329 results about "Wave packet" patented technology

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The embodiment of the invention provides an environment monitoring method and system based on 5G. According to the method, a dynamic coupling sensing field is constructed in a 5G network coverage domain, an adaptive topology with nonlinear interaction of parameters is established, and when parameter abnormity is detected, differential geometric reconstruction of a 5G network slice control surface is triggered based on Riemannian curvature change. And constructing a space-time manifold space at the 5G edge computing node, decomposing and extracting a diffusion principal curvature direction, and iteratively solving a propagation rate of an abnormal parameter in combination with a geodesic equation to generate a diffusion leading edge envelope surface. And according to the main curvature distribution characteristics of the diffusion leading edge envelope surface, constructing a dynamic sampling optimizer, generating an exponential decay sampling function, and deploying a directional sensing wavefront. And carrying out conformal tightening processing on the sampling function and the geographic grid to generate an interactive holographic manifold, establishing an aging judgment criterion of an abnormal level based on curvature 2-form characteristics, and triggering a graded early warning pulse wave packet. According to the technical scheme provided by the embodiment of the invention, the real-time performance and precision of environment monitoring are improved.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

The invention discloses a short-term load prediction method and system based on improved GWO-VMD-LSTM, and the method comprises the steps: carrying out the parameter optimization of a mode decomposition number and a penalty factor which need to be determined before VMD feature extraction, carrying out the variational mode decomposition of a load feature time sequence signal, decomposing the signal into a series of intrinsic mode function components, and carrying out the VMD-LSTM. An improved wavelet packet soft threshold function is used, a lugish-greed mechanism is provided to improve the function, denoising processing is performed on IMF component signals, feature coefficients and modal kurtosis values of all components are extracted, and the denoising effect is evaluated; a network structure of a long short-term neural network is improved, a parameter adaptive adjustment optimization algorithm is provided, the learning rate of the LSTM is dynamically optimized, and data is input into the network for training and prediction. The accuracy of short-term load prediction and the precision of classification can be improved, and short-term accurate prediction of the power load is facilitated.

The invention discloses an electrical fire monitoring method based on inherent residual current automatic compensation, and relates to the technical field of electrical safety monitoring, and the method comprises the following steps: S100, collecting a residual current signal, executing multi-component time-frequency deconstruction processing, extracting an energy distribution characteristic through wavelet packet transformation, and extracting a mutability index in combination with short-time spectrum entropy analysis, and constructing a preliminary distribution map of higher harmonic interference, and determining boundary features of interference signals in a time domain and a frequency domain. According to the method, high-frequency interference signals are accurately positioned through time-frequency deconstruction, wavelet packet analysis and spectral entropy indexes, non-fault harmonic components are effectively eliminated in combination with amplitude-frequency coupling recognition and a recursive rejection strategy, closed-loop control is constructed by introducing an adaptive compensation and stability backtracking mechanism, accurate recognition and dynamic correction of real electric leakage risks are achieved, and the method is suitable for large-scale popularization and application. The identification precision and the safety reliability of the monitoring system in a complex industrial environment are obviously improved, and the method has good engineering adaptability.

