149 results about "Hankel matrix" patented technology

The invention discloses a multi-modal fusion lithium iron phosphate battery thermal runaway early warning method and system, and the method comprises the steps: collecting multi-source heterogeneous data, such as temperature, voltage, gas concentration and shell strain pressure, in real time through deploying a heterogeneous sensor network, and carrying out the noise reduction and time sequence feature extraction through employing a sub-linear time low-rank approximation algorithm of a Hankel matrix; constructing a cross-modal feature association network by applying a secondary time algorithm of a maximum weight sparse subgraph problem, inputting a fused feature vector into a Bayesian network health degree evaluation model for probabilistic reasoning calculation to obtain a battery health degree score and a thermal runaway risk level, generating a graded early warning signal through multi-level early warning threshold comparison, and performing early warning on the battery health degree score and the thermal runaway risk level. And corresponding prevention and control suggestions are matched. The method solves the technical problems that single physical quantity monitoring is difficult to comprehensively reflect the complex change in the battery and the response delay of a centralized processing architecture causes the early warning lag, and achieves the timely capture and accurate early warning of the early weak characteristics of thermal runaway.

The invention provides a bearing fault diagnosis method based on a one-dimensional local binary pattern and a Hankel matrix. The bearing fault diagnosis method comprises the following steps: acquiring a discrete vibration signal; performing first-order differential operation on the discrete vibration signal to obtain a differential signal; performing inherent time scale decomposition on the differential signal to obtain an inherent rotation component signal; performing quantization and signal reconstruction on each inherent rotation component signal by taking a root mean square as a quantization criterion of a one-dimensional local binary mode method to obtain a decimal feature signal; constructing a Hankel matrix of the decimal characteristic signal and performing signal reconstruction according to a covariance matrix of the Hankel matrix; performing spectral analysis on the reconstructed signal, calculating the fault characteristic frequency of the bearing, and then judging the state and the fault type of the bearing through a frequency component obtained through spectral analysis and the fault characteristic frequency of the bearing obtained through calculation. According to the bearing fault diagnosis method, noise can be effectively suppressed, the bearing fault feature information can be effectively extracted, and the bearing state and the fault type can be accurately identified.

The invention relates to a multi-parameter estimation algorithm combining a disturbance quantification model and a rooting method in interference measurement. In order to solve the problems that in the prior art, the disturbance degree of a sequence is difficult to quantify, and harmonic key parameters are influenced by spectrum leakage, the invention provides the following innovative scheme: firstly, a sequence multi-classification measurement entropy quantification model is constructed, and quantitative evaluation of the signal noise degree is realized; secondly, through a Hankel matrix construction and rooting parameter estimation method, realizing accurate estimation of the harmonic frequency of the real-value signal; meanwhile, an amplitude correction algorithm based on window function characteristics is designed, and the signal sampling condition is effectively judged; and finally, through matrix weighting operation of a phase demodulation function, efficient reconstruction of an optical surface shape is realized, the problems of spectrum leakage and disturbance quantification in a traditional method are solved, and the method has relatively high measurement precision.

The invention discloses a PRFI suppression method based on a Hankel structure and truncation nuclear norm regularization. The method comprises the following steps: acquiring an SAR echo signal interfered by a PRFI signal; the SAR echo signals are constructed into a Hankel matrix, and the Hankel matrix comprises a low-rank PRFI signal interference component matrix and a sparse SAR useful signal component matrix; constructing a low-rank sparse decomposition model based on a Hankel matrix, wherein an objective function of the low-rank sparse decomposition model comprises a truncation nuclear norm regular term used for constraining a PRFI signal component and a sparse regular term used for constraining an SAR useful signal component; performing iterative solution on the low-rank sparse decomposition model by adopting an alternating direction multiplier method to obtain a PRFI signal interference component matrix and an SAR useful signal component matrix after separation; and carrying out inverse transformation on the SAR useful signal component matrix to obtain an SAR useful signal from which the PRFI signal is removed. According to the method, the loss of the SAR useful signal can be reduced while the PRFI signal interference is efficiently suppressed, so that the fidelity of the SAR useful signal is improved.

