224 results about "Noise intensity" patented technology

The invention relates to the technical field of environment detection, and discloses a GIS map-based multi-class environment parameter automatic prediction method, which comprises the following steps: collecting noise intensity, vibration spectrum, sewage turbidity, illumination intensity and PM2.5 concentration in real time through a distributed sensor network, combining social behavior data, carrying out space-time alignment, and generating a multi-dimensional space-time matrix; calculating an energy overlapping degree by adopting a space-time diagram attention network, marking a high-risk collaborative pollution area, and predicting a pollution diffusion path; constructing a self-adaptive prediction model containing physical and behavior driving channels, and dynamically adjusting the weight to optimize the prediction precision; based on the optimization model, reversely deducing a pollution source of an overproof area, matching equipment characteristics and generating a control instruction; and opening an AR (Augmented Reality) interface to verify the treatment effect, and when the virtual-real data deviation exceeds a data deviation threshold, triggering federal learning to update the global model, and generating an environmental protection compliance report. According to the invention, the accurate decision-making efficiency of environmental governance can be improved.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice feature processing method, device, equipment and medium. Performing time resolution analysis based on the fused Mel band energy to generate a multi-scale Mel spectrum amplitude value, and performing nonlinear transformation on the multi-scale Mel spectrum amplitude value according to the noise intensity parameter to generate a noise suppression Mel component; and generating a perception weighting coefficient according to an auditory perception model, and executing frequency domain energy adjustment on the noise suppression Mel component to generate Mel spectrum representation. On the basis of frequency resolution self-adaption, time resolution dynamic adjustment and auditory perception modeling, nonlinear transformation and perception weighting processing are applied to the multi-scale Mel spectrum amplitude value, the influence of noise interference on voice features can be effectively reduced, and the key information retention capacity of voice signals is enhanced.

The invention relates to the technical field of laser spectrum detection, in particular to an LIBS (Laser-induced Breakdown Spectroscopy) spectrum noise reduction method, system and equipment based on adaptive threshold wavelet transform and a storage medium. Acquiring an original spectral signal of the laser-induced breakdown spectroscopy; performing five-layer multi-layer wavelet decomposition on the original spectral signal by adopting a db4 wavelet basis function to obtain a high-frequency coefficient and a low-frequency coefficient of each layer; calculating a noise intensity standard deviation based on the detail coefficient of the highest decomposition layer; dynamically determining the optimal value of the regulation factor through a double-layer optimization strategy combining a grid search method and a golden section iterative optimization method; constructing an adaptive threshold value based on the noise intensity standard deviation and the adjustment factor; carrying out threshold value processing on the high-frequency coefficient by adopting a self-adaptive threshold value; and performing wavelet reconstruction on the processed high-frequency coefficient and low-frequency coefficient, and outputting a denoised spectral signal. While the LIBS spectral signal-to-noise ratio is remarkably improved, the spectral feature form is completely reserved, and reliable technical support is provided for laser-induced breakdown spectroscopy detection in a complex industrial environment.

The invention provides a training method of a generative denoising device and an image controllable generation and restoration method. The training method comprises the following steps: taking a pre-trained diffusion model as a teacher diffusion model; initializing a generative denoising device, a score model and a discriminator; obtaining a noiseless signal, and obtaining a noisy signal and Gaussian noise in combination with a generative denoising device; obtaining effective noise intensity and effective noise signals according to the noisy signals and the Gaussian noise; estimating a data score by using a teacher diffusion model according to the effective noise intensity, the effective noise signal and the noisy signal; according to the noisy signal, estimating a model score by using a score model; calculating and optimizing the gradient of learnable parameters of the generative denoising device; and according to the noisy signal, the data score, the model score and the optimized generative de-noising device, calculating de-noising score matching of the score model and adversarial loss of the discriminator, and optimizing the score model and the discriminator. And realizing image controllable generation and image restoration based on the generative de-noising device.

The invention relates to the technical field of medical monitoring, and particularly discloses a remote monitoring nursing method, device and equipment for old people and a storage medium, and the method comprises the steps: obtaining wrist acceleration information and environment sound information; acquiring a wrist acceleration frequency spectrum and an environment sound frequency spectrum; calculating the ratio of the noise intensity of the environment sound information at each frequency to the environment noise reference intensity to obtain a noise intensity proportion; acquiring a weighted wrist acceleration frequency spectrum; splicing to form a fusion feature vector; and adopting a preset health risk identification model to carry out risk level evaluation so as to judge the current health state of the old people, and generating alarm information when the risk level exceeds a preset threshold value. According to the method, monitoring and abnormal early warning of the health state of the old people are realized, the motion information and the environment sound information are comprehensively utilized, the interference of sound noise of household appliances on the acceleration information can be effectively inhibited, and the accuracy of health risk identification is improved.

