52 results about "Generalized s transform" patented technology

The invention discloses an offshore wind power prediction method and device based on multi-model collaborative fusion. The method comprises the steps that historical data and original complex environment data of offshore wind power are acquired; the method comprises the following steps: decomposing multivariate data by utilizing multivariate joint mode decomposition, decomposing a signal into a plurality of mode component IMFS with different frequencies, and introducing mutual information entropy to divide a mode component data set into high-frequency components and low-frequency components; through generalized S transformation, obtaining a time-frequency diagram feature of each modal component; a VTransform prediction model and a TiDE prediction model are established to predict a high-frequency component and a low-frequency component respectively, and an improved Newton-Raphson algorithm INRBO is used to optimize model hyper-parameters; and carrying out weighted fusion on prediction results of the two models by utilizing an INRBO algorithm, and carrying out error correction on weighted results by adopting an extended Kalman filter (EKF) model to obtain a final prediction result. According to the method, the precision of offshore wind power prediction can be improved, and the power grid dispatching and the operation of the offshore wind power plant are optimized.

The invention provides a cast-in-place pile quality online monitoring method and system based on an ultrasonic array, and the method comprises the steps: transmitting and receiving ultrasonic waves through the ultrasonic array arranged in a cast-in-place pile, and obtaining a multi-channel sound wave signal; performing generalized S transformation processing on the multichannel sound wave signals to obtain a time-frequency matrix, and calculating a cross correlation coefficient between each channel signal and the defect-free reference signal; an amplitude matrix and an instantaneous phase gradient matrix are calculated based on the time-frequency matrix, neighborhood amplitude energy and a phase consistency factor are fused in a time-frequency domain, and a defect energy indication diagram of the pile body section is generated; calculating the energy entropy of the defect energy indication diagram in a full frequency band; and extracting an actually measured energy arrival time sequence, comparing the actually measured energy arrival time sequence with a theoretical arrival time sequence to construct a space distortion vector, and setting a threshold value by combining energy entropy to judge defects.

Embodiments of the present disclosure provide a low-voltage fault arc identification method and apparatus, a medium and a device. The method comprises: using a period difference method to perform feature extraction on an acquired low-frequency signal within 3kHz of an electricity meter, and determining a low-frequency fault arc time domain feature vector; using generalized S-transform to perform feature extraction on an acquired intermediate-frequency current signal within 100kHz of the electricity meter, and determining an intermediate-frequency fault arc energy feature; inputting the low-frequency fault arc time domain feature vector and the intermediate-frequency fault arc energy feature into a pre-trained neural network identification model, and outputting fault arc signal presence information; and on the basis of the fault arc signal presence information, determining whether a fault arc signal is present or not.

The invention relates to the technical field of power grid equipment fault processing, provides a fault diagnosis method, device and equipment of a high-voltage vacuum circuit breaker, a storage medium and a program product, and can improve the fault diagnosis accuracy of the high-voltage vacuum circuit breaker. The method comprises the following steps: acquiring a target vibration signal of a target high-voltage vacuum circuit breaker in a closing operation process; changing the target vibration signal from one dimension to two dimensions according to a Grubrum angle field image coding mode, and obtaining a two-dimensional time domain resolution graph; according to a generalized S transformation mode, the target vibration signal is changed from one dimension to two dimensions, and a two-dimensional frequency domain resolution graph is obtained; and inputting the two-dimensional time domain resolution graph and the two-dimensional frequency domain resolution graph into a fault diagnosis model to obtain a fault diagnosis result of the target high-voltage vacuum circuit breaker.