The invention relates to the field of seismic data reconstruction, in particular to a seismic data low-rank reconstruction method fusing double-domain transformation, and the method comprises the steps: obtaining original incomplete seismic data, and initializing to-be-reconstructed seismic data and other model variables and parameters under an alternating direction multiplier method frame; performing fractional order gradient transformation on seismic data to be reconstructed along different directions, and mapping to obtain fractional order gradient domain data; performing space-time Hankel tensor expansion transformation on each piece of fractional order gradient domain data, and converting the data into a space-time Hankel matrix; performing low-rank constraint on each space-time Hankel matrix by adopting a Schatten norm to obtain a seismic data low-rank reconstruction model fused with double-domain transformation; and carrying out iterative solution on the model by using an ADMM framework until the model is stable, and obtaining a final seismic data reconstruction result. The method provided by the invention can effectively reconstruct the seismic data with the missing trace, and has a reconstruction effect with strong robustness and relatively good precision for incomplete seismic data with different missing degrees.

The invention discloses a wind turbine generator blade vibration mode feature recognition method, which mainly comprises the following steps of: arranging three-axis piezoelectric acceleration sensors at equal intervals from a blade root to a maximum chord length position, and adaptively selecting an optimal channel as a target input vibration signal according to envelope dispersion and a period proportion; introducing a vibration signal denoising method based on regenerative phase shift sine-assisted empirical mode decomposition, and constructing a Fisher ratio-based multi-dimensional fusion index to remove a noise component; estimating a system order range according to a singular entropy jump value theory, and designing modal similarity and a confidence index to accurately estimate a real order of a blade system; introducing three types of constraints of structure maintenance, modal sparsity and energy smoothness to jointly optimize a low-rank approximation strategy so as to realize optimal reconstruction of the Hankel matrix; constructing a fitness function selected by a clustering center by combining the point set density of the sample and Euclidean distance information, and optimizing a modal extraction result by adopting inter-class dispersivity and an intra-class sample number; according to the method, the environmental noise can be effectively removed, the system order can be accurately determined, and finally the modal parameters of the system can be accurately identified.

The invention discloses a hydroelectric generating set vibration signal noise reduction method based on SW-EEMD (Single Wave-Ensemble Empirical Mode Decomposition) and Hansel-SVD (Hanker-Singular Value Decomposition). The method comprises the following steps: processing an original noisy signal by adopting an SW-EEMD method; screening effective IMF components through correlation coefficients; respectively constructing Hankel matrixes for the screened IMF components and residual components; respectively carrying out singular value decomposition on the constructed Hankel matrix; carrying out soft threshold processing on the obtained singular value; the method aims at restraining the end effect of EEMD, noise in the vibration signals can be effectively filtered out, signal features are enhanced, more real and effective signal components can be obtained easily, and more reliable data are provided for state operation and maintenance of the hydroelectric generating set.

The invention discloses a data-driven multi-rate ore grinding loop control method and system, and the method comprises the steps: collecting the historical data of an ore grinding loop control system at different time scales, and estimating the future trajectory of the control system according to the characteristics of the historical data; the method comprises the following steps: predicting future data by taking known historical data of a set moment as a reference, rolling the set moment as the reference forwards along a time domain, constructing time-varying Hankel matrixes of historical output, historical input, future output and future input, and predicting future output data according to historical data characteristics; and a correction item is introduced for controller correction, the corrected frequency of the electric vibration feeder and the corrected opening degree of the water supplementing valve act on an ore grinding loop control system, and the ore grinding loop ore feeding amount, the water feeding amount and the set value are tracked through the predicted value. According to the control method, the control strategy is adjusted in real time in a data driving mode, and the control precision is improved so as to adapt to the time-varying characteristic and the multi-rate characteristic of the ore grinding loop.

The invention relates to the technical field of electrostatic dust collection and discloses an accurate frequency adjustment method for an intelligent frequency modulation high-voltage power supply of an electrostatic dust collector. The method comprises the steps of collecting voltage signals with frequency fluctuation in operation of a high-voltage power supply to form a voltage sequence; dividing period segments by using wave crest distribution in the voltage sequence, and clustering to obtain adjustment subsequences; obtaining a trend fluctuation index based on local signal change trend fluctuation and signal differences of the adjustment subsequences, and obtaining an amplitude drop difference and an amplitude interval difference according to an amplitude change trend difference and an amplitude bit sequence change trend difference; obtaining a period stability coefficient by combining the difference, the average condition of the trend fluctuation index and the average condition of the sub-sequence similarity; constructing a Hankel matrix and performing singular value decomposition, and obtaining a main component proportion and a difference index based on a singular value condition; and according to the Hankel matrix line data correlation, the period stability coefficient, the main component proportion and the difference index, obtaining a signal stability index, and according to the signal stability index, accurately adjusting the high-voltage power supply frequency.

