A vibration noise intelligent control method and system using spectrum characteristics

Abstract

The present invention relates to the technical field related to vibration and noise control, and specifically includes a method and system for intelligent vibration and noise control using spectral characteristics. The method comprises: collecting rail transit vibration and noise signals, extracting time-frequency domain mixed features and separating multiple sources, generating a noise source contribution matrix, configuring a strategy set, monitoring changes to generate feedback, and adjusting active dampers for noise reduction in real time. This solves the technical problem that vibration and noise control has difficulty adapting to dynamically changing vibration and noise environments, resulting in limited noise suppression effects and an inability to accurately match the noise reduction requirements of complex time-varying scenarios. The system achieves the technical effect of extracting time-frequency domain mixed features, more accurately identifying and separating overlapping noise sources, comprehensively considering the complex characteristics of vibration and noise, dynamically adjusting active dampers, and thus adapting to changes in vibration and noise, thereby improving the accuracy and effectiveness of dynamic adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field related to vibration noise control, and in particular to a vibration noise intelligent control method and system using spectral characteristics. Background Art

[0002] With the acceleration of urbanization and the increasing development of transportation networks, the rail transit system, as an important part of urban public transportation, has become increasingly prominent in terms of vibration and noise problems caused by its operation. These vibrations and noise not only affect the quality of life of surrounding residents, but also cause damage to surrounding buildings and infrastructure.

[0003] At present, there are various methods for controlling rail transit vibration and noise, but most of them have limitations. Conventional passive control methods, such as installing soundproof walls and vibration-damping pads, can reduce noise and vibration to a certain extent, but they are still difficult to adapt to the complex and changeable traffic environment and noise source characteristics. Vibration and noise control mostly relies on a single spectral feature or time domain feature, which makes it difficult to fully and accurately describe the complex characteristics of vibration and noise, and the control effect is limited.

[0004] In summary, the existing technology has the technical problem that vibration noise control is difficult to adapt to the dynamically changing vibration noise environment, resulting in limited noise suppression effect and inability to accurately match the noise reduction requirements of complex time-varying scenes. Summary of the Invention

[0005] This application provides an intelligent vibration noise control system that utilizes spectral characteristics, aiming to solve the technical problem that vibration noise control in the existing technology is difficult to adapt to the dynamically changing vibration noise environment, resulting in limited noise suppression effect and inability to accurately match the noise reduction requirements of complex time-varying scenarios.

[0006] In view of the above problems, the technical solution to implement this application is:

[0007] On the one hand, the present application provides an intelligent vibration noise control method using spectral characteristics, wherein the method includes: collecting vibration noise signals of rail transit and extracting time-frequency domain mixing characteristics; performing multi-source separation on the vibration noise signals to generate a noise source contribution matrix, wherein the noise source contribution matrix includes wheel-rail contact noise contribution value, aerodynamic noise contribution value and environmental reflection superposition coefficient; configuring an adaptive control strategy set including active damper frequency band adjustment parameters, sound absorption material layout optimization scheme, vibration isolator stiffness dynamic threshold and phase compensation strategy based on the time-frequency domain mixing characteristics and the noise source contribution matrix; at the same time, monitoring the noise synchronous attenuation rate and the vibration transfer function change to generate a dynamic feedback signal; based on the adaptive control strategy set and in combination with the dynamic feedback signal, dynamically adjusting the active damper, wherein the active damper is used to actively suppress vibration noise.

[0008] On the other hand, the present application provides an intelligent vibration noise control system using spectral characteristics, wherein the system includes: a signal acquisition module for collecting vibration noise signals of rail transit and extracting time-frequency domain mixing characteristics; a multi-source separation module for performing multi-source separation on the vibration noise signals and generating a noise source contribution matrix, wherein the noise source contribution matrix includes wheel-rail contact noise contribution value, aerodynamic noise contribution value and environmental reflection superposition coefficient; an adaptive configuration module for configuring an adaptive control strategy set including active damper frequency band adjustment parameters, sound absorption material layout optimization scheme, vibration isolator stiffness dynamic threshold and phase compensation strategy based on the time-frequency domain mixing characteristics and the noise source contribution matrix; a monitoring module for simultaneously monitoring the noise synchronous attenuation rate and the vibration transfer function change to generate a dynamic feedback signal; and a dynamic adjustment module for dynamically adjusting the active damper based on the adaptive control strategy set and in combination with the dynamic feedback signal, wherein the active damper is used to suppress vibration noise.

[0009] In summary, the one or more technical solutions provided in this application extract mixed features in the time-frequency domain, more accurately identify and separate overlapping noise sources, comprehensively consider the complex characteristics of vibration and noise, dynamically adjust the active damper, and thus adapt to changes in vibration and noise, thereby improving the accuracy and effectiveness of dynamic adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This application provides a flow chart of a vibration noise intelligent control method using spectral characteristics;

[0011] Figure 2 The present application provides a structural schematic diagram of a vibration noise intelligent control system utilizing spectral characteristics.

[0012] Description of reference numerals: signal acquisition module M100, multi-source separation module M200, adaptive configuration module M300, monitoring module M400, dynamic adjustment module M500. DETAILED DESCRIPTION

[0013] Example 1

[0014] The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present application provides a vibration noise intelligent control method using spectral characteristics, wherein the method includes:

[0015] S1: Collect vibration and noise signals from rail transit and extract mixed features in the time and frequency domains. S2: Perform multi-source separation on the vibration and noise signals to generate a noise source contribution matrix, which includes the wheel-rail contact noise contribution value, aerodynamic noise contribution value, and environmental reflection superposition coefficient.

[0016] Specifically, collecting vibration and noise signals refers to acquiring real-time vibration and noise data generated by rail transit through sensors (such as accelerometers and microphones). Hybrid time-frequency features refer to characteristic parameters extracted simultaneously in the time and frequency domains to comprehensively describe the complex characteristics of vibration and noise. Time-domain features include transient impact peaks and background noise baselines; frequency-domain features include spectral energy distribution and harmonic distortion rate. Feature extraction involves extracting key characteristic parameters from the raw signal using signal processing algorithms (such as Fourier transform and wavelet transform).