The invention belongs to the field of power distribution network traveling wave echo analysis, and particularly discloses a power distribution network traveling wave echo identification method based on time-frequency enhancement and multi-evidence fusion. The method comprises a closed-loop process of adaptive frequency band generalized S transformation, low-rank SVD time-frequency enhancement, double-threshold hysteresis starting point detection, physical template GLRT discrimination, double-peak fixed interval scanning and multi-band consistency rechecking. Firstly, a clear time-frequency energy diagram is obtained through preprocessing and self-adaptive frequency band S transformation, then power frequency and a slowly-changing background are suppressed through low-rank reconstruction, and then a candidate starting point is locked on a time energy track by adopting a hysteresis mechanism of'low-threshold triggering-high-threshold confirmation '; carrying out GLRT statistical discrimination in the candidate window by using single-peak / double-peak / damped ringing three types of physical templates, and quantifying time delay between peaks through fixed interval scanning when branch reflection exists; and finally, taking the energy consistency of the low / medium / high bands as cross-band physical evidence, and eliminating false detection only caused by high-frequency ringing.

The invention belongs to the technical field of discharge noise processing, and particularly relates to a staff noise reduction method for partial discharge of an ethylene propylene rubber cable. Comprising the following steps of: 1, processing a periodic narrow-band interference signal by adopting an energy ratio method, and removing the narrow-band interference in a partial discharge signal; 2, reprocessing the processed point signal by adopting a generalized S transformation mode; carrying out modular matrix algorithm transformation on the high-frequency partial discharge signal; 3, setting a basic value gamma by adopting a smaller threshold method, and obtaining the number of all narrowband interferences; 4, acquiring effective data of the center frequency; 5, wave crest data below the standard curve are set as interference frequency data, and the wave crest value is an interference wave crest; 6, extracting frequency peak values corresponding to all narrowband interference signals in the atlas according to the interference peak definition; and 7, setting a frequency spectral line corresponding to the peak value as kp. The method is more thorough in noise processing and has good application value.

The invention discloses a section positioning method for a latent fault of an active medium-voltage distribution cable, relates to the technical field of power distribution network fault positioning, and solves the problems of short duration, small fault energy and difficulty in fault positioning of the latent fault of the cable in the prior art. The method comprises the following steps: firstly, detecting whether a latent fault occurs in real time, if so, collecting 1 / 4 period zero-sequence current data of each monitoring point of a fault line after the fault, and performing generalized S transformation; then, zero-sequence current transient energy value features, zero-sequence current transient energy polarity features and zero-sequence current amplitude time-frequency matrix correlation features of the sections are extracted; based on the three-dimensional coordinates of the sections, Euclidean distances among the sections are calculated, and a correlation matrix is established; and finally, calculating a comprehensive distance similarity coefficient between each section and other sections, and judging a fault section based on the comprehensive similarity coefficient of each section.

The invention discloses a low-slow-small target micro-motion feature extraction method based on a GST-WT combination, and relates to the field of low-slow-small target micro-motion feature extraction, and the method comprises the steps: obtaining a multi-band radar echo signal, carrying out the preprocessing of the multi-band radar echo signal, carrying out the time-frequency analysis of the preprocessed signal through a GST-WT combination model, and carrying out the extraction of the micro-motion feature of a low-slow-small target. Obtaining time-frequency characteristics of improved generalized S transform and Morlet wavelet transform; carrying out linear interpolation on the time-frequency characteristics of the improved generalized S transform; fusing the time-frequency features by adopting a linear weighting strategy to obtain fused time-frequency features; the feature extraction effect is quantitatively evaluated in combination with the energy entropy and the energy concentration degree index, the advantages of GST global time-frequency analysis and WT local self-adaption are fully played, the micro-motion features of the target are effectively separated and accurately extracted, the time-frequency resolution, the anti-noise capability and the feature stability are improved, and the target tracking accuracy is improved. Technical support is provided for low-altitude, slow and small target detection in scenes of low-altitude security and protection, unmanned aerial vehicle supervision and the like.