The invention discloses a lithology identification-oriented while-drilling signal optimization reconstruction method, which is used for processing a vibration signal when a drill bit drills a rock sample, and belongs to the technical field of geological drilling, and comprises the following steps of: acquiring the vibration signal; performing Fourier transform to obtain a frequency spectrum; adaptively generating a frequency band boundary on the frequency spectrum; and constructing an initial matrix A and a Hankel matrix, performing singular value decomposition on the Hankel matrix, screening singular values, and sequentially reconstructing the Hankel matrix, the initial matrix and the signal. And a multi-domain feature fusion vector F is extracted based on the reconstructed signal. According to the method, the empirical wavelet transform method is improved, the problem of inaccurate frequency band division in empirical wavelet transform is avoided, the intrinsic mode function is accurately extracted, the function is ensured to better capture the local features of the signal at different frequencies, and the reconstructed signal has stronger feature retention capability and anti-noise performance; and the F can comprehensively capture time domain, frequency domain and entropy domain information of the signal, so that efficient characterization of the vibration signal is realized.

The invention belongs to the technical field of partial discharge detection, particularly relates to a partial discharge signal denoising method based on SVDTQWT, and aims to effectively remove periodic narrow-band interference and white noise in partial discharge signals. Comprising the following steps: performing Fourier transform on a noisy partial discharge signal to obtain a frequency spectrum, determining the number of periodic narrowband interferences through singular value decomposition, constructing a Hankel matrix to eliminate the periodic narrowband interferences, and obtaining a preliminary de-noised signal; and decomposing the preliminarily denoised signal by adopting adjustable quality factor wavelet transform to obtain a plurality of sub-bands. And dividing the plurality of sub-bands into high-frequency sub-bands and low-frequency sub-bands through sample entropy. Wherein the sample entropy indicates measurement of the complexity of the time series. And de-noising the high-frequency sub-band by using a group sparse total variation de-noising algorithm, de-noising the low-frequency sub-band by using an improved wavelet threshold de-noising algorithm, and reconstructing by using adjustable quality factor wavelet transform to obtain a pure partial discharge signal.

The invention discloses a mining area load spectrum anomaly detection method based on standard variable analysis, and belongs to the technical field of hydraulic pump anomaly detection.The mining area load spectrum anomaly detection method comprises the steps that S1, standardized data are obtained through data preprocessing, and a typical working condition data set of a hydraulic pump is constructed in combination with working conditions of an excavator; s2, constructing a historical vector and a future vector, and constructing a historical observation matrix and a future observation matrix according to the historical vector and the future vector; s3, constructing a Hankel matrix according to the autocorrelation matrix and the cross-correlation matrix, decomposing the Hankel matrix and determining a model order; s4, mapping the original data to a standard variable space and a residual space, and respectively evaluating the total variable quantity of the standard variable in the state space and the sum of squares of change errors in the residual space; and S5, determining an evaluation threshold value, and if the evaluation threshold value exceeds the control line, judging that the hydraulic pump operates abnormally. The method is based on standard variable analysis, adopts the pressure pulsation data of the hydraulic pump to perform anomaly detection, is sensitive to the internal running state of the pump, is not easily influenced by the external environment, and can perform early warning on faults of the hydraulic pump.

The invention discloses a noise reduction and rub-impact fault identification method based on information entropy and eigenvalue decomposition, and relates to the technical field of rotating machinery fault diagnosis, and the method comprises the following steps: obtaining an original discrete vibration signal through a sensor and a data collection card; according to the original discrete vibration signal, constructing a Hankel matrix, and through eigenvalue decomposition, obtaining an eigenvector of the Hankel matrix and an eigenvalue matrix; performing signal reconstruction according to different feature values in the feature value matrix and feature vectors corresponding to the feature values to obtain a reconstructed signal set; and selecting the reconstructed signal with the minimum information entropy in the reconstructed signal set as an optimal reconstructed signal, performing spectrum analysis on the optimal reconstructed signal, and judging the rub-impact fault according to the frequency component. Rub-impact fault recognition is realized through the optimal reconstruction signal determined by the feature value and the feature vector corresponding to the minimum information entropy, the influence of noise interference is reduced, the fault feature information is relatively prominent, and the fault recognition result is more accurate.

