23 results about "Geometric space" patented technology

The invention discloses a Chinese character structure and geometric space interactive writing method, which relates to the technical field of interactive writing methods, and comprises the following steps: carrying out geometric classification processing on Chinese characters by adopting a structural analysis method to obtain a Chinese character type set comprising a left-right structure, an up-down structure and a surrounding structure; carrying out geometric rule definition processing on the obtained Chinese character type set to obtain a geometric constraint rule base containing component proportion, gravity center alignment and stroke topological relation; performing structured storage processing on the obtained geometric constraint rule base by adopting a database construction method to obtain a dynamically-called Chinese character writing template database; the writing process of the user is monitored in real time, and a writing feature data set containing stroke coordinates, writing speed and pressure data is obtained; performing matching processing on the obtained writing feature data by adopting a dynamic grid generation method to obtain a self-adaptive reference grid corresponding to the currently written Chinese character; and carrying out deviation detection processing on the obtained self-adaptive reference grid by adopting a computer vision method.

Systems and methods for guiding or steering thought processes on a persistent cognitive machine (PCM) that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for guiding or steering thought processes on the thought manifold are disclosed that involve mathematical manipulations of the geometric space of the thought manifold inspired by gravitational lensing.

A system and method for a digital thought architecture, otherwise called a persistent cognitive machine (PCM), that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. Not only does the PCM with thought manifold maintain persistent cognitive processes regardless of external interaction, overcoming limitations of existing AI systems that operate within a prompt-response paradigm where they await input, generate output, and return to a waiting state, it also performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. In some embodiments, the thought manifold may be implemented as a neuromorphic platform.

The invention relates to the field of knowledge graph data processing, and discloses an indoor space automatic layout and rendering method and system based on a design knowledge graph, and the method comprises the steps: maintaining an entity knowledge graph containing a variation path, and discretizing a continuous geometric space into an environment topological graph; executing node mapping and resource topology handshake verification based on subgraph isomorphism; and responding to the mapping deadlock, triggering recursive topology rewriting along the variation path to reconstruct the query sub-graph, and generating a configuration instruction. According to the method, continuous geometric search is converted into discrete atlas isomorphism and attribute propagation, the high-dimensional space layout calculation complexity is reduced, the deadlock problem under the over-constraint condition is solved by utilizing a recursive variation mechanism, and the configuration efficiency is improved. And the logic leakproofness and engineering compliance of the layout scheme are ensured.

The invention discloses an HBIM reconstruction method in a historic building digital twinning technology. Firstly, fine modeling is carried out, and a standard parameter model and a triangulated irregular network model are fused to construct a basic geometric model; digital-analog decoupling is carried out, geometric space and semantic attribute information are separated, and an IFC format geometric model is generated; then entering a geometric conversion link, dividing a high-risk region, a medium-risk region and a low-risk region according to the ratio of the number of vertexes per unit area to the area, subdividing the high-risk region, merging the low-risk regions, converting the high-risk region and the medium-risk region into an OBJ format by adopting a geometric element extraction and reconstruction algorithm and a Delaunay triangulation algorithm, and finally converting the OBJ format into a glTF format; and finally, carrying out attribute mapping, associating attribute data with the glyTF-format geometric model by utilizing a unique identifier of the component, setting an updating mechanism to keep synchronization, completing HBIM reconstruction, and building a foundation for digital protection and application of the ancient building.

The invention discloses a method for reducing large model illusion of a question answering system based on knowledge graph representation learning, which comprises the following steps of: firstly, based on Ollivier-Ricci curvature calculation, embedding different structures in a knowledge graph into a plurality of approximate geometric spaces so as to analyze geometric patterns in data; secondly, generating a new entity representation by aggregating neighbor information, and transmitting and fusing information in different geometric spaces by using indexes; and finally, a dynamic curvature adaptive adjustment strategy is adopted to promote cooperative training and efficient fusion of multi-geometric space representation. According to the method, a complex semantic relationship is accurately described by combining geometric space and knowledge graph representation learning, geometric distortion in an embedding process is effectively reduced, and a structured knowledge system is constructed. According to the method, the accuracy and credibility of a large model in knowledge reasoning are improved, the illusion problem caused by incomplete or contradictory knowledge representation is solved, and powerful support is provided for a question and answer system.

