25 results about "Hankel matrix" patented technology

The application discloses a kind of high-resolution tomography SAR four-dimensional imaging methods of space-time baseline decoupling processing, which comprises the following steps: step 1, reconstruct original satellite data into uniform baseline observation data, and utilize the low rank characteristic of Hankel matrix, solve its noiseless estimation;Step 2, elevation information is inverted based on noiseless estimation, and elevation information is used as priori, deformation spectrum estimation dictionary is constructed by space-time decoupling, and deformation information estimation is carried out;Step 3, the elevation and deformation variable estimated are matched using maximum likelihood method to realize four-dimensional imaging.The method realizes high-precision imaging while exponentially reducing the algorithm complexity of four-dimensional imaging, and the differential tomography SAR four-dimensional imaging result verification is carried out based on domestic land exploration No.1 satellite data.

The present disclosure provides an unknown unmanned system data-driven multi-step kernel prediction control method and system. The method is aimed at the problem of unknown non-parametric unmanned system model. Only the off-line collected system input and output noise data are used. The Hankel matrix is constructed to extract the time sequence characteristics. The kernel matrix is constructed by combining the renewable kernel function. The nonlinear system rule is converted into high-dimensional linear representable data rule. The multi-step kernel prediction optimization problem based on the kernel matrix is solved on-line and rolling. The optimal control input is output to realize closed-loop control. The present invention does not need to construct an accurate mathematical model and does not need the full-dimensional state of the system. The problem of poor adaptability and long scene error accumulation of the traditional method is solved. The robustness and engineering landing performance are strong.

The application provides a seismic data noise attenuation method and device, the method comprising: obtaining seismic data to be denoised; inputting the seismic data into a pre-constructed artificial neural network to output noise intensity data; the artificial neural network is a seismic data noise intensity prediction model obtained by training seismic data samples based on a machine learning algorithm; limiting noisy seismic data based on Casado filtering setting space parameters, determining the size of the Hankel matrix in Casado filtering; obtaining noise intensity data limited by Casado filtering setting space parameters; performing Casado filtering on the noisy seismic data limited by the setting space parameters with the number of ranks to complete random noise attenuation of the seismic data; and performing iterative cycles with the setting space parameters to complete random noise attenuation of all seismic data. The application converts the noise intensity of the seismic data into the number of ranks, so that the rank of the Casado filtering changes with the signal-to-noise ratio of the seismic data, and the random noise attenuation effect is good.

The present application relates to the technical field of data processing, more particularly, the present application relates to a kind of power supply monitoring method for atmosphere light bar production, the method comprises: obtaining and preprocessing power supply signal in current time window, obtain current estimated noise sequence;Power supply signal is converted into Hankel matrix and is carried out singular value decomposition, the cumulative energy ratio of each truncation order is calculated;According to current noise level and the degree of aggregation of data in current estimated noise sequence, calculate adaptive energy threshold;Select the minimum truncation order of cumulative energy ratio greater than the threshold as target truncation order, reconstruct Hankel matrix and restore to obtain target power supply signal;Through abnormal detection to target power supply signal, identify power supply anomaly.The present application can improve the filtering effect of SVD filtering to power supply signal, realize the accurate monitoring of power supply anomaly in light bar production process.

The application relates to the field of power distribution system simulation, in particular to a distributed power distribution system unified scale load modeling simulation method and system, which comprises the following steps: denoising and fitting a wideband signal to obtain a multiscale signal; obtaining a Hankel matrix time-varying extended model based on the multiscale signal and historical modal parameters and solving to obtain real-time modal parameters; constructing a coupling matrix based on the real-time modal parameters, fitting a fast action layer state equation and a slow action layer state equation by using the coupling matrix to obtain a unified coupling equation; transforming and decoupling the unified coupling equation to obtain a mixed differential-algebraic equation group; and solving the mixed differential-algebraic equation group by using a preset step length switching rule based on a dominant dynamic type to obtain full-time domain simulation results of the distributed power distribution system. The application can effectively solve the problems of time scale fragmentation and insufficient parameter identification accuracy in traditional load modeling.

