114 results about "Basis function" patented technology

The invention relates to the field of remote sensing image processing, and provides a remote sensing image terrain radiation correction method, device and equipment, and the method comprises the steps: obtaining a to-be-corrected remote sensing image, calculating the terrain parameter of each pixel, dividing the to-be-corrected remote sensing image into a shadow region and a non-shadow region, and determining the direction weighting function of each pixel; calculating a topographic modulation spherical harmonic primary function corresponding to each pixel; training samples are selected in the shadow area and the non-shadow area respectively, a linear model is constructed, and spherical harmonic illumination coefficients representing overall three-dimensional illumination distribution of the shadow area and the non-shadow area are solved; and based on the topographic modulation spherical harmonic primary function, combining the spherical harmonic illumination coefficient corresponding to the area to which each pixel belongs, reconstructing the average incident radiance of each pixel, and then performing inversion to obtain the real reflectivity of the earth surface. According to the method, the problem of inaccurate radiation distortion correction of the remote sensing image under the complex terrain in the prior art is solved, and accurate inversion of the real reflectivity of the earth surface is realized.

The invention relates to the field of rotating machinery fault diagnosis, and discloses a bearing fault diagnosis method based on a physical perception KAM network, and the method comprises the steps: carrying out the discretization of a bearing vibration signal through a Gabor filter group based on physical prior initialization, and generating modal feature lexical elements with physical frequency band meanings; and inputting the lexical elements into a PC-KAM backbone network, calculating a hidden state vector by using a state space model branch, and dynamically adjusting the position of a primary function node of a Kolmogov-Arnod network branch to realize collaborative dynamic feature extraction. In the training stage, an orthogonal subspace constraint and physical perception low-rank adaptation fine tuning mechanism is introduced. And finally, searching a historical fault case, performing multi-modal fusion on the historical fault case and the deep feature sequence, mapping a fusion representation into a soft prompt, and inputting the soft prompt into a large language model to generate a diagnosis report. According to the method, the problems of poor physical interpretability of characteristics and few-sample diagnosis under variable working conditions are effectively solved, and the generalization and decision-making ability of a diagnosis system are improved.

The invention discloses a fracture pressure prediction and internal friction angle joint inversion method for a heterogeneous stratum. The invention belongs to the technical field of petroleum engineering and artificial intelligence cross application, well depth is used as an independent variable, the minimum / maximum horizontal stress, pore pressure and cohesion are combined to construct well depth mixed input, only multi-scale periodic basis function transformation is carried out on the well depth, and a linear well depth channel is reserved; establishing a rupture pressure depth mapping model, introducing an augmented Lagrange physical constraint containing dual variables and a dynamic threshold, adaptively adjusting a physical weight according to a physical residual error and a threshold difference value, realizing dynamic balance of a rupture criterion and data fitting, and searching and optimizing a model topology by using a multi-target network structure; under a limited sample, a fracture pressure profile and a physically reasonable internal friction angle can be obtained at the same time, and the prediction precision and the physical consistency are improved.

The invention discloses a split type three-box girder nonlinear vortex vibration steady-state amplitude intelligent prediction method, belongs to the field of bridge vortex-induced vibration control, and aims to solve the problem of low accuracy of three-box girder vortex vibration nonlinear modeling and amplitude prediction. The method comprises the following steps: acquiring displacement time sequence data through a reduced scale model wind tunnel test, preprocessing to obtain displacement, speed and acceleration dimensionless data, and dividing a data set; constructing a candidate function library containing high-order polynomial terms of the primary function; performing nonlinear system sparse recognition by taking acceleration data as a target item and combining an improved algorithm to obtain a preliminary feature set; a final feature set is obtained through energy-statistics-self-adaption three-stage screening; and finally, constructing a control equation to predict the steady-state amplitude. The method can accurately predict amplitude, coincide test and real bridge observation results, and is less in computing resource occupation and high in numerical value precision.

The invention provides a frequency dispersion curve calculation method based on a decoupling type Legendre-Gauss-Labatto spectral element method, and belongs to the technical field of discrete solution of guided wave propagation in a thin plate, and the method comprises the following steps: S1, constructing a wave equation which comprises a Cartesian space coordinate system based on an orthotropic plate, and combining a constitutive equation, a geometrical relationship under the coordinate system and guided wave displacement to obtain a wave dispersion curve; substituting into a control equation when the volume force is zero to obtain an improved three-dimensional elastic wave equation; s2, a differential matrix is constructed, wherein discretization and decoupling are carried out on a wave equation on the basis of a Lagrange interpolation basis function of a Legendre-Gauss-Labatto node; s3, explicitly embedding the stress free boundary condition into the solving process through matrix line transformation, and solving to obtain a frequency dispersion curve, the derivation process is simplified, the boundary and the interior of the material are decoupled, and the physical significance is clearer. And meanwhile, the high-precision advantage of the spectrum method is maintained, and the calculation rate is remarkably improved.

