1988 results about "Fourier transform" patented technology

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 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 discloses a cross-modal target detection method based on learnable Fourier transform, and mainly solves the problem of insufficient fusion of a visible light image and an infrared image in a complex scene due to inter-domain difference in the prior art. According to the implementation scheme, the method comprises the steps that bimodal features are extracted through a double-flow CSPDarknet53 network; a target position guiding module is utilized to enhance target area representation and suppress background interference; the features are converted to a frequency domain, and amplitude texture information of the visible light image and phase contour information of the infrared image are adaptively enhanced through a learnable frequency domain feature enhancement module; suppressing noise through global filtering and then inversely transforming back to a spatial domain; and finally, outputting a target detection result of the multi-modal image by the detection head. According to the method, frequency domain physical characteristics are fully utilized, full complementation and adaptive fusion of cross-modal features are realized, the detection precision and robustness of vehicles, pedestrians and other targets under low-illumination and complex backgrounds are remarkably improved, meanwhile, high calculation efficiency is kept, and the method can be applied to the fields of automatic driving, intelligent monitoring and the like.

The invention is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention discloses a detection method of an ocean magnetic field sensor based on a diamond NV color center. The detection method comprises the following steps: S1, calibrating a base line; s2, alternately measuring fluorescence between two single quantum resonance frequencies, and obtaining a difference to realize DQ reading; s3, three-axis compensation current is adjusted based on closed-loop control, and the NV working point is maintained at zero magnetization bias; s4, obtaining background magnetic field change through digital demodulation and adaptive filtering; and S5, calculating a gradient based on the spatial difference of the multi-point light spots, judging the existence and trajectory of the underwater vehicle in combination with a threshold value and a spectrum matching method, calibrating the threshold value according to the magnetic field characteristics of the existing underwater vehicle, adopting short-time Fourier transform signal spectrum, comparing the threshold value with known submarine characteristic spectrum data, and prompting if the similarity exceeds a set threshold value. According to the detection method, the measurement precision and the anti-interference capability can be improved, and the method is suitable for real-time monitoring of weak magnetic anomalies (such as magnetic moment disturbance of an underwater vehicle).

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.

The invention provides an electroencephalogram signal artifact removing method and device, equipment and a medium, and relates to the technical field of biological signal processing, collected electroencephalogram signal data is processed to obtain an optimal modal number and an optimal bandwidth parameter, and the optimal modal number and the optimal bandwidth parameter are used for conducting self-adaptive variational mode decomposition on the electroencephalogram signal data to obtain a plurality of modal components; then carrying out multi-dimensional feature analysis to obtain a plurality of feature indexes for judging an artifact suspicion mode and an effective mode component; performing short-time Fourier transform on the artifact suspicion mode, and constructing a time-frequency confidence map to guide weighted time-frequency independent component separation on the artifact suspicion mode to obtain an artifact component; suppressing the artifact component to obtain a suppressed independent component, and performing weighted reconstruction and multi-component fusion on the suppressed independent component and the effective modal component based on the time-frequency confidence map to obtain an artifact-removed electroencephalogram signal; and the fidelity, the robustness and the real-time performance of the electroencephalogram signal are improved.

The invention discloses a ship attitude prediction method and system based on FT-Mama architecture. The method comprises the following steps: collecting training data of multiple modes; performing feature extraction on the training data of various modes to obtain time domain features and Fourier transform frequency domain features corresponding to the training data of various modes; according to the time domain feature and the Fourier transform frequency domain feature, training a preset FT-Mama multi-modal fusion model to obtain a ship attitude prediction model; obtaining to-be-processed multi-modal ship data, inputting the to-be-processed multi-modal ship data into the ship attitude prediction model, and outputting a ship attitude prediction result; wherein the training data comprises ship attitude data, sea condition data and meteorological data. The method can effectively improve the ship attitude prediction precision under the complex sea condition, reduces the prediction delay, provides reliable look-ahead information for boarding compensation control, and can be widely applied to the technical field of ship control.