This invention discloses a method, device, medium, and product for extracting parameters from propeller-modulated signals, relating to the field of electromagnetic signal processing technology. The method includes: transmitting a single-carrier continuous wave signal; receiving a radio frequency signal modulated by periodic propeller shielding and generating a complex baseband sequence; physically modeling the sequence as the sum of complex exponential components containing attenuation factors; constructing a Hankel matrix, using adaptive singular value decomposition for noise reduction and estimating the number of signal components; constructing a lightweight generalized matrix bundle based on the extracted sub-matrices, solving for generalized eigenvalues ​​to separate frequency and attenuation factors; and using the least squares method to obtain amplitude and phase information, thereby extracting the actual modulation parameters. This invention breaks through the limitations of traditional constant amplitude superposition modeling, closely aligns with the physical nature of asymmetric spatial shielding, and enables high-resolution, low-computational-complexity, and high-precision synchronous parameter extraction in environments with low signal-to-noise ratios and short data lengths.

The application particularly relates to a UAV target main body and micro-motion component echo signal separation method based on factor decomposition group sparse regularization, which comprises the following steps: firstly, constructing a radar echo time-frequency representation into a Hankel matrix; secondly, using the characteristic that the echo Hankel matrix of the target main body uniform motion has low rank, establishing a signal separation model containing a low rank term, a sparse term and a noise term; thirdly, using a factor decomposition group sparse regularization method to effectively relax the rank function in the model, so as to improve the robustness of the algorithm; finally, using a linearized alternating direction multiplier method to iteratively solve the optimization model, so as to realize effective separation of the target main body echo and the micro-motion component echo in the time-frequency domain. The application is particularly suitable for narrow-band radar and short coherent accumulation time detection conditions, can effectively overcome the problems of model mismatch and low calculation efficiency existing in traditional methods, and simulation and measured data verify that the method has good separation precision and robustness in a noise environment.

This invention discloses a five-dimensional seismic data reconstruction method based on preconditioned Riemann gradient descent, belonging to the field of seismic data processing technology. The method involves performing a Hankel transform on fixed-frequency four-dimensional seismic data to construct a fourth-order block Hankel matrix; performing hard thresholding on the fourth-order block Hankel matrix to obtain a low-rank approximation matrix, and calculating the Euclidean gradient at the low-rank approximation matrix in the current iteration; calculating a preconditioner based on the diagonal part of the Euclidean gradient outer product; calculating the projection of the Euclidean gradient onto the tangent space of the Riemann manifold based on the Euclidean gradient and the preconditioner, and updating the low-rank approximation matrix based on the projected gradient to obtain the low-rank approximation matrix for the next iteration; iterative updates continue until a termination condition is met; the low-rank approximation matrix from the last iteration is inversely transformed into a fourth-order tensor, and the reconstructed five-dimensional seismic data is obtained based on the fourth-order tensor and the frequency components in the five-dimensional seismic data. This method can improve the reconstruction efficiency and quality of five-dimensional seismic data.

The application discloses a kind of data synchronization system and method under power distribution network terminal equipment wireless communication, the system includes data acquisition module, mutation variable detection module, data matrix construction module, singular value decomposition module, synchronization point selection module and wireless synchronization module;The method obtains voltage sampling data by data acquisition module, identifies fault or disturbance trigger time by mutation variable detection module, constructs Hankel matrix after data matrix construction module, extracts effective intermediate by singular value decomposition module, determines synchronization point by synchronization point selection module, finally realizes cross-terminal synchronization with relative time scale data by wireless synchronization module.The application does not need to rely on external time service equipment and system parameter, algorithm is simple, real-time is good, anti-interference ability is strong, suitable for complex communication environment in mountainous area, tunnel etc., with extremely high engineering popularization value in 10kV power distribution network scene.

The application relates to the technical field of oil exploration, and particularly discloses a multi-seismic-source aliasing seismic data decoupling separation method, which comprises the following steps: inputting aliasing seismic data and establishing a mathematical model, converting a separation problem into a signal recovery problem with constraints; reorganizing the data into a common offset domain, constructing a cascade shift embedded Hankel matrix to form a low-rank prior; designing an energy-aware modulation function based on spectral component values to adaptively distinguish effective signals and crosstalk; fusing a weighted spectral norm and a transform domain sparsity constraint to construct a joint structured penalty optimization model; and adopting a variable splitting iteration framework to decompose a sub-problem, alternately solving and adaptively updating weights. The application protects effective signals by relying on an adaptive weight strategy, separates crosstalk by combining low-rank and sparse double characteristics, overcomes signal leakage and amplitude distortion problems, improves waveform fidelity and separation signal-to-noise ratio, and takes into account convergence stability and calculation efficiency, so that the application has important application value in complex geological oil and gas exploration and high-precision seismic imaging.