The invention belongs to the field of real-time monitoring, particularly relates to a chemical process real-time monitoring method and system based on digital twinning, and aims to solve the technical problem that an existing method lacks an online model correction and updating mechanism. The monitoring method comprises the following steps: S1, constructing a state snapshot data set, and synchronously collecting real-time measurement data; s2, identifying a process fluctuation interval, and selecting to-be-selected state snapshots to form an optimal snapshot matrix; s3, calculating a reduced-order truncation error energy ratio, and constructing a reduced-order model based on a reduced-order basis function; s4, in the real-time monitoring stage, if the norm of the prediction residual error is smaller than a monitoring threshold value, a Kalman filtering algorithm is adopted to correct the state coefficient of the reduced-order model; otherwise, calculating the projection error of the prediction residual on each primary function in the standby primary function library; and outputting a full-order state vector reconstructed based on the corrected or updated reduced-order model. The monitoring method provided by the invention ensures the continuous effectiveness and accuracy of monitoring when the chemical process changes.

The invention belongs to the technical field of space geodetic survey observation data inversion regional land water reserve change, and provides a method and related equipment for inversion of land water reserves based on a Slepian basis function combined with GNSS three-dimensional deformation, and the method comprises the steps: obtaining vertical and horizontal deformation observation values of GNSS through simulation; determining a Slepian primary function of the target area according to the boundary data and a preset maximum expansion order, and further determining a function relationship between a Slepian coefficient of GNSS three-dimensional deformation and a Slepian coefficient driven by mass load change; determining an observation equation according to the function relationship, and resolving the observation equation by adopting least square joint adjustment; and according to the Slepian coefficient driven by the mass load change, carrying out inversion on the regional land water reserves of the target region to obtain a regional land water reserve change result. The method has the beneficial effect that the precision and efficiency of regional land water reserve inversion are improved.

The invention discloses a load curve decomposition method and system based on a curvature equipartition rational B-spline basis function, and the method comprises the steps: calculating the curvature of a historical load sequence, generating a continuous curvature function, determining a group of B-spline node sequences meeting the cumulative absolute curvature equipartition condition, and enabling the node distribution to be adaptive to the load fluctuation intensity; a B-spline primary function is generated based on the node sequence, and a rational B-spline primary function is constructed through weight optimization so as to finely describe a continuous time fluctuation mode; a fitting model is established in combination with time period virtual variables, and a load curve is decomposed into two parts, namely a time period characteristic load level representing different time period reference power consumption intensity and a time characteristic load curve representing a continuous change fluctuation form. The defect that a traditional uniform spline is poor in adaptability to a non-uniform load form is overcome, self-adaptive decomposition with definite physical significance is achieved, and a better-quality data basis is provided for load analysis and prediction.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

The invention provides a numerical feature embedding method based on a scale perception radial basis function and a situation awareness method for the health degree of power equipment, and the method comprises the steps: obtaining a logarithm of to-be-coded numerical data x, and obtaining a base number D and an index L; performing RBF (Radial Basis Function) expansion on the base number D to obtain the expression of the base number D; carrying out soft sub-bucket distribution on the index L to obtain a distance from the index L to each soft sub-bucket, taking the distance as a coefficient of the soft sub-bucket, and weighting each soft sub-bucket according to the coefficient of the soft sub-bucket to obtain representation of the index L; and converting the representation of the index L into two numbers by adopting a gated linear layer network, and scaling the representation of the base number D to obtain the coded representation of the numerical data x.

