44 results about "Nonlinear structure" patented technology

The invention relates to the technical field of pulsed ion beam processing, and discloses a positioning system for a pulsed ion beam processing optical element, which can accurately predict nonlinear structure deformation in a complex thermal environment and eliminate compensation deviation caused by a traditional linear model. A multi-sensor data fusion mechanism effectively suppresses the interference of local measurement noise on a control decision, and improves the reliability of a compensation strategy. And a closed-loop control system realizes real-time verification and dynamic optimization of the compensation effect, and machining precision reduction caused by error accumulation is avoided. The submicron executing mechanism ensures that the thermal drift compensation amount is accurately converted into platform pose adjustment, the high-precision optical element machining requirement is met, and the problem that a traditional positioning system is incomplete in model input due to the single temperature field information collection dimension is solved. Meanwhile, the dynamic characteristics of the temperature-displacement coupling relation are verified through high-precision displacement data.

The invention discloses a wind turbine blade semi-coupling aeroelastic modeling method considering nonlinear deformation, and belongs to the technical field of fan simulation calculation. In combination with calculation parameters used by three-party software, establishing a blade structured load model and a blade correction aerodynamic model, calculating an aerodynamic load and a structural load, and superposing: superposing the aerodynamic load, the gravity load and the centrifugal force load of the blade as an external load of a nonlinear structure control equation of the blade, and taking the external load as a calculation parameter; according to the method, the blade nonlinear structure model and the semi-coupling simulation process of the three-party wind turbine simulation software are constructed, and under the same simulation environment and load conditions, the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation, so that the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation calculation. The higher-precision non-linear motion response characteristics of the super-long flexible wind turbine blade are calculated, and a good effect can be achieved in the aspects of initial structure design and response evaluation of the large wind turbine blade.

The embodiment of the invention discloses a nonlinear system dynamic characteristic calculation method based on a structural dynamic modification method, and the method achieves the high-precision reduced-order modeling of a nonlinear structure through enabling a complex nonlinear effect to be equivalent to an additional stiffness field and combining with a structural dynamic modification algorithm. And based on the decoupled linear substructure modal space, a nonlinear correction operator is introduced, and the global dynamic response of the nonlinear structure is solved through a resolving method after modification. Structural vibration frequency, vibration shape and energy dissipation characteristics under a nonlinear effect can be efficiently obtained, and dynamic characteristics under different nonlinear amplitudes can be obtained. Compared with a traditional iteration method, the method has the advantage that the calculation complexity is remarkably reduced by equivalently linearizing a nonlinear part.

The invention discloses a constitutive model constrained physical information neural network seismic response prediction method, and relates to the field of artificial intelligence and big data analysis, a CM-PINNs framework shows reliable performance in the aspect of predicting nonlinear structure response under seismic excitation, and compared with an existing PhyLSTM model, the method has the advantages that the reliability is high, and the reliability is high. Excellent performance is shown in the aspects of prediction precision and robustness; the framework has good expandability, can be expanded from a single-degree-of-freedom system to a multi-layer shearing building structure, and provides an efficient and reliable method for earthquake response prediction of a complex structure system.

The invention provides a structure vulnerability analysis method and system based on earthquake magnitude, distance and site conditions, a storage medium and a computer program product. The method comprises the following steps: adopting a preset sampling algorithm to obtain preset earthquake magnitude, distance and site conditions, and acceleration time history samples corresponding to the preset earthquake magnitude, distance and site conditions; based on the acceleration time-history sample, performing nonlinear dynamic time-history analysis on a to-be-evaluated structure by using a finite element analysis method to obtain nonlinear structure seismic response corresponding to the to-be-evaluated structure and the acceleration time-history sample; establishing a change relationship between the statistical parameters of the seismic response of the nonlinear structure and the preset magnitude, distance and site conditions by using a regression fitting method; and constructing a vulnerability curved surface of a to-be-evaluated structure based on the preset earthquake magnitude, distance and site conditions based on the statistical parameters of the seismic response of the nonlinear structure and the change relationship between the preset earthquake magnitude, distance and site conditions. According to the embodiment of the invention, the four elements of a seismic source, a propagation path, a site and a structure can be completely coupled, and the method can be suitable for earthquake disaster risk analysis and rapid evaluation of post-earthquake disasters under various medium-short period structure systems.

