4593 results about "Fourier transform" patented technology

The present invention discloses an aero engine bearing fault diagnosis method based on STFT-IncepNext, which includes the following steps: step 1: collect acceleration signals under various working conditions and fault conditions of the bearing as sample data; step 2: determine the number of sampling points of each sensor first, and perform data merging by permuting and combining data collected by sensors at different positions in the same time series, and then use short-time Fourier transform to convert the fused vibration signal into a time-frequency diagram; step 3: data preprocessing: divide into training set, test set and validation set according to fault type, and normalize the converted time-frequency diagram using average value and standard difference; step 4: build an IncepNext model, train and validate it; and step 5: use the trained IncepNext model to diagnose aero engine bearing faults and obtain a diagnosis result. The present invention can effectively identify aero engine bearing faults, can guarantee the fault identification rate, and has good accuracy.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention relates to the technical field of transformer harmonic monitoring, in particular to a harmonic detection and early warning system for an intelligent transformer, which obtains voltage and current signals of the transformer in real time, performs frequency domain decomposition by using Fourier transform, extracts multi-dimensional harmonic characteristics such as amplitude, phase, frequency and the like, and performs harmonic detection and early warning on the intelligent transformer. And inputting into a deep learning model to train and generate a dynamic harmonic characteristic model. On the basis, a harmonic wave anomaly classification module analyzes harmonic wave signals in real time, carries out type judgment and position association on abnormal harmonic waves, and generates a report containing anomaly types and positions. Through a dynamic early warning module, a power grid load coefficient is calculated based on power grid load state parameters collected in real time, and a harmonic detection threshold is dynamically adjusted in combination with an initially set amplitude and frequency threshold. The application of the dynamic threshold enhances the adaptability of the system to the load state change, and significantly improves the sensitivity and accuracy of harmonic detection and early warning.

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention discloses a multi-dimensional time sequence photovoltaic grid-connected harmonic current prediction control method, and particularly relates to the technical field of power system control. Grid-connected original multi-dimensional electrical signals are collected and decomposed into multi-dimensional frequency signals through Fourier transform, the characteristic components of an independent harmonic source are separated through the blind source separation and non-negative matrix factorization technology, and the frequency and amplitude characteristics of the characteristic components are extracted; then, a multi-dimensional time sequence prediction model is constructed through a recurrent neural network, dynamic behaviors of multiple harmonic sources are predicted at the same time, and frequency domain data are reconstructed and frequency aliasing errors are corrected in combination with high-frequency compensation and multi-scale analysis; and finally, inputting a prediction result into a photovoltaic inverter control system, dynamically generating a compensation current opposite to a target harmonic wave, realizing effective offset of the harmonic wave, accurately processing the multi-source harmonic wave problem in a complex power grid environment, and greatly improving the harmonic wave suppression capability, the electric energy quality and the system operation stability of a photovoltaic grid-connected system.

The invention provides a hyperspectral image open set spectral spatial feature extraction and classification method, and aims to improve the classification precision of a known category and an unknown category in a hyperspectral image and the robustness of a classification model. According to the method, input data are preprocessed through fractional Fourier transform, and rotation analysis of a signal time-frequency plane is achieved. Multi-scale features of multi-branch cavity convolution are fused through an enhanced spectrum space residual module, deep separable convolution enhancement nonlinear representation of a lightweight convolution enhancement block is combined, and self-adaptive separation and weighted fusion of high / low frequency features are realized by innovating a dual-frequency enhancement module. Proposing a class perception comparison loss function, integrating anchoring loss, triple loss and regularization terms, and collaboratively optimizing intra-class compactness, inter-class separability and an open set decision boundary. The model framework provided by the invention is combined with the improved loss function, so that the classification precision of the known class and the unknown class of the hyperspectral image is remarkably improved.

