47 results about "Wavelet reconstruction" patented technology

The invention discloses an edge-guided self-adaptive fusion remote sensing image change detection method based on spatial frequency domain interaction. The method comprises the following steps: preparing data of front and rear remote sensing images and real change labels; preprocessing based on the previous and later remote sensing images and labels to obtain a standardized data set; an edge-guided adaptive fusion network based on spatial frequency domain interaction is constructed, feature enhancement is carried out through a spatial and frequency double-branch structure, and an adaptive orthogonal fusion module is designed to carry out multi-construction interactive fusion on double-time features. Introducing an edge driving wavelet reconstructor as an auxiliary supervision branch to guide the feature change region edge modeling capability; and finally, a high-precision change prediction map is generated by inputting a double-time-phase remote sensing image. The method aims at solving the problems that edge prediction details are insufficient and background noise is difficult to restrain in a traditional change detection technology, the detection performance is effectively improved, the advanced change detection performance is shown on a data set, and the method has high competitiveness and practical value.

The present application relates to the technical field of image processing, in particular to an insulator image enhancement method based on infrared image denoising of a UAV. The original infrared image collected by the UAV is preprocessed, and the preprocessed image is decomposed in multiple scales by wavelet transform to obtain high-frequency detail coefficients and low-frequency approximation coefficients; the high-frequency detail coefficients are processed by adaptive thresholding, while the low-frequency approximation coefficients are subjected to Wiener filtering, and after wavelet reconstruction and guided filtering, a denoised image is obtained; based on the denoised image, an edge detection operator is used for preliminary edge extraction, edge information is obtained by combining non-maximum suppression and double thresholding, and the edge gap is connected by using morphological edge reconstruction technology; through adaptive contrast enhancement based on local temperature distribution and image fusion processing, the final image with enhanced features is generated. The present application can realize efficient denoising and accurate feature enhancement of the insulator infrared image collected by the UAV.

The invention belongs to the technical field of petroleum and natural gas exploration and development, and discloses a method and a device for engraving a tiny fracture geologic body, electronic equipment and a medium, the method for engraving the tiny fracture geologic body comprises the following steps: based on a multi-wavelet decomposition result, carrying out wavelet energy optimization sorting and stripping a strong energy quantum wave, and then completing multi-wavelet reconstruction; seismic signals related to the tiny fracture system are highlighted, and the spatial form of the tiny fracture geologic body is obtained through engraving. The method effectively improves the spatial form engraving precision of the tiny fractured geologic body, and is suitable for spatial engraving of the tiny fractured geologic body.

This invention discloses a baseline removal method for multi-channel magnetocardiogram (MCC) signals based on wavelet low-frequency reconstruction and morphological secondary smoothing. The invention includes: acquiring multi-channel MCC signal data; performing power frequency notch filtering on each channel to suppress the power frequency fundamental frequency and at least one harmonic component; performing configurable IIR low-pass filtering on the notched signal; setting a discrete wavelet transform extension mode and performing discrete wavelet decomposition on the filtered signal; selectively retaining decomposition coefficients based on the retention layer set and setting the remaining coefficients to zero, then performing inverse wavelet reconstruction to obtain an initial baseline estimate; performing morphological secondary smoothing on the median-smoothed baseline signal to obtain a corrected baseline signal; subtracting the corrected baseline signal from the filtered signal to obtain the baseline-corrected channel signals; and assembling and outputting a multi-channel baseline-corrected signal matrix. This invention can improve the stability and cross-channel consistency of baseline estimation for multi-channel MCC signals, reduce the risk of over-correction and under-correction, and is applicable to multi-channel batch processing scenarios.

The embodiment of the application provides a cascade anti-interference circuit suitable for an electrocardio monitoring device, which comprises a sliding mean filter module, a notch filter module, a lifting wavelet decomposition module, a threshold calculation module, a threshold processing module and a lifting wavelet reconstruction module; while ensuring a small circuit scale, the ECG signal collected by the wearable electrocardio monitoring device is subjected to hierarchical noise reduction processing, and high signal-to-noise ratio ECG signal output is realized; for the ECG signal collected by the wearable electrocardio monitoring device, first, sliding mean filtering is performed to filter out baseline drift noise caused by human respiratory movement and other activities; the power frequency interference caused by the wired and wireless connection of the electromagnetic environment around the device is suppressed by using the notch filter module; finally, wavelet noise reduction is realized by using the lifting wavelet decomposition and reconstruction and threshold noise reduction method, the electromyographic interference caused by the autonomous or unconscious movement of the wearer is suppressed, and high signal-to-noise ratio ECG signal is output, which is used for physiological parameter extraction and electrocardio diagnosis.

