69 results about "Eigenvalues and eigenvectors" patented technology

A system for extracting glacier boundaries using multiparametric analysis, consisting of: a data acquisition module configured to acquire Synthetic Aperture Radar (SAR) data of a glacial region; a data processing unit configured to process the SAR data to generate coherence images and backscatter intensity maps, to extract terrain parameters including slope and curvature information from a digital elevation model (DEM), to apply thresholds to the coherence images, backscatter intensity maps, and terrain parameters, to generate a multi-band stack comprising the thresholded coherence images, the thresholded backscatter intensity maps, the thresholded slope information, and the curvature information, and a principal component analysis module configured to standardize the multi-band stack, calculate a covariance matrix from the standardized data, calculate eigenvalues and eigenvectors of the covariance matrix, and transform the original data into a principal component space with reduced dimensionality; a texture feature extraction module configured to calculate first- and second-order statistics from the output of the principal component analysis; generating a texture feature set comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module configured to: convert the set of textural features into a binary format that distinguishes glacier ice from the background; group spatially connected pixels of similar intensity into segments; extract a vector shape file corresponding to the glacier boundary; an output generation module configured to generate a glacier boundary delineation output; and a user interface having a display configured to display the output generated by the output generation module.

Systems and methods for new systems and methods for detecting power quality disturbances, such as faults. The approach is based on the dynamic mode decomposition (DMD)—a data-driven method to estimate linear dynamics whose eigenvalues and eigenvectors approximate those of the Koopman operator. The approach uses the real part of the main eigenvalue estimated by the DMD as the key indicator that a power quality event has occurred. The disclosed systems and methods can be used to detect events using current and voltage signals to distinguish different faults. Because the proposed method is window-based, the effect that the window size has on the performance of the approach is analyzed.

The invention discloses a wire harness processing quality traceability management method based on big data, and relates to the technical field of wire harness processing management, and the method comprises the steps: selecting a principal component of which the cumulative contribution rate is greater than a contribution rate threshold after calculating a feature value and a feature vector of a covariance matrix, and projecting a decentralized data matrix to a principal component space to obtain feature data; and a plurality of batches of historical feature data are substituted into the gating circulation unit, the gating circulation unit performs time sequence prediction to predict the quality trend of the wire harness in the future, the SHAP algorithm is utilized to analyze the influence of each piece of feature data on quality reduction, and a quality management strategy is output based on the prediction result of the gating circulation unit and the analysis result of the SHAP algorithm. According to the management method, high-dimensional data are projected to a low-dimensional principal component space, the calculation complexity is reduced, time sequence prediction is carried out by adopting a gating circulation unit, possible quality degradation is warned in advance, and the generation efficiency of the automobile wire harness is guaranteed.

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 low-redundancy high-order direction finding method and system based on co-prime characteristics and a computer storage medium, and the method comprises the steps: receiving signals from a plurality of signal sources through employing a low-redundancy array obtained through improvement based on a co-prime array, and obtaining a received signal; calculating a fourth-order cumulant matrix of the received signal according to the received signal; obtaining a covariance matrix according to the fourth-order cumulant matrix; according to the covariance matrix, calculating to obtain a characteristic value and a characteristic vector of the covariance matrix; dividing the feature vector into a signal subspace and a noise subspace according to the size of the feature value; constructing a spectral function according to the projection of the steering vector on the noise subspace; and searching peak values of the spectral function in all possible directions, and determining the direction of the signal source. According to the low-redundancy array obtained based on improvement of the co-prime array, on the basis that the number of receiving array elements is reduced, the number of estimated signal sources is increased, the fourth-order cumulant is adopted, and the positioning precision in a non-Gaussian noise environment can be 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 present invention discloses a method for monitoring an on-load tap changer transmission mechanism based on sparse filtering, which relates to the technical field of on-load tap changer transmission mechanism detection. The method involves performing multiple gear switching on the on-load tap changer to collect vibration signals of the transmission mechanism during the on-load tap changer switching process. A sparse filtering network-based vibration signal characteristic distribution matrix of the on-load tap changer transmission mechanism is calculated based on the vibration signals. The eigenvalues ​​and eigenvectors of the principal component components of the vibration signal characteristic distribution matrix are calculated. The weighted mean of the elements of the statistic and the corresponding control limits are calculated based on the eigenvalues ​​and eigenvectors of the principal component components of the characteristic distribution matrix. The mechanical state of the transmission mechanism is determined based on the weighted mean of the elements of the statistic and the corresponding control limits. The method of the present invention efficiently and accurately determines the mechanical state of the on-load tap changer transmission mechanism by performing real-time monitoring and computational analysis of the vibration signals of the transmission mechanism during the on-load tap changer switching process.