The invention relates to the field of electrical parameter measurement of power distribution switch equipment, in particular to an arc fault measurement method, system and device in the power distribution switch equipment and a medium, and the method comprises the steps: collecting a current signal in a power distribution loop in real time; separating out target frequency band components of which the frequency is greater than a preset frequency threshold in the current signal; performing envelope extraction on the target frequency band component to obtain an envelope signal; performing time domain analysis on the envelope signal to obtain the regularity intensity of the pulse of the envelope signal; performing short-time Fourier transform on the target frequency band component, and calculating a frequency spectrum variance; taking the regularity intensity and the spectrum variance as feature vectors, and inputting the feature vectors into a pre-trained artificial intelligence model; outputting a first fault diagnosis result by the artificial intelligence model; and generating a control instruction based on the first fault diagnosis result to trigger the protection device to act. The method has the advantage that the dangerous electric arc in the power distribution loop can be accurately identified and quickly responded.

The invention relates to the field of welding quality detection, and discloses a steel structure welding quality detection method and system based on machine vision, and the method comprises the steps: obtaining an original welding seam image sequence through high-frequency sampling, and carrying out the preprocessing to generate a clear sequence; extracting features from the sequence to obtain a welding seam static defect index; time sequence data are constructed according to the time arrangement characteristics, and a dynamic time sequence data set is obtained by calculating the welding seam symmetry fluctuation amplitude and the molten pool shape change trend; inputting the data set into a neural network model, analyzing a surface profile curve time sequence evolution mode, and determining potential defect points; extracting a crack period amplitude sequence from the potential defect points, and performing Fourier transform to obtain a defect frequency distribution diagram; extracting a frequency peak value position from the graph, judging process parameter deviation, generating an alarm and updating a database; and determining a sampling frequency deviation value according to the alarm, adjusting the high-frequency sampling frequency through feedback circulation, and optimizing dynamic detection. According to the invention, the detection real-time performance, the accuracy and the parameter adjustment accuracy are improved.

The invention discloses a small target detection method based on a Fourier mixed attention mechanism. An improved small target image detection network model based on RT-DETR is researched and designed, and a Basic Block module in a backbone network is replaced by a self-developed Fourier mixed attention enhancement module (FTABlock). The module is composed of a Fourier transform attention module (FTAModule) and a hybrid dynamic convolution feedforward network (DKMixFFN). The FFTModule calculates the attention weight in the frequency domain through Fourier transform, and strengthens small target feature expression in combination with position coding and multi-head attention; the DKMixFFN adopts a dynamic convolution kernel and a multi-scale dynamic convolution layer to realize adaptive modeling of multi-scale features. According to the method, under the synergistic effect of frequency domain attention and dynamic convolution, the small target feature extraction and perception capability of the RT-DETR model is effectively enhanced, and the small target detection precision is improved.

The invention discloses an OCT (Optical Coherence Tomography) retina image denoising method based on a high-frequency enhanced diffusion model, which is characterized in that a double-branch diffusion model network constructed based on frequency perception Fourier transform attention (FFTA) is used for separating different frequency domains, enhancing specific frequency domain features and fusing the specific frequency domain features into a spatial domain feature map to realize stronger frequency domain perception and processing capability; the invention relates to a time step T coding method for a module of frequency domain attention. The time step T of the diffusion model is used as a parameter to be coded into a frequency domain attention module to participate in self-attention matrix calculation, so that frequency domain feature processing is aligned with the iteration step number of the diffusion model, and different frequency domain information is pointedly processed at different time steps T; the frequency selective hopping mechanism uses pooling operation to obtain a high / low frequency characteristic pattern, so that the network has adaptive frequency domain retention capability for different local parts.

The invention relates to a building structure health monitoring method and system based on machine learning, and the method specifically comprises the following steps: firstly, building a target building three-dimensional numerical model through finite element simulation, generating a simulation signal, injecting Gaussian white noise, and adjusting model parameters to form a data set with health category labels; performing data enhancement by combining adaptive wavelet denoising with dynamic normalization, and extracting and enhancing high-resolution time-frequency features through adaptive window short-time Fourier transform and adaptive frequency band enhancement; then, a neural network model fusing structure physical prior guidance and multi-scale space-time interaction is constructed, a feature matrix is modulated, fused and coded to obtain a refined feature vector, and damage state probability distribution output is achieved; and training is carried out by using a feature consistency and prediction smoothness regularization term constraint model, and finally, the trained model is deployed, so that building structure health state evaluation and safety early warning are realized, the monitoring accuracy and reliability are improved, and effective technical support is provided for building safety guarantee.

