Noise reduction method for traffic noise and road sound barrier system

Abstract

The invention discloses a noise reduction method for traffic noise and a road sound barrier system. The method comprises the following steps: acquiring speed information of a target vehicle on a target road; a target control instruction set is generated based on the speed information, the target control instruction set is used for controlling a noise reduction mode of the road sound barrier system and adjusting working parameters of an adaptive filter, and the noise reduction mode comprises at least one of an engine order noise tracking mode, a tire noise broadband suppression mode and a Doppler effect compensation mode; the working parameters comprise at least one of filter step length and filter length. The technical problem that an existing traffic noise reduction method is poor in effect is solved.

Description

Technical Field

[0001] The present invention relates to the field of traffic noise control, and in particular to a traffic noise reduction method and a road sound barrier system. Background Art

[0002] Traffic noise primarily refers to the noise generated by vehicles, such as motor vehicles, railway locomotives, and urban rail transit vehicles, that disrupts the surrounding living environment during operation. This noise primarily includes tire noise from the interaction between the tires and the ground, engine noise from vehicles or locomotives, and transmission system noise such as brakes. With the increasing severity of traffic noise pollution, sound barriers have been widely used on roads, railways, and other locations as a primary noise reduction measure. Prior art approaches to controlling traffic noise generally include active and passive control. Passive control primarily employs sound-absorbing materials and sound-isolating structures to reduce noise transmission by reflecting or absorbing sound energy. Active control typically deploys a speaker array and error microphone on one side of the barrier, using adaptive algorithms (such as FxLMS) to generate anti-phase sound waves to cancel out the noise. However, these passive control schemes offer limited noise reduction effectiveness. Active control schemes fail to account for frequency shifts caused by vehicle movement, resulting in a mismatch between the cancellation signal and the noise band. The noise sources and control methods vary at different vehicle speeds, making existing, crude noise reduction methods inadequate. Furthermore, single-sided control can lead to interference between actuator signals on both sides, potentially forming a positive feedback loop that can cause system instability and poor traffic noise reduction effectiveness.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present invention provide a method for reducing traffic noise and a road sound barrier system, so as to at least solve the technical problem that the existing traffic noise reduction methods have poor effects.

[0005] According to one aspect of an embodiment of the present invention, a method for reducing traffic noise is provided, comprising: obtaining speed information of a target vehicle on a target road; generating a target control instruction set based on the speed information, the target control instruction set being used to control a noise reduction mode of a road sound barrier system and adjust operating parameters of an adaptive filter, wherein the noise reduction mode comprises at least one of the following: an engine order noise tracking mode, a tire noise broadband suppression mode, and a Doppler effect compensation mode, and the operating parameters comprise at least one of the following: a filter step size and a filter length.

[0006] Furthermore, a target control instruction set is generated based on the speed information, including: determining the instantaneous speed of the target vehicle based on the speed information; in response to the instantaneous speed being less than or equal to the first speed, generating a first target control instruction in the target control instruction set, wherein the first target control instruction is used to control the road sound barrier system to adopt an engine order noise tracking mode, control the filter step length to be the first step length, and control the filter length to be the first length, and the first step length is determined by the instantaneous speed.

[0007] Furthermore, generating a target control instruction set based on speed information also includes: in response to the instantaneous speed being greater than or equal to the second speed, generating a second target control instruction in the target control instruction set, wherein the second target control instruction is used to control the road sound barrier system to adopt a Doppler effect compensation mode, control the filter step length to be a second step length, and control the filter length to be a second length, the second step length is determined by the instantaneous speed, the second step length is smaller than the first step length, the second length is greater than the first length, and the second speed is greater than the first speed.

[0008] Furthermore, generating a target control instruction set based on speed information also includes: in response to the instantaneous speed being greater than the first speed and less than the second speed, generating a third target control instruction in the target control instruction set, wherein the second target control instruction is used to control the road sound barrier system to adopt a tire noise broadband suppression mode, control the filter step length to be a third step length, and control the filter length to be a third length, the third step length is determined by the instantaneous speed, the third step length is smaller than the first step length and larger than the second step length, and the third length is larger than the first length and smaller than the second length.

[0009] Furthermore, obtaining speed information of a target vehicle on a target road includes: obtaining first speed information, the first speed information being obtained by performing radar speed measurement on the target vehicle; obtaining second speed information, the second speed information being obtained by performing sound source positioning on the target vehicle; and determining speed information based on the first speed information and the second speed information.

[0010] Furthermore, after generating a target control instruction set based on the speed information, the method also includes: obtaining sound source noise and wind speed information, the sound source noise being collected by a reference microphone, and the wind speed information being collected by a wind speed sensor from a point within the target road; generating a cancellation signal based on the sound source noise and the wind speed information; determining an actuator drive signal based on the cancellation signal; and controlling the actuator to output a reverse sound wave based on the actuator drive signal, the reverse sound wave being used to cancel at least part of the sound source noise.

[0011] Furthermore, a cancellation signal is generated based on the sound source noise and wind speed information, including: determining wind noise power spectrum information based on the sound source noise and wind speed information, the wind noise power spectrum information is used to determine the power and energy distribution of wind noise at various frequencies; determining traffic noise power spectrum information based on the sound source noise and wind speed information, the traffic noise power spectrum information is used to determine the energy distribution of noise generated by vehicles at different frequencies; determining a filtering signal based on the wind noise power spectrum information and the traffic noise power spectrum information; performing blind source separation on the filtered signal to obtain a wind noise component and a traffic noise component; and inputting the wind noise component into an adaptive filter to generate a cancellation signal.

[0012] Furthermore, the actuator is a bilateral actuator distributed on both sides of the target road, and the actuator is controlled to output a reverse sound wave based on the actuator driving signal, including: determining the left actuator control signal and the right actuator control signal based on the actuator driving signal; obtaining a coupling matrix, the coupling matrix is obtained by actual measurement of the road sound barrier system, and the coupling matrix is used to characterize the mutual coupling relationship and system response characteristics between the left actuator control signal and the right actuator control signal; determining the optimization target and constraint conditions based on the left actuator control signal, the right actuator control signal and the coupling matrix; using the gradient descent algorithm to solve the optimization target to obtain the decoupled left actuator control signal and the decoupled right actuator control signal.