The invention discloses a dynamic visual noise monitoring method and system, and relates to the technical field of noise monitoring, and the method comprises the steps: obtaining the position coordinates of noise monitoring points in a to-be-monitored region, and collecting the noise intensity sequence of each noise monitoring point in real time; based on the noise intensity sequence, obtaining position coordinates and noise intensity of the estimated noise source; dividing a to-be-monitored area into a plurality of sub-areas, and obtaining a target area of a to-be-interpolated position; calculating interpolation weights of each noise monitoring point and each estimated noise source in the target area based on the position coordinates of the noise monitoring points and the estimated noise sources; performing bimodal interpolation on the to-be-interpolated position to obtain the noise intensity of the to-be-interpolated position; mapping the noise intensity and color of each position to be interpolated to obtain a noise intensity thermodynamic diagram of the area to be monitored; according to the invention, accurate interpolation can be carried out on the noise intensity of an unknown position, the accuracy of noise monitoring is enhanced, and a dynamic visualization effect of noise monitoring is realized.

The invention discloses a large-scale antenna for RFID (Radio Frequency Identification) tag inventory, which relates to the technical field of wireless communication, extracts channel state information by using a communication link, extracts main characteristics from a sparse channel through a compressed sensing technology, constructs a channel model, effectively captures a signal path, a multi-path attenuation characteristic and noise intensity, and improves the performance of the antenna in a dynamic environment. According to the signal strength and return delay of the tag, dynamically dividing the inventory area, and adjusting the phase and amplitude distribution of the antenna array through real-time feedback, so that the beam coverage is matched with the channel characteristics, and the signal focusing capability of the target area is enhanced; a recurrent neural network (RNN) is introduced to analyze CSI information and feedback signals, multi-path interference and effective signals are separated by learning time sequence features, independent responses of tags are extracted, and the influence of reflected signal interference on tag identification is reduced; the identification success rate of the weak signal label is improved by preferentially processing the label with the weak signal or the shielded label and combining a dynamic recording and alarming mechanism.

The invention relates to the technical field of crushing noise control, in particular to a noise control method, system and equipment for a mobile crushing station and a medium, and the method comprises the steps: obtaining a vibration noise signal of the crushing station, and carrying out the frequency domain conversion of the vibration noise signal to obtain spectrum analysis information; analyzing a frequency band with concentrated noise energy according to the spectrum analysis information to obtain distribution condition information and noise intensity information to generate first reverse sound wave information; historical information of a crushing station is obtained, and a sound wave prediction model is constructed according to the historical information of the crushing station; analyzing the distribution condition information, the noise intensity information and the working point position information according to a sound wave prediction model to obtain second reverse sound wave information; the first reverse sound wave information is adjusted according to the second reverse sound wave information, reverse sound wave output information acting on the loudspeaker is obtained, noise generated in multiple use environments and multiple working conditions of the mobile crushing station can be accurately and rapidly dealt with, and the operation stability of the mobile crushing station is improved.

The invention discloses a method for optimizing the quality of a playing code stream in real time, and relates to the technical field of multimedia processing and streaming media transmission. The method comprises the following steps: in a code stream playing process, a preprocessing module decodes a real-time video frame, obtains noise intensity through a frequency domain analysis method, and performs noise grade division on the video frame; the de-noising module is used for de-noising the video frame through a lightweight de-noising method; the color evaluation module performs color richness grade division on the video frame through a color richness evaluation method; the super-resolution module carries out super-resolution processing on the video frames with different color richness levels through different super-resolution technologies; and the display module displays the video frame subjected to super-resolution processing on a user interface. According to the playing code stream quality real-time optimization method, the noise problem can be adaptively recognized, the resolution of the video is improved, the quality of the live video and the user experience are remarkably improved, meanwhile, low calculation complexity is kept, and the real-time processing capacity is ensured.