[0017] During the acquisition phase, high-precision sensors (such as triaxial accelerometers and array microphones) are used to acquire vibration and noise signals in real time. The signal acquisition frequency is typically set to 10kHz to 20kHz to cover the common vibration and noise frequency bands in rail transit (such as vehicle engine vibration from 100Hz to 500Hz and wheel-rail contact noise from 500Hz to 2kHz). Furthermore, when extracting mixed time-frequency domain features, the short-time Fourier transform (STFT) or continuous wavelet transform (CWT) is used to analyze the signal. For example, the spectral energy distribution is obtained by calculating the power spectral density (PSD) of the signal, the harmonic distortion rate is quantified by calculating the total harmonic distortion (THD), the transient impact peak is extracted by detecting the mutation point of the signal envelope, and the background noise baseline is estimated by the average energy in the low-frequency band (0Hz to 50Hz). Through these steps, the dynamic characteristics of the vibration and noise are fully described, providing a data foundation for subsequent noise source isolation and control strategies.

[0018] Multi-source separation involves decomposing mixed vibration noise signals into multiple independent noise sources using signal processing algorithms (such as independent component analysis (ICA) and blind source separation (BSS). A noise source contribution matrix represents the proportion of each noise source to the total noise. Parameters in the matrix include wheel-rail contact noise contribution, aerodynamic noise contribution, and environmental reflection superposition coefficient. The contribution value represents the proportion or intensity of a noise source in the total noise, typically calculated using energy or power.

[0019] Multi-source separation uses the Independent Component Analysis (ICA) algorithm to decompose the collected vibration noise signal into multiple independent noise sources. Furthermore, in rail transit scenarios, the wheel-rail contact noise contribution is calculated based on the energy share in the mid-frequency band (500Hz to 2kHz), the aerodynamic noise contribution is calculated based on the energy share in the high-frequency band (2kHz to 10kHz), and the environmental reflection superposition coefficient is estimated by analyzing the signal coherence and the phase delay of the reflection path. The noise source contribution matrix is ​​generated by: normalizing the energy of each noise source's characteristic frequency band; calculating the energy share of each noise source to form a matrix; and introducing the environmental reflection superposition coefficient to correct for the superposition effect of the reflection path on the noise. By generating the noise source contribution matrix in these steps, the primary noise sources and their contribution ratios can be accurately identified, providing a quantitative basis for subsequent adaptive control strategies.

[0020] S3: Based on the time-frequency domain mixed characteristics and the noise source contribution matrix, an adaptive control strategy set is configured, including the frequency band adjustment parameters of the active damper, the sound-absorbing material layout optimization scheme, the vibration isolator stiffness dynamic threshold and the phase compensation strategy; S4: At the same time, the noise synchronous attenuation rate and the change in the vibration transfer function are monitored to generate a dynamic feedback signal; S5: Based on the adaptive control strategy set and combined with the dynamic feedback signal, the active damper is dynamically adjusted, and the active damper is used to actively suppress vibration noise.

[0021] Specifically, the frequency band adjustment parameters of the active damper refer to the adjustment parameters of the active damper within a specific frequency band, which are used to suppress vibration noise within a specific frequency range; the sound-absorbing material layout optimization plan refers to the layout plan of the sound-absorbing material in space, which improves the sound absorption effect by optimizing the layout; the dynamic threshold of the vibration isolator stiffness refers to the stiffness adjustment range of the vibration isolator in a dynamic environment, which is used to adapt to different vibration loads; the phase compensation strategy refers to compensating for the propagation delay of vibration noise through phase adjustment to ensure that the control signal is synchronized with the noise signal; the adaptive control strategy set refers to a set of dynamically adjusted control strategies that can be optimized in real time according to noise characteristics and environmental changes.

[0022] Based on the spectral energy distribution and noise source contribution matrix, the frequency band to be suppressed is determined. Specifically, if the wheel-rail contact noise contribution is high (e.g., 45%), the active damper's suppression strength in the 500Hz to 2kHz frequency band is prioritized. Combined with the noise source spatial distribution heat map, the placement of sound-absorbing materials is optimized, increasing their coverage density in areas with high noise source contributions (e.g., wheel-rail contact noise). The stiffness of the vibration isolator is dynamically adjusted based on the change in the vibration transfer function. For example, when the change in the vibration transfer function exceeds 5%, the dynamic stiffness adjustment mechanism is triggered, adjusting the stiffness threshold from 10kN / m to 15kN / m. Phase compensation parameters are adjusted in real time by monitoring the synchronous noise attenuation rate and the change in the vibration transfer function. For example, if the phase delay exceeds 1ms, the phase compensation value is adjusted to ensure synchronization between the control signal and the noise signal. Through these steps, an adaptive control strategy set is configured to dynamically adapt to the characteristics of different noise sources, improving the flexibility and adaptability of the control system.

[0023] The noise synchronous attenuation rate refers to the attenuation consistency of noise under different paths or different control strategies; the change in the vibration transfer function refers to the change in the transfer characteristics of the vibration signal in the propagation path, usually expressed by the frequency response function; the dynamic feedback signal refers to the real-time monitored noise and vibration change data, which is used to dynamically adjust the control strategy.

[0024] Multi-point sound pressure level sensors are used to monitor noise attenuation in real time, and the consistency of attenuation rates under different paths is calculated. Acceleration sensors and transfer function analysis tools are used to monitor changes in vibration transfer functions in real time. The role of the dynamic feedback signal is to perceive changes in noise and vibration in real time, providing an adjustment basis for the adaptive control strategy. For example, if the noise attenuation rate of a certain section of rail transit is lower than 10% of the overall average, the first dynamic feedback signal is triggered. At the same time, if the amplitude change of the transfer function exceeds 10% or the phase change exceeds 5°, a second dynamic feedback signal is generated, which then triggers the optimization of the frequency band adjustment parameters of the active damper to ensure the noise reduction effect.

