44 results about "Eigenvalues and eigenvectors" patented technology

The invention belongs to the technical field of image analysis and processing, and particularly relates to a flame image-based rotary furnace combustion state prediction method and system. The method comprises the following steps: acquiring a video image sequence of flames in the rotary furnace, and converting each frame of image into a YUV color space; calculating a combustion contribution degree based on the brightness component and the chromaticity component, and screening out a core flame pixel set; taking the combustion contribution degree as a weight, calculating a weighted covariance matrix, determining a confidence ellipse according to the eigenvalue and eigenvector of the matrix, taking the center of the confidence ellipse as a weighted centroid, determining a rotation angle by the eigenvector, and making the length of long and short semi-axes in direct proportion to the square root of the eigenvalue; extracting the area, eccentricity rate, rotation angle and center position of the confidence ellipse as combustion state feature vectors at the current moment; and inputting a time sequence formed by the combustion state feature vectors at the multiple moments into a pre-trained hidden Markov model, and outputting the combustion state of the rotary furnace. According to the invention, the accuracy and anti-interference capability of combustion state prediction are improved.

The invention discloses a field programmable gate array (FPGA) implementation method for Hermite matrix eigenvalue decomposition, which comprises the following steps of: performing real number processing on a Hermite matrix according to a corresponding relationship between the Hermite matrix and eigenvalues and eigenvectors; grouping the dimension real matrix obtained by the real number processing; calculating rotation matrixes of diagonal units and non-diagonal units; performing Jacobi rotation operation on the diagonal units and the non-diagonal units, and updating the feature vector matrix according to a rotation matrix; after a group of Jacobi rotation is completed, data exchange is carried out according to grouping of a parallel Jacobi algorithm; and multi-stage cleaning can be carried out according to actual requirements. According to the method, a parallel Jacobi algorithm is selected, a series of matrix rotation is adopted, the matrix is converted into a diagonal matrix, and therefore the eigenvalue and the eigenvector of the matrix are calculated. According to the method, the parallel computing advantages of the FPGA and the Jacobi algorithm are fully utilized, and the real-time performance of the algorithm is effectively improved.

The application belongs to the technical field of power systems, and discloses an EIGD time-delay power system stability analysis method and device, wherein the method comprises the following steps: a linear model of the time-delay power system is established, and the differential equation in the model is converted into an abstract Cauchy problem by using an infinitesimal generator; a group of discrete points is selected for each time-delay interval of the time-delay power system, a discrete function space is established according to the discrete points, and the time-delay variable is discretized to generate a low-order partial infinitesimal generator discretization matrix; displacement and inverse transformation are performed on the discretization matrix to obtain an inverse matrix, and the characteristic value of the system is obtained according to the inverse matrix; the characteristic value is checked by using the Newton method to obtain the accurate characteristic value and the characteristic vector, and the stability of the time-delay power system is analyzed and judged. The method solves the problem of large matrix LU decomposition calculation amount in the existing EIGD time-delay power system characteristic value sparse calculation method based on the DDAE model.

Methods, systems and apparatus for simulating physical systems. In one aspect, a method includes the actions of selecting a first set of basis functions for the simulation, wherein the first set of basis functions comprises an active and a virtual set of orbitals; defining a set of expansion operators for the simulation, wherein expansion operators in the set of expansion operators approximate fermionic excitations in an active space spanned by the active set of orbitals and a virtual space spanned by the virtual set of orbitals; performing multiple quantum computations to determine a matrix representation of a Hamiltonian characterizing the system in a second set of basis functions, computing, using the determined matrix representation of the Hamiltonian, eigenvalues and eigenvectors of the Hamiltonian; and determining properties of the physical system using the computed eigenvalues and eigenvectors.

