120 results about "Frequency axis" patented technology

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention provides a hemispherical resonator full spherical surface quality-rigidity combined trimming method and system, and belongs to the field of solid-state vibrating gyroscopes. The invention aims to solve the problem of low trimming efficiency caused by mass and rigidity coupling in the trimming frequency splitting process because etching is not carried out on a full-hemispherical shell in the existing trimming process of the hemispherical harmonic oscillator. A laser Doppler vibration meter is used for obtaining the low-frequency axis, high-frequency axis orientation and frequency splitting decomposition value of a hemispherical resonator, a focused ion beam is controlled to conduct rigidity fine adjustment on the positions of the four high-frequency axes in a high-latitude area, and quality-rigidity coupling fine adjustment is conducted on the positions of the four low-frequency axes in a low-latitude area. Etching on the full-hemispherical shell is realized, the quality and rigidity of the hemispherical harmonic oscillator can be decoupled, and the trimming efficiency of the hemispherical harmonic oscillator is improved; the mass and the rigidity are decoupled through mass-rigidity combined trimming, and the uniformity of the mass and the rigidity of the hemispherical resonator gyroscope is improved; the method has certain universality and can be popularized and used for mass rigidity trimming of harmonic oscillators in different shapes.

The present invention relates to the field of underwater acoustic engineering technology, and discloses a method and device for passively locating deep-sea targets using a horizontal array. The method comprises: using a spatial power spectrum estimation method to calculate the target azimuth angle and broadband beam output sound field intensity estimation results of the sound pressure signal collected by the horizontal array; performing spectrum analysis on the broadband beam output sound field intensity on the target azimuth angle along the frequency axis to obtain the target measured multi-path arrival delay spectrum; constructing a cost function to match the target measured multi-path arrival delay spectrum with multiple relative arrival delay template values, and estimating the distance and depth of the target based on the matching results. The method utilizes the relationship between multiple arrival delays and the target distance and depth to achieve the decoupling of the target distance and depth, and gets rid of the limitation that the existing passive positioning method based on the multi-path arrival structural characteristics requires the receiving array to have the ability to estimate the pitch angle. Compared with the traditional matching field method, the constructed cost function greatly reduces the amount of calculation, which can improve the efficiency of passive target real-time positioning.

The present disclosure describes techniques for implementing improved audio source separation. A complex spectrum X is split into a plurality of K bands along a frequency axis by applying band-split operations on the complex spectrum X. The complex spectrum is a time-frequency representation of audio signals. Each of the plurality of K bands is denoted as Xk, k=1, . . . , K. Each band Xk comprises one or more frequency bins. Each individual multilayer perceptron is applied to each band Xk to extract latent representations and obtain outputs Hk0. A time-domain transformer and a frequency-domain transformer are applied on a stacked representation H0. Time-domain and frequency-domain transformers are repeatedly applying in an interleaved manner for L times to obtain HL output from the transformer blocks. The HL is input into a multi-band mask estimation sub-model. A complex ideal ratio mask is generated based on outputs from the multi-band mask estimation sub-model.

The invention belongs to the technical field of cognitive radio, and particularly relates to a broadband multi-signal detection method based on a large-size convolution kernel. The method mainly comprises the following steps of: receiving a signal by adopting a receiving antenna through electromagnetic spectrum monitoring equipment, capturing a space electromagnetic radiation signal and converting the space electromagnetic radiation signal into a baseband complex signal, carrying out large FFT point short-time Fourier transform on signal sampling data to form a high-resolution and large-size time-frequency matrix, and carrying out time-frequency spectrum detection along a frequency axis of the time-frequency matrix; a time-frequency matrix is cut into a plurality of time-frequency matrix fragments adapted to neural network processing, and parameters such as confidence probability, center frequency, bandwidth, arrival time, duration and the like of existence of signals in each time-frequency matrix fragment are automatically calculated and generated by using a specially designed and trained deep neural network model. The method has the advantages that the operation of monitoring equipment is simplified, the signal detection speed is high, the detection accuracy is high, the detection recall rate is high, and the time-frequency occupancy parameter estimation precision is high.

