37 results about "S transform" patented technology

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.

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 application discloses a partial discharge signal denoising method, comprising the following steps: building a partial discharge signal detection platform and a partial signal collection platform; measuring and detecting characteristic signals in the partial discharge signals; screening and classifying the measured signals through a Gaussian test to obtain the characteristic signals; processing and denoising the signals through a fast S transform algorithm and a singular value decomposition SVD algorithm; testing and diagnosing the denoising effect by using two coefficients of noise suppression ratio and amplitude attenuation ratio; and displaying the diagnostic analysis result on a platform display interface. The application discloses a partial discharge signal diagnostic system based on a fast S transform and an adaptive singular value, and solves the problems that the existing technical methods are difficult to balance the denoising effect and extraction speed in the characteristic extraction of the partial discharge signals and are difficult to extract the characteristics.

The application discloses a flexible DC transmission line intra-zone and extra-zone fault discrimination method based on S transform, a method of waveform continuation is used to cover the subsequent wave with the wave tail of the first wave, the influence of the subsequent wave head on the frequency characteristics is reduced, the S transform is used to make the modulus time frequency matrix of the continued waveform, the row vectors of the modulus time frequency matrix are integrated, the corresponding amplitude-frequency curve is obtained, the energy operator is used to weight the amplitude-frequency curve to improve the proportion of high-frequency components, the energy accumulation of different frequency bands is constructed, the ratio of the high-frequency band energy accumulation and the total frequency band energy accumulation is taken as the intra-zone and extra-zone fault recognition criterion, when the ratio is greater than the threshold value, the intra-zone fault is determined, otherwise, the extra-zone fault is determined. The method meets the rapidity requirement of the flexible DC transmission line protection, has high sensitivity, and has important reference value for the development of the flexible DC transmission protection.

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 discloses a method for predicting the remaining service life of a tool. The method comprises the steps that 1, multi-source sensor signal data are obtained and preprocessed; step 2, constructing a time-frequency feature set for the obtained signal data by applying S transformation; step 3, a Kolmogorov-Arnold attention distribution network (KA-AAN) is built, and the KA-AAN is used for attention distribution; 4, predicting the residual service life RUL of the milling cutter through the KAN; and 5, model training and performance verification are carried out. By introducing the KAN, the feature importance is dynamically allocated in the time-frequency domain, and the accuracy and robustness of prediction of the remaining service life of the tool are improved. Through combination of an attention mechanism and a learnable spline function, the model can capture a complex non-linear relationship in a cutter degradation process, the adaptive capacity under different processing conditions is enhanced, the over-fitting risk is reduced, and the generalization energy of the prediction model is improved.

The present application relates to the technical field of fault location of UHVDC transmission, in particular to a single-ended accurate fault location method for UHVDC transmission system, when single-pole grounding fault occurs in the line, positive and negative voltage and current signals at the installation position of the protection are collected, line mode and zero mode voltage reverse waves are obtained through Kalman-Bell transformation decoupling, the first wave head is positioned by difference and effective time domain data is intercepted, S transform is carried out for time-frequency mapping by using adaptive window width, time-frequency energy redistribution is realized by iterative optimization group delay fixed point, time direction high convergence time-frequency distribution is obtained, double-mode wave arrival time is extracted frequency by frequency and modulus time difference is calculated, initial fault distance sample is obtained by combining frequency variable propagation speed, the probability density peak value is taken as the final fault location result through non-parametric kernel density estimation, the present application does not need double-ended communication and synchronization, and greatly improves the wave arrival time extraction accuracy, anti-interference ability and fault location robustness.

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 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 synthetic aperture radar radio frequency interference suppression method and system based on S transform, a storage device and electronic equipment, and comprises the following steps: detecting interference-containing echo data by a method based on distance spectrum flatness and symmetry, and marking interference-containing pulses; calculating the amplitude spectrum of the interference-containing pulses, and performing smoothing and normalization processing; calculating the optimized window parameters after scaling the normalized amplitude spectrum; converting the interference-containing pulses to the time-frequency domain based on the optimized window parameters; positioning the interference position in the time-frequency domain through the OSTU algorithm, and eliminating the radio frequency interference through the wave-trap technology; inversely converting the processed interference-free pulses to the time domain based on the optimized window parameters; and combining all the processed interference-containing pulses and interference-free pulses to form new interference-free echo data. The application can effectively separate the radio frequency interference from the useful signal, thereby significantly reducing the influence of the radio frequency interference on the image quality of the synthetic aperture radar.