The present application belongs to the technical field of image analysis and processing, and particularly relates to a rotary furnace combustion state prediction method and system based on flame images. The method comprises: collecting video image sequences of the flame in the rotary furnace, and converting each image to the YUV color space; calculating the combustion contribution degree based on the luminance component and the chrominance component, and screening out the core flame pixel set; taking the combustion contribution degree as the weight, calculating the weighted covariance matrix, determining the confidence ellipse according to the eigenvalue and eigenvector of the matrix, the center of the confidence ellipse being the weighted centroid, the rotation angle being determined by the eigenvector, and the long and short semi-axis lengths being proportional to the square root of the eigenvalue; extracting the area, eccentricity, rotation angle and center position of the confidence ellipse as the combustion state feature vector at the current time; inputting the time sequence sequence composed of the combustion state feature vectors at multiple times into the pre-trained hidden Markov model, and outputting the rotary furnace combustion state. The present application improves the accuracy and anti-interference ability of the combustion state prediction.

The invention belongs to the field of unit operation optimization control, and particularly relates to a bearing bush gap regulation and control method based on an LSTM-XGB integrated prediction model, and the method comprises the steps: collecting unit data affecting the temperature of a bearing bush, and carrying out the preprocessing; a principal component analysis method is used to calculate the eigenvalue and eigenvector of a covariance matrix, and a projection direction with the maximum data variance is found to carry out dimensionality reduction on unit data; constructing an LSTM prediction model and an XGB prediction model, and optimizing the LSTM prediction model and the XGB prediction model by using a sparrow search algorithm; finding an optimal combination strategy of the LSTM prediction model and the XGB prediction model through a stacking integration architecture; and obtaining a prediction result of the optimal LSTM-XGB integrated prediction model, and adjusting the bearing bush gap according to the prediction result of the optimal LSTM-XGB integrated prediction model. Through LSTM and XGBoost models, in combination with a sparrow search algorithm SSA optimization and Stacking integration strategy, the accuracy and stability of bearing bush gap prediction are improved, compared with a traditional method, errors are reduced, and millisecond-level prediction response is achieved.

The application discloses a low-redundancy high-order measurement direction method and system based on the coprime characteristic and a computer storage medium, and the method comprises the following steps: receiving signals from multiple signal sources by using a low-redundancy array improved based on a coprime array to obtain received signals; calculating a fourth-order cumulant matrix of the received signals according to the received signals; obtaining a covariance matrix according to the fourth-order cumulant matrix; calculating eigenvalues and eigenvectors of the covariance matrix according to the covariance matrix; dividing the eigenvectors into a signal subspace and a noise subspace according to the sizes of the eigenvalues; constructing a spectral function according to projections of the steering vectors on the noise subspace; searching for peak values of the spectral function in all possible directions to determine the directions of the signal sources. The low-redundancy array improved based on the coprime array can not only expand the estimated number of signal sources on the basis of reducing the number of received array elements, but also adopt the fourth-order cumulant, so that the positioning precision in a non-Gaussian noise environment can be improved.

The invention discloses a station weighting-based micro-seismic event plane positioning confidence ellipse calculation method, which comprises the following steps of: screening P-wave arrival time data simultaneously received by a plurality of stations, and calculating an initial seismic source position coordinate and an arrival time residual error of each station; the arrival time equation is linearized, and a design matrix and an initial weight matrix are constructed; constructing a normal equation based on the design matrix and the station weight matrix, solving a parameter correction amount, and updating a seismic source position coordinate and a seismic moment; calculating a covariance matrix and extracting a sub-matrix, performing eigenvalue decomposition to obtain eigenvalues and eigenvectors, and calculating an ellipse area under a preset confidence level; respectively resetting the weight of each station to zero, recalculating the area of the confidence ellipse, constructing an influence index of the station on the area of the ellipse, and adjusting the weight of the station according to the influence to obtain an updated weight matrix; and judging a convergence state based on the relative change rate of the confidence ellipse area, and outputting a final seismic source plane position and confidence ellipse parameters during convergence. According to the invention, rapid and accurate positioning operation of a micro-seismic monitoring scene can be realized.