The invention discloses a fault tracing and positioning method in an FTU section, and belongs to the technical field of distribution automation fault positioning. The method comprises the following steps: synchronously acquiring current abrupt change signals of multiple FTU sections, reconstructing a transient waveform through EMD decomposition and cubic spline interpolation, extracting wavelet packet energy characteristics, and generating multi-dimensional transient characteristics in combination with wave head polarity and timestamps; fusing the power distribution network topology and traveling wave time delay to construct a space-time correlation graph, introducing virtual nodes to compensate communication interruption, dynamically assigning node attributes and marking a reflection path; based on graph neural network cooperative training, iteratively aggregating neighborhood information and dynamically optimizing edge weights, and generating candidate section fault probability distribution; and judging a conflict level by using information entropy, carrying out multi-level digestion in combination with polarity matching and time delay consistency, and outputting a high-confidence positioning result. According to the method, the problems of difficulty in multi-FTU cooperative positioning, poor communication interruption adaptability, inaccurate feature fusion and the like are solved, and the accuracy and robustness of power distribution network fault tracing are remarkably improved.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention discloses a power grid cross-correlation harmonic suppression method, device and equipment and a storage medium, and the method comprises the steps: carrying out the wavelet packet decomposition of a subnet electric signal, and obtaining the harmonic component of a subnet at each frequency band; calculating the harmonic phase difference between the subnets according to the cross-correlation function between the harmonic components of the subnets and the topological impedance matrix, and calculating the total harmonic distortion rate according to the harmonic components of the subnets in the frequency bands; constructing a harmonic transfer equation according to the harmonic phase difference between the subnets and the equivalent output impedance of the inverter so as to calculate the harmonic responsibility proportion of the subnets; solving the double-layer game model based on the total harmonic distortion rate, the reference value of the total harmonic distortion rate and the harmonic responsibility proportion of the subnet, and obtaining the output of the governance equipment; and inputting the output of the treatment equipment into the fuzzy controller to obtain the impedance adjusting quantity of the subnet, and adjusting the virtual impedance according to the impedance adjusting quantity to update the harmonic backflow path. According to the invention, harmonic suppression among multiple subgrids during high-permeability distributed grid connection is realized, and the operation stability of the power grid is improved.

The invention provides a magnetostriction-based multi-physical-field defect detection method and system for pressure-bearing equipment, and relates to the technical field of detection, and the method comprises the steps: constructing a reconfigurable magnetic field excitation array and a multi-physical-quantity sensing network through an intelligent excitation and sensing device, and dynamically adjusting the excitation phase to improve the detection precision. According to the layered coupling calculation scheme, a coupling relationship among a magnetic field, a stress field and a temperature field is established, and multi-physical field data calculation is carried out in combination with physical constraint conditions. After multi-level wavelet packet decomposition and noise reduction, the deep neural network is utilized to extract features and generate a defect feature dictionary, so that efficient defect positioning and size evaluation are realized, and the accuracy and reliability of detection are remarkably improved.

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention discloses a nondestructive testing method for composite material defects, and relates to the technical field of nondestructive testing, and the method comprises the following steps: firstly, collecting a scanning waveform of a test piece A, determining a potential defect area based on an echo amplitude and a time difference, and outputting a sequence containing space coordinates, an original waveform and focusing parameters; an effective time window is intercepted after preprocessing, sub-bands are generated through wavelet packet decomposition, and total energy is calculated and normalized to obtain an energy vector; secondly, based on a known sample, evaluating the separability of sub-bands by using a Fisher criterion, sorting the sub-bands, determining an optimal energy dimension through cross validation, combining sub-band energy features with phase and time difference features into composite vectors, inputting the composite vectors into a dual-channel lightweight deep network, outputting defect categories and confidence coefficients, and mapping the defect categories and confidence coefficients to a C scanning frame image; and finally, backtracking the three-dimensional coordinates, calculating the defect volume and the residual wall thickness, and comparing with a material performance database to output a conclusion. The problem of misjudgment caused by echo waveform similarity is solved, and detection closed-loop optimization is achieved.

The invention discloses a power transmission line icing intelligent early warning method, system and device and a medium, and the method comprises the steps: obtaining icing state data and meteorological data, and dynamically adjusting the collection frequency and a dormancy strategy; performing data preprocessing and cleaning on the acquired data; extracting time-frequency features through wavelet packet transformation and a self-attention mechanism, and fusing the spatial dependency relationship and cross-modal interaction information of multiple monitoring points by using a graph neural network to obtain enhanced icing state characterization; performing icing risk prediction by adopting a gradient boosting decision tree model to obtain an icing risk prediction result; and analyzing an icing risk prediction result by using an interpretable tool, identifying a key factor which has the greatest influence on icing risk prediction, dynamically adjusting an early warning level according to the key factor, and generating an early warning and maintenance suggestion. Therefore, the monitoring real-time performance and the early warning timeliness are improved.