The invention relates to a damage prediction method and device for a tunnel lining structure, equipment, a medium and a program product. The method comprises the following steps: acquiring a strain monitoring data set of a plurality of lining structure monitoring points of a tunnel; the strain detection data set is composed of strain monitoring sub-data sets corresponding to load action times of each lining structure monitoring point at a plurality of historical moments; constructing a Hankel matrix of each lining structure monitoring point under the action of each load based on the strain monitoring sub-data set, so as to obtain the strain modal frequency of each lining structure monitoring point; determining a damage early warning index of each lining structure monitoring point based on the strain modal frequency; training and verifying a pre-constructed tunnel lining damage prediction model based on the plurality of damage early warning indexes to obtain a trained tunnel lining damage prediction model; and inputting the damage early-warning index set to be subjected to damage prediction into the trained tunnel lining damage prediction model to obtain an output damage prediction result, namely a predicted damage early-warning index.

The invention discloses a distributed traffic time sequence anomaly detection method for preventing data pollution, which comprises the following steps of: acquiring time sequence data of different characteristics of same equipment to be detected, performing adaptive multi-step coarse-grained denoising processing on the data, constructing the time sequence data into a Hankel matrix, and performing data processing on the Hankel matrix; dimensionality reduction and time sequence reconstruction are carried out according to a singular value decomposition result; multi-dimensional vector representation of each device is obtained by using a statistical method, the similarity between every two feature vectors is calculated by using cosine similarity, and a graph structure is constructed according to a similarity calculation result; and predicting and scoring based on the graph attention. According to the method, the relationship between the equipment and the variables can be fully mined on the premise of eliminating data pollution, so that more accurate behavior prediction is carried out.

The invention discloses a high-resolution tomographic SAR four-dimensional imaging method for space-time baseline decoupling processing, and the method comprises the following steps: 1, reconstructing original satellite data into uniform baseline observation data, and solving the noise-free estimation of the uniform baseline observation data through the low-rank characteristic of a Hankel matrix; step 2, inverting elevation information based on noise-free estimation, taking the elevation information as priori, constructing a deformation spectrum estimation dictionary through space-time decoupling, and performing deformation information estimation; and step 3, matching the estimated elevation and deformation quantity by using a maximum likelihood method so as to realize four-dimensional imaging. According to the method, the algorithm complexity of four-dimensional imaging is reduced exponentially while high-precision imaging is realized, and differential tomography SAR four-dimensional imaging result verification is carried out based on domestic land exploration No.1 satellite data.

The invention is based on a method for data-driven predictive control of a heat pump system (10), wherein at least one future input trajectory (28) of the heat pump system (10) and in particular at least one future output trajectory (38) of the heat pump system (10) is determined at least as a function of system data (30) of the heat pump system (10) measured at an earlier point in time, using a defined target function, which is in particular minimized, wherein the system data (30) comprise at least input measurement data (12) and output measurement data (14) of the heat pump system (10), wherein for the data-driven predictive control of the heat pump system (10), a control equation filled with the system data (30) is solved by means of a computing unit (22), wherein the computing unit (22, 22') uses the control equation to calculate the trajectories (28,38) of the heat pump system (10) are determined from a matrix-vector multiplication of at least one measurement data matrix with a decision variable vector, and wherein the measurement data matrix is ​​formed from at least two Hankel matrices written one above the other, each comprising only one measurement data type of the system data (30). , It is proposed that, in order to take into account a lower operating limit of the heat pump system (10), at least several entries of the future input trajectory (28) in the control equation are each assigned a binary variable, in particular by multiplication, wherein in particular the entries of the future input trajectory (28) each correspond to predicted time steps of a predicted input for the heat pump system (10).

The invention provides a low-rank matrix analysis seismic data adaptive noise reduction method, which comprises the steps of collecting seismic data of a preset area, and establishing a three-dimensional seismic data volume according to the seismic data; the three-dimensional seismic data volume is a three-dimensional matrix of a space-time dimension; converting the three-dimensional matrix from a time domain to a matrix of a corresponding frequency domain; constructing Hankel matrixes line by line for the matrix of the frequency domain under a preset frequency to obtain the Hankel matrix of each line; building a block Hankel matrix by using each row of Hankel matrix; performing singular value decomposition on the block Hankel matrix to obtain a plurality of singular values; performing singular value truncation on the block Hankel matrix to obtain a frequency domain matrix after noise reduction; and converting the matrix of the frequency domain after noise reduction into a matrix of a time domain after noise reduction. According to the method, random noise in the seismic data can be effectively removed, and adaptive noise reduction of the seismic data is realized.

