19 results about "Matrix analysis" patented technology

The invention relates to a weak power distribution network fault rapid identification and positioning method, and belongs to the field of power distribution network fault identification. According to the method, a fault type identification method based on multi-point time-frequency matrix fusion analysis is provided, an improved adaptive noise complete set empirical mode decomposition method and a Hilbert band-pass filtering algorithm are utilized to construct time-frequency matrixes corresponding to three-phase voltage, current and zero-sequence voltage waveforms at a bus after a fault; performing singular value decomposition, obtaining a singular spectrum and a characteristic index corresponding to the singular spectrum, and inputting the singular spectrum and the characteristic index into a multi-classification SVM model to realize identification of a weak power distribution network fault type; the invention further provides a fault section positioning method based on synchronous waveform feature extraction and matrix analysis, synchronous waveform features at multiple measurement points are extracted from a multi-domain angle, and an analysis object is converted into a random matrix feature value from a fault feature matrix by introducing a random matrix theory. And determining a cluster center point of the fault section in the feature value space by using a historical sample, and further performing online fault positioning to determine the fault section.

The invention discloses a method for accurately predicting upheaval deformation of adjacent tunnels caused by foundation pit excavation by considering the influence of soil friction, which comprises the following steps of: firstly, determining size parameters of a foundation pit and an existing tunnel and physical and mechanical parameters of the tunnel and a soil body, and determining a stress mode of the tunnel and a model sketch of interaction of the foundation pit and the tunnel; determining the additional stress of the tunnel; then establishing a total energy formula of a tunnel upheaval deformation system of a Pasternak foundation model considering soil friction, and obtaining a tunnel upheaval deformation w matrix analysis based on an energy variation principle; and finally, according to an Euler-Bernoulli beam theory, a bending moment expression and a shearing force expression of the tunnel are obtained. According to the method, based on the Pasternak foundation model, the influence of axial soil body friction of the tunnel on tunnel deformation is considered, so that the calculated data better conform to the measured value. The method is simple and practical in matrix analysis, and has guiding significance for predicting the upheaval deformation of the adjacent tunnel caused by excavation of the foundation pit.

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

The invention discloses an actuator vulnerability analysis method for a non-unique identifiable information physical system, and the method comprises the steps: combing an attack scene and parameters, constructing a vulnerability analysis target model set based on the information of attackers and defenders, extracting the determined line components of all possible system model parameters through matrix analysis and operation, and carrying out the vulnerability analysis. And constructing a criterion matrix of a plurality of sub-problems. And based on the property of the criterion matrix, determining the minimum number of the attack executors, and obtaining an optimal attack executor set of the sub-problem by taking whether the criterion matrix is in a full rank or not as a judgment standard through combined traversal. And generating an intersection of the optimal solutions of all the sub-problems as a global optimal solution. And constructing a simulation system to verify the defense effectiveness, and effectively attacking all states of the system. Attack and defense cognition depth alignment is achieved, and the problem that fragile point pre-judgment is not accurate is solved.

The invention relates to the technical field of test design, in particular to a key factor mining-based accommodating and arranging condition optimization method, medium and equipment, which are characterized in that accommodating and arranging conditions are generated by analyzing a target text, an initial simulation queue is constructed in combination with database retrieval, and baseline feature distribution is analyzed, so that representative research crowds can be obtained without real object recruitment; the test cost is reduced; a panoramic object feature matrix is constructed by integrating multi-dimensional data, the limitation of a single feature is broken through, and comprehensive data support is provided for key factor mining; the contribution degree of each characteristic variable to the preset key outcome is analyzed based on the matrix, and the key characteristic variables are positioned, so that the experience driving is changed into the data driving, and the scientificity and the pertinence of the accommodating and arranging condition design are improved; the accommodating and arranging conditions are dynamically adjusted through the closed-loop optimization process, it is ensured that the target accommodating and arranging condition set gives consideration to queue representativeness, feature pertinence and distribution rationality, and the design scientificity and success rate of clinical tests are improved.