The invention discloses an intelligent dynamics rapid simulation method. The invention relates to the technical field of finite element simulation, and solves the problem that real-time prediction of high-dimensional nonlinear structural dynamics and online inversion of impact acceleration cannot be met in the prior art. According to the invention, based on a deep learning algorithm, structure-level reconstruction of an existing operator learning normal form is realized through a mapping thought from a graph structure depth operator function to a function. According to the method, the double-tower type branch-trunk topology on which DeepONet leans is thoroughly got rid of; according to the method, a space correlation mode and a time evolution rule are synchronously coded in a hidden space and are directly projected to a function at a target moment to be expressed without following a frequency domain kernel integral path of the FNO instead of an end-to-end serial progressive single calculation path. According to the design, the function-to-function micro-mapping capability is reserved on the semantics of operator approximation, and induction deviation caused by structure splitting of a frame is overcome in a graph domain time sequence task.

The invention relates to the technical field of gene editing, and discloses an sgRNA activity prediction method and device based on deep features and automatic machine learning. The method comprises the following steps: acquiring sgRNA training data with editing efficiency labels and discretizing the sgRNA training data into classification labels; after the sgRNA and the PAM sequence are spliced, inputting the spliced sgRNA and PAM sequence into a pre-training depth sequence model to extract high-dimensional features; obtaining a low-dimensional feature vector through nonlinear dimensionality reduction; an automatic machine learning framework is combined with cross validation, and a prediction model is automatically trained and optimized based on low-dimensional features; and finally, carrying out activity prediction and sorting screening on candidate sgRNA by utilizing the model. According to the method, through fusion of deep semantic extraction, nonlinear structure maintenance and a full-automatic modeling process, the accuracy, robustness and development efficiency of sgRNA activity prediction are effectively improved, and an efficient pilot screening tool is provided for a gene editing experiment.

The invention relates to a multi-target equivalent static wind load calculation method suitable for a nonlinear structure. The method comprises the following steps: firstly, carrying out a nonlinear structure wind tunnel test and establishing a corresponding finite element model; secondly, obtaining an average component of the equivalent static wind load through a wind tunnel test result, obtaining an equivalent static wind load not including the average component through time-history analysis of a finite element model, namely a pulsation component, and obtaining a multi-target linear equivalent static wind load through combined analysis of the pulsation component and the average component; and finally, on the basis of the multi-target linear equivalent quiet wind load, correcting the multi-target linear equivalent quiet wind load in combination with a nonlinear coefficient, calculating to obtain a multi-target nonlinear equivalent quiet wind load, and expressing the multi-target nonlinear equivalent quiet wind load as a wind pressure coefficient form. The simultaneous equivalence of multiple extreme responses of a nonlinear structure is realized, and a convenient and accurate equivalent static wind load is provided for engineering design.

The invention relates to a structure period segment division method based on structure dynamic response similarity, which is characterized in that on the basis of structure dynamic response under seismic action, objective division of structure period segments is realized by constructing response characteristic matrixes corresponding to different structure periods and introducing a fuzzy clustering mechanism to perform unsupervised classification on the structure periods. According to the method, linear and non-linear structure response characteristics are considered at the same time, the optimal period segmentation number and the boundary threshold are determined in combination with the clustering effectiveness evaluation index, and a more accurate thought and method are provided for more rapidly evaluating seismic damage of the structure in the specific period range and matching input vibration of the structure in the specific period range.

The application discloses a kind of sensor fault detection method and device of structural health monitoring system, it is related to structural health monitoring technical field, comprising: obtaining the nonlinear structure monitoring data of sensor system collected by structural health monitoring system, nonlinear structure monitoring data is handled using improved geometric PNL hybrid model, obtain linear to-be-separated mixed signal, using FastICA model to process to-be-separated mixed signal, obtain multiple independent elements, determine the separation matrix of improved geometric post-nonlinear independent component analysis model according to multiple independent elements, using the processing of improved geometric post-nonlinear independent component analysis to real-time collection nonlinear structure monitoring data, determine whether sensor system exists fault and the sensor of fault occurrence.The method can still complete linearization processing to sensor mixed signal under the condition that prior knowledge is unknown, compared with simple linear ICA analysis algorithm, it is more suitable for complex nonlinear structure.