The invention discloses an intelligent fault detection system and method for a wind driven generator, and relates to the technical field of deep learning, and the method comprises the steps: collecting multi-dimensional data, and carrying out the preprocessing; extracting multi-dimensional features from the preprocessed multi-dimensional data, fusing the multi-dimensional features through a graph neural network and short-time Fourier transform, and generating a time-frequency domain signal; key features are extracted from the time-frequency domain signals, and fault features of the operation state of the wind driven generator are detected through a support vector machine model; based on the fault features, a fusion neural network model is constructed, fault prediction is generated, a diagnosis report is output, and a maintenance suggestion is provided; according to the method, the neural network model is fused, the convolutional neural network and the long-short-term memory network are combined, the spatial features and the time sequence data are extracted, and multi-dimensional characterization of the fault features of the wind driven generator is achieved.

The invention discloses a cable joint health state assessment method and system based on multi-dimensional data fusion, and relates to the technical field of cable state assessment, and the method comprises the steps: collecting cable joint operation parameters, unifying a time sequence, and forming a two-dimensional matrix; performing spectral analysis on the operation parameters, and calculating energy characteristics of each sub-band by applying multi-stage wavelet decomposition; and constructing a Bayesian network structure, and calculating probability distribution according to the energy characteristics of the sub-bands. According to the method, spectral analysis is carried out through Fourier transform, the effect of identifying main frequency components and noise characteristics in signals is achieved, the effect of extracting energy characteristics of the signals in different frequency ranges is achieved by decomposing a two-dimensional matrix into a plurality of sub-bands by applying multi-stage wavelet decomposition, and the method has the advantages of being simple in structure and convenient to operate. By constructing a Bayesian network structure and defining feature nodes and state nodes, the health state evaluation of the cable can be dynamically updated according to data collected in real time.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The invention discloses an image restoration method, an image restoration device and a storage medium, which are used for improving the structure restoration precision and the detail restoration capability of image restoration. The method comprises the following steps: acquiring multi-dimensional inertial data in real time; performing frequency domain analysis on the multi-dimensional inertial data by adopting sliding window short-time Fourier transform to obtain vibration intensity; if the vibration intensity does not exceed the preset threshold value, acquiring an image; calculating a definition index of the image; determining whether the image is a blurred image according to a preset definition standard and the definition index of the image; if the image is judged to be a blurred image, dividing the blurred image into a motion blurred image and a focusing blurred image; respectively constructing point spread function models of the motion blurred image and the focusing blurred image; performing deconvolution processing or depth reconstruction on the blurred image through a point spread function model to obtain a clear image; and recalculating the definition index of the clear image, and if the definition index does not exceed the definition threshold, triggering reacquisition or switching the repair model to execute secondary repair.

The invention provides an arc fault detection method and device based on lightweight, and a storage medium, and the method comprises the steps: obtaining an arc current signal of a power distribution network load in a preset time period, determining a signal-to-noise ratio parameter, determining a dynamic window length according to the signal-to-noise ratio parameter, carrying out the Hilbert transformation of the arc current signal according to the dynamic window length, and obtaining an arc fault detection result. The method comprises the steps of obtaining m amplitude envelope data, extracting features from the m amplitude envelope data to obtain p time domain statistical features, calculating arc current signals by adopting a preset Fourier transform method to obtain arc current frequency spectrum data, processing the arc current frequency spectrum data to obtain q frequency domain features, and fusing the p time domain statistical features and the q frequency domain features based on a preset feature fusion algorithm to obtain a target arc current feature, and inputting the target arc current feature into a preset lightweight arc fault detection model to obtain an arc fault detection result. Therefore, the accuracy of arc fault detection is improved.

The invention discloses a drilling multi-source sensing signal denoising method based on dynamic noise decoupling and a self-adaptive mode, and belongs to the technical field of underground processing. The method comprises the following steps of: separating common noise of a noise energy distribution matrix of a sensor and separating specific noise; carrying out adaptive noise set empirical mode decomposition on the separated signals, carrying out variational mode decomposition on residual signals in the signals, and screening effective modes through a kurtosis-entropy joint criterion to obtain signals with effective characteristic components reserved; high-frequency fluctuation of the signal is punished through total variation regularization, an improved alternating direction multiplier method algorithm is used for solving, short-time Fourier transform is carried out on the solved signal, low-frequency and high-frequency features are extracted through a multi-scale convolutional network, and a final denoised signal is output through gating weight fusion. According to the method, the signal denoising precision in a complex noise environment is remarkably improved, the processing time is shortened, and the resource consumption is reduced.

