1811 results about "Center frequency" patented technology

The invention discloses a power distribution network state panoramic intelligent sensing method, relates to the technical field of power distribution network monitoring management, and is used for solving the problem of poor power distribution network abnormity monitoring. According to the method, multi-scale decomposition and baseline construction of the signals are realized through accurate acquisition and preprocessing of the node signals of the power distribution network, steady-state and abnormal dynamic characteristics of the power distribution network are effectively extracted, a variational mode decomposition technology is used, and a low-frequency mode is screened through a center frequency to construct a baseline, so that the reliability of the power distribution network is improved. Constructing a robust residual error and an abnormal energy index by combining the difference between a baseline and an original signal, accurately determining an abnormal event, switching a power distribution network node to an ultrahigh frequency sampling mode after abnormal triggering, extracting a local disturbance characteristic by using time domain difference, and adaptively adjusting a filtering parameter according to a local edge ratio through robust filtering noise reduction, so as to achieve the purpose of reducing noise. Therefore, the abnormal edge information is fully reserved, the real-time and high-precision perception of the abnormal state of the power distribution network is realized, and the response speed and accuracy of the panoramic perception of the power distribution network are improved.

The invention provides a multi-band radio frequency power amplifier dynamic range adaptive test method and a system thereof. The method comprises the following steps: acquiring an output signal frequency spectrum of a tested radio frequency power amplifier, extracting characteristic frequency points including a center frequency and a bandwidth, matching the characteristic frequency points with a preset frequency band database, and automatically loading a test scheme of a corresponding frequency band. According to the method, the frequency bands are automatically matched through spectrum sensing, the test scheme is loaded, the multi-frequency-band characteristics can be rapidly adapted without manual parameter configuration, index missing test is avoided, and the switching efficiency is improved; dynamically optimizing test parameters by using a genetic algorithm, and accurately capturing a nonlinear distortion critical point while balancing coverage and efficiency; by combining multi-dimensional signal analysis and a deep learning model, the problem of missing detection of traditional single-domain analysis is solved, and the abnormal signal recognition capability is remarkably enhanced; through linear model correction, impedance dynamic matching and index threshold iteration, full automation and high precision of the test are realized, the environmental interference influence is effectively reduced, and a self-adaptive test system is constructed.

The present invention relates to the technical field of power source power allocation. Disclosed are a direct current power source power allocation method and system for a generator status monitoring apparatus. The method comprises the following steps: by means of system data, constructing a variational problem model; solving the constructed variational problem model, and using a particle swarm optimization algorithm to optimize computing parameters of the variational problem model; and, on the basis of a computing result of the variational problem model, allocating the total output power of a hybrid energy storage system to an energy-type storage device and a power-type storage device according to a ratio. By means of using the convergence-guaranteed particle swarm optimization algorithm, the present invention not only excels in the solving speed but also shows significant advantages in computational accuracy; furthermore, by means of solving the variational problem model, more accurate allocation ratios are obtained; variational mode decomposition can achieve adaptive matching of the optimal center frequency and bandwidth for each mode, thus effectively separating intrinsic mode components and achieving frequency domain partitioning of signals.

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

The invention belongs to the technical field of radar signal processing and target identification, and particularly relates to a maritime hybrid target identification method and system based on modal decomposition and reconstruction, and the method comprises the steps: carrying out the variational modal decomposition of a radar echo signal of a maritime hybrid target, and decomposing an original signal into a plurality of intrinsic modal signals; estimating a background noise energy reference and removing noise modals based on the decomposed modal signals, clustering the remaining modals according to the center frequency, and combining the modals with the frequency within the center frequency range into an independent single-target signal which is of the same target and is reconstructed into an independent single-target signal; respectively carrying out time-frequency analysis on each reconstructed single target signal to obtain a corresponding time-frequency diagram, and extracting time-frequency domain features from the time-frequency diagram; and inputting the extracted features into a trained support vector machine classifier to realize automatic identification of the ship target and the floating target. A ship target and a floating target can be distinguished more accurately in a mixture scene, and the stability of tracking identification is improved.

