69 results about "Wavelet reconstruction" patented technology

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 is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention relates to the technical field of image processing, in particular to an insulator image enhancement method based on unmanned aerial vehicle infrared image denoising. The method comprises the following steps: preprocessing an original infrared image acquired by an unmanned aerial vehicle, and performing multi-scale decomposition on the preprocessed image by using wavelet transform to obtain a high-frequency detail coefficient and a low-frequency approximation coefficient; a high-frequency detail coefficient is processed by adopting an adaptive threshold value, Wiener filtering is carried out on a low-frequency approximation coefficient, and a de-noised image is obtained after wavelet reconstruction and guide filtering processing; based on the de-noised image, an edge detection operator is adopted to perform preliminary edge extraction, non-maximum suppression and dual-threshold processing are combined to obtain edge information, and a morphological edge reconstruction technology is adopted to connect edge gaps; and generating a final image after feature enhancement through adaptive contrast enhancement based on local temperature distribution and image fusion processing. According to the invention, efficient denoising and accurate feature enhancement of the insulator infrared image collected by the unmanned aerial vehicle can be realized.

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 invention discloses a cloud resource use prediction method and system based on wavelets and an attention mechanism. The method comprises the following steps: firstly, acquiring a multi-dimensional resource use sequence of a target server and dividing the multi-dimensional resource use sequence into a training subset and a test subset; generating an input sample set by adopting a sliding window mechanism; then constructing a cloud resource prediction model comprising a wavelet attention network, a multi-attention module, a wavelet reconstruction module and a memory enhancement self-attention module; decomposing the time window sample into a low-frequency feature component and a high-frequency feature component through a wavelet attention network; respectively generating a low-frequency feature vector and a high-frequency feature vector by using a multiple attention module; fusing into a reconstructed sample sequence through a wavelet reconstruction module; generating enhanced feature representation through a memory enhanced self-attention module; and finally, outputting a cloud resource use prediction sequence in a future time period through a full-connection projection layer. According to the method, the multi-frequency-domain time sequence characteristics can be effectively separated, the interaction relationship among the multi-element resources can be modeled, and the prediction precision and generalization ability are remarkably improved.

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 invention discloses an image noise reduction method and device, equipment and a medium, which are used for realizing multi-channel wavelet high-frequency noise reduction. The method provided by the invention comprises the steps of performing wavelet decomposition on a to-be-denoised image to obtain a plurality of high-frequency components, and determining a plurality of high-frequency channel vectors; each high-frequency channel vector is obtained by combining high-frequency components of a plurality of channels of the to-be-denoised image at the same image coordinate position; for each high-frequency channel vector, determining a similar vector of the high-frequency channel vector, and generating a high-frequency similar vector matrix; on the basis of the high-frequency similar vector matrix, determining a parameter for performing noise reduction on the high-frequency channel vector; performing noise reduction processing on the high-frequency channel vector by using a parameter for performing noise reduction on the high-frequency channel vector to obtain a high-frequency channel vector after noise reduction; and on the basis of each denoised high-frequency channel vector, performing wavelet reconstruction by adopting a mode corresponding to wavelet decomposition to obtain a denoised image.

The application discloses a current signal denoising method and system for fault arc detection and a storage medium, comprising: collecting the working current of the load at the current time, and analyzing to obtain the noise estimation spectrum of the working current at the current time; wavelet decomposing the noise estimation spectrum and the working current of the load at the subsequent time respectively to obtain first wavelet coefficients and second wavelet coefficients; correcting the second wavelet coefficients based on the first wavelet coefficients to obtain third wavelet coefficients; and wavelet reconstructing the third wavelet coefficients to obtain a time domain enhanced signal. The improved wavelet threshold function improves the defects of poor continuity and constant deviation of the traditional wavelet threshold function, and the current signal processed by the denoising algorithm not only suppresses the existence of noise, but also improves the arc characteristics of the signal, so that the detection performance of the fault arc detection algorithm is significantly improved.

The invention discloses a cardiac mechanical signal processing method and device based on wavelet reconstruction. The method comprises the following steps: performing trend removal and frequency band extraction on an original SCG signal, and reserving main mechanical energy components corresponding to a heart sound event; then wavelet decomposition and main scale reconstruction are carried out, and time domain features of key components such as S1 and S2 structures similar to PCG signals are strengthened, so that heart sound-like signals which are similar to PCG in form and clear in rhythm are generated. Compared with an original SCG signal, the heart sound-like signal obtained through reconstruction is remarkably improved in the aspects of rhythm definition, form consistency and peak stability. And further performing rhythm analysis on the reconstructed signal, introducing physiological constraints, and screening to obtain a cardiac trigger point sequence, thereby realizing high-precision cardiac recognition. The device can be integrated with medical image equipment and wearable equipment, and the functions of heart sound gating, synchronous triggering or auscultation auxiliary analysis and the like are achieved.

