423 results about "Frequency analysis" patented technology

The invention provides a passenger and goods elevator roof bearing capacity evaluation method with a self-adaptive early warning function, which comprises the following steps of: acquiring data of a multi-source sensor, and performing data acquisition and preprocessing by adopting a dynamic synchronous sampling optimization algorithm; extracting time-frequency characteristics, separating load modes by adopting a multi-dimensional time-frequency analysis algorithm, and constructing a dynamic load spectrum; establishing multi-scale state mapping, calculating system response by adopting a generalized dynamic response analysis method, and evaluating structural risk; and constructing a self-adaptive early warning system, dynamically optimizing an early warning threshold value, and generating an optimal decision scheme. According to the method, the sampling strategy can be adaptively adjusted, the load characteristics can be accurately recognized, multi-scale state evaluation is achieved, early warning decisions are dynamically optimized, and the accuracy and reliability of people and goods elevator top plate bearing capacity evaluation are remarkably improved.

The invention relates to the technical field of data classification management, in particular to a knowledge graph-based foreign trade data classification management system, which comprises a term word order modeling module, a graph path generation module, a path cross analysis module, a semantic category judgment module and a classification structure output module. According to the method, morpheme-level word segmentation and word order extraction are performed on foreign trade terms, an original word order template is constructed, the reduction capability of a semantic expression sequence is enhanced, a directed path is established through morpheme-level label sorting, and the level definition of a term structure and the logic relation between nodes are enhanced; a semantic intersection area is extracted through node intersection and end point frequency analysis, the accuracy of term association judgment is improved, semantic affiliation is selected according to end point label frequency, a node classification closed loop is formed by combining labels and path mapping, links of term graph modeling, path construction, intersection analysis and affiliation output are broken through, and the term association judgment accuracy is improved. And the classification management process of foreign trade data is fully improved.

The invention discloses a method for identifying a multi-model fusion signal modulation mode based on a time-frequency diagram, and belongs to the field of signal processing. Preprocessing the original signal to obtain a signal sample; constructing a data set by using the normalized signal samples; constructing a fusion model; training a modulation identification module in the fusion model; performing modulation mode identification on an input unknown signal by using the fusion model to obtain a preliminary identification result; and performing comprehensive judgment on the preliminary recognition result of the fusion model by using a comprehensive judgment device to obtain a final recognition result. According to the invention, by combining a plurality of time-frequency analysis methods, the time-frequency domain feature information of the signal is fully extracted; the reliability and robustness of a final decision are improved by adopting a multi-model fusion framework, and the modulation recognition performance of the system under the condition of a low signal-to-noise ratio is improved.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention discloses a wellhead data acquisition modeling system. The wellhead data acquisition modeling system comprises a data acquisition module, a hydraulic characteristic processing module, a well layer coupling modeling module and an interaction control module, the invention belongs to the technical field of well water monitoring management, based on an adaptive time-frequency analysis method, a dynamic window function enhanced by local frequency spectrum entropy is introduced, feature extraction and abnormal early warning are performed on transient hydraulic fluctuation signals, and the time-frequency resolution and change recognition capability of the hydraulic signals are remarkably improved; a dynamic coupling model between a water pumping system and an aquifer is constructed by combining a multi-scale parameter coordination mechanism, and accuracy and dynamic adaptability of well layer response modeling are realized by introducing a gap compression lag function and a joint parameter calibration method; and the interaction control module implements model prediction control according to the hydraulic feature vector, the hydraulic anomaly early warning signal and the well layer coupling prediction parameter combination, and outputs wellhead data acquisition decision support data.