The invention relates to the technical field of three-dimensional measurement, and discloses a coating thickness measurement method and system based on a multi-source sensor, and the method comprises the steps: obtaining and fusing point cloud data of the multi-source sensor, and constructing a three-dimensional curved surface model of a component through curvature analysis and normal vector estimation; calculating an optimal incident angle of the sensor relative to each area of the curved surface based on the model, and generating a dynamic attitude adjustment parameter; according to the parameters, the three-axis attitude of the sensor is adjusted in real time for signal acquisition, and original measurement data are obtained; frequency domain analysis is carried out on the original data to diagnose reflection distortion, and correction is carried out in combination with time domain filtering and spatial domain smoothing; and finally, calculating a thickness value in combination with the three-dimensional model, detecting and repairing a data missing region by using Fourier transform and an interpolation algorithm, and generating a complete thickness distribution diagram. According to the invention, high-precision and automatic measurement of the thickness of the complex curved surface coating is realized.

The invention relates to the technical field of federated learning security, and discloses a federated learning poisoning defense method based on time-frequency spectrogram and comparative learning, which comprises the following steps: receiving model update uploaded by each client, grouping and vectorizing parameters according to model layers, and generating a time-frequency spectrogram by applying short-time Fourier transform to parameter vectors of each layer; based on the time-frequency spectrogram, constructing a positive sample pair through data enhancement, carrying out difficult negative sample mining, and training an encoder by using a contrast loss function to extract an embedded vector with high discriminant power; and performing unsupervised clustering on the embedded vector by using a DBSCAN clustering algorithm, judging the maximum cluster as a benign client, performing final judgment in combination with historical malicious records, and only aggregating model parameters of the benign client to update a global model. According to the invention, high-precision detection of attack features can be realized, and a more universal, more efficient and more practical federal learning poisoning attack defense method is realized.

The invention relates to an encrypted traffic classification method fusing space, time sequence and frequency spectrum features, and belongs to the field of encrypted traffic classification and deep learning. The method comprises the following steps: analyzing a network original flow Pcap packet, segmenting the packet into different sessions according to a quintuple, and preprocessing each session: extracting first n data packets, and then extracting first m bytes and t time sequence data from each packet; utilizing byte data of the session to train a spatial feature module, and extracting spatial features of the session; training a time sequence feature module by using the time sequence data of the session, and extracting time sequence features of the session; the byte data of the session are converted into frequency spectrum information through fast Fourier transform, a frequency spectrum feature module is trained, and frequency spectrum features of the session are extracted; and fusing the extracted global spatial features, time sequence features and frequency spectrum features, and training by using multi-modal joint feature representation to realize fine classification of encrypted traffic. According to the invention, the precision and robustness of traffic classification can be improved.

