110 results about "Instantaneous amplitude" patented technology

The invention discloses an HPLCamp (High Performance Liquid Chromatography) based on deep learning. The invention discloses an HRF dual-mode communication adaptive coding modulation and anti-noise method. The method comprises the following steps: acquiring an optical radio frequency signal amplitude-phase change rate and synchronously sampling and normalizing; calculating a node amplitude-phase residual error to generate a nonlinear mapping coefficient; monitoring coherent change to solve a drift trend, adjusting a modulation coding optimization scheme, compensating distortion and outputting an anti-noise result. According to the method, the instantaneous amplitude and phase of the optical radio frequency dual-mode signal are extracted, a multi-dimensional amplitude-phase characteristic matrix is formed in combination with time domain synchronization and a normalization template, differential residual modeling and nonlinear mapping coefficient calculation are carried out between impedance nodes, and dynamic compensation of amplitude-phase mismatch and envelope offset is achieved. A drift trend quantity is generated based on coherent offset parameter differentiation, feedback is provided for modulation format and coding strategy optimization, amplitude equalization and phase correction are completed, the signal synchronization degree and amplitude-phase consistency are improved, and the steady-state response and anti-disturbance performance of a transmission link are enhanced.

The invention discloses an underground physical characteristic prediction method and system combining seismic waves and drilling data, and relates to the technical field of geophysical exploration, and the method comprises the steps: collecting original seismic records, carrying out the preprocessing, obtaining a three-dimensional seismic data body, carrying out the complex channel analysis of the three-dimensional seismic data body, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes, and carrying out the calculation of the instantaneous amplitude, instantaneous phase and instantaneous frequency attributes. Fault features are identified through a coherent body algorithm, an earthquake feature matrix is obtained, depth alignment and correction are carried out on density logging, interval transit time and neutron porosity curves of drill holes, and a standardized drill hole data table is generated; and based on the seismic feature matrix and the standardized drilling data table. According to the method, deep complementation of the earthquake macroscopic information and the borehole local truth value is realized through the geometric perception multi-modal graph neural network and the dynamic gating fusion mechanism, the problems of insufficient cross-modal feature fusion and misalignment of physical rules in the prior art are solved, and the prediction precision and reliability under complex geological conditions 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 invention provides a vibration nonlinear signal energy analysis method and system based on variational mode decomposition, and relates to the technical field of signal processing, and the method comprises the following steps: obtaining a monitoring signal of a vibratory roller, determining a corresponding state based on root-mean-square data of the monitoring signal, and determining the state of the monitoring signal; performing segmentation processing on the monitoring signal based on the state corresponding to the monitoring signal to obtain a signal segmentation result; performing variational mode decomposition processing on the signal segmentation result to obtain at least two second mode components; performing Hilbert transformation processing on the second modal component, and combining the instantaneous frequency and the instantaneous amplitude of each modal component obtained through transformation to obtain frequency spectrum information of the monitoring signal; and performing marginal spectrum calculation and integration on the frequency spectrum information to obtain the total energy of the monitoring signal. According to the method, interference signals can be efficiently identified and eliminated in the vibration signals, so that the precision and robustness of compaction quality evaluation are improved.

The invention discloses a flow field data modal decomposition method, device and equipment suitable for a non-stationary flow field of an aircraft, and a storage medium, and relates to the technical field of hydrodynamics, and the method comprises the steps: obtaining the spatial-temporal data of the non-stationary flow field of the aircraft, determining a physical representation form, and obtaining the spatial-temporal data of the non-stationary flow field of the aircraft; the method comprises the following steps of: decomposing a plurality of low-order dynamic process components consisting of spatial modals and time evolution coefficients by utilizing a frequency modulation technology, constructing a frequency modulation operator, converting the time evolution coefficient of each component into a narrowband frequency modulation component time sequence signal, and optimizing the narrowband signal bandwidth by taking minimization of the narrowband signal bandwidth as an optimization target; and constructing a to-be-solved variational optimization problem by combining the flow field reconstruction constraint and the frequency modulation structure constraint. According to the method, a time evolution coefficient, an instantaneous frequency, an instantaneous amplitude and a spatial mode are obtained through iterative solution by adopting an alternating direction multiplier method, and a modal decomposition result is determined based on a solution result, so that adaptive extraction and dynamic characteristic description of the non-stationary flow field are realized, and the modal separation precision and the extraction efficiency of nonlinear and transient characteristics of the non-stationary flow field are improved.