The invention discloses an unmanned aerial vehicle radio frequency fingerprint identification method and device, a medium and equipment, and relates to the technical field of computers. According to a time-frequency graph corresponding to a radio frequency signal, time domain convolution is carried out by sliding along a time axis of the time-frequency graph, time domain features of the time-frequency graph are extracted, frequency domain convolution is carried out by sliding along a frequency axis of the time-frequency graph, frequency domain features of the time-frequency graph are extracted, and heterogeneous combined convolution kernels with different sizes are adopted in the time domain convolution and the frequency domain convolution, so that the time-frequency graph is more accurate. Therefore, time-frequency anisotropic convolution is realized, and short-time dynamic evolution and local energy distribution characteristics are respectively captured. According to the method, the uniform operation defect of traditional isotropic convolution is avoided, parameter redundancy is reduced and feature discrimination is enhanced through physically interpretable dimension decoupling, and the accuracy of radio frequency fingerprint identification of the unmanned aerial vehicle is improved.

The invention relates to the technical field of data processing, and discloses a signal intra-pulse modulation pattern recognition method and system, and the method comprises the steps: receiving a radar pulse signal; performing short-time Fourier transform on the radar pulse signal; in the time-frequency matrix, calculating an energy distribution gradient of each time point along a frequency axis; according to the energy distribution gradient of each time point, identifying gradient abrupt change points; determining a filtering bandwidth boundary based on the gradient catastrophe point by adopting a negative feedback regulation mechanism of bandwidth and signal-to-noise ratio; based on the filtering bandwidth boundary, performing bandwidth filtering on the radar pulse signal; calculating an instantaneous frequency, a first-order differential frequency and a second-order differential frequency of the filtered signal through Hilbert transform, and forming a feature vector; predicting a cluster number range through a long and short term memory neural network according to the feature vectors; according to the feature vector, through a K-Means clustering algorithm, carrying out clustering analysis in a clustering number range; and outputting a modulation style label according to a clustering analysis result.

The invention discloses a mirror image rejection method and device based on spectrum entropy, and belongs to the technical field of radio frequency signal processing. Multiple groups of frequency spectrums are collected by moving a local oscillator, the frequency spectrums are mapped to a unified reference frequency axis to achieve frequency alignment, a spectrum entropy sequence of the same frequency point after alignment is calculated, and the problem that the spectrum entropy fluctuation of the same frequency point is large due to nonlinear deviation of the local oscillator frequency of a mirror image signal is solved. And accurate identification and suppression of the mirror image signal are realized due to the difference of spectrum entropy stability caused by linear offset of the useful signal. According to the method, multiple groups of frequency spectrums are mapped to a unified reference axis through frequency alignment, high-precision mirror image suppression is effectively realized by utilizing the characteristics of large fluctuation of mirror image signals and stable useful signals in a spectrum entropy sequence of the same frequency point, the distinguishing capability in a complex scene is improved, and the problems of high cost and insufficient robustness of an existing mirror image suppression method are solved.

The invention discloses a fault diagnosis method, system, equipment and medium for a wind power gear box, and relates to the technical field of fault detection, and the method comprises the steps: collecting a vibration signal of the wind power gear box, and obtaining a time-frequency diagram of the vibration signal; capsule feature tensors in the time-frequency graph are obtained, a time-frequency two-dimensional network is formed in space, and corresponding query tensors are extracted from a time axis and a frequency axis of the time-frequency two-dimensional network by adopting different convolution kernels; dot product matching is carried out on the corresponding query tensors and a preset shared key value pair, attention distribution of each query tensor is obtained, and aggregation output in the directions of the time axis and the frequency axis is fused through the attention distribution; and performing class activation mapping on the aggregated output, determining a fault category of the wind power gear box, generating a class activation graph focused on the key time-frequency region through the fault category, and displaying a discrimination basis. The method can focus on a key time-frequency region with clear physical significance, and obviously improves the discrimination transparency and engineering credibility of the model.

The invention discloses a bearing fault acoustic detection method and system based on fractional harmonic phase-locked amplification, and the method comprises the steps: obtaining an acoustic signal of a target motor, measuring the shaft rotation frequency of a motor shaft in real time, synchronizing the shaft rotation frequency to a fractional phase-locked loop, and carrying out the calculation of a bearing fault characteristic frequency according to the geometric parameters of a bearing, the bearing fault characteristic frequency is a non-integer multiple of the shaft rotation frequency, generating a reference signal synchronous with the bearing fault characteristic frequency through a fractional phase-locked loop, and performing phase sensitive detection on the obtained acoustic signal and the generated reference signal by using a phase-locked amplifier, specifically, performing orthogonal demodulation on the acoustic signal and the reference signal, and performing phase sensitive detection on the obtained acoustic signal and the reference signal. The method comprises the following steps: generating an in-phase component and an orthogonal component, extracting a direct-current component through low-pass filtering, calculating to obtain a vibration amplitude and phase information corresponding to a bearing fault characteristic frequency, and judging the type and severity of a bearing fault according to the comparison between the vibration amplitude and a preset baseline, the harmonic consistency and the phase stability.