This invention provides a power load forecasting method based on time-frequency enhancement and multi-level wavelet modeling, comprising: constructing a multivariate historical power load time series and generating instance normalized sequences according to variable channels; performing time-domain enhancement and spectral enhancement on the instance normalized sequences to obtain enhanced feature sequences; performing multi-level discrete wavelet decomposition on the enhanced feature sequences to obtain a first-level approximation coefficient sequence and a detail coefficient sequence of wavelet coefficients at each level; performing independent resolution branch modeling based on the decomposition coefficient sequences, modeling and predicting the coefficient sequences corresponding to each resolution branch, obtaining predicted approximation coefficient sequences and predicted detail sequences at each level, and then performing multi-level wavelet reconstruction; and performing inverse normalization on the reconstruction results to obtain the power load forecasting results. Applying this method can coordinate time-domain and frequency-domain feature enhancement with multi-resolution modeling, improving the accuracy and stability of long-term power load forecasting while reducing computational complexity.

This invention relates to a wavelet threshold-based method and system for denoising cold test vibrations, comprising the following steps: constructing a vibration parameter database based on the acquired diesel engine cold test vibration signal; performing wavelet decomposition on the vibration parameters in the database according to the selected wavelet basis function and decomposition level to obtain wavelet coefficients; performing wavelet reconstruction on the vibration parameters based on a threshold function to obtain denoised cold test vibration parameters, specifically: shrinking the wavelet coefficients within the threshold range to zero, while keeping the wavelet coefficients outside the threshold unchanged; and processing the acquired diesel engine cold test vibration signal using the denoised cold test vibration parameters to obtain a denoised cold test vibration signal. The threshold function is used to solve the oscillation and distortion problems existing in the denoising process, thereby improving the denoising effect of the cold test vibration signal.

The present application relates to the technical field of fault detection, and relates to a relay contact fault identification method based on wavelet denoising and a support vector machine, comprising the following steps: S1, constructing a current signal acquisition and double-channel reconstruction circuit: a Rogowski coil is sleeved on an output loop of a relay contact, and a differential voltage signal is inducted and output by the Rogowski coil; S2, respectively adopting a first wavelet base and a second wavelet base to perform multi-scale wavelet decomposition, threshold denoising and wavelet reconstruction on a complete current digital sequence and a transient current digital sequence; S3, extracting time domain statistical features from the complete current digital sequence after denoising, extracting time-frequency energy features from the transient current digital sequence after denoising, and outputting a fault category of the relay contact after classification and decision by a support vector machine classifier. The present application overcomes the inherent contradiction that a single acquisition channel is difficult to simultaneously consider slow-changing contact components and transient disturbance components in terms of dynamic range and denoising strategy.

The application discloses a power load prediction method and system based on multi-stage time sequence preprocessing and double-branch wavelet Mamba, and relates to the technical field of intelligent power grids and time sequence analysis. In view of the problems that the existing power load prediction technology has insufficient long sequence modeling capability, low prediction robustness and precision, and cannot simultaneously consider global trend fitting and local mutation capturing when facing high-noise non-stationary data, a high-quality target sequence is first constructed through multi-stage time sequence preprocessing, and after channel independence and block embedding processing, the high and low frequency characteristic components are decoupled through discrete wavelet transform, the global long-range dependence is extracted through a bidirectional trend Mamba module, the local mutation characteristics are purified through a detail Mamba module, and noise is suppressed, and finally, the prediction result is output through inverse wavelet reconstruction. The application significantly improves the precision and robustness of long sequence power load prediction and reduces the computational complexity.

The invention discloses a hybrid model-based wind power medium-and-long-term generating capacity prediction method and device, and belongs to the technical field of wind power generation. The method comprises the following steps: acquiring historical actually measured wind power data and historical meteorological data as input, and obtaining a cleaned power generation time sequence; performing discrete wavelet decomposition on the cleaned power generation time sequence to obtain multiple layers of subsequences of different frequency bands; based on the sub-sequence and the historical meteorological data, selecting an optimal prediction feature subset from the data; constructing a plurality of multi-level perceptron neural network models running in parallel, and outputting corresponding prediction results; and performing wavelet recombination on all the output prediction results to obtain a final prediction value of the medium-and-long-term generating capacity of the wind power. According to the technical scheme, high-precision prediction of the medium-and-long-term generating capacity of wind power is achieved, abnormal data can be effectively removed, multi-scale features are extracted, multi-model collaborative learning is achieved, and the reliability and adaptability of a prediction result are improved.

