92 results about "Mesh optimization" patented technology

The invention discloses a deep learning-based middle-deep layer geothermal pipe group multi-field coupling simulation method, which comprises the following steps: carrying out numerical simulation cross mutual verification to obtain a middle-deep layer buried pipe heat pump system model, and carrying out system configuration by adopting a Monte Carlo method to obtain a first parameter environment set; screening the first parameter environment set according to a parameter environment function to obtain a second parameter environment set, performing data augmentation to obtain mid-deep layer geothermal pipe group simulation data, constructing a mid-deep layer geothermal pipe group multi-field coupling deep learning model, and obtaining simulation heat exchange data and prediction coupling heat exchange data; and adaptive grid optimization is carried out to obtain an optimized grid size, and the optimized grid size is adopted to adjust the mid-deep layer buried pipe heat pump system model to output the mid-deep layer buried pipe multi-field coupling model. According to the method, accurate and rapid simulation of the multi-field coupling process of the medium-deep geothermal pipe group can be realized under complex geological conditions, and the method has important practical significance for promoting large-scale development and utilization of medium-deep geothermal resources.

The invention provides a substation equipment multi-view point cloud data fusion three-dimensional modeling method and system. The method comprises the steps of obtaining original point cloud data under multiple views and synchronously updating an equipment surface feature topological relation; setting a registration reference surface matched with the geometric structure of the equipment according to the spatial distribution characteristics of the point cloud; carrying out rigid alignment by adopting a point cloud registration algorithm based on a feature weight; and calling a curvature constraint-based three-dimensional reconstruction algorithm through the registration error parameters to generate a high-precision grid model. Wherein the registration reference plane setting needs to judge the point cloud coverage integrity and the overlapping area condition, and when the condition is not met, the collection view angle is adjusted or the point cloud data is complemented; the registration condition is judged through a curvature consistency index; the three-dimensional reconstruction process comprises voxel down-sampling, curved surface function construction and grid optimization, and the final model needs to be subjected to electrical safety verification. According to the method, high-precision three-dimensional modeling of the substation equipment can be realized, and the geometric feature reduction degree and the electrical safety verification reliability are improved.

The invention provides a GPU-based parallel tetrahedral mesh optimization method, system and device and a medium, and relates to the technical field of numerical simulation. Comprising the steps of generating a triangular mesh covering a surface for a three-dimensional geometric model; generating an initial tetrahedral mesh based on the triangular mesh; performing a round of point smoothing operation on each tetrahedron by using a plurality of GPU threads; carrying out iterative circulation of the combination optimization operation, and simultaneously carrying out a round of point smoothing, edge deletion and multi-surface deletion operation on a plurality of initial tetrahedrons by utilizing a plurality of GPU threads; and when the edge deletion operation and the multi-surface deletion operation cannot further improve the grid quality, simultaneously performing a round of edge contraction and edge segmentation operation on the plurality of initial tetrahedrons by utilizing the plurality of GPU threads. According to the method, the large-scale grid with reliable quality is quickly generated based on the low-quality input grid, and the grid optimization rate is improved, so that the requirement of large-scale unstructured grid optimization in a three-dimensional space is met.

The invention relates to the technical field of video processing, and particularly discloses a video anti-shake processing method, a medium and equipment, and the method comprises the steps: carrying out the down-sampling of a first video frame, extracting feature points, calculating a homography transformation matrix between adjacent frames, and generating a global transformation matrix to achieve the frame alignment; generating a comprehensive grid block set covering the key area and the overall situation in combination with self-adaptive detailed grid division and uniform grid division, and marking the type of each grid block; and carrying out pixel-level fusion based on the distance weight and the type weight coefficient through a weighted fusion recombination method, and outputting a stable video frame. According to the method, through combination of global motion compensation and local grid optimization, video frame images in a violent shaking scene can be processed, picture content can be restored, stable video frame images can be obtained, monitoring picture missing is avoided, and port operation safety is guaranteed.