[0025] Active vibration noise suppression refers to the real-time suppression of vibration noise through active control means (such as active dampers); adjusting the frequency band adjustment parameters and phase compensation strategy of the active damper in real time according to the dynamic feedback signal; optimizing the layout of sound-absorbing materials and the stiffness of the vibration isolator through the dynamic feedback signal; if the noise attenuation effect of a certain frequency band is detected to be reduced, the suppression strength of this frequency band is increased; if the vibration transfer function of a certain area is detected to have a large change, the dynamic threshold of the stiffness of the vibration isolator is adjusted to ensure the isolation effect; this dynamic adjustment mechanism can significantly improve the suppression effect of the active damper.

[0026] Furthermore, the method further comprises:

[0027] The time-frequency domain mixing features include spectral energy distribution, harmonic distortion rate, transient impulse peak and background noise baseline; based on the time-frequency domain mixing features, combined with wave direction of arrival estimation and coherence analysis, a noise source spatial distribution heat map is generated; independent components of overlapping noise sources in the noise source spatial distribution heat map are extracted, and the vibration sensor data is correlated to verify the multi-source separation results.

[0028] Specifically, spectral energy distribution refers to the energy distribution of vibration noise signals in different frequency bands, which is usually calculated by power spectral density (PSD); harmonic distortion rate refers to the ratio of harmonic components to fundamental components in the signal, which is used to quantify the degree of distortion of the signal; transient impact peak refers to the sudden high-amplitude short-term impact in the signal; background noise baseline refers to the static level of environmental noise.

[0029] The spectral energy distribution is calculated through short-time Fourier transform (STFT) or wavelet transform (WT), which can identify the frequency bands of major noise sources. The harmonic distortion rate is quantified by calculating the total harmonic distortion (THD), which can identify harmonic distortion in the signal. The transient impact peak is extracted through signal envelope detection, which can locate sudden noise events. Furthermore, in rail transit, vehicle engine vibration is mainly concentrated in the range of 100Hz to 500Hz, wheel-rail contact noise is concentrated in the range of 500Hz to 2kHz, and aerodynamic noise is concentrated in the range of 2kHz to 10kHz. If the THD exceeds 5%, it indicates that there is significant harmonic distortion in the signal, which may be caused by mechanical vibration or electrical interference.

[0030] It has been verified that in rail transit, the transient impact peak of wheel-rail contact typically occurs when the train passes through the track joint, and its amplitude can reach more than 10 times that of the background noise. Furthermore, the background noise baseline can be used to assess the static level of environmental noise by estimating the average energy in the low-frequency band (0Hz to 50Hz). For example, in urban rail transit, the background noise baseline is typically between 30dB and 40dB. The spectral energy distribution, harmonic distortion rate, transient impact peak, and background noise baseline in the time-frequency domain hybrid features are used to comprehensively describe the dynamic characteristics of vibration noise, providing a data basis for subsequent noise source isolation and control strategies.

[0031] Direction of Arrival (DOA) estimation uses array signal processing technology to estimate the direction of a noise source. Coherence analysis evaluates the correlation between different noise sources by calculating the coherence between signals. A noise source spatial distribution heatmap displays the spatial distribution of noise sources in the form of a heatmap. Direction of Arrival estimation uses high-resolution algorithms based on array microphones (such as the MUSIC algorithm) to accurately estimate the direction of the noise source. Coherence analysis evaluates the correlation between noise sources by calculating the coherence matrix between different sensors. A noise source spatial distribution heatmap visualizes the results of direction of arrival estimation and coherence analysis to intuitively display the spatial distribution of noise sources. Furthermore, if the coherence coefficient of two noise sources exceeds 0.7, it indicates that the two noise sources come from the same source or are coupled. Correspondingly, the heatmap shows that wheel-rail contact noise is mainly concentrated on both sides of the track, while aerodynamic noise is distributed in front of the train in the direction of travel. The noise source spatial distribution heatmap is used to identify the spatial distribution characteristics of noise sources, providing a basis for subsequent optimization of sound absorption material layout and placement of vibration isolators.

[0032] Independent component extraction (ICA) decomposes mixed signals into independent noise sources. Vibration sensor data association correlates vibration sensor data with noise source separation results to verify the accuracy of the separation. Independent component extraction uses the Fast Independent Component Analysis (FastICA) algorithm to separate overlapping noise sources into independent components. Vibration sensor data association verifies the accuracy of multi-source separation results by comparing vibration sensor data with separated noise sources. For example, if a separated noise source is highly consistent with the wheel-rail contact vibration characteristics recorded by the vibration sensor, the separation result is reliable. This verification mechanism ensures the accuracy of multi-source separation results and provides reliable data support for subsequent adaptive control strategies.

[0033] Furthermore, the active damper is dynamically adjusted, and the method includes:

[0034] The resonance frequency band is predicted using the spectrum energy distribution and harmonic distortion rate in the time-frequency domain hybrid feature to determine the target suppression frequency band of the active damper; and the frequency band adjustment parameters of the active damper are determined based on the target suppression frequency band.

[0035] Specifically, spectral energy distribution refers to the energy distribution of vibration noise signals in different frequency bands, which is usually calculated by power spectral density (PSD); harmonic distortion rate refers to the ratio of harmonic components to fundamental components in the signal, which is used to quantify the degree of distortion of the signal, and is usually calculated by total harmonic distortion (THD); resonance frequency band prediction is to predict the frequency band where resonance may occur in vibration noise by analyzing spectral energy distribution and harmonic distortion rate; target suppression frequency band refers to the frequency band that the active damper needs to focus on suppressing, which is usually the resonance frequency band or the frequency band with a high noise contribution value.

[0036] The spectrum energy distribution is calculated through short-time Fourier transform (STFT) or wavelet transform (WT), which can identify the frequency band of the main noise source; the harmonic distortion rate is quantified by calculating the total harmonic distortion (THD), which can identify the harmonic distortion in the signal; the resonance frequency band prediction identifies the frequency band with concentrated energy and high harmonic distortion rate by analyzing the spectrum energy distribution and harmonic distortion rate; after determining the target suppression frequency band, the active damper will focus on suppressing the vibration noise in this frequency band to improve the control effect.