The application discloses a kind of reciprocating compressor air valve fault diagnosis methods, it is related to reciprocating compressor fault diagnosis technical field.The method steps are:S1, collection reciprocating compressor air valve vibration acceleration signal;S2, based on the window length of Hankel matrix in dynamic modal decomposition is optimized in chaos evolution optimization algorithm;S3, air valve vibration acceleration signal is decomposed and reconstructed;S4, the two-dimensional multiscale attention entropy of reconstructed signal is calculated, and two-dimensional multiscale attention entropy feature matrix is constructed;S5, two-dimensional multiscale attention entropy feature matrix is input least square support vector machine optimized by particle swarm optimization algorithm, and the fault diagnosis of reciprocating compressor air valve is realized.The application solves the problem that parameter selection of traditional method has no universality, feature extraction is not comprehensive and diagnostic precision is insufficient, realizes the high reliability and high accuracy of reciprocating compressor air valve fault diagnosis.

The application discloses a large-interval array ultrasonic signal anti-symmetry Lamb wave dispersion extraction method for a cased well, an electronic device and a program product. The method comprises the following steps: constructing a Hankel matrix according to a frequency domain space sequence of a large-interval ultrasonic Lamb wave array signal, and then solving spatial poles; constructing a Vandermonde matrix according to the spatial poles, and then solving real complex amplitudes of the spatial poles; performing adaptive dynamic phase unwrapping on the spatial poles according to a phase velocity of a prior A0 mode, and obtaining phase velocities and spatial attenuation coefficients of all spatial poles in a full frequency band; selecting an initial anchor point according to the real complex amplitudes of the poles in a dynamic pole library; eliminating cross modes and noise interference according to the initial anchor point and a monotonicity constraint of the phase velocity; eliminating artifact points according to a difference between an error mean of imaginary parts of the dynamic poles and a fitting curve and a fitting variance, and obtaining a final A0 mode dispersion curve. The application effectively suppresses spatial aliasing and mode cross artifact.

The present application relates to the field of structural health detection, and particularly relates to a device abnormality detection method for wind-induced deformation measurement of large deployable structures, comprising the following steps: obtaining time series response data of a healthy structure under wind load excitation to form baseline reference data; and normalizing the data; combining empirical mode decomposition and Hurst index analysis to carry out signal denoising; establishing a Hankel matrix of each channel data based on the denoised data; calculating the orthogonal projection vector of the matrix; obtaining a state matrix and an output matrix of the large deployable structure through matrix decomposition; and calculating a reference mode matrix of the structure in a healthy state; in the service process of the structure, data acquisition, preprocessing and signal denoising are carried out in the same way as the above steps to obtain a current mode matrix corresponding to the service state; the distance between the current mode matrix and the reference mode matrix is calculated; the damage position of the deployable structure is determined by comparing the distance changes of each order; and the abnormality detection of the structure is realized.

The application discloses a kind of amplitude and phase high-precision estimation method and device suitable for fast dynamic change signal, the method of the application includes the discrete signal sequence of continuous signal sampling, constructs Hankel matrix and decomposes left singular matrix U, diagonal matrix D, right singular matrix V;The estimated value of the number of frequency components p is estimated by D, combined with extracting the subspace of right singular matrix V and using least square method to obtain diagonal matrix Q;The damping coefficient of frequency component is calculated by Q and constructs system matrix A;The dynamic phasor coefficient vector S is calculated by A and discrete signal sequence using least square method;The amplitude and phase of continuous signal are calculated by S and damping coefficient.The application aims at solving the problem that the main lobe overlap of dense harmonic / interharmonic with small frequency interval in power system causes spectral leakage interference, extremely serious even can submerge part of main spectrum line, and then lead to the accuracy of spectral leakage affecting the amplitude and phase estimation of dynamic change signal.

This application discloses a method, device, and medium for fuel cell health diagnosis under the "four low" conditions of high-altitude areas, relating to the field of proton exchange membrane fuel cells. The method includes: real-time acquisition of key parameters of the fuel cell, such as voltage, current, ambient temperature, humidity, and oxygen concentration, based on nanosensors; processing various data using BIRCH clustering, multimodal fusion, and GCN model methods to obtain dynamic features, high-altitude environmental features, and static features; extracting the dynamic features into a Hankel matrix field based on the dynamic feature information; extracting the high-altitude environmental features and static features into a co-occurrence matrix field based on the high-altitude environmental features and the co-occurrence matrix field; standardizing and tensorizing the features into a three-dimensional space based on the Hankel matrix field and the co-occurrence matrix field; and fusing a multilayer perceptron using the sample entropy method to obtain the fuel cell diagnosis results for high-altitude areas based on the three-dimensional feature space. This application ensures the safe operation of the fuel cell system.

