48 results about "S transform" patented technology

The invention discloses a Fourier transform and S transform combined battery impedance spectroscopy measurement method and device, and aims to improve the precision, speed and reliability of impedance measurement of a battery energy storage system. According to the method, a multi-frequency component excitation signal is generated through a controllable current source, and current and voltage response signals of a battery are synchronously acquired and are respectively input into a Fourier transform module and an S transform module for frequency domain analysis. The Fourier transform module extracts impedance spectrums of low and medium frequency bands, and the S transform module extracts impedance spectrums of medium and high frequency bands through Gaussian window time-frequency analysis. And a data fusion algorithm is adopted to perform composite processing on results of the two modules, including a direct selection method, an arithmetic average method, an amplitude and phase calibration method and a weighted average method, so that smooth transition and optimization between frequency bands are realized, and finally the electrochemical impedance spectrum is generated. Through collaborative analysis of Fourier transform and S transform, parameter optimization and a fusion algorithm, the efficiency and accuracy of impedance spectroscopy measurement are remarkably improved, and the method is suitable for rapid monitoring and diagnosis of the battery state.

The invention relates to the technical field of American ginseng quality detection, in particular to an American ginseng quality detection method and system based on S-transformation and multi-task deep learning. Near infrared spectrum data of American ginseng to be detected are extracted, and time-frequency conversion is performed on the near infrared spectrum data through S-transformation; obtaining a time-frequency characteristic pattern used for representing time-frequency domain information of the near infrared spectrum; and inputting the to-be-detected American ginseng time-frequency feature map into a pre-trained multi-task deep learning model, and identifying the producing area of the to-be-detected American ginseng and predicting the quality index of the to-be-detected American ginseng by using the multi-task deep learning model. The multi-task deep learning model comprises a feature extraction network used for extracting time-frequency features of each detection task of the feature map, a feature interaction network used for enhancing feature complementation between the tasks and performing feature fusion, and a multi-task head network used for executing a production place identification task and a quality index prediction task according to the time-frequency features and outputting the tasks. The method can be suitable for quality analysis and origin classification of small American ginseng samples.

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 invention discloses a broadband noisy signal rapid detection method based on a frequency band adaptive variable Kaiser window, and the method comprises the steps: building a broadband noisy signal model, and enabling a broadband noisy signal to comprise a fundamental wave and a high-frequency oscillation signal; a variable Kaiser window function with a self-adaptive frequency band is constructed; based on a Kaiser window function, carrying out fast S transformation on the broadband noisy signal to generate a time-frequency matrix; extracting time-frequency characteristics from the time-frequency matrix, wherein the time-frequency characteristics comprise a fundamental frequency amplitude curve, a frequency amplitude envelope curve and a standard deviation curve; and calculating the amplitude and the frequency of the oscillation component based on the time-frequency characteristics to complete the identification of the broadband noisy signal. According to the invention, the high-precision detection of harmonic waves, inter-harmonic waves and transient components in broadband signals is realized by designing a frequency band adaptive Kaiser window adjustment factor and combining with an improved fast S transform algorithm.

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 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 present disclosure provides a method and related equipment for identifying the same source of voltage sags. The method processes the data to be identified as the same source, corresponding to the voltage sag fault waveform recorded by an intelligent power distribution terminal, at a unified sampling rate. The S transform is used to extract the sag data segments from the data to be identified as the same source. The Gram angular field matrix is ​​obtained by sequentially using a preset transformer transfer matrix and time-series two-dimensional graphical processing. The voltage sag similarity is obtained through image matching. The voltage sag similarity is compared with a preset same-source identification threshold to obtain an effective voltage sag same-source identification result. The present disclosure uses the voltage sag fault waveform as a reference, extracts the sag data segments through the S transform, and performs image matching using the Gram angular field matrix. This preserves the characteristics of the voltage sag fault waveform, can obtain true voltage sag similarity, and thus achieves effective voltage sag same-source identification.

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 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 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 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 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 present application provides a method and device for denoising wellbore DAS seismic data, belonging to the technical field of geophysical exploration. In the embodiments of the present application, the improved median filter is used to separate the first effective data and noise data from the original seismic data; for the noise data, time-frequency domain analysis is carried out through the S transform, and the S transform result function is filtered through the time-frequency domain filter constructed according to the first effective data and noise data, and the second effective data is further extracted from the noise data; finally, the sum of the first effective data and the second effective data is output, so as to obtain the seismic data after suppressing noise. On the basis of the improved median filter, the embodiments of the present application further extract effective data from the noise data through the S transform, effectively suppressing the noise of the wellbore DAS seismic data while maximizing the retention of effective signals, providing effective technical support for improving the imaging accuracy and interpretation reliability of the wellbore DAS seismic data.

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.