The invention discloses a three-dimensional model wall thickness measuring method, device and equipment combining a ray method and a spherical surface method, and a storage medium, and is applied to the field of three-dimensional models. Processing the input three-dimensional model by adopting a self-adaptive subdivision algorithm of a quadtree to generate an optimized triangular mesh; the method comprises the following steps: constructing an axial alignment bounding box (AABB) hierarchical tree structure optimized based on a superficial area heuristic (SAH) cost function, and in a wall thickness calculation stage, combining the advantages of a ray method and a spherical method: performing dynamic judgment according to an included angle between a normal vector at an intersection point of a ray and the inner surface of a model and an initial ray direction; the hybrid strategy ensures that reliable and efficient measurement results can be obtained in different geometric feature regions; a visual distribution diagram of color coding can be generated; according to the method, through adaptive grid optimization, efficient SAH-AABB acceleration and ray / spherical surface method mixed measurement, high-precision and efficient global measurement of the wall thickness of any three-dimensional model, especially a complex structure model, is realized.

The invention provides a thermal imaging automatic calibration and key region segmentation method. The method comprises the steps of obtaining a thermal imaging image, and then performing preliminary denoising processing on the image to obtain a denoised image; constructing a high-density grid in an image pixel dot matrix by adopting a dense sampling method according to the de-noised image so as to determine an initial pixel coordinate of each grid point; obtaining neighborhood feature vectors through an abnormal point set, and grouping the vectors by adopting K-means clustering so as to determine a temperature change mode, and fusing a temperature gradient to extract a boundary extraction basis as grid optimization input; and extracting a boundary extraction basis of the key part from the temperature change mode, adjusting the high-density grid according to the boundary extraction basis to obtain an optimized grid, and starting relocation iteration adjustment through coordinate deviation calculation. According to the method, a closed-loop optimization process of high-density grid construction, abnormal point identification, clustering analysis, grid dynamic optimization and iterative relocation is constructed, so that refinement and adaptive segmentation of a thermal anomaly region are realized.

The invention discloses a topological network global optimization method based on graph reinforcement learning, and relates to the field of computational geometry and grid optimizing.The method comprises the steps that a topological optimization process is modeled into a Markov decision process, a topological network containing singular points and angular points is converted into heterogeneous graph representation, and a graph state is constructed; selecting a topology editing action by a reinforcement learning agent fused with the graph neural network based on the current graph state; after executing the action, updating a topological structure and a graph state, and calculating an instant reward based on the global grid quality index change; training intelligent agents in batches through experience playback and time sequence differential learning, so that the intelligent agents learn a mapping strategy from a graph state to an optimal action; and applying the trained policy network deployment to automatic optimization of the new topology. According to the method, full-process automation of topological optimization is achieved, non-local negative effects of local modification are effectively avoided, a global optimal optimization strategy is learned, the grid quality and optimization efficiency are remarkably improved, and the method is suitable for simulation grid generation of aircrafts.

The invention discloses a structured grid optimization method and device and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: determining regular family fragments in a structured grid, calculating a spacing distance corresponding to the regular family fragments, and obtaining the optimized structured grid according to the spacing distance and a pre-stored ideal spacing distance. And optimizing the regular family fragments, determining optimized grid point coordinates, and finally updating the structured grid according to the grid point coordinates. When the spacing distance is adjusted, the spacing distance of the regular family fragments is optimized through the ideal spacing distance, the optimized grid point coordinates are obtained, and finally the structured grid is updated according to the grid point coordinates, so that the accuracy of a grid optimization mode can be improved.

The present invention relates to the field of video processing technology, and specifically discloses a video anti-shake processing method, medium, and device. The method comprises: extracting feature points by downsampling a first video frame, calculating the homography transformation matrix between adjacent frames, and generating a global transformation matrix to achieve frame alignment; combining adaptive detail grid division and uniform grid division to generate a comprehensive grid block set covering key areas and the global area, and marking the type of each grid block; performing pixel-level fusion based on distance weight and type weight coefficient through a weighted fusion and recombination method, and outputting a stable video frame. The method combines global motion compensation with local grid optimization, and can process video frame images in violent shaking scenes, restore the picture content, obtain stable video frame images, avoid missing monitoring images, and ensure the safety of port operations.

This invention discloses a fully automated, high-speed, and highly accurate three-dimensional reconstruction method and system, relating to the field of three-dimensional dynamic reconstruction. The invention introduces the construction of a spectral-point cloud fusion model and the calculation of a dynamic reflectance field by Monte Carlo ray tracing. By combining this with Fresnel's equation and dynamic ray tracing techniques, the reflectance calculation conforms to physical laws, thereby improving the accuracy of spectral mapping. Furthermore, the invention optimizes spectral data based on an error correction matrix using a spectral error compensation network, reducing the accumulation of errors caused by factors such as measurement angle and surface roughness. Moreover, in the three-dimensional reconstruction process, the invention improves the continuity and spectral consistency of the three-dimensional model by using Poisson surface reconstruction and a spectral reflectance-driven mesh optimization method based on spectrally enhanced point cloud data.