The present invention discloses a neural network accelerated simulation method for the radar scattering field of a wide-band target group, belonging to the technical field of electromagnetic simulation. The method of the present invention constructs a KAN neural network model at the central frequency of the wide band, and constructs KAN neural network models at other target frequencies through transfer learning or direct training; uses the trained KAN neural network model to generate a multi-frequency point numerical Green's function data set, and then constructs a wide-band training sample to train the multi-task learning model to realize the global prediction of the numerical Green's function of the entire target wide band; uses the numerical Green's function output by the multi-task learning model to realize the fast simulation of the scattering field of the wide-band target group. The method of the present invention can realize the fast and accurate simulation of the radar scattering field of the wide-band target group.

The invention relates to a nonlinear ultrasonic guided wave phased array imaging method based on mixed array elements and time reversal. The method comprises the following steps: exciting a fundamental wave and receiving a second harmonic wave at piezoelectric array elements of the fundamental wave and the second harmonic wave of an ultrasonic guided wave by using a center frequency respectively; full-matrix data of ultrasonic guided wave signals in the detected workpiece are obtained in a one-transmitting and one-receiving mode; a fundamental wave signal is suppressed and a second harmonic signal is enhanced by adopting a pulse inversion mode; for the full-matrix data after pulse inversion, utilizing continuous wavelet transform filtering to extract a second harmonic signal; respectively calculating the distance from the discretized imaging area to the excitation array element and the distance from the discretized imaging area to the receiving array element; and for each discrete pixel point, according to an excitation signal of a distance forward propagation fundamental wave and a received second harmonic signal back propagation, a pixel value is calculated by using a time domain topology energy equation, and local acoustic nonlinear imaging of the plate-shaped structure is realized.

The invention discloses an optical cable icing galloping online monitoring system based on a BOTDR, and belongs to the technical field of distributed optical fiber sensing. The system comprises an optical module, a signal processing module and a data processing module, the optical module is used for generating a detection light signal and interacting with a sensing optical fiber to obtain a Brillouin scattering signal, and then the Brillouin scattering signal is converted into an electric signal; the signal processing module is used for performing frequency down-conversion, signal filtering and digital acquisition on the electric signal to obtain multiple groups of Brillouin scattering signal data; the data processing module is used for processing the multiple groups of Brillouin scattering signal data and extracting a Brillouin frequency shift center frequency; temperature and strain distribution along the optical cable is obtained according to the frequency shift, and temperature and strain states are transmitted to a QT upper computer through PCIE for real-time display. According to the invention, high-precision and long-distance optical cable icing galloping monitoring can be realized, reliable early warning and safety guarantee are provided, and technical support is provided for maintenance and management of an electric power system.

The invention provides a bridge dynamic displacement measurement method based on an unmanned aerial vehicle, and relates to the field of bridge health monitoring, and the method comprises the steps: obtaining a bridge image sequence through the unmanned aerial vehicle; obtaining transformation matrixes according to the relation between the other frames of images and the fixed measurement points of the reference frame, correcting the coordinates of the motion measurement points of the corresponding frames of images according to the transformation matrixes, obtaining a corrected bridge image sequence, and further obtaining an initial displacement signal; variational mode decomposition is carried out on the initial displacement signal, a constraint variational optimization problem is constructed, the optimization problem is solved through iteration to obtain each mode component of variational mode decomposition and a corresponding center frequency, and a low-frequency mode component is removed to obtain a displacement signal after secondary correction of the influence of the unmanned aerial vehicle; and obtaining the real displacement according to the scale factor between the image obtained by the unmanned aerial vehicle and the real world. The influence caused by the movement of the unmanned aerial vehicle is corrected, and the accuracy, reliability and stability of a measurement result are effectively improved.

Disclosed are techniques for wireless communication. In an aspect, a network node receives, from a network entity, a configuration for reporting carrier phase measurements of one or more reference signal resources spanning a plurality of subcarriers, wherein the configuration indicates one or more center frequencies to use for reporting the carrier phase measurements, obtains one or more carrier phase measurements of the one or more reference signal resources based on the one or more center frequencies, and reports, to the network entity, the one or more carrier phase measurements.

