16 results about "Modal matrix" patented technology

The invention discloses a wind field rapid prediction method and system based on optimized Latin hypercube sampling and POD-BPNN, and the method comprises the steps: carrying out Latin hypercube sampling to generate an initial sample point set, introducing a sensitivity weight, and carrying out the iterative optimization of sample distribution through a greedy strategy; assembling the CFD numerical simulation flow field data of all sample points in the sample point set into a flow field snapshot matrix, and determining a dynamic multi-target truncation order and a corresponding POD mode and coefficient; building and training a BPNN model, packaging the trained BPNN model, the POD modal matrix, the mean field and the flow field reconstruction logic into an FMU module, obtaining the FMU module which can be called in a cross-platform manner, and achieving the real-time prediction of a wind field. The invention relates to the technical field of wind field prediction, significantly improves the precision and calculation efficiency of wind field prediction, and provides an efficient and accurate solution for the rapid prediction of a wind field.

The invention relates to the technical field of signal processing, in particular to a pipeline guided wave signal processing method and system. The method comprises the following steps: acquiring a multi-channel ultrasonic guided wave signal through a sensor array circumferentially arranged around a pipeline, and converting the multi-channel ultrasonic guided wave signal into a frequency domain response matrix; calculating a circumferential amplitude distortion degree based on the power spectrum amplitude distribution of each channel; constructing a modal coupling matrix by combining the circumferential amplitude distortion degree and the theoretical wave number of the ideal pipeline; correcting the standard orthogonal modal matrix to generate a modal projection matrix; and decomposing the frequency domain response matrix by using the projection matrix, and separating a structure echo component and a defect signal component. According to the method, the influence of geometric defects such as ovality deviation is evaluated more accurately, energy exchange between modeling modes is accurately established, and orthogonality hypothesis is corrected; modal energy leakage is inhibited, and the decomposition accuracy is improved; signals are effectively separated, coherent noise interference is solved, and the accuracy and reliability of pipeline defect detection are improved.

The invention relates to the technical field of wind power plant power control, in particular to a wind power plant dynamic power distribution optimization method based on projection dimensionality reduction. The method comprises the following steps: collecting a high-dimensional original operation data stream of a wind power plant, and preprocessing to output a high-dimensional state vector; extracting a dominant mode matrix through an intrinsic orthogonal decomposition algorithm, and projecting a high-dimensional state vector to a low-dimensional space; future modal coefficient evolution is predicted through an autoregression prediction model, and a low-dimensional rolling optimization problem is constructed and solved through a sequential quadratic programming algorithm; reconstructing the optimal low-dimensional modal coefficient vector into a target power instruction of each fan, and issuing and executing the target power instruction; and updating the dominant mode matrix through an incremental singular value decomposition algorithm and correcting parameters of the autoregressive prediction model. According to the method, projection dimensionality reduction from high-dimensional data to a low-dimensional space is realized through intrinsic orthogonal decomposition, and the problem of low optimization solution efficiency caused by high data dimensionality of a traditional method is solved.

The invention is suitable for the field of portal frames, and discloses a portal frame dynamics analysis method which comprises the following steps: establishing a full-order finite element model of a portal frame, and obtaining a mass matrix and a stiffness matrix of the full-order finite element model; dividing the full-order finite element model into a plurality of substructures based on the modular structure characteristics of the portal frame, and decomposing the degree of freedom of each substructure into an internal degree of freedom and a boundary degree of freedom; constructing a fixed boundary main modal matrix and a constraint modal matrix of each substructure, and constructing a reduced-order basis matrix; performing modal polycondensation on a mass matrix and a stiffness matrix of the full-order finite element model by using the reduced-order basis matrix to generate a reduced-order mass matrix and a reduced-order stiffness matrix, and constructing a reduced-order kinetic equation; and solving the reduced-order kinetic equation to obtain reduced-order modal coordinates, carrying out inversion reduction in combination with the reduced-order basis matrix and the reduced-order modal coordinates to obtain a displacement response vector of the full-order finite element model, and retaining key kinetic information of the portal frame to the greatest extent through reduced-order efficiency improvement.

The invention discloses a transient signal detection and sorting method based on time-frequency image deep learning, and relates to the technical field of signal sorting. Based on a hypergraph model and core polymorphic sites, the hypergraph model is divided into a plurality of independent substructures by analyzing the distribution characteristics of hyperedge dynamic weights, independent modal analysis is performed on each substructure, and a transient signal detection and sorting result is obtained. Extracting a time-frequency distortion coefficient matrix and an interference response mode matrix; inputting the multi-modal data into a multi-modal network to generate a homogeneous transformation matrix; and based on the homogeneous transformation matrix, deriving attitude error parameters and translation error parameters, feeding back the attitude error parameters and the translation error parameters to a feature input layer and a decision layer of the multi-modal deep learning detection and sorting model, completing dynamic correction of model parameters, and detecting and sorting real-time signals again after model correction. According to the sorting method, the detection rate and sorting accuracy of transient signals are remarkably improved in real-time processing, and meanwhile, the false alarm and leak detection risks are reduced.