The invention discloses a time-frequency analysis method based on local entropy optimization synchronous demodulation reassignment transformation, relates to the technical field of signal processing, and solves the problem that a traditional time-frequency analysis method is not easy to carry out when complex non-stable vibration signals generated by rotating machinery and the like are processed. The problems of difficulty in balancing time and frequency resolution, easiness in energy diffusion, difficulty in multi-component signal separation and the like exist; according to the method, zero padding is carried out on the two ends of a target signal, a discrete Gaussian window is constructed to generate a sliding windowing signal matrix, a projection angle sequence is generated in an angle space, a time axis and a frequency axis are discretized, a demodulation function containing undetermined parameters is constructed to generate a complex number time-frequency representation matrix, the Renyi entropy value of time-frequency sub-blocks is calculated to determine an optimal demodulation parameter, and the optimal demodulation parameter is obtained. And setting a frequency difference threshold to construct a frequency redistribution operator to generate an optimized time-frequency matrix. The method is mainly used for time-frequency analysis of complex non-stationary vibration signals in the fields of rotating machinery and the like, and high-precision frequency modulation feature extraction and enhancement processing are achieved.

The invention relates to the technical field of voice signal processing, and particularly provides a voice separation method, device and system irrelevant to array geometry. The method is suitable for various microphone array structures, adopts a virtual microphone estimation mechanism to generate a virtual channel signal for enhancing spatial information density, combines frequency spectrum-time features and spatial direction features, and extracts multi-modal representation through a spatial dictionary learning and attention fusion module. And the extracted features are further input into a layered double-path modeling network, and global dependency relationships are respectively modeled on a time axis and a frequency axis, so that high-precision separation of voices of multiple speakers is realized. The system has good array structure adaptivity, can adapt to channel number changes and array shape differences, and has good application value in scenes such as teleconferences, voice recognition front ends and vehicle-mounted voice processing.

The invention relates to the technical field of power equipment state monitoring, and discloses a partial discharge detection method and system based on synchronous compression wavelet-synchronous extrusion. The method comprises the following steps: preprocessing a partial discharge signal; calculating a coefficient through continuous wavelet transform and estimating an instantaneous frequency and a chirp rate; synchronous compression is carried out to compensate the phase, and scale remarking is carried out; energy is reallocated to a frequency axis through synchronous extrusion, and a high-aggregation-degree time-frequency diagram is generated; extracting a peak domain based on the saliency spectrum, demodulating along a ridge line, and estimating event parameters; and finally, robust judgment and auditing output are completed by using the joint statistics. According to the method, the sensitivity and robustness of partial discharge signal detection under the conditions of low signal-to-noise ratio and strong interference are remarkably improved.

The invention relates to the technical field of impedance measurement, in particular to an impedance measurement method and system for a common-mode inductor, and the method comprises the steps: obtaining voltage and current signals of the common-mode inductor in a test circuit, and obtaining frequency domain response atlas data containing impedance characteristics through time-frequency transformation; carrying out non-linear segmentation division on the frequency axis to form a plurality of frequency segmentation regions; extracting the phase amplitude of each segmented region, carrying out clustering segmentation, and recognizing an effective impedance frequency band; analyzing phase synchronization characteristics in the effective impedance section, and determining a common-mode resonant frequency point through clustering analysis; and fusing multi-section frequency region division, effective impedance section and resonance point information to finish accurate measurement of the impedance characteristic of the common-mode inductor. According to the method, through non-linear scanning and phase information joint analysis, the recognition precision of impedance response key characteristics in a broadband range is improved, the capturing capacity of parasitic parameters and resonance behaviors is enhanced, and efficient and high-precision comprehensive evaluation of the impedance characteristics of the common-mode inductor is achieved.

