Adaptive computing power optimization in-vehicle noise active control system and method

Abstract

The invention discloses a self-adaptive computing power optimization in-vehicle noise active control system and method. Order optimization is carried out on a secondary path filter of the secondary path model, so that the weight coefficient of the secondary path filter meets the convergence requirement and the order of the secondary path filter meets the minimization requirement, and the secondary path filter after order optimization is used for carrying out pre-filtering on the noise reduction reference signal; the active noise reduction control filter is subjected to order optimization, so that the weight coefficient of the active noise reduction control filter meets the convergence requirement, the order of the active noise reduction control filter meets the minimum requirement, the active noise reduction control filter after order optimization is used for generating noise reduction sound waves, noise reduction is carried out on in-vehicle noise corresponding to the noise reduction reference signal, and noise reduction is carried out on the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction processing result meets the set requirement, it is judged that the order optimization of the active noise reduction control filter is completed, and the active noise reduction control filter after the order optimization is completed is used for conducting noise reduction processing on the in-vehicle noise corresponding to the noise reduction reference signal.

Description

Technical Field

[0001] This invention relates to the field of in-vehicle noise control, specifically to an adaptive computing power-optimized in-vehicle noise active control system and method. Background Technology

[0002] Active noise cancellation technology is a promising technology in the automotive NVH field, applicable to in-vehicle noise reduction and sound quality adjustment. An active noise cancellation system involves the design and application of two filter modules: 1) Secondary path modeling filter. The secondary path modeling method involves the noise cancellation system emitting a test sound wave, and a noise cancellation microphone collecting the response at the noise reduction point. Based on the recorded test sound wave and the collected response signal, a minimum mean square error adaptive filtering algorithm is used to establish the system's secondary path model. The secondary path model is used to pre-filter the reference signal to compensate for the phase shift caused by the secondary path delay. In existing technologies, the order of the secondary path modeling filter is fixed. Once the secondary path model is established, the computational power of the module that pre-filters the noise reduction reference signal is determined and difficult to change, resulting in poor system adaptability and portability. 2) Noise reduction control filter. An adaptive minimum mean square error algorithm is used to update the control filter coefficients in real time based on the reference signal and residual signal. The noise reduction control filter is convolved with the reference signal to generate a noise-reduced inverse sound wave. The order of the noise reduction control filter is fixed. Under certain conditions, the filter order may be too high, resulting in slow convergence speed and wasted computing power; or under certain conditions, the system may be unstable and the noise reduction effect may be unsatisfactory due to insufficient filter order. Summary of the Invention

[0003] The purpose of this invention is to provide, on the one hand, an adaptive computing power-optimized active control system for in-vehicle noise, and on the other hand, an adaptive computing power-optimized active control method for in-vehicle noise. This system and method can automatically adjust the order of the modeling filter and the control filter according to the current noise reduction conditions, thereby adjusting the system's computing power requirements and maintaining the system in an optimal state that balances noise reduction effectiveness with computing power demand.

[0004] To achieve this objective, the present invention provides an adaptive computing power-optimized active control system for in-vehicle noise, comprising: The secondary path optimization module is used to establish a secondary path model based on the test signal and the corresponding response signal, optimize the order of the secondary path filter of the secondary path model, so that the weight coefficients of the secondary path filter meet the convergence requirement and the order of the secondary path filter meets the minimization requirement. The order-optimized secondary path filter is used to pre-filter the noise reduction reference signal, and the pre-filtered noise reduction reference signal is used to update the weight coefficients of the active noise reduction control filter. The active noise reduction optimization module is used to optimize the order of the active noise reduction control filter of the active noise reduction model, so that the weight coefficients of the active noise reduction control filter meet the convergence requirement and the order of the active noise reduction control filter meets the minimum requirement. The order-optimized active noise reduction control filter is used to generate noise reduction waves to reduce the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction result meets the set requirements, it is determined that the order optimization of the active noise reduction control filter is completed. The active noise reduction control filter after order optimization is used to perform noise reduction processing on the in-vehicle noise corresponding to the noise reduction reference signal.

[0005] The active noise reduction optimization module is used to optimize the order of the active noise reduction control filter of the active noise reduction model, so that the weight coefficients of the active noise reduction control filter meet the convergence requirement and the order of the active noise reduction control filter meets the minimum requirement. The order-optimized active noise reduction control filter is used to generate noise reduction waves to reduce the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction result meets the set requirements, it is determined that the order optimization of the active noise reduction control filter is completed. The active noise reduction control filter after the order optimization is used to perform noise reduction processing on the in-vehicle noise.

