358 results about "Aliasing" patented technology

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses a wind turbine generator anti-impact noise fault identification method based on feature embedding deep learning, and the method comprises the steps: carrying out the feature mode decomposition of an original vibration signal of a wind turbine generator, screening out an optimal mode component, and converting a time domain signal of the optimal mode component into an envelope spectrum; using the minimum envelope entropy as a fitness function, and using a sparrow search algorithm to globally optimize the filter length and the decomposition modal number of characteristic modal decomposition; and extracting time-frequency domain features, constructing a multi-dimensional time-frequency domain feature vector, inputting the multi-dimensional time-frequency domain feature vector into the combined fault recognition model, and outputting a fault classification result. According to the method, on the basis of an FMD and SSA-MEE joint optimization framework, the sensitivity limitation of a traditional envelope demodulation method on impact noise is broken through, modal aliasing is restrained, and noise robustness is enhanced. According to the method, the vibration signals are subjected to characteristic mode decomposition, the influence of early impact noise is inhibited, a CNN-GRU-Attention fault recognition model is provided, and the accuracy of fault recognition is greatly improved.

The invention relates to the technical field of dynamic deformation measurement error real-time correction based on Beidou satellite navigation, in particular to a pumped storage dam deformation monitoring method based on Beidou positioning, and aims to solve the technical problem of monitoring data pseudo fluctuation caused by Beidou signal multi-path time-varying error and structure real displacement frequency domain aliasing in a high-dynamic environment. Millimeter-level real-time resolving is achieved through multi-mode cooperative monitoring network construction and dynamic feature analysis. The method comprises the following steps: deploying a double-frequency Beidou receiver array and an environment sensor, fusing Beidou observation data, reservoir water pressure gradient, foundation vibration spectrum and three-dimensional geologic structure data, constructing a robust solution model constrained by a geologic model, outputting a deformation parameter confidence interval, and suppressing an abnormal value through robust estimation; a historical working condition library is matched by adopting a dynamic time warping algorithm, and an early warning threshold is dynamically calibrated and a monitoring network is reversely optimized in combination with a closed-loop feedback mechanism. According to the invention, the multi-path time-varying error is effectively suppressed.

The invention discloses a sub-industry load prediction method based on time sequence decomposition and multi-core Transform fusion. Firstly, industry loads are divided into a new energy dominant type, an industrial dominant type, a meteorological sensitive type and a mixed type through a clustering algorithm in combination with correlation coefficients of new energy output, policy factors and meteorological elements; secondly, decomposing the load data into trend components and day, weekly and monthly periodic components by adopting a time sequence decomposition layer, and respectively extracting features through a multi-kernel Transform architecture: capturing short-term fluctuation features by a time convolution kernel, capturing long-term trend features by a Transform layer based on a multi-head self-attention mechanism, fusing and outputting sub-industry load prediction results, and finally, accumulating four types of industry load prediction results to obtain a system load prediction result. According to the method, the problems of periodic component aliasing, difficulty in multi-factor coupling classification, insufficient long-term and short-term feature fusion and the like of a traditional method are solved, and the precision and robustness of sub-industry load prediction are improved.

The invention provides an OTFS radar communication integrated signal processing method and system based on time-frequency-space domain joint optimization. According to the method, pilot symbol positions and densities are configured in an OTFS time-frequency grid according to channel time-varying characteristics and bandwidths, and a Doppler index range is set to optimize channel information acquisition; deploying a controllable reflecting surface, constructing a phase modulation matrix based on a target position and communication channel parameters, dynamically adjusting a phase gradient direction, and separating energy of a target echo and a communication signal; within a set Doppler index range, calculating frequency offset estimation, correcting a time domain sampling point of a received signal, and generating a phase correction vector; and positioning a spatial aliasing main interference area, and inhibiting symbol interference and spatial aliasing caused by a high-speed target through frequency domain beam forming weighting and phase rotation compensation in combination with a phase correction vector and a sub-Doppler parameter in the area. According to the invention, time-frequency-space domain cooperative efficient processing of OTFS radar communication integrated signals is realized.