[0013] According to another aspect of an embodiment of the present invention, a road sound barrier system is also provided, including: a sound noise collection module, the sound noise collection module is used to collect sound source noise; a calculation module, the calculation module includes an anti-wind noise unit, the anti-wind noise unit is used to separate the wind noise component based on the sound source noise, the calculation module includes an adaptive algorithm unit, the adaptive algorithm unit includes an adaptive filter, the adaptive algorithm unit is used to generate a cancellation signal according to the sound source noise, the calculation module includes a decoupling optimizer unit, the decoupling optimizer unit is used to decouple the actuator control signals on both sides; an actuator, the actuator is used to receive the decoupled actuator control signal and generate a reverse sound wave based on the decoupled actuator control signal.

[0014] Furthermore, the road sound barrier system includes: a sound barrier structure, which is arranged on both sides of the target road, and is provided with a wind speed sensor, an actuator array, an error microphone array and a reference microphone array. The reference microphone array is used to collect sound source noise, and the error microphone array is used to collect residual noise signals. The error microphone array and the reference microphone array are respectively arranged on both sides of the sound barrier structure in the thickness direction.

[0015] Furthermore, the sound barrier structure is also provided with a gradient impedance structure, which includes a micro-perforated plate layer, a gradient density sound-absorbing cotton layer and a Helmholtz resonance cavity layer arranged along the thickness direction, wherein the gradient density sound-absorbing cotton layer is located between the micro-perforated plate layer and the Helmholtz resonance cavity layer, and the gradient density sound-absorbing cotton layer is arranged with increasing density along the thickness direction of the sound barrier structure.

[0016] Furthermore, the sound barrier structure includes a guide airfoil portion, the inclination angle of the guide airfoil portion is A, the surface curvature radius of the guide airfoil portion is R, and 60mm≥R≥40mm.

[0017] In an embodiment of the present invention, speed information is used to control the noise reduction mode of the road sound barrier system and adjust the working parameters of the adaptive filter. By adaptively adjusting the noise reduction at different vehicle speeds, the purpose of ensuring the optimal noise reduction effect at different target vehicle speeds and compensating for the frequency offset caused by the movement of the target vehicle is achieved, thereby solving the technical problem of poor effect of existing traffic noise reduction methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 is a flow chart of an optional method for reducing traffic noise according to an embodiment of the present invention;

[0020] Figure 2 is a flow chart of an optional method for reducing traffic noise according to an embodiment of the present invention;

[0021] Figure 3 is a flow chart of an optional method for reducing traffic noise according to an embodiment of the present invention;

[0022] Figure 4 is a flow chart of an optional method for reducing traffic noise according to an embodiment of the present invention;

[0023] Figure 5 is a module schematic diagram of an optional traffic noise reduction system according to an embodiment of the present invention;

[0024] Figure 6 is a schematic structural diagram of an optional sound barrier structure according to an embodiment of the present invention;

[0025] Figure 7 is a schematic structural diagram of an optional sound barrier structure according to an embodiment of the present invention;

[0026] Figure 8 Schematic diagram of the structure of an optional Helmholtz resonant cavity according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] According to an embodiment of the present invention, a method embodiment of a method for reducing traffic noise is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Traffic noise currently exhibits a high degree of randomness in both space and time, affecting a wide area and causing long-lasting damage. Traffic noise control can be achieved in three stages: suppressing the noise source, inhibiting its propagation, and controlling it at the receiving end. In principle, these technologies can be categorized as active and passive.

[0031] Existing technologies are mainly divided into two categories: passive sound barriers and active noise reduction systems. Their implementation solutions and limitations are as follows:

[0032] 1. Passive sound barriers mainly use sound-absorbing materials (such as porous fiberboard, micro-perforated board) and sound-insulating structures (such as concrete slabs, composite interlayers) to reduce noise transmission by reflecting or absorbing sound energy. Traditional sound-absorbing materials have insufficient absorption rate for low-frequency noise (<500Hz) (usually <30%), which makes it difficult to suppress the low-frequency components of tires / engines in traffic noise. Vertical rigid barriers will reflect sound waves to the opposite side, forming standing waves or sound focusing, which is more significant in double-sided barrier scenarios. It is unable to adapt to the changes in the direction and speed of dynamic sound sources (such as moving vehicles), and the noise reduction effect decays rapidly with distance.

[0033] 2. Unilateral active noise reduction system: A speaker array and error microphone are deployed on a single-sided barrier, and an adaptive algorithm (such as FxLMS) is used to generate anti-phase sound waves to cancel out noise.

[0034] Unilateral control only covers the near-field (typically <5m), making it difficult to protect wide areas and ineffective at the far end of multiple lanes. It also fails to account for the superposition effect of reflected sound from opposite-side barriers, resulting in phase mismatch in the cancellation signals. It is also susceptible to environmental interference such as wind noise and vibration, which can contaminate the reference signal and lead to poor algorithm stability.

[0035] 3. Existing technologies also offer some methods for combating wind noise. Wind-proof hardware primarily involves adding a windscreen or waveguide to the microphone to physically filter out wind noise. Frequency-domain filtering primarily involves using a high-pass filter to suppress low-frequency wind noise.

[0036] Windscreens only suppress high-frequency wind noise (>1kHz) and are ineffective against mid- and low-frequency wind noise (100-500Hz). Frequency-domain filtering can misinterpret the effective frequency band of traffic noise (such as the engine fundamental frequency), causing signal distortion. Furthermore, without a combined wind-traffic noise model, these systems are unable to cope with time-varying wind conditions, resulting in poor dynamic adaptability.