In a dynamic response test method for a nonlinear structure using a virtual signal provided in the present application, a load is applied to the free end of the nonlinear structure by an actuator according to the target dynamic parameter A(t) of the nonlinear structure, and then an excitation signal is applied to the nonlinear structure to capture the dynamic response of the nonlinear structure. The present application uses a virtual signal to control the actuator to generate a target load to load onto the nonlinear physical structure to simulate target parameters, such as stiffness, damping, and the force under action. Load simulation can be accurately performed through virtual signals, and accurate load simulation can finely control and adjust dynamic parameters such as damping and stiffness to solve the problem of sensitivity to dynamic parameters of nonlinear structures.

The invention relates to a nonlinear structure reduced-order model construction method based on sparse recognition and a mixed mode, belongs to the technical field of structural dynamics analysis and aeroelastic mechanics analysis, and solves the problem that complex motion caused by geometric nonlinearity under large deformation cannot be accurately described in the prior art. Comprising the following steps: S1, establishing a nonlinear finite element model of a target large flexible wing to obtain a training data set; s2, solving a mixed modal basis based on the displacement residual error and SVD (Singular Value Decomposition); s3, establishing a nonlinear stiffness coefficient solving problem model, introducing LASSO regression to establish an LASSO regression optimization objective function, and solving to obtain a sparse nonlinear stiffness coefficient; s4, based on the sparse nonlinear stiffness coefficient and the structural kinetic equation, establishing a nonlinear structure reduced-order model; and S5, applying the nonlinear structure reduced-order model to statics response solution and dynamics response solution of the large flexible wing to obtain statics response and dynamics response results.

The invention provides a seismic data filtering method and device based on kernel principal component analysis and a medium, and belongs to the field of seismic data processing. The method comprises the following steps: extracting a trend time difference attribute of an input three-dimensional seismic data volume; setting a surface element size and extracting a corresponding line data volume according to the surface element size; selecting a target point and calculating surface element coordinates according to the trend time difference data; setting the size of a time window and extracting a small three-dimensional data volume in combination with surface element coordinates; and performing kernel principal component analysis on the small three-dimensional data volume to obtain a first principal component, and taking central point data of the first principal component as filtered data of the target point. According to the method, original data are mapped into a high-dimensional space through nonlinear mapping by adopting kernel principal component analysis, so that nonlinear structures and characteristics in the original data can be better reserved; in addition, the trend time difference attribute of seismic data is also considered, geologic structure data can be more accurately obtained by opening up a time window along the event trend, the problem of data discontinuity caused by the influence of stratum inclination is improved, and the filtering effect is improved.

The application provides a high-order interaction prediction method and device with mixed graph deep learning, which first constructs a drug molecule graph, a microorganism weighted graph, a disease weighted graph and a supergraph connecting the three based on multi-source heterogeneous data such as drug molecular structure, microorganism classification information and disease semantic network, forming a mixed graph structure. Subsequently, through a mixed graph deep learning module fusing a graph convolution network and a supergraph neural network, nonlinear structure features and high-order interaction features of each entity are extracted, and the adaptive fusion of the features is realized by using an attention mechanism. Then, the fused deep features are mapped to the prior expectation of the latent factor matrix in the Bayesian logic tensor decomposition model, a probabilistic graph model is constructed, and the joint adaptive inference of the model parameters, latent variables and deep learning mapping is carried out through a variational expectation maximization algorithm, so that the high-order correlation probability prediction of the whole tensor space is realized without negative sampling.