The invention provides a real-time human body detection method and system based on a microwave radar, and is applied to the field of signal data processing. By constructing a sparse reconstruction model and combining multi-dimensional feature decomposition, the method effectively relieves the recognition difficulty caused by target signal aliasing, continuously outputs and receives radar signals, extracts distance, Doppler and angle features, constructs target distribution point clouds in a three-dimensional feature space, further utilizes angle continuity judgment and point cloud density analysis, and improves the recognition accuracy of target distribution point clouds. According to the method, the number and distribution of overlapped targets are accurately estimated, a Kalman filter is introduced for dynamic tracking for a scene in which a continuous area is not formed, continuous separation and pseudo-color image reconstruction of multiple targets are realized, and thus the resolution precision and target positioning capability of a radar in a multi-person close-range aggregation scene are remarkably improved.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention relates to a variable sampling start-based satellite-borne sliding spotlight SAR data processing method, which comprises the following steps of: carrying out distance direction blocking on echo data of variable sampling start, carrying out Fourier transform on sub-block data along the distance direction, multiplying by a phase compensation function, and carrying out sampling start alignment; performing two-step azimuth frequency domain aliasing removal on the sub-block data after sampling initial alignment; performing two-dimensional frequency domain matched filtering on the sub-block data after the azimuth frequency domain aliasing is removed; carrying out resampling on the sub-block data subjected to two-dimensional frequency domain matched filtering; and splicing the resampled sub-block data to obtain an imaging result of the whole imaging scene. According to the method, sampling initial alignment is carried out through a distance frequency domain azimuth time domain phase compensation function, zero padding does not need to be carried out on echo data, and the data processing difficulty is reduced; the focusing quality is improved by eliminating azimuth frequency domain aliasing, two-dimensional frequency domain matched filtering and two-dimensional frequency domain resampling.

The invention provides synthetic aperture sonar equipment towed by an unmanned aerial vehicle and a method, and belongs to the technical field of underwater sonar detection. The equipment comprises an unmanned aerial vehicle, a sonar towed body, a towing cable and a water surface control unit, the method comprises: establishing a measurement reference; based on the measurement basis, acquiring state data of the synthetic aperture sonar under the dragging of the unmanned aerial vehicle, and resolving in real time to obtain pose data; extracting a track direction velocity component from the pose data, performing displacement integration on the track direction velocity component, and generating a pulse trigger signal when the displacement integration quantity reaches a sound wave half-wavelength threshold value; synchronously latching current pose data and collecting acoustic echoes based on the pulse trigger signal, and generating a timestamp binding data packet; geometric phase compensation is applied to the timestamp binding data packet, an aliasing-free image is reconstructed through wave number domain resampling, and spatial spectrum aliasing caused by carrier motion disturbance is eliminated.

According to the plateau area load prediction method and system based on the signal decomposition and the neural network, an original signal is decomposed into a plurality of intrinsic mode components with physical significance by adopting completely adaptive noise set empirical mode decomposition, mode aliasing is suppressed, and multi-scale features are extracted to effectively suppress load demand oscillation; the problem of non-stationarity of the load of the service area is solved; then, K-means clustering grouping is carried out on the components, similar modes are combined to reduce calculation redundancy, and the problems of heterogeneity and local feature redundancy in complex time series data are effectively solved; performing nonlinear dimension reduction processing on the grouped reconstructed signals by adopting kernel principal component analysis, eliminating noise interference and extracting key features, thereby effectively solving the multiple nonlinear problem; and finally, inputting the dimensionality-reduced features into a long-short-term memory network modeling time sequence dependency relationship, and realizing high-precision prediction through adaptive weighted integration of component prediction results.

The invention discloses a multi-parameter coupling detection method of an integrated optical sensor and related equipment. The detection method comprises the following steps: injecting different trace medicament combinations into a to-be-detected sample in a plurality of micro-fluidic sub-channels according to a preset time sequence; performing phase shift coding acquisition on the differential optical response signal, splitting a broadband light source into a plurality of wavelength subintervals, and generating a coded light signal with a preset phase shift amount; calculating a cross-correlation function between the channels, and performing signal amplification or suppression processing on the channel group with high coupling degree; multi-dimensional feature extraction is carried out, and the differential interference amount, the amplitude ratio and the phase synchronization degree between channels are calculated; baseline drift and noise distribution are monitored in a preset time window, and parameter adjustment is carried out; and performing self-learning evaluation on the detection data, updating identification model parameters, and generating a multi-parameter fingerprint spectrum. According to the technical scheme, the problems of signal aliasing and insufficient sensitivity in multi-parameter detection are effectively solved, and high-precision synchronous detection can be carried out on various target objects.