The invention provides a radar interference signal identification method and system based on a deep fusion neural network, and the method comprises the steps: obtaining an echo signal of a radar, carrying out the generalized S transformation of the echo signal to generate a time-frequency image, carrying out the preprocessing of the time-frequency image, and obtaining a preprocessed time-frequency image; and identifying the preprocessed time-frequency image by using a pre-trained deep fusion neural network model, and outputting an interference signal type. According to the method, the time-frequency image of the radar interference signal with high time-frequency resolution is obtained by adopting generalized S transformation, the time-frequency domain characteristics of the radar interference signal can be flexibly described, and the quality of a learning sample is ensured.

The application provides a clastic rock reservoir seismic sequence high-resolution division method and system, S1 obtains the sensitive well logging curve of the well closest to the seismic trace, adopts a cumulative prediction error method to process the sensitive well logging curve to obtain a cumulative prediction error curve, and performs generalized S transformation on the sensitive well logging curve to obtain time-frequency distribution characteristics; S2 performs long-term and short-term cyclic sequence interface identification by using the cumulative prediction error curve and the time-frequency distribution characteristics to obtain long-term and short-term cyclic sequence interfaces; S3 calibrates the long-term and short-term cyclic sequence interfaces to the well seismic trace, performs well seismic trace seismic sequence division, adopts an atomic matching tracking technology to perform well-seismic time-frequency matching analysis in the time-frequency domain, identifies seismic implicit sequence interfaces, and obtains a clastic rock reservoir high-resolution seismic sequence framework after all seismic traces are calculated. The application obtains a high-resolution seismic sequence framework, and provides strong support for improving the precision and accuracy of clastic rock reservoir prediction.

The invention belongs to the technical field of geophysical signal processing and mine micro-seismic monitoring, and relates to a micro-seismic signal first arrival pickup method based on sparse generalized S transformation. Aiming at the problems of low signal-to-noise ratio, complex noise type, weak first-arrival wave energy and difficulty in accurately picking up first arrival of mine micro-seismic signals, the method comprises the following steps of: firstly, performing time-frequency analysis on the micro-seismic signals by utilizing sparse generalized S-transform, then extracting a maximum energy point in a time-frequency domain, performing connected domain decomposition on a time-frequency spectrum, screening a time-frequency connected region containing main energy, and finally extracting the maximum energy point from the time-frequency domain; and irrelevant noise interference is suppressed. And on the basis, performing energy superposition and normalization processing on the main energy time-frequency block in a constraint frequency band, and combining an energy peak position and an energy transition characteristic in a time window to realize accurate judgment of the arrival time of the microseismic signal first arrival wave.

The invention relates to the technical field of seismic data processing, in particular to an angle gather random noise adaptive suppression method based on generalized S transformation. The method comprises the following steps: selecting a seismic signal in angle gather data to carry out generalized S transformation time window adjustment parameter optimization to obtain a time window parameter under a CM optimal criterion; calculating a corresponding generalized S transform time-frequency spectrum for angle gather data under the same elastic parameter and different angles by using the obtained time window parameter; performing superposition, normalization and threshold calculation on the obtained time-frequency spectrum to obtain a corresponding time-frequency domain adaptive filter; performing point multiplication on the time-frequency spectrum of each seismic signal and the time-frequency domain adaptive filter to obtain a filtered time-frequency spectrum; and inverse transformation is carried out on the filtered time-frequency spectrum of each seismic signal, and random noise adaptive suppression of angle gather data is completed. According to the method, the problem of parameter selection in a traditional model-driven denoising algorithm is avoided by utilizing an adaptive algorithm, manual intervention is reduced, the analysis cost is reduced, and the processing efficiency is improved.

The invention discloses a method for tracing a dissipated energy flow of a power system. The method comprises the following steps: firstly, setting a sensitive subsynchronous oscillation online detection link with relatively low calculation complexity, and positioning subsynchronous oscillation in time at the initial stage of oscillation; secondly, retaining a subsynchronous frequency band component in the signal based on synchronous compression generalized S-transform; taking a 60 Hz system as an example, the subsynchronous oscillation (SSO) frequency is between 5 Hz and 55 Hz. Therefore, the band-pass filter in the frequency range can be used for variables such as voltage and active / reactive power; then, for the preprocessed and filtered signals, calculating instantaneous energy dissipation of each branch or each bus; and finally, comparing the instantaneous energy dissipation of the branches and the bus, and judging the oscillation source. The method is simple in calculation, can accurately calculate the frequency, the amplitude and the phase angle of each harmonic wave of the power system, and has a very high practical value.