The invention discloses an inertial navigation system periodic oscillation suppression method based on time-frequency transformation, and relates to the technical field of underwater navigation, and the method comprises the following steps: S1, modeling an error propagation model of an INS, and defining coordinate systems required by INS navigation, including an inertial coordinate system, a navigation coordinate system and a body coordinate system; based on the INS navigation equation, obtaining an INS error model, and carrying out time domain analysis to obtain an analytical solution of time domain error divergence; s2, designing a time-frequency transformation NTFT, proposing a normal phase time-frequency transformation method, and constructing a complete observation equation; s3, substituting a null method into a calculated longitude and latitude result, and accurately extracting frequency, phase and instantaneous amplitude in inertial navigation longitude and latitude; and S4, establishing a resolving framework of the inertial navigation system periodic oscillation suppression method based on time-frequency transformation, and constructing a negative feedback loop through input gyroscope and acceleration information to suppress periodic errors.

The invention discloses a pre-distortion calibration method and system based on a single chip microcomputer, a medium and a product, and relates to the field of radio frequency front ends. The method comprises the following steps: extracting an instantaneous amplitude value of an input signal sequence to obtain a first envelope to represent instantaneous nonlinearity; the periodic average power of the input signal is calculated and filtered through timer interruption, and a second envelope representing the heat effect is obtained. According to the method, the first envelope and the second envelope serve as double indexes, the preset two-dimensional lookup table is inquired, so that the pre-distortion calibration coefficient capable of being matched with the instantaneous nonlinearity and the thermal memory effect of the power amplifier at the same time is determined, and the input signal is calibrated. In addition, a feedback signal output by the power amplifier is obtained to calculate a distortion calibration error, and the two-dimensional lookup table is adaptively updated according to the error. According to the scheme, a composite calibration model is constructed on a low-cost single-chip microcomputer platform, and the accuracy and stability of nonlinear pre-distortion calibration of the power amplifier are guaranteed.

The invention relates to the field of railway data analysis and processing, in particular to a railway irregularity data processing method based on cEEMDAN-Hilbert. The method comprises the following steps: acquiring and preprocessing track irregularity original signals to obtain a target track irregularity signal sequence; performing CEEMDAN (adaptive noise complete ensemble empirical mode decomposition) on the target track irregularity signal sequence, and performing iterative decomposition on the signal added with Gaussian white noise to obtain a plurality of IMF (intrinsic mode function) components and a residual component; performing Hilbert transformation on each IMF component, calculating an instantaneous frequency and an instantaneous amplitude, constructing an analysis signal in a complex form, and extracting time and frequency joint distribution information; according to the instantaneous frequency and the energy result, false wavelength components are removed, signal reconstruction is carried out, and reconstructed track irregularity signals are generated. According to the invention, the identification capability of local structure disturbance can be enhanced.

The invention relates to the technical field of nuclear reactor fault detection, in particular to a method and system for extracting weak collision and abrasion noise of loose parts in a reactor based on signal processing and machine learning, and the method comprises the steps: obtaining a reactor vibration signal, and decomposing the vibration signal into a plurality of sub-signals through empirical mode decomposition; performing Hilbert transform processing on each sub-signal, extracting an instantaneous frequency and an instantaneous amplitude of each sub-signal, and constructing a time-frequency two-dimensional matrix of each sub-signal; superposing the time-frequency two-dimensional matrixes of the sub-signals along the dimensions of the sub-signals to generate a three-dimensional instantaneous time-frequency matrix; and inputting the three-dimensional instantaneous time-frequency matrix into a pre-trained convolutional neural network model, inhibiting the operation noise of the reactor, and extracting a weak collision and abrasion noise signal after noise reduction. The method aims at extracting weak collision and abrasion noise signals of the loose part in the reactor and achieving collision and abrasion fault recognition of the loose part in the reactor.