The invention provides an intelligent grabbing control method and system based on tactile perception, and the method comprises the steps: obtaining a tactile feedback signal through a sensor array of a humanoid robot, carrying out the frequency domain decomposition of the signal through a Fourier transform method, and carrying out the analysis of the pressure and deformation data of each contact point on the surface of a to-be-grabbed object, calculating to obtain frequency characteristic distribution of the tactile feedback signal; if the initial estimation value of the object rigidity gradient exceeds a threshold value, performing finite element analysis on the object rigidity gradient, and combining a time domain processing result of the tactile feedback signal to obtain object rigidity gradient distribution; carrying out probability modeling on the deformation field by adopting a Bayesian inference method according to the gradient distribution of the rigidity of the object, and carrying out iterative optimization on a modeling result to obtain dynamic update parameters mapped by the deformation field; and regenerating an action sequence of the end effector of the humanoid robot based on the optimized strategy according to a trigger condition of grabbing failure detection, and determining a final grabbing control parameter.

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention relates to the technical field of fault diagnosis, in particular to an oil monitoring fault diagnosis method and system based on the Internet of Things, and the method comprises the following steps: collecting a mechanical vibration signal through oil monitoring, setting a time window, executing short-time Fourier transform, converting a time domain signal into a frequency domain signal, and obtaining frequency spectrum recognition data. According to the method, batch data can be processed in real time through the Internet of Things technology, a classifier is trained through a machine learning method, the fault type recognition precision is further improved, the operation state and efficiency of the machine are allowed to be evaluated through introduction of entropy calculation, and through comparison with a set fault level standard, the fault type recognition accuracy is improved. The fault risk of the machine is found and sorted in time, and the combination of real-time monitoring and automatic identification greatly improves the capability of prevention and maintenance and the operation efficiency of the equipment, reduces the risk of accidental shutdown, and brings remarkable benefits in the aspects of cost control and productivity improvement.

The invention relates to the technical field of audio signal processing, and discloses a high-sampling-rate audio analysis optimization method and system, a storage medium and equipment, and the method comprises the steps: upgrading an FFTW (Fast Fourier Transform) library, and building an optimized memory management mechanism; carrying out adaptive framing processing on the input audio data, and optimizing the data access efficiency by adopting a multi-stage cache system; the calculation precision of the FFT fast Fourier transform is dynamically adjusted, and the calculation of the FFT fast Fourier transform is optimized by pre-calculating and caching an FFTW plan; performing clock synchronization optimization and jitter elimination on the COAX digital audio signal; establishing a multi-dimensional result cache matrix and an intelligent multiplexing decision tree, and reducing repeated operation through intelligent cache prediction and similarity calculation; system performance indexes are monitored in real time, adaptive parameter adjustment is realized based on an optimization objective function, audio quality and system stability are monitored in combination with subjective and objective evaluation mechanisms, repeated operation is remarkably reduced, and calculation resource allocation is optimized on the premise of ensuring sound quality.

The invention relates to the field of data signal processing, in particular to a network algorithm cable terminal discharge early warning method, device and system based on dual-channel waveform identification. The method comprises the following steps of: firstly, acquiring a periodic ultrasonic signal and an ultrahigh frequency electromagnetic field signal generated by discharging of a cable terminal, converting the two signals into a corresponding time-frequency diagram by utilizing short-time Fourier transform, and performing normalization processing to serve as input of a subsequent network; a dual-channel high-order feature extraction network formed by cascading a ConvNeXt backbone network and a convolution block attention module is constructed, and is used for extracting time-frequency diagram detail fault features of ultrasonic signals and high-frequency electromagnetic field signals. And finally, by constructing an adaptive feature fusion network, fusing the fault features of the two channels, and obtaining cross-channel fault joint features by using an adaptive weight coefficient vector, thereby outputting a diagnosis result, and realizing accurate evaluation of a defect state of an adjacent cable terminal.