The invention provides a real-time estimation method and system for a small-target radar adaptive signal-to-noise ratio. According to the method, a motion model is established based on a historical trajectory of a maneuvering target, and a dynamic position prediction sequence is generated; acquiring a scanning beam pointing angle sequence and a timestamp echo signal intensity sequence; starting multiple rounds of iterative detection for each target position point in the prediction sequence, and shrinking a detection window bidirectionally until the boundary is smaller than the width of a beam half-power point; a signal intensity peak value and background noise intensity in a final iteration window are extracted, a beam irradiation efficiency factor is generated in combination with the spatial matching degree of the scanning beam directional angle sequence and the position prediction sequence, and a signal-to-noise ratio dynamic correction is cooperatively calculated; and weighting and fusing the correction, the signal peak value and the noise intensity, and outputting a final signal-to-noise ratio value of the maneuvering target in real time. According to the invention, the signal-to-noise ratio real-time estimation precision and stability of a small target radar when a high-speed maneuvering target passes through a beam edge are improved.

The invention relates to the field of intelligent analysis, and discloses a transformer operation state analysis system and method for transformer substation noise, and the method comprises the steps: collecting the sound signal and noise intensity of a transformer in each region during operation at a set frequency; performing feature extraction on the sound signal of each area to obtain corresponding waterfall spectrogram feature information; inputting the waterfall spectrogram feature information into a pre-trained convolutional neural network model, and outputting a transformer operation state of each region; the transformer operation state of each area in the transformer operation state analysis module is compared with the noise intensity, if the transformer operation state is matched with the noise intensity, it is judged that the current transformer operation state is accurate, and if the transformer operation state is not matched with the noise intensity, it is judged that the current transformer operation state is not accurate, all operation parameters of the transformer are obtained and then analyzed, and transformer operation coefficients are obtained; and updating the current transformer operation state according to the transformer operation coefficient.

The invention relates to the field of power line communication, in particular to a power line communication system based on a multi-carrier aggregation technology, which is characterized in that transmission interference characterization values of power line networks in regions are determined based on collected environment characteristics of each region in combination with corresponding sudden noise intensity; the method comprises the following steps of: dividing abnormal tendency categories of transmission work of a power line network in each region according to different regions, and calling related modules for analysis, including obtaining interference characteristic analysis transmission parameters of each branched transmission path in the region, so as to judge whether data signal transmission of the branched transmission path meets a transmission standard or not, and identifying a corresponding offset branched transmission path; analyzing a carrier transmission abnormity characterization coefficient according to the path data signal intensity and the data signal transmission rate so as to identify whether the carrier frequency band of the data signal in each branch transmission path needs to be adjusted; according to the invention, interference factor changes affecting power line communication can be captured in time, a path area where a fault may occur can be identified in time, and the quality of data signal transmission is ensured.

The invention provides an audio noise reduction method and system based on local attention and feature fusion in the technical field of crossing of audio signal processing and artificial intelligence, and the method comprises the steps: S1, building an audio noise reduction model based on an encoder, a noise reduction module and a decoder, and setting a loss function of the audio noise reduction model; s2, acquiring a large amount of historical audio data, preprocessing each piece of historical audio data, and labeling each piece of preprocessed historical audio data at least including clean audio, noise type, noise intensity, audio scene and audio content to construct a data set; s3, dividing the data set into a training set, a verification set and a test set to train, verify and test the audio noise reduction model; and S4, deploying the audio noise reduction model passing the test, and executing audio noise reduction operation through the deployed audio noise reduction model. The method has the advantages that the quality, generalization and efficiency of audio noise reduction are greatly improved.

The invention relates to the technical field of geological survey, in particular to a rapid stratum disease risk detection method based on a ground penetrating radar, and solves the technical problem of depth positioning misalignment caused by propagation parameter fluctuation due to heterogeneity of an underground medium in stratum disease detection of the ground penetrating radar in the prior art. The method comprises the following steps: acquiring a reflected wave signal at each acquisition moment through a ground penetrating radar; performing signal decomposition processing on the reflected wave signal, separating an intrinsic mode component representing dielectric noise, and performing feature extraction on the intrinsic mode component to determine a dielectric noise intensity index; constructing a dielectric noise intensity sequence according to the dielectric noise intensity index, and performing spatial-temporal characteristic analysis to determine the confidence coefficient of stratum diseases; and according to the dielectric noise intensity index and the stratum disease confidence coefficient, in combination with a pre-calibrated stratum average dielectric constant, constructing a depth correction weight, carrying out adaptive correction on the original stratum disease depth, and outputting the corrected stratum disease depth.