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 application discloses a wavelet denoising method and system for SAR images and electronic equipment, and relates to the technical field of remote sensing image processing. The method comprises the following steps: determining a wavelet and a wavelet decomposition level, and calculating a wavelet coefficient matrix obtained by wavelet decomposition of a to-be-processed SAR image; determining an initial adaptive threshold and a corresponding estimated wavelet coefficient matrix according to the wavelet coefficient matrix based on a preset adaptive threshold estimation function; removing a wavelet coefficient when any wavelet coefficient is less than the initial adaptive threshold; when any wavelet coefficient is greater than or equal to the initial adaptive threshold, constructing a risk function based on the wavelet coefficient and the estimated wavelet coefficient matrix; performing minimum calculation on the risk function based on a minimum maximum estimation criterion to obtain optimal wavelet coefficients after threshold processing; and performing wavelet reconstruction on the optimal wavelet coefficients to obtain a denoised SAR image. The application effectively removes noise in the image while better maintaining the image edge, thereby improving the denoising effect.

The invention discloses an image noise reduction method and device, equipment and a medium, which are used for improving an image noise reduction effect. The method provided by the invention comprises the steps of obtaining a to-be-denoised image, and performing wavelet decomposition on the to-be-denoised image to obtain a wavelet decomposition result which comprises a low-frequency component and a high-frequency component; carrying out pixel point clustering on the to-be-denoised image to obtain at least one cluster; for each cluster, based on a wavelet decomposition result, determining a corresponding relation between a brightness value and a noise estimation value of at least one pixel point of the cluster; determining a noise reduction threshold value corresponding to the cluster based on the pixel value of each pixel point of the cluster and the corresponding relationship; performing noise reduction processing on the high-frequency component of each pixel point of the cluster by using the noise reduction threshold value corresponding to the cluster; and performing wavelet reconstruction based on the low-frequency component of each cluster and the high-frequency component after noise reduction processing to obtain an image after noise reduction processing.

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 a fault diagnosis method for an aircraft executing mechanism. The fault diagnosis method comprises the following steps: collecting a motor signal of the aircraft executing mechanism; sequentially carrying out wavelet decomposition, threshold processing, wavelet reconstruction and denoising preprocessing to obtain denoised motor current one-dimensional data; constructing a fault diagnosis model, wherein the fault diagnosis model comprises a convolutional neural network CNN layer, a long short-term memory network LSTM layer, a channel attention mechanism and a full connection layer; inputting the motor current one-dimensional data into a convolutional neural network (CNN) layer and a long short-term memory (LSTM) layer, applying a channel attention mechanism on the LSTM layer, and performing weight scoring on the output of the LSTM layer to obtain a trained fault diagnosis model; and carrying out aircraft fault detection on the test set by using the trained fault diagnosis model to obtain a fault diagnosis result. According to the method, online learning can be realized, new and unknown fault modes can be immediately adapted, and dependence on historical fault data is reduced.

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 present invention proposes a method and system for detecting early-stage faults of distribution network insulators taking interference events into consideration, comprising: obtaining the zero-sequence current of the distribution network insulator and calculating the zero-sequence current mutation rate based on the zero-sequence current; if the zero-sequence current mutation rate is greater than a set threshold, determining that a fault or disturbance event has occurred in the distribution network; obtaining original recorded wave data of the distribution network insulator and filtering the original recorded wave data based on a wavelet reconstruction algorithm; for the filtered signal, identifying a spike waveform based on the extreme value point of the fault phase voltage; identifying a nonlinear distorted waveform based on sinusoidal fitting of the zero-sequence current, and, based on the identification result, satisfying a set first criterion or a set second criterion, determining that an early-stage insulator fault may have occurred; and then calculating the wavelet energy entropy of the signal; if it is greater than a set entropy threshold, determining that an early-stage insulator fault has occurred in the distribution network; otherwise, determining that an interference event has occurred.

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 anti-interference detection of ultrasonic gas meters, and discloses an anti-interference detection system and method of an ultrasonic gas meter. The method comprises the following steps: synchronously sampling an analog echo amplitude signal and a digital emission trigger pulse and removing a direct current component; sending the zero-average signal into a self-created three-point filter, and extracting approximation and detail coefficients of each layer based on three-layer wavelet decomposition; self-adaptive thresholds are calculated respectively, and soft threshold denoising is carried out; an anti-interference waveform is recovered through reverse wavelet reconstruction; sound wave round-trip time difference is accurately extracted by combining digital triggering and Hilbert envelope detection; a pre-collected known flow sample is used, and online least square linear fitting is carried out to obtain a calibration constant; the method is applied to time difference-flow conversion, and an anti-interference flow value is output in real time. The method is compact in link and cooperative in module, and can dynamically suppress field noise, retain echo details, stabilize a positioning peak value and automatically complete calibration and calibration.