A deep transfer reinforcement learning (DTRL) method based on transfer learning within a deep reinforcement learning (DRL) framework is provided to accelerate the GoB optimization decisions when experiencing environment changes in the same source radio network agent or when being applied from a source radio network agent to a target radio network agent. The transferability of the knowledge embedded in a pre-trained neural network model as a Q-approximator is exploited, and a mechanism to transfer parameters from a source agent to a target agent is provided, where the transferability criterion is based on the similarity measure between the source and target domain.

The invention relates to the technical field of EMB brake disc visual inspection, and discloses an EMB brake disc defect detection method and system based on visual inspection, and the method comprises the steps: obtaining a complete image set containing a plurality of visual angles, and carrying out the space alignment and feature extraction, and obtaining a surface feature data set; performing interpolation complementation on the surface feature data set, and performing defect detection and space construction on the complemented data set to obtain a space information model; according to the spatial information model, generating point cloud data of the surface of the brake disc, and performing grid optimization to obtain an optimized point cloud grid data set; performing texture mapping and defect analysis on the optimized point cloud grid data set to obtain defect position data; according to the defect position data and the three-dimensional distribution view, depth information of the defect area is extracted, and defect form description data is obtained. According to the method, the multi-angle defect detection precision of the complex curved surface can be improved, so that the surface defects of the brake disc can be comprehensively and accurately detected at multiple angles and multiple dimensions.

In order to solve the problems of high calculation complexity and low efficiency in hydraulic fracturing numerical simulation, the invention provides a method for parallel simulation of hydraulic fracturing crack propagation. A three-dimensional fracture grid is constructed by adopting a displacement discontinuity method (DDM) and a finite volume method (FVM), and stress and fluid pressure distribution in fractures are calculated. Crack expansion is driven by optimizing DDM stress coefficient matrix generation and combining FVM to calculate crack tip pressure. OpenMP multi-thread scheduling and Intel MKL and Eigen library optimization are adopted, LU or QR decomposition is dynamically selected according to the matrix scale, and therefore the calculation efficiency is improved. According to the method, by initializing a three-dimensional fracture grid, optimizing a stress matrix, constructing a fluid-solid coupling equation set, carrying out iterative solution, checking convergence and generating a three-dimensional fracture expansion model, efficient fracture expansion simulation is achieved, and an efficient and accurate numerical tool is provided for fracture design of unconventional oil and gas reservoirs.

The invention provides a hyperbolic variable cross-section construction process optimization method, and belongs to the technical field of building construction.The method includes the steps that precise geometric data of a hyperbolic variable cross-section structure is obtained to establish a geometric parameter matrix, and a high-precision finite element analysis model is established through a self-adaptive mesh refinement algorithm; establishing a double-layer game grid optimization model to realize coordination and balance of calculation precision and efficiency, establishing a prestressed tendon three-dimensional path planning model, optimizing a spatial arrangement path of the prestressed tendon in a hyperboloid by adopting a genetic algorithm, accurately calculating a curvature radius and a friction loss coefficient of the prestressed tendon, and adjusting arrangement parameters according to a prestress loss rate. And a real-time monitoring system is established to dynamically adjust construction parameters, and the technical problem of low prestress transmission efficiency caused by complex spatial path planning of the prestressed tendons in the construction process of the hyperbolic variable cross-section structure is solved.

The invention discloses a vehicle body grid parameter dynamic optimization method and system based on model simulation, and belongs to the crossing field of computer-aided engineering and artificial intelligence technology. According to an existing vehicle body grid optimization scheme, deep mining and analysis of grid data cannot be achieved, and consequently the accuracy and reliability of a grid optimization result are not high. According to the vehicle body grid parameter dynamic optimization method based on model simulation, a vehicle body historical database module, a vehicle body grid optimization module and a domain knowledge base module are utilized to match vehicle body grid data to be optimized with vehicle body historical data to obtain the most similar vehicle body historical data; the information is used as experience information and verification basis of vehicle body grid parameter optimization; and then experience information and a verification basis are reused, the to-be-optimized grid data of the vehicle body are analyzed and processed, and optimization suggestion information is generated, so that historical data can be deeply mined and analyzed, and the accuracy and reliability of a grid optimization result can be effectively improved.