A noise reduction processing method and a noise reduction processing device are provided. The noise reduction processing method comprises the following steps: applying a first filter having a first filter window to an object signal to obtain a low-frequency signal, applying a second filter having a second filter window independent of the first filter window to a high-frequency signal obtained based on the object signal and the low-frequency signal to estimate a noise intensity signal; and obtaining a low-noise signal.

The invention provides a multi-redundancy gyroscope weighted fusion method and device based on real-time noise estimation, and relates to the technical field of inertial navigation and multi-sensor fusion. The method comprises the following steps: firstly, carrying out sliding window sampling and multi-scale wavelet decomposition on measurement signals of a plurality of redundant gyroscopes, and extracting wavelet coefficients under different frequency scales; then, applying a noise variance estimation method based on mutual difference observation on each scale, and estimating the noise intensity of each channel in real time; and finally, constructing a fusion weight according to a minimum variance criterion, and realizing weighted fusion of redundant signals and signal output of the virtual gyroscope. The system is simple in structure and efficient in calculation, can improve the measurement precision and robustness of an inertial navigation system in a dynamic environment, and is suitable for an embedded high-precision navigation system in a dynamic complex working condition.

The invention discloses a noise detection method and device for a dual-mode communication network of a power system. The noise detection method comprises the following steps: acquiring an analog signal transmitted by the dual-mode communication network of the power system in a current detection period and filtering the analog signal; sequentially carrying out signal amplification and analog-to-digital conversion on the filtered analog signal according to a preset sequence of a plurality of preset gain values to obtain a digital signal, carrying out validity verification on the digital signal, and terminating after obtaining a first preset gain value which is verified to be valid; and calculating a noise intensity value of the dual-mode communication network of the power system based on the preset gain value which is verified to be valid for the first time. Through orderly traversal of a preset gain sequence and a first effective immediate stop strategy, orderly matching of effective gains is realized, an ADC signal overflow risk is thoroughly avoided, and complete waveform capture in a strong noise environment is ensured; and meanwhile, traversal times are compressed, and detection time consumption is remarkably reduced compared with a traditional multi-stage gain scheme.

The invention relates to an AMT signal denoising method and device based on self-adaptive multi-level U-Net. According to the method, noise segment classification is performed on a noisy AMT signal, a corresponding position mask of the clean AMT signal is recorded, and noise positioning information is provided for training of a denoising model, so that the training effect of the denoising model is improved, and loss of non-noise segments can be avoided; the method also uses two U-Net networks to form a U-Net cascade structure, the first network performs preliminary denoising, the second network processes fragment data output by the first network, generates a noise intensity probability by using a residual error, uses the noise intensity probability as a driving signal, distributes a corresponding weight value, and outputs the driving signal to the U-Net cascade structure. According to the method and the system, the second network is dynamically controlled to selectively absorb and fuse fragment data output by the first network, an area with remarkable residual noise can be adaptively concerned, complementation and optimization of a feature level are realized, and finally, the denoising effect of the denoising model on the noisy AMT signal is improved.

The invention discloses a city noise map generation method, system and device and a storage medium, and relates to the technical field of environment monitoring, and the method comprises the steps: collecting the noise data of each region of a city through a fixed sensor and a mobile sensor, extracting the spatial-temporal characteristics related to the noise intensity in the noise data, and carrying out the integration, the method comprises the following steps: acquiring noise intensity and noise source positions of different types of noise sources, performing noise level classification according to the noise intensity, acquiring noise levels, integrating the noise source positions, the noise levels and acquisition time of the different types of noise sources, placing an integration result in a map, and generating a noise distribution map with different noise levels. According to the method, the change of noise pollution and the region with high noise level are found and dealt with, the noise distribution condition of each region of a city is accurately described through data integration of the data, and a scientific basis is provided for city planning, environmental protection and public health management.

The invention relates to the technical field of radio frequency starting of medical cyclotrons, in particular to a radio frequency starting method and system of a medical cyclotron, and the method comprises the steps: carrying out the frequency sweeping when a radio frequency pulse in the medical cyclotron is started, and converting a radio frequency signal which is continuously adjusted in the frequency sweeping process into a radio frequency digital signal; analyzing noise characteristics formed after the radio frequency digital signals are converted into the radio frequency digital signals, and filtering noise interference in the radio frequency digital signals; the method specifically comprises the steps of calculating noise intensity at a current moment; acquiring a frequency sweeping rate sequence; calculating the Kalman gain at the current moment; filtering the radio frequency digital signal at the current moment by adopting a Kalman filtering method; and controlling the starting frequency of a radio frequency circuit in the medical cyclotron according to the filtered radio frequency digital signal at the current moment. According to the invention, the frequency sweeping operation is more accurate when the radio frequency of the medical cyclotron is started, and the tuning effect of the medical cyclotron is improved.