The invention provides a coal measure strata tight sandstone gas earthquake identification method and device, and relates to the technical field of earthquake gas reservoir prediction, and the method comprises the steps: carrying out the coal seam multiple suppression processing of the seismic data of a target work area, and obtaining the preprocessed seismic data; generalized S transformation is carried out on the preprocessed seismic data to obtain time-frequency spectrum data; extracting a plurality of gas layer sensitive time-frequency attributes from the time-frequency spectrum data; and performing multi-attribute well control fusion processing on the plurality of gas reservoir sensitive time-frequency attributes to obtain a gas reservoir sensitive factor, and based on the gas reservoir sensitive factor, generating an earthquake identification map for identifying the tight sandstone gas of the coal measure strata. According to the method, the data signal-to-noise ratio is increased by pressing coal bed interlayer multiples, multi-angle gas bed sensitive time-frequency attributes are extracted through high-resolution time-frequency analysis, a visual recognition map is generated through well control fusion, the problem that tight sandstone gas weakness response is difficult to recognize due to coal bed strong shielding is systematically solved, and the method has the advantages of being high in practicability and the like. And the identification precision and reliability are improved.

A method and apparatus for identifying arc discharge patterns in short-gap oil of transformers are disclosed. The method involves conducting arc discharge experiments on oil-paper insulation based on different discharge defect models of oil-immersed power transformers; collecting multi-physical feature signals of short-gap oil arc discharge from the oil-paper insulation arc discharge experiments using sensors; processing the multi-physical feature signals using generalized S-transform to generate a multi-physical signal time-frequency spectrum; and inputting the generated multi-physical signal time-frequency spectrum into a multi-channel deep separable convolutional neural network model to achieve arc discharge pattern identification through joint detection of multiple physical quantities.

The application provides a time-varying coherence estimation method of ground motion based on Bayesian collaborative optimization. The method constructs double target constraints covering physical consistency and spectrum graph distinguishability through generalized S transform and time-frequency bidirectional exponential smoothing method, cooperatively determines the optimal combination of generalized S transform kernel parameters and exponential smoothing operator by using a Bayesian optimization algorithm, and outputs an optimal parameter set and a corresponding time-varying coherence spectrum graph. Therefore, more real data reflecting the spatial variation characteristics of ground motion can be obtained, and the design requirements under the limit adverse state can be met.

The application discloses a kind of sparse generalized W transform gas reservoir characterization time-frequency analysis method, for the first time L1 norm is introduced to the mathematical relationship between generalized W transform and seismic signal for sparse constraint, and is solved using Bregman iteration algorithm, to obtain a kind of analysis result with higher time-frequency resolution.The method absorbs the advantage that generalized W transform highlights low-frequency information of seismic signal, avoids the problem of main frequency splitting, and provides a more sparse time-frequency representation for non-stationary seismic signals. When applied to the time-frequency analysis of actual seismic data, it can provide a high-precision seismic spectral decomposition result for gas reservoirs, thereby more accurately delineating the low-frequency abnormal area of gas reservoir.

The invention relates to the field of electrical equipment fault diagnosis and safety protection calculation, in particular to a series arc fault and reignition detection method based on time-frequency characteristics. The method comprises the following steps: collecting and preprocessing a line current signal; performing high-resolution time-frequency analysis on the signal by adopting generalized S-transform, and extracting time-frequency characteristics in a frequency band; constructing a two-dimensional feature vector; and global optimization is carried out on the support vector machine hyper-parameters by using a grey wolf optimization-particle swarm hybrid algorithm, a classification model is established, and accurate identification of three working conditions of a normal state, a fault arc and contact arc reignition is realized. According to the method, the classification model is established, the defects of a traditional method in the aspect of distinguishing instantaneous reignition and continuous fault arcs are overcome, the method has the advantages of being high in recognition accuracy, high in anti-interference capacity, clear in physical significance and the like, and a reliable technical scheme is provided for early fault early warning and intelligent protection of an electrical system.