The invention belongs to the technical field of power system power distribution network relay protection, and provides a distribution network T-connection line fault positioning method based on SVMD, which comprises the following steps: step 1, acquiring three-phase voltage by using a traveling wave detection device installed on a distribution network line, and performing phase-mode conversion to obtain a voltage traveling wave alpha-mode component; step 2, decomposing the voltage traveling wave alpha-mode component by using continuous variational mode decomposition (SVMD) to obtain intrinsic mode components of different frequencies; 3, calculating the kurtosis value of each modal component, and selecting the modal component corresponding to the maximum kurtosis value as a characteristic modal; compared with the traditional VMD, the SVMD method using the successive decomposition strategy is different in that the mode number does not need to be preset, and the mode component is extracted by optimizing the spectrum compactness, the residual signal energy minimization and the mode orthogonality principle, so that the method can eliminate the dependence on the prior knowledge, can better adapt to different signals, and can improve the robustness of the signal. The method can avoid the problem of mode aliasing, and has the characteristics of low calculation complexity and strong anti-noise capability.

The invention discloses systems and methods for privacy-preserving digital communications compliant with frameworks such as GDPR, DPDPA, HIPAA, and PSD2. A Virtual Identity (VI) is instantiated within a secure enclave and bound to one or more Compliance Jurisdiction Tokens (CJTs). Unlike conventional tokenisation limited to payment or aliasing, the invention enables multiple concurrent VIs (Multi-VI), each scoped to a declared purpose, jurisdiction, subnet, or session. Communication is permitted only if all bound CJTs validate inline, including Multi-CJT bindings where jurisdiction, purpose, and consent must all succeed, and Multi-Purpose CJTs (MCJTs) where multiple lawful purposes must be simultaneously satisfied. Each validation produces a Ledger-Anchored Validation Receipt (LAVR) containing pseudonymised metadata, anchored into tamper-evident ledgers for transparency. Regulators access receipts through a Regulator Query Interface (RQI), enabling filtered oversight without exposure of raw identifiers. The technical effect is to transform privacy and compliance into cryptographic enforcement across device, telecom, and network layers

The invention provides a fatigue assessment method and system based on multi-modal feature fusion, and the method comprises the steps: carrying out the preprocessing of collected multi-dimensional physiological signals, including denoising and baseline drift removal; a forehead electroencephalogram signal and an electro-oculogram signal are adaptively separated from the forehead mixed electroencephalogram signal by adopting a variational mode decomposition algorithm, so that the problems of mode aliasing and endpoint effect are solved; and extracting a multi-modal feature vector, and inputting the multi-modal feature vector into the fatigue state evaluation model for training to obtain a trained fatigue state evaluation model. According to the method, the anti-interference performance of mixed electroencephalogram signal separation can be improved, so that the accuracy and stability of fatigue monitoring are improved.

The invention relates to the technical field of mechanical vibration and fluid excitation coupling effect analysis, in particular to a runner crown modification pressure pulsation amplitude measurement method, which comprises the following steps of: extracting pressure gradient distribution and cavitation phase change characteristic parameters of a runner surface through fluid dynamics simulation; targeted arrangement of a high-frequency pressure sensor array in a vortex core area, a boundary layer separation critical position and a modification structure mutation area is guided, and the signal acquisition precision is improved by adopting an annular encryption and axial equal-interval arrangement strategy. And establishing a dynamic error feedback mechanism of actual measurement and simulation. And verifying an abnormal frequency band proportion through frequency domain kurtosis and analyzing envelope spectrum harmonic waves, and screening a modification parameter combination with the pressure pulsation amplitude decreasing to reach the standard and stable frequency band distribution. According to the method, the problem of measurement errors caused by multi-source high-frequency noise and effective signal frequency domain aliasing is solved, a high-precision quantification basis is provided for runner modification optimization, and the method is suitable for the field of mechanical system dynamic characteristic testing.

The hybrid energy storage frequency modulation method based on two-dimensional mode decomposition comprises the following steps: acquiring an original frequency modulation instruction signal; generating a primary modal subsequence according to the frequency modulation instruction signal based on a two-dimensional modal decomposition algorithm; generating a plurality of secondary modal subsequences from the undecomposed frequency modulation instruction signal based on a two-dimensional modal decomposition algorithm through loop iteration; distributing the primary modal sub-sequence and the plurality of secondary modal sub-sequences to different energy storage units in the hybrid energy storage system for power regulation and control; when the power grid frequency of the power plant fluctuates, response can be made in time, the optimal decomposition sub-sequence is obtained through the two-dimensional mode decomposition algorithm, frequency modulation instruction distribution is more accurate, the problem of inaccurate frequency modulation instruction signal distribution caused by mode aliasing is solved, the cost of an energy storage system is reduced, and the income of the power plant is improved.

