107 results about "Z eigenvalue" patented technology

The invention relates to the technical field of data processing, in particular to a multi-modal sensor data real-time fusion processing method for a smart community. According to the method, a sensor network topological graph is constructed, a connection weight is optimized, distributed clock synchronization is realized by using a graph Laplacian matrix, and clock drift prediction and compensation are performed in combination with a fractional Brownian motion model; performing wavelet transform decomposition on the sensor data after time sequence alignment, calculating each scale Hurst index, predicting a load trend through a fractal prediction model, and outputting an optimal resource allocation scheme through hybrid evolution calculation; the method comprises the following steps: constructing multi-modal sensor data into a graph structure, extracting node features by using a graph convolutional neural network, obtaining global feature representation by using a self-attention mechanism, and performing anomaly detection classification in combination with a resource utilization rate and a prediction error; an anomaly detection feedback mechanism is established, and Laplacian matrix eigenvalues and weight parameters are dynamically adjusted; the real-time performance, the accuracy and the robustness of data fusion processing are improved.

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.

Methods, systems and apparatuses may provide for technology that determines a major axis length of an ellipse in texture space based on a transformation matrix associated with a circle in screen space, inverts the major axis length, determines a product between the inverted major axis length and a determinant of the transformation matrix, and determines a minor axis length based on a modulus of the product between the inverted major axis length and the determinant of the transformation matrix. In one example, the technology selects a level of detail in the texture space based on the major axis length, the minor axis length, a direction of the major axis length, and a direction of the minor axis length.

The invention discloses a multi-converter parameter collaborative optimization method for improving the small interference stability of an active power distribution network, and belongs to the technical field of power systems, and the method comprises the steps: building a differential algebraic equation model, and carrying out the linearization of a steady-state operation point, and obtaining a state space model; and a control parameter optimization problem taking the minimization of the spectrum intercept construction as a target is defined, and control parameters are a GFL converter phase-locked loop parameter and a GFM converter virtual damping parameter. First-order sensitivity and second-order sensitivity of eigenvalues to control parameters are calculated based on a matrix perturbation theory and are converted into local quadratic constraint sub-problems. And performing convexity solution by adopting a positive semidefinite relaxation method, adjusting the step length in combination with a line search mechanism, iteratively updating control parameters, and outputting an optimization result after a convergence condition is met. According to the method, the spectral intercept is reduced through multi-converter parameter collaborative optimization, and the weak stability characteristic is improved; the convergence and solvability are improved by utilizing matrix perturbation and positive semidefinite relaxation; and the stability is enhanced and optimized by adopting line search step length adjustment.

A method for a quantum computer is presented. The method comprises receiving a target matrix comprising only real eigenvalues and block encoding the target matrix. A polynomial approximation is precomputed for a function to be applied to the target matrix. Coefficients are selected for a generating function that match the precomputed polynomial approximation. A polynomial history state is generated, the polynomial history state comprising a superposition of polynomials onto the block encoded target matrix by at least mapping the generating function to a quantum algorithm.

The present invention relates to an information compilation technology method based on the Julia fractal set. The method includes the following steps: Step S1, information encoding: converting the input information into binary ASCII code and mapping it to complex numbers to generate a Julia set image with specific fractal features; Step S2, obtaining the fractal dimension D: obtaining the fractal dimension D of the fractal image through the box-counting method as the eigenvalue of the information; Step S3, information hiding and storage: storing the fractal image feature data and the fractal dimension D in a database to achieve dynamic storage of information; Step S4, information decoding and error correction: during the decoding process, obtaining the fractal dimension D of the fractal image, inversely deriving the original information based on this as an index, and using the fractal self-similarity to achieve error correction. The present invention can use the self-similarity and high complexity of the fractal image to achieve the concealment of information, the efficient compression of data, and the anti-interference ability.