The invention discloses a partial discharge signal denoising method based on STFT-SVD and Bayesian kurtosis threshold adaptive optimization, and the method comprises the steps: collecting an analog signal outputted by a high-frequency current transformer, carrying out the analog-to-digital conversion, obtaining a one-dimensional time domain signal sequence, carrying out the DC component removal and amplitude normalization of the signal, and obtaining a preprocessing time domain signal; performing short-time Fourier transform on the preprocessed time-domain signal to obtain a time-frequency spectrum, suppressing low-amplitude noise by adopting a soft mask method, and retaining main characteristics of partial discharge pulses; performing singular value decomposition on the time-frequency spectrum after soft masking, automatically selecting a principal component number according to a principal component, and adaptively reserving a main signal component to obtain a principal component spectrum; and performing inverse short-time Fourier transform on the principal component atlas to reconstruct a time domain signal, adaptively selecting a kurtosis threshold in combination with a Bayesian optimization algorithm, and outputting a denoised time domain signal.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention relates to the technical field of power quality monitoring, in particular to a method and system for constructing a power quality disturbance recognition model based on multidimensional data, and the method comprises the steps: obtaining voltage and current waveform data and environmental parameter data of a key node of a power transmission line, carrying out the hardware defect self-inspection and phase compensation of the voltage and current waveform data, and obtaining a power quality disturbance recognition model; clean transmission electric energy data is obtained; and performing multi-scale noise suppression and time sequence correlation analysis on the clean transmission electric energy data, and constructing a high-fidelity disturbance sequence. According to the method, through the hardware defect self-inspection and phase compensation steps, denoising preprocessing is carried out by utilizing wavelet packet transformation, the frequency response deviation of equipment is identified through Fourier transformation, a frequency domain interpolation method is adopted to reconstruct a frequency-closed defect mark segment, the phase deviation error can be accurately compensated, and the detection accuracy is improved. And self-systematic errors of hardware are eliminated from a data acquisition source, high fidelity of clean transmission electric energy data used for subsequent analysis is ensured, and a foundation is laid for high-precision identification.

According to the invention, the multi-mode time-frequency domain conversion and the deep learning technology are combined, and a multi-component mixed Raman spectrum unmixing method is developed, so that clinical in-vivo and in-situ detection and disease diagnosis of novel Raman probes, instruments and the like are facilitated. The method comprises the following steps: (1) converting a mixed Raman spectrum from a time domain to a frequency domain by using fast Fourier transform (FFT), discrete cosine transform (DCT) and discrete sine transform (DST), and extracting frequency domain features; (2) extracting multi-scale local time-frequency domain characteristics of the mixed spectrum by using short-time Fourier transform (STFT) and discrete wavelet transform (DWT); (3) carrying out spectral unmixing calculation in each mode in combination with a one-dimensional attention mechanism U-shaped neural network model; and (4) fusing various modal unmixing results by using a meta-learning method, and analyzing the weight of each modal to obtain an accurate unmixing spectrum. Compared with a traditional Raman spectrum analysis method, the Raman spectrum multi-component signal unmixing method based on multi-modal time-frequency domain transformation and deep learning can accurately separate independent Raman signals of different tissue structures and biochemical components in a complex environment in a living body, so that the unmixing accuracy of the Raman spectrum multi-component signal is improved. Therefore, convenience is provided for subsequent disease mechanism analysis and diagnosis. The method provides an innovative and potential solution for in-vivo and in-situ detection analysis and disease diagnosis of medical clinical Raman spectroscopy.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a low-light remote sensing image restoration method and system based on double-frequency domain processing, and the method comprises the steps: constructing an image restoration network which comprises a coding module, an intermediate enhancement module and a decoding module which are connected in sequence, the coding module and the decoding module are in jump connection; wherein the coding module, the intermediate enhancement module and the decoding module are each internally provided with a double-frequency-domain attention module, and each double-frequency-domain attention module comprises a Fourier attention sub-module used for global frequency domain feature modeling and a wavelet attention sub-module used for multi-scale detail feature extraction; by introducing the Fourier transform frequency domain processing technology, the global frequency characteristic analysis capability is provided, and efficient global modeling is realized. Secondly, introducing a wavelet decomposition frequency domain processing technology, decomposing the image into sub-bands with different scales and frequencies, and effectively separating a clear image and a degenerated component;

The invention relates to the technical field of optical detection, and discloses an object surface three-dimensional topography measurement method based on optical interference, which comprises the following steps: acquiring a mixed time domain interference signal sequence in an axial scanning process; extracting seed pixels and constructing a spatial topology connectivity feature matrix; introducing fractional Fourier transform, and constructing a joint decoupling objective function containing a frequency domain nonlinear phase distortion evaluation index and a time domain physical invariant evaluation index; executing multi-stage optimization for the seed pixels, and locking a global optimal rotation angle; array self-adaptive decoupling is executed in combination with the spatial topology connectivity feature matrix, and an optimal rotation angle matrix is generated; and performing signal decoupling and reconstruction on the mixed signal, and extracting the absolute surface height of each pixel point to generate three-dimensional shape data. According to the invention, through multi-dimensional joint decoupling and spatial prior constraint, the problem of phase decoupling caused by film interference aliasing is solved, and the calculation overhead of a large-pixel array is reduced while the measurement precision is improved.