The invention relates to the technical field of navigation, and particularly provides a multi-attribute quantitative evaluation method and system for inertial satellite integrated navigation equipment, and the method comprises the steps: collecting corresponding observation sample data for an inertial integrated satellite according to a plurality of evaluation indexes, and extracting a feature value and a feature vector corresponding to each evaluation index from the observation sample data; screening out a plurality of core indexes from the plurality of evaluation indexes according to the characteristic value of each evaluation index by utilizing an accumulative contribution rate inequality, and generating evaluation data of each core index according to the characteristic vector corresponding to each core index and the observation sample data; determining the weight of each core index based on the Euclidean distance between the evaluation data of each core index and a corresponding preset standard value; and calculating a grey correlation coefficient matrix of the plurality of core indexes based on the evaluation data, the weight and a preset standard value of each core index, and calculating a quantitative evaluation value of each core index based on the grey correlation coefficient matrix. According to the method, the accuracy and scientificity of an evaluation result are remarkably 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 present invention discloses an adaptive underwater sound source passive localization method and system. The method includes: acquiring the signals received by a hydrophone array; converting the signals received by the array to the frequency domain by using fast Fourier transform, obtaining the effective frequency band of the target signal source through spectrum analysis, and constructing a steering vector estimation; calculating the covariance matrix of the signals received by the array, and obtaining the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition; constructing a weight matrix by using the eigenvalues of the covariance matrix, weighting all the eigenvalues of the covariance matrix with the weight matrix to form a fully weighted spatial vector, and realizing adaptive fully weighted MUSIC spatial spectrum estimation through iterative operation and setting of iterative termination conditions; and obtaining the estimated value of the arrival azimuth. The present invention avoids the need for estimating the number of signal sources in the conventional MUSIC algorithm, and solves the situation of performance degradation of the beamformer caused by steering vector mismatch and noise subspace division error in practical applications.

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 present invention provides an evaluation method for the traffic connection plan of intercity railway stations based on passenger flow influence, and the specific steps are as follows: S1: Establish an index model for the influencing factors of passenger flow at intercity railway stations; S2: Conduct principal component analysis on continuous variables; S21: Construct a sample data matrix and standardize it; S22: Calculate the sample covariance matrix; S23: Calculate the eigenvalues and eigenvectors of the covariance matrix; S24: Calculate the principal component contribution rate and the cumulative contribution rate; S25: Write out the principal components; S3: Establish a multiple linear regression model for the influencing factors of passenger flow at stations; S4: Calculate the scores of traffic connection plans; The present invention can be used as a basis for evaluating the traffic connection plan before and after adjustment and comparing multiple plans from the perspective of passenger flow. The present invention selects influencing factors from three perspectives: line, station, and traffic connection, which is more comprehensive than existing research in the study of passenger flow at intercity railway stations.

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 invention belongs to the technical field of linear system dynamics analysis, and particularly discloses a controllable and observable quantitative analysis method and device for a linear period time-varying system mode. According to the method, the solution of the state variable of the system under multiple groups of initial values is solved through a numerical calculation method, so that the state transition matrix is obtained more accurately. The eigenvalue and the eigenvector of the state transition matrix correspond to the oscillation mode of the system, and the incidence relation between the input / output and the oscillation mode is established by solving the eigenvalue and the eigenvector. By calculating the mode controllability and the mode observability of the linear period time-varying system, the incidence relation between the input / output of the linear period time-varying system and the oscillation mode is accurately quantified, and a dynamic stability analysis theory system of the linear period time-varying system is further improved; the method is of great significance to stability mechanism analysis, stability rule description and the like of a large-scale linear periodic time-varying system, and theoretical support is provided for design and implementation of stabilization control of the linear periodic time-varying system.

The invention provides a multi-source heterogeneous data source fusion processing method, and belongs to the field of data fusion. The problem of low multi-source heterogeneous data fusion efficiency is solved; the method specifically comprises the following steps: acquiring and storing numeric data and non-numeric data; dividing the numerical data into mixed data, one-dimensional data and multi-dimensional data; the mixed data are integrated, dimensions are unified, and dominant fusion data are obtained; normalizing the one-dimensional data to obtain primary fusion data, and constructing a correlation function; extracting feature values and feature vectors of the multi-dimensional data to obtain dominant fusion data of the multi-dimensional data; carrying out secondary fusion on the multi-dimensional data with correlation; in the non-numeric data, screening and fusing the non-numeric data corresponding to the extraction target to obtain non-numeric fused data; according to the method, various different structural data or non-structural data are analyzed, extracted and integrated, so that the interpretability of the data is improved.

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 a method and system for health evaluation of a hydropower unit based on characteristic statistical analysis, wherein the method comprises: obtaining sample data of each associated measuring point in the evaluation project, and determining new sample variables of each evaluation item in the hydropower unit components by principal component analysis; calculating the eigenvalues ​​and eigenvectors of the original sample variables in the sample data, and obtaining the principal component eigenvalues ​​and load matrix corresponding to the new sample variables; calculating the project health index of the evaluation project based on the eigenvalues, eigenvectors, principal component eigenvalues ​​and load matrix; determining the weight coefficient of the evaluation item in the hydropower unit components by hierarchical analysis, and then calculating the component health index of the hydropower unit components. Through the present application, the problem of poor health status evaluation effect of the hydropower unit is solved, and the cause of the failure from the associated measuring point to the hydropower unit equipment is determined, the health status of the hydropower unit is accurately grasped, and the failure is prevented in advance.

The invention provides a random phase shift measurement method based on dynamic mode decomposition, and the method comprises the steps: obtaining three frames of random phase shift interferograms of a to-be-measured object, and carrying out the difference and normalization processing, and obtaining a background-removed first interferogram sequence; performing difference and algebraic operation on the first interferogram sequence to generate a second interferogram sequence with equal step length; expanding the second interferogram sequence into a column vector form, and constructing a data matrix X and a data matrix according to a time sequence; performing singular value decomposition on the data matrix, and calculating 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 second interferogram sequence, and reconstructing a dynamic mode of the second 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. The method breaks through the limitation that an existing algorithm needs at least four frames of interferograms, and can achieve stable phase demodulation under any unknown phase shift condition.

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.