The application relates to the technical field of fault detection of industrial systems, in particular to an industrial system fault detection method and system based on distributed equivalent space optimization. The method comprises the following steps: a Hankel matrix is constructed to identify an equivalent matrix with zero projection under normal working conditions, and a residual signal is generated; based on local data of each sensor node, an average consistency algorithm is used to calculate a covariance matrix in an offline stage; parameter optimization design is performed on the equivalent matrix; the residual signal is calculated when the system is running online, and a globally consistent detection statistic is obtained through the average consistency algorithm; a detection threshold is determined in combination with normal operation data, and fault detection of a complex industrial process is realized. The application combines an equivalent space optimization method, a distributed sensor network cooperative computing mechanism and an average consistency algorithm, reduces communication and computing burden, improves system robustness and scalability, and enhances the sensitivity and reliability of fault detection.

The application relates to a fault detection method, device, equipment, medium and product of a low-voltage distribution network. The method comprises the following steps: constructing a Hankel matrix according to a current sequence of a target measuring point in the low-voltage distribution network; determining a delay characteristic sequence according to the Hankel matrix; constructing a HAVOK linear forced model according to the delay characteristic sequence; performing fitting processing on the HAVOK linear forced model, and determining a fault detection result of the target measuring point according to the amplitude of a forced operator in the fitting processing process; wherein the forced operator is a delay signal located at the end of the delay characteristic sequence. The method can improve the accuracy of fault detection.

The application discloses a signal delay estimation method based on block processing, comprising the following steps: acquiring CSI data and preprocessing (validity test, noise filtering, selecting valid index); obtaining overlapping data blocks by block processing according to a preset block quantity and overlapping ratio; building Hankel matrix and enhancement matrix for each block, and obtaining delay estimation value through singular value decomposition, subspace separation and characteristic polynomial solving; assigning weight to each estimation value in combination with signal quality evaluation; and determining the final result based on the delay estimation value and weight information. In the application, a large matrix is disassembled through block overlapping, the singular value decomposition complexity and storage occupation are reduced, and lightweight hardware is adapted; the processing time consumption is shortened, the real-time demand in multiple scenes is met, the application can be run on ordinary equipment, and positioning deviation caused by delay is avoided.

The application discloses a high-speed rail seismic data wave field separation method and system using random Cadzow filtering, and belongs to the technical field of exploration geophysics. The method comprises the following steps: collecting seismic data of high-speed rail passing time; estimating seismic wave propagation velocity through Radon transformation; correcting the time difference of the same direction wave field event to obtain aligned data; dividing the aligned data into blocks and randomly rearranging; performing Fourier transformation on the rearranged data and constructing a Hankel matrix; performing SVD decomposition and low-rank reconstruction on the Hankel matrix; reconstructing the frequency domain data block through inverse Hankel operation; performing inverse Fourier transformation and recovering the channel sequence; and extracting and averaging the central channel to obtain unidirectional wave field data. The system comprises corresponding functional modules. The application can effectively separate the mixed wave field of the high-speed rail seismic source, improve the data signal-to-noise ratio, and provide a reliable data basis for underground imaging.

This invention proposes a priori-enhanced multi-kernel canonical variable analysis method and device for monitoring blast furnace ironmaking. First, historical multivariate data is collected, standardized, and past and future Hankel matrices are constructed. Then, a CSI-MKCVA model is constructed, and an enhanced hybrid kernel function is built to simultaneously capture local, global, and time-related nonlinear features of the blast furnace ironmaking process, and the covariance matrix and Laplace matrix are reconstructed using weighted averages. Next, the projection matrices of past and future data are iteratively trained to extract priori spatiotemporal nonlinear features (CSNF) with conformal and discriminative capabilities. Finally, based on the extracted CSNF, the kernel density estimation method is used to calculate control limits, enabling real-time monitoring. This invention enhances the spatial correlation modeling and fault category identification capabilities in the blast furnace ironmaking process, significantly improving the anomaly monitoring accuracy under conditions of data nonlinearity, dynamism, and class imbalance, ensuring the safe and stable operation of blast furnace production.