The application relates to a hyperspectral image denoising diffusion model construction method and system, and belongs to the technical field of computer vision. The method generates a noisy low-resolution hyperspectral image for testing by adding noise to a clean image; the number of compressed channels is dynamically determined by using an elbow point method, spectrum compression is performed, and a coefficient matrix is obtained; each band complex noise image after spectrum compression is converted into an approximate Gaussian noise image by using a noise conversion technology; the number of inverse diffusion embedding steps of each band after noise conversion is dynamically determined by using a pre-trained diffusion model, and a more accurate denoising process is performed through the determined number of inverse diffusion steps. The application can dynamically determine the number of spectrum compression channels of the noisy hyperspectral image, thereby effectively improving the recovery accuracy of the hyperspectral image, can adapt to various noisy hyperspectral image scenes, can dynamically determine the number of inverse diffusion embedding steps according to the noise intensity, can sufficiently denoise high-noise channels and moderately constrain low-noise channels, and can dynamically balance the denoising intensity and spectrum fidelity.

The invention discloses a data noise reduction method and device for magnetotelluric signals and a medium, belongs to the technical field of magnetotelluric signals, and aims to solve the problems that signal-noise separation of existing magnetotelluric signals is not accurate enough, and effective magnetotelluric signals are difficult to obtain. And main effective components of the magnetotelluric signal cannot be accurately extracted. The method comprises the following steps: performing signal superposition processing on an effective magnetotelluric signal and a simulated synthetic noise waveform to generate a magnetotelluric signal sample pair; sequentially carrying out related noise intensity perception, weight dynamic adjustment and feature noise reduction processing under self-attention weighted output on feature variables in the magnetotelluric signal sample pair; performing two-stage noise reduction feature sequence generation strategy calculation based on a convolutional layer on an original signal time sequence in the magnetotelluric signal sample pair; and performing noise reduction control and feature extraction on the current original magnetotelluric signal to obtain an effective signal feature.

The invention discloses a method and a device for positioning a low-frequency oscillation source of a wind-thermal bundling system. The method comprises the following steps: acquiring multi-source signals of the wind-thermal bundling system; carrying out adaptive wavelet threshold anti-noise preprocessing; carrying out adaptive noise intensity set empirical mode decomposition; screening a wind-fire coupling key mode; calculating a wind-fire adaptive dissipated energy flow; and oscillation source determination based on phase coupling: constructing a multi-condition fusion determination logic in combination with a phase coupling relationship between a dissipated energy flow trend and a wind-fire key mode and a phase coupling relationship between the dissipated energy flow trend and an electric energy transmission signal so as to realize oscillation source type identification. According to the invention, accurate identification of oscillation sources (a wind power side, a thermal power side and a tie line) in a frequency band of 0.1-2Hz is realized, and both positioning accuracy and engineering real-time performance are considered.

The invention provides a noise reduction method for a voice processing system of a helicopter, which comprises the following steps: firstly, inputting an audio signal, inputting the audio signal into window noise estimation for preset caching, setting the size of a window to be between 10ms and 30ms, determining that the voice with the time length is constant, and when the input signal is smaller than a threshold value theta, determining that the voice with the time length is not changed; when the input signal is larger than a threshold theta value, the input signal is considered as a noise signal, when the input signal is larger than the threshold theta value, the input signal is considered as a real-time voice signal, a useful signal can be obtained by subtracting a noise signal frequency spectrum from the real-time voice signal, and according to the logic, a prior pure noise lambda x (k) and a useful signal lambda d (k) are obtained; according to the noise reduction method for the voice processing system of the helicopter provided by the invention, the Wiener filtering algorithm is operated inside, so that the noise intensity in the collected voice is reduced, the vibration of a rotor wing, the vibration of a helicopter body, the noise of an engine and the like are suppressed, and the voice heard in an earphone is ensured to be clear and free of noise.

