561 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 relates to a TCN-LSTM wind power generation capacity prediction method combining time-frequency conjoint analysis and a multi-head self-attention mechanism. The TCN-LSTM wind power generation capacity prediction method comprises the following steps: preprocessing original data of wind power generation; frequency domain features of the wind power generation data are extracted through wavelet transform, adaptive weighted fusion is carried out on the frequency domain features and time domain features, and a time-frequency conjoint analysis module is constructed; a TCN-LSTM combined network of a time convolution network TCN and a long short-term memory network LSTM is constructed; a multi-head self-attention mechanism is introduced into the TCN-LSTM combination network, and a TCN-LSTM wind energy generating capacity prediction model combining time-frequency analysis and the attention mechanism is generated; training the model by using the wind power generation data set; the trained prediction performance is evaluated; and performing wind power generation prediction by using the final wind power generation prediction model. According to the method, the accuracy of wind power generation prediction and the calculation efficiency of the model are remarkably improved, and the precision of wind power prediction 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 discloses a music data identification method and device based on multiple sound parts, equipment and a storage medium. The method comprises the steps that music data is collected and then preprocessed, noise interference is removed, the music data is converted into a spectrogram through multiple kinds of time-frequency analysis, features are extracted through a deep learning algorithm fusing CNN, RNN and an attention mechanism, then sound parts are classified and labeled, adaptive noise elimination and a filter are adopted in preprocessing, and volume normalization is carried out. The device comprises a data acquisition module, a preprocessing module, a time-frequency analysis module, a feature extraction module and a classification labeling module, and the equipment comprises a processor and a memory to execute the method. The method is also realized when the program stored in the storage medium is executed; through adaptive denoising, a multi-analysis method, a composite deep learning model and a classification labeling technology, noise is effectively removed, sound features are clearly displayed, features are accurately extracted, sound functions are determined, processing efficiency is improved, large-scale real-time processing requirements are met, and digital development of the music industry is assisted.

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.

Method and apparatus for predicting maintenance and failure of X-ray tubes are disclosed. X-ray radiation signals are received directly from one or more X-ray detectors without being attenuated by an object or subject under examination. The unattenuated X-ray radiation signals and scan metadata data are stored in memory, and time domain analysis and frequency domain analysis is performed on the stored X-ray radiation signals. Amplitude and frequency data for all peaks greater than a predetermined Signal to Noise Ratio (SNR) from a spectrum derived from the frequency analysis are stored. Components of an X-ray tube are monitored according to the stored amplitude and frequency data. The stored amplitude and frequency data for all peaks greater than the predetermined SNR may be written to a data file for further analysis.

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 relates to the technical field of concrete defect detection and data processing, in particular to a high-precision concrete defect detection data processing method and terminal, and the method comprises the steps: collecting the original data of a detected work area along a detection line; establishing a wavelet model based on the original data, and calculating a received signal through a reflection coefficient and a wavelet; preprocessing the received signal to obtain a preprocessed signal; converting the pre-processed signal into a time-frequency domain signal through improved S transformation, extracting a main frequency signal, and performing interpolation processing on the main frequency signal along a measuring line; converting a time sequence of the original data into a depth sequence through time-depth conversion, and generating a depth detection map through mapping of the depth sequence and the dominant frequency signal; according to the invention, the time-frequency analysis method is adopted to obtain the time-frequency spectrum of the collected signal, more abundant defect detection information is provided, and the problems that the traditional impact echo processing method only judges defects through dominant frequency translation, seriously depends on the working experience of field personnel and is not strong in underlying logic are solved.

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 discloses a self-adaptive time-frequency analysis method based on kurtosis maximization and continuity criterion. The method comprises the following steps: 1, acquiring a pulse signal sampling data sequence to be processed; 2, initializing an initial window length set and time frame parameters of adaptive time-frequency analysis; 3, updating the adaptive window length set according to the initial window length set and the time frame parameters; 4, calculating the kurtosis corresponding to each window length in the adaptive window length set of the current time frame, and obtaining the initial adaptive time-frequency analysis window length of the current time frame; 5, correcting the initial adaptive time-frequency analysis window length according to a window length change continuity criterion to obtain the optimal time-frequency analysis window length of the current time frame; 6, obtaining the self-adaptive short-time power spectrum and the instantaneous discretization frequency of the current time frame, and obtaining a contraction window length set according to the change trend of the instantaneous discretization frequency; 7, judging whether all time frames are processed or not, if not, returning to the step 3, and otherwise, turning to the step 8; and 8, reconstructing an adaptive time-frequency analysis result according to the optimal time-frequency analysis window length sequence.