[0037] Frequency band adjustment parameters refer to the adjustment parameters of the active damper within a specific frequency band, including gain, phase, bandwidth, etc., which are used to optimize the suppression effect. An active damper is a device that suppresses vibration noise through active control means and can dynamically adjust the damping characteristics according to the control signal. The gain parameters of the active damper are set according to the energy level of the target suppression frequency band; the phase compensation value of the active damper is adjusted according to the phase characteristics of the vibration noise to ensure synchronization between the control signal and the noise signal; and the bandwidth parameters of the active damper are adjusted according to the width of the target suppression frequency band. By optimizing the frequency band adjustment parameters, the active damper can more accurately suppress the vibration noise in the target frequency band.

[0038] Furthermore, the method comprises:

[0039] The transient impact peak and background noise baseline in the time-frequency domain hybrid feature are used to determine the dynamic adaptation range of the porosity of the sound absorbing material; and the sound absorbing material layout optimization scheme is adjusted according to the dynamic adaptation range of the porosity of the sound absorbing material.

[0040] Specifically, the transient impact peak refers to a sudden high-amplitude short-term impact in the vibration noise signal, which is usually extracted through signal envelope detection; the background noise baseline refers to the static level of ambient noise; the porosity dynamic adaptation range refers to the adjustable range of the porosity of the sound-absorbing material under different noise environments, which is used to optimize the sound absorption effect; the transient impact peak is extracted through signal envelope detection and can identify sudden noise events; the background noise baseline is estimated by the average energy in the low-frequency band (0Hz to 50Hz) to evaluate the static level of ambient noise; the porosity dynamic adaptation range is determined by analyzing the transient impact peak and the background noise baseline. For example, if the transient impact peak is high (such as exceeding 15dB of the background noise baseline), the porosity of the sound-absorbing material needs to be increased to improve the sound absorption effect. By adjusting the porosity, the sound-absorbing material can more effectively absorb noise of different frequencies and intensities.

[0041] The sound-absorbing material layout optimization plan refers to the arrangement plan of the sound-absorbing material in space, which improves the sound absorption effect by optimizing the layout; the porosity dynamic adaptation range refers to the adjustable range of the porosity of the sound-absorbing material under different noise environments, which is used to optimize the sound absorption effect.

[0042] The distribution of noise sources can be identified through the heat map of the spatial distribution of noise sources. The optimal layout of the sound-absorbing materials can be determined based on the distribution of noise sources and the dynamic adaptation range of the porosity. The layout of the sound-absorbing materials can be optimized through simulation and experimental verification. In the rail transit scenario, the heat map shows that wheel-rail contact noise is mainly concentrated on both sides of the track, while aerodynamic noise is distributed in front of the train's running direction. In areas with higher wheel-rail contact noise, the coverage density of the sound-absorbing material is increased, and the porosity is adjusted to a higher value (such as 30% to 40%). By adjusting the layout position and porosity of the sound-absorbing material, the sound pressure level attenuation is increased, the sound absorption effect is significantly improved, and the impact of environmental noise on surrounding residents and infrastructure is reduced.

[0043] Furthermore, the method comprises:

[0044] Noise control effect evaluation indicators are set, and the noise control effect evaluation indicators include sound pressure level attenuation, vibration acceleration reduction rate and strategy execution energy consumption index; at the same time, parameter optimization is performed on the adaptive control strategy set to balance the noise reduction effect and equipment life loss, and the triggering conditions of the phase compensation strategy are dynamically adjusted according to historical control records.

[0045] Specifically, the sound pressure level attenuation refers to the difference in sound pressure levels before and after noise control, usually expressed in decibels (dB); the vibration acceleration reduction rate refers to the reduction ratio of vibration acceleration before and after vibration control, usually expressed as a percentage; the strategy execution energy consumption index refers to the energy consumed during the execution of the noise control strategy, usually expressed in watt-hours (Wh).

[0046] The sound pressure level attenuation is calculated by comparing the sound pressure levels before and after control. If the sound pressure level before control is 80dB and the sound pressure level after control is 65dB, the sound pressure level attenuation is 15dB. The vibration acceleration reduction rate is calculated by comparing the peak values ​​of vibration acceleration before and after control. If the peak value of vibration acceleration before control is 0.5m / s² and the peak value after control is 0.2m / s², the vibration acceleration reduction rate is 60%. The strategy execution energy consumption index is obtained by monitoring the cumulative energy consumption of the active damper and other equipment.

[0047] Optimization refers to the process of finding the optimal parameters in the control strategy through optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.); equipment life loss refers to the reduction in equipment service life due to wear, aging and other factors during operation; the triggering condition of the phase compensation strategy refers to the conditions for starting the phase compensation mechanism, which is usually based on real-time monitoring data of noise and vibration; parameter optimization is carried out through genetic algorithms (GA) or particle swarm optimization (PSO) algorithms, and the gain, phase and bandwidth parameters of the active damper are optimized through genetic algorithms to maximize the sound pressure level attenuation while minimizing equipment life loss; in the above steps, the noise control effect is fully quantified, and the adaptability and efficiency of vibration noise control can be significantly improved according to the dynamic adjustment mechanism.

[0048] Furthermore, the resonance frequency band is predicted using the spectrum energy distribution and harmonic distortion rate in the time-frequency domain hybrid feature, and the method further includes:

[0049] Based on the non-stationary characteristics of the spectrum of the vibration noise signal, a time-varying transfer function is set, and the time-varying transfer function is used to correct the phase delay parameter of the noise propagation path; wavelet packet decomposition is used to separate the broadband random component and the narrowband impact component in the vibration noise signal to generate a frequency band priority weight coefficient; based on the time-varying transfer function and combined with the frequency band priority weight coefficient, the center frequency tracking rate of the adaptive notch filter is adjusted to suppress the secondary reflection of periodic noise.