[0037] 4. Beamforming (such as Delay-and-Sum) or TDOA positioning based on the assumption of a fixed sound source. These methods have the following limitations: they do not compensate for frequency shifts caused by vehicle movement, offset the mismatch between the signal and noise frequency bands, and ignore the Doppler effect. Traditional positioning algorithms have high calculation delays (>50ms) and cannot track high-speed vehicles (e.g., a 120km / h vehicle moves 1.67m / 50ms). In scenarios with multiple sound sources overlapping in dense traffic, they cannot resolve independent noise components, resulting in failure to separate multiple targets.

[0038] 5. Use distributed controllers to independently drive the actuators on both sides, or use a central controller for unified scheduling. This solution has the following limitations: The signals of the actuators on both sides interfere with each other, which may form a positive feedback loop and cause system instability. The global optimization algorithm has high computational complexity (O(N 3)), making it difficult to process large arrays in real time. Wind speed, temperature, and humidity sensors are not integrated, and the acoustic field model is static, making it impossible to correct for wind noise distortion.

[0039] Figure 1 is a flow chart of a method according to an embodiment of the present invention, such as Figure 1 As shown, the method includes the following steps:

[0040] Step S202: Obtain speed information of a target vehicle on a target road.

[0041] Step S204: Generate a target control instruction set based on the speed information. The target control instruction set is used to control the noise reduction mode of the road sound barrier system and adjust the working parameters of the adaptive filter. The noise reduction mode includes at least one of the following: engine order noise tracking mode, tire noise broadband suppression mode, and Doppler effect compensation mode. The working parameters include at least one of the following: filter step size and filter length.

[0042] Through the above steps, the noise reduction mode of the road sound barrier system is controlled by using speed information and the working parameters of the adaptive filter are adjusted. By adaptively adjusting the noise reduction at different vehicle speeds, the optimal noise reduction effect can be ensured at different target vehicle speeds and the frequency offset caused by the movement of the target vehicle can be compensated, thereby solving the technical problem of the poor effect of existing traffic noise reduction methods.

[0043] Activating engine order noise tracking refers to a technology used in active traffic noise reduction systems to precisely track and cancel out order components in engine noise. Engine order noise refers to the spectral components of engine noise generated during operation that are related to engine speed. These components typically appear on a spectrum as a series of fixed frequencies that are related to multiples of engine speed, known as order frequencies. For example, the engine fundamental frequency (which is typically directly related to speed) and its double and triple frequencies can all be considered order noise. In practice, noise tracking relies on the following key steps: 1. Engine speed measurement: Real-time engine speed is acquired using a vehicle-mounted speed sensor or tachometer. This speed information serves as the basis for calculating order frequencies. 2. FFT spectrum analysis: The FFT (Fast Fourier Transform) is a fast algorithm used to convert a time-domain signal into a frequency-domain representation, namely the signal's spectrum. By using high-resolution FFTs, the system can more accurately identify and separate the individual order components in engine noise. FFT resolution refers to the frequency interval of the spectrum analysis. An FFT resolution of 1 Hz means the system can distinguish signal components with frequencies as close as 1 Hz. 3. Order Frequency Calculation: Using the real-time measured engine speed, the specific values of each order frequency are calculated. For example, if the engine speed is 1500 RPM (revolutions per minute), the fundamental frequency is 1500 / 60 = 25 Hz (assuming 60 seconds per revolution), then the second-order, third-order, and so on, frequencies are 50 Hz, 75 Hz, and so on. 4. Adaptive Filter Tracking: After obtaining the order frequencies, the system uses an adaptive filter (such as the FxLMS or NLMS algorithm) to track these frequency components. The filter adjusts its internal coefficients based on the error signal (i.e., the difference between the actual and expected noise) to generate a cancellation signal corresponding to the engine order noise. Because the engine speed constantly changes during vehicle operation, the filter must be able to quickly adapt to these changes. 5. Noise Cancellation: The generated cancellation signal is transmitted through an actuator (such as a speaker), spatially interfering with the original engine order noise, thereby attenuating or canceling the noise at these specific frequencies. Wideband tire noise suppression uses a sub-band FxLMS (Filtered-x Least Mean Squares) algorithm to actively control tire rolling noise. Wideband tire noise suppression means the system not only processes noise at a specific frequency, but also addresses tire noise components across a wider frequency range. This is because tire noise typically has a wide spectrum, encompassing multiple components from low to high frequencies.

[0044] The core idea of the sub-band FxLMS algorithm is to decompose the signal into multiple sub-bands in the frequency domain, and then apply adaptive filtering technology independently on each sub-band. Compared with directly applying the FxLMS algorithm in the time domain or full frequency domain, this has the following advantages: 1. Reduced computational complexity: The filter coefficients that need to be processed by the full-frequency domain FxLMS algorithm are usually long, which increases the computational burden. Sub-band FxLMS decomposes the signal into multiple frequency bands, and each frequency band can use a shorter filter, thereby reducing computational complexity and system delay. 2. Improved control accuracy: The spectrum of tire noise changes with vehicle speed. Especially when driving at high speeds, the Doppler effect and wind noise will make the noise spectrum more complex. The sub-band FxLMS algorithm can more accurately track and control the noise in each sub-band, thereby improving the control effect.

[0045] The specific implementation steps of the sub-band FxLMS algorithm are as follows:

[0046] 1. Subband Decomposition: First, the tire noise signal collected from the microphone is decomposed into multiple frequency bands through a subband decomposition filter bank. The width and number of each subband can be determined based on the characteristics of the noise spectrum and computing resources.

[0047] 2. Adaptive Filtering: Within each subband, the FxLMS algorithm is independently applied to generate a cancellation signal. The FxLMS algorithm adjusts the coefficients of the adaptive filter based on the error microphone feedback signal, generating a signal with a phase opposite to the tire noise. In this way, the tire noise component within each subband is specifically canceled.

[0048] 3. Sub-band synthesis: The cancellation signals generated in each sub-band are recombined into a full-frequency signal, which is then transmitted through an actuator (such as a speaker). These cancellation signals interfere spatially with the original tire noise, thereby suppressing the tire noise.