The invention relates to the technical field of feature detection, in particular to a radar micro-motion feature extraction method based on deep learning, and the method comprises the following steps: collecting the instantaneous amplitude, slope, time interval and phase change of a radar signal, normalizing the instantaneous amplitude, slope, time interval and phase change into a time sequence feature group, constructing and fusing a feature matrix, and extracting the feature matrix; and inputting a time sequence convolutional network model to carry out multi-layer extraction and modeling, implementing multi-stage judgment and dynamic screening, and finally outputting a fragment sequence through threshold judgment and multivariate joint screening based on the attribution confidence probability. According to the method, quaternary time sequence features are constructed by capturing instantaneous amplitude, slope, extreme point interval and phase change, internal association of weak dynamic of a target is revealed, spatial-temporal correlation is automatically modeled to mine nonlinear and non-stationary features, and multi-stage discrimination, dynamic screening and attribution confidence probability joint determination are performed on the features. Therefore, effective micro-motion signal segments are accurately locked under complex background interference, and the robustness and accuracy of feature extraction are remarkably improved.

The application provides an oil and gas detection method and device based on instantaneous energy frequency value, a computer readable storage medium and an electronic device. The method comprises the following steps: for each seismic signal in a three-dimensional seismic data volume of a target area, decomposing the seismic signal into a data set composed of a plurality of IMF components, extracting the instantaneous amplitude and instantaneous frequency of each IMF component, calculating the instantaneous energy frequency value of each IMF component based on the instantaneous amplitude and instantaneous frequency, and calculating the instantaneous energy frequency value of the seismic signal based on the instantaneous energy frequency value of each IMF component; analyzing the change characteristics of energy and frequency in the stratum of the target area based on the instantaneous energy frequency value of each seismic signal in the three-dimensional seismic data volume and the instantaneous energy frequency value of each IMF component of each seismic signal, and further analyzing the oil and gas bearing property of the stratum. The method provided by the application can more accurately identify the energy and frequency anomalies in the stratum and can also assist in oil and gas identification.

The application discloses a rolling bearing fault diagnosis method based on acoustic framing. Firstly, the collected acoustic signal of the rolling bearing is subjected to Hilbert transform to construct a double-channel analytic representation containing instantaneous amplitude and instantaneous phase information. Then, the analytic signal is divided into short-time frame segments by the acoustic framing embedding module according to a preset frame length and step, and a uniform dimension frame-level embedding vector is generated by linear mapping, so as to highlight the local transient impact feature and effectively compress the sequence length. An improved lightweight Transformer encoder is adopted for timing feature extraction, the encoder combines a low-dimensional multi-head self-attention mechanism and a feedforward network based on SiLU activation and deep convolution, so as to simultaneously model the cross-frame global dependence and the short-range local structure, and finally, the intelligent identification of the multi-class faults of the rolling bearing is completed through the global feature vector of the class token.

The application provides a replay voice attack detection method for a voiceprint security authentication system. First, the voice signal is preprocessed, then a plurality of sub-band signals are obtained through linear equal-width Gabor filters, each sub-band signal is processed through FDEO to obtain an instantaneous amplitude and an instantaneous frequency, then the instantaneous amplitude and the instantaneous frequency are respectively taken as inputs of an SENet to obtain enhanced IACC and IFCC features, and the features are respectively processed through windowing and averaging and discrete cosine transformation to obtain respective low-dimensional feature vectors. Then, the extracted IACC and IFCC feature vectors are respectively used to train respective Gaussian mixture model classifiers to obtain respective classifier model parameters. In detection, the IACC and IFCC feature vectors of the voice to be detected are respectively input into the respective GMM classifiers and are scored for credibility, and finally score level fusion is performed to realize discrimination of true and false voices.