This invention provides a dual-motor steer-by-wire system and its active fault-tolerant control method. The method includes: a front wheel steering angle tracking controller, a vehicle stability controller, and a nonlinear model control method based on a variable parameter (LPV) model. The front wheel steering angle tracking controller and the stability controller are designed based on a linear quadratic regulator (LQR) and an optimal control algorithm, including: solving for the ideal front wheel steering angle; establishing a dynamic model of the dual-motor steering actuation system; and designing the steering angle tracking controller and the stability controller based on the ideal front wheel steering angle and the steering system dynamic model. The dual-motor active fault-tolerant control includes: comparing the control torque output by the dual-motor torque sensors with that output by the front wheel steering angle tracking controller, calculating the fault coefficient, and optimizing the steering actuator model into a variable parameter (LPV) nonlinear structure containing motor fault parameters. This invention can ensure vehicle safety after a dual-motor failure.

The invention provides a variance-based aircraft structure design parameter global sensitivity analysis method, and aims to solve the problems of high calculation cost, difficulty in quantizing parameter interaction effect and coupling propagation influence and the like in the application of existing Sobol and other methods, the method comprises the following steps: firstly, identifying key structure parameters and establishing a probability model of the key structure parameters; then, a Sobol sequence is adopted to generate a sample matrix, and output response is calculated through an aircraft structure system model; constructing a mixed sample matrix to perform variance decomposition, and respectively calculating a first-order sensitivity index and a total sensitivity index of each parameter to quantify main effect and interaction effect contribution; and finally identifying performance key parameters and interaction sensitive parameters according to the index sequence. According to the method, global analysis of a high-dimensional nonlinear structure system is realized under acceptable calculation cost, the result can provide a quantitative decision basis for lightweight design, tolerance allocation and multidisciplinary collaborative optimization of an aircraft structure, and the design efficiency and reliability are remarkably improved.

The present application relates to the field of structural dynamics analysis and test technology, and particularly relates to a large flexible structure reduced order model parameter identification method based on ground test data, comprising the following steps: installing force sensors and exciters on a large flexible structure to be tested, and dispersively installing multiple acceleration sensors and strain sensors; applying excitation to the large flexible structure by the exciters, and recording the measurement values of the acceleration sensors, strain sensors and force sensors; converting the local acceleration signals to a global coordinate system, and integrating the global acceleration signals to obtain velocity signals and displacement signals; performing modal test on the large flexible structure to obtain modal information, inputting the global acceleration signals, velocity signals and displacement signals into a nonlinear structure reduced order model after modal conversion, and calculating the nonlinear stiffness coefficient by a least square method; the present application can improve the reliability and accuracy of the identification result.

The invention discloses a typical tree species growth prediction method, system and device based on multi-stage multi-factor regression and a medium, and relates to the technical field of tree species growth prediction.The method comprises the steps that multi-source heterogeneous data are collected and preprocessed; dividing the independent variables into different types of influence factors, and performing statistical test on each factor to obtain a preliminary candidate variable set; calculating a variance expansion factor of the candidate variables, and when the expansion factor exceeds a threshold value, reducing the correlation among the preliminary candidate variables by adopting a collaborative path method to obtain a final variable; performing regression modeling through a three-stage modeling method based on the final variable to generate a regression model, and establishing a regression sub-model for each partition; and based on the obtaining mode of the final variable, extracting a judgment rule of the tree species and outputting the judgment rule in a structured format. According to the method, high-precision prediction of the growth under multi-factor driving can be realized, and the problems of multi-collinearity, unstable variable selection, insufficient nonlinear structure expression and the like in a traditional regression model are solved.

The application relates to the field of electric digital data processing and discloses a sensing data fusion abnormality detection system and method based on edge computing, which comprises a data acquisition module, a feature storage module and a data analysis module. The data analysis module acquires N-path heterogeneous digital signals of the real-time state of a controlled object, constructs an observation vector mapped to an N-dimensional feature space through normalization processing, calls a pre-stored coupling feature matrix, projects the observation vector to a stable manifold space, extracts an orthogonal residual vector of the observation vector and calculates the module length, and when the module length continuously exceeds a judgment threshold for a period reaching a time threshold, it is judged that the controlled object has nonlinear structural decoupling. The application identifies abnormalities by monitoring the topological offset of the observation vector relative to the stable manifold, realizes deep mining of the physical coupling logic among multi-source signals, and effectively resists signal slow drift caused by environmental fluctuations.