[0049] 4. Dynamic Parameter Adjustment: In medium-speed mode, the system adjusts sub-band FxLMS algorithm parameters, such as step size μ and filter length, based on real-time vehicle speed. This helps the system adapt to the characteristics and spectral distribution of tire noise at different vehicle speeds, improving noise control effectiveness and stability. Sub-band FxLMS tire noise broadband suppression technology, through precise control within the frequency domain, significantly enhances the noise suppression capabilities of the active traffic noise cancellation system at medium speeds, demonstrating improved performance and adaptability when processing complex, broadband signals such as tire noise.

[0050] In Doppler effect compensation mode, the method involves first calculating the Doppler frequency shift Δf based on the vehicle's instantaneous velocity v(t) and the speed of sound waves c (typically 343 m / s under standard atmospheric conditions). Then, within the FxLMS algorithm, the frequency response of the adaptive filter is updated in real time to compensate for the Doppler frequency shift. This typically involves adjusting the filter coefficients so that its frequency response curve matches the compensated noise spectrum. Finally, a compensation signal is generated: By dynamically adjusting the filter to the Doppler frequency shift, the system generates a cancellation signal that matches the frequency variation of the moving sound source.

[0051] Furthermore, in step S204, a target control instruction set is generated based on the speed information, including:

[0052] determining an instantaneous speed of the target vehicle based on the speed information;

[0053] In response to the instantaneous speed being less than or equal to the first speed, a first target control instruction in the target control instruction set is generated, wherein the first target control instruction is used to control the road sound barrier system to adopt an engine order noise tracking mode, control the filter step length to be the first step length, and control the filter length to be the first length, and the first step length is determined by the instantaneous speed.

[0054] Furthermore, in step S204, generating a target control instruction set based on the speed information further includes:

[0055] In response to the instantaneous speed being greater than or equal to the second speed, a second target control instruction in the target control instruction set is generated, wherein the second target control instruction is used to control the road sound barrier system to adopt a Doppler effect compensation mode, control the filter step length to be a second step length, and control the filter length to be a second length, the second step length is determined by the instantaneous speed, the second step length is smaller than the first step length, and the second length is greater than the first length.

[0056] Furthermore, in step S204, generating a target control instruction set based on the speed information further includes:

[0057] In response to the instantaneous speed being greater than the first speed and less than the second speed, a third target control instruction in the target control instruction set is generated, wherein the second target control instruction is used to control the road sound barrier system to adopt a tire noise broadband suppression mode, control the filter step length to be a third step length, and control the filter length to be a third length. The third step length is determined by the instantaneous speed, the third step length is smaller than the first step length and larger than the second step length, and the third length is larger than the first length and smaller than the second length.

[0058] like Figure 2 The figure shows the flow chart of the multi-vehicle speed adaptive control mode switching. For distance, the first speed is 30km / h.

[0059] The second speed is 80km / h. The speed range classification includes:

[0060] Low speed mode (v<30km / h): Activate engine order noise tracking (FFT resolution 1Hz).

[0061] Medium-speed mode (30km / h≤v≤80km / h): Enable broadband tire noise suppression (sub-band FxLMS).

[0062] High-speed mode (v>80km / h): triggers Doppler compensation and predictive beamforming.

[0063] Optionally, in addition to the noise reduction mode control, the step size and filter length of the adaptive algorithm control need to be controlled. The dynamic parameter adjustment includes:

[0064] The step size μ is adjusted according to the formula:

[0065]

[0066] Filter length: 512 points in low-speed mode, 2048 points in high-speed mode. Step size (μ) generally refers to the update rate or learning rate of the adaptive filter. It is a key parameter in the adaptive algorithm, determining the speed and stability of the algorithm's convergence to the optimal state. The step size directly affects how the algorithm adjusts its filter coefficients to better adapt to changes in the input signal.

[0067] For example, in adaptive algorithms for FIR (Finite Impulse Response) filters, such as Least Mean Squares (LMS) or Filtered-x Least Mean Squares (FxLMS), the amount of update to the filter coefficients at each iteration depends on the current error signal, input signal, and step size. A smaller step size μ means a smaller step size for each update, resulting in slower but relatively stable convergence. A larger step size μ means a larger step size for each update, potentially leading to faster convergence to the optimal state, but also more prone to instability or overfitting.

[0068] In multi-speed adaptive control mode, the step size μ is adjusted to accommodate changes in noise characteristics at different speeds. In high-speed mode (e.g., vehicle speeds exceeding 80 km / h), the location and characteristics of noise sources (e.g., vehicles) change more rapidly. Therefore, a smaller but sufficiently fast step size is required to ensure the filter can track these changes while maintaining system stability.

[0069] Using the technical solution of this application, a multi-vehicle speed adaptive control strategy is proposed, which provides a radar / TDOA-based vehicle speed classification and Doppler frequency shift compensation mechanism and a frequency domain-spatial domain joint control mode (low speed / medium speed / high speed) of vehicle speed partitioning.

[0070] Doppler compensation algorithm:

[0071] (Compensation accuracy <0.5Hz).

[0072] Predictive beamforming delay compensation formula: (Prediction error <0.1m).

[0073] Predictive beamforming is used to process noise generated by moving sound sources. In a traffic noise active noise reduction system with an adaptive double-sided sound barrier that is resistant to wind noise, the vehicle moves at a high speed in high-speed mode, causing the position of the sound source relative to the sound barrier and microphone array to constantly change. This change produces a Doppler shift effect, that is, the frequency of the sound source is perceived as a changing frequency at the receiving end. In addition, due to the rapid movement of the vehicle, traditional beamforming technology may not be able to track the position of the sound source in real time, resulting in poor noise reduction effect. The system needs to estimate the speed of the sound source in real time and calculate the offset of the sound source frequency relative to the stationary reference point based on the Doppler shift formula. Then, the frequency of the anti-phase sound wave generated by the actuator array is adjusted to compensate for this frequency shift, ensuring that the anti-phase sound wave and the sound source noise are accurately matched in frequency to achieve the best noise reduction effect.