A method, apparatus, and processor-readable medium for detecting a physical change in an environment are provided, the method comprising: acquiring a first set of sound frames representing sound waves reflected in the environment; applying frequency filtering to extract from the first set of sound frames a corresponding first set of filtered frames limited to a predefined frequency band, the predefined frequency band corresponding to a frequency band of a predefined cyclic acoustic detection signal; calculating an instantaneous amplitude of the filtered frame in the first set of filtered frames; evaluating whether the first set of sound frames is indicative of a physical change in the environment based on the calculated instantaneous amplitude and a predefined reference parameter corresponding to a reference state of the environment; when the evaluation indicates the physical change, a predefined operation is caused to be performed.

This application relates to a method, apparatus, device, medium, and product for under-range protection of a line. The method includes: upon detecting a fault in the protected line, determining the zero-mode current component corresponding to the voltage and current data of the protected line; decomposing the zero-mode current component to obtain two preset sub-frequency band signals, and determining the instantaneous amplitude and instantaneous phase corresponding to each preset sub-frequency band signal; determining the actual time difference between the two preset sub-frequency band signals based on their respective instantaneous amplitudes; and determining the theoretical time difference between the two preset sub-frequency band signals based on their respective instantaneous phases; and performing protection processing on the under-range protection area of ​​the protected line based on the actual time difference and the theoretical time difference. This application can promptly perform under-range protection actions and improve the accuracy of under-range protection actions.

A kind of DAS signal adaptive demodulation method and system based on FPGA, wherein, the method comprises: obtaining the in-phase component signal and quadrature component signal of DAS signal;When the address of write pointer occurs incremental change, the historical in-phase component signal and historical quadrature component signal in the preset history sampling window are read;The instantaneous amplitude energy of each time stamp is calculated, and the instantaneous amplitude energy sequence is obtained;The amplitude energy mean and variance eigenvalue of DAS signal are calculated;Adaptive gain factor is generated;The in-phase component signal and quadrature component signal are compensated and iteratively demodulated by adaptive gain, and the phase output value and demodulation module length value are obtained;The module length deviation amount of demodulation module length value and preset standard module length is calculated, and when the cumulative value is greater than preset locking threshold, the correction coefficient is calculated according to the current amplitude energy mean and module length deviation amount;The storage value of index address is updated based on correction coefficient.The application can improve the reliability of DAS system.

The invention discloses a power distribution terminal automatic test method and system based on a dynamic fault record and analysis oscillograph, and the method comprises the steps: S1, obtaining a signal collected by the dynamic fault record and analysis oscillograph, carrying out the wavelet packet decomposition and noise reduction processing, carrying out the transformation, obtaining a signal feature containing an instantaneous frequency and an instantaneous amplitude, and carrying out the detection of the instantaneous frequency and the instantaneous amplitude; time domain features and frequency domain features in the signal features are extracted, and fault feature classification is carried out; s2, reconstructing the fault feature vector, carrying out time sequence feature association mapping, and predicting the action response of the power distribution terminal through a pre-trained intelligent prediction model; generating a test case according to the prediction result and the corresponding fault feature vector; and S3, reconstructing a test signal, outputting the test signal to the power distribution terminal to generate an actual response signal, evaluating the actual response signal by using a prediction result of the intelligent prediction model, and generating a test evaluation result responded by the power distribution terminal. The intelligent level of power distribution terminal testing can be improved.

The invention relates to the technical field of sound field control, in particular to a sound equipment sound field low-frequency increasing and decreasing method and system based on volume amplitude recognition, and the method comprises the following steps: collecting an original audio stream, and extracting instantaneous amplitude features; calculating a low-frequency compensation coefficient according to the nonlinear auditory model; constructing a dynamic safety threshold and correcting a coefficient in combination with the physical limit of the equipment; and adjusting the low-frequency component based on the corrected parameter, and generating an optimized sound field signal. According to the invention, by establishing a dynamic mapping and constraint mechanism between the instantaneous volume amplitude and the low-frequency gain, the technical defects that the low-frequency hearing feeling of a traditional sound system is deficient under the low volume and overload distortion is extremely easy to occur under the high volume are thoroughly solved, and under the premise of strictly guaranteeing the physical safe operation of loudspeaker hardware, the sound quality is greatly improved. And full-dynamic-range sound field adaptive equalization and restoration conforming to human ear psychological acoustic characteristics are realized.