The application provides a kind of addressing matrix analysis method for multi-dimensional identification system, by the instruction of terminal input is disassembled into different components, combined into addressing information;Structured data based on multi-dimensional identification attribute, namely addressing matrix, is constructed;Terminal sends addressing request message to access routing device;The addressing request message is forwarded to regional server;K-d tree index is constructed;Using the pre-constructed k-d tree index, the terminal node coordinate C closest to the query point P in Euclidean distance is found;Match appropriate terminal node coordinate;After locking the target terminal through coordinate mapping and similarity matching algorithm, extract the key identifier as the analysis result of query;The method can significantly improve the positioning accuracy and query efficiency of terminal node, can significantly reduce the delay of data transmission, and provide faster response time.

The present invention discloses a method for precise modeling of multi-energy and mass flow integration of green hydrogen metallurgical integrated energy system. First, by focusing on the green hydrogen metallurgical integrated energy system, and aiming at the problem that the transmission characteristics of heterogeneous energy flows and material flows are greatly different and difficult to describe in a unified manner, a unified mathematical equation for multi-energy and mass flow transmission based on differential dynamics is proposed. Secondly, in order to simplify complex dynamic problems, the time domain dynamics is converted into a static algebraic problem, and a generalized transmission model based on distributed parameters is constructed. In response to the challenge of heterogeneous distribution parameters of multi-energy and mass flow branches and nodes, an equivalent modeling method for branches and node external ports is further proposed, and the description system of multi-energy and mass flow integrated modeling is improved. Finally, based on the above-mentioned algebraic model, the matrix modeling of the multi-energy and mass flow network is studied, a standardized matrix analysis paradigm is proposed, and by constructing a unified Jacobian matrix, the rapid calculation of the multi-energy and mass flow distribution is realized, providing precise support for the coordinated regulation of multiple links in the system.

The present invention discloses a method for fast mode decomposition of anti-noise fiber laser, including: obtaining a spot to be decomposed with noise, rearranging the eigenmodes into vectors in sequence, and then multiplying the N eigenmode vectors pairwise to obtain a coefficient matrix A; obtaining a mode information vector X based on the coefficient matrix A; determining the errors of the elements in the mode information vector X based on the L2 norm of the pseudo-inverse matrix A<supgt;‑1< / supgt>, and deleting the elements with larger errors until at least 2N‑1 elements remain in the mode information vector X; establishing a nonlinear equation system based on the remaining elements in the mode information vector X, obtaining the mode coefficients and mode phases of each eigenmode restored, and generating a first reconstructed spot therefrom to obtain the mode decomposition result. The present invention is applied to the field of fiber laser mode decomposition. Based on the matrix analysis method, redundant feature information is screened and removed, so as to improve the measurement accuracy of mode coefficients and mode phases in a noise environment.

The invention provides a matrix analysis method, which comprises the following steps of: opening client software of battery detection equipment, opening a working step editor on the client software, and performing working step flow parameter and matrix configuration editing; after the work step process is edited, a to-be-tested channel is selected and clicked to start, the client packages work step process parameters and sends the work step process parameters to the server, the server sends the work step process parameters to the middle computer, and the middle computer analyzes the work step process parameters and then starts the channel to start testing; the middle computer is charged or discharged or shelved according to the working step flow parameters, and when the matrix analysis function is executed, the table look-up step is cyclically executed by taking the table look-up time interval of the matrix subitems as a period, so that a table look-up operation result value is obtained; and ending the test until the step flow of the test channel is executed. According to the method, the process flow test is more flexible, and richer analog data test requirements of users are met; the process step flow conversion and editing difficulty is reduced, the process step flow editing efficiency is greatly improved, and the user experience is improved.

The utility model provides a kind of power station parameter matrix analysis method, belongs to power station fault diagnosis technical field, solve how to design a kind of power station parameter matrix analysis method, solve the numerous power station parameters, data effective information density is low, lead to the problem that fault cause analysis is difficult, and the massive data stream generated occupies a large amount of storage space, by power station parameter data matrixization representation, constructs parameter data matrix, by matrix operation, the rank of matrix is as quantization index, effectively distinguishes the information density of parameter, the correlation between different parameters can be analyzed by the multi-parameter matrix constructed, not limited by parameter number and time sequence length, and by matrix similarity operation, judge the similarity of unit state, provide quick judgment basis for similar fault in later period;Dimensionality reduction is carried out to operation data matrix using matrix singular value decomposition method, retain the part of large singular value proportion, can extract fault feature, realize compressed storage, save storage space.