This invention discloses a method and system for fitting safety monitoring data based on adaptive hybrid basis functions, belonging to the field of computer-aided data analysis technology. The method achieves fully automatic high-precision curve fitting through a six-layer adaptive architecture: it automatically analyzes the trend complexity, periodicity, noise level, and nonlinearity of the data using multi-dimensional feature recognition technology; it calculates the data complexity score based on the feature analysis results and intelligently selects basis function combinations from an extended basis function library containing 32 sub-functions across 8 categories; it constructs a dynamic weighted hybrid basis function model, achieving adaptive adjustment of model parameters through time-varying weight functions and coupling correction terms; and it employs a multi-model dynamic fusion mechanism, integrating multiple candidate models based on six-dimensional confidence evaluation. This invention innovatively introduces adaptive regularization technology and a hierarchical optimization strategy, effectively balancing fitting accuracy and generalization ability, and possesses advantages such as full automation, strong robustness, and complete uncertainty quantification.

The present application relates to the field of electromagnetic numerical calculation, and discloses a kind of method for solving electromagnetic scattering based on adaptive block Krylov subspace base function, method includes the following steps: step one: target region is carried out adaptive region decomposition, and target subdomain is obtained;Step two: target subdomain is expanded, and extended subdomain is formed;Step three: using optimized extended subdomain, the solving calculation of electromagnetic scattering is carried out.The main advantage of the present application is that the efficiency of constructing Krylov subspace base function is higher by using adaptive block technology, which significantly reduces the generation time of Krylov subspace base function based on block, optimizes the block expansion of subdomain, improves the solving calculation time, uses clustering algorithm to carry out region decomposition on the calculation target, and uses the average distance of clustering data points to cluster center to expand each subdomain to ensure the continuity of current, which not only ensures the calculation accuracy, but also improves the construction efficiency of base function, significantly reduces the generation time of Krylov subspace base function based on block.

The invention relates to the technical field of high-current particle beam diagnosis, in particular to a high-energy ion beam three-dimensional reconstruction method and system based on secondary electron emission signals. According to the technical scheme, the method comprises the steps of data preprocessing and conversion, geometric space up-sampling, point assignment generation and model boundary division and output. According to the method, the spatial sparsity of original data is overcome through geometric upsampling, the fidelity of physical attribute reconstruction is remarkably improved by utilizing a topological structure based on a minimum spanning tree and a primary function frequency superposition model, automatic and accurate division of beam boundaries is realized according to a physical threshold value, an end-to-end automatic processing flow is formed, and the method has the advantages that the method is simple and convenient to operate and high in practicability. And outputting a visual three-dimensional model supporting interactive analysis, systematically solving the problems of data sparsity, interpolation distortion and boundary fuzziness in the prior art, and realizing high-precision and high-efficiency reconstruction and visual analysis of the three-dimensional form of the high-energy ion beam.

The invention discloses a meshless Galerkin-Newmark method for calculating a transient wave propagation problem. The meshless Galerkin-Newmark method comprises the following steps: dispersing a transient wave propagation problem domain into nodes; selecting an interpolation primary function and a weight function; constructing a node interpolation primary function and a global interpolation format by adopting a moving least square method based on the selected function; based on a global interpolation format, combining a partial differential control equation and a Galerkin weighted residual value method to obtain a transient wave propagation problem system matrix equation; performing time domain discretization through a Newmark time integration method to obtain a discrete time equation; solving the discrete time equation to obtain a numerical calculation result; and analyzing a result and comparing the result with a common method to finish solving and analyzing a transient wave propagation problem. The meshless Galerkin-Newmark method for calculating the transient wave propagation problem has a monotonous consistent convergence characteristic in the numerical calculation of the transient wave propagation problem, and the calculation precision can be continuously improved along with the reduction of the time integral step length.

This invention belongs to the field of artificial intelligence and system response calculation technology, and provides a method and system for calculating system response based on a generalizable physical information neural network. The technical solution involves establishing a family of differential equations consisting of multiple governing equations based on the physical characteristics of the system to be analyzed, and obtaining the corresponding dataset; constructing a neural network model consisting of a main network and multiple channel networks, where the main network learns the basis functions representing the system response and shares them with all channel networks; each channel network processes a specific differential equation task within the task family and learns the basis function coefficients corresponding to that task; inputting the dataset into the neural network model, training the model by minimizing a loss function containing multiple constraints, and obtaining the optimal solution for the system response under different stimuli based on the trained model. This method can solve complex differential equations more accurately and improve the model's generalization performance on unseen tasks.