The invention relates to the field of dynamic jump behavior simulation analysis of a thin-wall arch structure, in particular to a thin-wall arch structure finite element approximate calculation method and a lightweight system. The invention particularly provides a thin-wall arch structure finite element approximate calculation method and a lightweight system, and the method comprises the steps: carrying out one-time quasi-static equilibrium path analysis, obtaining a characteristic value closest to zero of each increment step, recognizing characteristic values before and after sign change, carrying out two-time perturbation finite element analysis, and carrying out two-time perturbation finite element analysis; the method comprises the following steps of: obtaining an approximate transient dynamic jump parameter and a feature vector, obtaining an approximate jump parameter and a feature vector at a saddle point based on a feature value interpolation closest to zero, finally obtaining an approximate transient jump response through a Gaussian hyper-geometric function, and proposing a Python / C-based hybrid programming method to realize the lightweight of a finite element analysis system. And the calculation cost and the analysis difficulty of the flexible shallow arch saddle point transient jump analysis can be effectively reduced.

The invention provides a multi-party privacy set intersection method and system in an unbalanced scene, and the method comprises the steps: taking an input party with a minimum set as a central participant, taking the set as an anchor point, and embedding elements into a hash table through a hash function, each participant respectively runs a batch member condition random number generation protocol under a balance scene constructed based on a batch oblivious pseudo-random function and an oblivious key value pair storage protocol or a batch member condition random number generation protocol under a non-balance scene constructed based on the batch oblivious pseudo-random function and a fully homomorphic encryption technology; the member relationship of the elements is converted into a feature value equivalence relationship, and the central participant and other parameter parties jointly operate a joint privacy equivalence test protocol, so that the central participant obtains a corresponding feature vector, and multi-party privacy set intersection is realized. According to the method, the communication complexity can break through the linear boundary of a large set size, and any collusion attack can be resisted.

The invention provides a BEC ground state and excitation state determination method and device, equipment and a storage medium. Relates to the technical field of quantum computing. The method comprises the following steps: describing a rotation BEC in a coordinate system by adopting a multi-dimensional dimensionless Gross-Pitaevskii (G-P) equation with an angular momentum rotation item to obtain a representation equation of the rotation BEC, and processing the representation equation of the rotation BEC by adopting a virtual time method and a projection method to obtain a space-time sequence equation of a characteristic value; based on the space-time sequence equation of the characteristic values, using an OC method in the space direction and using a backward Euler method and / or a time midpoint method in the time direction to carry out complete discretization, obtaining a backward Euler type high-order format and a time midpoint type high-order format of the space-time sequence equation, carrying out iterative solution on the high-order formats, and determining a ground state and an excitation state. The device comprises a plurality of modules and is used for executing the method, the equipment and the storage medium comprise a processor, the method is executed through the processor, more calculation space can be saved, and universality is better.

The invention provides a flow field evolution method and device based on quantum calculation and vortex characterization, and the method comprises the steps: defining a complex scalar field, constructing a Hermi matrix based on an initial velocity field, and converting a vortex thread positioning problem into a quantum ground state solving problem; solving the minimum characteristic value of the matrix through a quantum algorithm, and outputting an initial vortex quantum state; according to a fluid dynamic control equation, constructing Hamiltonian through discretization processing; constructing a Hamiltonian quantum circuit, and driving the initial vortex quantum state to evolve to a target quantum state; and measuring the evolved quantum state, obtaining a discrete numerical value of the complex scalar field on the central point of the grid unit, positioning a zero equivalence point through a discrete winding number and interpolation, and reconstructing a vortex thread structure after flow field evolution. According to the method, the parallel computing advantage of quantum computing in high-dimensional data processing is fully exerted, the computing efficiency of flow field simulation is remarkably improved, the data processing speed is increased, and meanwhile the high requirement for storage resources is effectively relieved.

The invention discloses a multi-modal practical dynamic security domain construction method and system based on the maximum Lyapunov exponent theory, and the method comprises the steps: obtaining the measurement information of the active power, the power angle, the angular velocity and the like of a generator node from an actual power system, and determining a fault line and a fault state corresponding to a required dynamic security domain; transient simulation of an initial operation point of the system is carried out to obtain a transient stability condition and a power angle leading cluster of the system; for generators in the leading cluster S, calculating to obtain a maximum Lyapunov exponent characteristic value and related characteristics of a control variable, and solving the maximum Lyapunov exponent sensitivity; and constructing a multi-modal practical dynamic security domain based on the maximum Lyapunov index by combining the upper limit and the lower limit of generator output and the output limit of a balancing machine. According to the method, accurate transient stability evaluation before a fault occurs is realized, the application range is expanded, and the limitation that the stability margin cannot be quantified by track stability judgment is changed.