The application provides a self-adaptive signal beam forming method, device, equipment and storage medium. The method is based on a 2N+1 element uniform linear array receiving signal. The method comprises the following steps: a multiple Toeplitz matrix is constructed, and a covariance matrix is obtained by merging; a signal direction vector is calculated according to an eigenvalue and an eigenvector, and an expected signal direction vector is obtained by optimization; a noise power is estimated by using a minimum eigenvalue, a corresponding relationship between a signal power and the eigenvector is established, and an expected signal power is determined; a covariance matrix of mutually independent noise is reconstructed according to the noise power, the corresponding relationship and the expected signal power; and the expected signal direction vector and the reconstructed covariance matrix are brought into a Cap on beam former, and an optimal weight vector is calculated to control beam forming. The method improves the performance and reliability of the beam forming system in a complex signal environment by constructing a multiple Toeplitz matrix and using the characteristics of the multiple Toeplitz matrix for signal decorrelation and covariance matrix reconstruction.

The application provides a new energy unit clustering method based on a modified node admittance matrix, comprising the following steps: (1) reading power grid topology data and element parameters to obtain a branch table of the power grid; (2) constructing a modified node admittance matrix according to the branch table of the power grid; (3) calculating eigenvalues and eigenvectors of the modified node admittance matrix; (4) sorting n eigenvalues in ascending order to obtain new eigenvalues and corresponding eigenvector sequences; and (5) based on the new eigenvalues and corresponding eigenvector sequences obtained in step (4), using a mean value division method based on Laplacian spectrum to divide nodes in the power grid. The network topology is described by the constructed modified node admittance matrix, and the nodes are divided by the mean value division method based on the Laplacian spectrum, so that the new energy units can be accurately clustered. The application is suitable for grouping of various new energy stations.

The invention relates to a petrochemical engineering emergency early warning method and device based on large model data distillation. The method comprises the steps of obtaining original data, preprocessing and standardizing the original data, and constructing a data set. The method comprises the following steps: mapping a data set from a high-dimensional space to a low-dimensional space through linear transformation, and performing eigenvalue decomposition on a covariance matrix of data in the data set to obtain eigenvalues and eigenvectors; and sorting the feature vectors according to the feature values from large to small, selecting the feature vectors of which the feature value ranking is not lower than a first threshold value to form a new feature space, and extracting important features from the feature space through LASSO regression. And calling a CNN deep learning model to extract key features from the important features, distilling representative features in the original data, and outputting a feature set after distillation. And performing anomaly detection on the petrochemical engineering system through a clustering algorithm according to the feature set after distillation, and triggering early warning when a detection result does not meet a preset condition.

The invention discloses a power distribution network communication fault diagnosis method, and relates to the technical field of power distribution network communication, and the method comprises the steps: dividing a multi-dimensional data variable into a plurality of data groups, forming a neighborhood graph, and constructing a similarity matrix; obtaining a low-dimensional data variable based on the feature value and the feature vector of the similarity matrix; calculating a corresponding influence weight and correlation between any two variables based on the low-dimensional data variables; performing preliminary grouping on the low-dimensional data variables according to the correlation, and dividing manifold groups according to the total group weight ratio of each group; acquiring a real-time data group, calculating a manifold coordinate and determining a manifold group, calculating a final credibility according to the manifold group, and determining fault data according to the final credibility; the influence degree of dominant paths of different factors is accurately measured; according to the manifold group condition to which the real-time data group belongs, different methods are adopted to calculate the final trust degree so as to determine the fault data, and the problems of high calculation complexity and limitation and accuracy which are difficult to understand in a traditional analysis method are solved.

The invention belongs to the technical field of stability evaluation of a new energy station grid-connected system, and discloses a participation factor calculation method of repeated and similar oscillation modes and related equipment, and the method comprises the steps: obtaining a linear state space model of a new energy station system; acquiring a linearized state matrix according to the linearized state space model of the new energy station system; calculating a characteristic value of the new energy station system and a characteristic vector corresponding to the characteristic value according to the linearized state matrix, and respectively establishing a modal matrix according to the characteristic value and the characteristic vector; identifying a repeated oscillation mode or a similar oscillation mode of the new energy station system, and selecting a key repeated oscillation mode or a similar oscillation mode; based on a key repeated oscillation mode or a similar oscillation mode, a group participation factor of the repeated oscillation mode or the similar oscillation mode is calculated according to a modal matrix of a characteristic value and a characteristic vector, only one summation operation is involved for calculation of the group participation factor, and the calculation process is simple and efficient.