The invention discloses a method for analyzing time-frequency characteristics of collision vibration by using a segmented B-spline wavelet collocation method, which comprises the following steps of: (1) establishing a dynamic equation for wavelet discretization, discretizing a dynamic control equation of a collision-vibrator system by using a 4-order B-spline wavelet basis function, and establishing a nonlinear algebraic equation set about a wavelet coefficient; (2) carrying out segmented iterative solution on the time domain, dividing the whole time domain into a plurality of sub-time periods which are connected in sequence, and carrying out iterative solution on the nonlinear algebraic equation set in each sub-time period to obtain a global wavelet coefficient; and (3) analyzing the time-frequency characteristics, and directly calculating energy distribution based on the wavelet coefficient to realize time-frequency joint characterization. The invention aims to provide a method which can fuse solution of system response and extraction of time-frequency characteristics in a unified mathematical framework, and realizes high-precision time-frequency joint characterization by directly solving wavelet coefficients, thereby overcoming inherent defects of an existing two-step method.

A method for maintaining physical assets based on recommendations generated from operational data analysis and a composite model representing multiple models of the physical assets includes: constructing the composite model by a computing device using a port-reduced static condensed reduction primitive approximation with respect to at least a portion of a partial differential equation; the computing device analyzing error indices associated with at least one model within the composite model to determine if the error indices exceed a tolerance level, and accordingly increasing the number of basis functions in the port-reduced static condensed reduction primitive approximation; the computing device receiving first operational data associated with at least one region of the physical asset and updating the composite model; and the computing device providing recommendations for maintaining the physical asset based on the updated composite model.

This invention relates to the field of pet food spectral detection technology, and discloses a pet food spectral detection device and method. The method involves sampling at equal intervals within a preset wavelength range, eliminating noise interference through dark current and standard white plate calibration; taking the natural logarithm of reflectance to balance the dynamic range; constructing a linear spline basis at equal intervals, and adaptively calculating the regularization coefficient based on the energy ratio of the signal and basis functions; constructing an augmented matrix based on the inner product of the basis functions and the signal, and solving for the spline coefficients using Gaussian elimination; reconstructing the spectrum using the coefficients and quantifying the residuals; establishing a reference model based on the mean and standard deviation of multiple qualified sample coefficients; comparing the Euclidean distance between the coefficients of the sample to be tested and the model mean with a threshold, and generating residuals, distances, thresholds, and a judgment report; the entire process requires no empirical parameters or manual tuning, enabling batch online adaptive detection, achieving rapid and accurate detection with noise correction, feature enhancement, overfitting suppression, and traceability.

The application discloses a multi-constraint geometric nonlinear level set topology optimization method, device and medium, and aims to solve the problem that the current linear condition level set method is difficult to well optimize the large deformation of a to-be-optimized component. The method divides a design domain corresponding to the to-be-optimized component into a plurality of finite element units, and establishes corresponding level set radial basis functions based on volume constraints; according to the relationship between the element strain increment and the node displacement increment of the finite element unit, displacement constraint terms and frequency constraint terms are introduced to extend the Lagrange function; according to the nonlinear element unbalanced force, the element thermal load and the overall thermal load are calculated; based on the extended Lagrange function and the overall thermal load of the to-be-optimized component under the set temperature, the design domain is topologically optimized. The application considers the displacement constraint and the frequency constraint under the nonlinearity, is suitable for the to-be-optimized component under the large deformation condition, and can achieve an optimal topology result.

A kind of open pit model dynamic updating method based on real-time positioning of excavator and SDF belongs to the technical field of digital mine. The initial pit triangular mesh model is voxelized to generate a three-dimensional symbolic distance field based on the definition of vertical coordinate symbol. The three-dimensional positioning data of the center of rotation of the excavator is collected in real time through the GNSS-RTK positioning module, and the working state is judged based on the preset time window and displacement threshold. When the excavator switches from displacement state to digging state, a spherical mining influence domain is constructed with the current positioning data as the center of the sphere and the maximum digging radius as the radius, and the voxel symbol distance field value in the domain is set to 0. A control point set is constructed by boundary constraint points and background constraint points, and global symbol distance field smoothing reconstruction is realized by using radial basis function interpolation. The zero value isosurface is extracted by moving cube algorithm to generate the updated pit model. The present application realizes high-frequency model updating synchronized with operation, has low deployment cost and strong robustness, and solves the problems of model updating lag and visualization penetration.

