32 results about "Level set" patented technology

The invention discloses a self-adaptive point cloud implicit curved surface reconstruction method based on a truncation level B spline, and the method comprises the steps: constructing a first-layer truncation level B spline primary function space, and obtaining an initial implicit function through solving an optimization model; evaluating the fitting error of each point of the input point cloud, and determining the fitting error as a region which needs to be further refined; establishing a new truncation hierarchy B-spline primary function set, and expanding a current function space; performing truncation operation on the THB spline primary function which is completely positioned in the refined region; combining the truncated primary function with the primary function set generated by the new layer, and updating the whole primary function library; re-optimizing the model, and solving the updated implicit function; refining and optimizing layer by layer, and outputting a final implicit function and a zero level set thereof; and performing triangular meshing on the final zero level set to finish curved surface reconstruction. According to the method, an extra training process is not needed, excellent point-to-surface distance and curvature indexes can be kept at the noise level up to 10%, and the memory and calculation overhead can be greatly reduced.

The embodiment of the invention discloses a bill key field identification method and device. The method comprises the following steps: acquiring a bill image; defining the key field, wherein the key field comprises at least one of an invoice number, an amount and a date; fractional order differential calculation is carried out on the bill image, an enhanced image and fractional order differential features are generated, and the fractional order differential features comprise a fractional order gradient field and a multi-scale feature vector; semantic features of the bill image are extracted according to a predefined bill semantic template, semantic priori is generated, and the semantic priori represents the relative position and expected content of the key field; a level set function is initialized based on the fractional order differential features and the semantic priori, an energy function is constructed, the energy function is minimized to drive the level set function to evolve, and a segmentation result is generated; and performing feature extraction on the segmentation result to generate structured data. According to the invention, the accuracy and efficiency of key field identification are improved.

The application belongs to the technical field of image segmentation, and particularly relates to a complex scene target segmentation method and system based on image recognition, which comprises the following steps: performing superpixel adaptive division on an input image, fusing gray scale and texture features to determine a superpixel boundary, mapping the superpixel boundary into a graph node and calculating an edge weight, and constructing an undirected weighted graph; adaptively encoding a graph signal, utilizing a hybrid graph wavelet-Fourier joint transform to optimize and separate features, and obtaining purified graph frequency domain features; sparsely reconstructing features through adaptive super-complete dictionary learning, and obtaining target enhanced features; extracting topological parameters based on an improved persistent homology, constructing a topological constraint feature graph, mapping the topological constraint feature graph into an initial contour field, iteratively optimizing a level set and a contour through an adaptive partial differential equation, and obtaining a high-fidelity coarse segmentation result; extracting geometric features to construct a joint constraint model to repair an occluded area, and outputting a precise segmentation result. In the application, sparse topological modeling is adopted, weak features are strengthened, and target discrimination accuracy is improved.

The present application belongs to the technical field of wind power generation, and specifically relates to a wind power tower material crack propagation prediction method and system, which is suitable for fatigue life evaluation of large supporting structures in the wind energy industry. The method comprises: deploying a multi-source heterogeneous sensor network to collect strain, acoustic emission and environmental load data; constructing a digital twin high-fidelity finite element submodel to obtain a dynamic stress intensity factor; constructing a deep neural network, embedding the Paris law of fracture mechanics in the form of residual into the loss function to form a physical information neural network, and realizing crack propagation rate prediction; using the extended finite element method combined with the level set function and the maximum circumferential stress criterion to dynamically track the three-dimensional space expansion path of the crack; combining Monte Carlo simulation and recursive Bayesian update to evaluate the remaining life and trigger graded early warning. The present application solves the problems of insufficient fusion of physical mechanisms and data-driven in the prior art, low prediction accuracy under small samples, and lack of crack space path tracking.

This invention discloses a multi-objective topology optimization method for aircraft design, belonging to the field of aerospace technology. The method includes: establishing a three-dimensional geometric model and defining the optimization region and aerodynamic preservation region; describing the structural boundary using level set functions; applying volume and manufacturing constraints with the objectives of maximizing stiffness and natural frequency; constructing a comprehensive objective function that minimizes the distance between ideal points using a compromise programming method; transforming the constraint problem using the augmented Lagrangian method; calculating shape sensitivity and deriving the normal velocity field; updating the level set functions to drive topology evolution; iterating until convergence to generate a Pareto front solution set; selecting the optimal solution and reconstructing it into a manufacturable three-dimensional model. This invention solves the multi-objective collaborative optimization problem while preserving the aerodynamic shape, resulting in clear boundaries, strong manufacturability, and significantly improved structural performance and engineering applicability.