[0006] Furthermore, the method for establishing a secondary path model based on the test signal and its corresponding response signal includes: a test signal generator emitting a test signal, and the test signal being recorded as... The microphone collects and records the test sound wave response signal. Response signal The least mean square error algorithm is used to establish the secondary path model.

[0007] Furthermore, the method for optimizing the order of the secondary path filter of the secondary path model so that the weight coefficients of the secondary path filter meet the convergence requirements includes: performing convergence detection on the weight coefficients of the secondary path filter of the secondary path model, extracting the amplitude frequency and phase frequency characteristics of the secondary path filter, and determining whether the order of the secondary path filter meets the minimization requirement and whether the order optimization of the secondary path filter of the secondary path model is completed based on the convergence detection results. in, These are the weighting coefficients of the secondary path filter; This is the convergence threshold for the weight coefficients of the secondary path filter; Secondary path filter order indivual Composition of secondary pathway model .

[0008] Furthermore, methods for determining whether the order of the secondary path filter meets the minimization requirement and whether the order optimization of the secondary path filter is complete based on the convergence test results include: when When the convergence requirement is met, update the order of the secondary path filter to... Secondary pathways were remodeled and extracted. The amplitude-frequency and phase-frequency characteristics of the secondary path model, and Compare the amplitude-frequency and phase-frequency characteristics, when and If the difference in the amplitude-frequency response curve is within the amplitude-frequency setting error range, and the difference in the phase-frequency response curve is within the phase-frequency setting error range, then it is considered... The convergence requirement is met; repeat the above steps until... If the convergence requirement is no longer met, the order of the optimized secondary path filter is N-M+1, and the weight coefficients of the secondary path filter are... ;when If the convergence requirement is not met, update the order of the secondary path filter to... Remodel the secondary pathways and determine... Does it meet the convergence requirement? Repeat the above steps until... If the convergence requirement is met, the order of the optimized secondary path filter is N+M, and the weight coefficients of the secondary path filter are... The secondary path model after order optimization is used to pre-filter the noise reduction reference signal.

[0009] Furthermore, methods for optimizing the order of the active noise reduction control filter in the active noise reduction model to ensure that the weight coefficients of the active noise reduction control filter meet the convergence requirements include: establishing the active noise reduction control model using the normalized minimum mean square error algorithm. The convergence of the weight coefficients of the active noise reduction control filter in the active noise reduction module is tested. Based on the convergence test results, it is determined whether the order of the active noise reduction control filter meets the minimization requirement and whether the optimization of the order of the active noise reduction control filter is completed. ; in, To control the filter weight coefficients; To control the convergence threshold of the filter weight coefficients; To control the filter order, Composition of active noise reduction control model .

[0010] Furthermore, methods for determining whether the order of the active noise cancellation control filter meets the minimization requirement and whether the optimization of the active noise cancellation control filter order is completed based on the convergence test results include: when When the convergence requirement is met, the order of the active noise reduction control filter is updated to [value]. Repeat the noise reduction iteration and judge. Does it satisfy the convergence condition? If the convergence condition is met, continue the noise reduction iteration and repeat the above steps until... The convergence condition is no longer met; therefore, the order of the active noise reduction control filter is modified to... 1- 1+1, completing the optimization of the active noise reduction control filter order; when If the convergence condition is not met, update the order of the active noise reduction control filter to [value]. Repeat the noise reduction iteration and judge. Does it satisfy the convergence condition? If the convergence condition is not met, continue the noise reduction iteration and repeat the above steps until... If the convergence condition is met, the order of the active noise reduction control filter is modified to... 1+ 1. Complete the optimization of the order of the active noise reduction control filter.

[0011] Furthermore, the method for reducing in-vehicle noise by generating a noise-reduced wave using an optimized active noise reduction control filter includes: convolving the weight coefficients of the active noise reduction control filter with the reference signal. ; in, The anti-phase sound wave signal is used to cancel out in-vehicle noise. As a noise reduction reference signal, This refers to the discrete-time sampling sequence number. To satisfy the convergence requirement, control the filter weights. The noise reduction residual signal is obtained after amplification by the power amplifier, speaker output, and cancellation with in-vehicle noise. .

[0012] Furthermore, the active noise cancellation control filter with optimized order is used to reduce in-vehicle noise. When the noise reduction result meets the set requirements, the method for determining that the active noise cancellation control filter has completed order optimization includes: when... At that time, it is determined that the active noise reduction control filter has completed the order optimization. The weighting coefficients of the active noise reduction control filter are The corresponding noise reduction residual signal at that time The weighting coefficients of the active noise reduction control filter are or The corresponding noise reduction residual signal at that time, and the optimized active noise reduction control filter order is: 1- 1+1 or 1+ 1. The weighting coefficients of the active noise reduction control filter are: or .