The present application relates to a gyro signal denoising method based on Kalman filtering and Visushrink threshold processing, comprising the following steps: establishing a time series model of a gyro signal; using adaptive anti-outlier Kalman filtering to denoise the gyro signal; using wavelet analysis to respectively perform Visushrink threshold processing on low-frequency components and high-frequency components of the Kalman filtered gyro signal; and performing wavelet reconstruction on the high-frequency and low-frequency signals of the threshold processed gyro signal. The present application provides an adaptive anti-outlier denoising scheme for gyro signals, which combines Kalman filtering and wavelets, can more effectively improve the accuracy of sensors and reduce errors.

The invention discloses an offshore culture video identification method and device based on spectrum semantic consistency. The method comprises the following steps: receiving underwater video data of an offshore culture scene, and carrying out time sequence alignment and steady-state preprocessing on the underwater video data to obtain a steady-state frame sequence; frequency domain enhancement processing is carried out on the steady-state frame sequence, the processing comprises wavelet decomposition, sub-band residual refinement based on historical statistical reference and wavelet reconstruction, and the processing logic is optimized by the spectrum-semantic quality consistency constraint introduced in the training stage; and finally, inputting the enhanced image sequence into a pre-trained multi-task neural network for feature extraction and semantic recognition, and outputting a recognition result. According to the invention, through a collaborative optimization mechanism of frequency spectrum and semantics, in combination with a quality-gated self-learning framework and a cost-driven deployment strategy, the recognition accuracy, robustness and system adaptability of an offshore culture video in a complex underwater environment are significantly improved.

The invention relates to a cable insulation fault positioning method and system, and the method comprises the following steps: collecting a three-phase voltage traveling wave signal when a cable has a single-phase insulation damage grounding fault, and carrying out the signal decoupling, and obtaining an independent line mode component and a zero mode component; a Db45 wavelet basis is used as a mother wavelet, multi-scale wavelet decomposition and reconstruction are carried out on the decoupled line mode component so as to extract fault traveling wave head features, and the number of layers of multi-scale wavelet decomposition is determined according to the sampling frequency and the power grid fundamental wave frequency; and according to a wavelet reconstruction result, detecting the moment when a fault traveling wave head arrives at monitoring points at two ends of the line, thereby calculating the distance from a fault point to one end of the line. Compared with the prior art, the method has the advantages that inter-phase coupling interference is eliminated through phase-mode transformation, and serious voltage distortion and harmonic interference in a power grid are effectively suppressed by using the excellent time-frequency localization characteristic of the Db45 wavelet basis, so that the accuracy and reliability of cable insulation fault positioning are greatly improved.

The application is applied to the field of mechanical fault diagnosis signal processing, and particularly discloses a mechanical fault diagnosis method based on STFT dimension transformation, which comprises the following steps: collecting a one-dimensional mechanical vibration signal, and performing wavelet decomposition; performing wavelet reconstruction on low-frequency components and high-frequency components after denoising processing, so as to obtain a one-dimensional vibration signal after denoising; performing short-time Fourier transform, so as to convert the signal into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, performing Poisson sparse coding on the time-frequency two-dimensional matrix, and only performing pulse response on signal significant features; constructing a threshold coding convolution network with residual connection, inputting a sparse coding matrix, training by using an unsupervised learning rule based on STDP, and adaptively adjusting network synaptic weights; inputting into the trained threshold coding convolution network, and determining a fault diagnosis result by means of pulse firing activities of output layer neurons obtained through network forward propagation.

Super-resolution label-free microscopy is provided using multiplexed, temporally modulated acquisition patterns of emission point spread functions (“PSFs”). Supercontinuum ultrafast pulses can be used to enhance nonlinear processes, such as autofluorescence and harmonic generation, in order to provide super-resolution imaging of nonlinear label-free signals. Images can be reconstructed using various reconstruction techniques, including pixel reassignment, wavelet reconstruction, and deep learning model-based reconstructions.