The present application relates to the field of additive manufacturing, and specifically discloses a kind of generation method of manufacturing three-dimensional Chinese character cup model based on energy driving.The present application constructs the smooth time evolution path from multiple outline initial region to single target outline, adopts level set function to describe the outline evolution process, and establishes energy functional including curve length regularization term, adaptive inflation term and shape prior term;Through introducing the dynamic coupling control mechanism based on area difference of region, shape error and boundary length, the adaptive adjustment of inflation coefficient is realized;Then the level set evolution equation is obtained by variational derivation, and after numerical discrete solution, two-dimensional evolution sequence is added to three-dimensional volume field along time axis;Finally, through isosurface extraction and thick shell processing, the 3D printable three-dimensional Chinese character cup model with complete structure and uniform wall thickness is generated.

The invention belongs to the technical field related to structure optimization design, and discloses a lattice structure optimization design method and device combining a multivariable cutting level set and progressive optimization, and the method comprises the steps: (1) building a lattice structure optimization model by taking structure flexibility as a target function and cutting height as a design variable; (2) performing finite element analysis based on the lattice structure optimization model to obtain a structure volume fraction; (3) if the current number of iterations is an odd number or the structural volume fraction is lower than the set threshold value, calculating the sensitivity of the target function and the constraint function to the design variable, and updating the design variable by adopting a moving asymptote algorithm based on the obtained sensitivity; if the number of iterations is an even number and the volume fraction of the structure exceeds a set threshold value, calculating a unit sensitivity degree so as to obtain a sensitivity degree of the virtual microstructure, and removing a part of the virtual microstructure based on the sensitivity degree of the virtual microstructure; and a final lattice structure is further obtained. According to the invention, the optimization efficiency is improved.

The application provides a kind of marine structure flow field simulation method, the method comprises: generating calculation grid and object surface grid for flow field simulation;Initialize object surface grid level set function field;According to flow field information, calculate the force condition of object surface, calculate object translation vector and rotation vector;Complete data mapping of object surface grid to calculation grid, obtain the level set function field of calculation grid;According to the obtained calculation grid level set function field, divide cell type;Carry out geometric calculation to cut cell, based on cut cell flow field reconstruction strategy, complete fluid cell, cut cell estimated speed update;Solve pressure Poisson equation, obtain pressure field, pressure gradient, update velocity field according to the obtained pressure gradient, obtain the final velocity field satisfying continuity condition.The marine structure flow field simulation method of the application greatly improves the numerical simulation efficiency of complex structure flow field, and reduces the implementation difficulty.

The present application belongs to the technical field of structure optimization design, and discloses a structure topology optimization design method and device, steps are as follows: (1) dividing a physical design domain into a background grid, based on coordinate information of background nodes of the background grid, a sparse filtering matrix is constructed by using a Gaussian kernel function with row normalization; (2) using the sparse filtering matrix to perform front filtering on design variables on the background nodes to obtain a smooth density field, and then obtaining a level set function field; (3) using an adaptive grid subdivision algorithm to cut and locally adaptively reconstruct the level set function field to obtain an analysis grid; (4) interpolating the level set function field to the analysis grid through a sparse global interpolation matrix from the background grid to the analysis grid to obtain a level set function field; (5) projecting the level set function field to obtain a pseudo-density field on the analysis grid nodes, and then obtaining an optimal topology configuration. The present application realizes good boundary smoothness while improving optimization efficiency.

The invention relates to a high-order interface capturing method for a coupling level set and a fluid volume based on mixing precision, which comprises the following steps of: firstly, dividing a calculation area into a grid comprising a plurality of control body units, initializing, then identifying interface units, and reconstructing a THINC interface function by adopting a high-order polynomial based on an LS value; thirdly, introducing a mixed precision iterative algorithm to solve an interface position nonlinear equation, and adaptively switching to multi-precision operation to obtain a robust solution when double precision cannot converge; then, flux is calculated through an accurate interface, a VOF transport equation is solved, and mass conservation is guaranteed; and finally, on the basis of the predicted interface geometry, an LS field which does not need to be reinitialized is directly reconstructed through pure geometric methods such as interface point cloud generation through projection and nearest point searching in a narrow band. According to the method, the capture precision and geometric fidelity of complex interfaces with high curvature, sharp corners and the like on the unstructured grids are remarkably improved, and the method has high precision and strong robustness.