[0013] Furthermore, an adaptive computing power-optimized active control method for in-vehicle noise based on the system includes: A secondary path model is established based on the test signal and the corresponding response signal. The order of the secondary path filter of the secondary path model is optimized so that the weight coefficients of the secondary path filter meet the convergence requirement and the order of the secondary path filter meets the minimization requirement. The order-optimized secondary path filter is used to pre-filter the noise reduction reference signal. The pre-filtered noise reduction reference signal is used to update the weight coefficients of the active noise reduction control filter. The order of the active noise reduction control filter in the active noise reduction model is optimized to ensure that the weight coefficients of the active noise reduction control filter meet the convergence requirement and the order of the active noise reduction control filter meets the minimization requirement. The order-optimized active noise reduction control filter is used to generate noise reduction waves to reduce the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction result meets the set requirements, the order optimization of the active noise reduction control filter is determined to be complete. The order-optimized active noise reduction control filter is then used to reduce the in-vehicle noise corresponding to the noise reduction reference signal.

[0014] The beneficial effects of this invention are as follows: In existing active noise cancellation technologies, the fixed order of the secondary path filter and the active noise cancellation control filter leads to redundant and wasted computing power in low-noise scenarios, while insufficient order causes inadequate convergence and deteriorated noise reduction performance under high-complexity conditions. This invention designs a dynamic order optimization mechanism. By real-time detection of the convergence status of the secondary path filter weight coefficients and comparative verification using the amplitude-frequency / phase-frequency characteristics of the secondary path filter, the order of the secondary path filter is optimized. Simultaneously, the convergence of the active noise cancellation control filter weight coefficients is detected, and the order is dynamically adjusted based on the noise reduction effect verification of the residual signal. This dual optimization process enables the system to adaptively balance computing power allocation according to the real-time noise environment. It eliminates the ineffective computational energy consumption caused by order redundancy in traditional solutions and ensures system convergence stability and noise reduction depth by intelligently increasing the order under complex conditions. Ultimately, it significantly improves the environmental adaptability of the in-vehicle active noise cancellation system under different driving conditions, achieving efficient utilization of computing resources while maintaining optimal noise reduction performance. Attached Figure Description

[0015] Figure 1 This is a block diagram of the adaptive computing power optimized in-vehicle noise active control system of the present invention; Figure 2 This is a diagram of the secondary path modeling filter order optimization module of the present invention; Figure 3 shows the secondary path filter model and its amplitude-frequency and phase-frequency characteristics of the present invention. Figure 4 This is a diagram of the control filter order optimization module of the present invention; Figure 5 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 5 As shown, an adaptive computing power optimized in-vehicle noise active control system includes: The secondary path optimization module is used to establish a secondary path model based on the test signal and the corresponding response signal, optimize the order of the secondary path filter of the secondary path model, so that the weight coefficients of the secondary path filter meet the convergence requirement and the order of the secondary path filter meets the minimization requirement. The order-optimized secondary path filter is used to pre-filter the noise reduction reference signal, and the pre-filtered noise reduction reference signal is used to update the weight coefficients of the active noise reduction control filter. The active noise reduction optimization module is used to optimize the order of the active noise reduction control filter of the active noise reduction model, so that the weight coefficients of the active noise reduction control filter meet the convergence requirement and the order of the active noise reduction control filter meets the minimum requirement. The order-optimized active noise reduction control filter is used to generate noise reduction waves to reduce the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction result meets the set requirements, it is determined that the order optimization of the active noise reduction control filter is completed. The active noise reduction control filter after order optimization is used to perform noise reduction processing on the in-vehicle noise corresponding to the noise reduction reference signal.

[0018] The noise reduction reference signal is the source signal that generates noise. In some embodiments, the road noise inside the vehicle is generated by the vibration of the road surface exciting the tires, which is transmitted to the vehicle body through the suspension and causes the body panels to vibrate. The vibration signal transmitted from the road surface can be used as the noise reduction reference signal to reduce road noise. The active noise reduction control system uses the vibration signal transmitted from the road surface to generate noise-reducing anti-phase sound waves to cancel out the road noise inside the vehicle.