Quantum Entanglement Model-Matrix Analysis systems / processes are designed to detect and mitigate accuracy downgrades in Explainable AI (XAI) systems. Using quantum entanglement principles, it monitors relationships within data matrices during AI computations, identifying anomalies and deviations caused by poisoned data or cyberattacks. The system includes hash-key tracking that verifies the integrity of both the final output and the computational steps leading to it, ensuring any manipulation of formulas or macros is detected. A traceability matrix tracks the origin of data, enhancing transparency and accountability. The system's continuous scanning feature detects anomalies in real time, while swarm intelligence-based task distribution allows decentralized auditing of data integrity. Additionally, the system includes a self-healing capability to correct detected anomalies, ensuring consistent reliability of AI outputs. This invention offers a robust solution for maintaining the accuracy and security of AI computations in complex environments, protecting against data corruption and computational manipulation.

The invention discloses a directed ring causal structure discrimination and estimation method for network intelligent operation and maintenance, relates to the technical field of network intelligent operation and maintenance, and provides a solution based on high-order cumulant matrix analysis for causal discovery and effect estimation requirements of a feedback loop in a network system. A unified cumulant matrix construction principle is established, so that cumulant total orders of main diagonal elements and auxiliary diagonal elements of the matrix are the same; on the basis, the system firstly utilizes rank conditions of a forward three-order cumulant matrix and a reverse three-order cumulant matrix to realize loop detection; and after the existence of the feedback loop is confirmed, constructing a mixed third-order cumulant matrix, constructing a solvable equation about a causal coefficient by utilizing determinant properties, directly completing loop effect estimation, and realizing integration of loop discrimination and effect estimation. According to the method, a complex intra-ring causal effect estimation problem is converted into an algebraic problem which can be solved directly, and accurate causal support is provided for network performance optimization, resource scheduling and safety protection.

The application discloses a profile optimization design method in a positioning system based on RIS, and the method comprises the following steps: constructing a system model, determining system component elements, i.e., an access point (AP), RIS, UE and their position relationship, establishing a signal model and a measurement model, performing Fisher information analysis, calculating a correlation matrix and parameters, and deducing a position error bound expression. Furthermore, an optimal RIS profile design strategy is proposed, an optimization problem is constructed, and the optimal covariance matrix and RIS profile design are derived by using matrix analysis and deduction. Meanwhile, considering the hardware limitation, a constrained RIS profile design is proposed, a constrained optimization problem is constructed, and the RIS profile satisfying the actual hardware constraint is obtained by using the alternating minimization technology. The application improves the positioning accuracy of user equipment (UE).

The invention discloses a carrier rocket generalization test optimization method based on matrix analysis, which is obtained by performing continuous pressure reduction optimization on the carrier rocket generalization test optimization method, and adopts a matrix analysis technology, a test modeling method based on matrix analysis and a test recombination optimization method based on a heuristic algorithm from the perspective of forward design. The method focuses on a combination optimization technology represented by matrix analysis, takes a test state as input, forms a related test flow through optimal combination of test items, and completes algorithm optimization and test flow optimization.