In an abnormality detector apparatus, a signal processor unit performs frequency analysis on data of a current or a voltage in different first and second time intervals to retrieve a first drive frequency corresponding to a maximum peak value of a spectrum in the first time interval and a second drive frequency corresponding to a maximum peak value of a spectrum in the second time interval; adjusts a frequency axis of one spectrum such that the maximum peak values coincide with each other, and calculates an averaged spectrum relating to the adjusted and unadjusted spectra; and retrieve an abnormal peak value equal to or more than a predetermined threshold value at a frequency different from the drive frequency having the maximum peak value based on the averaged spectrum; and determine an abnormal state of the motor depending on presence or absence of the abnormal peak value.

The application discloses a rotating machinery characteristic frequency enhancement extraction method based on an optimal weighted envelope spectrum, and comprises the following steps: (1) collecting vibration or noise data of the rotating machinery as a monitoring signal and calculating a spectrum correlation function; (2) normalizing the spectrum correlation function and constructing an enhanced envelope spectrum; (3) determining a basic modulation frequency; (4) performing slice processing on the spectrum correlation function and estimating a carrier-to-noise ratio; (5) constructing a cyclic stationary measure function of the basic modulation frequency and harmonic components thereof; (6) taking the carrier-to-noise ratio as an initial weighting function and searching for an optimal weighting function by using a cuckoo optimization algorithm; (7) performing weighted processing on the spectrum correlation function by using the optimal weighting function to obtain an optimal weighted spectrum correlation; and (8) integrating the optimal weighted spectrum correlation along a spectrum frequency axis to obtain an optimal weighted envelope spectrum. The application can effectively extract the modulation frequency components in the rotating machinery vibration noise signal under complex and strong noise interference.

The present invention discloses a speech enhancement method that combines hearing loss compensation and speech noise reduction, comprising: extending and embedding a hearing loss map along the frequency axis to obtain a hearing loss spectrum, and superimposing the hearing loss spectrum with the complex spectrum features of the noisy training speech; constructing a metric generative adversarial network model based on frequency-time convolution recursion, the model's main structure comprising a compensation generator and a metric discriminator; alternately training the compensation generator and the metric discriminator to optimize the metric generative adversarial network model; superimposing the complex spectrum features of the test speech with the hearing loss spectrum and inputting them into the trained compensation generator, and reconstructing an enhanced speech waveform of the test speech based on the output of the compensation generator. The present invention uses a metric generative adversarial network to simultaneously achieve noise reduction and hearing loss compensation for a specific audiogram, capable of stably and effectively improving the effectiveness of hearing loss compensation in noisy environments. The method is ingenious and novel, with promising application prospects.

A communication device according to an embodiment includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: receive an OFDM signal and convert the OFDM signal into a time-axis waveform signal of a baseband; extract a part of the time-axis waveform signal, and calculate a correlation value between the extracted signal and a known signal; execute FFT on the time-axis waveform signal; extract a part of a frequency-axis waveform signal on which the FFT is executed, and calculate a degree of similarity between the extracted signal and a known signal; and estimate a switching timing between uplink communication and downlink communication in the communication device based on a calculation result of the similarity degree.

This application proposes a high-precision detection method and device for stationary human targets based on a radar life detector. The method involves performing an inverse Fourier transform on the radar's fast time to convert the frequency axis into the range axis, iteratively updating the low-rank matrix and the sparse matrix, updating the dual variables and modifying the penalty parameters, and obtaining the separation result between the target and the background after convergence. The background information is stored in the low-rank matrix, while the signal containing life characteristic information is stored in the sparse matrix, thereby achieving high-precision detection of stationary human targets.

The invention discloses an equipment anomaly monitoring method based on audio signal analysis, which comprises the following steps of: S1, generating a logarithmic Mel spectrogram through time-frequency transformation, generating a gating vector by utilizing a self-adaptive gating module based on median, root mean square and variance, and after weighting fusion with an original spectrogram, splicing supplementary audio features to form a multi-channel fusion feature; and S2, the multi-channel fusion features are partitioned along a time axis and a frequency axis, parallel time domain and frequency domain branches are respectively input, a selective state space model is adopted, the time domain branches capture long-time-history steady-state features and trend anomalies, and the frequency domain branches analyze cross-band energy distribution and a harmonic structure. And S3, performing linear alignment on the two branch features, adding the two branch features element by element to obtain a fusion feature, training a model through a self-supervised ID classification task in combination with an angle interval loss function, and taking a negative logarithmic probability of a target ID category as an abnormal score during reasoning, thereby improving the robustness, generalization ability and accuracy of detection.