[0019] In existing technologies, the orders of the secondary path filters in the secondary path model and the active noise reduction control filters in the active noise reduction model are fixed. This can easily lead to order redundancy during actual noise reduction control, resulting in wasted computational power or, under certain complex conditions, insufficient order causing inadequate system convergence and unsatisfactory noise reduction performance. This solution optimizes the orders of the secondary path filters and the active noise reduction control filters, thereby optimizing computational power and improving noise reduction performance during the active noise reduction process.

[0020] In some technical solutions, the method for establishing a secondary path model based on the test signal and its corresponding response signal includes: a test signal generator emitting a test signal, and the test signal being recorded as... The test signal is emitted as a test sound wave through a speaker. The microphone collects and records the test sound wave response signal. The test acoustic wave is the response signal corresponding to the test signal; a secondary path model is established using the minimum mean square error algorithm. : ; ; ; when ; have ; in, For actual physical secondary pathways, This represents the discrete-time sampling sequence number.

[0021] The test signal generator is part of the active noise reduction control program, used to generate and record test signals according to preset test commands. (e.g., white noise digital signal with a frequency range of 20Hz-2kHz), the test signal is recorded as follows: The test signal is amplified by the circuit and power amplifier and output as a test sound wave signal by the speaker. The test sound wave response signal was collected by the microphone. The secondary path modeling filter is used to filter the noise reduction reference signal, eliminating the phase mismatch problem caused by the secondary path delay. In some embodiments, the speaker is a horn for playing music inside the vehicle, typically installed in the dashboard, door panels, or trunk side panel; the secondary path includes the sound path inside the vehicle, i.e., the sound path from the noise-canceling speaker to the noise-canceling microphone. The microphone is the in-vehicle sound-receiving device, such as a microphone in a headset that collects human speech, installed in the headrest or ceiling above the seat; A test signal (such as white noise) is generated using a test signal generator, and recorded as follows: The signal drives the speaker to emit a test sound wave; the noise-canceling microphone collects and records the sound wave response signal. The actual response corresponding to the test signal is used; the minimum mean square error (LMS) algorithm is used to establish the secondary path model. The secondary path model is used to solve the phase mismatch problem caused by the sound wave propagation delay, thereby ensuring that there is no phase shift between the noise-reduced anti-phase sound wave emitted by the active noise cancellation control module and the original noise, thus improving the stability and accuracy of the active noise cancellation system. like Figure 1 As shown, the adaptive computing power optimization process for active control of in-vehicle noise is as follows: the secondary path modeling module starts the test signal generator to generate test signals. The test signal is emitted as a test sound wave through a speaker. The microphone collects and records the test sound wave response signal. P(n) represents the primary path, such as the transmission path of road noise from road surface vibration to the vehicle interior, forming in-vehicle noise; d(n) represents the original in-vehicle noise without noise reduction, based on... , and Secondary pathway model based on least mean square (LMS) algorithm Secondary pathway model This is used for pre-filtering the noise reduction reference signal x(n). The active noise reduction module combines the reference noise signal x(n) filtered by the secondary path filter with... The (active noise reduction control filter weight coefficients) are convolved to obtain the inverse acoustic wave signal used to cancel the in-vehicle noise corresponding to the noise reduction reference signal. , The in-vehicle noise corresponding to the noise reduction reference signal is superimposed to achieve noise reduction of the in-vehicle noise corresponding to the noise reduction reference signal.

[0022] In some technical solutions, the method for optimizing the order of the secondary path filter of the secondary path model so that the weight coefficients of the secondary path filter meet the convergence requirements includes: performing convergence detection on the weight coefficients of the secondary path filter of the secondary path model, extracting the amplitude frequency and phase frequency characteristics of the secondary path filter, and judging whether the order of the secondary path filter meets the minimization requirement and whether the order optimization of the secondary path filter of the secondary path model is completed based on the convergence detection results. in, These are the weighting coefficients of the secondary path filter; This is the convergence threshold for the weight coefficients of the secondary path filter; The order of the secondary path filter. indivual Composition of secondary pathway model .

[0023] The amplitude-frequency and phase-frequency characteristics of the secondary path filter are its amplitude-frequency response curve and phase-frequency response curve. These curves are obtained using a filter characteristic analysis function.

[0024] The convergence threshold is set within 10% of the filter coefficient range, meaning the tail coefficients fluctuate within 10% of the filter coefficient range. The filter coefficient range refers to the absolute difference between the maximum and minimum values ​​in a set of filter weight coefficients. The absolute difference between the maximum and minimum values ​​in the filter weight coefficients is: Δ = max( )−min( ); in ={ …, } represents the sequence of filter weight coefficients. In some embodiments, when the order of the secondary path filter is N=128, ,=−1.8; =0.2 When = 0.01, Δ = |max(0.2)−min(−1.8)| = |0.2−(−1.8)| = 2.0, and the convergence threshold = σ = 10% × Δ = 0.2; By setting a convergence threshold, the order of the secondary path filter can be optimized to prevent order redundancy from easily occurring in the actual noise reduction control process, which would lead to wasted computing power or insufficient system convergence and unsatisfactory noise reduction effect under certain complex conditions due to insufficient order.