The invention discloses an e-commerce digital marketing advertisement pushing method and device based on a distributed polyhedron matrix. The method comprises the following steps: representing user and advertisement features as high-dimensional matrixes respectively; a matching process of user portraits and advertisement features is expressed as a geometric problem in a high-dimensional space, an advertisement pushing task is decomposed into a plurality of sub-tasks, and the sub-tasks are executed in parallel in a multi-core processor or a distributed system; performing matrix decomposition on the user portrait matrix and the advertisement feature matrix, extracting potential features of the user and the advertisement, calculating the matching degree between the user and the advertisement, and optimizing the calculation process; defining a target function, defining constraint conditions through a polyhedral model and representing the constraint conditions as linear inequalities, and solving an optimal advertisement pushing scheme through an iterative optimization algorithm in combination with matrix analysis and the polyhedral model; reinforcing learning is introduced to dynamically adjust a pushing strategy of the advertisement, and the pushing strategy is combined with a polyhedral model and matrix analysis to continuously learn an optimal pushing strategy; in the user portrait matrix, a social network polyhedral model is constructed in combination with multi-modal data based on the structure of the social network and the influence relationship between the users; data changes are captured in real time, user feedback is collected, a feature matrix is updated, unified construction is carried out in cooperation with multi-platform data, meanwhile, a self-adaptive algorithm parameter adjusting mechanism is designed, and related parameters are automatically adjusted according to the real-time changes of the data and the operation effect of an algorithm.

The invention discloses a convolution blind source separation method and system based on impulse response compression and a storage medium, and belongs to the field of blind source separation. According to the method, an impulse response compression model composed of compression transformation and infinite norms is designed, an objective function is constructed according to the model, the objective function is solved based on a gradient descent algorithm, impulse response compression is achieved, the impulse response length is shortened, and the influence of a high reverberation environment is reduced. In addition, an independent low-rank matrix analysis method for iterative source control is introduced, inversion of multiple matrixes is avoided, the robustness of the convolutional blind source separation method is improved, and the method is easier for engineering implementation. Compared with an existing convolution blind source separation method, the designed method has a better high reverberation suppression effect, the impulse response compression model does not depend on parameter setting, and the method has the advantages of high precision, high robustness and the like and is wide in application prospect.

A method for determining the reconstruction target based on a dimension-reduced controllability matrix includes: S1, providing the dynamic equations of a spacecraft control system; S2, providing the Lie derivative calculation rules applicable to the system described in S1; S3, establishing the controllability matrix of the spacecraft control system using the Lie derivative calculation rules given in S2; S4, decomposing the common subspace of the controllability matrix by performing matrix transpose and polar coordinate transformation on the system controllability matrix in S3; and S5, determining the reconstruction target using the common subspace obtained in S4. The dimension reduction representation method of the system controllability matrix obtained in this invention eliminates the need for complex Lie derivative calculations and reduces the dimension of the observability matrix analysis. This algorithm is reliable and consumes few computational resources.

The invention provides a multi-channel electroencephalogram acquisition system based on Hessian matrix analysis and a signal processing method. The system comprises a multi-channel brain electrode array, a signal acquisition unit, a signal processing unit and a visual display unit, the multi-channel brain electrode array comprises a central electrode and at least eight surrounding electrodes surrounding the central electrode to form a Hessian electrode array; brain potential signals of at least nine electrode points of the Hessian electrode array are synchronously collected, and a Hessian matrix representing the local curvature of a potential field at the position of a central electrode is constructed; and performing eigenvalue decomposition on the Hessian matrix to obtain at least one main eigenvalue and a main eigenvector corresponding to the main eigenvalue, and calculating a main direction angle according to the main eigenvalue to represent the intensity of the electroencephalogram activity collected at the position of the central electrode and the direction of an electroencephalogram signal source. According to the method, accurate analysis of the directivity of the electroencephalogram activity source can be achieved, good spatial filtering capacity and high signal-to-noise ratio are achieved, and weak local electroencephalogram signals can be captured.

The invention provides a time-delay neural network non-fragile state estimation method and device, a medium and a product, and relates to the field of networked system control. In order to obtain state information of a time-delay memristor neural network circuit system, a non-fragile state estimator is established by introducing a dynamic saturation function. Determining an estimation error system based on the established system model and the estimation system model, and performing augmentation processing to obtain an augmented system model; through constructing an energy function and setting a constraint condition of an estimation error, a matrix analysis technology and a linear matrix inequality method are used to obtain a sufficient condition satisfying augmented system performance constraint, and a gain parameter of a non-fragile state estimator model is determined to obtain a final non-fragile state estimator model. And determining the state of the neurons of the time-delay neural network. According to the method, the conditions of negative effects and estimation performance reduction caused by time lag and abnormal disturbance interference in actual production can be inhibited or attenuated.