The application discloses a silicon carbide grain size detection method based on image processing and relates to the technical field of semiconductor material quality detection. A first feature map is generated by calculating the phase consistency distribution of a silicon carbide metallographic image to suppress scratch interference. A topological anchor sequence is identified by using the eigenvalues of a Hessian matrix, and a balanced first gradient potential field matrix is generated by combining local entropy density to suppress polycrystalline contrast fluctuation. Under the constraint of the topological anchor, an optimization evolution is performed by using a path cost function with curvature penalty and energy saturation characteristics, physical inertia closure of a grain boundary signal fracture is realized, and a closed grid vector is generated. Twin grain boundaries are identified and logically merged based on geometric parameters, and grain size detection data consistent with the physical structure of the material is output.

The invention discloses a system recommendation method based on Lanczos algorithm orthogonality. The system recommendation method comprises the following steps: firstly, modeling a problem of searching a community structure in a network structure into a problem of solving first k minimum feature pairs of a network Laplacian matrix; secondly, projecting a Laplacian matrix in a high-dimensional Euclidean space into a symmetric three-diagonal matrix in a low-dimensional Krylov subspace; then, using a feature value convergence criterion to screen out converged feature pairs of the symmetric tridiagonal matrix, and calculating feature vectors corresponding to the converged feature pairs; thirdly, calculating a feature vector of a Laplacian matrix according to the feature vector of the symmetric matrix; and finally, carrying out de-orthogonalization on the convergent feature vector of the Laplacian matrix and the Lancozs vector, storing the convergent feature vector of the Laplacian, and using the convergent feature vector of the Laplacian to detect the community structure through a standard k-means algorithm. According to the method, the real community detection data set is used as a drive, the Lanczos algorithm is used as a model, and the community structure contained in the graph network can be accurately detected, so that the recommendation quality of the system is improved.

The invention discloses a rapid calculation method and device for the sensitivity of a linear periodic time-varying system based on a matrix sparse technology, and belongs to the technical field of modeling analysis of the linear periodic time-varying system.The rapid calculation method comprises the steps that an LTP system is converted into an equivalent LTI system, and the eigenvalue, the left eigenvector and the right eigenvector of the LTP system are obtained; based on a functional derivative method, obtaining a sensitivity analytical expression of the LTP system characteristic value to the parameter; according to the influence mechanism of the parameters on the system matrix, obtaining an analysis form of the influence of the parameters on the LTP system matrix in the sensitivity analysis expression; and converting a partial derivative of a system matrix to a steady-state trajectory into sparse calculation of a vector field Hessian matrix by using a matrix sparse technology, thereby obtaining a calculation result of the characteristic value sensitivity of the LTP system. According to the method, the sparse characteristic of the system matrix in the PEPS is utilized, the calculated amount and the storage requirement of the system matrix on trajectory partial derivative solving are reduced, and therefore calculation of the LTP characteristic value sensitivity is accelerated.

A novel and useful hybrid quantum-classical mechanism for approximating geometric entanglement of multi-qubit pure states with noise mitigation in a quantum device. The mechanism applies the Higher-Order Power Method that approximates solutions to rank-1 tensor approximation and is executed virtually solely on a quantum computer with only angle computation performed on a classical computer. The mechanism iteratively estimates a closest separable state and an estimate of the corresponding entanglement eigenvalue in a quantum system. During each iteration, the mechanism applies a special unitary operator that skips one of the qubits. The state of the skipped qubit is recovered using one-qubit tomography to obtain updated angles used for a subsequent qubit. This procedure is repeated to converge to a final entanglement eigenvalue. A depolarizing noise mitigation procedure is also provided to generate a reduced noise estimate of geometric entanglement.