The invention discloses a GIS basin-type insulator bolt loosening state intelligent classification method and system based on a selective state space depth network, and the method comprises the steps: collecting a multi-channel signal through a plurality of pairs of ultrasonic transducers disposed on the two sides of a basin-type insulator flange, and carrying out the microsecond time alignment of the multi-channel signal; then synchronously performing synchronous compression wavelet transform and S transform on the signals, extracting high-resolution time-frequency features, and extracting modal features with physical significance in combination with variational modal decomposition; fusing the two types of features into a sequential sequence, inputting the sequential sequence into a selective state space deep network, modeling long-range dependence by using a state space dynamic evolution unit of the selective state space deep network, and adaptively distributing modeling weights through a selective gating mechanism to realize fine classification of the number, the position and the degree of bolt looseness; and finally, obtaining steady-state working condition judgment through confidence coefficient calibration and time sequence smoothing, and mapping the steady-state working condition judgment into multi-level risk levels for early warning. According to the method, the recognition precision and robustness under the working conditions of strong noise and small samples are remarkably improved, and the method is suitable for online intelligent monitoring and preventive maintenance of GIS equipment.

The application discloses a hybrid power quality disturbance classification method based on multi-source feature selection, and steps of the method comprise the following steps: a, hybrid power quality disturbance signal collection; b, using multi-source methods such as time domain, Fourier transform, short-time Fourier transform, wavelet transform and S transform to extract hybrid power quality disturbance signal features, and forming a feature set matrix; c, using a near neighbor component analysis (NCA) to calculate feature weights of various features, and performing feature selection; d, using selected key features to perform support vector machine (SVM) training, and using an improved particle swarm algorithm to optimize SVM parameters; e, identifying power quality disturbance types and outputting category labels. The application can not only comprehensively integrate advantages of various feature extraction methods, but also solve problems of large data sample quantity and unobvious feature performance, and can also improve classification performance of the support vector machine, so that various power quality disturbance signals can be accurately identified.

The invention discloses a deep coal bed gas containing heterogeneity detection method, a storage medium and equipment. The method comprises the following steps: 1, acquiring post-stack seismic data of a corresponding coal bed gas work area; 2, according to the post-stack seismic data, obtaining a seismic interpretation horizon of a coal seam, and marking the seismic interpretation horizon as T; 3, extracting a plurality of position pairs of post-stack seismic data to obtain a plurality of seismic traces; 4, performing time-frequency analysis on the extracted seismic traces by adopting synchronous extrusion S transformation to obtain a time-frequency distribution diagram; according to the method, the coal seam seismic data is analyzed through synchronous extrusion S transformation, compared with a traditional time-frequency analysis method, the obtained time-frequency analysis result has higher time-frequency resolution and better time-frequency energy focusing performance, in the application of actual seismic data, instantaneous spectrum anomaly characteristics such as high-frequency attenuation related to deep coal reservoirs can be effectively detected, and the method has the advantages of being high in accuracy and high in reliability. The prediction precision of the deep coal reservoir is improved, and the gas-bearing heterogeneity of the deep coal reservoir can be accurately described.

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 application discloses a Green function reconstruction method combining S transform and dictionary learning. The method is as follows: S transform denoising is performed on a background noise cross-correlation function containing noise to obtain time-frequency spectrum coefficients and extract real coefficient matrix and imaginary coefficient matrix of the time-frequency spectrum coefficients; dictionary learning is performed on the real coefficient matrix and the imaginary coefficient matrix respectively to obtain a real coefficient matrix dictionary and an imaginary coefficient matrix dictionary; real coefficients after dictionary learning and imaginary coefficients after dictionary learning are obtained; reconstructed spectrum coefficients are obtained, and S inverse transform is performed on the reconstructed spectrum coefficients to obtain reconstructed Green functions. The application can eliminate the interference of noise sources with different directions in the seismic background noise cross-correlation function, and good results can also be achieved for background noise cross-correlation data with low signal-to-noise ratio; the application has low complexity, high timeliness and high reconstruction precision.