The present invention proposes a demand response adjustable user potential assessment method and system based on WGCNA, which relates to the field of distribution network reconstruction optimization technology. Compared with traditional methods that can only process point-to-point, linear or Euclidean distance relationships, the WGCNA of the present invention can capture more complex nonlinear structural associations between user load curves by constructing a network, realize higher-dimensional user load behavior pattern recognition, and can characterize the complex correlation structure between load curves. Traditional clustering methods often rely on cluster center vectors, resulting in poor aggregation effects on non-convex and asymmetric user curves, and are unable to retain the true peak and valley fluctuation information of user load curves. The present invention retains the dynamic morphological consistency characteristics between individuals, retains the overall dynamic morphology of the load curve, and does not rely on the "averaging loss" of traditional clustering centers.

The invention provides a nonlinear mode discrimination method based on a convolutional neural network, and the method employs a response time-frequency graph of a nonlinear structure as input, and cooperates with a trained convolutional neural network to achieve the rapid discrimination of a nonlinear mode. Carrying out special processing on the displacement, speed and acceleration time-frequency diagrams and synthesizing a time-frequency diagram; in order to adapt to the aim of nonlinear mode recognition, simplification and improvement are carried out on the basis of a classical residual convolutional neural network, and finally a convolutional neural network model containing 32 layers is obtained.

The application discloses a wind turbine blade semi-coupling aeroelastic modeling method considering nonlinear deformation and belongs to the technical field of wind turbine simulation calculation. The application combines calculation parameters used by three-party software, establishes a blade structural load model and a blade corrected aerodynamic model, calculates aerodynamic load and structural load, and superimposes the aerodynamic load, gravity load and centrifugal force load of the blade to serve as external load of a blade nonlinear structure control equation. The external load is taken as a calculation parameter and is brought into a self-constructed iteration scheme, and then simulation calculation of the wind turbine is carried out in a semi-coupling mode. The application adopts the above method, constructs a semi-coupling simulation process of the blade nonlinear structure model and three-party wind turbine simulation software, can calculate higher-precision nonlinear motion response characteristics of an ultra-long flexible wind turbine blade under the same simulation environment and load conditions, and can achieve good effects in preliminary structural design and response evaluation of large wind turbine blades.

The invention belongs to the technical field of aerodynamic performance characteristic analysis of aircrafts, and particularly relates to a geometric nonlinear flutter boundary prediction method based on a chaos phase space principal component. According to the method, nonlinear flutter characteristics are analyzed and flutter boundaries are predicted by analyzing attractor characteristics of system response signals, and planarity of attractors is represented by analyzing variance contribution rates EVR of first two dimensions of a phase space matrix by utilizing characteristics of pie-shaped attractors of the response signals in a phase space when flutter occurs in a geometric nonlinear structure. The flutter characteristics of a wing with geometric nonlinear characteristics are quantitatively analyzed and described, a brand-new extrapolation prediction method of the flutter critical speed is provided based on the rule that the index changes along with the wind speed, and accurate prediction of the nonlinear flutter boundary can be achieved.

The application provides a scene elevation difference high-precision monitoring method based on a Riemann manifold, and specifically comprises the following steps: constructing a global navigation satellite system (GNSS) four-receiver vertical plane array, acquiring spatial data of a scene to be measured, extracting GNSS vertical plane four-baseline data according to the relative position relationship of the receivers, combining baseline length and angle constraints to construct an optimization objective function, constructing a Riemann manifold structure based on the objective function, converting the baseline optimization problem into a geometric pose adjustment problem of a rigid body formed by the four vertical plane baselines in Euclidean space, gradually adjusting the baseline position by calculating the gradient of the objective function, and finding an optimal solution, and further calculating the elevation difference in combination with the optimization solution result. The method realizes scene elevation difference monitoring through the GNSS vertical plane four-baseline structure, captures the nonlinear structure of the terrain data by using the Riemann manifold, maps the complex Riemann manifold calculation to the Euclidean space, simplifies the optimization process, and improves the scene elevation difference precision.