The invention relates to the technical field of power equipment state monitoring, and discloses an extra-high voltage transformer partial discharge signal denoising method and system.The method comprises the steps that firstly, the high-frequency interference energy proportion is detected in real time, the wavelet packet decomposition layer number is dynamically adjusted, and the high-frequency interference fine isolation capacity is improved; a clustering combination threshold value is dynamically adjusted based on a noise cluster proportion, and an interference frequency band is accurately identified; and finally, reconstructing the signal through an inverse wavelet packet, and carrying out optimized output. According to the method, the signal-to-noise ratio can be remarkably improved in a complex noise environment, the pulse detection rate is improved, meanwhile, real-time optimization is achieved through sliding window processing and FPGA acceleration, and efficient and reliable technical support is provided for online monitoring of the insulation state of the extra-high voltage transformer.

The invention discloses a cross-working-condition fault diagnosis method based on knowledge embedding and multi-scale attention, belongs to the technical field of cross-working-condition fault diagnosis, designs a domain knowledge embedding signal processing method based on wavelet packet transformation, envelope spectrum analysis and statistical feature analysis, and constructs a knowledge feature matrix and a statistical feature matrix. Fault information which is not easily influenced by working condition changes is highlighted, and the dependence of a deep learning model on a target domain sample is effectively reduced; a multi-scale attention mechanism is designed to extract depth fault features in an input matrix, and compared with an original multi-head attention mechanism, multi-scale is introduced, so that the parameter quantity of a model is reduced, and the feature extraction capability is more flexible; by embedding domain knowledge highlighting domain invariant fault information into a signal processing end, the method can show excellent variable working condition fault diagnosis performance when a target domain sample is completely lacked.

The invention discloses a distribution line fault detection and positioning method based on current signal analysis, and belongs to the technical field of power system fault diagnosis. The method comprises the steps of collecting three-phase and zero-sequence current data before and after a fault, calculating current variation, judging whether the fault is an interphase short-circuit fault, and determining a first fault interval by combining an impedance method; and performing wavelet packet transformation on the transient voltage signal to obtain an actual transient signal, injecting a test signal in the first fault interval, collecting a response signal, constructing an autoregression model, solving an inverse noise function, and performing denoising processing on the actual transient signal to obtain a target transient signal. Wavelet packet decomposition is carried out on the denoised target transient signals, energy distribution and signal-to-noise ratio information of all frequency bands are combined, a corrected positioning formula is introduced, and accurate positioning of a second fault interval of the fault point is achieved. The method effectively inhibits noise interference, improves the positioning precision, and is suitable for a real-time fault detection and positioning system in an intelligent power distribution network.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention provides an ultrasonic echo signal joint noise reduction method based on improved EKF and wavelet packet cooperation, belongs to the technical field of ultrasonic detection, and solves the problems that the noise reduction effect is poor in a traditional ultrasonic signal noise reduction method and noise reduction and feature retention are difficult to balance in the wavelet packet noise reduction process. Modeling the ultrasonic echo signal and the noise signal to obtain an ultrasonic echo signal with noise; extracting a target ultrasonic echo signal by using an improved extended Kalman filter to obtain an observation equation; predicting and updating on the basis of the observation equation to obtain ultrasonic echo signal estimation; optimizing the noise covariance matrix and the observation noise covariance matrix by adopting a differential evolution algorithm to obtain an optimal parameter, executing extended Kalman filter filtering based on the optimal parameter, and generating an ultrasonic echo signal estimated value after preliminary denoising; and carrying out wavelet packet threshold denoising to obtain a final denoised ultrasonic echo signal.