The invention discloses a wavelet reconstruction seismic response characterization method and device, and the method comprises the steps: obtaining an initial wavelet which is a seismic numerical simulation conventional wavelet; determining a wavelet reconstruction demand corresponding to the at least one geologic model; modifying the initial wavelet form according to the wavelet reconstruction requirement to obtain a reconstructed wavelet; seismic numerical simulation is carried out based on the reconstructed wavelets, a seismic response representation result is obtained, and targeted wavelets are generated according to simulation requirements, so that the influence of a sidelobe effect and a tuning effect on seismic numerical simulation is effectively reduced, and the numerical simulation precision is improved.

The present application belongs to the technical field of oil liquid abrasive particle monitoring, and particularly relates to a kind of abrasive particle feature extraction methods based on differential signal band selection adaptive filtering, including obtaining the differential signal to be detected by carrying out common mode rejection to oil liquid abrasive particle signal;Obtain multi-decomposition scale feature vector by wavelet packet decomposition to the differential signal to be detected through orthogonal wavelet base function;Calculate similarity index on each decomposition scale, extract target scale feature vector;Obtain filter vector by processing target scale feature vector using improved least mean square root adaptive filter;Carry out wavelet reconstruction to zero vector and filter vector to obtain noise reduction signal;Divide noise reduction signal into multiple segments, calculate composite recognition index of each segment;According to composite recognition index, calculate the weight of each segment, and obtain oil liquid abrasive particle feature signal extraction result by weighting all segments;The present application improves the signal-to-noise ratio of abrasive particle signal, has stronger adaptive ability, and helps to improve the accurate detection of oil liquid abrasive particle feature.

The invention discloses a thin sand body identification method and device based on multi-wavelet decomposition, electronic equipment and a medium. The method comprises the following steps: preprocessing a prestack angle gather, and further performing multi-wavelet decomposition; through comparative analysis of well data and an actual decomposition gather, screening a frequency band range sensitive to a thin sand body reservoir; performing multi-wavelet reconstruction on the data subjected to multi-wavelet decomposition to obtain a seismic angle gather data body; screening dominant angles according to the seismic angle gather data body; partial superposition is carried out according to the dominant angle, and superposed seismic data are acquired; and based on the superposed seismic data, sand body sensitive attributes are extracted to carry out thin sand body identification. According to the method, the frequency component sensitive to the thin sand body is fully utilized, interference of the tuning effect on thin sand body recognition can be effectively avoided, and high-precision recognition of the thin sand body reservoir is achieved.

The invention relates to the technical field of seismic signal processing and interpretation, and particularly discloses a frequency domain fusion gas-bearing prediction method and device, and the method comprises the steps: obtaining the logging and post-stack seismic data of a target region reservoir; de-noising processing is carried out on the post-stack seismic data to obtain seismic data with a high signal-to-noise ratio; performing time-frequency analysis on the logging well bypass to determine a fluid sensitive frequency band; performing multi-wavelet reconstruction of a fluid-sensitive frequency band on the seismic data with the high signal-to-noise ratio to obtain a reconstructed data volume; performing high-precision time-frequency analysis on the reconstructed data volume to obtain a gradient frequency attribute volume; and carrying out normalization processing and fusion on the gradient frequency attribute body to obtain a gas-bearing indicator factor data body. According to the method, the gas-bearing property is detected by adopting the thinking of frequency attribute fusion, more abnormalities caused by oil and gas can be represented, a more accurate target stratum gas-bearing property prediction result is finally obtained, and the method does not depend on logging data and seismic pre-stack data, is high in stability and strong in certainty, and still has applicability in a work area with fewer drilling samples.

The application discloses a low signal-to-noise ratio condition modulation identification method based on wavelet transform and channel attention, comprising: acquiring a received signal; inputting the received signal into a pre-trained neural network identification model to output a classification identification result and a classification accuracy rate. Based on the signal reconstruction and the identification model of the neural network, the wavelet threshold estimation module and the wavelet reconstruction module constitute a signal reconstruction path, the multi-scale feature extraction module and the prediction classification module constitute an identification path, the two paths are mutually enhanced, the advantages of the digital signal processing technology and the neural network are combined, the wavelet threshold estimation module is introduced to predict a denoising threshold through the neural network, and the network parameters of the wavelet threshold estimation module are updated according to the back propagation; the signal reconstruction path and the identification path are combined, the network interpretability is enhanced, the multi-scale modulation features are utilized, and the modulation identification accuracy rate in the low signal-to-noise ratio scene is significantly improved.