[0025] like Figure 2 As shown, the optimization process for the secondary path modeling filter order is as follows: Test signal The acoustic signal e(n) collected and recorded by the microphone is input to the LMS (Least Mean Square Error) algorithm module, which constructs a secondary path model using the LMS algorithm. The order of the secondary path filter in the secondary path model is adjusted to meet the convergence requirement, and the amplitude-frequency and phase-frequency characteristics of the secondary path filter are extracted. The amplitude-frequency and phase-frequency characteristics corresponding to the initial order of the secondary path filter are compared with those corresponding to the adjusted order. If the changes in the amplitude-frequency and phase-frequency characteristics corresponding to the initial order of the secondary path filter and those corresponding to the adjusted order of the secondary path filter are within a preset error range, the order adjustment of the secondary path filter is complete; otherwise, the order of the secondary path filter is readjusted.

[0026] As shown in Figure 3, (a) is a graph of the weight coefficients of the secondary path filter versus the order of the secondary path filter. The horizontal axis represents the order of the secondary path filter, and the vertical axis represents the coefficients of the secondary path filter.

[0027] (b) shows the amplitude and phase response characteristic curves of the secondary path filter model. The horizontal axis is frequency, the left vertical axis is the amplitude response in dB, and the right vertical axis is the phase response in radians. Filter 1 and filter 2 represent the gain on the signal amplitude, respectively. The amplitude, phase and frequency curves of filter 1 and filter 2 in the figure are basically the same, which shows that the characteristics of the two filters are basically the same and they can be substituted for each other.

[0028] In some technical solutions, methods for determining whether the order of the secondary path filter meets the minimization requirement and whether the order optimization of the secondary path filter is completed based on the convergence test results include: when When the convergence requirement is met, update the order of the secondary path filter to... Secondary pathways were remodeled and extracted. The amplitude-frequency and phase-frequency characteristics of the secondary path model, and Compare the amplitude-frequency and phase-frequency characteristics, when and If the difference in the amplitude-frequency response curve is within the amplitude-frequency setting error range, and the difference in the phase-frequency response curve is within the phase-frequency setting error range, then it is considered... The convergence requirement is met; repeat the above steps until... If the convergence requirement is no longer met, the order of the optimized secondary path filter is N-M+1, and the weight coefficients of the secondary path filter are... ;when If the convergence requirement is not met, update the order of the secondary path filter to... Remodel the secondary pathways and determine... Does it meet the convergence requirement? Repeat the above steps until... If the convergence requirement is met, the order of the optimized secondary path filter is N+M, and the weight coefficients of the secondary path filter are... The secondary path model after order optimization is used to pre-filter the noise reduction reference signal.

[0029] The In this context, N is an initial value set based on historical experience. In some embodiments, N is set to 128. Convergence testing is performed on the secondary path model based on this empirical value, and the order of the secondary path filter is optimized. In some embodiments, the amplitude-frequency error range is set to within ±5dB, and the phase-frequency error range is set to within ±10°. When the error range of the amplitude-frequency characteristic curve between the optimized secondary path model and the initial secondary path model is within ±5dB, and the error range of the phase-frequency characteristic curve is within ±10°, it indicates that the optimized secondary path model's order maintains essentially the same effect on pre-filtering the noise reduction reference signal. Optimizing the order of the secondary path filter by combining amplitude-frequency and phase-frequency characteristics prevents blindly increasing the order of the secondary path filter from causing wasted computing power or decreasing its order from causing a decrease in the accuracy of the secondary path model. The optimized secondary path model is used for pre-filtering the noise reduction reference signal to compensate for the phase shift caused by the secondary path delay. In some technical solutions, the normalized minimum mean square error algorithm is used to establish an active noise reduction control model. The method for optimizing the order of the active noise reduction control filter in the active noise reduction model to ensure that the weight coefficients of the active noise reduction control filter meet the convergence requirement includes: performing convergence detection on the weight coefficients of the active noise reduction control filter in the active noise reduction module, and determining whether the order of the active noise reduction control filter meets the minimization requirement and whether the order optimization of the active noise reduction control filter is completed based on the convergence detection results. in, To control the filter weight coefficients; To control the convergence threshold of the filter weight coefficients; To control the filter order, Composition of active noise reduction control model .