The application discloses a lightning disturbance discrimination method for flexible HVDC transmission line based on instantaneous frequency characteristics, and specifically comprises the following steps: step 1, extracting voltage and current data in a time window from 1ms before protection starting to 1.0ms after protection starting, and obtaining line mode voltage forward and reverse traveling waves; step 2, performing discrete S transform on the line mode voltage reverse traveling wave obtained in step 1 to obtain a mode time-frequency matrix, and extracting an amplitude-frequency curve at a fault occurrence moment; step 3, obtaining a maximum value number Q of the instantaneous amplitude-frequency curve, if Q=1, it is lightning disturbance, and the discrimination ends; if Q>1, it is a fault, and step 4 is entered; step 4, defining a frequency of a point with the maximum amplitude in the amplitude-frequency curve as a main frequency F1, comparing the main frequency F1 with a threshold value F set , and judging a fault type according to a comparison result. The discrimination method provided by the application is simple in calculation, and can accurately judge lightning disturbance and lightning fault.

The application discloses a CFRP interface weak bonding defect dynamic detection method based on peak entropy, and specifically comprises the following steps: step 1, collecting particle velocity signals on the back of a nondestructive test piece under laser impact through a photonic Doppler velocimetry system; step 2, pre-processing the particle velocity signals; step 3, performing S transform time-frequency analysis and extreme value analysis on the pre-processed particle velocity signals, and calculating amplitude differences between extreme value points; step 4, calculating Shannon entropy of the amplitude difference sequence between the extreme value points, and generating a peak entropy feature; and step 5, extracting the peak entropy feature, and judging a damage state of the nondestructive test piece. The CFRP interface weak bonding defect dynamic detection method based on the peak entropy collects dynamic response signals through single laser impact, adopts S transform time-frequency analysis and Shannon entropy feature extraction, and realizes single impact detection.

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 application discloses a dynamic power quality disturbance signal detection method, device and medium, and the method steps comprise the following steps: S01, acquiring a dynamic power quality disturbance signal to be detected; S02, dividing a signal frequency band, and performing time-frequency analysis on the dynamic power quality disturbance signal by using an improved variable resolution S transform method; the improved variable resolution S transform method is an S transform based on a Gaussian window, and a Gaussian window scale factor is used to optimize the Gaussian window function; S03, performing a synchronous squeezing rearrangement operation on the improved variable resolution S transform result, so that energy components scattered in adjacent frequency intervals are aggregated to real frequency axis positions, and a time-frequency matrix after synchronous squeezing rearrangement is obtained; and S04, detecting signal components of the time-frequency matrix after synchronous squeezing rearrangement. The application can reduce energy diffusion and realize high-precision detection of non-stationary multi-scale complex dynamic power quality disturbance signals.

The application relates to the field of defect detection, and specifically discloses a cable defect positioning method, which comprises the following steps: injecting a Gaussian envelope linear chirp signal into a cable to be detected, and obtaining a corresponding reflection signal; adding a generalized adjustment factor to the reflection signal to perform generalized adjustment, and obtaining a first time-frequency distribution after the generalized adjustment; performing energy rearrangement under the same frequency on the first time-frequency distribution to obtain a second time-frequency distribution after the energy rearrangement; determining a cross-correlation function of the incident signal and the second video distribution to obtain a defect positioning curve of the cable to be detected; and determining a defect position of the cable to be detected based on the defect positioning curve. The traditional S transform is subjected to generalized adjustment and energy rearrangement, so that the cross terms are suppressed and the positioning resolution of the method is improved; the time-frequency distribution in the effective frequency band range of the signal is subjected to inverse transformation, so that the interference of noise on defect polarity discrimination is effectively reduced.