The invention relates to the technical field of finite element optimization, in particular to a performance optimization method and system based on finite elements, and the method comprises the steps: obtaining a finite element model of a gear in a transplanting mechanism of a rice transplanter, and dividing the finite element model into a plurality of grids; corresponding loads are applied to the finite element model according to simulation data of gears when the rice transplanter works, simulation indexes of all the grids are obtained, the grid sizes of all the grids are optimized based on the simulation indexes, and optimized target grids are output; determining a performance index of each target grid, optimizing design parameters of the target grids according to the performance indexes, and updating the finite element model based on the optimized design parameters to serve as a final finite element model of the gear; according to the method, through deep coupling of grid optimization and parameter optimization, the analysis precision and optimization efficiency of the gear can be improved.

The invention discloses a multi-model collaborative two-dimensional Gaussian splash three-dimensional reconstruction method, which comprises the following steps: acquiring a multi-view RGB image, outputting camera parameters and sparse point clouds by using a VGGT model, and generating a depth image and a mask image through a monocular depth estimation network and an image segmentation network; carrying out densification processing by utilizing a visual shell algorithm; initializing a two-dimensional Gaussian primitive by using the dense point cloud; carrying out training on the initialized two-dimensional Gaussian primitives; re-rendering depth maps of all visual angles, and generating a three-dimensional grid through a truncated symbol distance function and a contour surface extraction algorithm; performing grid optimization based on local bilateral filtering on the three-dimensional grid to obtain a smooth grid; and carrying out scale correction and smoothing the grid to obtain a three-dimensional model with a real scale. According to the method, an omnibearing three-dimensional model with high-fidelity geometry, real scale and new view rendering capability is generated through a multi-model collaborative initialization and optimization strategy.

The invention relates to the technical field of point cloud completion, in particular to a point cloud completion method and system based on secondary Poisson reconstruction, and the method comprises the steps: obtaining three-dimensional space coordinates and color information obtained through the analysis and recognition of original high-density point cloud data, converting the three-dimensional space coordinates and color information into a uniform data format, and carrying out the preprocessing; poisson surface reconstruction is carried out on the to-be-completed point cloud data based on a Poisson reconstruction algorithm and an Open3D framework, wherein the Poisson surface reconstruction comprises normal vector estimation, density-driven grid optimization and implicit surface generation; performing secondary reconstruction on the primary Poisson reconstruction point cloud data based on a secondary Poisson reconstruction algorithm; and acquiring small-range point cloud data around the missing part of the point cloud profile obtained by cutting based on FAROSCENE software, and comparing and fitting the original high-density point cloud data, the small-range point cloud data around the missing part and the secondary Poisson reconstruction point cloud data. The method can greatly improve the complementation precision, and has high fidelity and high visualization quality.

The application discloses a fluid simulation method and system based on adaptive grid optimization, and belongs to the technical field of computational fluid dynamics, and comprises the following contents: receiving fluid state data at a current moment, state data at a historical moment and global physical parameters, performing scale alignment and normalization processing on the data and encapsulating the data as structured input; performing initial blocking on the whole calculation domain, converting original regular grid expression into a candidate unit set with hierarchical relationship; calculating physical indexes of each candidate unit based on fluid physical properties, judging whether each candidate unit needs to be refined, reserved or discarded; performing word tokenization and position coding on a multi-scale unit sequence, inputting the sequence into a neural solver to establish a long-range dependence relationship across regions, and outputting state word tokens at a next moment; and mapping the state word tokens back to an original regular grid space to generate a flow field simulation result. By using the method, accurate identification of a key region of a flow field and efficient modeling of multi-scale flow characteristics are realized.

The application relates to the technical field of CAD model processing, in particular to a triangular mesh quality optimization method and system based on local reconstruction, which comprises the following steps: acquiring a triangular mesh, and screening a first triangular mesh to be optimized based on a preset regularity; performing basic topological adjustment on the first triangular mesh to be optimized, and obtaining a second triangular mesh to be optimized; acquiring a first local neighborhood mesh of the second triangular mesh to be optimized, moving the vertexes of the triangular mesh in the first local neighborhood mesh, and obtaining a third triangular mesh to be optimized; acquiring a second local neighborhood mesh of the third triangular mesh to be optimized and a projection plane of the second local neighborhood mesh, determining a layout triangle according to the area of the projection plane and a target side length, and mapping the layout triangle to the second local neighborhood mesh. The application can optimize the quality of the triangular mesh on the basis of retaining the original geometric characteristics of the triangular mesh, and greatly improve the calculation efficiency of the triangular mesh optimization.

