] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0251] Since the computer-readable storage medium can implement the computer program that can be beneficially stored in any of the vehicle noise reduction methods provided in the embodiments of the present application, any of the vehicle noise reduction methods provided in the embodiments of the present application can be executed. Therefore, the effects are detailed in the previous embodiments and will not be repeated here.

[0252] Optionally, an embodiment of the present application further provides a vehicle, which includes any of the above electronic devices, electronic devices, computer-readable storage media, and computer program products.

[0253] In the aforementioned vehicle noise reduction method, electronic device, electronic device, vehicle, computer-readable storage medium, computer program product, etc., the descriptions of various embodiments each have their own focus. For portions not described in detail in a particular embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for ease and brevity of description, the specific operating processes and beneficial effects of the aforementioned electronic device, vehicle, computer-readable storage medium, computer program product, and their corresponding units can be referred to in the description of the vehicle noise reduction method in the aforementioned embodiments, and the details will not be repeated here.

[0254] The above is a detailed introduction to a vehicle noise reduction method, electronic device, electronic device, vehicle, computer-readable storage medium, and computer program product provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present application.

Claims

1. A vehicle noise reduction method, characterized in that: The method comprises: The vehicle is controlled to perform noise reduction processing according to target iteration step data, wherein the target iteration step data is determined based on iteration step data under at least two secondary channels.

2. The vehicle noise reduction method according to claim 1, wherein: The controlling the vehicle to perform noise reduction processing according to the target iteration step data includes: obtaining a reference signal of the vehicle noise; generating an excitation signal based on the reference signal and the target iteration step data; The speaker of the vehicle is controlled to emit sound based on the excitation signal to perform noise reduction processing.

3. The vehicle noise reduction method according to claim 1, wherein: The iteration step data includes an iteration step boundary corresponding to a secondary channel and a frequency corresponding to the iteration step boundary. Determining target iteration step data based on the iteration step data under at least two secondary channels includes: Obtaining an iteration step boundary corresponding to each frequency in the iteration step length data of the at least two secondary channels; Determining a target iteration step size boundary corresponding to the frequency according to the iteration step size boundary corresponding to the frequency; The target iteration step size data is determined according to the target iteration step size boundary corresponding to the frequency.

4. The vehicle noise reduction method according to claim 3, wherein: The determining, according to the iteration step size boundary corresponding to the frequency, a target iteration step size boundary corresponding to the frequency, includes: The maximum iteration step size boundary among the iteration step size boundaries corresponding to the frequency is set as the target iteration step size boundary corresponding to the frequency.

5. The vehicle noise reduction method according to claim 3, wherein: The steps for determining the iterative step data under the secondary channel include: Acquiring a secondary channel transfer function corresponding to the secondary channel; The secondary channel transfer function is input into a convergence statistical analysis model to obtain iterative step length data under the secondary channel.

6. The vehicle noise reduction method according to claim 5, wherein: The construction process of the convergence statistical analysis model includes the following steps: Based on the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function and the theoretical secondary channel transfer function, the theoretical iteration step size boundary corresponding to the target frequency is calculated; The convergence statistical analysis model is constructed based on the theoretical iteration step size boundary corresponding to the target frequency.

7. The vehicle noise reduction method according to claim 6, wherein: After constructing the convergence statistical analysis model based on the theoretical iteration step size boundary corresponding to the target frequency, the method further includes: The target frequency is updated, and based on the new target frequency, the theoretical reference signal corresponding to the target frequency, the algorithm built-in transfer function and the theoretical secondary channel transfer function are re-executed to calculate the theoretical iteration step corresponding to the target frequency.

8. The vehicle noise reduction method according to claim 6, wherein: The step of calculating the theoretical iteration step size boundary corresponding to the target frequency based on the theoretical reference signal corresponding to the target frequency, the algorithm built-in transfer function and the theoretical secondary channel transfer function includes: Based on the theoretical reference signal corresponding to the target frequency, the algorithm's built-in transfer function and the theoretical secondary channel transfer function, an error iteration matrix of the adaptive filter corresponding to the target frequency is constructed; Obtaining a convergence condition equation based on the error iteration matrix and a preset convergence condition; According to the convergence condition equation, the theoretical iteration step size boundary corresponding to the target frequency is solved.