[0050] Specifically, the spectral non-stationary characteristic refers to the characteristic that the spectral characteristics of the vibration noise signal change over time; the time-varying transfer function refers to the transfer function that changes over time and is used to describe the dynamic characteristics of the noise propagation path; the phase delay parameter refers to the phase delay caused by the path difference during the noise propagation process.

[0051] The non-stationary characteristics of the spectrum are obtained through short-time Fourier transform (STFT) or wavelet transform (WT) analysis. Changes in train speed will cause non-stationary characteristics of the noise spectrum. The time-varying transfer function is updated in real time through recursive least squares (RLS) or adaptive filtering algorithm. If the train speed increases from 60km / h to 100km / h, the amplitude of the time-varying transfer function increases accordingly, thereby increasing the phase delay. The phase delay parameter is calculated by measuring the phase difference of the noise signal. If the phase difference of the noise signal between the two sensors is 10°, the phase delay parameter is 10°. By setting the time-varying transfer function, the phase delay parameter of the noise propagation path can be dynamically corrected to improve the accuracy of noise control. By correcting the phase delay parameter, the sound pressure level attenuation is increased from 20dB to 25dB.

[0052] Wavelet packet decomposition can decompose the signal into sub-bands of different frequency bands. Broadband random components refer to random noise components with a wide frequency distribution in the signal. Narrowband impact components refer to impact noise components with concentrated frequency and high amplitude in the signal. The frequency band priority weight coefficient refers to the weight assigned according to the noise contribution of different frequency bands, which is used to optimize the control strategy. Wavelet packet decomposition decomposes the vibration noise signal into sub-bands of multiple frequency bands through multi-resolution analysis. Using Daubechies wavelet to perform 3-layer decomposition of the signal, 8 sub-bands of frequency bands can be obtained. Broadband random components and narrowband impact components are the same. The sub-bands are separated by analyzing the energy distribution and amplitude characteristics of each sub-band. If the energy distribution of a sub-band is relatively uniform and the amplitude is low, it is determined to be a broadband random component; if the energy of a sub-band is concentrated and the amplitude is high, it is determined to be a narrowband impact component; the frequency band priority weight coefficient is calculated based on the energy proportion and noise contribution of each sub-band. If the energy proportion of a frequency band is 30% and the noise contribution is high, a higher weight (such as 0.4) is assigned to it; by separating the broadband random component and the narrowband impact component and generating the frequency band priority weight coefficient, the control strategy can be optimized more accurately.

[0053] The adaptive notch filter can dynamically adjust its center frequency to suppress noise in a specific frequency band. The center frequency tracking rate refers to the speed at which the center frequency of the adaptive notch filter is adjusted. Secondary reflections of periodic noise refer to secondary interference caused by reflections of periodic noise in the propagation path. Furthermore, the adaptive notch filter dynamically adjusts its center frequency using a minimum mean square error (LMS) or recursive least squares (RLS) algorithm. If the center frequency of the periodic noise is detected to be 300 Hz, the center frequency of the adaptive notch filter will track and lock at 300 Hz. The center frequency tracking rate is adjusted based on the frequency band priority weight coefficient and the time-varying transfer function. If the weight coefficient of a frequency band is 0.4 and the time-varying transfer function shows an increase in phase delay, the center frequency tracking rate is increased from 50 Hz / s to 100 Hz / s. By adjusting the center frequency tracking rate, secondary reflections of periodic noise can be effectively suppressed, improving the noise control effect.

[0054] Furthermore, the method comprises:

[0055] A cluster analysis is performed on the time domain correlation of multi-source noise under the noise propagation path positioning to identify the coupling relationship between the dominant noise source and the secondary noise source; based on the coupling relationship between the dominant noise source and the secondary noise source, a collaborative control logic chain is generated to synchronously adjust the action timing of the active damper and the vibration isolator.

[0056] Specifically, noise propagation path positioning refers to determining the specific path of noise propagation through signal processing technology; time domain correlation refers to the correlation of noise signals in the time domain, which is used to evaluate the synchronization between different noise sources; cluster analysis is used to group similar data points; the dominant noise source refers to the noise source that contributes the most to the total noise; the secondary noise source refers to the indirect noise source caused by the dominant noise source; and the coupling relationship refers to the interaction relationship between different noise sources.

[0057] Noise propagation path positioning is achieved through direction of arrival (DOA) estimation and time difference of arrival (TDOA) technology. The location of the noise source can be accurately determined through a 16-channel microphone array and accelerometer. Time domain correlation is evaluated by calculating the cross-correlation function of the signals of different noise sources. If the cross-correlation coefficient of two noise sources exceeds 0.7, it indicates that there is a significant correlation between the two noise sources. Cluster analysis uses K-means clustering or hierarchical clustering algorithm to group multi-source noise according to correlation. Specifically, K-means clustering is used to divide the noise sources into three groups: vehicle engine vibration, wheel-rail contact noise, and aerodynamic noise. The coupling relationship between the dominant noise source and the secondary noise source is determined by analyzing the energy contribution and correlation of each noise source. Furthermore, if the energy contribution of wheel-rail contact noise is 45% and the correlation coefficient with aerodynamic noise is 0.8, the wheel-rail contact noise is the dominant noise source and the aerodynamic noise is the secondary noise source. Identifying the dominant and secondary noise sources and identifying the coupling relationship through clustering analysis can more accurately optimize the control strategy.

[0058] The collaborative control logic chain refers to the control logic generated according to the coupling relationship of the noise source, which is used to synchronously adjust multiple control devices; the action timing refers to the time sequence and delay of the control device action. Specifically, the collaborative control logic chain is generated by a state machine or Petri net to ensure that the action timing of the active damper and the vibration isolator are synchronized. When the wheel-rail contact noise is detected as the dominant noise source, a logic chain is generated: the active damper is first started to suppress the wheel-rail contact noise, and the vibration isolator is started 20ms later to block the vibration propagation; the action timing is dynamically adjusted through real-time monitoring and feedback mechanism. If it is monitored that the suppression effect of the active damper decreases, the start delay of the vibration isolator is shortened from 20ms to 10ms to ensure that the two work together; in the above steps, the noise control effect can be significantly improved through the collaborative control logic chain.