[0074] Using the known speed and direction of the sound source, as well as information about its location over the past few cycles, the system predicts the likely location of the sound source within a certain timeframe. The prediction window, typically ranging from tens to hundreds of milliseconds, requires a balance between accuracy and computational complexity. The predictive beamforming algorithm optimizes the actuator array's signal phase and amplitude based on the predicted location, adjusting the beam direction in advance to ensure effective noise cancellation when the source reaches the predicted location, even if the source is moving rapidly.

[0075] The implementation of predictive beamforming may include the following steps: Radar speed measurement and TDOA sound source localization: First, the system uses radar and Time Difference of Arrival (TDOA) technology to accurately measure and locate high-speed moving sound sources. The filter length and algorithm step size are adjusted according to the vehicle speed to adapt to the computational requirements and stability in high-speed scenarios. The Doppler shift is calculated and applied to ensure that the frequency of the antiphase sound wave is consistent with the frequency of the sound source noise. The beam direction is adjusted based on the predicted sound source location, and the actuator signal phase and amplitude are optimized to perform noise cancellation in advance.

[0076] Furthermore, in step S202, obtaining speed information of a target vehicle on a target road includes:

[0077] Acquiring first speed information, where the first speed information is obtained by measuring the speed of the target vehicle through a radar;

[0078] Acquiring second speed information, where the second speed information is obtained by performing sound source positioning on the target vehicle;

[0079] Speed information is determined based on the first speed information and the second speed information.

[0080] That is, the input of vehicle speed detection includes radar speed measurement data + TDOA sound source positioning results, and its output is the vehicle instantaneous speed v(t).

[0081] Combine Figure 2 As shown, in a specific embodiment, the method includes the following steps:

[0082] Step S1, vehicle speed detection. Input: radar speed measurement data + TDOA sound source positioning results. The radar speed gun can provide the vehicle's instantaneous speed information, which is achieved by transmitting and receiving radar waves and measuring the echo time. TDOA (Time Difference of Arrival) sound source positioning technology determines the location of the sound source by comparing the time difference between multiple microphones receiving sound waves, thereby indirectly inferring the vehicle's moving speed and direction. Output: vehicle instantaneous speed v(t). Combining radar speed measurement data with TDOA positioning results, the system can monitor and accurately calculate the instantaneous speed of each vehicle in real time. This speed information will be used for subsequent multi-vehicle speed adaptive control strategies.

[0083] Step S2, speed range classification. The system automatically switches to different control modes according to the instantaneous speed v(t) of the vehicle: Low-speed mode (v<30km / h): In this mode, the system focuses on tracking the engine order noise, because the engine noise is relatively significant when driving at low speeds. Medium-speed mode (30km / h≤v≤80km / h): As the vehicle speed increases, tire rolling noise and wind resistance noise become the main noise sources, and the system needs to be adjusted to a wider-band noise suppression strategy. High-speed mode (v>80km / h): When driving at high speeds, wind noise and Doppler effect are significant, so it is necessary to trigger Doppler compensation and predictive beamforming technology to adapt to the rapidly changing noise environment and sound source position.

[0084] Step S3, dynamic parameter adjustment. Step size μ adjustment: In adaptive filters (such as FxLMS), step size μ is a key parameter that determines the speed and stability of the filter coefficient update. The formula μ = 0.01 / (1+0.1v) means that as the vehicle speed v increases, the step size μ gradually decreases. This is because the faster the vehicle speed, the faster the noise environment changes, and more cautious adjustment of the filter coefficient is required to avoid overreaction and instability. For example, when the vehicle is traveling at 30km / h, μ = 0.01 / (1+0.1*30) ≈ 0.0077; and when the speed increases to 120km / h, μ = 0.01 / (1+0.1*120) ≈ 0.0008.

[0085] Step S4, filter length adjustment. The length or order of the filter directly affects its processing capability, especially in frequency domain analysis and noise cancellation. In low-speed mode, using a shorter filter length (such as 512 points) is sufficient to process relatively simple and slowly changing noise spectra. In high-speed mode, the noise spectrum changes more complexly and the Doppler effect is significant, requiring a longer filter length (such as 2048 points) to obtain finer frequency resolution and more accurate noise feature tracking.

[0086] Furthermore, after generating the target control instruction set based on the speed information, the method further includes:

[0087] Acquire sound source noise and wind speed information. The sound source noise is collected by a reference microphone, and the wind speed information is collected by a wind speed sensor at points within the target road.

[0088] generating a cancellation signal based on the sound source noise and wind speed information;

[0089] determining an actuator drive signal based on the cancellation signal;

[0090] The actuator is controlled to output a reverse sound wave based on the actuator driving signal, and the reverse sound wave is used to offset at least part of the sound source noise.

[0091] Furthermore, if Figure 3 The flow chart of anti-wind noise signal processing is shown, which generates a cancellation signal based on the sound source noise and wind speed information, including:

[0092] Determine wind noise power spectrum information based on sound source noise and wind speed information, and use the wind noise power spectrum information to determine the power and energy distribution of wind noise at various frequencies;

[0093] Determine traffic noise power spectrum information based on sound source noise and wind speed information, where the traffic noise power spectrum information is used to determine the energy distribution of noise generated by vehicles at different frequencies;

[0094] The signal input includes the reference microphone original signal x(n) + wind speed sensor data v(n), and then performs Wiener filter preprocessing: Calculate the wind noise power spectrum P wind (f) and traffic noise power spectrum P traffic (f).

[0095] determining a filtering signal based on wind noise power spectrum information and traffic noise power spectrum information;

[0096] Generate a filter function:

[0097]

[0098] And output the filtered signal x filtered (n).

[0099] Perform blind source separation on the filtered signal to obtain wind noise component and traffic noise component;

[0100] Blind Source Separation (BSS): Use the FastICA algorithm to separate the wind noise component wind (n) and traffic noise component s traffic (n).

[0101] Blind source separation techniques can help isolate different noise sources, such as wind noise and traffic noise, from mixed microphone signals. For example, the FastICA algorithm can quickly analyze signals from multiple microphones and decompose them into independent components, distinguishing wind noise from other noises such as car engine sounds and tire rolling sounds. Once these source signals are separated, different noise control strategies can be applied accordingly, such as using specific filters to eliminate wind noise while retaining or processing traffic noise, achieving better noise reduction results.