The invention provides an unstructured parallel grid quality optimization method based on grid unit merging. According to the method, when it is detected that a negative volume unit exists in a computational grid, the negative volume unit and an adjacent unit are combined, and optimized grid geometric information, topological information, boundary condition information and butt joint surface information are reconstructed. And through loop iteration until no negative volume unit exists in the optimized grid, the quality of the computational grid is remarkably improved, and the efficiency and precision of CFD numerical simulation are improved. The method can be applied in a multi-block parallel grid environment, supports grid optimization of a large-scale complex geometric model, and is suitable for two-dimensional and three-dimensional unstructured grids.

The application discloses a kind of complex tide river network's multi-source compound disaster inundation risk simulation method, contains the construction method of astronomic tide-storm surge-flood-salinity-falling multi-scale dynamic process full coupling model, can analyze complex river network in climate change scenario mountain torrent, storm surge, salinity and rainstorm etc. extreme compound disaster's inundation and salinity upstream risk.Through potential inundation area determination, model grid optimization, numerical format setting and parallel acceleration etc. technical means, on the basis of guaranteeing momentum and material flux conservation in river network, maximum degree optimizes calculation efficiency, can realize in estuary-coastal cross-scale spatial region high-precision simulation of the purpose of giving priority to multi-source disaster inundation and saltwater stratification.Based on the application, the problem that water dynamics, salinity, relatively isolated nearshore complex topography cannot interact in traditional simulation can be solved, technical support is provided for scientific evaluation of estuary area multi-source disaster risk and return period, with outstanding social benefits and disaster prevention and mitigation economic benefits.

The invention relates to the field of radiator design analysis, in particular to a radiator flow channel three-dimensional reconstruction and fluid performance prediction system and a method thereof.The method comprises the steps that a parameter input module receives a radiator three-dimensional model and boundary information and calculates parameters of a flow channel section and an inlet section; the streamline drawing module receives the parameters and calculates and determines starting coordinates and boundary points of a streamline; and the streamline smoothing module is used for smoothing the streamline. The flow channel curved surface gridding module receives the smoothed streamline coordinates, generates a three-dimensional curved surface based on spatial structure points, and performs adaptive non-uniform mesh generation and boundary constraint intelligent mesh optimization; the fluid performance prediction module receives the flow channel curved surface grid, inputs fluid performance analysis parameters and predicts fluid performance; the calculation result output module receives the prediction result, arranges and summarizes the prediction result and outputs the result; and through streamline tracking and smoothing processing, accurate reconstruction of the complex flow channel of the radiator is realized, the modeling precision is improved by more than 40%, and the design accuracy and efficiency of the radiator are improved.

A method and apparatus that renders a mesh from one or more virtual camera views of an object, the one or more virtual camera views being projections of the mesh onto a plurality of 2D planes; inputs the one or more virtual camera views into a fully-convolutional model for real-time segmentation; generates one or more 2D pixel-wise masks based on the fully-convolutional model, the 2D pixel-wise masks being associated with one or more segmented elements; performs remeshing and mesh optimization using the one or more 2D pixel-wise masks; and generates an output mesh based on the remeshing and mesh optimization.

The application discloses an intelligent identification and modeling method for an aviation composite special-shaped component inner preform, and belongs to the technical field of aviation composite material microstructure digitization reconstruction and modeling. The method comprises the following steps: S1, data acquisition; S2, data preprocessing; S3, model training preparation; S4, intelligent segmentation; S5, three-dimensional reconstruction; S6, grid optimization; S7, topology processing; and S8, model output. The aviation composite special-shaped component inner preform intelligent identification and modeling method is adopted, topology errors and grid conforming problems that are prone to occur in explicit modeling are combined with grid Boolean operation and post-processing repair algorithms, grid intersections and discontinuous regions are accurately processed, grid gaps, missing surfaces and other closed defects introduced by the Boolean operation are repaired, the node conformality, topology connectivity and spatial closedness of the preform substructure grid are ensured, manual participation is reduced, and the modeling efficiency and stability are improved.