The application discloses a Pick iterative random characteristic flow field deduction and reconstruction method and system for bridge wind engineering and belongs to the technical field of bridge wind engineering and structural health monitoring. The application divides a global space-time calculation domain, constructs local affine mapping and a space-time random characteristic base function, adopts Pick iterative linearization incompressible Navier-Stokes equations, avoids Jacobian matrix calculation, assembles a global over-determined linear equation group containing momentum, mass conservation, boundary and interface constraints, forms a double-enhanced linear system through algebraic row balance and physical enhancement layered weighting, realizes high-fidelity reconstruction of a flow field through least square solution, supports unknown fluid parameter alternating inversion under sparse data, and can automatically generate bridge wind-induced vibration early warning. The application eliminates edge device memory bottleneck, improves solution robustness and precision, strictly guarantees physical conservation, and is suitable for real-time deduction of bridge complex flow fields, sparse observation reconstruction and safety monitoring.

The invention relates to the field of financial data, and discloses a medium-and-long-term adaptive transaction signal system based on a large model, which comprises the following steps: after obtaining target signal data, inquiring a hidden variable association graph of the target signal data, analyzing a structure association element and generating an evolution path, and by analyzing a path response sequence, identifying a sequence transition node and calculating a node chaos index, obtaining the target signal data; the method comprises the following steps: performing modal decomposition on target signal data to obtain a signal sub-data cluster, performing label coding to obtain a primary function label set, querying a sparse representation coefficient, marking a weight vector, coding the weight vector to a historical memory module, dynamically adjusting a hyper-parameter combination according to a convergence state, calculating a generalization performance ratio, generating a distribution characteristic key, and finally, performing data distribution on the basis of the distribution characteristic key. And constructing a self-adaptive transaction signal mechanism according to various identification elements in the secret key. According to the invention, the transaction signal adaptation accuracy can be improved.

The invention discloses a coated target electromagnetic scattering modeling method based on multi-core heterogeneous parallel computing, and belongs to the field of computational electromagnetism and high-performance computing. The method comprises the steps that an MPI + OpenMP + DSP three-level heterogeneous parallel framework is constructed, and optimal configuration of computing resources is achieved through one-to-one binding of an MPI process and a DSP acceleration card and one-to-one mapping of an OpenMP thread and a CPU core; dispersing a to-be-solved coating target by adopting a triangular grid, and defining an RWG primary function to expand surface current; substituting the surface current approximate expansion into an electric field integral equation based on impedance boundary conditions; carrying out impedance matrix calculation by adopting a process-based parallel filling strategy, and carrying out parallel solution on a matrix equation based on a ScaLAPACK library; and finally, calculating a far-region scattering field and a radar scattering sectional area according to the solved current coefficient. According to the method, through an innovative heterogeneous parallel architecture and a calculation task scheduling strategy, the problems of insufficient memory and low calculation efficiency during processing of an electrically large coated target in a traditional method are effectively solved, and an efficient and reliable technical means is provided for electromagnetic stealth design of a complex target.

The invention provides an electromagnetic scattering modeling method based on a learnable primary function, and relates to the technical field of radar detection and remote sensing imaging, and the method comprises the steps: carrying out the parameter analysis of a discrete triangular mesh on the surface of an electromagnetic scattering three-dimensional conductor target, and obtaining an initial feature set composed of mesh edge features, surface patch geometric features and geometric and incident parameters; based on the learnable basis function, constructing a current prediction model by using the local branch layer, the global branch layer and the feature fusion layer; based on a residual error loss function, training a current prediction model by using the initial feature set to obtain a trained current prediction model, and completing electromagnetic scattering modeling; the trained current prediction model is used for analyzing the discrete triangular mesh to obtain a plurality of induced current distribution of each patch. The electromagnetic scattering modeling method solves the problems that an existing electromagnetic scattering modeling method depends on specific physical approximation and is high in calculation complexity.

This invention discloses a method and system for extracting S-parameters of microwave passive devices, comprising: geometrically modeling the microwave passive device and then meshing it to obtain a processed device model; discretizing the processed device model according to basis functions, and performing mode function expansion on the ports of the processed device model to establish a transfinite element matrix equation; performing partial LU decomposition of the system matrix in the transfinite element matrix equation using the multi-wavefront method to obtain the Schur complement matrix of the system matrix and the decomposed system matrix; obtaining the dense S-parameter matrix equation for the microwave passive device based on the Schur complement matrix of the system matrix and the decomposed system matrix; and solving the dense S-parameter matrix equation to obtain the S-parameters of the microwave passive device. This invention effectively reduces the memory usage of matrix calculations and reduces computation time.