A structural damage identification method based on random sparse regularization comprises the following steps: S1, establishing a finite element model of a to-be-identified damage structure, dispersing the structure into a finite number of units, setting a damage working condition, and simulating the damage working condition; s2, performing modal analysis, calculating a stiffness matrix and a mass matrix of the structure, adding boundary conditions, and solving feature values and feature vectors; s3, constructing an objective function of damage identification; s4, introducing an l1 / 2 regularization optimization method, and establishing a structural damage identification equation; s5, introducing a random class algorithm, optimizing and solving the structural damage identification target equation, and obtaining a solution vector; and S6, determining the damage position and the damage degree in the structure according to the solution vector of the damage identification equation. According to the method, the damage position can be quickly positioned, the damage degree can be accurately quantified, the accuracy of a damage identification result is improved, and the identification time of damage identification is shortened.

The application belongs to the technical field of electronic circuits, and particularly relates to a high-order topological insulator circuit design method based on graphdiyne structure. The method comprises the following steps: (1) establishing an LC circuit model of a two-dimensional rhombus-shaped graphdiyne structure; (2) obtaining the admittance matrix of the circuit according to the Kirchhoff equation, solving the matrix eigenvalues and eigenvectors by using MATLAB, and obtaining the admittance band structure; (3) changing the size of the capacitance proportion parameter, so that the topological phase transition of the circuit system can be realized; (4) in the topological non-trivial phase parameter region, the impedance distribution under different frequencies is calculated, and it can be observed that the system has zero energy angle state, i.e. second-order topological state; (5) the impedance distribution under a specific frequency is measured, and it is observed in the experiment that the topological angle state is localized in the obtuse angle or acute angle region of the rhombus-shaped circuit board, and the system has high-order topological properties.

The invention belongs to the technical field of hydromechanics vortex recognition and analysis, and relates to a method for quantitatively analyzing vortex intensity based on velocity gradient characteristics, which comprises the following steps of: 1, inputting three-dimensional flow field velocity distribution data; 2, calculating the speed gradient tensor of each grid point; 3, solving a characteristic value and a characteristic vector of the velocity gradient tensor; 4, extracting a vortex core line of the flow field vortex, and a characteristic value and a characteristic vector of a velocity gradient tensor at the vortex core line; and 5, performing continuous quantitative analysis on the vortex intensity at the vortex core line by analyzing the characteristic value and the characteristic vector to obtain quantitative analysis of the vortex evolution process. According to the method, the technical defects of subjective misjudgment, pseudo vortex interference and incapability of quantizing intensity distribution caused by manual setting of an experience threshold in an existing vortex identification method are overcome, and threshold-independent objective identification and continuous intensity quantitative analysis of flow field vortexes are realized.

The invention discloses a wave direction angle analysis method and equipment, and belongs to the field of wave test simulation. The method comprises the following steps: 1) generating a target area wave height data array; 2) performing forward and backward smoothing on the wave height data array of the target area to obtain a doubled data array; 3) calculating a covariance matrix of the doubled data array; 4) calculating an optimized covariance matrix by using a spatial smoothing method; 5) calculating all eigenvalues and eigenvectors of the optimized covariance matrix; 6) arranging according to the sizes of the characteristic values, distinguishing signal subspaces / noise subspaces, and constructing a noise matrix; 7) performing translation and reciprocal weighting processing on the characteristic value of the noise subspace to obtain a weighted noise matrix; 8) constructing a spectral function by using an orthogonal relationship between the wave steering vector and the weighted noise matrix, and 9) calculating the spectral function corresponding to all angles, wherein the angle corresponding to the peak value of the spectral function is the oblique wave direction estimation angle.The method can quickly and accurately calculate the wave field direction angle in real time.