The invention belongs to the technical field of aero-engine cooling design and flow heat exchange optimization, and provides a turbine blade cooling turbulence rib construction method, system and configuration based on level set topological optimization and a blade. The method comprises the steps that a fluid-solid coupling calculation model containing a cooling channel fluid domain and a blade wall surface solid domain is constructed, defining a level set function to implicitly represent the two-phase region; establishing a unified control equation set covering a full computational domain based on the function, and introducing a flow velocity suppression mechanism into a solid domain to carry out single-domain coupling solution of fluid flow and solid heat conduction; according to the physical field solution, constructing an optimization model taking the cooling performance as a target function and the volume fraction of the turbulent flow rib as a constraint, and calculating the sensitivity of the target function to a level set function; using the sensitivity to drive a level set function update to evolve a spoiler rib boundary; iterating until convergence and outputting the final geometric configuration of the turbulent flow rib. According to the invention, the irregular turbulent flow structure with high heat exchange efficiency and low flow resistance can be automatically generated without presetting a rib type.

The invention discloses a shot peening forming process planning method based on a level set algorithm, and the method comprises the steps: S1, obtaining the features of a target wallboard, and selecting the type and number of level set functions according to the features of the target wallboard, so as to describe the boundary between a shot peening region and a non-shot peening region of the target wallboard; s2, establishing a shot peening forming process optimization mathematical model based on the level set function; s3, calculating the sensitivity of an objective function of the optimization model, and deducing an iterative formula of the level set movement velocity field; s4, the optimization model is initialized, the level set function is solved, the shot blasting area boundary is updated, if the boundary is not convergent, the step S3 is executed, and if the boundary is convergent, the step S5 is executed; and S5, discretely generating a shot blasting impact point set based on the converged shot blasting area boundary. According to the method, the problems existing in shot peening forming of complex wallboards in an existing shot peening process are solved, the process planning cost is remarkably reduced, and the forming precision is improved.

This invention belongs to the field of aero-engine cooling design and flow heat transfer optimization technology. It provides a method, system, configuration, and blade for constructing turbine blade cooling fringe based on level set topology optimization. The method includes: constructing a fluid-structure interaction (FSI) computational model encompassing the fluid domain of the cooling channel and the solid domain of the blade wall; defining a level set function to implicitly represent the two-phase region; establishing a unified set of governing equations covering the entire computational domain based on this function; introducing a velocity suppression mechanism in the solid domain to perform single-domain coupling solution of fluid flow and solid heat conduction; constructing an optimization model based on the physical field solution, with cooling performance as the objective function and fringe volume fraction as the constraint; calculating the sensitivity of the objective function to the level set function; using this sensitivity to drive the level set function update to evolve the fringe boundary; iterating until convergence to output the final fringe geometry. This invention does not require pre-setting the fringe type and can automatically generate irregular fringe structures with high heat transfer efficiency and low flow resistance.

A solution search device (1) comprises: a setting unit (12) for setting a first desired level, which is a target value for a plurality of objective functions, in a multi-objective optimization problem; a first-optimal-solution computation unit (13) for computing a first optimal solution using the first desired level set by the setting unit and a bound, which is a solution set set for optimizing the plurality of objective functions; and a first-optimal-solution fitting unit (15) for receiving an input of an adjustment amount to fit the first optimal solution from a user when an instruction not to use the first optimal solution computed by the first-optimal-solution computation unit is received from the user, and for fitting the first optimal solution within a range of the bound based on the received adjustment amount.

Techniques for level-set based shape optimization with shape constraints include simulating a state of a current shape of a component, calculating a sensitivity of an objective function to with respect to modification to the current shape based on the simulated shape based on the simulated state, generating a source function for an optimization equation based on the sensitivity, adding one or more constraint terms to the optimization equation based on one or more constraints on an interface of the shape to generate a constrained optimization equation, solving the constrained optimization equation to generate a solution to the constrained optimization equation, updating a velocity field based on the solution to the constrained optimization equation, solving a transport function based on the updated velocity field to generate a solution of the transport equation, and updating the current shape based on the solution to the transport equation to generate an updated shape.