The invention discloses an intelligent pipe network management system based on the Internet of Things technology, and relates to the technical field of data management. Pressure bearing capacity and water pressure data of a pipeline are collected in real time through stress and pressure sensors, and whether water flow exceeds a pipeline safety range or not is accurately assessed by combining pipeline pressure bearing capacity monitoring, frequency analysis and risk assessment; based on the data, the risk division module divides risks into high, medium and low levels, and takes targeted management measures; and for medium-risk conditions, the maintenance module reduces the pipeline fracture risk by predicting and dynamically adjusting the starting frequency of the water pump and the opening degree of the valve, and effectively prevents secondary pollution and water quality decline, thereby ensuring safe and stable operation of the water supply system.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention provides a large-scale equipment early fault detection system and method based on energy flow time-frequency analysis. The large-scale equipment early fault detection system based on energy flow time-frequency analysis comprises a multi-sensor data acquisition and preprocessing module, an energy flow time-frequency feature extraction module, a deep transfer learning fault identification module, a data enhancement and dynamic balance module and a multi-scale energy flow modeling module. According to the energy flow time-frequency analysis-based large-scale equipment early-stage fault detection system and method provided by the invention, in order to solve the problems of poor time-frequency feature extraction adaptability, insufficient noise robustness and weak cross-domain migration capability in a variable working condition scene in the prior art, through collaborative optimization of multi-modal data fusion, adaptive time-frequency analysis and dynamic migration learning, the time-frequency feature extraction adaptability is improved; and the fault detection precision and generalization capability under the complex working condition are obviously improved.

The invention belongs to the field of artificial intelligence, particularly relates to a method for predicting the residual life of a key part of a coal mill, and aims to solve the problems of low prediction precision and poor extrapolation caused by working condition disturbance interference and inaccurate degradation characterization in a traditional method. The method comprises the following steps: collecting vibration, temperature, current and acoustic emission multi-source synchronous data; constructing a dynamic working condition decoupling model of fusion of the variational auto-encoder and the attention mechanism, and separating degradation sensitive components; and a high-fidelity degradation index sequence is generated through fusion of time-frequency analysis and a gating circulation unit. According to the method, multi-modal sensing and physical priori knowledge are fused, the monotonicity of a degradation index is remarkably improved to 98% or above, the extrapolation error of an extreme working condition is reduced by 40%, edge-cloud collaborative deployment and digital twinborn visualization are supported, multi-component collaborative early warning is realized, and the early warning amount of non-planned shutdown is improved from 7 days to 21 days or above; and a high-precision, strong-robustness and landing life prediction solution is provided for intelligent operation and maintenance.

The invention discloses a multi-factor coupling dynamic error compensation method, system and device and a storage medium, and the method comprises the steps: obtaining an original signal sequence, and carrying out the time-frequency analysis of the original signal sequence, and obtaining a time-frequency matrix; performing feature extraction on the time-frequency matrix to obtain feature information; according to the feature information, calculating a Lagrange interpolation reference node of a corresponding time point, and carrying out signal reconstruction to obtain a synchronous sampling sequence; performing harmonic analysis on the synchronous sampling sequence to obtain harmonic parameters; the dynamic compensation amount is calculated in combination with the characteristic information and the harmonic parameters, the electric energy metering value is corrected, and the real-time performance and accuracy of electric energy measurement are effectively improved.

The invention discloses a time-frequency analysis method for impact signal positioning based on transient scale extraction transformation, and belongs to the technical field of mechanical vibration signal processing. The method comprises the following steps: collecting a rotating machine fault vibration signal, and reconstructing a signal model through Hilbert transform and a Dirac function; a transition matrix is generated by using a Gaussian window function traversal model, and matching and Fourier transform are carried out in combination with a discretized scale basis function; solving a frequency partial derivative of a transformation result to generate a time redistribution operator, and redefining by a Dirac function to obtain a transient scale extraction operator; based on the sub-time-frequency representation of scale-based rotation discretization, screening optimal matching results of each time center through a maximum kurtosis value, and integrating the optimal matching results into a complete time-frequency representation; and finally, redistributing a time-frequency coefficient by using a transient extraction operator to realize accurate positioning of the impact component. According to the method, the problems of serious impact energy diffusion and insufficient positioning precision in existing time-frequency analysis are solved, and the time-frequency representation readability and the impact positioning reliability are remarkably improved.