The present application relates to the field of information technology, and more particularly to a data principal component acquisition method based on transverse federated learning, comprising: respectively obtaining features and vectors of local sample data; participating parties negotiate to generate a random number vector; adding the features and the vectors and sending to a trusted coordinator; the trusted coordinator calculates the mean; the participating parties calculate the difference and the covariance matrix; again generating a random number vector, adding the covariance matrix and sending to the trusted coordinator; calculating the global covariance matrix; obtaining m eigenvalues and eigenvectors of the covariance matrix; selecting d eigenvalues and corresponding eigenvectors from the m eigenvalues in descending order and sending to each participating party; the participating party projects the local sample data into a d-dimensional space formed by the eigenvalues to obtain a projection, which is the principal component of the local sample data. The beneficial technical effects of the present application include: while protecting data privacy, allowing more sources of data to be used, and improving the accuracy of model analysis.

The application provides a photovoltaic power generation power prediction model training method, a prediction method, an apparatus, a device and a medium, comprising: obtaining historical data and extracting feature data, calculating feature importance scores and determining important feature data; using principal component analysis to standardize the important feature data, calculating a covariance matrix and decomposing to obtain eigenvalues and eigenvectors, constructing a principal component loading matrix and calculating a principal component feature matrix, and adding the principal component feature with the largest variance contribution rate to the important feature data; dividing a training set, a validation set and a test set; establishing a hybrid attention mechanism-bidirectional long short-term memory network model, selecting a hyperparameter type and setting an initial value; training the model using the training set, verifying whether the model performance reaches the expected effect using the validation set, if not, adjusting the hyperparameters and training the model; if yes, outputting the trained photovoltaic power generation power prediction model; and testing and evaluating using the test set. The application improves the model performance and improves the prediction accuracy.

The invention provides a three-frame dynamic mode decomposition-based phase-shift interference in-situ measurement method, which comprises the following steps of: acquiring a three-frame phase-shift interferogram of an object to be measured, and carrying out difference and normalization processing on the three-frame phase-shift interferogram to obtain a background-removed first interferogram sequence; expanding the first interferogram sequence into a column vector form, and constructing a data matrix and a data matrix according to a time sequence; performing singular value decomposition on the data matrix, and calculating to obtain an approximate matrix; performing feature analysis on the approximate matrix, extracting a feature value and a feature vector, mapping the feature vector back to the space of the first interferogram sequence, and reconstructing a dynamic mode of the first interferogram sequence; and recovering the phase distribution of the object to be measured according to the argument information of the main mode in the dynamic mode. According to the method, the limitation that at least four frames of interferograms are needed in a traditional dynamic mode decomposition algorithm is broken through by constructing a new phase shift interferogram sequence, and the method has good robustness for background intensity fluctuation and noise interference.

The present application relates to the technical field of identification of dielectric response characteristics and aging state evaluation of capacitive power equipment, and particularly relates to a dynamic modal decomposition-based extended Debye model parameter identification method, which comprises the following steps: obtaining depolarization current data changing with time, constructing the data into a Hankel matrix, taking the first N columns and the last N columns to form a matrix A and a matrix B; performing economic SVD decomposition on the matrix A, setting a singular value threshold, discarding singular values less than the singular value threshold, and reconstructing the SVD decomposition matrix; solving X=A ‑1 B, then solving the eigenvalues and eigenvectors of X; solving the modes of X and sorting them in the order of decreasing energy; screening the modes, restoring the depolarization current by using the screened modes, judging the effect of dynamic modal decomposition, and judging whether to adjust the singular value threshold according to the restoration effect; calculating the branch number, time constant and relaxation intensity coefficient of the insulation extended Debye model. The parameter identification result obtained by the method has clear physical meaning.