The invention relates to the technical field of image processing, and discloses a target segmentation method for a complex background image, and the method comprises the steps: carrying out the pre-fitting of foreground and background features of a to-be-segmented filling image through fuzzy C-means clustering based on parallelization and down-sampling strategy improvement, and reconstructing a pre-fitting denoising image; jS divergence and Euclidean distance are introduced to carry out joint multiplicative measurement analysis on three components of the foreground, the background and the de-noised image obtained through pre-fitting calculation, and difference energy is obtained; and constructing a data driving item containing a global fidelity fitting item and a local fitting item based on the filling image to be segmented, the pre-fitting component and the difference energy, performing iterative updating on the initial level set function to obtain a final contour line, and segmenting the image to be segmented to obtain an image segmentation result. According to the method, the accuracy and robustness of target segmentation under a complex background image are improved through fuzzy C-means clustering based on parallelization and down-sampling strategy improvement and difference energy of JS divergence introduction.

The invention discloses a transient synchronous stability analysis method and system for a multi-type converter grid-connected system based on coordinate transformation and combined Lyapunov. According to the method, a combined Lyapunov function theory is taken as a core, a trainable coordinate transformation layer is introduced into a Lyapunov neural network structure, decoupling and dimension reduction of a high-dimensional coupling system are realized through implicit coordinate transformation, a complex multi-type converter grid-connected system is decomposed into a plurality of low-dimensional subsystems for independent modeling, and a combined Lyapunov function is constructed on the basis. And estimating the stability domain boundary of the system based on the level set structure of the function. The method effectively overcomes the dimension disaster problem of a high-dimensional coupling system, has calculation high efficiency and theoretical interpretability, is especially suitable for transient synchronization stability analysis of a large-scale grid-connected system with multiple fans and multiple converters, and provides a systematic solution for practical engineering application.

This invention relates to the field of image processing technology and discloses a method for polyp image segmentation and a computer-readable storage medium. First, the polyp image to be segmented is input into a backbone network to extract multi-layer initial feature maps. Then, the initial feature maps are sequentially input into a level set edge enhancement module. Learnable parameters and the geometric properties of the level set function are used to explicitly model and dynamically enhance the edges to suppress feature confusion and obtain an optimized feature map. The optimized feature map is then input into a dual-branch attention module, which coordinates global and local information to adaptively calibrate channel weights. This optimized feature map is then input into a cross-layer fusion module for multi-scale feature complementarity, outputting an attention-enhanced feature map. Based on the cross-layer fusion module, deep feature aggregation, and layer-by-layer feature aggregation, layer-by-layer fusion is performed to obtain the segmentation result map. This invention effectively solves the problems of over-segmentation and under-segmentation of polyp images caused by edge blurring and feature confusion, significantly improving the accuracy of polyp segmentation tasks.

The application discloses a topology optimization design method for integrated emission antenna heat dissipation flow channels, and specifically relates to the following steps: establishing a simplified two-dimensional plane heat source model; initializing and applying boundary conditions at the inlet; distributing compact support radial basis functions with an expansion coefficient on the obtained finite element grid nodes divided in a design domain, then linearly superimposing the radial basis functions to obtain a parameterized level set function; obtaining the distribution of solid and fluid in the design domain according to the level set function value; solving the heat exchange performance of the heat dissipation flow channel configuration represented by the parameterized level set function; establishing a mathematical model of the topology optimization problem, calculating the sensitivity of the objective function and the constraint condition with respect to the design variable expansion coefficient; and obtaining a complete heat dissipation flow channel design. The method can meet the demand of maximizing the heat exchange capacity at a high heat flow density heat source while simplifying the calculation, and improve the heat dissipation performance.

The invention discloses a gravity imaging method and system based on a level set physical information neural network, and belongs to the technical field of geophysical exploration. According to the method, a level set and gravitational potential dual network is constructed, and level set output is mapped into a density model; and pre-training initialization and interface adaptive sampling are adopted to construct a loss function fusing physical, data and geometric constraints, a differentiation smoothing strategy is utilized to iteratively optimize the network, and finally high-precision density imaging is output. According to the method, the implicit geometric expression capability of the level set is combined with the physical constraint of the physical information neural network, so that the problems of fuzzy boundary, unstable calculation and incapability of processing complex topological change in the traditional gravity inversion are effectively solved, and the imaging precision and the calculation efficiency are improved.