The invention provides a medium loading resonant cavity analysis method based on a volume-surface integral equation, and belongs to the technical field of resonance characteristic numerical analysis. According to the method, an internal resonance generalized eigenvalue equation based on a body-surface integral equation and internal resonance factors representing internal resonance attributes of body and surface current characteristic modes are defined, so that accurate solution of a unique internal resonance mode is realized; the method avoids the problem that the traditional characteristic value method is difficult to directly solve the internal resonance mode and the mode field of the metal cavity with the aperture after the medium is loaded, is suitable for the loading medium and the metal cavity with any shape and placement relation, and achieves accurate modeling through the volume-surface mixed grid on the premise of guaranteeing the solving precision. Compared with a finite element method, the method has lower modeling complexity.

The invention discloses a circuit fault diagnosis method based on a fractional order observer, and the method comprises the steps: constructing a fractional order circuit model based on the Kirchhoff's law, and describing a capacitor end voltage and an inductive current as a Caputo fractional order derivative form; a double-disturbance decoupling mechanism is designed, total disturbance is decomposed into a decoupling disturbance component and a non-decoupling disturbance component, physical isolation of the decoupling disturbance is achieved through a projection matrix, and the influence of the non-decoupling disturbance is eliminated through a dynamic suppression coefficient. Constructing a state observer to generate a state estimation error and a fault error vector, and dynamically compensating a fault estimation value in combination with an adaptive gain matrix; and ensuring the system stability through a Lyapunov function, and proving that an error state is converged to a preset boundary domain under the condition of satisfying a characteristic value correlation matrix inequality. Compared with the prior art, rapid and accurate diagnosis of faults such as inductance saturation and capacitance aging can be realized.

To provide an intervention device capable of estimating which factors governing the state of the system should be intervened in order to return the system to then original stable state just before the system transitions from the stable state to a critical state.SOLUTION: An intervention device 1 for suppressing fluctuations in networks where nodes with state quantities are connected includes: a state sample acquiring unit 11 that acquires multiple samples of state; an estimation unit 12; an intervention unit 13; and a control unit 14. The estimation unit 12 selects eigenvectors related to the eigenvalues of the covariance matrix of the acquired multiple samples as an estimate of the dominant right eigenvector. The control unit 14 is configured to control the intervention unit 13 so as to intervene to suppress the state quantity corresponding to the eigenvector component related to the selected eigenvalue on the network.SELECTED DRAWING: Figure 5

The invention provides a heterogeneous symmetric matrix eigenvalue decomposition method, system and device based on GPU parallel acceleration, and belongs to the technical field of high-performance computing. According to the method, the design that five sub-processes can only be strictly and serially executed in the traditional symmetric matrix eigenvalue decomposition process is broken through, the problem that CPU and GPU computing resources are idle in a serial algorithm is solved, and the computing efficiency is greatly improved. Meanwhile, aiming at the BC-Backk stage, the invention provides a novel BC-Backk method realized based on a GPU (Graphics Processing Unit), the BC-Backk process is transferred from a CPU (Central Processing Unit) with limited parallel capability to a GPU capable of realizing large-scale parallel, and the parallel efficiency of the BC-Backk stage is greatly improved. According to the method, heterogeneous architecture characteristics and GPU parallelism are fully utilized, parallel between symmetric matrix eigenvalue solving sub-processes and internal parallel of BC-Back are achieved, CPU and GPU resources are fully utilized, and the calculation performance of the interior of a computer for the symmetric matrix eigenvalue solving process is greatly improved.

A system and method for spectral learning in persistent cognitive machines implements learning through controlled evolution of a spectral decomposition of a cognitive manifold. The spectral decomposition, comprising eigenvectors and eigenvalues, encodes long-term memory as global geometric structure rather than as stored data or network parameters. The system performs inference operations by projecting incoming data onto the cognitive manifold using a fixed spectral decomposition without modification. Geometric invariants including principal angles, spectral gap ratios, projection residuals, and curvature statistics are continuously monitored to detect structural inadequacy. When invariants exceed thresholds, a learning event modifies the spectral decomposition through eigen decomposition with warm-start initialization while enforcing mode-specific plasticity bounds that are tighter for low-frequency eigenvectors than high-frequency eigenvectors, thereby preventing catastrophic forgetting. The system operates continuously by alternating between inference using fixed spectral decompositions and learning events that modify spectral decompositions through controlled spectral evolution.