9. The vehicle noise reduction method according to claim 8, wherein: The method of constructing an error iteration matrix of the adaptive filtering corresponding to the target frequency based on the theoretical reference signal corresponding to the target frequency, the algorithm built-in transfer function and the theoretical secondary channel transfer function includes: Calculating a theoretical excitation signal based on the algorithm's built-in transfer function and the theoretical reference signal; Obtaining a theoretical error signal according to the theoretical excitation signal, the theoretical secondary channel transfer function, and the theoretical original noise signal; Obtaining an adaptive filtering error corresponding to the target frequency according to the theoretical error signal; The error iteration matrix is constructed according to the adaptive filtering error corresponding to the target frequency.

10. The vehicle noise reduction method according to claim 6, wherein: The algorithm built-in transfer function is obtained by regressing the first secondary channel transfer function measured under at least two actual vehicle working conditions.

11. The vehicle noise reduction method according to claim 5, wherein: Before obtaining the secondary channel transfer function corresponding to the secondary channel, the method includes: Obtain the first secondary channel transfer function measured under actual vehicle operating conditions; adjusting the delay and / or order of the first secondary channel transfer function to obtain a second secondary channel transfer function; Secondary channel transfer functions corresponding to at least two secondary channels are set based on the first secondary channel transfer function and the second secondary channel transfer function.

12. The vehicle noise reduction method according to claim 1, wherein: Before controlling the vehicle to perform noise reduction processing according to the target iterative step length data, the method further includes: Performing simulation noise reduction processing based on first real noise data of a first real vehicle operating condition, a built-in transfer function of the algorithm, and an initial iteration step size; Adjusting the initial iteration step size according to the divergence of the simulation noise reduction process to obtain a simulation iteration step size limit value; The target iteration step data is adjusted according to the simulation iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle working condition.

13. The vehicle noise reduction method according to claim 12, wherein: After adjusting the target iteration step data according to the simulation iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle operating condition, the method further includes: Acquiring second real noise data measured by the vehicle in at least two second real vehicle operating conditions; The adjusted target iteration step data is dynamically simulated and corrected according to at least two of the second real noise data.

14. The vehicle noise reduction method according to claim 1, wherein: Before controlling the vehicle to perform noise reduction processing according to the target iterative step length data, the method further includes: Based on the algorithm's built-in transfer function and initial iteration step size, test noise reduction was performed under the first real vehicle operating condition; Adjusting the initial iteration step size according to the noise reduction condition of the experimental noise reduction process to obtain a limit value of the experimental iteration step size; The target iteration step data is adjusted according to the test iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle operating condition.

15. The vehicle noise reduction method according to claim 14, wherein: After adjusting the target iteration step data according to the test iteration step limit value and the iteration step data of the secondary channel corresponding to the first actual vehicle operating condition, the method further includes: Under at least two second actual vehicle operating conditions of the vehicle, dynamic test correction is performed on the adjusted target iteration step data.

16. The vehicle noise reduction method according to any one of claims 1 to 15, characterized in that: The controlling the vehicle to perform noise reduction processing according to the target iteration step data includes: Adjusting the target iteration step data according to the reserved margin coefficient; The vehicle is controlled to perform noise reduction processing based on the adjusted target iteration step data.

17. An electronic device, characterized in that: include: The noise reduction module is used to control the vehicle to perform noise reduction processing according to target iteration step data, wherein the target iteration step data is determined based on iteration step data under at least two secondary channels.

18. An electronic device, characterized in that: The vehicle noise reduction method comprises a processor connected to a memory, the memory storing a computer program, and the processor is configured to run the computer program in the memory to execute the vehicle noise reduction method according to any one of claims 1 to 16.

19. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle noise reduction method according to any one of claims 1 to 16 is implemented.

20. A computer program product, characterized in that The method comprises a computer program, wherein the computer program is executed by a processor to implement the vehicle noise reduction method according to any one of claims 1 to 16.

21. A vehicle, characterized in that: The vehicle performs the vehicle noise reduction method according to any one of claims 1 to 16, or includes the electronic device according to claim 17 or the electronic device according to claim 18.

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