The application provides a medium-voltage distribution network line fault positioning method and a smart switch, and belongs to the technical field of distribution networks. The method comprises the following steps: performing Hilbert-Huang transformation on a distribution network fault current signal connected with a distributed energy source to obtain the instantaneous frequency and instantaneous amplitude of a key component signal; performing multidimensional signal phase space reconstruction based on the instantaneous frequency and instantaneous amplitude of the key component signal to obtain a phase space reconstruction signal; using a convolutional neural network to extract the characteristic quantity of the time sequence of the distribution network fault current signal and the characteristic quantity of the phase space reconstruction signal, mixing them, and determining the fault occurrence position according to the mixed characteristic quantity. The application firstly uses Hilbert-Huang transformation to mine the fault current signal and its time-frequency characteristics in the medium-voltage distribution network containing distributed resources, then uses a phase space reconstruction method to capture the internal law of the fault signal, and finally realizes fault line selection and accurate positioning of the fault in the medium-voltage distribution network containing distributed resources through a convolutional neural network.

The application provides a speech separation post-filtering method and system based on sub-band envelope characteristics, which comprises the following steps: designing an m times frequency band pass filter set, performing sub-band decomposition on the separated estimated sound source signal and the original mixed signal to obtain the frequency band division representation thereof; performing Hilbert transform on each sub-band of the estimated sound source signal and the original mixed signal to construct an analytic signal, calculating the instantaneous amplitude thereof and removing the high frequency component through low pass filtering to obtain the corresponding sub-band envelope; calculating an initial masking value, limiting the initial masking value by setting a lower threshold, performing nonlinear mapping on the initial masking value through a sine function to obtain an envelope masking coefficient; applying the envelope masking coefficient to the corresponding sub-band signal of the estimated sound source; and performing full-band reconstruction on each sub-band signal subjected to the masking processing to obtain an enhanced target sound source signal. The application has the advantage that the performance of a speech separation system in a complex acoustic environment is effectively improved.

Embodiments of the present application relate to a stratum Q value determination method, device and equipment based on CMP gathers and a storage medium. The method determines the first two-way reflection time corresponding to each non-zero offset seismic trace in any two non-zero offset seismic traces in the CMP gathers and the instantaneous amplitude spectrum at the first two-way reflection time. The first two-way reflection time and the instantaneous amplitude spectrum corresponding to each non-zero offset seismic trace are brought into a preset attenuation model to obtain a corresponding attenuation equation. A stratum equivalent Q value equation about non-zero offset is established according to the attenuation equations of the two non-zero offset seismic traces. The above steps are repeatedly executed to obtain an over-determined equation set. The over-determined equation set is solved to obtain stratum equivalent Q values at different non-zero offsets. The stratum equivalent Q values at different non-zero offsets and the first two-way reflection time are fitted to determine the stratum equivalent Q corresponding to the zero offset seismic trace in the CMP gathers. The accuracy of the stratum Q value determination is improved.

This invention belongs to the field of data analysis technology for structural health monitoring in civil engineering, and particularly relates to a method for identifying time-varying cable forces in bridges based on nonlinear frequency-modal distribution. The method includes: firstly, extracting the free decay vibration response of bridge cables; iteratively decomposing the signal using the Hilbert vibration decomposition method to obtain the time-varying frequency; modeling the time-varying frequency based on an autoregressive moving average model, eliminating the endpoint effect of the time-varying frequency through predictive extension; using this as the initial input, further accurately extracting the instantaneous frequency, instantaneous amplitude, and time-varying damping ratio of each component using an adaptive frequency-modal decomposition algorithm; and outputting a stable time-varying cable force curve after calculating the foundation cable force. This invention has the advantages of good modal aliasing suppression and thorough elimination of endpoint effects, and is suitable for time-varying cable force monitoring scenarios for various cable-stayed bridges, such as cable-stayed bridges and suspension bridges.