The invention discloses a window-based decoupling short-time fractional Fourier transform time-frequency analysis method. The method comprises the following steps: S1, acquiring non-stationary signal data generated under power quality disturbance in a power grid; s2, segmenting the non-stationary signal into a plurality of parts based on signal waveform characteristics, and setting an initialized virtual window function for discrete short-time fractional Fourier transform, wherein the window function only provides an interface for calling of a specific window function; s3, calling different window functions for each segmented sub-segment signal to obtain windowed sub-segment signals under a specific window function; and S4, discrete fractional Fourier transform is applied to the sub-segment signals obtained in the step S3 to obtain time-frequency results of the sub-segment signals under different windows, and finally, frequency spectrums of the sub-segment signals are spliced according to a time sequence and output by an oscilloscope. Based on the characteristics of non-stationary signals generated under power quality disturbance in a power grid, different window function analysis signals can be called in one task by setting a virtual window function interface, and the window-based decoupling short-time fractional order Fourier transform time-frequency analysis method is formed. According to the method, the coupling between fractional Fourier transform and a specific window is reduced, the expandability of a program is improved, and more accurate signal local spectrum features can be obtained.

The invention relates to the technical field of data processing, in particular to a multi-series short-duration rainstorm data frequency analysis method. The method comprises the following steps: obtaining rainfall original data of multiple series of short duration, and carrying out missing data interpolation and sequence extension processing, duration consistency and rainfall intensity rule check to obtain a rainstorm data set; respectively performing reliability, representativeness and consistency review and sample validity screening on the rainstorm data set; a Pearson III type probability density function model is constructed, probability distribution fitting is carried out, and a frequency distribution curve is generated; and carrying out extra heavy rain value analysis and empirical frequency adaptive line analysis adjustment, generating a corrected frequency parameter, further calculating a design heavy rain value of each design return period, and carrying out duration consistency and spatial rationality check to obtain a final design result output data set. According to the invention, through multi-dimensional data complementation, checking and optimization fitting, the system improves the accuracy, reliability and engineering applicability of short-duration rainstorm frequency analysis.

The invention belongs to the field of polar acoustic measurement, and relates to a method for estimating the sound velocity and thickness of an ice layer by using airwaves and ice layer bending waves, which comprises the following steps of: acquiring broadband pulse sound signals by using a sensor fixed on an ice surface; recording the distance between the detonating device and the ice surface sensor; performing time-frequency analysis on the broadband pulse signal acquired by the ice surface sensor; obtaining a time-frequency spectrum of the signal through time-frequency analysis, calculating the propagation velocity of the airwave, converting the time-frequency spectrum into a group velocity spectrum, and extracting a group velocity dispersion curve of the bending wave; initializing the longitudinal wave velocity, the transverse wave velocity, the ice layer thickness and the seawater sound velocity of the ice layer; calculating an expected group velocity curve according to the sound propagation model, and constructing a cost function; and optimizing the cost function by adopting a particle swarm algorithm, and outputting a final parameter estimation value. The method can be realized only by using a single sensor, and the synchronization of transmitting equipment and receiving equipment is not required; the sound velocity and thickness of the ice layer can be inverted by using the relationship between the group velocity and the acoustic parameters.

An electronic device includes: a transmission unit that transmits a transmission wave; a receiving unit that receives a reflected wave of the transmission wave from the target; and a signal processing unit that detects the distance, direction, and speed of the target on the basis of a converted signal obtained by Fourier-converting a beat signal of the transmission wave and the reception wave. The signal processing unit extracts a signal component corresponding to heart sound vibration accompanying the heartbeat of the subject from the converted signal using a plurality of window functions, and performs frequency analysis on an envelope signal obtained by performing envelope processing on the signal component corresponding to the heart sound vibration. And performing calculation processing of a statistical result on a frequency analysis result, and outputting time sequence data of the heartbeat frequency of the object based on a calculation result.

The invention discloses a bearing fault dynamic ridge tracking and feature matching diagnosis method based on high-resolution time-frequency analysis, which is applied to the technical field of fault diagnosis and signal processing of rotating machinery of a wind turbine generator, and comprises the following steps: acquiring an acceleration vibration signal of a bearing, and utilizing an optimized variational mode decomposition algorithm to obtain an acceleration vibration signal of the bearing; decomposing the acceleration vibration signal of the bearing to obtain an optimal intrinsic mode function; transforming the optimal intrinsic mode function component by using a synchronous compression transformation method to obtain a high-resolution time-frequency graph, and extracting a time-frequency ridge line in the high-resolution time-frequency graph by using a dynamic programming algorithm; constructing a fault characteristic curve based on the time-frequency ridge line, and calculating a fault actual characteristic coefficient of the fault characteristic curve; and according to a comparison result of the actual fault characteristic coefficient and the fault characteristic coefficient corresponding to the theoretical fault type, identifying the fault type of the bearing. The problem that the rotation frequency of the bearing is unstable under variable working conditions is solved, and the fault type of the bearing can be accurately recognized.