[0030] In some embodiments, N is set to 128 based on empirical values. Based on the set empirical values, the order of the active noise reduction control filter is optimized by setting a convergence threshold. This prevents order redundancy from easily occurring in the actual noise reduction control process, which would lead to wasted computing power or insufficient system convergence and unsatisfactory noise reduction effect under certain complex conditions due to insufficient order.

[0031] In some technical solutions, methods for determining whether the order of the active noise cancellation control filter meets the minimization requirement and whether the optimization of the active noise cancellation control filter order is completed based on the convergence test results include: when When the convergence requirement is met, the order of the active noise reduction control filter is updated to [value]. Repeat the noise reduction iteration and judge. Does it satisfy the convergence condition? If the convergence condition is met, continue the noise reduction iteration and repeat the above steps until... The convergence condition is no longer met; therefore, the order of the active noise reduction control filter is modified to... 1- 1+1, completing the optimization of the active noise reduction control filter order; when If the convergence condition is not met, update the order of the active noise reduction control filter to [value]. Repeat the noise reduction iteration and judge. Does it satisfy the convergence condition? If the convergence condition is not met, continue the noise reduction iteration and repeat the above steps until... If the convergence condition is met, the order of the active noise reduction control filter is modified to... 1+ 1. Complete the optimization of the order of the active noise reduction control filter.

[0032] By implementing a closed-loop iterative optimization mechanism to dynamically adjust the order, the optimal order of the active noise cancellation control filter is accurately located while ensuring that the noise reduction effect is not degraded. This significantly reduces the real-time computational load of the active noise cancellation system, ensures the stability of the system under all operating conditions, and ultimately achieves an adaptive balance between computing power requirements and noise reduction performance. In some technical solutions, an order-optimized active noise cancellation control filter is used to generate a noise-reducing wave. Methods for reducing in-vehicle noise corresponding to the noise reduction reference signal include: convolving the weight coefficients of the active noise cancellation control filter with the reference signal. ; in, The anti-phase sound wave signal is used to cancel out in-vehicle noise. As a noise reduction reference signal, This refers to the discrete-time sampling sequence number. To satisfy the convergence requirement, control the filter weights. The noise reduction residual signal is obtained after amplification by the power amplifier, speaker output, and cancellation with in-vehicle noise. .

[0033] The active noise cancellation control filter updates its coefficients in real time based on the reference signal and the residual signal. The noise cancellation control filter is convolved with the reference signal to generate a noise-canceling inverse sound wave, which cancels out in-vehicle noise.

[0034] In some technical solutions, an order-optimized active noise cancellation control filter is used to reduce in-vehicle noise corresponding to the noise reduction reference signal. The method for determining that the order optimization of the active noise cancellation control filter is complete when the noise reduction result meets the set requirements includes: when... At that time, it is determined that the active noise reduction control filter has completed the order optimization. The weighting coefficients of the active noise reduction control filter are The corresponding noise reduction residual signal at that time The weighting coefficients of the active noise reduction control filter are or The corresponding noise reduction residual signal at that time, and the optimized active noise reduction control filter order is: 1- 1+1 or 1+ 1. The weighting coefficients of the active noise reduction control filter are: or .

[0035] set up As a result of the noise reduction process, it is ensured that the noise reduction effect on the in-vehicle noise will not decrease after the order of the active noise cancellation control filter is optimized.

[0036] like Figure 4 As shown, the active noise reduction control filter order optimization control logic is as follows: Step 1: Employ the Normalized Adaptive Least Mean Square Error (NLSM) algorithm, utilizing the secondary path modeling filter to filter the denoised reference signal x'(n) and the denoising error signal. Adjust the coefficients of the active noise reduction control filter in the active noise reduction control model, and then perform convergence detection on the weight coefficients of the active noise reduction control filter. If the weight coefficients of the active noise reduction control filter do not meet the convergence requirements, continue to adjust the order of the active noise reduction control filter. When the active noise reduction control model meets the convergence requirements, execute step 2. Step 2: Convolve the weight coefficients of the active noise cancellation control filter with the reference signal to obtain the inverted acoustic wave signal used to cancel in-vehicle noise. , The noise reduction residual signal is obtained after being amplified by the power amplifier, output by the speaker, and canceled out by the original noise d(n) inside the vehicle. ,when When the order of the active noise cancellation control filter is adjusted, the noise reduction effect is satisfactory, and the optimization of the active noise cancellation control filter is complete; if Not satisfied When the order of the active noise cancellation control filter is adjusted, the noise reduction effect is unsatisfactory and the noise reduction effect deteriorates, so the order of the active noise cancellation control filter is readjusted.