The application discloses a power quality disturbance feature extraction method and system based on segmented multi-resolution S transform, relates to the technical field of power quality monitoring, and aims to solve the problem that traditional time-frequency analysis methods are difficult to accurately capture power quality disturbance features in different frequency bands due to fixed resolution. The application comprises establishing a mathematical model of power quality disturbance signals, including various single and composite disturbance models; discretizing the signals based on continuous S transform and applying one-dimensional discrete S transform; dividing the frequency bands into three segments, i.e., low, medium and high, according to disturbance frequency characteristics; introducing a Gaussian window function with different adjustment factors into each frequency band to adaptively adjust the time-frequency resolution; combining S transform with the window function to obtain a discrete expression of segmented multi-resolution S transform; and extracting disturbance feature parameters based on the time-frequency matrix obtained through transformation. The technical scheme realizes adaptive optimization of time-frequency resolution, and improves the extraction accuracy and reliability of complex power quality disturbance features.

The present application relates to the technical field of geophysical exploration, in particular to a pre-stack OVT domain low-order fault identification method based on azimuth and frequency domain control. The method comprises the following steps: performing pre-stack OVT domain gather anisotropy analysis; analyzing target area tectonic stress characteristics, performing OVT domain gather data azimuth optimization and division, and performing partial data stacking on OVT gathers; constructing data volumes controlled by different azimuths and frequency domains through improved generalized S transform spectral decomposition; extracting coherent attribute analysis; and drawing a target area fault distribution plan. The method can effectively improve the accuracy and reliability of low-order fault interpretation, meet the needs of fine fault system interpretation at different exploration and development stages, and provide reliable basis for oilfield exploration and development target optimization and drilling and completion scheme design.

The application provides a current direction intelligent recognition method and system based on a current transformer, and relates to the technical field of current direction recognition.The method comprises the following steps: obtaining a secondary side output signal about a to-be-tested current through a current transformer; performing time-frequency decomposition on the secondary side output signal by using an S transform algorithm based on a Morlet wave kernel, and extracting a secondary side output effective signal; performing phase correction on the secondary side output effective signal in combination with a Preisach magnetic hysteresis model; constructing a current direction sensitivity factor based on the time-frequency distribution of the secondary side output signal obtained through time-frequency decomposition; mapping the time-frequency distribution to a sparse domain, and extracting a current direction sensitive component of the to-be-tested current; determining a direction discrimination threshold for judging the current direction in combination with the current direction sensitive component and the current direction sensitivity factor; and judging the current direction of the to-be-tested current according to the direction discrimination threshold.The application improves the accuracy and the applicable range of current direction recognition.

The invention relates to a well constraint-based seismic data frequency division weighted fusion optimization method. The method comprises the following steps of: processing seismic data by using high-resolution generalized S transform to obtain broadband seismic data; processing the broadband seismic data to obtain a frequency division seismic data set; according to the sound wave and density data of the actual drilling well and the dominant frequency of the seismic channel beside the well, a synthetic seismic record is obtained through convolution calculation; a single well optimal frequency division weighted fusion coefficient is calculated according to the synthetic seismic record and the well-side seismic trace; processing the optimal frequency division weighted fusion coefficient of each well to obtain an optimal frequency division weighted fusion coefficient distribution matrix; and processing the frequency division seismic data set according to the optimal frequency division weighted fusion coefficient distribution matrix to obtain optimized seismic data. The method can effectively solve the problems that an existing seismic data fusion optimization method is insufficient in quantification, high in multiplicity of solutions and low in precision, and has an obvious effect on improving seismic data quality and improving oil and gas field description precision.

The invention discloses a time-frequency domain cross-correlation reconstruction method and system for a rock breaking source of a drill jumbo, and relates to the technical field of seismic signal processing and intelligent detection, and the method comprises the steps: collecting the seismic signals of the rock breaking source in the drilling process of the drill jumbo, including a pilot signal and a side wall signal; respectively carrying out generalized S transformation on the pilot signal and the side wall signal to obtain respective time-frequency domain representation; performing sliding cross-correlation on the two time-frequency domain representations under set time delay to obtain a cross-correlation spectrum; and the cross-correlation spectrum is recovered to a time domain through inverse transformation of generalized S transformation, and an enhanced seismic signal is obtained. Based on generalized S transformation and time-frequency cross-correlation, underground reflection information is effectively extracted, and reconstruction of seismic source wave field features while drilling is realized.