The invention relates to the field of charging pile use, and discloses a charging pile use frequency analysis method based on charging frequency data, which comprises the following steps: acquiring charging frequency original data of a plurality of charging piles at different time periods and different dates, performing structured processing in combination with a time label, and constructing a charging behavior data set containing a time dimension label; introducing a sliding window algorithm and a periodic spectrum analysis method, and performing frequency feature extraction on the charging behavior data set; using a density peak clustering algorithm to classify the use frequency matrixes of different charging piles under the same time scale, and identifying use preferences and active features of the charging piles under various typical time scenes; based on a lightweight time sequence neural network structure, a frequency fluctuation sensing mechanism is introduced; and predicting the use condition of the charging pile in a future time period based on the trained frequency model in combination with regression prediction and an anomaly detection algorithm. The method has the advantage of improving operation scheduling.

The invention discloses an industrial control system anomaly detection method combining time-frequency analysis and pure consistency measurement, and belongs to the technical field of industrial control system monitoring and intelligent diagnosis. The problems that in the prior art, when industrial control system data are processed, precision is not high, robustness is poor and the like are solved, discrete fractional Fourier transform (DFRFT) is adopted to extract time-frequency features, important features are screened in combination with an energy extraction strategy, and complex nonlinear and non-stationary data are effectively processed. On the basis, inverse fractional Fourier transform (iDFRFT) is used for recovering time domain features, and the inherent rule of the data is further disclosed. Meanwhile, through K-S inspection and random consistency analysis, the consistency and stability of feature distribution are quantified, and the accuracy and reliability of feature extraction are ensured. According to the method, a normal behavior mode is modeled by using a generative adversarial auto-encoding network (AGAE), and potential abnormal behaviors are identified according to reconstruction errors, so that the accuracy and robustness of anomaly detection are improved.

The invention relates to the technical field of harmonic intelligent positioning, and discloses a coupling harmonic intelligent positioning method and system based on multi-modal fusion, and the method comprises the steps: obtaining the voltage amplitude time sequence data of each observation point, constructing a frequency distribution characteristic curve, and extracting a frequency center and a broadening parameter; parameter updating is realized in combination with priori information of power grid impedance, and an adaptive frequency analysis window is constructed on this basis to extract energy distribution characteristic quantity; the reliability weight of an observation point and the time sequence weight of harmonic source equipment are further introduced, and the dynamic characteristics of the space credibility and the operation state are comprehensively reflected; and finally, taking the two types of weights as weighting factors, carrying out weighted calculation on the harmonic propagation model deviation of the candidate nodes to obtain a comprehensive residual value, and positioning the node with the minimum comprehensive residual value as a coupling harmonic source node, thereby remarkably improving the accuracy of harmonic source positioning in a complex power grid environment.

The invention relates to a line loss positioning method and system based on artificial intelligence and big data analysis, and the method comprises the following steps: S1, collecting voltage, current, power and timestamp data from an intelligent ammeter, a sensor and historical records, and expanding a data set through employing a data enhancement and generative adversarial network; s2, features are extracted from the expanded data set obtained from the generator by fusing GNN and Transform, the features are fused into a comprehensive feature vector, and a training data set is constructed; s3, constructing a line loss prediction model; s4, on the basis of the line loss prediction model, data is obtained in real time for prediction and analysis, and line loss abnormal points are obtained; s5, based on the abnormal points, identifying potential reasons of the line loss abnormal points in combination with statistical analysis and frequency analysis; and S6, marking the position and time of an abnormal point based on the power grid topological graph, thereby facilitating field inspection and processing by operation and maintenance personnel. According to the invention, artificial intelligence and big data analysis are combined to realize line loss positioning, and the line loss positioning capability and accuracy are effectively improved.