The invention provides a feature extraction method for an electroacoustic background interference signal of operation power transformation equipment, which belongs to the technical field of power transformation equipment, and comprises the following steps: acquiring an electroacoustic signal, preprocessing the electroacoustic signal, and generating a controlled interference signal at the same time; and performing time-frequency analysis on the preprocessed electroacoustic signal and the controlled interference signal, and optimizing the interference signal parameter to enable the interference signal parameter to be highly similar to the electroacoustic signal. And constructing a wavelet basis function library based on the optimized controlled interference signal, and performing wavelet packet decomposition on the electroacoustic signal to obtain a plurality of frequency band sub-signals. A self-adaptive threshold model is established by using a controlled interference signal, and soft threshold denoising processing is performed on sub-signals. Time-frequency features of the denoised sub-signals and the controlled interference signals are extracted, a feature mapping relation is established, and an initial feature set is obtained; and performing nonlinear dimensionality reduction on the initial feature set by adopting principal component analysis to obtain a dimensionality-reduced feature set. And an improved support vector machine model is adopted to evaluate the importance of dimension reduction features, and an optimal feature set is selected as an electroacoustic background interference signal feature.

The invention relates to the cross technical field of geological disaster monitoring and signal processing, and provides a GNSS tunnel portal slope deformation time sequence noise reduction method, system and medium combining CEEMD and an adaptive wavelet packet threshold function, the method integrates the advantages of CEEMD and wavelet packet adaptation, processing of mixed entropy demarcation, adaptive threshold, dynamic smoothing and multi-path compensation is used, and the noise reduction efficiency is improved. Useful deformation features can be reserved while more comprehensive and finer noise reduction is performed, smooth compression can be performed in a large signal part through improved threshold function processing, excessive suppression can be avoided, smooth transition is performed in a small signal part through exponential attenuation, features of the large signal part are reserved, noise suppression is realized, signal details are reserved to the greatest extent, and the noise suppression effect is improved. The noise reduction requirement of tunnel portal side slope monitoring data is met, and particularly in side slope monitoring of a tunnel portal in a complex environment, the precision and reliability of GNSS deformation monitoring are improved.

The invention provides a harmonic reducer fault diagnosis method and system combining SAH with SSET under a time-varying rotating speed, and belongs to the field of harmonic reducer fault diagnosis. The invention aims to solve the problems of large time-frequency ridge line extraction error and inaccurate fault feature order extraction caused by the non-stable and signal modulation characteristics of a harmonic reducer vibration signal under a time-varying rotating speed. According to the SAH construction method based on sub-band rearrangement dual-tree complex wavelet packet transformation, a band elimination filter is designed according to an optimal frequency band, and a resonance frequency band where a fault impact component is located is filtered out; a time-frequency ridge extraction method combining SAH-SSET with a fast path optimization algorithm is provided, a frequency ambiguity phenomenon is improved, order analysis is carried out based on the frequency ambiguity phenomenon, and then the fault type of the harmonic reducer is judged according to an envelope order spectrum.

The invention discloses a slurry circulating pump state monitoring method and related equipment, and aims to overcome the defects of high false alarm rate and late early warning caused by multi-fault coupling effect and strong background noise interference in the prior art. The method comprises the following steps: synchronously acquiring vibration, acoustic emission, pressure pulsation and temperature signals of a pump volute, an outlet pipeline and a bearing seat; performing wavelet packet decomposition on the acoustic emission signals, extracting features and generating cavitation sensitive factors; a digital twinborn model adapted to fluid-structure coupling simulation and transfer learning is utilized to calculate a deviation value between the monitoring feature and a normal baseline; and when the deviation value exceeds a threshold value, fault identification and life prediction are carried out. Through multi-signal fusion and a dynamic threshold mechanism, a multi-fault coupling effect and strong background noise interference are effectively overcome, and the reliability of a monitoring system is improved.