A centimeter-resolution integrated photonic millimeter-wave (mmWave) radar chip operating in the mmWave V band based on a 4-inch wafer-scale thin-film lithium niobate (TFLN) technology is provided. The fabricated TFLN photonic mmWave integrated circuit has a first electro-optic modulator (EOM) capable of generating a broadband linear frequency modulated mmWave radar waveform through optical frequency multiplication of a low-frequency input signal, and a second EOM for frequency de-chirp of the reflected echo wave, thereby greatly relieving bandwidth requirements for the digital-to-analog converter in the transmitter and analog-to-digital converter in the receiver. In the absence of optical and electrical filters, the radar chip features continuous on-demand tunability of the center frequency and bandwidth, currently only limited by the bandwidths of electrical amplifiers. The radar chip provides a promising way to achieve high resolution sensing and detection for vehicle radar, airborne radar, and smart home in the 6G era.

The invention discloses a multipath interference suppression method and device during in-pipe detection and a storage medium, and belongs to the technical field of radar signal processing. According to the method, a CFAR detection and clustering algorithm combined method is adopted, and the target number and the center frequency are automatically extracted from a radar amplitude-frequency signal; an original noisy signal is decomposed into a target number of modal components by using an improved variational modal model, and an improved power reference analysis method is proposed to divide the modal components into effective modals and ineffective modals; sVD noise reduction is carried out on the effective mode; and performing signal reconstruction on the plurality of modal components after SVD noise reduction to obtain a signal after multipath interference suppression. According to the method, the problem that the decomposition number and the center frequency of the variational mode decomposition algorithm are difficult to determine is effectively solved, the influence of inaccurate parameters is reduced, multipath interference suppression during complex environment detection is facilitated, and the effectiveness of the algorithm is verified through actual measurement.

The invention discloses a method for jointly detecting corrosion defects of a pressure pipeline by utilizing multiple guided wave modes, relates to an ultrasonic non-destructive testing technology, and is used for solving the problem that the accuracy is relatively low when a single guided wave mode is used for detecting. The method for detecting the corrosion defect of the pressure pipeline comprises the following steps: calculating ultrasonic guided wave dispersion data of the pressure pipeline, and determining the center frequency of a transducer according to the dispersion data; the method comprises the following steps: arranging ultrasonic transducer annular arrays on two sides of a to-be-detected pressure pipeline, and acquiring ultrasonic guided wave data detected by the pressure pipeline in an L (0, 1) mode and a T (0, 2) mode; preprocessing the array waveform data to obtain detection data which can be used for guided wave imaging; according to the L (0, 1) mode guided wave data, a full waveform inversion method is adopted to invert the wall thickness of the pipeline corrosion defect; judging whether the pipeline defect exceeds the detection depth range of the T (0, 2) mode, and if the pipeline defect exceeds the detection depth range, ending; and if not, inverting the wall thickness of the pipeline corrosion defect by adopting a full-waveform inversion method according to the T (0, 2) mode guided wave data.

The invention discloses a LoRa-based parallel backscatter communication method and system. The method comprises the following steps: transmitting a single-tone radio frequency source signal as a signal excitation source; designing a backscattering label, and performing backscattering modulation processing on the environmental perception data in combination with the signal excitation source to obtain a modulated standard LoRa data packet; and the receiver presets the center frequency of a communication link, and performs frequency band distinguishing and demodulation processing on the modulated standard LoRa data packet to complete parallel backscatter communication of the LoRa data. According to the invention, while a parallel backscattering LoRa communication system compatible with commercial LoRa equipment is realized, the throughput of the system is improved, and frequency division multiple access parallel transmission is realized. The LoRa-based parallel backscatter communication method and system can be widely applied to the technical field of parallel backscatter communication.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

Example systems and methods for radio-frequency adversarial deep-learning inference for automated network coverage estimation include a generative adversarial network (GAN) based approach for synthesizing RF maps in indoor scenarios. In some examples, a semantic map is utilized—a high-level representation of the indoor environment to encode spatial relationships and attributes of objects within the environment and guide the radio frequency (RF) map generation process. A new gradient-based loss function is introduced that computes the magnitude and direction of change in RSS values from a point within the environment. Some examples incorporate this loss function along with the antenna pattern to capture signal propagation within a given indoor configuration and generate new patterns under new configuration, antenna (beam) pattern, and center frequency.