The invention discloses a wind power digital twinning robustness detection method based on time-frequency revolving door perception matching, and the method comprises the steps: obtaining a fan detection signal generated by digital twinning and a fan detection signal collected by a sensor, the fan detection signal being the temperature, radial vibration, axial vibration, current, voltage or power of a fan; the two kinds of fan detection signals are preprocessed through a rotating door algorithm SDA so that the characteristic frequency amplitude error of the preprocessed fan detection signals can be smaller than a preset threshold value; and respectively updating receptive fields of the corresponding convolutional neural networks based on the preprocessed fan detection signals so as to determine the number of convolutional layers of the convolutional neural networks, and outputting the difference between the number of convolutional layers of the convolutional neural networks of the two fan detection signals as a robustness detection result of digital twinning. The invention aims to improve the data processing capability, solve the problem of misdiagnosis possibly caused by spectrum deviation and aliasing and signal abrupt change when the signal stably changes, and improve the accuracy of robustness judgment.

The invention provides a rolling bearing fault diagnosis method based on deep learning, and relates to the technical field of mechanical fault diagnosis. According to the method, global optimization is carried out on a parameter combination in an ICEEMDAN decomposition process through a CPO optimization algorithm, the optimal amplitude of white noise and the number of times of adding the white noise are determined, an intrinsic mode function is screened and denoised in combination with a correlation coefficient, reconstruction of a vibration signal of a rolling bearing is completed, and the vibration signal of the rolling bearing is reconstructed. The problem of mode aliasing is solved while the reliability and the stability of a vibration signal of the rolling bearing are improved; a K nearest neighbor method is used to construct a graph model, an edge connection relation between nodes can be effectively established, so that a foundation is laid for subsequent deep analysis and processing of vibration signals based on a graph structure, a graph attention neural network is input, topological structure information of the nodes and edges is fully utilized, extracted node features are classified and identified, and the accuracy of vibration analysis is improved. Therefore, fault diagnosis of the rolling bearing is realized.

According to the method, a VanilaNet network structure in the field of computer vision is used for reference and improved and then is applied to the gesture recognition problem based on electromyographic signal processing, and the VanilaNet network is difficult to be directly applied to electromyographic signal processing in consideration of the difference of image signals and electromyographic signals in data dimension and feature complexity, so that the recognition accuracy is improved. According to the invention, the VanilaNet network structure is improved in terms of data input and structure simplification, so that the VanilaNet network structure can be applied to electromyographic signals. And finally, under the condition that the recognition precision is ensured, model lightweight can be realized. Furthermore, in the data preprocessing stage, the mode decomposition mode and the data multi-domain fusion are improved, the problem of mode aliasing is avoided, the accuracy is further improved, and the robustness is improved; meanwhile, in the real-time control process, a data caching method is further designed to solve the delay phenomenon caused by the intelligent bionic hand device.

The invention belongs to the technical field of radar detection, and discloses a bird positioning method of a quantum radar bird detection system, which is characterized in that quantum characteristics such as polarization state and phase of echo photons are accurately analyzed by means of a quantum state demodulation technology, and high-precision measurement of the flight time of the photons is combined, so that the resolution limitation of the traditional radar is broken through from the quantum level; the technical combination not only can capture fine actions such as wing flapping frequency and steering angle of birds, but also can exert remarkable advantages in a dense bird flock scene: by utilizing quantum state differences of echo photons of different birds, overlapping signals are decomposed into independent individual signals through a quantum state separation algorithm; the individual distinguishing problem caused by traditional radar signal aliasing is solved; in high-precision demand scenes such as airports and the like, small bird targets can be accurately locked, flight potential safety hazards caused by misjudgment are greatly reduced, and the system can physically resist copying and deception interference of the outside to quantum signals based on the quantum unclonable principle.

The embodiment of the invention relates to the technical field of electromagnetic space signal processing, in particular to an intelligent estimation method for the number of information sources of multiple time-frequency overlapping signals under non-Gaussian interference, and the method comprises the steps: carrying out the sparse decomposition of an obtained time-frequency overlapping signal into a plurality of matching signals through a matching pursuit algorithm, a nonlinear converter is adopted to weaken the influence of alpha stable noise on time-frequency overlapping signals, a plurality of matching signals and original signals are utilized to form virtual multi-channel signals, and a generalized correlation matrix is constructed; performing data preprocessing operation including eigenvalue decomposition and similarity transformation on the generalized correlation matrix to obtain a processed generalized correlation matrix; and inputting the processed generalized correlation matrix into an improved U-Net network to obtain a segmentation result output by the improved U-Net network, and realizing intelligent estimation of the number of time-frequency overlapped multiple signal sources under the non-Gaussian interference according to the segmentation result. The method is high in estimation precision, the estimation performance is not influenced by the modulation mode and the frequency spectrum aliasing degree, and the robustness is high.