The present invention provides a method, device, equipment, and medium for testing electrochemical impedance spectroscopy of new energy batteries, and relates to the technical field of electrochemical impedance spectroscopy of new energy batteries. The method comprises: using a preset generalized S transform to process a one-dimensional time-series signal of output voltage and a one-dimensional time-series signal of output current to obtain a two-dimensional time-frequency matrix of output voltage and a two-dimensional time-frequency matrix of output current; determining a target frequency response function estimation method based on the minimum value of the average current noise level and the average voltage noise level; when it is determined that the noise comes from voltage, calculating a weight matrix based on the energy of the measured current signal to obtain a target impedance value; when it is determined that the noise comes from current, calculating a weight matrix based on the energy of the measured voltage signal to obtain a target impedance value. The energy-weighted frequency response function estimation technology of the present invention extracts effective harmonic information from the two-dimensional time-frequency matrix, and accurately obtains the electrochemical impedance spectrum by giving priority to the more contributing frequency components.

The present invention discloses a method for extracting micro-motion features of low-altitude, slow and small targets based on a GST-WT combination, which relates to the field of micro-motion feature extraction of low-altitude, slow and small targets. The method comprises the following steps: acquiring a multi-band radar echo signal, preprocessing the multi-band radar echo signal, and adopting a GST-WT combination model to perform time-frequency analysis on the preprocessed signal to obtain time-frequency features of an improved generalized S transform and a Morlet wavelet transform; performing linear interpolation on the time-frequency features of the improved generalized S transform; adopting a linear weighting strategy to fuse the time-frequency features to obtain fused time-frequency features; and quantitatively evaluating the feature extraction effect by combining energy entropy and energy concentration indicators. By giving full play to the advantages of GST global time-frequency analysis and WT local adaptation, the micro-motion features of the target are effectively separated and accurately extracted, and the time-frequency resolution, noise resistance and feature stability are improved, thereby providing technical support for the detection of low-altitude, slow and small targets in scenarios such as low-altitude security and unmanned aerial vehicle monitoring.

The invention provides a fracture detection method based on sub-azimuth frequency variation inversion, and the method comprises the steps: building a suitable HTI medium shale equivalent model based on the geological data of shale through employing a self-consistent theory (SCA), a differential equivalent medium theory (DEM) and a Chapman fracture theory; a shale rock physical model is used for researching the influence of fracture density and gas saturation on the dispersion and attenuation of a shale reservoir, and sensitive parameters are optimized to construct a rock physical interpretation quantity version of a dispersion attribute; carrying out azimuth processing and ellipse fitting on the original OVT data to obtain a dominant azimuth of fracture development, and carrying out time-frequency analysis on pre-stack seismic data based on an inversion spectrum decomposition method of generalized S transformation to obtain near, medium and far offset data of different frequencies; and performing frequency variation inversion by using a further derived Ruger approximation formula to obtain a frequency dispersion attribute, and predicting the fracture density and the gas saturation through a rock physical quantity version. The method can be used for predicting the fracture density and the gas saturation of the shale, and the prediction precision is higher.