The application discloses a method for impedance mismatch design of a multi-flame tube combined flame system of a gas turbine combustion chamber, comprising the following steps: based on graph theory, 12 flame tubes in a gas turbine are combined through a combined flame pipe to form a combustion chamber, modeling is performed as a cycle graph, and an adjacency matrix and a Laplace matrix are constructed; based on the angular frequency of the flame tube and the coupling strength of the combined flame pipe, a system matrix is constructed; eigenvalues and eigenvectors of the system matrix are calculated, oscillation frequency and vibration mode are determined, and degenerate modes are identified; by modifying the coupling strength of at least one combined flame pipe, the eigenvalues and eigenvectors of the system matrix are recalculated, degenerate frequency splitting is performed, and symmetry is broken; according to the split frequency and mode, the parameters of the combustion chamber are adjusted to optimize the combustion stability. The application simplifies the modeling of a complex system based on a graph theory method, reduces the calculation cost, breaks the symmetry of the system by modifying the coupling strength of the combined flame pipe, splits the degenerate frequency, and relieves the combustion instability.

Provided in the present application is a data processing method for improving the efficiency of solving for an eigenvalue and an eigenvector of a matrix. The data processing method comprises: acquiring a target matrix; on the basis of a plurality of characteristic parameters of the target matrix, searching a first database to determine at least one target reference eigenvector, wherein the first database stores a plurality of groups of first reference data, each group of first reference data corresponds to one first reference matrix, each group of first reference data comprises a plurality of characteristic parameters and eigenvectors of the first reference matrix, a plurality of target reference eigenvectors are eigenvectors of a target first reference matrix, and a plurality of characteristic parameters of the target first reference matrix match the plurality of characteristic parameters of the target matrix; and on the basis of the at least one target reference eigenvector, determining an initial reference vector, wherein the initial reference vector is used for solving for eigenvalues and eigenvectors of the target matrix. In addition, further provided in the present application are a corresponding apparatus, a computing device cluster, a computer-readable storage medium and a computer program product.

The invention discloses a polarized synthetic aperture radar forest and urban target scattering confusion removal method considering wavelength dependence, and relates to the technical field of polarized synthetic aperture radar image processing and ground object classification. The method comprises the following steps: inputting PolSAR data of a target area, obtaining a polarization covariance matrix, and carrying out geocoding on the polarization covariance matrix; extracting eigenvalues and eigenvectors in the data based on eigenvalue-eigenvector polarization decomposition to obtain odd scattering power, even scattering power and volume scattering power; targets including forest targets and urban targets are regarded as strong body scattering radar targets, wherein the proportion of the body scattering power to the total power is larger than a specified value; and performing urban area target and forest target discrimination on the strong body scattering radar target based on the scattering feature separation factor, and outputting a target identification result with target geographic position information. The method is effective and simple, radar target recognition is carried out by comprehensively considering the mutual relation of the three scattering mechanisms, and the accuracy and robustness of the result are ensured.

This invention discloses a method, apparatus, equipment, and storage medium for non-stop wind turbine inspection, relating to the field of wind turbine inspection technology. The method includes: acquiring point cloud data of the wind turbine to be inspected and determining the covariance matrix of the blade point cloud in the point cloud data; determining the eigenvalues ​​and corresponding eigenvectors of the covariance matrix, and determining the rotation axis direction of the wind turbine to be inspected based on the eigenvalues ​​and eigenvectors; determining the yaw angle of the wind turbine to be inspected based on the rotation axis direction, and determining the blade azimuth angle of the wind turbine to be inspected based on the point cloud data; and determining the UAV inspection route of the wind turbine to be inspected based on the yaw angle and blade azimuth angle at different times. The technical solution of this invention achieves non-stop, adaptive, and highly secure intelligent wind turbine inspection.