The invention discloses an on-line brightness uniformity automatic test method and system, and relates to the technical field of brightness test, and the method comprises the following steps: obtaining a stroboscopic signal of an illumination light source in a test environment, and extracting the stroboscopic characteristic frequency of the illumination light source through a frequency domain analysis algorithm, establishing a phase tracking baseline between the frequency of the illumination light source and a sampling clock of the optical detection equipment, and constructing a phase difference mapping matrix based on the baseline; and based on the phase difference mapping matrix, constructing a self-correction model of the sampling clock of the optical detection equipment. Through phase tracking and self-correction control, optical sampling and illumination frequency are synchronized for a long time, brightness fluctuation caused by stroboscopic aliasing is eliminated, and measurement stability and consistency are improved; and through frequency domain residual suppression and brightness index re-calibration, closed-loop control from sampling to output is realized, so that the detection result is higher in precision, the judgment is more reliable, and accurate support is provided for brightness detection and quality tracing of a production line.

The invention discloses an aerodynamic noise time sequence prediction method based on a VMD-ESN, and relates to the technical field of hydromechanics noise prediction.The method comprises the steps that firstly, aerodynamic noise signals are collected in real time through a sound pressure sensor; secondly, performing adaptive frequency domain decomposition on the original noise signal by adopting VMD, obtaining a plurality of orthogonal narrowband intrinsic mode functions through a constraint variation optimization framework, effectively separating vortex shedding harmonic and turbulence pulsation characteristics, and inhibiting spectrum aliasing; then, inputting each IMF into an ESN, performing high-dimensional mapping on a modal time sequence evolution rule by using a dynamic reserve pool of sparse connection of a neural network, and training an output layer weight matrix through a ridge regression algorithm; and finally, linearly superposing prediction results of all modes to generate a complete aerodynamic noise time sequence. The method does not need to depend on high-resolution grid iterative calculation, and achieves the accurate prediction of the aerodynamic noise time sequence in a long time interval through a cooperation mechanism of VMD signal adaptive decomposition and ESN machine learning dynamic modeling, and greatly improves the calculation efficiency.

The invention relates to the technical field of hot melting salt storage, and discloses a photo-thermal system mirror surface micro-angle linkage adjustment method based on thermal perception, which breaks through the limitation of traditional single data monitoring and experience judgment through multi-source data fusion and triple physical constraint decoupling, and remarkably improves the fault diagnosis accuracy. And secondly, the problem of poor model timeliness caused by traditional static partitioning and long-period updating is solved through dynamic partitioning deployment and real-time contribution identification, so that the contribution degree matrix can be updated in real time along with the mirror surface state. Accurate tracking of parameters of different time scales and safe rehearsal of a control strategy are achieved through parameter hierarchical updating and digital twinborn verification, and parameter aliasing and optimization failure caused by a traditional single updating strategy are avoided.

The invention discloses a FDA-MIMO radar multi-target distance angle joint super-resolution method and system, and mainly solves the problem that the existing phased array only depends on the angle to identify multiple targets, resulting in low resolution ability, and the implementation scheme of the method comprises the steps of obtaining FDA-MIMO radar echo data, and performing separation aliasing on the FDA-MIMO radar echo data to obtain a data matrix; performing conversion stacking on the data matrix to obtain NM * L-dimensional data, and calculating a covariance matrix of the NM * L-dimensional data; performing reverse array decoherence processing on the covariance matrix to obtain a new source signal covariance matrix; constructing a two-dimensional spatial spectrum function of a target distance and a target angle for the new source signal covariance matrix through a MUSIC algorithm; and performing spectrum peak search on the two-dimensional spatial spectrum function, and detecting a local maximum value of the two-dimensional spatial spectrum function to obtain a distance and angle joint estimation value of a plurality of targets. The method can improve the angle resolution capability of the target when the target angle interval is lower than the traditional resolution, reduces the influence of the amplitude-phase error on the resolution capability when the target distance interval is large enough, and can be used for target recognition.