[0102] The wind noise component is input into the adaptive filter to generate a cancellation signal.

[0103] Will s wind (n) Input the secondary channel of the FxLMS algorithm to generate the cancellation signal y wind (n).

[0104] The FxLMS (Filtered-x Least Mean Squares) algorithm is a widely used adaptive noise control technology, particularly suitable for active noise control (ANC) systems, to cancel or reduce unwanted noise signals. In traffic noise active noise reduction systems, the specific process of using the FxLMS algorithm to cancel wind noise is as follows:

[0105] First, a mixed signal s(n) containing traffic noise and wind noise is captured by a reference microphone array, and wind speed data v(n) is obtained from an environmental sensor. The wind noise signal s"wind"(n) is then separated using Wiener filtering, blind source separation (BSS), or other preprocessing techniques.

[0106] In the FxLMS system, there are two main paths: the primary path and the secondary path. The primary path processes the path directly from the noise source to the error microphone, while the secondary path simulates or directly measures the signal transmission path from the actuator to the error microphone.

[0107] In this scenario, the separated wind noise signal s"wind"(n) is input to the secondary path of the FxLMS algorithm. The secondary path usually includes a digital filter G(z), which simulates the actual signal path from the actuator to the error microphone in a physical environment, that is, how the wind noise is transmitted to the error microphone.

[0108] The core of the FxLMS algorithm is to generate a cancellation signal y"wind"(n) by adjusting the coefficients of an adaptive filter W(z), thereby canceling wind noise. This adaptive filter receives the input signal s"wind"(n) and converts it into a signal that matches the characteristics of the wind noise.

[0109] Finally, through spatial beamforming: the beam direction is adjusted based on wind speed data to suppress wind noise in non-target directions.

[0110] The FxLMS algorithm adjusts the coefficients of the adaptive filter through feedback of the error signal. At each sampling moment, the error signal e(n) and the output of the secondary channel filter G(z) are used together to update the filter coefficients W(n) to better generate the cancellation signal y"wind"(n).

[0111] In the active noise reduction system of traffic noise with anti-wind noise adaptive double-sided sound barriers, changes in wind speed will affect the transfer function of the secondary channel. Therefore, the secondary channel filter G(z) needs to be dynamically adjusted according to the real-time wind speed data to ensure that y"wind"(n) and s"wind"(n) match in phase and amplitude, so as to achieve efficient wind noise cancellation. The technical solution of this application is adopted, based on the wind noise-traffic noise joint processing framework of blind source separation (BSS) and Wiener filtering, the model reference adaptive cancellation (MRAC) architecture, and the integrated wind speed data-driven spatial beamforming. The real-time estimation formula of the wind noise power spectrum is:

[0112] Pwind (f) = α·v 2.5 ·f -1.2 .

[0113] Beamforming weight dynamic adjustment rules:

[0114]

[0115] like Figure 4 As shown, further, the actuators are bilateral actuators distributed on both sides of the target road, and the actuators are controlled to output reverse sound waves based on the actuator driving signal, including:

[0116] determining a left actuator control signal and a right actuator control signal based on the actuator drive signal;

[0117] Obtaining a coupling matrix. The coupling matrix is obtained by actual measurement of the road sound barrier system. The coupling matrix is used to characterize the mutual coupling relationship between the left actuator control signal and the right actuator control signal and the system response characteristics.

[0118] Determining optimization objectives and constraints based on the left actuator control signal, the right actuator control signal, and the coupling matrix;

[0119] The optimization objective is solved using the gradient descent algorithm to obtain the decoupled left actuator control signal and the decoupled right actuator control signal.

[0120] like Figure 5 As shown in the figure, it is a flow chart of the double-side actuator decoupling optimization algorithm. In this method, the input includes the left actuator control signal u L +Right signal u R The coupling matrix C is obtained through offline measurement.

[0121] In the traffic noise active noise reduction system with wind-resistant, adaptive, double-sided sound barriers, the coupling matrix C is a core mathematical model used to describe the interaction between the left-side actuator control signal u_L and the right-side signal u_R, as well as the system response characteristics. The coupling matrix C is introduced and used to address the signal interference problem when the two-sided sound barriers work together. This is because the sound waves generated by one barrier inevitably affect the noise control effect of the other barrier. By introducing the coupling matrix, the system can perform decoupling optimization. That is, when controlling one barrier, the interference from the other barrier is simultaneously considered and compensated, thereby achieving more refined and effective noise suppression.

[0122] C quantifies how the sound waves generated by one sound barrier propagate through the space between the barriers and affect the actuator and error microphone on the other side. The coupling matrix also reflects the propagation characteristics of sound waves at different frequencies, which is crucial because frequency characteristics affect the intensity and direction of sound waves. By calculating the coupling matrix, the system can adjust the control signals to minimize signal interference between the two sound barriers, thereby improving the overall noise reduction effect. This typically involves using an optimization algorithm to find a set of control signals that minimizes the residual noise energy.

[0123] The optimization goal is where e is the error signal, i.e., the difference between the actual residual noise and the target noise; γ is the regularization parameter used to balance the noise suppression effect and the output energy of the actuator.

[0124] The first component, ‖e‖^2, represents the energy of the residual noise. We minimize the squared norm of e to optimize noise suppression. The second component, γ(‖u_L‖^2 + ‖u_R‖^2), introduces a regularization term that penalizes actuator signals with excessive output energy. This helps prevent the algorithm from over-outputting, potentially generating unnecessary acoustic feedback or noise, while also saving energy.

[0125] Solution: Use the Lagrange multiplier method to transform it into an unconstrained problem and solve it iteratively through gradient descent.

[0126] The output of the algorithm is the decoupled actuator drive signal

[0127] The technical solution of this application is adopted, and the test scenario is: two-way 6-lane highway, traffic volume 40 vehicles / minute, average speed 60km / h, and wind speed 8m / s.

[0128] Noise reduction (dB(A)): 18.6 for the present invention and 9.2 for the traditional solution.