The invention discloses a non-linear structure impact load identification method based on physically guided deep learning, and belongs to the technical field of structure health monitoring and intelligent diagnosis. By constructing an enhanced robust neural network, innovatively combining deep learning technologies such as multi-scale convolution, a self-adaptive long-short-term memory network and a multi-head attention mechanism, and integrating knowledge such as physical constraints, high-precision recognition of impact loads of a nonlinear structure system is realized. The core innovation of the method comprises the following steps: designing a self-adaptive preprocessing assembly line, and automatically selecting an optimal processing strategy according to data characteristics; a multi-path nonlinear feature extractor with physical significance is constructed, and nonlinear features of different orders are effectively captured through linear, secondary and tertiary feature path parallel processing; providing a physically guided adaptive loss function, and comprehensively considering a plurality of physical constraints such as time-frequency domain consistency, peak value, energy conservation, gradient continuity and the like; a self-adaptive LSTM block is developed, and the time sequence modeling capability is enhanced through a gating mechanism and residual connection.

The invention provides an electricity purchase side settlement business ticket optimization matching method, device and system, and relates to the technical field of electric power grids. According to the method, key original features are extracted from original settlement data, a deep potential structure is mined through a nonlinear structure, a feature set with high characterization force is formed by fusing an explicit and implicit feature interaction relation, and finally accurate matching is completed by combining dynamic similarity measurement and an adaptive threshold value. The method effectively overcomes the limitation that a traditional linear method is difficult to process a complex non-linear relation, improves the automation degree and accuracy of matching, reduces manual intervention and subjective errors, solves the technical problem that the matching of settlement business tickets is inaccurate by using the linear method, and improves the matching efficiency. And a reliable technical support is provided for rapid and accurate processing of the settlement service of the electricity purchasing side.

The application relates to the field of electric digital data processing and discloses a sensing data fusion abnormality detection system and method based on edge computing, which comprises a data acquisition module, a feature storage module and a data analysis module. The data analysis module acquires N-path heterogeneous digital signals of the real-time state of a controlled object, constructs an observation vector mapped to an N-dimensional feature space through normalization processing, calls a pre-stored coupling feature matrix, projects the observation vector to a stable manifold space, extracts an orthogonal residual vector of the observation vector and calculates the module length, and when the module length continuously exceeds a judgment threshold for a period reaching a time threshold, it is judged that the controlled object has nonlinear structural decoupling. The application identifies abnormalities by monitoring the topological offset of the observation vector relative to the stable manifold, realizes deep mining of the physical coupling logic among multi-source signals, and effectively resists signal slow drift caused by environmental fluctuations.

The invention provides a high-order interactive prediction method and device with mixed graph deep learning, and the method comprises the steps: firstly constructing a drug molecule graph, a microorganism weighted graph, a disease weighted graph and a hypergraph connecting the three graphs based on multi-source heterogeneous data, such as a drug molecule structure, microorganism classification information and a disease semantic network, and forming a mixed graph structure; and then, through a mixed graph deep learning module fusing a graph convolutional network and a hypergraph neural network, nonlinear structure features and high-order interaction features of each entity are extracted, and adaptive fusion of the features is realized by using an attention mechanism. Next, the fused deep features are mapped into priori expectation of a potential factor matrix in a Bayesian logic tensor decomposition model, a probability graph model is constructed, and joint adaptive inference is performed on model parameters, latent variables and deep learning mapping through a variational expectation maximization algorithm, so that the probability graph model is obtained under the condition that negative sampling is not needed; and high-order association probability prediction of the full tensor space is realized.

The present invention relates to the technical field of heating pipe network operation efficiency evaluation, and discloses a heating pipe network operation efficiency evaluation method based on data prediction. The method includes: constructing a structural transformation result matrix, establishing a graph modeling structure, extracting disturbance features and constructing a graph propagation modulation mechanism, generating a structural disturbance response representation, identifying the structural behavior type, combining a basic predictor to complete node flow prediction, and comparing and analyzing the prediction results with the actual operation data to determine the degree of fit between the node operation status and the target heating demand, thereby outputting the operation efficiency evaluation results of the heating pipe network. By introducing a disturbance-driven graph propagation mechanism and a multi-mode structure recognition strategy, this method effectively improves the modeling capability and prediction accuracy of nonlinear structural responses in complex heating systems, and provides support for real-time evaluation of system operation status and efficiency management.