The invention provides an inter-beam bunch signal aliasing calibration method based on a cavity probe. The inter-beam bunch signal aliasing calibration method comprises the following steps: acquiring a non-aliasing reference signal from a high repetition frequency free electron laser device; extracting a signal waveform of each beam bunch and a measurement result thereof from the aliasing-free reference signal; modeling to obtain a residual signal formula; obtaining a calibration result of the beam bunch based on a residual signal formula and a vector calibration algorithm according to the measurement result of the aliasing signal waveform and the standard waveform of the previous beam bunch; taking the next beam bunch as the current beam bunch, and returning to the step of obtaining the calibration result; and obtaining calibration results of all the beam bunches until all the beam bunches are calibrated. According to the method, the amplitude-phase characteristics of the attenuation wake of the adjacent beam bunches are modeled and compensated through the vector calibration algorithm, the time domain aliasing effect in the high-repetition-frequency free electron laser device is effectively eliminated, and the phase absolute deviation measured by the beam arrival time of the cavity probe is reduced to the femtosecond magnitude.

The invention relates to an orthogonal energy information transmission magnetic coupling structure and system, and relates to the technical field of wireless energy information transmission, the orthogonal energy information transmission magnetic coupling structure comprises a transmitting end and a receiving end, the transmitting end comprises a first magnetic core and a transmitting end integrated coil, and the receiving end comprises a second magnetic core and a receiving end integrated coil; each of the transmitting end integrated coil and the receiving end integrated coil comprises an energy coil for energy transmission and an information coil for information transmission, the information coil comprises a forward information coil and a reverse information coil, the forward information coil is parallel to a plane where the first direction is located, and the reverse information coil is parallel to a plane where the second direction is located. Therefore, the directions of magnetic fields generated by the three coils can be perpendicular to one another, so that signal aliasing and signal crosstalk are reduced, energy loss is reduced, and energy information transmission efficiency is improved.

The invention discloses a blind separation and time-frequency noise reduction combined comprehensive pulsar time establishing method. The method comprises the following steps: selecting a pulsar meeting a preset requirement; processing the timing residual error of the selected pulsar; establishing a mixed signal model, iteratively solving an anti-mixing matrix through an approximate negentropy contrast function, and separating independent components from the multi-dimensional features; carrying out Pearson correlation analysis on each separated independent component and reference time; and performing multi-scale decomposition on the selected common-mode clock difference signal by adopting a plurality of wavelet bases, and performing Bayesian soft threshold processing to obtain the final comprehensive pulsar time. According to the method, the long-term stability of the comprehensive pulsar is improved, the common problems of aliasing and modal mixing in a high-noise environment in the prior art are solved, and the extracted signal is more stable.

The invention relates to the technical field of remote sensing image processing, in particular to a wetland information extraction method and system based on multi-source remote sensing data fusion, and the method comprises the following steps: obtaining and fusing a wetland optical radar image and water level time sequence data, extracting near-infrared and back-scattering multi-source characteristic parameters to discriminate water body pixels, and extracting a water body image; and carrying out statistics on spatial connectivity aggregation to generate a water body plaque boundary, calculating an area perimeter and shape index comprehensive model to classify wetland types, and adjusting mismatching labels in combination with vegetation coverage and soil moisture to form a wetland information extraction result map. According to the method, the water body differential expression is enhanced through combined reflection and scattering characteristics, the water body and peripheral differential expression is enhanced through combined reflection and scattering characterization, boundary transition aliasing is restrained through bidirectional threshold constraint, plaque space integrity is restored and maintained through connectivity analysis, and type classification sensitivity is enhanced through morphological parameter combination. And the wetland expression accuracy and the type stability are improved through ecological attribute consistency correction.

The invention discloses a multi-frequency-band preprocessing machine learning seismic oscillation data denoising method, relates to the technical field of seismic signal processing, and solves the problems that noise and effective signals are aliasing, accurate recognition is difficult and noise remains due to the fact that seismic data cannot be effectively denoised in an existing method. The adopted seismic data de-noising network has a good de-noising effect on noise in a full frequency range, the noise can be effectively separated from effective signals, the effective signals and interference noise can be automatically distinguished through the network, the situation that the effective signals are mistakenly recognized as noise and filtered out is avoided, the two sub-modules are combined together, and the noise reduction efficiency is improved. According to the method, more details can be extracted, the denoising effect is improved, and a good denoising effect is achieved in actually measured seismic data.