The invention discloses a real-time fault diagnosis method, device and system for a rotating machinery bearing based on a deep learning algorithm, and the method comprises the steps: designing a data collection board card, and collecting the vibration data and sound data of a bearing in different fault states on an electromechanical fault simulation platform; carrying out normalization processing on the collected signals, and constructing a data set by adopting a sliding window method; performing multi-scale decomposition on the collected bearing vibration signal and sound signal by using a signal time-frequency analysis method to obtain a time-frequency signal with a multi-scale fault feature; taking the decomposed signals as input, training a lightweight deep neural network model and storing optimal model parameters; deploying the trained lightweight deep neural network model based on time-frequency analysis to an edge computing embedded board card; a display screen of the edge computing embedded board card outputs a bearing fault diagnosis result in real time, and an emergency alarm is given through an acousto-optic signal when a fault is detected. According to the method, the advantages of signal time-frequency analysis in processing non-stationary signals and the powerful capability of the deep neural network in feature extraction are fully combined, the bearing fault can be accurately diagnosed in real time in a complex industrial environment, and the problems of poor real-time performance, high calculation complexity and the like existing in bearing fault diagnosis in the prior art are solved.

The invention provides a ship maintenance scheme design recommendation system, and relates to the technical field of ship fault prediction, and the system comprises a processing module which is used for building a coordinate system with a riveting geometric center as an original point, forming a rectangular analysis region through extending a set distance, dividing the rectangular analysis region into m * n grids, and filling grid units column by column and line by line according to a lower left corner starting point; constructing a candidate set by using a central grid and 8 adjacent grids, screening abnormal grids through vibration signal time-frequency analysis, generating a high-risk set in combination with historical stress standard exceeding data, and obtaining a target position A and a target position B after cross comparison; and the calculation module is used for calculating the stress data coupling degree and the vibration data coupling degree of the grid at the main riveting position and the grids at the target position A and the target position B. According to the invention, the early recognition capability of the looseness risk of the riveting structure and the accuracy of the maintenance scheme are improved.

According to the anti-frequency analysis searchable encryption method and system, a certificateless encryption system is introduced, for a scene with limited computing resources, the scheme is ensured to be safe and suitable for the scene with the limited computing resources, meanwhile, a trap door algorithm is designed based on a probability trap door generation algorithm, and the security of the scheme is improved. The method improves the resistance of a system to frequency analysis attacks, solves the problem of secret key trusteeship due to the fact that identity information and secret key distribution are concentrated in a private key generation center in a traditional encryption scheme, and improves the encryption efficiency. The technical problem that malicious users are easy to obtain the privacy information of the target keyword by performing frequency analysis on trap doors and initiating keyword guessing attacks is solved.

The invention discloses a time-frequency analysis method and system for non-stationary signals of periodic pulses, and the method comprises the following steps: S1, carrying out the preprocessing of a vibration signal through a rapid CMSP method, and obtaining a periodic pulse component in the vibration signal; s2, carrying out iterative estimation on group delay by utilizing fixed point iteration; s3, utilizing iteration group delay estimation, and redistributing time-frequency coefficients of the STFT through time rearrangement multiple times of synchronous extrusion transformation; s4, performing integration on a time rearrangement multiple-time synchronous extrusion transformation result along a time direction so as to reconstruct the signal; according to the method, the problem that STFT time-frequency representation is fuzzy is solved through a fixed point iterative algorithm, meanwhile, signal reconstruction can be achieved, synchronous extrusion operation is carried out through iterative group delay estimation, an estimated value is closer to real group delay, divergent time-frequency coefficients in STFT after FC processing are redistributed, and the estimation accuracy is improved. Therefore, the energy concentration degree of time-frequency representation is improved.