The invention discloses a composite fault signal separation method based on adaptive spectral kurtosis deconvolution. The method comprises the following steps: S1, carrying out noise reduction processing on an original composite fault signal; s2, separating the composite fault signal after noise reduction through Hilbert envelope demodulation; s3, optimizing a low-pass filter and a high-pass filter through a Bayesian objective function, and constructing a band-pass filter and a band elimination filter; and S4, carrying out iterative filtering on the composite fault signal after noise reduction through a band-pass filter and a band elimination filter, constructing a frequency-bandwidth fast spectral kurtosis graph according to a fast spectral kurtosis deconvolution method, obtaining a center frequency and bandwidth range corresponding to the maximum spectral kurtosis, and separating out the weak fault signal in the step S2. According to the method, a spectral kurtosis graph is constructed by combining a low-pass filter and a high-pass filter through a fast spectral kurtosis deconvolution method to determine the center frequency and the bandwidth range, targeted separation is carried out on composite fault signals, an initial filtering parameter range is provided for Bayesian optimization, and the fault separation efficiency and accuracy are improved.

The invention discloses a hydroelectric generating set state online monitoring system and method based on data analysis, and relates to the technical field of data analysis. The method comprises the following steps: setting a training period, extracting a time domain feature vector and a frequency domain feature vector of each data packet, and training to generate a gated attention mechanism model; training and generating a mixed density network model according to the data packet containing the fault state label; according to the real-time fault risk probability, the latent variable representation vector and the real-time center frequency, whether abnormal features are analyzed or not is judged, if the abnormal features are analyzed, the contribution degree of each input feature to the real-time fault risk probability is calculated through a gradient approximation method, and an influence degree score is calculated; and taking the corresponding input features exceeding the influence degree score threshold as abnormal features, and outputting prompt information for operation and maintenance personnel to check, thereby improving dynamic environment response, identification precision degree and reliability of a monitoring system.

The invention belongs to the technical field of metal crack detection, and discloses a metal microcrack ultrasonic detection method, which comprises the following modules: an ultrahigh frequency composite array probe with a center frequency dynamic adjustable range of 5-50 MHz; the dual-mode excitation and receiving module is used for independently generating a pulse reflection mode excitation signal and a diffraction mode chirp frequency modulation signal; the phase-controlled synthetic aperture dynamic focusing module is used for realizing + / -60-degree deflection and sub-wavelength level focusing of acoustic beams; the nonlinear harmonic enhancement module is used for exciting a crack nonlinear effect by superposing 1-2MHz fundamental waves; and a time-space-frequency three-domain data fusion module. According to the method, signal-to-noise ratio collapse caused by the diffraction effect in a traditional method is overcome, more importantly, the system has low-frequency penetrability and high-frequency resolution at the same time due to 80%-120% of broadband coverage of chirp frequency modulation signals, the diffraction signal gain is increased by 20 dB or above, a near-surface blind area is compressed to be within 0.2 mm, and the system can be applied to the field of radar imaging. And the core pain point of missing detection of the near-surface microcracks of the thin-wall component is solved.

The invention discloses an interference filtering system for power equipment partial discharge signal monitoring, relates to the technical field of power equipment partial discharge monitoring, and effectively inhibits interference of interference signals on partial discharge signal detection. According to the method, the partial discharge signal of the power equipment is collected, Fourier transform is performed on the partial discharge signal to generate the corresponding partial discharge spectrum signal, the center frequency of the interference signal is extracted from the partial discharge spectrum data, and the suppression frequency point is established according to the center frequency of the interference signal. The method comprises the following steps of: generating real-time partial discharge spectrum data, extracting spectrum data of an interference signal from the real-time partial discharge spectrum data according to a suppression frequency point, further obtaining pure spectrum data, generating a partial discharge time domain signal according to the pure spectrum data, and further performing peak searching processing on the partial discharge time domain signal to obtain PRPS characteristic spectrogram data.