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 a rail corrugation detection and identification method, device, equipment, medium and program, and the method comprises the steps: obtaining an original vibration response signal, decomposing the original vibration response signal, and obtaining a modal function component; screening out a target component from the modal function components according to a preset correlation coefficient criterion, and reconstructing the target component to obtain a preprocessed signal; performing time-frequency analysis on the preprocessed signal to obtain a time-frequency ridge line, and intercepting a corrugation section according to a vibration response signal positioned by the time-frequency ridge line; and extracting time domain features of the corrugation section, and generating a wave depth grade result according to the time domain features.

The invention relates to the technical field of data storage, in particular to a construction engineering measurement data storage method and system.The construction engineering measurement data storage method comprises the steps that the overall character frequency and the stage character frequency are obtained by analyzing the occurrence frequency of characters in each piece of measurement data in the measurement data; analyzing key data in the measurement data based on the stage character frequency to obtain the key data representation degree of each character; the change degree of the stage character frequency of each character in different construction stages is analyzed, the change degree of the total data amount of the measurement data in different construction stages is analyzed, and the future change trend degree of each character is obtained; according to the future change trend degree and the key data representation degree, the coding priority of each character is obtained in combination with the overall character frequency; and according to the coding priority, endowing each character with a coding length to complete the compression and storage of the data. The compression efficiency can be effectively improved, and the subsequent access efficiency of key data can be taken into account at the same time.

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 discloses a dynamic event propagation prediction system for urban social governance, which is used for accurately classifying and analyzing dynamic event propagation of urban social governance, identifying early propagation signs of events in time and predicting the propagation range, influence and possible evolution paths of the events. The system comprises a public opinion information acquisition subsystem, a public opinion data center, a public opinion data preprocessing subsystem and a public opinion analysis and prediction subsystem, the public opinion information acquisition subsystem is used for performing directional information acquisition on websites in a specified area and a specified field, and the public opinion data preprocessing subsystem is used for performing code recognition, webpage noise removal, text content extraction, Chinese word segmentation and automatic abstract generation on the acquired information and then providing the information to the public opinion analysis and prediction subsystem; and the public opinion analysis and prediction subsystem is used for carrying out keyword frequency analysis, topic detection and tracking and sensitive topic identification on the preprocessed information.

The invention relates to the technical field of power system load prediction, and discloses a microgrid-based PCVMD-CNN-GRU-Attention load prediction method, which comprises the following steps: S1, collecting microgrid power load data, and preprocessing the data; s2, constructing a fitness function for decomposing VMD by a sparrow algorithm; s3, for a sparrow algorithm optimization VMD parameter, after the parameter is determined, dividing an original load into a plurality of sub-modals and a residual quantity; s4, performing frequency analysis on the sub-modal data and the residual data; s5, inputting the normalized component data into a CNN-GRU-Attention model for training and prediction, and storing the number of the models which is the same as the number of the components; and S6, respectively predicting each component. According to the method, feature extraction is carried out on the multi-dimensional historical data by using the CNN, modeling is carried out on the time sequence front-back relation of the extracted features by using an Attention mechanism in combination with the GRU model, and finally short-term load prediction is completed, so that the beneficial effect of preprocessing the original data according to the historical data of the microgrid load and the generated model is achieved.

The invention relates to the technical field of radar weak target detection, in particular to a radar weak target detection method and device based on deep learning. According to the technical scheme, the method comprises the following steps: data preprocessing and enhancement: receiving an original echo signal of a radar, generating three-dimensional time frequency-wavelet coefficient spatial data by combining time frequency analysis with wavelet transform, and expanding a data set in multiple modes; and feature extraction network construction: constructing a 3D-CNN and LSTM fused network, wherein the 3D-CNN has a convolution operation with a variable convolution kernel size. According to the method, the data are richer and more comprehensive through time-frequency analysis in combination with wavelet transform and data enhancement, and multi-dimensional features can be accurately captured through 3D-CNN and LSTM fusion and an adaptive convolution kernel; and the training effect is ensured through the adaptive learning rate and the early stop method, and the accuracy and adaptability of weak target detection are jointly 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 belongs to the technical field of radars, and particularly relates to an intermittent sampling and forwarding interference suppression method based on Fast-SCNN. According to the method, a time-frequency analysis and deep learning semantic segmentation method is adopted, interference and target superposed receiving signals are used as input data, and regional distribution of interference signals is used as label data. And after preprocessing, the features are converted into time-frequency domain two-dimensional features, so that the interference can be conveniently extracted and suppressed. And subsequently, utilizing a Fast-scnn semantic segmentation network to more accurately predict the interference region in the time-frequency two-dimensional matrix. And in combination with a signal reconstruction method, the target echo signal is further restored, so that the suppression effect on intermittent sampling forwarding interference is better realized.