The invention discloses an image segmentation method of a regional active contour model based on a Laplacian operator. According to the method, an adaptive-Laplacian operator is introduced, and a corresponding energy functional is used as a length regular term of a level set function, so that effective regularization of the level set function is realized. The regularization mechanism not only can suppress false contours generated in the curve evolution process, but also can adaptively adjust the diffusion process, so that noise interference in the level set evolution process is effectively suppressed, image edge structure details are kept, and segmentation robustness and accuracy are improved. Experimental results show that the method has high segmentation precision and robustness in complex natural image segmentation.

The invention provides a topological optimization method and system based on a physical information radial basis deep neural network. The method comprises the following steps: S1, initializing parameters and a network model; s2, calculating a volume fraction; s3, training a network to predict a displacement field; s4, calculating a total potential energy loss function of the system; s5, judging network convergence; s6, calculating an optimization target value; s7, calculating a Lagrange multiplier and a velocity field; s8, calculating an expansion coefficient and an updating level set function; and S9, optimizing cyclic convergence judgment. Wherein the network model used for displacement prediction uses a physical information radial basis depth neural network (PIRDN), and a first hidden layer of the PIRDN is designed to be a self-adaptive radial basis function layer, so that the calculation precision and efficiency of a displacement field are improved; the total potential energy of the system is calculated through Gaussian integral, and training of the PIRDN model is achieved by minimizing the total potential energy of the system; and calculating unit strain energy according to the displacement field predicted by the PIRDN, and further calculating a level set function evolution velocity field to realize updating of the structure topology.

Techniques for level-set based shape optimization with shape constraints include simulating a state of a current shape of a component, calculating a sensitivity of an objective function to with respect to modification to the current shape based on the simulated shape based on the simulated state, generating a source function for an optimization equation based on the sensitivity, adding one or more constraint terms to the optimization equation based on one or more constraints on an interface of the shape to generate a constrained optimization equation, solving the constrained optimization equation to generate a solution to the constrained optimization equation, updating a velocity field based on the solution to the constrained optimization equation, solving a transport function based on the updated velocity field to generate a solution of the transport equation, and updating the current shape based on the solution to the transport equation to generate an updated shape.

A solution search device (1) is provided with: a setting unit (12) that sets a first desired level, which is a target value for a plurality of target functions in a multi-target optimization problem; a first optimal solution calculation unit (13) that calculates a first optimal solution using the first desired level set by the setting unit and a boundary that is a solution set for optimizing the plurality of objective functions; and a first optimal solution adjustment unit (15) that, upon receiving, from a user, an instruction indicating that the first optimal solution calculated by the first optimal solution calculation unit is not adopted, receives, from the user, an input of an adjustment amount for adjusting the first optimal solution, and adjusts the first optimal solution within the range of the boundary on the basis of the received adjustment amount.

The invention discloses an isogeometric non-probabilistic reliability topological optimization and geometric post-processing integration method, and belongs to the technical field of structural topological optimization design. The method comprises the following steps: constructing a geometric model by using the same group of NURBS primary functions, analyzing the model and parameterizing a level set function, and realizing unification of CAD and CAE models; for material attributes and loads with interval uncertainty, non-probability displacement reliability constraints are established based on a vertex combination method and an optimized feature distance; taking the minimization of the structural volume as a target, performing sensitivity analysis in combination with a shape derivative and an adjoint method, and driving boundary evolution of a level set to obtain a topological configuration meeting a reliability requirement; and automatically outputting the explicit geometric model through zero level set boundary extraction and parameterized projection-based boundary repair. According to the method, integration from reliability topological optimization to manufacturable geometric model is realized, and the safety and design efficiency of the structure in an uncertain environment are improved.