The invention discloses a crystal oscillator circuit fault classification technology based on a multi-source information fusion network, and belongs to the technical field of analog circuit fault diagnosis. Firstly, a multi-source data set of different measurement points of the crystal oscillator circuit is acquired; carrying out conversion from a time domain to a frequency domain on the data set by utilizing fast Fourier transform; extracting data fault features by using a convolutional neural network, and performing a trust distribution function under each piece of source data by using a softmax classifier; fusing different trust distribution functions under the multi-source data by adopting a D-S evidence theory to obtain a final diagnosis result and diagnosis probability output, and calculating cross entropy loss; and finally, training model parameters through back propagation to obtain a final diagnosis model. According to the method, the time-frequency transformation algorithm, the deep neural network algorithm and the information fusion algorithm are combined, a multi-source information fusion network is constructed, the defects of an existing diagnosis model in crystal oscillator circuit fault classification are overcome, and the accuracy and stability of crystal oscillator circuit fault diagnosis are remarkably improved.

The invention relates to the technical field of pantograph-catenary system monitoring, and discloses a nonlinear pantograph-catenary system state prediction method based on a physical information neural network, and the method comprises the steps: obtaining the structural parameters of a catenary and a pantograph in a nonlinear pantograph-catenary system, and the train speed; a multi-expert Mama network based on Fourier transform is constructed; utilizing the first full connection layer to map input features formed by the structural parameters of the contact net and the pantograph and the train speed to a high-dimensional feature space to obtain first high-dimensional features; frequency domain features are extracted from the first high-dimensional features by using a plurality of cascaded multi-expert Mamba network layers, and time sequence features are modeled at the same time; fusing the spliced frequency domain feature and the time sequence feature by using a second full connection layer to obtain a second high-dimensional feature fusing the time-frequency energy and the dynamic evolution information; and performing nonlinear pantograph-catenary system state prediction according to the second high-dimensional features. According to the invention, the state prediction accuracy of the nonlinear pantograph-catenary system can be improved.

The invention relates to the technical field of power distribution lines, in particular to a power distribution line abnormal line loss analysis and treatment method and system, and the method comprises the following steps: synchronously extracting real-time monitoring data and historical data based on a power distribution line network, carrying out the data alignment and data cleaning, screening features associated with current leakage and voltage drop, and carrying out the analysis and treatment of the abnormal line loss of the power distribution line. Comprising load change rate and temperature fluctuation. According to the invention, through application of real-time and historical data analysis, the detection and management efficiency of the abnormal line loss of the distribution line is improved, the accuracy of data processing is improved through synchronous extraction and data alignment, early recognition of abnormal modes is allowed, the possibility of energy loss and unplanned power failure is reduced, and through adoption of Fourier transform and wavelet transform, the detection efficiency of the abnormal line loss of the distribution line is improved. According to the method, abnormal signals are accurately recognized, the analysis capability of the signals is enhanced, the accuracy of abnormal detection is improved, fault positioning and maintenance plan making are effectively optimized through combination of the GIS system, and the pertinence and efficiency of maintenance work are remarkably improved.

The present invention relates to the technical field of fault diagnosis, and provides a fault diagnosis method for a wind-turbine drive train based on time-frequency plane expectation maximization. The method comprises the following steps: configuring a cylindrical MEMS acceleration sensor on a drive chain of a wind turbine, and acquiring a vibration signal of the drive chain of the wind turbine via the cylindrical MEMS acceleration sensor; adopting a spectrogram-zero-based unsupervised classification method, acquiring a vibration signal and a rotational speed signal of the wind turbine during operation, generating a time-frequency representation of the vibration signal by using a short-time Fourier transform, and extracting spectrogram zeros and performing unsupervised classification to implement denoising processing of the vibration signal; on the basis of a multi-component signal estimation method under time-frequency plane expectation maximization, accurately estimating an instantaneous frequency and an instantaneous amplitude of a multi-component signal on a time-domain plane of the denoised signal; and performing order spectrum analysis to identify vibration signal characteristics of the drive chain of the wind turbine, so as to achieve fault diagnosis of the drive chain of the wind turbine under variable rotational speeds.