The present application relates to the technical field of feature detection, in particular to a radar micro-motion feature extraction method based on deep learning, comprising the following steps: collecting the instantaneous amplitude, slope, time interval and phase change of the radar signal, normalizing into a time sequence feature group, constructing and fusing into a feature matrix, inputting into a time sequence convolution network model for multi-layer extraction and modeling, implementing multi-level discrimination and dynamic screening, and finally outputting a segment sequence based on attribution confidence probability through threshold judgment and multi-element joint screening.In the present application, four-element time sequence features are constructed by capturing instantaneous amplitude, slope, extreme point interval and phase change, revealing the internal correlation of target weak dynamics, automatically modeling the space-time correlation to mine nonlinear and non-stationary features, and implementing multi-level discrimination, dynamic screening and attribution confidence probability joint judgment on the features, so as to accurately lock the effective micro-motion signal segment under complex background interference, and significantly improve the robustness and accuracy of feature extraction.

The application discloses a bidirectional Mamba-based YOLOv12 ground penetrating radar underground disease identification method and device, equipment and medium, and relates to the technical field of road disease identification. The method comprises the following steps: constructing a four-channel physical perception tensor containing original signals, instantaneous amplitudes, phases and frequencies by processing ground penetrating radar gray-scale B-Scan data; designing an improved MPS-YOLOv12 network, expanding the input layer to 4 channels, decoupling physical features through four differentiated projection heads, modeling global dependence through bidirectional Mamba scanning, and finally outputting disease detection results through a multi-scale detection head by using the residual fusion mechanism of amplitude gating and phase compensation. By fully mining the physical information of GPR signals and using bidirectional Mamba, the disease identification accuracy is improved, and the false detection rate under complex geological background is effectively reduced.

The invention discloses an unmanned aerial vehicle radio frequency fingerprint extraction method based on multi-dimensional feature field coding, and belongs to the field of wireless signal identification. In order to solve the problem that a traditional time-frequency diagram mainly represents signal energy distribution and is difficult to explicitly express a signal state transition relation, the invention provides an F-FD-A (instantaneous frequency-frequency difference-instantaneous amplitude) multi-dimensional feature coding strategy which can effectively extract information such as a frequency hopping rule and envelope deformation of a signal. The method comprises the following steps: firstly, extracting instantaneous frequency, frequency difference and instantaneous amplitude of a radio frequency signal; further, the Markov transfer field is utilized to encode the first two sequences, and the Gramer angle difference field is utilized to encode the amplitude sequence; and finally, fusing the feature images into an RGB image, and inputting the RGB image into a ResNet-50 network for identification. Experiments show that the method has excellent recognition precision in a mixed data set covering DJI multi-series and multi-brand universal models, and fine-grained and high-robustness recognition of similar models of unmanned aerial vehicles in a complex environment is realized.

The invention relates to a variable frequency driving equipment time-frequency ridge line adjacent / cross signal feature extraction method, which comprises the following steps: firstly, carrying out low-pass filtering on a non-stationary time-varying vibration signal, and processing by using a rotating frequency component enhancement technology; then rough estimation of the instantaneous rotating speed of the rotating shaft is obtained from the enhanced signal; inputting the rough estimation value into a variational nonlinear single-component Chirp modal extraction algorithm, and demodulating the accurate instantaneous rotating speed of the rotating shaft from the vibration signal after low-pass filtering; inputting an accurate rotating shaft instantaneous rotating speed and order priori related to mechanical part faults, and separating time-varying fault feature components by using a proposed variational time-varying amplitude modulation-frequency modulation mode extraction algorithm; and finally, integrating instantaneous frequency and instantaneous amplitude information demodulated by the variable time-variable amplitude modulation-frequency modulation extraction algorithm, and reconstructing optimized time-frequency distribution. According to the invention, accurate demodulation and visual display of time-varying fault features are realized under the condition that the time-frequency ridge lines are adjacent or crossed.