[0129] Wind noise interference fluctuation (dB): ±1.8 for the present invention and ±5.3 for the traditional solution.

[0130] System response delay (ms): 18 for the present invention and 95 for the traditional solution.

[0131] The technical solution of the present application provides a bilateral actuator decoupling optimization algorithm, which introduces a distributed optimization model with coupling matrix constraints;

[0132] The objective function constructed by this method is:

[0133]

[0134] Furthermore, the technical solution of the present application uses an online update mechanism of the sound field transfer function (cycle ≤ 5 minutes) to establish a sound speed correction model that integrates temperature, humidity and wind speed data.

[0135] The dynamic correction formula of the sound speed is: c=331.4+0.6T+0.0124h (T: temperature, h: humidity). Optionally, a recursive least squares (RLS) transfer function update algorithm is used (forgetting factor λ=0.98).

[0136] In an optional embodiment, the coupling matrix C is generally obtained by offline measurement and modeling, and the specific steps are as follows:

[0137] 1. System calibration: A series of tests are conducted on the actual deployed sound barrier system, including precisely controlling the actuators (speakers) at different locations to generate sound waves of different frequencies.

[0138] 2. Response measurement: A high-precision microphone array is used to measure the sound pressure response at various points on both sides of the sound barrier. These measurements reflect how the sound waves emitted from the actuator on one side affect the sound pressure on the other side.

[0139] 3. Data collection and analysis: Collect all measurement data and perform frequency domain transformation on them to obtain response data at each frequency. This data can be a complex transfer function that contains phase and amplitude information.

[0140] 4. Construct the coupling matrix: Organize the frequency domain response data into the form of a coupling matrix C. The coupling matrix is usually a four-dimensional matrix, where two dimensions correspond to the position and frequency of the actuator, and the other two dimensions correspond to the position and frequency of the error microphones on both sides of the sound barrier.

[0141] According to another aspect of an embodiment of the present invention, there is further provided a road sound barrier system, comprising:

[0142] A sound and noise collection module is used to collect sound source noise;

[0143] a calculation module, the calculation module including an anti-wind noise unit, the anti-wind noise unit being used to separate a wind noise component based on the sound source noise; an adaptive algorithm unit, the adaptive algorithm unit including an adaptive filter, the adaptive algorithm unit being used to generate a cancellation signal based on the sound source noise; and a decoupling optimizer unit being used to decouple actuator control signals on both sides;

[0144] The actuator is configured to receive the decoupled actuator control signal and generate a reverse acoustic wave based on the decoupled actuator control signal.

[0145] like Figure 5Shows the module diagram of the road sound barrier system (system overall architecture diagram), Figure 6 Shows a schematic diagram of the structure of the sound barrier structure, wherein, Figure 5 and Figure 6 As shown, the road sound barrier system includes: 1. The noise source (vehicle) emits sound waves, which are transmitted through the air to the sound barriers on both sides. 2. Sensor array: Reference microphone array (outside): arranged on the outside of the sound barrier, equipped with a windshield and waveguide structure to collect the original noise signal. Error microphone array (inside): distributed in a ring shape to monitor the residual noise in the protected area. Environmental sensor: Anemometer, temperature and humidity sensors are embedded in the top of the barrier. 3. Actuator array: 32 sets of actuators (speaker + piezoelectric ceramic composite unit) are embedded on the surface of each side of the sound barrier, covering the frequency band of 50-2000Hz. 4. Central controller: includes multi-core DSP (processing sound field reconstruction and adaptive algorithm), FPGA (real-time beamforming and delay compensation) and data bus to connect each module, supporting closed-loop control with a delay of ≤1ms. Figure 8 The noise reduction cavity is shown.

[0146] Furthermore, the road sound barrier system includes: a sound barrier structure, which is arranged on both sides of the target road, and is provided with a wind speed sensor, an actuator array, an error microphone array and a reference microphone array. The reference microphone array is used to collect sound source noise, and the error microphone array is used to collect residual noise signals. The error microphone array and the reference microphone array are respectively arranged on both sides of the sound barrier structure in the thickness direction.

[0147] Furthermore, the sound barrier structure is also provided with a gradient impedance structure, which includes a micro-perforated plate layer, a gradient density sound-absorbing cotton layer and a Helmholtz resonance cavity layer arranged along the thickness direction, wherein the gradient density sound-absorbing cotton layer is located between the micro-perforated plate layer and the Helmholtz resonance cavity layer, and the gradient density sound-absorbing cotton layer is arranged with increasing density along the thickness direction of the sound barrier structure.

[0148] like Figure 7 A cross-sectional view of a gradient impedance sound barrier structure is shown.

[0149] The surface layer (micro-perforated plate layer) is made of aluminum alloy with a thickness of 1mm, a pore diameter of 0.2mm, and a perforation rate of 28%. The surface layer absorbs high-frequency noise (>500Hz) and has a reflectivity of <15% @1kHz.

[0150] The density of the middle layer (gradient density sound absorbing cotton layer) is 80kg / m 3 To the bottom layer 200kg / m 3 Gradient, 80mm thick, used to improve low-frequency absorption rate (absorption coefficient>0.6@200Hz) through impedance gradient matching.

[0151] The bottom layer (Helmholtz resonant cavity layer) has a cavity size of 50mm in diameter, a neck length of 10mm, and a resonant frequency of 150Hz. It is used to selectively absorb the low-frequency peak (100-300Hz) of tire noise.

[0152] The inclination angle of the guide airfoil is 15°, and the surface curvature radius R=50mm, reducing the drag coefficient to 0.3.

[0153] Furthermore, the sound barrier structure includes a guide airfoil portion, the inclination angle of the guide airfoil portion is A, 20°≥A≥10°, and the surface curvature radius of the guide airfoil portion is R, 60mm≥R≥40mm.

[0154] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention when running.