This disclosure describes techniques for achieving improved audio source separation. The complex spectrum X is divided into K frequency bands along a frequency axis by applying a frequency band division operation to the complex spectrum X. The complex spectrum is a time-frequency representation of the audio signal. Each of the K frequency bands is represented as Xk, k = 1,..., K. Each frequency band Xk comprises one or more frequency intervals. Each individual multi-layer perceptron is applied to each frequency band Xk to extract potential representations and obtain an output Hk0. A time domain transformer and a frequency domain transformer are applied to the stacked representation H0. The time domain transformer and the frequency domain transformer are repeatedly applied L times in an interleaved manner to obtain an HL output from the transformer block. The HL is input into a multi-band mask estimation sub-model. The complex ideal ratio mask is generated based on the output from the multiband mask estimation sub-model.

The transmitter (1) has a frequency repetition modulation unit (18) which generates a signal in which information symbols, whose time domain signal is non-zero, and null symbols, whose time domain signal is zero, appear periodically by generating a transmission spectrum in which the spectrum of the modulated signal is continuously arranged on the frequency axis, the same transmission symbols are arranged at regular subcarrier intervals in the spectrum of the modulated signal, and the spectrum on the high band side and the spectrum on the low band side are equal to a center frequency.

The application relates to a fast demodulation system and method for a large-scale microcavity array on a chip, and the system comprises a frequency-sweeping laser, a first fiber coupler, a microcavity array transmission signal arm, an auxiliary interference reference arm, a same-frequency trigger arm and a signal processing device. The first fiber coupler is used for dividing a light source into a first light source and a second light source. The microcavity array transmission signal arm is used for receiving the first light source of the frequency-sweeping laser, and the first light source enters a large-array microcavity sensor device after polarization processing, and is converted into an electric signal after receiving the disturbance of an external sensing signal. The auxiliary interference reference arm is used for receiving the second light source of the frequency-sweeping laser, and two beams of coherent light with opposite phases are obtained after processing the second light source, and then the coherent light is converted into an electric signal. The same-frequency trigger arm is used for transmitting a same-frequency trigger signal emitted by the frequency-sweeping laser. The signal processing device is used for signal acquisition and processing. The application effectively suppresses the frequency-axis distortion and spectral line broadening caused by the frequency-sweeping nonlinearity of the laser, and can be applied to the accurate demodulation of a large-scale, high-Q microcavity sensing array.

The application provides a cross-domain small sample wideband signal detection and recognition method based on a visual basic model, and relates to the cross technical field of electromagnetic signal processing and computer vision. The application reduces the interference of background noise in the wideband time-frequency graph on the candidate area by performing double condition screening based on target confidence and aspect ratio features on the preliminary candidate frame, and combining the field of view expansion in the frequency axis direction, and improves the incomplete coverage problem of the slender signal candidate frame. By extracting multiple intermediate layer features and the final layer feature map of the visual basic model for fusion in the channel dimension, the problem that the local texture information in the wideband time-frequency graph is smoothed or covered in the layer-by-layer abstraction process is solved. The parameters of the visual segmentation model and the visual basic model are kept frozen, and only the small sample fine tuning of the front region proposal network is performed, so that the calculation overhead caused by full fine tuning of the visual basic model during cross-domain small sample adaptation is avoided, and the method is suitable for deployment on devices with limited computing resources.

In a communication device according to an embodiment, a signal reception unit converts an OFDM signal into an OFDM signal of a baseband. A time waveform computation unit extracts a part of a time-axis waveform signal and calculates the degree of similarity to a known signal. A carrier frequency correction unit corrects a carrier frequency of the time-axis waveform signal on the basis of the degree of similarity. An FFT unit performs Fourier transform on the time-axis waveform signal. A frequency waveform computation unit extracts a part of a frequency-axis waveform signal, and calculates the degree of similarity between the extracted signal and a known signal. A switching timing estimation unit estimates, on the basis of a result from the frequency waveform computation unit, a timing for switching between uplink communication and downlink communication in the device.

This computation device comprises: a video information acquisition unit that acquires video information including a plurality of continuous frame images in which a person subjected to measurement is captured; a region identification unit that identifies a region subjected to measurement among regions of the person captured in the frame images; a statistics computation unit that performs, regarding the identified region, statistics computation on at least two pixel values among RGB pixel values of the plurality of continuous frame images; a difference calculation unit that calculates a difference between the at least two pixel values on which the statistics computation has been performed; a computation unit that transforms information about a time-based difference value, which has been calculated by the difference calculation unit, onto the frequency axis; and an output unit that outputs, on the basis of a result having been transformed by the computation unit, a pulse wave amplitude value and a heart rate of the person subjected to measurement.