The present application relates to the technical field of signal processing, and particularly relates to a pipeline guided wave signal processing method and system. The method comprises the following steps: acquiring multi-channel ultrasonic guided wave signals through a sensor array arranged circumferentially around a pipeline, and converting the signals into a frequency domain response matrix; calculating a circumferential amplitude distortion degree based on the amplitude distribution of the power spectrum of each channel; constructing a modal coupling matrix in combination with the circumferential amplitude distortion degree and an ideal pipeline theoretical wave number; generating a modal projection matrix by correcting a standard orthogonal modal matrix; and decomposing the frequency domain response matrix by using the projection matrix to separate a structure echo component and a defect signal component. The present application more accurately evaluates the influence of geometric defects such as ellipticity deviation, accurately models the energy exchange between modes and corrects the orthogonality assumption, suppresses modal energy leakage, improves the decomposition accuracy, effectively separates signals, solves the interference of coherent noise, and improves the accuracy and reliability of pipeline defect detection.

The invention belongs to the technical field of steel structure floor design, and particularly relates to a transient response analysis method for converting structural damping into equivalent viscous damping in a full frequency band, which comprises the following steps of: 1, establishing a finite element model of a structure, and outputting characteristic matrixes of the structure, including a stiffness matrix and a mass matrix; 2, performing modal analysis based on the finite element model of the structure to obtain a modal matrix of the structure; 3, calculating a generalized stiffness matrix and a generalized mass matrix of the structure based on the stiffness matrix, the mass matrix and the modal matrix; step 4, calculating viscous damping of each order vibration mode of the structure based on the generalized stiffness matrix; 5, calculating the transient response of each order vibration mode of the structure based on the generalized stiffness matrix, the generalized mass matrix and the viscous damping of each order vibration mode of the structure; and 6, converting the transient response of each order vibration mode of the structure to a physical coordinate to obtain the transient response of the structure at each moment.

The invention provides a PCA-based rear duct variable area ejector generation method and device, equipment and a storage medium. The method comprises the following steps: converting original coordinate data of a basic ejector shape into low-dimensional controllable parameters through a CST parameterization method; the method comprises the following steps: performing disturbance on CST fitting parameters to generate a large number of disturbance shape samples, compressing a high-dimensional parameter space to a low-dimensional modal coefficient space formed by a small number of main modals by extracting a main modal matrix of shape deformation, further screening the first K main modals according to a variance contribution rate, determining a value range of modal coefficients, and determining the value range of the modal coefficients; uniformly sampling in the K-dimensional modal coefficient space by adopting a Latin hypercube sampling method; and finally, reconstructing the sampled target modal coefficient into the shape coordinate data of the target ejector through inverse principal component analysis transformation. According to the method, on the premise of ensuring physical rationality of the shape of the ejector, uniform coverage and efficient exploration of a design space are realized with a small number of samples, and the shape generation efficiency of the ejector is remarkably improved.

The invention discloses a lightweight optimization method of a prestress self-balancing tension lattice structure based on buckling dependence, and relates to the technical field of structural engineering and computational mechanics. Base structure generation and balance analysis: generating a node set by adopting a discretization method according to a design domain, forming a base structure, and establishing a balance matrix based on a geometric topological relation; carrying out dimensionality reduction preprocessing of symmetry driving, carrying out singular value decomposition on the balance matrix, obtaining a self-stress mode matrix, judging a symmetric group, converting a symmetric self-stress mode matrix, obtaining an independent base, and representing a force density vector as a product of the independent base and an independent design variable; and based on LPSO topological optimization, decoding, adaptive component classification, dual buckling stability check, fitness evaluation and convergence judgment are carried out until iteration is terminated. Through a symmetry-driven dimension reduction strategy and a dual buckling constraint mechanism, high-stability lightweight structure design capable of being periodically assembled is realized, and the optimization calculation efficiency is greatly improved.

The invention relates to a state prediction method and device for a multi-grid-connected inverter system. The method comprises the following steps: based on a topological structure of a single grid-connected inverter system, obtaining an impedance model corresponding to the single grid-connected inverter system, constructing a power grid structure node admittance matrix, further determining an impedance matrix corresponding to a multi-grid-connected inverter system, performing eigenvalue decomposition on the impedance matrix to obtain a modal matrix corresponding to the impedance matrix, and determining the impedance matrix corresponding to the multi-grid-connected inverter system. Analyzing a modal amplitude boundary condition corresponding to any parameter change in the multi-grid-connected inverter system to obtain a safe operation boundary condition, and predicting the state of the multi-grid-connected inverter system based on the safe operation boundary condition; by adopting the method, the accuracy of predicting the state of the multi-grid-connected inverter system can be improved.