The embodiment of the invention provides a beam coverage method and device of an antenna array, equipment and a storage medium. The method comprises the steps of determining an initial beam coverage area; determining a reference center position in the initial beam coverage area, and constructing a rectangular window according to the maximum spatial frequency deviation of the initial beam coverage area relative to the reference center position; constructing a rectangular window function; obtaining a space beam forming gain function and determining a first relation function between the beam forming weight vector and the beam forming vector; inverse Fourier transform is carried out according to the spatial beam forming gain function to obtain a beam forming weight vector function, and a second relation function is determined based on the rectangular window function and the beam forming weight vector function; and calculating a target beam forming vector of the antenna array in combination with the first relation function and the second relation function, and controlling the antenna array to perform signal coverage on the initial beam coverage area according to the target beam forming vector. In this way, the real-time performance of beam configuration of the antenna array can be improved.

The invention provides a comprehensive noise reduction performance evaluation method for a nonlinear ultrasonic detection signal noise reduction algorithm, and the method comprises the steps: firstly constructing a multi-working-condition noise reduction performance pre-screening mechanism based on error band analysis based on early-stage experimental data, and then introducing a radar map as an evaluation tool for the comprehensive noise reduction effect of the signal noise reduction algorithm. The noise reduction effects of a moving average method (MA), a spectral subtraction method (SS), a short-time Fourier transform method (STFT), a wavelet transform method (WT) and an orthogonal matching pursuit algorithm (OMP) are compared, and finally a signal noise reduction algorithm with the optimal comprehensive efficiency is screened out. On the basis of algorithm optimization, quantitative mapping rules between microcrack three-dimensional geometric parameters and relative nonlinear coefficients are analyzed through regression modeling, and the significant level of the correlation degree of the relative nonlinear coefficients and microcrack size parameters is effectively improved; and a high-confidence theoretical support is provided for quantitative nondestructive detection of the microcracks in engineering practice.

The invention relates to the technical field of satellite signal processing, and discloses a multi-constellation satellite signal intelligent identification and adaptive acquisition method and system. The method comprises the following steps: performing short-time Fourier transform on a received signal to extract a frequency trajectory matrix, calculating a Doppler frequency shift rate and an acceleration, inputting a multi-task neural network in combination with modulation domain characteristics, and outputting constellation category and interference type identification results, when a low-orbit satellite is identified, a carrier frequency deviation track and uncertainty are predicted through a long-short term memory network, the frequency of a numerical control oscillator is adjusted in advance according to a prediction result, a search window and integral time are dynamically set, and corresponding anti-interference strategies are executed for different interference types. And periodically monitoring the signal quality index, and triggering strategy upgrading when the performance improvement rate is lower than a threshold value. According to the invention, the signal capture success rate and tracking stability in a multi-constellation mixed environment are improved.

The invention provides a non-power-cut test method for mutual inductance parameters of a power transmission line under excitation of different-frequency signals and a related device, and solves the problems of difficulty in accompanying stop and high safety risk of adjacent lines. The method comprises the following steps: selecting one circuit in the same corridor for power failure, connecting the head end with an induced electricity suppression device, and performing three-phase short-circuit grounding at the tail end; injecting a different-frequency sinusoidal current of 45-55Hz into the power failure line; the synchronous measurement device collects head and tail voltage and current of a running line based on a satellite synchronous clock; extracting pilot frequency signals through two-stage notch filtering and Fourier transform; and different-frequency zero-sequence self-impedance and mutual impedance are calculated and converted into power-frequency impedance. According to the invention, accurate test without power failure can be realized, and safety and power supply reliability are guaranteed.