[0037] Example 2 An adaptive computing power-optimized active control method for in-vehicle noise based on the system includes: A secondary path model is established based on the test signal and the corresponding response signal. The order of the secondary path filter of the secondary path model is optimized so that the weight coefficients of the secondary path filter meet the convergence requirement and the order of the secondary path filter meets the minimization requirement. The order-optimized secondary path filter is used to pre-filter the noise reduction reference signal. The pre-filtered noise reduction reference signal is used to update the weight coefficients of the active noise reduction control filter. The order of the active noise reduction control filter in the active noise reduction model is optimized to ensure that the weight coefficients of the active noise reduction control filter meet the convergence requirement and the order of the active noise reduction control filter meets the minimization requirement. The order-optimized active noise reduction control filter is used to generate noise reduction waves to reduce the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction result meets the set requirements, the order optimization of the active noise reduction control filter is determined to be complete. The order-optimized active noise reduction control filter is then used to reduce the in-vehicle noise corresponding to the noise reduction reference signal.

[0038] Example 3 A computer program product includes a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the method described in Embodiment 2.

[0039] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0040] Those skilled in the art will readily understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, combinations, substitutions, improvements, etc., made under the spirit and principles of the present invention are included within the protection scope of the present invention.

[0041] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. An adaptive computing power-optimized active control system for in-vehicle noise, characterized in that, include: The secondary path optimization module is used to establish a secondary path model based on the test signal and the corresponding response signal, optimize the order of the secondary path filter of the secondary path model, so that the weight coefficients of the secondary path filter meet the convergence requirement and the order of the secondary path filter meets the minimization requirement. The order-optimized secondary path filter is used to pre-filter the noise reduction reference signal, and the pre-filtered noise reduction reference signal is used to update the weight coefficients of the active noise reduction control filter. The active noise reduction optimization module is used to optimize the order of the active noise reduction control filter of the active noise reduction model, so that the weight coefficients of the active noise reduction control filter meet the convergence requirement and the order of the active noise reduction control filter meets the minimum requirement. The order-optimized active noise reduction control filter is used to generate noise reduction waves to reduce the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction result meets the set requirements, it is determined that the order optimization of the active noise reduction control filter is completed. The active noise reduction control filter after order optimization is used to perform noise reduction processing on the in-vehicle noise corresponding to the noise reduction reference signal.

2. The adaptive computing power optimization in-vehicle noise active control system according to claim 1, characterized in that: The method for establishing a secondary path model based on a test signal and its corresponding response signal includes: a test signal generator emitting a test signal, which is recorded; the test signal being emitted as a test sound wave via a loudspeaker, and a microphone acquiring and recording the test sound wave response signal, wherein the test sound wave response signal is the response signal corresponding to the test signal; and a minimum mean square error algorithm is used to establish the secondary path model. .

3. The adaptive computing power optimization in-vehicle noise active control system according to claim 2, characterized in that: The method for optimizing the order of the secondary path filter of the secondary path model so that the weight coefficients of the secondary path filter meet the convergence requirement includes: performing convergence detection on the weight coefficients of the secondary path filter of the secondary path model, extracting the amplitude frequency and phase frequency characteristics of the secondary path filter, and judging whether the order of the secondary path filter meets the minimization requirement and whether the order optimization of the secondary path filter of the secondary path model is completed based on the convergence detection result. ； in, These are the weighting coefficients of the secondary path filter; This is the convergence threshold for the weight coefficients of the secondary path filter; The order of the secondary path filter. indivual Composition of secondary pathway model .

4. The adaptive computing power optimization in-vehicle noise active control system according to claim 3, characterized in that: Methods for determining whether the order of the secondary path filter meets the minimization requirement and whether the order optimization of the secondary path filter is complete based on the convergence test results include: when When the convergence requirement is met, update the order of the secondary path filter to... Secondary pathways were remodeled and extracted. The amplitude-frequency and phase-frequency characteristics of the secondary path model, and Compare the amplitude-frequency and phase-frequency characteristics, when and If the difference in the amplitude-frequency response curve is within the amplitude-frequency setting error range, and the difference in the phase-frequency response curve is within the phase-frequency setting error range, then it is considered... The convergence requirement is met; repeat the above steps until... If the convergence requirement is no longer met, the order of the optimized secondary path filter is N-M+1, and the weight coefficients of the secondary path filter are... ;when If the convergence requirement is not met, update the order of the secondary path filter to... Remodel the secondary pathways and determine... Does it meet the convergence requirement? Repeat the above steps until... If the convergence requirement is met, the order of the optimized secondary path filter is N+M, and the weight coefficients of the secondary path filter are... The secondary path model after order optimization is used to pre-filter the noise reduction reference signal.