The invention relates to the technical field of radio-frequency signal processing, and discloses a 5G radio-frequency signal amplification method and system, and the method comprises the steps: decomposing an obtained radio-frequency signal into a high-frequency sub-band and a low-frequency sub-band through a multilayer wavelet packet, judging whether the radio-frequency signal is sparse in a wavelet packet domain, and carrying out the sparse reconstruction of an LI norm minimization problem if the radio-frequency signal is sparse; obtaining feature data of each sub-band, extracting a nonlinear transfer function of each sub-band through a CNN-GRU network, and carrying out transfer learning pre-training and dynamic adaptation updating; on the basis of dynamic adaptation update ending, layered memory polynomial calculation and layered model joint output construction, layered model parameter optimization is converted into a least square problem to obtain an error compensation function, and resource allocation is dynamically adjusted according to the peak-to-average power ratio of an input signal; and based on power amplifier state data, holographic sensing is performed on nonlinear parameters of the device through a fusion model, and dynamic changes of the device are compensated through multi-stage calibration and calibration delay is controlled.

The invention discloses a vibration signal processing method based on self-adaptive wavelet packet and deep learning fusion, and belongs to the field of sewage plant equipment fault diagnosis. The method aims at solving the problems that traditional signal processing is poor in flexibility, the non-stationary signal processing capacity is weak, the deep learning data requirement is large, and the high-frequency weak feature capturing capacity is limited. According to the method, the high-frequency acceleration sensor is adopted, the vibration signals of the sewage plant equipment are accurately collected, the self-adaptive wavelet packet decomposition technology is applied, the primary function is dynamically selected, the number of decomposition layers is optimized, self-adaptive threshold noise reduction is achieved, and the signal processing quality is improved. Meanwhile, in combination with a one-dimensional convolutional neural network and a bidirectional LSTM model, local and global features of the signal are extracted respectively, and pre-processed data are formed through gating weighted fusion. According to the method, the signal-to-noise ratio and the weak fault detection rate are remarkably improved, feature redundancy and data requirements are reduced, the calculation efficiency and diagnosis accuracy are improved, the method is suitable for sewage plant equipment fault diagnosis, and the industrial applicability is enhanced.

The invention discloses a rotor acoustic anomaly detection method, system and device based on an auto-encoder and wavelet packet energy entropy and a medium, and belongs to the technical field of hydroelectric generating sets, and the method comprises the steps: collecting an original acoustic signal, carrying out the noise reduction of the original acoustic signal, and carrying out the multi-channel data fusion to obtain an integrated acoustic signal; inputting the integrated acoustic signal into a wavelet packet for transformation processing to obtain wavelet packet coefficients under different scales; calculating energy values of different frequency band signals based on wavelet packet coefficients to form an energy entropy feature vector; constructing an implicit feature model of the hydroelectric generating set rotor in a normal state according to the energy entropy feature vector; and carrying out feature reconstruction on the energy entropy feature vector, calculating a reconstruction error, carrying out dynamic statistical analysis, and carrying out real-time monitoring and abnormity judgment on the operation state of the hydroelectric generating set rotor. According to the method, the detection precision and the response speed are improved in actual hydroelectric generating set rotor acoustic anomaly detection, and automatic identification and dynamic threshold adaptive adjustment of irregular perturbation are realized.

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

The invention provides a sensor fusion-based pig feed crushing particle size monitoring method, which comprises the following steps of: synchronously acquiring vibration, acoustic emission and optical signals, carrying out band-pass filtering, normalization, feature extraction and wavelet packet decomposition, and dynamically adjusting the confidence coefficient of each channel in combination with working condition perception and a sensor performance knowledge base so as to monitor the crushing particle size of the pig feed. Weighted fusion and conflict evidence modulation of multi-source signals are achieved, the improved Dempster-Shafer evidence theory is introduced to improve the abnormal granularity recognition accuracy, key parameters of crushing equipment are controlled in a linkage mode through a closed-loop feedback mechanism, high-robustness detection and self-adaptive adjustment of the granularity state are achieved, and the method has the advantages of being high in robustness and high in robustness. And the stability and the automation level of the feed crushing process are improved.