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 invention discloses a high-fidelity magnetic resonance spectrum reconstruction method, and relates to a magnetic resonance spectrum reconstruction method. Comprising the following steps: 1) acquiring a to-be-reconstructed magnetic resonance spectrum time domain signal; 2) proposing a magnetic resonance spectrum reconstruction model based on symmetric rank-one Hankel matrix decomposition; and 3) solving the model in the step 2) by using a projection gradient algorithm to obtain a magnetic resonance spectrum time domain signal, and performing Fourier transform on the magnetic resonance spectrum time domain signal to obtain a final spectrum signal. According to the method, the high-fidelity reconstruction of the magnetic resonance spectrum is realized based on the symmetry of the Hankel matrix and the rank-one characteristic of the magnetic resonance spectrum signal.

The application discloses a frequency-space domain alternating iteration seismic random noise suppression method and system, transforms a seismic signal from a time-space domain to a frequency-space domain, and obtains a frequency slice by taking data of a fixed frequency domain index; arranges the frequency slice into a Hankel matrix and performs singular value decomposition, selects singular values and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; obtains the reconstructed frequency slice, and takes the next frequency slice to complete spatial direction denoising; takes single seismic trace data of a fixed space domain index in the frequency-space domain data, arranges the single seismic trace data into a Hankel matrix and performs singular value decomposition, selects singular values and corresponding singular vectors to perform rank reduction reconstruction on the Hankel matrix; then, the reconstructed single seismic trace data is obtained through anti-diagonal average, the next seismic trace data is taken, the obtained frequency-space domain data after denoising is transformed back to the time-space domain, and a signal after noise suppression is obtained.

The present invention is an Internet of Things data reconstruction method based on structured low-rank tensor completion. First, the monitoring area is discretized into multiple grid points, and a sensor node is deployed inside each grid point. Assuming that the sensor node senses data every other time slot, the data received by the base station within time T forms a third-order tensor. Secondly, the data reconstruction is converted into a basic low-rank tensor completion problem, and a low-rank tensor completion model is constructed. Finally, the block Hankel matrix transformation is performed on the unfolding matrix of each mode i of the third-order tensor, and the basic low-rank tensor completion model is improved into a structured low-rank tensor completion model. The augmented Lagrangian function of the structured low-rank tensor completion model is solved to obtain the third-order tensor, and the Internet of Things data reconstruction is completed. The data collected at continuous moments are arranged in a third-order tensor to make full use of the spatial correlation of the data. The block Hankel matrix transformation is performed on the unfolding matrix of each mode i of the third-order tensor, and data reconstruction is carried out by combining structured and low-rank tensor completion, further exploring and utilizing the spatio-temporal correlation of the data, alleviating the influence of basis mismatch on the reconstruction performance in the sparse constraint-based method, and improving the data reconstruction accuracy.

The invention discloses a signal delay estimation method based on block processing. The signal delay estimation method comprises the following steps: acquiring CSI (Channel State Information) data and pre-processing (validity check, noise filtering and effective index selection); blocking according to a preset block number and an overlapping proportion to obtain overlapped data blocks; a Hankel matrix and an enhancement matrix are established for each block, and a delay estimation value is obtained through singular value decomposition, subspace separation and characteristic polynomial solution; distributing weights for each estimated value in combination with signal quality evaluation; and determining a final result based on the delay estimation value and the weight information. According to the method, the large matrix is disassembled through block overlapping, the singular value decomposition complexity and storage occupation are reduced, and lightweight hardware is adapted; processing time consumption is shortened, multi-scene real-time requirements are met, the method can be operated on common equipment, and positioning deviation caused by delay is avoided.

The application discloses a ground microseismic event signal enhancement method, comprising the following steps: 1) obtaining a ground microseismic signal after dynamic correction containing noise according to a ground microseismic event signal; 2) obtaining time slice data at a given time point t1; 3) constructing a hankel matrix of each trace for the time slice data; 4) performing random singular value decomposition on the rearranged Hankel matrix of each time slice; 5) adding a damping operator to suppress the random noise of the residual error; 6) using Hankel matrix inverse transformation to transform the reconstructed matrix after truncation of singular values back to time slice data, and obtaining signal data after noise suppression; 7) repeating steps 2) to 6) until all time slices are processed, and obtaining an enhanced signal of the original ground microseismic event signal. The application can highlight the effective signal energy of the ground microseismic event, suppress noise interference, improve the ground microseismic event signal energy ratio, and enhance the effective signal identification capability.