The invention discloses a high-precision spectral interference signal demodulation method for micro distance measurement. The method comprises the steps of S1, interference spectrum shaping; measuring an interference spectrum and a reference spectrum; selecting a six-item combined cosine window as a primary function of an interference signal to obtain a shaped interference spectrum; s2, performing non-uniform Fourier transform to obtain a frequency spectrum of the target signal; s3, on the basis of a multi-spectral-line interpolation algorithm, interpolation is carried out on the window function to solve a fitting function, and a center frequency k is obtained; and S4, outputting a result. And solving and outputting an optical path difference d. According to the method, a six-item cosine combination window is selected as a primary function of an interference signal, and the problem that the accuracy of signal extraction is influenced by mutual superposition of side lobes and even main lobes of a direct current item and a solved frequency item is solved; an interference spectrum is processed by using a fast non-uniform Fourier transform algorithm, accuracy can still be kept under the conditions that the distance to be measured is increased and sampling is gradually sparse, and the problem of resampling deviation caused by a traditional interpolation method can be solved.

In a wireless local area network (WLAN) system, a transmission STA can transmit a PPDU via a 320 MHz channel, and a Medium Access Control (MAC) signal may be generated for the PPDU. The MAC signal may include puncturing pattern information and channel center frequency segment (CCFS) information for a 320 MHz band. The CCFS information may include a first CCFS field related to channel center frequency (CCF) information of a primary 160 MHz channel, and a second CCFS field related to CCF information of a 320 MHz channel.

The invention discloses a multi-mode communication and perception channel twinborn AI data set generation method. The method comprises the following steps: establishing a high-precision three-dimensional indoor scene model and an outdoor scene model; configuring parameters of a signal receiving and transmitting end and the RIS, determining dielectric constants and magnetic conductivities of different object surfaces in a scene according to a signal center frequency, and determining reflection, refraction, scattering and diffraction effects of electromagnetic waves in different media based on a geometric optical theory and a geometric diffraction theory; calculating channel parameters of each ray, generating a channel matrix and a channel intensity thermodynamic diagram based on the channel parameters, and generating a complete data set in combination with scene information; and aligning the coordinates of the scene and the ray, loading and rendering ray data and a channel intensity thermodynamic diagram based on a moving path selected by a user, and obtaining a visual file of the multi-modal channel twin AI data set. According to the invention, refined materials and electromagnetic properties are given to objects in a scene, and efficient simulation and second speed visualization of electromagnetic wave propagation are realized.

The invention relates to the technical field of radar waveform optimization, in particular to a synthetic broadband waveform optimization method based on reinforcement learning. According to the method, a deep reinforcement learning framework is adopted, and end-to-end optimization is carried out on the center frequency and amplitude parameters of multiple sub-pulses. The method comprises the following steps: constructing a synthetic broadband waveform signal model, and parameterizing sub-waveforms to determine a search space of an optimization problem; converting a waveform optimization problem into a reinforcement learning form, and defining a state space, an action space, a reward function and a state transition mechanism; based on a deterministic soft action value iteration strategy algorithm, point spread function features are extracted in combination with a Transform encoder, and sub-pulse parameters are dynamically adjusted. Through the method, the main lobe resolution can be effectively suppressed and improved, the target detection performance of a radar system is improved, and the method has good global optimization capability and environmental adaptability and is suitable for cognitive radar and adaptive waveform design scenes.

The invention belongs to the field of filter optimization of coupling matrixes and neural networks, and discloses a filter optimization design method of a neural network based on guidance of a coupling matrix, and physical elements and mechanisms are introduced into the neural network. The method comprises the following steps: firstly, determining filter optimization indexes, randomly generating sample data in a range near the size of the optimization indexes, and screening data samples by adopting a sample screening method to obtain high-quality samples; then, extracting a coupling matrix from the obtained S parameter according to a vector fitting algorithm, and forming new coupling information M'by combining the center frequency f0 and the bandwidth BW; a full-connection neural network is used as a medium of filter device size parameters and output frequency response, the size of a to-be-optimized device of the filter is input, and coupling information M'representing S parameters is output. And the forward simulator is connected with a genetic optimization algorithm GA to realize optimization design of a specific index parameter filter. According to the method, the optimization design time is effectively saved, the optimization efficiency is improved, and the optimization dimension is reduced.