[0059] Furthermore, the method further comprises:

[0060] When it is detected that the deviation of the uniformity of the sound pressure distribution exceeds the deviation threshold, the path parameter recalibration mechanism is triggered; under the path parameter recalibration mechanism, the geographic data and meteorological parameters are integrated to establish a noise propagation path optimization model, and the multi-path reflection superposition effect compensation value is determined; based on the multi-path reflection superposition effect compensation value, the stiffness dynamic threshold fluctuation corresponding to the vibration isolator is dynamically optimized to generate a stiffness adjustment gradient that matches the terrain undulation.

[0061] Specifically, the sound pressure distribution uniformity deviation refers to the difference in sound pressure level distribution at different locations and is used to assess the uniformity of the noise field. The deviation threshold is the critical value of the sound pressure distribution uniformity deviation that triggers the calibration mechanism. The path parameter recalibration mechanism refers to the process of recalibrating the noise propagation path parameters to optimize noise control. The sound pressure distribution uniformity deviation is measured using multi-point sound pressure level sensors. Once the path parameter recalibration mechanism is triggered, the parameters of the noise propagation path will be reassessed. If the sound pressure distribution uniformity deviation is detected to exceed the threshold, the calibration mechanism will be activated to remeasure the noise source location and propagation path, thereby ensuring the accuracy and effectiveness of the noise control strategy.

[0062] Geographic data, including terrain, building distribution, and other geographic information, includes meteorological parameters, including wind speed, temperature, and humidity. Multipath reflection and superposition effect compensation refers to the value used to compensate for the noise superposition effect caused by multipath reflection. The integration of geographic data and meteorological parameters is achieved through GIS (Geographic Information System) and meteorological sensors. A noise propagation path optimization model is established through ray tracing or finite element analysis. This ray tracing model, combined with geographic data and meteorological parameters, simulates noise propagation paths and identifies reflection and superposition effects.

[0063] Preferably, geographic data such as topography and building distribution is obtained from a geographic information system (GIS). This data provides information about the physical environment in which noise propagates. Meteorological parameters such as wind speed, temperature, and humidity are obtained from meteorological sensors, as these parameters influence the speed and direction of sound wave propagation. Data fusion algorithms integrate geographic data and meteorological parameters into a unified framework, providing a comprehensive environmental description for modeling noise propagation paths. Based on this fused data, an optimized noise propagation path model is established. This model typically employs ray tracing or finite element analysis to simulate the propagation path of noise in a complex environment, taking into account multipath reflections and the superposition effect—the phenomenon in which noise reflects off different surfaces (such as buildings and the ground) and then overlaps. This simulation can identify the primary reflection paths and overlapping areas.

[0064] Select a suitable noise propagation model, such as a ray tracing model or a finite element analysis model, and connect it to the noise propagation path optimization model. Set model parameters based on the fused data, including the sound source location, propagation medium characteristics, and reflective surface characteristics. Furthermore, utilize the established noise propagation path optimization model to obtain a multipath reflection superposition effect compensation value. This multipath reflection superposition effect compensation value is used to adjust noise control strategies to offset the impact of reflection and superposition effects on noise measurement and control. Within the noise propagation path optimization model, the intensity and phase difference of the reflection paths are simulated to determine the multipath reflection superposition effect compensation value. This multipath reflection superposition effect compensation value is then applied to the dynamic optimization process for the dynamic threshold fluctuation of the vibration isolator's stiffness. By adjusting the vibration isolator's stiffness, the reflected and superposed noise is more effectively blocked. The optimized noise control effect is evaluated using metrics such as sound pressure level attenuation. If performance indicators do not meet expectations, the model parameters or compensation values ​​need to be readjusted and iterative optimization performed.

[0065] The dynamic threshold fluctuation of stiffness refers to the range of fluctuations in the stiffness of a vibration isolator over time or with environmental changes. Dynamic optimization refers to the dynamic adjustment of parameters through an optimization algorithm to achieve optimal results. The stiffness adjustment gradient refers to the rate at which the stiffness of a vibration isolator adjusts as the terrain fluctuates. The multipath reflection superposition effect compensation value is used to adjust the dynamic threshold of the stiffness of the vibration isolator. Optimization algorithms (such as genetic algorithms and particle swarm optimization) are used to dynamically adjust the stiffness parameters of the vibration isolator to achieve optimal noise control. Terrain information is obtained through geographic data. Based on the terrain fluctuation and the compensation value, a stiffness adjustment gradient is generated to match the terrain. If the terrain is highly undulating, the stiffness adjustment gradient is increased to improve isolation effectiveness. The stiffness of the vibration isolator is dynamically adjusted through real-time monitoring of sound pressure level and vibration data to ensure that the control strategy can adapt to environmental changes. The optimized noise control effect is evaluated using indicators such as sound pressure level attenuation. If it does not meet expectations, the model parameters or compensation values ​​are readjusted and iterative optimization is performed. Through multiple iterative adjustments, the vibration isolation effect can be significantly improved.

[0066] In summary, the beneficial effects of the embodiments of the present application are:

[0067] The vibration and noise signals of rail transit are collected to extract the mixed features in the time and frequency domains; the vibration and noise signals are separated from multiple sources to generate a noise source contribution matrix; an adaptive control strategy set is configured according to the mixed features in the time and frequency domains and the noise source contribution matrix; at the same time, the noise synchronous attenuation rate and the change in the vibration transfer function are monitored to generate a dynamic feedback signal; based on the adaptive control strategy set and in combination with the dynamic feedback signal, the active damper is dynamically adjusted, and the active damper is used to suppress vibration noise. The present application provides a vibration and noise intelligent control method and system using spectral characteristics, extracts mixed features in the time and frequency domains, more accurately identifies and separates overlapping noise sources, comprehensively considers the complex characteristics of vibration and noise, dynamically adjusts the active damper, and thus adapts to changes in vibration and noise, thereby improving the accuracy and effectiveness of dynamic adjustment.