The invention discloses a composite material structure impact positioning method based on wavelet packet Tsallis entropy and LLE, and the method comprises the following steps: employing an EFPI optical fiber sensor network to monitor impact response signals at different positions on a structure, extracting wavelet packet Tsallis entropy spectrums of the impact response signals, carrying out dimension reduction on the wavelet packet Tsallis entropy spectrum by adopting a local linear embedding method; according to the relation between the wavelet packet Tsallis entropy spectrum of the impact point response signal after dimension reduction and the impact point position, impact position identification is achieved through a GA-CNN-BiLSTM-Attention model; according to the method, the wavelet packet Tsallis entropy spectrum of the impact response signal monitored by the EFPI sensor on the composite material structure is analyzed, the feature vector is extracted from the wavelet packet Tsallis entropy spectrum by using the LLE technology, and the impact load position of the composite material structure is accurately identified by using the GA-CNN-BiLSTM-Attention neural network model, so that the method has the advantages of no need of a high-speed data acquisition module, small signal feature data volume, high accuracy and the like. The application condition is wide, and the identification model generalization ability is good.

The present application discloses a method, apparatus, device and storage medium for cross-correlated harmonic control in power grids. The method includes: performing wavelet packet decomposition on subnet electrical signals to obtain the harmonic components of the subnet in each frequency band; calculating the harmonic phase difference between subnets based on the cross-correlation function and topological impedance matrix between the harmonic components of each subnet, and calculating the total harmonic distortion rate based on the harmonic components of the subnet in each frequency band; constructing a harmonic transfer equation based on the harmonic phase difference between subnets and the equivalent output impedance of the inverter to calculate the harmonic responsibility ratio of the subnet; solving a two-layer game model based on the total harmonic distortion rate and its reference value and the harmonic responsibility ratio of the subnet to obtain the output of the control equipment; inputting the output of the control equipment into the fuzzy controller to obtain the impedance adjustment amount of the subnet, and adjusting the virtual impedance according to the impedance adjustment amount to update the harmonic return path. The present application realizes the harmonic control between multiple subnets during high-penetration distributed grid connection, thereby improving the stability of power grid operation.

The invention discloses a comprehensive pulsar time determination method and device based on time-frequency combination and dynamic weighted fusion, and relates to the field of comprehensive pulsar time calculation. The method comprises the following steps: acquiring a timing residual sequence of each pulsar; the timing residual sequence is determined based on timing residuals collected at set sampling intervals; the timing residual error is the difference value between the actual arrival time and the preset arrival time of the pulsar pulse; carrying out adaptive wavelet packet decomposition and empirical mode decomposition on the timing residual sequence of each pulsar, and carrying out time-frequency combined filtering processing to obtain a processing signal; and performing fusion processing according to the processed signal by adopting a dynamic weighted fusion algorithm to obtain comprehensive pulsar time. According to the invention, dynamic fusion is realized, the noise suppression precision is improved, and the anti-interference capability is improved.

The invention relates to the technical field of composite material structure monitoring, in particular to a composite material structure damage identification method based on acoustic emission wavelet packet energy characteristics, and the method comprises the steps: S1, obtaining acoustic emission signal data of a composite material, and carrying out the preprocessing; s2, performing wavelet packet decomposition on the denoised composite material acoustic emission signal data by using a wavelet basis function and discrete wavelet inverse transformation; s3, acquiring the processed acoustic emission signal data of the composite material, and extracting the characteristic energy of each piece of acoustic emission signal data; and S4, performing clustering analysis by using a clustering method to complete damage identification of the composite material structure. According to the method, the acoustic emission signal is used as an analysis basis, wavelet transform and k-means clustering analysis are used for accurately realizing structural damage identification and judgment of the composite material, dynamic continuous monitoring can be realized, the method is not influenced by structural complexity, and the method has wide applicability and is suitable for various composite materials.