The invention provides a method for eliminating echo of a talkback system, which comprises the following steps of: according to separated near-end speech evaluation and a plurality of recognized speaker speech segments, respectively performing residual echo suppression, constructing binary time-frequency masking and reducing residual echo spectrum caused by dynamic change of echo power; the phase information of the original mixed signal is combined with the enhanced near-end voice power spectrum, the voice waveform of each speaker is reconstructed through inverse short-time Fourier transform, and transition between voice frames is smoothed in an overlap-add mode; whether voice exists at the near end and the far end or not is judged, if the double-talk state is detected, parameters of a nonlinear echo cancellation algorithm are adjusted, and the situation that the voice at the near end is mistakenly suppressed is avoided; and carrying out transmission path modeling on the processed near-end voice signal, and evaluating propagation characteristics of the voice signal from a speaker to a microphone, including time delay and attenuation of direct sound and early reflected sound.

The invention discloses a mechanical rotating part fault diagnosis method and device, equipment and a medium, and relates to the technical field of fault diagnosis, and the method comprises the steps: monitoring and obtaining the current operation data of a mechanical rotating part, and carrying out the Fourier transform and continuous wavelet transform of the current operation data, obtaining a target frequency domain image and a target time frequency image under the same pixel size; inputting the target frequency domain image and the target time frequency image into a pre-training fault diagnosis model, so that a trained one-dimensional convolutional network and a trained two-dimensional convolutional network of the model carry out feature extraction and fusion on the target frequency domain image and the target time frequency image respectively, and carrying out fault classification prediction on fused image features. Outputting probability distribution results of the current operation data relative to various standard fault data sets; and taking the standard fault category corresponding to the maximum probability value in the probability distribution result as a predicted fault diagnosis result. And through similarity calculation, the fault type of the mechanical rotating part is accurately judged.

The invention discloses a metering equipment interference signal suppression method and system in an electromagnetic compatibility test, and the method comprises the steps: obtaining an input signal of metering equipment in the electromagnetic compatibility test, performing windowing processing on the input signal by adopting a self-adaptive fractional order window function, and then obtaining a frequency spectrum component of the input signal through self-adaptive fractional order Fourier transform; calculating the amplitude of the frequency spectrum component, comparing the amplitude with a power threshold value, and screening out the frequency spectrum component of the interference signal; establishing a spectrum model of the interference signal based on the interference signal spectrum component; and generating a frequency domain compensation signal considering interference signal phase correction, converting the frequency domain compensation signal into a time domain compensation signal, and superposing the time domain compensation signal and the input signal to obtain a signal after interference suppression. According to the method, the frequency spectrum characteristics of the signals in different frequency bands can be ensured to be clear, the interference signal components can be accurately identified, the energy distribution of the interference signals can be effectively simulated, and the high-efficiency interference suppression capability can still be kept in a variable signal environment.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention relates to the technical field of noise filtering, in particular to a communication line noise filtering system and method, and the system comprises a temperature coupling module, a voltage response module, a parameter joint debugging module and a link checking module. According to the method, inductance and capacitance node temperature data are collected and input into a piecewise linear regression model, the temperature-to-resonant frequency deviation gradient is calculated, a noise suppression bandwidth threshold value is dynamically matched in combination with a reference table, the frequency stability under temperature fluctuation is improved, Kalman filtering is used for processing voltage sampling, and a fluctuation predicted value is generated. According to the method, interval indexes are extracted based on a parameter mapping table, instant inductance and capacitance parameters are generated through least square fitting, voltage transient fluctuation self-adaption is achieved, noise power spectrum density is calculated through fast Fourier transform, a 3dB attenuation point is compared with a threshold value difference value to trigger parameter rollback, closed-loop control is formed, it is ensured that suppression bandwidth is continuously matched with a noise frequency band, and noise suppression is achieved. The bandwidth offset is reduced to be within 1.2%, and the optimization response time is shortened to 200 ms level.