[0155] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0156] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0157] In the above embodiments of the present invention, 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.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0159] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0160] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0162] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for reducing traffic noise, characterized in that: include: Obtain speed information of target vehicles on target roads; A target control instruction set is generated based on the speed information, and the target control instruction set is used to control the noise reduction mode of the road sound barrier system and adjust the working parameters of the adaptive filter, wherein the noise reduction mode includes at least one of the following: engine order noise tracking mode, tire noise broadband suppression mode, and Doppler effect compensation mode, and the working parameters include at least one of the following: filter step size and filter length.

2. The noise reduction method according to claim 1, wherein: Generating a target control instruction set based on the speed information includes: determining an instantaneous speed of the target vehicle based on the speed information; In response to the instantaneous speed being less than or equal to the first speed, a first target control instruction in the target control instruction set is generated, wherein the first target control instruction is used to control the road sound barrier system to adopt an engine order noise tracking mode, control the filter step size to be the first step size, and control the filter length to be the first length, and the first step size is determined by the instantaneous speed.

3. The noise reduction method according to claim 2, characterized in that: Generating a target control instruction set based on the speed information further includes: In response to the instantaneous speed being greater than or equal to the second speed, a second target control instruction in the target control instruction set is generated, wherein the second target control instruction is used to control the road sound barrier system to adopt a Doppler effect compensation mode, control the filter step size to be a second step size, and control the filter length to be a second length, the second step size is determined by the instantaneous speed, the second step size is smaller than the first step size, the second length is greater than the first length, and the second speed is greater than the first speed.

4. The noise reduction method according to claim 3, wherein: Generating a target control instruction set based on the speed information further includes: In response to the instantaneous speed being greater than the first speed and less than the second speed, a third target control instruction in the target control instruction set is generated, wherein the third target control instruction is used to control the road sound barrier system to adopt a tire noise broadband suppression mode, control the filter step size to be a third step size, and control the filter length to be a third length, the third step size is determined by the instantaneous speed, the third step size is smaller than the first step size and larger than the second step size, and the third length is larger than the first length and smaller than the second length.

5. The noise reduction method according to claim 1, wherein: Obtain speed information of target vehicles on target roads, including: Acquiring first speed information, where the first speed information is obtained by measuring the speed of the target vehicle through a radar; Acquiring second speed information, where the second speed information is obtained by performing sound source positioning on the target vehicle; The speed information is determined based on the first speed information and the second speed information.

6. The noise reduction method according to claim 1, wherein: After generating a target control instruction set based on the speed information, the noise reduction method further includes: Acquiring sound source noise and wind speed information, wherein the sound source noise is collected by a reference microphone, and the wind speed information is collected by a wind speed sensor at points within the target road; generating a cancellation signal based on the sound source noise and the wind speed information; determining an actuator drive signal based on the cancellation signal; The actuator is controlled to output a reverse sound wave based on the actuator driving signal, and the reverse sound wave is used to offset at least part of the sound source noise.

7. The noise reduction method according to claim 6, characterized in that: Generating a cancellation signal based on the sound source noise and the wind speed information, comprising: determining wind noise power spectrum information based on the sound source noise and the wind speed information, wherein the wind noise power spectrum information is used to determine the power and energy distribution of the wind noise at each frequency; determining traffic noise power spectrum information based on the sound source noise and the wind speed information, wherein the traffic noise power spectrum information is used to determine energy distribution of noise generated by a vehicle at different frequencies; determining a filtering signal based on the wind noise power spectrum information and the traffic noise power spectrum information; Performing blind source separation on the filtered signal to obtain a wind noise component and a traffic noise component; The wind noise component is input into the adaptive filter to generate the cancellation signal.

8. The noise reduction method according to claim 6, wherein: The actuators are bilateral actuators distributed on both sides of the target road, and the actuators are controlled to output reverse sound waves based on the actuator drive signal, including: determining a left actuator control signal and a right actuator control signal based on the actuator drive signal; Obtaining a coupling matrix, where the coupling matrix is obtained by actually measuring the road sound barrier system, and the coupling matrix is used to characterize the mutual coupling relationship between the left actuator control signal and the right actuator control signal and the system response characteristics; determining an optimization objective and constraint conditions based on the left actuator control signal, the right actuator control signal, and the coupling matrix; The optimization objective is solved using a gradient descent algorithm to obtain a decoupled left actuator control signal and a decoupled right actuator control signal.

9. A road sound barrier system, characterized in that: The road sound barrier system is used to perform the method according to any one of claims 1 to 8, comprising: A sound and noise collection module, the sound and noise collection module is used to collect sound source noise; a calculation module, the calculation module including an anti-wind noise unit, the anti-wind noise unit being configured to separate a wind noise component based on the sound source noise, the calculation module including an adaptive algorithm unit, the adaptive algorithm unit including an adaptive filter, the adaptive algorithm unit being configured to generate a cancellation signal based on the sound source noise, and the calculation module including a decoupling optimizer unit, the decoupling optimizer unit being configured to decouple actuator control signals on both sides; An actuator is configured to receive the decoupled actuator control signal and generate a reverse acoustic wave based on the decoupled actuator control signal.

10. The road sound barrier system according to claim 9, characterized in that: The road sound barrier system comprises: A sound barrier structure, wherein the sound barrier structure is arranged on both sides of the target road, and a wind speed sensor, an actuator array, an error microphone array and a reference microphone array are provided on the sound barrier structure. The reference microphone array is used to collect sound source noise, and the error microphone array is used to collect residual noise signals. The error microphone array and the reference microphone array are respectively arranged on both sides of the sound barrier structure in the thickness direction.

11. The road sound barrier system according to claim 10, characterized in that: The sound barrier structure is also provided with a gradient impedance structure, which includes a micro-perforated plate layer, a gradient density sound-absorbing cotton layer and a Helmholtz resonance cavity layer arranged along the thickness direction, wherein the gradient density sound-absorbing cotton layer is located between the micro-perforated plate layer and the Helmholtz resonance cavity layer, and the gradient density sound-absorbing cotton layer is arranged with increasing density along the thickness direction of the sound barrier structure.

12. The road sound barrier system according to claim 11, characterized in that: The sound barrier structure includes a guide airfoil portion, and the surface curvature radius of the guide airfoil portion is R, 60mm≥R≥40mm.

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