This invention proposes a low-light image enhancement method based on a wavelet gradient-illuminance fusion network, belonging to the field of image processing technology. The method first decomposes the low-light input image into a reflectance component and an illuminance component based on Retinex theory. Then, the reflectance component undergoes channel attention and spatial attention enhancement, wavelet decomposition denoising, and inverse wavelet reconstruction sequentially to obtain the denoised reflectance features. Furthermore, a gradient-guided residual aggregation module is used to extract edge information, achieving detail enhancement of the reflectance component. Simultaneously, the illuminance component is jointly modeled with the HSV brightness component of the original input image. An illuminance enhancement network predicts the illumination distribution weight map, and a dual-path attention mechanism is combined to achieve adaptive enhancement of the illuminance component.

The application discloses a wavelet denoising method, system, device and medium based on a measured ship distance image, and relates to the field of target identification.The method comprises the following steps: acquiring one-dimensional distance image data of a ship target; determining a wavelet base and a decomposition order; performing wavelet decomposition on the one-dimensional distance image data according to the wavelet base and the decomposition order to obtain first wavelet coefficients; determining a wavelet threshold; determining second wavelet coefficients according to the wavelet threshold and the first wavelet coefficients by using an improved threshold function; the improved threshold function is provided with a variable factor, and the variable factor has a value range of [0, 1); when the value of the variable factor is equal to 0, the improved threshold function is a soft threshold function; when the value of the variable factor tends to 1, the improved threshold function tends to a hard threshold function; performing wavelet reconstruction on the second wavelet coefficients to obtain one-dimensional distance image data after denoising, so that the ship target can be identified.The application can improve the denoising effect and improve the ship target identification rate.

The invention discloses a climate data error correction method, and relates to the technical field of meteorological data processing, and the method comprises the steps: determining a target region, obtaining CMIP6 global climate mode data and ERA5 reanalysis data of the target region, and unifying the computational grids of the two types of data; in the time dimension, correcting the data sequence by using methods of wavelet analysis, wavelet decomposition, quantile mapping and wavelet reconstruction; in the spatial dimension, correcting the spatial sequence in the longitude direction by using wavelet analysis, quantile mapping and wavelet reconstruction methods; correcting the space sequence in the latitude direction by using wavelet analysis, quantile mapping and wavelet reconstruction methods; and storing time, longitude and latitude correction fields as standard data files. According to the method, systematic deviation of CMIP6 data can be remarkably reduced, extreme value performance and spatial resolution are improved, and a reliable data basis is provided for high-precision downscaling simulation of future climate scenes.

The invention provides a cabinet equipment power prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of the Internet of Things, and the method comprises the steps: carrying out the wavelet decomposition of a cabinet equipment power sequence, obtaining a plurality of components, and enabling the plurality of components to comprise an approximate component and a plurality of detail components; inputting any component in the plurality of components and the associated features into a base learner corresponding to the component to obtain a prediction result output by the base learner; the associated features comprise feature data of a plurality of feature factors, and the feature factors are features associated with the power of the cabinet equipment; the prediction result comprises a cabinet equipment prediction power sequence and prediction correlation characteristics; inputting the prediction result corresponding to each component into a meta-learner to obtain frequency domain prediction data output by the meta-learner; and performing wavelet reconstruction processing on the frequency domain prediction data to obtain time domain prediction power. According to the invention, pre-judgment of the operation state of the cabinet equipment is realized, and equipment loss caused by power over-limit is avoided.

The application provides an air target adaptive segmented track recognition method and system based on a Softmax multi-classification network, which comprises the following steps: traversing a whole complex track sequence in time through a sliding window, fitting the track in the window by using a designed fitting equation to obtain equation parameters; training a Softmax classification network model based on the equation parameters, and recognizing the track pattern in the window based on the network model; and correcting the recognition result based on Haar wavelet reconstruction to eliminate discrete wrong recognition results. The application solves the problem of poor complex track pattern recognition effect and can effectively recognize the complex track pattern in an adaptive segmented manner.