The invention relates to the technical field of fault diagnosis, in particular to a wind driven generator fault diagnosis method and system based on Mamba-ResNet. The method comprises the following steps: carrying out feature extraction and feature fusion by utilizing preprocessed data, namely constructing adaptive window short-time Fourier transform (AW-STFT) to carry out dynamic time-frequency resolution analysis, carrying out parallel feature extraction and constructing a multi-dimensional heterogeneous feature vector, and carrying out a cross-modal adaptive gating fusion mechanism based on a bidirectional cross gating unit; the method comprises the following steps: constructing a Mamba-ResNet hybrid deep network model architecture; performing model training on the constructed network model architecture; and performing fault diagnosis on the wind driven generator by using the trained model architecture. A tedious manual feature design process in a traditional method is avoided, and the automation level and adaptability of a diagnosis system are remarkably improved.

The invention relates to the technical field of electromagnetic spectrum monitoring, and particularly discloses a distance-based electromagnetic spectrum monitoring abnormal data detection method, which comprises the following steps of: performing short-time Fourier transform and normalization processing on an acquired original signal to generate an energy density distribution characteristic graph; constructing a multivariate Gaussian distribution model based on non-abnormal historical data; during real-time monitoring, the mahalanobis distance between the collected data and the mean vector of the historical model is calculated after the collected data is preprocessed. And comparing the distance metric value with a preset threshold value to preliminarily judge abnormity, and calculating a distance fluctuation variance through a sliding window mechanism to perform secondary verification. And finally, processing and positioning anomalies by using image morphology, and dividing the degree of anomalies according to the relative deviation between the energy density and the mean value of the historical model. According to the method, the mahalanobis distance and the multivariate Gaussian distribution are introduced, secondary verification and abnormal positioning are combined, the limitation of a traditional method is overcome, the detection accuracy and reliability are effectively improved, the misjudgment and missing judgment rate is reduced, and the method does not depend on a large amount of labeled data and is high in practicability.

The invention relates to the technical field of information transmission, in particular to a multi-band processing system for electronic communication, which comprises a signal separation module, a frequency deviation compensation module, an amplitude correction module, a phase correction module and a dynamic feedback adjustment module. According to the invention, a plurality of frequency bands are extracted to execute band-pass filtering to separate signal components, short-time power spectral density is calculated to describe energy distribution, normalization coefficient operation is carried out to enhance frequency domain balance, high-energy component screening is carried out to reduce noise interference, signal effectiveness is improved, and frequency offset compensation calculates a change rate based on adjacent phase data and eliminates abnormal fluctuation. A time domain coefficient is adjusted to correct a frequency offset error, amplitude correction depends on short-time Fourier transform to screen the maximum amplitude to optimize signal consistency, phase adjustment fits a group delay error curve to optimize compensation parameters, dynamic feedback optimizes a strategy according to a signal-to-noise ratio to enhance compensation precision, and signal transmission quality and stability are improved.

The invention discloses a photovoltaic grid-connected harmonic current prediction method based on deep learning, and relates to the technical field of power systems and photovoltaic grid connection. According to the method, three-phase load current and grid-connected point voltage data in a photovoltaic grid-connected system are obtained, the three-phase load current is converted into a two-phase direct-current component through Clark and Park conversion, fundamental current is extracted through a low-pass filter, and a harmonic current component is obtained. Time domain and frequency domain characteristics of grid-connected point voltage data are extracted through fast Fourier transform, correlation between voltage harmonic waves and load current harmonic waves is analyzed, and grid-connected point voltage characteristics are extracted. Local features of harmonic current components are extracted through a convolutional neural network, time sequence features are captured through a bidirectional long-short-term memory network, and a harmonic current prediction model is constructed. And finally, inputting the preprocessed current and voltage characteristics, and generating a harmonic current predicted value. And the problems of nonlinearity and uncertainty in the photovoltaic grid-connected system can be more effectively solved.