The invention provides a self-adaptive wavelet noise reduction method and system based on mass spectrum signal processing, and the method comprises the following steps: S1, collecting a data set outputted by a mass spectrometer, and inputting the data set into a noise reduction process in the form of a text document; s2, reading related data in the text document, analyzing spectral peak characteristic parameters, and initializing related parameters; s3, performing multilayer wavelet decomposition on the document data, and outputting an approximate component and a detail component of each layer; s4, calculating the noise intensity of different layers according to the output approximate component and the detail component, and adaptively calculating a wavelet threshold value based on the signal length and the noise intensity; s5, threshold processing is applied to the detail components, and wavelet signals are reconstructed; and S6, calculating signal-to-noise ratios under different decomposition layer numbers, and selecting the optimal decomposition layer number to output the mass spectrum data after noise reduction. According to the method, the related spectrogram information output by the mass spectrometer is denoised, and the signal-to-noise ratio of the mass spectrum data is improved while the spectrum peak information is reserved to a great extent.

The application relates to a fixed-pattern noise correction method of an infrared detector and belongs to the technical field of infrared imaging. The application can eliminate the fixed-pattern noise of the infrared detector in a real-time image by taking the fixed-pattern noise phenomenon of the infrared detector as a template and calculating the fixed-pattern noise intensity in the real-time image through correlation.

A code error rate estimation device included in an optical transmission system using a direct detection receiver, the code error rate estimation device including: a transmission path model creation unit that creates a physical model of a transmission path for each candidate path for performing communication between user devices that perform communication; a propagation waveform calculation unit that generates an electric field signal waveform to be output from a transmitter assumed in the physical model of the transmission path and generates a reception signal waveform at the time of direct detection by using linear fiber propagation simulation; a nonlinear noise calculation unit that calculates nonlinear noise light intensity of light on the basis of the physical model of the transmission path; a noise intensity conversion unit that converts the calculated nonlinear noise light intensity of the light into noise in an electrical stage; a reception signal waveform calculation unit that calculates a Gaussian distribution of each symbol or each sample in a reception signal waveform at the time of the direct direction on the basis of the reception signal waveform at the time of the direct detection and the converted noise in the electrical stage; and a code error rate calculation unit that calculates a code error rate on the basis of the Gaussian distribution of each symbol or each sample.

An encoder for providing an audio stream on the basis of a transform-domain representation of an input audio signal includes a quantization error calculator configured to determine a multi-band quantization error over a plurality of frequency bands of the input audio signal for which separate band gain information is available. The encoder also includes an audio stream provider for providing the audio stream such that the audio stream includes information describing an audio content of the frequency bands and information describing the multi-band quantization error.A decoder for providing a decoded representation of an audio signal on the basis of an encoded audio stream representing spectral components of frequency bands of the audio signal includes a noise filler for introducing noise into spectral components of a plurality of frequency bands to which separate frequency band gain information is associated on the basis of a common multi-band noise intensity value.

The invention relates to the technical field of voice recognition, in particular to an audio device intelligent control method and system based on plant electric signals. The method comprises the following steps: acquiring a voltage signal, noise intensity and different environmental parameter values, and obtaining resting potential reference values and respective reference values; acquiring potential deviation and signal fluctuation amplitude based on the fluctuation amplitude of the voltage deviation in the time window and the overall condition; acquiring an environment gain factor and a noise suppression factor based on the difference between the environment parameter and the reference value and the difference between the noise and the reference value; pitch and tone quality are respectively adjusted through an environment gain factor, a noise suppression factor, potential deviation and signal fluctuation amplitude, and a special sound effect is determined in combination with voltage deviation; and music playing is controlled by combining musical instrument timbre recognition voice. According to the invention, the interactivity between the user and the plant is improved.

This invention relates to the field of wind power generation technology, and in particular to a method for early warning of wind turbine blade flutter based on low-frequency noise. First, the geometric model information and physical parameters of the wind turbine blade are acquired. Then, the modal frequencies and mode shapes of each order of the blade are calculated. According to the superposition principle of vibration modes, blade flutter will inevitably radiate low-frequency noise at the corresponding modal frequencies. Microphones installed on the wind turbine are used to monitor wind force and low-frequency noise generated during operation in real time. The noise intensity at the blade flutter frequency is obtained and monitored through signal processing, and an early warning intensity threshold is set. When the low-frequency noise signal at the flutter modal frequency exceeds the early warning threshold, a flutter warning is immediately issued. This allows for early warning before significant blade deformation occurs, greatly improving the safety margin.