[0068] Example 2

[0069] Based on the same inventive concept as the vibration noise intelligent control method using spectrum characteristics in the above embodiment, Figure 2 As shown, an embodiment of the present application provides an intelligent vibration noise control system using spectral characteristics, wherein the system includes:

[0070] The signal acquisition module M100 is used to collect vibration and noise signals of rail transit and extract time-frequency domain mixed features.

[0071] The multi-source separation module M200 is used to perform multi-source separation on the vibration noise signal to generate a noise source contribution matrix, which includes the wheel-rail contact noise contribution value, the aerodynamic noise contribution value and the environmental reflection superposition coefficient.

[0072] The adaptive configuration module M300 is used to configure an adaptive control strategy set including active damper frequency band adjustment parameters, sound absorption material layout optimization scheme, vibration isolator stiffness dynamic threshold and phase compensation strategy based on the time-frequency domain mixing characteristics and the noise source contribution matrix.

[0073] The monitoring module M400 is used to simultaneously monitor the noise synchronous attenuation rate and the change in the vibration transfer function to generate a dynamic feedback signal.

[0074] The dynamic adjustment module M500 is used to dynamically adjust the active damper based on the adaptive control strategy set and in combination with the dynamic feedback signal, and the active damper is used to suppress vibration noise.

[0075] Furthermore, the signal acquisition module M100 is used to perform the following method:

[0076] The time-frequency domain mixing features include spectral energy distribution, harmonic distortion rate, transient impulse peak and background noise baseline; based on the time-frequency domain mixing features, combined with wave direction of arrival estimation and coherence analysis, a noise source spatial distribution heat map is generated; independent components of overlapping noise sources in the noise source spatial distribution heat map are extracted, and the vibration sensor data is correlated to verify the multi-source separation results.

[0077] Furthermore, the dynamic adjustment module M500 is configured to perform the following method:

[0078] The resonance frequency band is predicted using the spectrum energy distribution and harmonic distortion rate in the time-frequency domain hybrid feature to determine the target suppression frequency band of the active damper; and the frequency band adjustment parameters of the active damper are determined based on the target suppression frequency band.

[0079] Furthermore, the adaptive configuration module M300 is further configured to execute the following method:

[0080] The transient impact peak and background noise baseline in the time-frequency domain hybrid feature are used to determine the dynamic adaptation range of the porosity of the sound absorbing material; and the sound absorbing material layout optimization scheme is adjusted according to the dynamic adaptation range of the porosity of the sound absorbing material.

[0081] Furthermore, the adaptive configuration module M300 is further configured to execute the following method:

[0082] Noise control effect evaluation indicators are set, and the noise control effect evaluation indicators include sound pressure level attenuation, vibration acceleration reduction rate and strategy execution energy consumption index; at the same time, parameter optimization is performed on the adaptive control strategy set to balance the noise reduction effect and equipment life loss, and the triggering conditions of the phase compensation strategy are dynamically adjusted according to historical control records.

[0083] Furthermore, the dynamic adjustment module M500 is further configured to execute the following method:

[0084] Based on the non-stationary characteristics of the spectrum of the vibration noise signal, a time-varying transfer function is set, and the time-varying transfer function is used to correct the phase delay parameter of the noise propagation path; wavelet packet decomposition is used to separate the broadband random component and the narrowband impact component in the vibration noise signal to generate a frequency band priority weight coefficient; based on the time-varying transfer function and combined with the frequency band priority weight coefficient, the center frequency tracking rate of the adaptive notch filter is adjusted to suppress the secondary reflection of periodic noise.

[0085] Furthermore, the dynamic adjustment module M500 is further configured to execute the following method:

[0086] A cluster analysis is performed on the time domain correlation of multi-source noise under the noise propagation path positioning to identify the coupling relationship between the dominant noise source and the secondary noise source; based on the coupling relationship between the dominant noise source and the secondary noise source, a collaborative control logic chain is generated to synchronously adjust the action timing of the active damper and the vibration isolator.

[0087] Furthermore, the dynamic adjustment module M500 is further configured to execute the following method:

[0088] When it is detected that the deviation of the uniformity of the sound pressure distribution exceeds the deviation threshold, the path parameter recalibration mechanism is triggered; under the path parameter recalibration mechanism, the geographic data and meteorological parameters are integrated to establish a noise propagation path optimization model, and the multi-path reflection superposition effect compensation value is determined; based on the multi-path reflection superposition effect compensation value, the stiffness dynamic threshold fluctuation corresponding to the vibration isolator is dynamically optimized to generate a stiffness adjustment gradient that matches the terrain undulation.

[0089] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory and can be called and recognized by an unlimited computer processor, without any unnecessary restrictions.