5. The adaptive computing power optimization in-vehicle noise active control system according to claim 1, characterized in that: Methods for optimizing the order of the active noise reduction control filter in an active noise reduction model to ensure the convergence of the filter's weight coefficients include: establishing the active noise reduction control model using the normalized minimum mean square error algorithm. The convergence of the weight coefficients of the active noise reduction control filter in the active noise reduction module is tested. Based on the convergence test results, it is determined whether the order of the active noise reduction control filter meets the minimization requirement and whether the optimization of the order of the active noise reduction control filter is completed. ; in, To control the filter weight coefficients; To control the convergence threshold of the filter weight coefficients; To control the filter order, Composition of active noise reduction control model .

6. The adaptive computing power optimization in-vehicle noise active control system according to claim 5, characterized in that: Methods for determining whether the order of the active noise cancellation control filter meets the minimization requirement and whether the optimization of the active noise cancellation control filter order is complete based on the convergence test results include: when When the convergence requirement is met, the order of the active noise reduction control filter is updated to [value]. Repeat the noise reduction iteration and judge. Does it satisfy the convergence condition? If the convergence condition is met, continue the noise reduction iteration and repeat the above steps until... The convergence condition is no longer met; therefore, the order of the active noise reduction control filter is modified to... 1- 1+1, completing the optimization of the active noise reduction control filter order; when If the convergence condition is not met, update the order of the active noise reduction control filter to [value]. Repeat the noise reduction iteration and judge. Does it satisfy the convergence condition? If the convergence condition is not met, continue the noise reduction iteration and repeat the above steps until... If the convergence condition is met, the order of the active noise reduction control filter is modified to... 1+ 1. Complete the optimization of the order of the active noise reduction control filter.

7. The adaptive computing power optimization in-vehicle noise active control system according to claim 6, characterized in that: The method for reducing in-vehicle noise corresponding to the noise reduction reference signal by generating a noise-reduced wave using an order-optimized active noise reduction control filter includes: convolving the weight coefficients of the active noise reduction control filter with the reference signal. ; in, The anti-phase sound wave signal is used to cancel out in-vehicle noise. As a noise reduction reference signal, This refers to the discrete-time sampling sequence number. To satisfy the convergence requirement, control the filter weights. The noise reduction residual signal is obtained after amplification by the power amplifier, speaker output, and cancellation with in-vehicle noise. .

8. The adaptive computing power optimization in-vehicle noise active control system according to claim 7, characterized in that: The active noise cancellation control filter with optimized order is used to reduce the in-vehicle noise corresponding to the noise reduction reference signal. The method for determining whether the order optimization of the active noise cancellation control filter is complete when the noise reduction result meets the set requirements includes: when... At that time, it is determined that the active noise reduction control filter has completed the order optimization. The weighting coefficients of the active noise reduction control filter are The corresponding noise reduction residual signal at that time The weighting coefficients of the active noise reduction control filter are or The corresponding noise reduction residual signal at that time, and the optimized active noise reduction control filter order is: 1- 1+1 or 1+ 1. The weighting coefficients of the active noise reduction control filter are: or .

9. An adaptive computing power-optimized active control method for in-vehicle noise based on the system of claim 1, characterized in that, include: A secondary path model is established based on the test signal and the corresponding response signal. The order of the secondary path filter of the secondary path model is optimized so that the weight coefficients of the secondary path filter meet the convergence requirement and the order of the secondary path filter meets the minimization requirement. The order-optimized secondary path filter is used to pre-filter the noise reduction reference signal. The pre-filtered noise reduction reference signal is used to update the weight coefficients of the active noise reduction control filter. The order of the active noise reduction control filter in the active noise reduction model is optimized to ensure that the weight coefficients of the active noise reduction control filter meet the convergence requirement and the order of the active noise reduction control filter meets the minimization requirement. The order-optimized active noise reduction control filter is used to generate noise reduction waves to reduce the in-vehicle noise corresponding to the noise reduction reference signal. When the noise reduction result meets the set requirements, the order optimization of the active noise reduction control filter is determined to be complete. The order-optimized active noise reduction control filter is then used to reduce the in-vehicle noise corresponding to the noise reduction reference signal.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 9.

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