The application is a fractional order controller parameter setting method based on a rotating Hankel matrix and a multi-objective genetic algorithm NSGA3. The application relates to the technical field of target constraint controller optimization design, and utilizes the multi-objective genetic algorithm NSGA3 to optimize fractional order controller parameters, so as to complete the design of a fractional order controller meeting the required indexes of a system; the setting of a reference point, the adaptive standardization of a population, the correlation operation and the individual reservation operation. The fractional order controller design method provided by the application has a significant improvement in overshoot and regulation time compared with existing methods. Under the condition of meeting the same required indexes of a system, the slope of a phase characteristic curve obtained by the fractional order controller designed by the method of the application is closer to 0, and the robustness of the system is improved.

The application discloses a low-rank seismic data denoising processing method of adaptive symplectic geometric decomposition, comprising the following steps: converting three-dimensional seismic data into a frequency domain by using Fourier transform; forming a Hankel matrix by frequency slicing of the frequency domain seismic data; forming a Hamilton matrix form by the Hankel matrix; using symplectic QR decomposition to obtain eigenvalues and eigenvectors of the Hamilton matrix; obtaining symplectic geometric entropy of the eigenvalues, and adaptively selecting the number of ranks through the symplectic geometric entropy; reconstructing the block Hankel matrix through low-rank approximation; and converting the processed frequency domain into a time domain through inverse Fourier transform. The application extracts eigenvalues and eigenvectors of the Hankel matrix by constructing the Hankel matrix and applying symplectic QR decomposition, and adaptively determines the number of ranks by using symplectic geometric entropy, so that low-rank approximation reconstruction is realized, effective denoising is achieved, and effective signals in the seismic data are preserved.

This invention discloses a method for suppressing data-driven oscillations in grid-connected inverters based on transient prediction. The method includes: collecting input and output data of the inverter on the grid-connected inverter side and constructing a Hankel matrix; establishing a single-step transient predictive controller, and then generating a multi-step predictive model; establishing a regularized comprehensive cost function, and obtaining the optimal input control sequence of the inverter as an additional value for the current loop reference command after online rolling optimization, and then controlling the inverter after processing and modulation to achieve data-driven oscillation suppression in the grid-connected inverter. This invention effectively eliminates estimation bias caused by the existence of controller feedback loops by utilizing a transient predictor. Combined with a regularization mechanism, it significantly improves the system's transient response speed to sudden changes in grid parameters, and also achieves dynamic stability control and oscillation suppression from single-machine to multi-machine operation, and from steady-state operation to fault recovery, exhibiting both excellent dynamic response capability and robustness.

The present invention belongs to the technical field of hydraulic pump anomaly detection. Disclosed is a mining area load spectrum anomaly detection method based on canonical variate analysis. The method comprises: S1, performing data preprocessing to obtain standardized data, and in combination with operating conditions of an excavator, constructing a typical operating condition data set of a hydraulic pump; S2, constructing a historical vector and a future vector, and on the basis of the historical vector and the future vector, constructing a historical observation matrix and a future observation matrix; S3, constructing a Hankel matrix on the basis of an auto-correlation matrix and a cross-correlation matrix, decomposing the Hankel matrix, and determining a model order; S4, mapping original data into a canonical variate space and a residual space, and evaluating the total variation of canonical variates in a state space and the sum of squared variation errors in the residual space; and S5, determining an evaluation threshold value, and if a control limit is exceeded, determining that the hydraulic pump operates abnormally. In the present invention, pressure pulsation data of a hydraulic pump is used to perform anomaly detection on the basis of canonical variate analysis, and the method in the present invention is sensitive to the internal operating state of the pump, is not prone to the impact of an external environment, and enables early warning of faults in the hydraulic pump.

The application discloses a laser communication coarse tracking system comprehensive modeling and multi-target complementary control method, and belongs to the field of laser communication technology control. The laser communication coarse tracking system is used for control, comprising linear modeling of the coarse tracking system, combination of low frequency bands of sinusoidal sweep signals and middle and high frequency bands of pseudo-random signals to obtain system frequency response, and system transfer function obtained through Hankel matrix identification method; nonlinear modeling of the coarse tracking system, establishment of a Stribeck friction model, and design of a feedforward compensation algorithm based on the Stribeck friction model to obtain a multi-target complementary control method for controlling the coarse tracking system. The application realizes accurate identification of model order and parameters in the linear part, significantly improves low-speed tracking performance in the nonlinear part, effectively solves the contradiction between system performance and robustness, and significantly improves tracking precision and anti-interference ability.