The embodiment of the invention provides a question recommendation method and device based on multi-modal matrix fusion and dynamic weighting, and the method comprises the steps: constructing a basic matrix through historical user related data, determining a plurality of corresponding recommendation algorithms through the basic matrix, setting a recommendation algorithm weight for each recommendation algorithm, comprising a basic weight and a user behavior weight, the basic matrix and the user behavior weight are updated in an incremental updating and momentum updating mode, recommendation calculation is conducted on the updated basic matrix through recommendation algorithms in parallel, and recommendation scores output by the recommendation algorithms are obtained. The method comprises the steps of updating user behavior weights, updating recommendation algorithm weights through the updated user behavior weights, performing weighted fusion on scores of recommendation algorithms through the recommendation algorithm weights, determining a final recommendation result, and collecting new user related data for performing new incremental updating and momentum updating. The recommendation accuracy and efficiency of the question recommendation system can be improved.

The application provides a sensor optimization arrangement method and system based on structural vibration response, and belongs to the technical field of equipment safety detection. The application comprises the following steps: establishing a finite element model of a structure to be monitored; calculating an equivalent energy matrix and an average value of diagonal elements of the equivalent energy matrix; starting from all degrees of freedom, sequentially deleting the minimum value on the diagonal of the equivalent energy matrix, and simultaneously deleting the degree of freedom represented by the value; using the remaining degrees of freedom to construct an iteration matrix; sorting the main diagonal elements on the iteration matrix, selecting the smallest element, and deleting the degree of freedom vector corresponding to the element from the modal matrix; repeating the step until the number of remaining degrees of freedom is equal to the number of sensors to be arranged, wherein the sensors are arranged on the remaining degrees of freedom. The application not only considers high-energy measuring points and improves the noise resistance of the measuring point combination, but also fully guarantees the modal independence and modal information amount of the measuring point combination, and is more suitable for the application of sensor optimization arrangement in actual engineering practice.

The application belongs to the field of gantry cranes, and discloses a gantry dynamics analysis method, which comprises the following steps: establishing a full-order finite element model of a gantry crane, and obtaining a mass matrix and a stiffness matrix of the full-order finite element model; based on the modular structure characteristics of the gantry crane, dividing the full-order finite element model into a plurality of substructures, and decomposing the degrees of freedom of each substructure into internal degrees of freedom and boundary degrees of freedom; constructing a fixed boundary principal modal matrix and a constraint modal matrix of each substructure, and constructing a reduced-order basis matrix; using the reduced-order basis matrix, performing modal condensation on the mass matrix and the stiffness matrix of the full-order finite element model, generating a reduced-order mass matrix and a reduced-order stiffness matrix, and constructing a reduced-order dynamics equation; solving the reduced-order dynamics equation to obtain a reduced-order modal coordinate, and combining the reduced-order basis matrix and the reduced-order modal coordinate to inversely restore a displacement response vector of the full-order finite element model, which maximally retains the key dynamics information of the gantry crane through reduction.

The invention discloses an excitation point optimization selection and aerodynamic force equivalence implementation method in a ground flutter test, and relates to the technical field of aeroelasticity tests of aircrafts. The method comprises the following steps: constructing a system modal matrix, introducing a weighting coefficient matrix based on a prior aeroelasticity analysis result, and constructing a weighting effective independent matrix; calculating diagonal elements of the weighted effective independent matrix, and gradually removing candidate measuring points which contribute the minimum linear independence of the modal vector by adopting an iterative deletion method until a preset number of optimal excitation points are left; and estimating modal coordinates of the structure based on response data of the optimal excitation point, and realizing aerodynamic equivalence through generalized force condensation. A traditional measuring point arrangement method is often difficult to give consideration to measuring point representativeness and global recognition precision, and according to the method, excitation points are optimally selected by introducing a weighting coefficient matrix, so that a limited number of excitation points can reflect a main vibration mode of a structure to the maximum extent, the measurement precision of a key flutter mode is ensured, and thus the estimation precision of modal coordinates is improved.

The invention discloses a short-time traffic flow prediction method of a traffic flow combination model based on graph convolution. The method comprises the following steps: 1, abstracting a traffic network into a graph formed by nodes and edges; 2, processing the traffic flow sample by using a fast ensemble empirical mode decomposition algorithm to obtain a modal matrix of the traffic flow, and performing singular value decomposition so as to construct a traffic flow characteristic matrix; 3, inputting the normalized traffic flow characteristic matrix into a short-time traffic flow prediction network formed by a simplified graph convolutional neural network, a Transform layer and a full connection layer for processing to obtain predicted traffic flow data; and 4, training the short-time traffic flow prediction network, and obtaining an optimal traffic flow combination prediction model for predicting future traffic flow data. According to the method, the characteristics of short-time non-stationarity, spatial correlation and time dependence of the traffic flow time sequence can be combined, so that the prediction precision and the prediction speed of the traffic flow can be improved, and intelligent management and control of a traffic system are realized.