The present invention discloses an ECG identity recognition method based on quality assessment and deep transfer learning. First, an ECG signal quality assessment algorithm based on Support Vector Machine (SVM) is proposed to classify the original ECG signal into three different quality levels. Second, a wavelet transform denoising algorithm is proposed to denoise the "suspicious" signal, thereby obtaining a denoised "qualified" signal. Then, a generalized S transform is used to perform time-frequency domain analysis on the "qualified" signal and the denoised "qualified" signal, converting the one-dimensional ECG signal into a two-dimensional ECG trajectory diagram as a model input. Finally, by improving and optimizing the original GoogleNet network model, a secondary deep transfer recognition model based on GoogleNet is constructed. The GoogleNet model with a deeper network layer is selected for transfer learning recognition training based on ECG signals, which improves recognition accuracy while reducing recognition and authentication time.

The embodiment of the invention provides a cable defect positioning method and device, equipment, a storage medium and a program product. The method comprises the following steps: generating a Gaussian envelope linear frequency modulation excitation signal through a signal generator; determining a Gaussian envelope linear frequency modulation excitation signal as an incident signal of the cable to be detected; injecting the incident signal into the to-be-detected cable to obtain real-time waveform information of the incident signal and real-time waveform information of the reflected signal of the to-be-detected cable; determining adaptive parameters; respectively carrying out discrete generalized transformation calculation on the real-time waveform information of the incident signal and the real-time waveform information of the reflected signal according to the self-adaptive parameters to obtain a time-frequency distribution result of the incident signal and a time-frequency distribution result of the reflected signal; and determining the defect position of the to-be-detected cable according to the time-frequency distribution result of the incident signal and the time-frequency distribution result of the reflected signal. According to the method, the purposes of high response speed and high positioning precision in the cable defect positioning process can be achieved.

This invention addresses the limitations of single signal sources in containing limited fault information, failing to comprehensively reflect the operating status of centrifugal fire pumps, and the low accuracy of fault diagnosis in centrifugal fire pumps due to the difficulty of dynamically and adaptively differentiating feature weights during fault diagnosis using convolutional neural networks (CNNs). To address these issues, a fault diagnosis method for centrifugal fire pumps based on multi-source information fusion and an improved EfficientNet-b0-TL model is proposed. Vibration signals from centrifugal fire pumps are collected from different angles and locations using multiple sensors, and the signals are cleaned, aligned, and denoised. A generalized S-transform is used to perform time-frequency transformation on the denoised signals, and time-frequency features are extracted and fused. A CBAM module is introduced to improve the EfficientNet-b0-TL model, constructing a fault diagnosis model. This model is then applied to actual centrifugal fire pump experimental data, and the results show that this method has high fault diagnosis accuracy.

The invention relates to the technical field of oil and gas geophysics, and discloses an earthquake characterization method and device for predicting the fluidity of underground shale oil and application. The method comprises the following steps: performing generalized S transformation based on post-stack seismic data to obtain a time-frequency domain result; fitting a wavelet spectrum; and based on a time-frequency domain result and the fitted wavelet spectrum, extracting a fluid fluidity attribute as a measurement index of the overall fluidity of the shale oil reservoir. According to the device, a transformation module is used for performing generalized S transformation based on post-stack seismic data to obtain a time-frequency domain result; the fitting module is used for fitting a wavelet spectrum; and the extraction module is used for extracting the fluid flowability attribute as a measurement index of the overall flowability of the shale oil reservoir based on the time-frequency domain result and the fitted wavelet spectrum. According to the technical scheme, the reservoir fluidity is mainly represented by using the earthquake low-frequency information, and the actual position of the underground shale oil dessert can be well predicted through the attribute value.

The invention discloses a coherent MIMO radar multi-channel accumulation detection method. According to the method, firstly, single-channel signals are accumulated through generalized Radon-Fourier transform (GRFT), then a virtual direction vector is introduced to replace a traditional direction vector, inter-channel differences and target motion parameters are effectively decoupled, and therefore inter-channel envelope differences and phase differences can be estimated in a lower dimension. And based on the estimated difference information, constructing an envelope alignment function and a phase compensation function, and finally realizing coherent accumulation of the multi-channel signals. According to the method, the echo signals of the high-speed maneuvering target can be effectively accumulated under the condition of low signal-to-noise ratio, and the calculation complexity is remarkably reduced.