The invention provides a bearing fault identification method based on singular value decomposition. The method comprises the following steps: acquiring a discrete vibration signal of a bearing; a Hankel matrix of the vibration signals is constructed, the dimension of the matrix is smaller than < and is the length of the vibration signals, and the column number is determined based on the minimum characteristic frequency of typical components of the equipment; performing singular value decomposition on the matrix; selecting an effective row vector of the matrix by taking high periodicity, high cyclic stability and low complexity of the signal as targets, determining an effective singular value based on the effective row vector, and performing signal reconstruction based on the effective singular value; and carrying out spectrum analysis on the reconstructed signal and carrying out bearing fault identification according to a relation between a prominent frequency component in a spectrum and a bearing fault characteristic frequency. According to the bearing fault identification method, the features of the signals can be enhanced while noise reduction is carried out on the signals, and accurate judgment of bearing faults is facilitated.

The invention discloses a robust approximation method, device and system of a Koopman operator and a medium, and the method comprises the steps: constructing a Hankel matrix through time delay embedding for a nonlinear system in a noise environment, introducing a correction matrix to carry out the dynamic adjustment of the Hankel matrix, and obtaining a corrected Hankel matrix delta H; the Koopman operator approximation problem containing noise data is converted into a robust optimization problem, and a robust optimization objective function J is designed; and when a real-time data stream arrives, dynamically correcting delta H and re-solving J by utilizing the estimated value KN of the Koopman operator in the previous step and combining an incremental Hankel matrix updating strategy, and iteratively generating a Koopman operator KN + 1 at the current moment. According to the method, the influence of noise on the system is fully considered, high-precision data modeling is performed on the noisy nonlinear system, and modeling, analysis and prediction of the nonlinear system are realized at the modeling cost of the linear system.

The application discloses a two-dimensional sparse array single-shot DOA estimation method based on a deep unfolding convolutional network, and belongs to the technical field of cross array signal processing and deep learning. First, a two-dimensional uniform planar array is constructed, and sparse observation data are constructed. Then, an isomorphic tensor mapping mode of separating real parts and imaginary parts is adopted to decompose the sparse observation signals and splice them along the channel dimension, so that a three-dimensional input tensor containing real part channels and imaginary part channels is constructed to maintain the two-dimensional spatial topological structure of the array. Then, the overall structure of the deep unfolding network is unfolded, and a sliding window rule with a predetermined size is used to rearrange the two-dimensional array data into a block Hankel matrix. Finally, network training is performed to realize DOA estimation. The application maintains the two-dimensional spatial structure, improves the reconstruction accuracy, reduces the computational complexity, improves the robustness under a low signal-to-noise ratio condition, realizes high-precision DOA estimation under a single-shot condition, and has good engineering application value.

The invention discloses a series fault arc identification method and system based on singular spectrum statistical characteristics, and the method comprises the steps: S1, collecting the current waveform of a series AC fault arc, and obtaining a current sampling sequence; s2, constructing a Hankel matrix Y based on the current sampling sequence, and then performing singular value decomposition on the Hankel matrix Y to obtain a singular spectrum; s3, statistical features are calculated based on singular spectrums, and feature vectors are formed; and S4, inputting the feature vector into a trained XGBoost classifier to obtain a category label of the series AC fault arc, and realizing real-time identification of the fault arc. According to the method, the singular spectrum of the current waveform is constructed through Hankel matrix decomposition, and the high-frequency harmonic and amplitude characteristics of the fault arc are efficiently captured through the singular spectrum, so that the recognition precision is improved; and the hyper-parameters of the classifier are optimized in combination with a differential evolution algorithm, so that the recognition efficiency of the series alternating-current fault arc is improved.

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 invention discloses an anti-transition resistance semi-decoupling multi-terminal differential line fault positioning and distance measuring method, belongs to the field of power system relay protection, and solves the problems of complex calculation, weak anti-transition resistance and difficult equipment upgrading caused by multi-terminal data coupling in the existing method. The method comprises the following steps: S1, calculating a distance ratio based on local side voltage and current, and preliminarily positioning a fault branch; s2, constructing an equation containing transition resistance R and fault impedance ZL-1, and generating a transition resistance matrix r and a fault impedance matrix z (dimensionality is adaptive to CPU computing power); s3, denoising through a Hankel matrix and singular value decomposition to obtain an accurate sequence; and S4, verifying the sequence consistency within 20-40ms after the fault, and outputting a result or repositioning. The method is free of iteration and high in transition resistance resistance, the complexity is linearly improved along with the number of T-connection terminals, hardware computing power and old equipment upgrading are adapted, and the method is suitable for a new energy T-connection multi-terminal network.