The invention discloses a medium cavity filtering coupler with an adjustable center frequency, particularly relates to the technical field of wireless communication, and solves the problems that in the prior art, the fusion design between a coupler and a filter of the same type is mainly adopted, most designs are low-power planar circuits, and the cost is low. The technical problem of lack of fusion design of an adjustable cavity filter and a coupler is solved. According to the technical scheme, a metal stepped shaft assembly is inserted into a non-metallized through hole of a medium cavity in a segmented mode, disturbance of different magnitudes is generated on a TE110 mode electric field in the medium cavity, and then the resonant frequency of the medium cavity is shifted; the effect of flexibly regulating and controlling the center frequency of the filtering coupler can be achieved.

The invention discloses a method for adaptively identifying the thickness of a black soil layer based on a variational mode decomposition method and a ground penetrating radar technology, and the method can directly measure the thickness of the black soil layer of a large-area black land through the ground penetrating radar technology, automatically filters ground penetrating radar signal clutters, and corrects the ground position. According to the technical scheme, on the basis of a center frequency method and a residual error method, variational mode decomposition parameters suitable for black land ground penetrating radar signals are automatically obtained, then modal components of variational mode decomposition are subjected to counting analysis, a spectrogram of original ground penetrating radar signals and the actual black soil layer thickness are compared, an empirical model based on the peak area proportion is established, and the calculation accuracy is improved. The automatic screening can be used for identifying the post-screening center frequency and frequency sweep width of the thickness of the black soil layer, and the position of the black soil layer is adaptively identified after frequency filtering, so that the thickness of the black soil layer can be quickly obtained, and the identification accuracy and efficiency are improved.

The invention discloses a device and a method for accurately acquiring working face abnormal body information by a multi-frequency ground penetrating radar. The device comprises a multi-frequency data acquisition module, a data alignment module, a data fusion module, a data management module, a model training optimization module and an abnormal body information identification module. The method comprises the following steps: acquiring ground penetrating radar data of different center frequencies of a target by using a multi-frequency ground penetrating radar; preprocessing the original multi-frequency ground penetrating radar data; performing spatial alignment on the preprocessed ground penetrating radar data; fusing the ground penetrating radar data through improved non-subsampled contourlet transform (NSCT) to obtain a multi-frequency ground penetrating radar fusion profile; fused images are collected to make a ground penetrating radar image data set; and importing an improved YOLOv11 model to perform underground anomalous body identification and marking on the ground penetrating radar image data set. Through multi-frequency data complementation and deep learning optimization, anomalous body detection precision and efficiency are significantly improved, and the method is especially suitable for geological exploration of complex underground environments such as a coal mine.

The invention belongs to the field of hydroelectric generating set monitoring, and discloses a set vibration signal noise reduction and feature extraction method based on IAVMD and WOA, and the method comprises the steps: constructing a comprehensive evaluation index comprising an energy difference, an average energy entropy and a center frequency variation coefficient; combining an adaptive variational mode decomposition method and a whale optimization algorithm to obtain an optimal decomposition layer number K and a penalty factor; calculating an energy entropy and a Pearson correlation coefficient of each intrinsic mode component; screening the K intrinsic mode components through a double-threshold mechanism; and calculating to obtain a denoised unit vibration signal time sequence and fault features according to the screened intrinsic mode components. According to the method, the problems of large noise interference, fuzzy characteristics, parameter dependence on experience and the like in a traditional method are solved, the precision and efficiency of unit vibration signal processing are remarkably improved, nonlinear dynamic characteristics in unit vibration signals are captured through multi-scale analysis, and the sensitivity and diagnosis accuracy of early weak faults can be improved.

The invention relates to a height measurement method and device based on a multi-frequency linear combination InSAR and a storage medium, and the method comprises the steps: carrying out the segmentation of a used large-bandwidth signal, so as to extract different center frequencies, carrying out the linear combination of the center frequencies, so as to generate a plurality of synthesized measurement frequencies, measurement is carried out based on all measurement frequencies from small to large, a measurement phase difference of a next measurement result is updated based on a product invariant principle by utilizing a result of a previous measurement frequency, phase unwrapping is guided step by step through hierarchical classification, and the highest precision is achieved. According to the method, the maximum measurement range and the highest measurement precision of the extreme steep terrain (EST) are achieved at the same time, real-time measurement is achieved through single-time large-bandwidth signal collection, no time error exists, and meanwhile the large bandwidth provides excellent distance resolution.