The application discloses a high-speed maneuvering target detection method based on deep learning and generalized Radon-Fourier transformation, and the method comprises the following steps: obtaining three-dimensional data through echo signal preprocessing; inputting the three-dimensional data into a target / noise classification network to obtain the probability of existence of a target in each distance unit; inputting the data of the distance unit in which the target may exist into a velocity acceleration classification network to output the search interval of the velocity and acceleration of the target; accumulating energy in the search interval by using generalized Radon-Fourier transformation; and finally obtaining a detection result through constant false alarm detection. The application combines the neural network and the generalized Radon-Fourier transformation, and can effectively balance the detection performance and the calculation amount. The application has a high accuracy in estimating the distance unit where the target is located and the motion parameters of the target; in addition, unnecessary search operations can be reduced according to the rough estimation result of the network, so that the calculation amount is greatly reduced.

The application discloses a ship scattering point three-freedom rotation micro-Doppler frequency extraction method, belongs to the technical field of radar signal processing and target identification, is used for micro-Doppler frequency extraction, and comprises the following steps: constructing a ship motion geometric model, and constructing a three-component amplitude modulation and frequency modulation micro-Doppler frequency model; performing generalized S transformation on a one-dimensional echo in the azimuth direction of a range cell, synchronously compressing the generalized S transformation in combination with a time-frequency track and an instantaneous frequency operator, and calculating a micro-Doppler frequency estimation value; constructing an adaptive ridge filter, decomposing the micro-Doppler frequency estimation value to obtain a micro-Doppler frequency estimation value subcomponent, and linearly superimposing the subcomponent to obtain an unoptimized micro-Doppler frequency estimation value. Through the construction of the three-freedom rotation micro-Doppler frequency model and the synchronous compression generalized S transformation and adaptive ridge filter method, high-precision and high-resolution extraction of the rotation micro-Doppler frequency of the ship scattering point is realized, and the problems of multi-component time-frequency cross and noise interference are effectively solved.

The invention discloses a stratum quality factor Q estimation method based on a centroid frequency movement method. The method has the main functions of improving the signal-to-noise ratio of post-stack data and performing reservoir prediction according to seismic attenuation characteristics. And the time-frequency domain is focused for analysis to obtain a more accurate amplitude spectrum result. According to the method, an improved frequency shift method based on generalized S transformation is applied to attenuation estimation of post-stack seismic data, and the frequency resolution is increased as much as possible on the basis of ensuring that the time resolution can identify a target layer. According to the requirements of actual data, by adjusting parameters lambda and p under generalized S transformation, a more appropriate frequency resolution and a more accurate amplitude spectrum result are obtained, the seismic data resolution is improved, and finer interlayer information is identified. From the perspective of extracting the post-stack profile Q value model, the improved frequency shift method based on generalized S transformation can establish a relatively accurate underground Q value model, and the Q value model can be applied to the post-stack profile through conventional inverse Q filtering processing.

The invention provides a new energy battery electrochemical impedance spectroscopy test method, device, equipment and medium, and relates to the technical field of new energy battery electrochemical impedance spectroscopy, the method comprises the following steps: processing an output voltage one-dimensional time sequence signal and an output current one-dimensional time sequence signal by using preset generalized S transformation; obtaining an output voltage two-dimensional time-frequency matrix and an output current two-dimensional time-frequency matrix; determining a target frequency response function estimation mode according to the minimum value of the current average noise level and the voltage average noise level; under the condition that the noise is determined to be from the voltage, calculating a weight matrix based on the energy of the measured current signal to obtain a target impedance value; and under the condition that the noise is determined to be from the current, calculating a weight matrix based on measured voltage signal energy to obtain a target impedance value. Effective harmonic information is extracted from a two-dimensional time-frequency matrix based on a frequency response function estimation technology of energy weight, and an electrochemical impedance spectrum is accurately obtained by preferentially considering more contributive frequency components.