This invention relates to the field of artificial intelligence-based educational assessment, and discloses a cognitive diagnostic model and method based on knowledge text. The model includes a text feature extraction module for extracting and generating knowledge point association vectors representing the degree of association between questions and preset knowledge points; a knowledge proficiency embedding module for mapping knowledge proficiency vectors; a question attribute embedding module for mapping question knowledge difficulty vectors and question discrimination scalars; a knowledge association attention module for adjusting attention weights and generating weighted knowledge representations; and an adaptive nonlinear prediction module containing a multi-layer KANLinear structure composed of B-spline basis functions and outputting the prediction probability of correct student answers. This invention solves the problem of fragmented internal module functions in existing models and features high interpretability and independence from manual annotation.

The invention discloses a residence time iteration solving method and system, a medium and equipment, and the method comprises the steps: constructing an optical surface shape error mathematical model, and obtaining a discrete response matrix; setting an initial sparse B-spline node vector and a convergence condition; generating a B-spline primary function matrix according to the node vectors, performing matrix multiplication, and constructing an optimization coefficient matrix; solving the quadratic programming model to obtain a current control point vector and a surface shape residual error; calculating the projection of the full-aperture surface shape residual error on the parameter domain, and constructing a sensitivity index function; identifying a high-error hotspot interval and adaptively inserting a new node; and dynamically updating the topological structure of the model until a convergence condition is met, and finally determining a non-uniform node vector adaptive to the local error feature and corresponding residence time distribution. According to the method, the contradiction between undercorrection and dimensionality redundancy of a traditional uniform model in complex surface shape finishing is effectively solved, and the method has remarkable advantages in the aspects of local feature capture capability, calculation efficiency ratio, process intelligence level and the like.

The invention discloses a method for generating a quadrilateral curved surface grid, the quadrilateral curved surface grid can cover the curved surface of a three-dimensional object so as to analyze the three-dimensional object, and the method comprises the following steps: obtaining a curved surface digital geometric model of the three-dimensional object; constraint on grid singular point configuration on the curved surface is received, the singular point configuration comprises at least one designated position and a target price number of the designated position, and the price number represents the number of edges connected with grid vertexes; on the basis of the Riemannian-Rooch theory, a finite-dimensional quadrilateral grid generation space is constructed within the range meeting singular point configuration constraints, and construction comprises calculation of a primary function of a sub-pure function linear space L (D) corresponding to singular point configuration; selecting a specific grid generation parameter combination from the quadrilateral grid generation space; a quadrilateral curved surface grid is generated and output according to the grid generation parameter combination, and singular point distribution of the grid conforms to the received constraint; and the quadrilateral curved surface grid is provided for a finite element analysis or isogeometric analysis solver and is used for carrying out simulation analysis of mechanics, thermotics or fluid dynamics on a three-dimensional object.

The application belongs to the field of bridge health monitoring and rapid detection, and discloses a bridge bending stiffness identification method based on multi-point deflection influence line input, and steps are as follows: firstly, a linear model for solving bending stiffness is established by using measured deflection influence lines at multiple positions of a bridge and linear elasticity theory; then, a redundant dictionary composed of multiple types of global basis functions is used for sparse representation of the bending stiffness distribution curve, and the bending stiffness reconstruction problem is converted into a sparse vector solving problem; finally, a sparse vector solution model with 1-norm regularization constraint is established, and the optimal bending stiffness distribution is obtained by solving the model. l The application can realize continuous bending stiffness curve solving by using multi-point measured deflection influence lines only, is suitable for stiffness evaluation of statically indeterminate in-service bridges such as continuous girder bridges, and has the advantages of strong applicability, high solving efficiency and strong robustness.

The invention discloses a key area restoration parameter mapping method based on dynamic grid local re-subdivision, and belongs to the technical field of engineering simulation. The method comprises the following steps that octree and quadtree partitions of an old grid are searched for units of a re-subdivision area on a new grid; searching an old grid unit where the degree-of-freedom coordinate point is located in the unit array of the partition, and then interpolating the physical field through a primary function; and calculating the value of the freedom degree coordinate point of the new grid. Mapping operation is carried out in a key area near the moving part in a concentrated mode; performing a moving restoration operation on a re-subdivision area near the boundary of the moving part, and then performing search mapping; and a simplified treatment mode is adopted for the air domain. According to the method, the unit search range and the calculation overhead in the physical field parameter mapping process are reduced, the dynamic grid physical field mapping efficiency is improved, the accuracy and the consistency of physical field transmission in a key area are guaranteed, and efficient and accurate physical field mapping and comprehensive improvement of engineering simulation efficiency are achieved.