The application belongs to the technical field of rotating machinery fault diagnosis, and discloses a neural network explainable method for rotating machinery fault diagnosis and application, which comprises the following steps: (1) performing S transform processing on a vibration signal to obtain a time-frequency graph; (2) converting the time-frequency graph sample from a space domain to a frequency domain by using a two-dimensional Fourier transform, adding a mask to the frequency domain, and converting the sample after the mask into the space domain by using an inverse Fourier transform; (3) inputting the sample into a neural network to obtain a neural network last layer feature map and neural network output logits, and calculating a classification probability of a target category and a sparsity score of the masked sample; (4) calculating an out-of-domain distribution score of the sample; (5) optimizing the mask by using a stochastic gradient descent method; and (6) adding the optimized mask to a sample frequency coefficient to obtain an explanation for the classification of the sample. The application improves the feature resolution of the explanation.

The application provides a seismic reconstruction method based on low-frequency trend modeling of velocity. The method firstly reconstructs a low-frequency component of well logging wave impedance in a time domain based on a generalized S transform, calculates the low-frequency component of seismic wave impedance based on a seismic velocity data body and a fitting relationship between density and velocity, obtains a corrected three-dimensional space seismic wave impedance low-frequency component data body, calculates a wave impedance value of a target layer after desolidification, calculates a wave impedance desolidification coefficient, obtains a three-dimensional seismic compaction coefficient body, and further establishes a mathematical relationship between multi-channel seismic records, a seismic wavelet and a wave impedance difference. A wave impedance inversion target function after desolidification is established, a nonlinear inversion method is adopted to obtain a seismic record after desolidification correction, and the reconstructed seismic data effectively improve the prediction accuracy of thin reservoirs, reservoir properties and reservoir oil content in deep and low-lying zones.

The invention discloses a dynamic power quality disturbance signal detection method and device, and a medium. The method comprises the following steps: step S01, obtaining a to-be-detected dynamic power quality disturbance signal; s02, signal frequency bands are divided, an improved variable resolution S transformation method is adopted to carry out time-frequency analysis on the dynamic power quality disturbance signals, and the improved variable resolution S transformation method is that S transformation based on a Gaussian window is adopted, and a Gaussian window function is optimized by using a Gaussian window scale factor optimized by frequency bands; step S03, performing synchronous extrusion rearrangement operation on the improved variable-resolution S transformation result so as to aggregate energy components dispersed in adjacent frequency intervals to a real frequency axis position, and obtaining a time-frequency matrix after synchronous extrusion rearrangement; and step S04, detecting according to signal components of the time-frequency matrix disturbance signals after synchronous extrusion and rearrangement. According to the invention, energy diffusion can be reduced, and high-precision detection of non-stationary multi-scale complex dynamic power quality disturbance signals is realized.

The application provides a generalized S transform time-frequency analysis method based on dictionary learning and matching pursuit decomposition, comprising the following steps: step 1, inputting an original seismic signal set, and constructing a wavelet library based on K-SVD dictionary learning; step 2, combining the fast complex domain matching pursuit algorithm with the wavelet library constructed based on K-SVD dictionary learning to improve the speed and accuracy of seismic signal decomposition; step 3, obtaining the time-frequency spectrum of independent wavelets based on the generalized S transform, and superimposing the time-frequency spectrum of all independent wavelets to perform time-frequency joint analysis of the high-resolution time-frequency spectrum; step 4, studying the change of reservoir characteristics by using single-frequency attributes based on the high-resolution time-frequency spectrum; and step 5, eliminating the high-resolution time-frequency spectrum representing the change of reservoir characteristics after strong reflection. The generalized S transform time-frequency analysis method based on dictionary learning and matching pursuit decomposition forms a high-resolution time-frequency analysis technology according to the development of actual application requirements and the characteristics of non-stationary signals.