The present invention provides a control device that functions as a near-realtime RAN intelligent controller (RIC) in an open radio access network (O-RAN) that includes said near-realtime RIC and a non-realtime RIC. A prescribed range of frequency resources on the frequency axis is shared by a plurality of cells provided by the O-RAN, and configuration of a frequency resource range within the prescribed range of frequency resources that can be preferentially used by each of the plurality of cells is performed periodically by the non-realtime RIC. The configuration is such that the frequency resource range configured for a first cell included among the plurality of cells and the frequency resource range configured for a second cell, different from the first cell, included among the plurality of cells are at least partially different. The control device: acquires first information that can be used to identify radio resource utilization and / or amount of interference in each of the plurality of cells; determines, on the basis of the first information, whether or not a notification should be issued for causing the non-realtime RIC to perform an aperiodic reconfiguration of the configuration; and, on the basis of the result of the determination, issues to the non-realtime RIC a notification including second information which is generated on the basis of the first information and which is to be used when the non-realtime RIC performs the aperiodic reconfiguration.

The invention provides a multi-modulation signal robust identification method based on a time-frequency diagram and data enhancement. Original baseband signals of the traditional modulation mode and the novel modulation mode are generated and preprocessed; performing data set division according to a double sampling mode; performing STFT on the data to generate a time-frequency graph; performing enhancement operations of time axis offset, frequency axis offset, noise injection and tiny rotation on the time-frequency graph of the training set; a ResNet-18 model adaptive to single channel input is constructed; and training the model and carrying out a performance test of unknown signal modulation mode identification. According to the method, the novel data set comprising a traditional modulation mode and an emerging modulation technology is constructed, the defect that most of the current modulation data sets are the traditional modulation mode is overcome, a data basis closer to a future communication scene is provided for modulation recognition in a complex scene, multi-dimensional enhancement is performed on data, data diversity is improved, and the method is suitable for being applied to modulation recognition in a complex scene. The model generalization ability is significantly improved, and the identification precision of the modulation signal is also effectively improved.

The invention relates to the technical field of oil and gas exploration and development, and particularly discloses a robust mixed norm constraint high-dimensional seismic data reconstruction method and device, and the method comprises the steps: carrying out the Fourier transformation of five-dimensional data along a time axis, and obtaining the Fourier transformation data; unfolding 4D data of each frequency slice of the Fourier transform data along a mode-(m, n) to obtain a near-square matrix; a non-convex Frobenius / Nuclear mixed norm and L1 norm combined regularization constraint is applied to the near-square matrix, and a target functional of observation seismic data and reconstruction data is constructed; and alternately solving the target functional by adopting an ADMM algorithm, and carrying out inverse Fourier transform on a solving result along a frequency axis. According to the method, a high-dimensional tensor completion method under the robust Frobenius / Nuclear mixed norm constraint is introduced, accurate representation of nonlinear high-dimensional seismic data is achieved, the seismic data reconstruction and denoising precision is improved, and therefore regularized reconstruction and abnormal noise suppression of the seismic data with the low signal-to-noise ratio are achieved.

The application discloses a GIS device partial discharge wide-frequency electromagnetic signal characteristic analysis method and system, and relates to the technical field of partial discharge detection. The method comprises the following steps: collecting a GIS partial discharge wide-frequency electromagnetic signal, and adopting wavelet transform processing to obtain a time-frequency distribution matrix; uniformly dividing the time-frequency distribution matrix according to a frequency axis to obtain initial sub-frequency bands; calculating the effectiveness indexes of the sub-frequency bands and adaptively aggregating the sub-frequency bands to obtain candidate characteristic intervals; and performing collaborative contribution degree analysis on the candidate characteristic intervals to obtain optimal characteristic intervals. The technical problem that, in the existing GIS partial discharge wide-frequency electromagnetic signal characteristic extraction process, frequency band division relies on experience and lacks an adaptive optimization mechanism, resulting in inaccurate characteristic interval selection and weak anti-interference capability is solved, and the technical effect that the accuracy and anti-interference capability of partial discharge characteristic extraction are improved through adaptive sub-frequency band division of the wide-frequency electromagnetic signal and collaborative analysis of the characteristic intervals is achieved.