The present invention discloses a 3D scanning system for the medical field, which belongs to the field of 3D scanning technology and aims to solve the problems of dynamic changes, low data acquisition efficiency and high complexity of post-processing encountered in the 3D scanning process. The system comprises the following steps: projecting a light pattern onto a target object through a projector of a 3D scanner, capturing the reflection intensity with a camera and automatically adjusting the brightness and contrast of the projector; capturing a reflected image of the light pattern and obtaining an acquisition distance, and automatically adjusting the camera shutter speed to optimize data capture after ensuring that scanning rules are met; pre-processing the reflected data, filtering invalid data, converting it into a point cloud and performing posture transformation, perspective merging, density balancing and outlier removal; generating a 3D model based on the processed point cloud data, and completing target recognition and annotation through grid optimization; storing and managing the reflected data and processing results to achieve efficient and accurate 3D scanning and modeling.

The invention relates to the technical field of parameter design of a double-exponential pulse current source, and particularly discloses a pulse current source parameter prediction and optimization method based on artificial intelligence, which comprises the following steps of: constructing a two-stage parameter reverse framework of artificial intelligence prediction and local grid optimization, and taking target rise time, pulse width and load internal resistance as input; rough prediction of the capacitance C and the inductance L is given by using a regression neural network; then, a two-dimensional local parameter grid is constructed by taking the prediction as a center, a double-index analysis model is called to carry out rapid feature calculation and error evaluation, and a parameter group enabling the relative error of each feature to be minimum is selected as a final solution. The method aims at obtaining engineering balance between precision within 1% and second-level response.

The invention provides a coal mine vertical shaft model construction method and device and electronic equipment, and the method comprises the steps: obtaining a coal mine three-dimensional geologic model, and determining a model boundary of a to-be-constructed vertical shaft model based on vertical shaft model construction parameters; determining a to-be-cut area based on the model boundary and the coal mine three-dimensional geologic model; in response to the situation that the vertical shaft cutting condition is not met, optimizing the distorted triangular mesh area; determining a windowing boundary, and performing vertical shaft model cutting in the windowing boundary to generate candidate vertical shaft models; and the steps are repeated until the candidate vertical shaft model meets the cutting end condition, and the candidate vertical shaft model and the coal mine three-dimensional geologic model are fused to generate a target vertical shaft model. By triggering a local grid optimization + dynamic windowing retry mechanism, global model defects can be effectively avoided, an effective vertical shaft model can still be stably generated under severe geological conditions, dependence on professional modeling personnel is greatly reduced, modeling efficiency is improved, and the method is suitable for large-scale mine group batch processing scenes.

The invention discloses a grid optimization method based on a double delay depth deterministic policy gradient (TD3) algorithm, and belongs to the technical field of engineering simulation grid optimization, and the grid optimization method comprises the following steps: S1, reading a grid file in an industrial standard format, and extracting a node identifier, a coordinate and a connection relation; s2, constructing a reinforcement learning environment, and defining a state space, an action space and a reward function; s3, constructing a TD3 framework comprising an Actor strategy network and a dual Critic evaluation network; s4, iteratively training the network model; and S5, applying the trained model to optimize the grid, outputting a standard format optimization file, and generating a quality report and a visualization result. According to the method, autonomous grid optimization is achieved through deep reinforcement learning, the grid division quality in engineering simulation can be improved, the method has the advantages of being high in automation degree, high in optimization speed and the like, an efficient and reliable grid optimization solution is provided for the field of engineering simulation, and numerical simulation precision and research and development efficiency can be improved.

The present application discloses a method, device and product for generating a lattice shell structure model, which relates to the technical field of building structure optimization, and is aimed at the problems of material waste, excessive structural weight, low construction efficiency and difficulty in manufacturing complex nodes in the existing technology. The lattice shell structure is modeled by using a parametric modeling tool, and the basic grid shape is geometrically optimized using a geometric form-finding optimization tool. The optimized grid shape is further optimized using a grid optimization tool, and the number of nodes and material usage of the lattice shell structure are optimized using an optimization algorithm; the optimized grid shape is structurally simulated and analyzed in finite element analysis software to evaluate stress distribution and structural deformation; topological optimization software is used to perform topological optimization on the shape and stress conditions of the lattice shell nodes, and the optimized nodes are manufactured using additive manufacturing technology. The method provided by the present application can effectively improve material utilization, achieve structural lightweighting, reduce structural weight, and improve construction efficiency.