The present application belongs to the technical field of structural topology optimization, and discloses a level set topology optimization design method considering structural size constraints. The method comprises the following steps: S1, for a to-be-optimized structure, identifying a center axis in the to-be-optimized structure as a coarse skeleton of the to-be-optimized structure, performing morphological operation on the coarse skeleton to obtain a fine skeleton, calculating the nearest distance from each point in the to-be-optimized structure to the fine skeleton to obtain a distance field, and constructing a maximum size constraint and a minimum size constraint of the to-be-optimized structure according to the distance values in the distance field; S2, taking the minimum flexibility of the to-be-optimized structure as the target, and taking the volume constraint, the maximum size constraint and the minimum size constraint of the to-be-optimized structure as the constraint conditions, establishing a velocity field level set topology optimization design model, and solving the optimal solution of the model to realize the level set topology optimization of the to-be-optimized structure. Through the present application, the maximum and minimum sizes of the to-be-optimized structure are effectively controlled, and the manufacturability of the optimization design result is increased.

The invention discloses an intelligent identification method for boundary features of a water diversion disaster-causing structure in front of a tunnel, and relates to the technical field of underground engineering detection and disaster prevention and control. The method comprises the following steps: acquiring a tracing response curve and a water head observation data set, and defining a source and target endpoint set; establishing a level set function representing the geometric boundary of the disaster-causing structure, and establishing a travel time offset variable for each group of observation configuration; constructing a heterogeneous parameter field based on a level set function, and constructing an endpoint connectivity topology constraint for quantifying physical connectivity energy between endpoints; constructing a unified objective function including travel time residual, water head residual, topological constraint and bias variable sparse constraint; and executing synchronous joint inversion, updating the level set function, the physical parameter field and the bias variable, and outputting a disaster-causing structure geometric boundary based on the final level set function. Through level set evolution and topological constraint, the problems of fuzzy construction boundary and connectivity deficiency are solved, and the interference of adsorption delay on inversion precision is eliminated by using a bias separation mechanism.

The invention relates to the technical field of computer vision and image processing, and discloses a feature-sensitive image segmentation method, which comprises the following steps of: firstly, converting a medical image into a CIELAB space, constructing a five-dimensional feature vector, and clustering to generate a superpixel image block; then calculating local statistical features such as pixel mean value, information entropy and roughness in a block, aiming at pixel points, discriminating region attributes based on gradient continuity, calculating a self-adaptive fractional order differential order in combination with the local features so as to distinguish edge enhancement and noise smoothing operation, generating a saliency image by using a global background reference value, and on the basis, obtaining a significant image; and constructing a level set evolution equation containing a dual data fitting term and an adaptive fractional order length regular term, and driving the zero level set contour to evolve to a target boundary. According to the method, the differential strategy is dynamically adjusted, gray scale unevenness and weak boundary interference are effectively overcome, and the segmentation precision and robustness of the complex medical image are remarkably improved.

The application discloses a kind of topological independent heat source detection method and system based on level set-physical information neural network, belong to heat conduction and heat source detection technical field, including constructing heat conduction control equation and boundary condition;Heat source boundary and heat source term are implicitly represented using level set function;Build temperature prediction and level set double network collaborative architecture;Design fusion physical constraint, data fitting and geometric regularity multi-objective composite loss function;Network is trained using pre-training, joint optimization and fine convergence stage strategy;After convergence, level set zero isoline is extracted to output heat source boundary and temperature field and performance evaluation is carried out;System includes modeling module, level set representation module, network construction module, loss function module, training optimization module, result output module and evaluation module;The topological independent heat source detection method and system based on level set-physical information neural network provided by the application have significant engineering application value.

The invention relates to the field of additive manufacturing, and particularly discloses a method for generating a cup model capable of manufacturing three-dimensional Chinese characters based on energy driving. According to the method, a smooth time sequence evolution path from a multi-contour initial region to a single target contour is constructed, a contour evolution process is described by adopting a level set function, and an energy functional including a curve length regularization item, an adaptive expansion item and a shape prior item is established; by introducing a dynamic coupling control mechanism based on a region area difference, a shape error and a boundary length, adaptive adjustment of an expansion coefficient is realized; a level set evolution equation is obtained through variational derivation, and after numerical discrete solution, a two-dimensional evolution sequence is subjected to additive superposition along a time axis to form a three-dimensional volume field; and finally, through contour surface extraction and shell thickness processing, a 3D printing three-dimensional Chinese character cup model which is complete in structure and uniform in wall thickness is generated.