[0090] Furthermore, the above technical solution only reflects the preferred technical solution of the technical solution of the embodiment of the present application. Some changes that may be made to certain parts thereof by technical personnel in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technical personnel in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A vibration noise intelligent control method using spectrum characteristics, characterized in that: The method comprises: Collect vibration and noise signals from road traffic tracks and extract mixed features in the time and frequency domains; Performing multi-source separation on the vibration noise signal to generate a noise source contribution matrix, wherein the noise source contribution matrix includes a vehicle engine vibration contribution value, a wheel-rail contact noise contribution value, an aerodynamic noise contribution value, and an environmental reflection superposition coefficient; According to the time-frequency domain hybrid characteristics and the noise source contribution matrix, an adaptive control strategy set including active damper frequency band adjustment parameters, sound absorption material layout optimization scheme, vibration isolator stiffness dynamic threshold and phase compensation strategy is configured; At the same time, the synchronous attenuation rate of noise and the change of vibration transfer function are monitored to generate dynamic feedback signals; Based on the adaptive control strategy set and in combination with the dynamic feedback signal, dynamically adjusting the active damper, wherein the active damper is used to suppress vibration noise; The time-frequency domain mixing features include spectrum energy distribution, harmonic distortion rate, transient impulse peak value and background noise baseline; The active damper is dynamically adjusted, including: Using the spectrum energy distribution and harmonic distortion rate in the time-frequency domain hybrid features to predict the resonance frequency band, and determine the target suppression frequency band of the active damper; Determining a frequency band adjustment parameter of the active damper according to the target suppression frequency band; The dynamic adaptation range of the porosity of the sound-absorbing material is determined by utilizing the transient impact peak and background noise baseline in the time-frequency domain hybrid feature; Adjusting the layout optimization scheme of the sound absorbing material according to the dynamic adaptation range of the porosity of the sound absorbing material; Among them, setting noise control effect evaluation indicators, said noise control effect evaluation indicators include sound pressure level attenuation, vibration acceleration reduction rate and strategy execution energy consumption index; At the same time, the parameters of the adaptive control strategy set are optimized to balance the noise reduction effect and equipment life loss, and the triggering conditions of the phase compensation strategy are dynamically adjusted according to historical control records; The method of using the spectrum energy distribution and harmonic distortion rate in the time-frequency domain hybrid features to predict the resonance frequency band further includes: Based on the non-stationary characteristics of the spectrum of the vibration noise signal, a time-varying transfer function is set, wherein the time-varying transfer function is used to correct the phase delay parameter of the noise propagation path; Using wavelet packet decomposition to separate broadband random components and narrowband impact components in the vibration noise signal, and generate frequency band priority weight coefficients; Based on the time-varying transfer function and in combination with the frequency band priority weight coefficient, adjusting the center frequency tracking rate of the adaptive notch filter to suppress secondary reflections of periodic noise; wherein, cluster analysis is performed on the time domain correlation of the multi-source noise under the noise propagation path positioning to identify the coupling relationship between the dominant noise source and the secondary noise source; According to the coupling relationship between the dominant noise source and the secondary noise source, a cooperative control logic chain is generated to synchronously adjust the action timing of the active damper and the vibration isolator.

2. The vibration noise intelligent control method using spectrum characteristics according to claim 1, characterized in that: The method comprises: Based on the time-frequency domain hybrid features, combined with direction of arrival estimation and coherence analysis, a noise source spatial distribution heat map is generated; Independent components are extracted from overlapping noise sources in the noise source spatial distribution heat map and correlated with vibration sensor data to verify the multi-source separation results.

3. The vibration noise intelligent control method using spectrum characteristics according to claim 1, characterized in that: When it is detected that the sound pressure distribution uniformity deviation exceeds the deviation threshold, the path parameter recalibration mechanism is triggered; Under the path parameter recalibration mechanism, geographic data and meteorological parameters are integrated to establish a noise propagation path optimization model and determine the compensation value of the multipath reflection superposition effect; Based on the multipath reflection superposition effect compensation value, the stiffness dynamic threshold fluctuation corresponding to the vibration isolator is dynamically optimized to generate a stiffness adjustment gradient that matches the terrain undulation.

4. An intelligent vibration noise control system using spectrum characteristics, characterized in that: A system for implementing a vibration noise intelligent control method using spectral characteristics according to any one of claims 1 to 3, comprising: A signal acquisition module, which is used to collect vibration and noise signals of road traffic tracks and extract time-frequency domain mixed features; a multi-source separation module, the multi-source separation module being used to perform multi-source separation on the vibration noise signal to generate a noise source contribution matrix, the noise source contribution matrix including a vehicle engine vibration contribution value, a wheel-rail contact noise contribution value, an aerodynamic noise contribution value, and an environmental reflection superposition coefficient; An adaptive configuration module configured to configure an adaptive control strategy set including active damper frequency band adjustment parameters, sound absorption material layout optimization scheme, vibration isolator stiffness dynamic threshold and phase compensation strategy based on the time-frequency domain hybrid characteristics and the noise source contribution matrix; A monitoring module, the monitoring module is used to simultaneously monitor the noise synchronous attenuation rate and the change in the vibration transfer function to generate a dynamic feedback signal; a dynamic adjustment module, the dynamic adjustment module being configured to dynamically adjust an active damper based on the adaptive control strategy set and in combination with the dynamic feedback signal, the active damper being configured to suppress vibration noise; The time-frequency domain mixing features include spectrum energy distribution, harmonic distortion rate, transient impulse peak value and background noise baseline; The active damper is dynamically adjusted, including: Using the spectrum energy distribution and harmonic distortion rate in the time-frequency domain hybrid features to predict the resonance frequency band, and determine the target suppression frequency band of the active damper; Determining a frequency band adjustment parameter of the active damper according to the target suppression frequency band; The dynamic adaptation range of the porosity of the sound-absorbing material is determined by utilizing the transient impact peak and background noise baseline in the time-frequency domain hybrid feature; Adjusting the layout optimization scheme of the sound absorbing material according to the dynamic adaptation range of the porosity of the sound absorbing material; Among them, setting noise control effect evaluation indicators, said noise control effect evaluation indicators include sound pressure level attenuation, vibration acceleration reduction rate and strategy execution energy consumption index; At the same time, the parameters of the adaptive control strategy set are optimized to balance the noise reduction effect and equipment life loss, and the triggering conditions of the phase compensation strategy are dynamically adjusted according to historical control records; The resonance frequency band is predicted using the spectrum energy distribution and harmonic distortion rate in the time-frequency domain hybrid features, and the system is further configured to perform the following method: Based on the non-stationary characteristics of the spectrum of the vibration noise signal, a time-varying transfer function is set, wherein the time-varying transfer function is used to correct the phase delay parameter of the noise propagation path; Using wavelet packet decomposition to separate broadband random components and narrowband impact components in the vibration noise signal, and generate frequency band priority weight coefficients; Based on the time-varying transfer function and in combination with the frequency band priority weight coefficient, adjusting the center frequency tracking rate of the adaptive notch filter to suppress secondary reflections of periodic noise; wherein, cluster analysis is performed on the time domain correlation of the multi-source noise under the noise propagation path positioning to identify the coupling relationship between the dominant noise source and the secondary noise source; According to the coupling relationship between the dominant noise source and the secondary noise source, a cooperative control logic chain is generated to synchronously adjust the action timing of the active damper and the vibration isolator.

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