The invention discloses a cable fault positioning method and system based on reflected wave decomposition and kurtosis sorting, and aims to solve the problem that a cable fault is difficult to detect under a low impedance change rate. The method comprises the steps of transmitting an incident excitation signal to a cable to obtain a first reflection signal in the cable; an improved VMD algorithm is adopted for decomposition to obtain K IMF components, and the IMF with the maximum kurtosis is selected as a second reflection signal; acquiring a time-frequency distribution curve of the second reflected signal by using short-time Fourier transform and frequency spectrum redistribution technologies; and constructing a two-dimensional energy-time curve by extracting an energy value and time under the frequency of the maximum energy amplitude point, and determining a fault position. The system is composed of an incident excitation module, a decomposition module, an STFT-RSP module and a fault positioning output module, and the positioning method is achieved. According to the method, the limitation of a traditional spread spectrum time domain reflection positioning method in cable fault detection is effectively overcome, and an efficient and accurate instructive technology is provided for cable fault positioning and detection.

The invention discloses a space-variant self-focusing imaging method for a missile-borne synthetic aperture radar sea surface moving target. The method comprises the following steps: acquiring a radar fundamental frequency echo signal; performing range pulse pressure and range migration correction on the radar fundamental frequency echo signal to obtain a correction signal, and further obtaining a coarse focusing radar image; extracting a single defocusing target image in the coarse focusing radar image, and inverting the single defocusing target image to an echo data domain to obtain data of single target energy; performing correction to obtain data after correction of the residual range walk; selecting a part of distance units from the data after the residual range walk is corrected, performing Doppler frequency modulation rate estimation on the selected part of distance units by adopting fractional Fourier transform, fitting the estimated Doppler frequency modulation rate by adopting a space-variant phase error model, and constructing a space-variant phase error compensation function of the target; and compensating the data after the correction of the residual range walk, and imaging. According to the invention, the refocusing effect of the moving ship target can be improved.

The invention discloses an underwater acoustic signal denoising method based on a time-frequency adaptive dual-path Conformer network, and the method comprises the steps: carrying out the preprocessing of real marine environment noise and underwater acoustic target signals, and generating a multi-signal-to-noise-ratio noisy data set; performing short-time Fourier transform on the noisy data, and extracting real part and imaginary part features to form a feature tensor; extracting features by using a feature encoder, and generating intermediate feature representation; respectively extracting a time path feature and a frequency path feature through a time-frequency adaptive dual-path Conformer network; performing multi-scale convolution processing and weighted fusion on the extracted time-frequency features by adopting a multi-scale fusion dynamic gating network; performing nonlinear mapping on the fused features by using a feature decoder to generate a mask matrix; and restoring the complex frequency spectrum based on the mask matrix, and restoring the denoised underwater acoustic time domain signal. According to the method, time-frequency information is fully mined in combination with underwater sound noise characteristics, and the underwater sound signal denoising effect is improved.

Disclosed in the present invention are a dual-end monitoring-based partial discharge source localization method and system for high-frequency partial discharge of a high-voltage cable. The method comprises: acquiring operation data and phase velocity test data of a high-voltage cable; when a partial discharge fault occurs in the high-voltage cable, collecting partial discharge signals on two sides of the high-voltage cable by means of sensors on the two sides of the high-voltage cable; performing Fourier transform respectively on the basis of the partial discharge signals on the two sides to obtain amplitude-frequency characteristics of the partial discharge signals, and preprocessing the collected partial discharge signals on the basis of the amplitude-frequency characteristics to optimize and improve a phase difference algorithm; and by incorporating the phase velocity test data of the high-voltage cable, using the improved phase difference algorithm to perform partial discharge source localization on the faulty cable. In the present invention, dual-end monitoring is used to reduce the attenuation of signals caused by long-distance transmission, and synchronization of two sensors is performed using a PTP protocol, effectively reducing errors; and in addition, during monitoring, partial discharge source localization is performed using the improved phase difference algorithm, which is conducive to resisting external interference, and there is no need to determine the time of arrival, greatly improving the positioning accuracy.