This invention provides an in-situ phase-shift interferometry measurement method based on three-frame dynamic mode decomposition, comprising: acquiring three-frame phase-shift interferograms of the object under test, and performing differential and normalization processing on the three-frame phase-shift interferograms to obtain a first interferogram sequence after removing the background; expanding the first interferogram sequence into column vector form and constructing a data matrix and a data matrix in chronological order; performing singular value decomposition on the data matrix to calculate an approximate matrix; performing feature analysis on the approximate matrix to extract eigenvalues ​​and eigenvectors, and mapping the eigenvectors back to the space of the first interferogram sequence to reconstruct the dynamic mode of the first interferogram sequence; and recovering the phase distribution of the object under test based on the argument information of the principal mode in the dynamic mode. This invention overcomes the limitation of traditional dynamic mode decomposition algorithms requiring at least four interferograms by constructing a new phase-shift interferogram sequence, and exhibits good robustness to background intensity fluctuations and noise interference.

A beamformee receives a channel coefficient matrix H of a wireless communication channel between a beamformer and the beamformee. The channel coefficient matrix is a 2×N complex matrix having two rows corresponding to two antenna of the beamformee and N columns corresponding to N antenna of the beamformer. A real symmetric matrix M is determined based on the matrix H followed by determining eigenvalues and eigenvectors of matrix M. Singular vectors of the matrix H based on the eigenvectors are determined where the singular vectors define a steering matrix. The steering matrix is transmitted to a beamformer, wherein a beam is steered by the beamformer to the beamformee based on the steering matrix.

The invention discloses a customer credit rating method and device in the financial field, a medium and a product, and relates to the technical field of credit rating, and the method comprises the steps: obtaining basic economic data and basic rating data; standardizing the basic economic data to obtain a standardized matrix; calculating a covariance matrix according to the standardized matrix, and calculating a characteristic value and a characteristic vector of the covariance matrix; determining an eigenvector matrix based on the eigenvalues and the eigenvectors, and performing principal component analysis data dimension reduction on the standardized matrix according to the eigenvector matrix to obtain a dimension-reduced data matrix; establishing a multiple linear regression model based on the data matrix after dimension reduction and the basic rating data, and fitting to obtain a regression coefficient; and determining a principal component weight matrix based on the regression coefficient and the feature vector matrix, and calculating a credit score according to the principal component weight matrix and the standardized matrix. According to the invention, the time and cost required by evaluation can be reduced, and the reliability of the evaluation result is improved.

The invention discloses a radar-oriented order constraint Jacobi eigenvalue self-sorting method and system, solves the problem of resource consumption caused by an additional sorting module in a Jacobi iteration eigenvalue decomposition post-processing flow in the prior art, realizes direct output of ordered eigenvalues and eigenvectors, and omits an independent sorting module. The method comprises the steps that a real symmetric matrix A obtained through radar baseband signal sampling and covariance estimation is processed to obtain a compressed array, and a feature vector matrix V is initialized; in the parallel iteration process, index pairs are generated according to a scheduling sequence, sub-matrixes are extracted, two types of asymmetric rotation operators are generated through a condition driving mechanism, and a feature vector matrix is synchronously updated to track feature vectors; global data replacement is carried out, and non-diagonal element energy judgment convergence is calculated; after iteration is ended, feature values arranged in a descending order can be directly extracted from the main diagonal of the final compressed array, and corresponding feature vectors are output and used for direction of arrival estimation.

The application provides a random phase shift measurement method based on dynamic mode decomposition, comprising: obtaining three frames of random phase shift interferograms of an object to be measured, performing difference and normalization processing to obtain a first interferogram sequence without background; performing difference and algebraic operation on the first interferogram sequence to generate an equal-step second interferogram sequence; expanding the second interferogram sequence into a column vector form and constructing a data matrix X and a data matrix in time sequence; performing singular value decomposition on the data matrix to calculate an approximate matrix; performing characteristic analysis on the approximate matrix to extract eigenvalues and eigenvectors, and mapping the eigenvectors back to the space of the second interferogram sequence to reconstruct the dynamic mode of the second interferogram sequence; and recovering the phase distribution of the object to be measured according to the amplitude angle information of the main mode in the dynamic mode. The application breaks through the limitation of the existing algorithm that at least four frames of interferograms are required, and can realize stable phase demodulation under any unknown phase shift condition.