The application discloses a kind of graph data semantic analysis methods, this method will graph data semantic analysis process be disassembled into three parts of calculating N order inner eigenvalue, N order outer neighbor feature and feature combination, the calculation of neighbor feature most consumed resource is moved to offline stage, when receiving the data analysis request initiated by user, when certain node is as the N order neighbor node of target node, take out the X order historical neighbor feature pre-cached, the whole flow of deep neighbor modeling calculation can be recovered, can be guaranteed to analyze the depth as N+X order neighbor feature, while, the query order of online graph database is reduced to N order neighbor from N+X order neighbor, greatly reduce the query pressure of online graph database, guarantee the calculation of online stage, through the light degree of data to speed up the calculation speed, reduce the consumption of computing resources.The application also discloses a kind of graph data semantic analysis device, equipment and readable storage medium, with corresponding technical effects.

The application relates to a kind of computing method and device for improving the numerical shock stability of hypersonic speed, belonging to the technical field of computational fluid dynamics.The method comprises: obtaining the grid information of numerical simulation of hypersonic vehicle, and initializing flow field information; for each calculation grid unit, two calculation localities are constructed along the x direction and the y direction with two adjacent grids; two localities are respectively equipped with stability matrix, and the instability condition of grid in the calculation process is judged by the positive and negative of the eigenvalue thereof; according to the instability condition of the grid unit, a Riemann solver is selected: for the grid unit not prone to instability, a high-resolution HLLC Riemann solver is used for calculation; for the grid unit prone to shock instability, a high-robustness HLL Riemann solver is used for calculation. By using different Riemann solvers for calculation at different positions, the shock instability position in the flow field can be detected, identified and processed, and the stable capture of shock is realized.

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.

Methods, systems and apparatus for determining an error-mitigated expectation value of a target observable with respect to a noisy quantum state. In one aspect a method includes obtaining multiple copies of the noisy quantum state; performing measurements on tensor products of M copies of the noisy quantum state to compute an expectation value of the target observable with respect to an entangled quantum state, wherein M≥1 and eigenvalues corresponding to non-dominant eigenvectors of the noisy quantum state in the spectral decomposition of the entangled quantum state are suppressed exponentially in M; and using the computed expectation value of the target observable with respect to an entangled quantum state to determine the error-mitigated expectation value of the target observable with respect to the noisy quantum state.

The present disclosure provides a method, apparatus, device, and storage medium for determining quantum state components, relating to the field of computer technology, particularly to the field of quantum computing technology. A specific implementation scheme is as follows: a component analysis step, including: in a current sub-process, when the total initial quantum state is used as the total input quantum state of a block-coded quantum circuit corresponding to a simulated block-coded unitary operator #imgabs0#, obtaining a first characteristic phase λ corresponding to the block-coded unitary operator #imgabs1#, and a first characteristic state corresponding to the first characteristic phase λ; when it is determined that the measurement result corresponding to the target auxiliary register in the block-coded quantum circuit meets a preset requirement, obtaining first component information of the target quantum state ρ based on the first characteristic phase λ and the first characteristic state corresponding to the first characteristic phase λ; the first component information includes at least one of the following: a target eigenvalue of the target quantum state ρ, and a target eigenstate corresponding to the target eigenvalue.

The invention relates to a double-engine propeller noise prediction and optimization method based on an analytical method, and belongs to the field of aviation acoustics. The method disclosed by the invention comprises a noise prediction method and a noise optimization method, the noise prediction method establishes a noise prediction model based on an improved propeller characteristic theory, and the noise optimization method comprises the following steps: S1, establishing a noise objective function based on the noise prediction model, solving a first-order partial derivative and a Hessian matrix of the noise objective function, and obtaining a first-order partial derivative of the noise objective function; the method comprises the steps of S1, providing a noise target function, S2, providing a new Euler formula transformation method, and converting a triangular polynomial corresponding to a first-order partial derivative of the noise target function into a zero-equivalent algebraic polynomial, S3, analyzing and solving the zero point of the algebraic polynomial by using a Fourier-Frobenius matrix method, and S4, realizing synchronous maximum noise reduction control. According to the method, an improved propeller characteristic value theory and a Hessian + Fourier-Frobenius matrix method are used for rapidly and accurately solving an analytical solution of the minimum noise, and the method is combined with synchronous control, so that the limitation on the time and precision of predicting the minimum noise is overcome.