The application provides a time-frequency analysis method based on frequency multiplication symmetric wavelet. The time-frequency analysis is an important analysis method for non-stationary signals, and its essence is to filter the signal by using a band-pass filter family. In the method provided by the application, the way of constructing the band-pass filter family is different from the previous method, specifically: the minimum frequency and the maximum frequency of the signal to be analyzed are obtained by Fourier transform, and the minimum octave frequency and the maximum octave frequency are obtained by taking the logarithm with base 2, and then a plurality of octave symmetric wavelets with half-interval overlap in the octave frequency domain are generated according to the preset octave number of a single filter, which are used as a band-pass filter family to filter and decompose the signal, and after obtaining the time domain decomposition signal, the Hilbert transform is performed and the instantaneous amplitude spectrum is obtained, and the time-frequency spectrum is generated based on the instantaneous amplitude spectrum of each time domain decomposition signal. The application can enrich the time-frequency analysis theory, and is expected to expand the application range of the time-frequency analysis method.

The invention discloses a high-low voltage intelligent cabinet parameter monitoring method and system based on an intelligent power grid, and relates to the technical field of power grids, and the method comprises the following steps: obtaining an electromagnetic signal and an acoustic signal in a high-low voltage intelligent cabinet; dynamically adjusting a signal processing parameter based on an analysis result of analyzing the spectral characteristics of the electromagnetic signal and the acoustic signal, and filtering the electromagnetic signal and the acoustic signal by using the adjusted signal processing parameter to obtain a preprocessed signal; the instantaneous amplitude and the instantaneous change rate of the preprocessed signal are analyzed, and potential partial discharge pulses are identified; extracting waveform features of potential partial discharge pulses; matching the waveform features with a pre-stored standard partial discharge feature library to obtain a plurality of feature similarities; and if the highest value of the plurality of feature similarities exceeds a preset confidence threshold, generating early warning information. According to the invention, weak partial discharge signals can be effectively identified, and the accuracy and timeliness of insulation fault monitoring of the high-low voltage intelligent cabinet are improved.

The application discloses a kind of engineering structure dynamic comfort evaluation methods under wind load action, including the dynamic response data of high-rise building under wind load action is collected and obtained;Acceleration time series signal is decomposed into several intrinsic mode signals based on empirical mode decomposition method, and the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal are obtained based on Hilbert transform;According to the instantaneous frequency and instantaneous amplitude of each intrinsic mode signal, the weighted average instantaneous frequency and maximum instantaneous amplitude of each time are obtained;Based on Kalman filtering algorithm and introducing cumulative error correction mechanism, the estimated displacement data of engineering structure at each time under wind load action is obtained according to the acceleration time series signal collected, the comprehensive comfort score of each time is obtained, to realize the dynamic comfort evaluation of engineering structure under wind load action.The application solves the problem that the vibration generated by wind load often has the characteristics of randomness and time variation, so that the dynamic comfort evaluation of engineering structure under wind load action cannot be accurately realized due to the neglect of nonlinear and non-stationary dynamic response characteristics.

This invention discloses a method and system for fault location of charging piles in microgrids based on HHT and an improved BP neural network, belonging to the field of microgrid fault diagnosis technology. The method includes: acquiring transient current signals from each charging pile node; obtaining intrinsic mode function components (IMFs) through empirical mode decomposition; selecting the first IMF component and performing Hilbert transform to calculate its instantaneous amplitude vector sum as a single-dimensional fault feature; inputting the normalized feature into an improved BP neural network for fault location; and introducing a momentum term in the weight update of the improved BP neural network to optimize the training process. This invention solves the technical problems of insufficient feature extraction capability for non-stationary fault signals and slow and unstable convergence of the location model in traditional methods, achieving fast, accurate, and adaptive fault location of charging piles in microgrids. Simulation verification shows a location accuracy of 100%, significantly improving the safety and reliability of the microgrid system.