The invention belongs to the technical field of RSW pumps of CANDU6 nuclear power stations, aims to solve the problem that modal aliasing and false components exist in EMD decomposition in traditional HHT, and discloses a time-frequency analysis method and system for non-stationary vibration signals of an RSW pump of a CANDU6 nuclear power station, and the method comprises the steps: obtaining a rotor vibration signal, carrying out VMD decomposition on the rotor vibration signal, obtaining K modal components, and carrying out VMD decomposition on the K modal components; performing HT transformation on the K modal components, obtaining a Hilbert spectrum of an original signal, finally obtaining the original signal, and performing diagnosis on a rotor fault according to the change of a characteristic frequency in the Hilbert spectrum; the system comprises a vibration signal acquisition module, a signal decomposition and center frequency evaluation module, a modal component and center frequency solving module, a constraint problem conversion module, a modal component calculation module, a Hilbert spectrum acquisition module, an analysis signal construction module and an original signal restoration module. According to the method, the VMD method with excellent decomposition performance is combined with the HT, and the fault feature recognition capability of a traditional HHT method in processing rotor non-stationary vibration signals is optimized.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

According to the hardware mold machining control method and system, energy of flutter characteristic frequency components is obtained by collecting cutting torque signals and conducting frequency analysis, the flutter risk is evaluated by dynamically setting an energy threshold value based on the overhanging length of a tool, the feeding rate and the rotating speed of a main shaft are adjusted when the response risk exists, and the machining accuracy of the hardware mold is improved. Therefore, fluttering in the machining process can be effectively recognized and inhibited, and the method has the advantages that fluttering in the machining process can be effectively recognized and inhibited, the machining quality is improved, and the service life of a tool is prolonged.

The invention discloses a line fault detection method and device based on morphological wavelets, relates to the technical field of line detection, and solves the problem that in the prior art, when a single-phase grounding fault occurs in a small-current grounding system, the fault current is small, the characteristics are not obvious, and when line fault detection is carried out by using time domain, frequency domain and time frequency analysis methods, the fault current is not obvious. And the technical problems of low fault line selection accuracy and poor reliability exist. The method comprises the following steps: acquiring zero-sequence current signals in a period before and after a line fault occurs; pre-processing the zero sequence current signal; decomposing the preprocessed zero sequence current signal by using a morphological Haar wavelet; calculating modulus maxima of the plurality of lines according to the decomposition result; correcting the modulus maxima of the plurality of lines based on the line length; screening fault lines according to the corrected modulus maxima; the accuracy and reliability of single-phase earth fault line selection can be improved.

The invention provides an interest point recommendation method based on a dynamic hierarchical attention fusion space-time network, and the method comprises the following steps: 1, obtaining user historical sign-in and interest point feature data, and carrying out the preprocessing of the user data; 2, learning robust spatio-temporal information representation of the interest points through an adaptive spatio-temporal double-graph convolutional network module; 3, constructing a'interest point-functional unit-regional unit 'multi-level semantic hierarchical structure, and learning hierarchical semantic information representation of interest points by using a bidirectional hierarchical graph attention network module; 4, after fusing the two interest point representations, coding a user track and inputting the user track into an attention-frequency fusion Transform module, so as to obtain a multi-level semantic hierarchical structure; the module separates long and short term dynamic signals in a user behavior sequence through frequency analysis and dynamically fuses the long and short term dynamic signals with a self-attention mechanism to predict a next point of interest; and 5, training by adopting a combined loss function. According to the method, the complex characteristics of the user and the POI are effectively modeled, and the accuracy and the capability of capturing the dynamic preference of the user are improved.

The invention discloses a ship signal adaptive time-frequency analysis method based on instantaneous frequency direction self-adjustment. The ship signal adaptive time-frequency analysis method comprises the steps of collecting ship signals to be subjected to time-frequency analysis and performing short-time Fourier transform; estimating the instantaneous frequency of the ship signal based on short-time Fourier transform time-frequency distribution in combination with time-frequency narrow-band features; calculating the instantaneous frequency and the optimal window length after smoothing; segmenting the smoothed instantaneous frequency in time, calculating time-frequency domain direction pre-guidance of each time period, and constructing a Chirp atomic direction over-complete dictionary; segmenting the ship signal in time, and calculating the matching direction of the ship signal in each time period so as to calculate and improve Chirplet transformation time-frequency distribution; and calculating the optimal short-time Fourier transform in combination with the optimal window length, and correcting and improving Chirplet transform time-frequency distribution. According to the method, the robustness of short-time Fourier transform and the anisotropy of improved Chirplet transform are utilized at the same time, and time-frequency distribution with high robustness and high time-frequency resolution is obtained.