The application discloses a kind of space-time prediction methods based on wavelet coding and space-frequency dual-domain feature fusion, belong to space-time prediction technical field, comprising: obtaining by multiple time steps consisting of source space-time sequence, constructs input sequence and target sequence;Through wavelet downsampling, the input sequence is encoded step by step, and the spatial semantic representation with multi-scale characteristics is extracted;The representation after coding is organized as feature sequence according to time sequence, and time series modeling is carried out;Space-time feature containing state information and change clues is input into space-time conversion network, to realize cross space-time feature interaction;Through inverse wavelet reconstruction, the features after space-time conversion are decoded step by step, and future prediction sequence is generated.The application solves the problem that key information is easy to lose due to the use of conventional convolution sampling in existing methods, solves the problem that existing methods mainly rely on single spatial domain transformation, resulting in limited feature modeling, solves the problem that existing methods mainly rely on implicit learning, resulting in indirect time series modeling.

The application discloses an anti-interference low-frequency current signal extraction method, system and device, and belongs to the technical field of signal processing. The method comprises the following steps: acquiring a mixed current signal; using an adaptive notch filter to track power frequency fluctuation in real time, eliminating power frequency interference and harmonics thereof, and obtaining a first filtered signal; performing N-layer wavelet multi-scale decomposition on the first filtered signal to obtain high-frequency detail coefficients and low-frequency approximation coefficients of each layer; using an improved threshold function to perform threshold quantization processing on the high-frequency detail coefficients of each layer, the function being continuous at a threshold point and approximating a hard threshold characteristic when the amplitude of the coefficient is greater than the threshold; and performing wavelet reconstruction on the low-frequency approximation coefficients and the quantized high-frequency detail coefficients to obtain a pure low-frequency current signal. Through the cooperative processing of adaptive notch filtering and wavelet threshold denoising, and in combination with the improved threshold function, the application solves the problem of low-frequency current signal extraction difficulty in a strong noise background, and improves the accuracy of signal extraction and the anti-interference ability.

The invention discloses a wavelet noise reduction and gradient feature extraction method for buried pipeline welding seam positioning, and relates to the technical field of nondestructive testing. The method comprises: acquiring an original magnetic field signal of a pipeline area through a magnetic field acquisition device; wavelet transformation is carried out on the signal, wavelet reconstruction is carried out after a noise coefficient is suppressed through threshold processing, and a denoised signal is obtained; finally, gradient feature extraction is conducted, specifically, the gradient of the denoised signal is calculated and standardized, extreme points in the standardized gradient are recognized by setting a threshold value to serve as welding seam feature points, and therefore accurate positioning of the welding seam position is achieved. According to the method, non-periodic noise is effectively filtered through wavelet transformation, gradient analysis is combined to strengthen and extract the magnetic field abrupt change features of the welding seam, the problem that a traditional frequency domain method is poor in adaptability in buried pipeline welding seam positioning is solved, and the method has the advantages of being good in filtering effect, accurate in feature extraction and high in robustness.

The invention relates to the technical field of mining safety monitoring, in particular to a data noise reduction and error code reduction method and system for a mining rock burst monitoring sensor. The method comprises the following steps: firstly, collecting a vibration signal of a mine working face through a sensor, then carrying out wavelet analysis and decomposition on the vibration signal to obtain a high-frequency noise component and a low-frequency effective signal component, removing the high-frequency noise component, and retaining the low-frequency effective signal component; performing wavelet reconstruction on the reserved low-frequency effective signal component, generating an effective vibration signal after noise reduction, encoding the effective vibration signal, transmitting the effective vibration signal to a data receiving end, decoding encoded data at the data receiving end, identifying and correcting an error code generated in a transmission process through a verification rule of a data error correction code, and finally performing data transmission. And outputting effective vibration monitoring data without error codes. According to the invention, integrated cooperative processing of noise separation, hardware anti-interference and transmission error correction is realized, and the reliability and stability of a monitoring system are enhanced.

The present application relates to the transmission tower dynamic response analysis method, system, storage medium and equipment based on wind fluctuation process, its method includes steps: S1, obtains low frequency reconstruction component and high frequency reconstruction component, and selects component data representing data characteristics;S2, the nonlinear correlation coefficient is calculated using the processed data;S3, the time series of wind speed and tower response is divided into several fluctuation sections;The extreme value of wind speed trend term is used to divide wind speed stage;After wind speed data is normalized, wind speed fluctuation section is segmented according to time sequence;According to the division of wind speed fluctuation section, the fluctuation process of the wavelet reconstruction time sequence of the vibration acceleration and the inclination angle of each node of the tower is divided.The present application carries out wavelet decomposition to the response of the tower, uses neural network to learn and analyze the result, divides the data into segmented data related to wind field data, can obtain the fluctuation characteristics of the tower, and further provides reference basis for risk assessment and mechanical analysis of the tower.