The application discloses a kind of double box girder flow field dynamics modal and flow stability analysis method and system, belong to bridge engineering field.This method first establishes double box girder flow field model by computational fluid dynamics, solves the momentum equation and continuity equation of two-dimensional incompressible flow;Dynamic modal decomposition is carried out again, and eigenvalue, characteristic mode and other information are obtained;Finally, based on linear stability theory, the flow field component is expressed as the sum of steady-state solution and disturbance term, and the global linear flow stability equation is obtained by substituting into the equation, and the flow stability is analyzed by discrete.This method can accurately extract the modal characteristics of each order, providing a scientific basis for wind-induced vibration analysis of double box girder, and has important application value.The method of the application occupies less computing resources, has high numerical accuracy, and can be further expanded to higher precision calculation model according to computing resources, and is suitable for application in actual bridge engineering.

The invention relates to the technical field of computer vision, and discloses a road damage detection method based on vision assistance. The method comprises the following steps: firstly, collecting a pavement grayscale image, and establishing a pixel coordinate and a row-column index with the left upper corner as an original point; gaussian smoothing is carried out on the image, and first-order partial derivative is solved in row and column directions to obtain a gradient field; carrying out weighted accumulation on the gradient in a local window to form a symmetric structure tensor, and taking a characteristic value difference as structure energy; second-order and mixed partial derivatives are solved through second-order difference, and curvature tensor is constructed to obtain curvature energy; standardizing the two types of energy, and weighting the two types of energy into coupling energy according to a self-adaptive proportion; setting a threshold value according to the total graph statistics to generate candidate masks; and outputting structured results such as boundaries, geometric centers and the like through four neighborhood connected domain and area and shape ratio screening. The method gives consideration to cracks and pits, improves the accuracy and adaptability, reduces the manual dependence, and is convenient for large-scale batch application.

The invention discloses a seismic data adaptive denoising method and system based on quantum mechanics, and belongs to the field of seismic data denoising, and the method comprises the following steps: mapping seismic data into a quantum potential energy field based on the predictability of a seismic signal in a frequency-space domain, a self-adaptive quantum basis function is constructed by solving a characteristic value problem of a Schrodinger equation; performing sparse representation on the seismic data by adopting a quantum basis function to obtain an expansion coefficient of the signal on a quantum basis; performing noise suppression on the expansion coefficient by adopting an energy-based threshold processing mechanism to obtain a denoised coefficient; and reconstructing de-noised seismic data through quantum basis function linear combination by using the de-noised coefficient. The method can be seamlessly connected with an existing seismic data processing flow, is convenient for seismic data processing personnel to use, and improves the analysis precision of seismic data.

The invention relates to a large-scale power system discrete eigenvalue parallel computing method based on surrounding channel integration. Comprising the following steps: performing contour integral spectrum transformation on a system state matrix, and determining characteristic value dominance; differentiated integral curves are designed based on characteristic value dominance, the number of characteristic values in each integral curve is estimated, and the initial dimension of the corresponding characteristic subspace is determined; determining the number of initial integral points of each integral curve by adopting a self-adaptive method, and performing self-adaptive integral point configuration on each integral curve; determining an initial subspace dimension of each block based on the number of feature values and the number of parallel integral blocks; performing singular value decomposition on a block basis matrix to construct a standard orthogonal basis; combining the orthogonal basis of each block to form a global orthogonal subspace basis; performing Rayleigh-Ritz projection in a global space to solve a dimension-reduced generalized feature value; and outputting the discrete eigenvalue and the eigenvector. And accurate and efficient calculation of discrete characteristic values of a large-scale power system is realized.