The invention belongs to the technical field of robots, and discloses a hydraulic biped robot mechanical leg lightweight design method based on topological optimization, and the method comprises the steps: defining a three-dimensional geometric model according to an existing biped robot mechanical leg space; building of a finite element model is completed, definition of topological optimization parameters is completed, and topological optimization and post-processing design are carried out; and through result analysis, the optimal configuration is screened for simulation analysis. A mathematical model of the mechanical leg of the hydraulic biped robot is established based on a topological optimization method, and an initial geometric space and an analysis model are determined according to design requirements. Weight reduction optimization is carried out on the thigh assembly through topological optimization, and finally four topological structures are obtained, and the mass of the four topological structures is 535.4 g, 494.3 g, 423.8 g and 569.2 g respectively. Through simulation verification, the third scheme meets the requirements in the aspects of strength, safety coefficient and service life, the optimization effect is better than that of the original design, and the feasibility and superiority of the optimization design are proved.

The application discloses a 3D printing modeling method for heterogeneous material parts, which comprises the following steps: 1) obtaining each layer slice contour and filling path of a heterogeneous material part model to be printed, and constructing a feature point set E of the model in a geometric space; 2) constructing a parameter space transformation function matrix H, and transforming the feature point set E of the model from the geometric space to the parameter space of the parameter space transformation function matrix H; 3) constructing a material space distribution change matrix F, and obtaining the material volume fraction of each feature point by using the material space distribution change matrix F, and completing material modeling. The application directly uses the slice filling path points as the material feature points in the model, avoids manufacturing errors caused by the traditional voxel method or the finite element method, quickly obtains the material value of the feature points by the space transformation method, avoids the limitation of directly defining the material transformation on the model geometric space, and therefore can obtain a heterogeneous part model with an arbitrary complex transformation.

The application discloses a multi-entity meshing method, an electronic device and a storage medium, and is used for converting a target entity of a geometric space into a hexahedron-based mesh of a parameter space. The application divides the target entity into a plurality of simple-shaped sub-entities, performs multi-entity collaborative division, uses geometric feature matching and topological relationship analysis, automatically identifies and extracts contact surfaces between the multi-entities, and automatically defines a sweepable parameter, so that a target surface is extracted without manually selecting entities to perform Boolean operation or is swept one by one. The common node algorithm is adopted, common node constraints are applied in the meshing stage, common node meshes are generated, and error accumulation in traditional division and merging is avoided.

The disclosed systems, structures, and methods are directed to generating a three-dimensional (3D) representation of an object, the method comprising accessing a 3D point cloud reconstruction of the object, accessing a 3D model template, the 3D model template defining a generic version of the object, aligning, in a geometrical space, the 3D model template with respect to the 3D point cloud reconstruction, adjusting a scale of the 3D model template, and applying local deformations to a surface of the 3D model template.

The application discloses a geometric embedding knowledge graph question answering method based on a large language model, first, aiming at the structural heterogeneity of knowledge graph chain, level and cycle, entity and relationship embedding is learned in Euclidean space, hyperbolic space (Poincare ball model) and spherical space (unit sphere) respectively, and the triple score of the three geometric spaces is optimized by combining negative sampling and maximum interval loss. Secondly, in the reasoning stage, the large language model analyzes the problem and identifies the key entity, constructs the relevant subgraph, and generates the candidate path by using the breadth-first search or beam search; the path node is projected in three ways and spliced to calculate the attention weight, the fusion node representation and edge level fusion score are obtained, and the path score is accumulated. Finally, the structured prompt is formed by selecting the Top-K evidence path and its weight, contribution value, the large model is constrained to generate the answer and explanation, the traceability and accuracy are improved, the consistency is enhanced, and the illusion is reduced.

This invention relates to the field of thread turning technology, and discloses a thread turning cutting simulation method and system based on the analytical formula of a helical surface. The method includes: acquiring helical surface data during thread turning and constructing a geometric space model of the helical surface that can represent the tool sweep body; acquiring blank data and constructing a blank model using a tri-dexel model; for dexel segments parallel to the rotation axis, calculating the corresponding rotation angle and axial intersection point using the helical surface geometric space model, and calculating the axial cutting result of the tool based on the axial intersection point; for dexel segments perpendicular to the rotation axis, solving multiple solutions for the rotation angle and multiple intersection points using the helical surface geometric space model, selecting the effective intersection points as non-axial intersection points, and calculating the non-axial cutting result of the tool based on the non-axial intersection points. This invention can improve the efficiency and accuracy of thread turning cutting simulation.

The application relates to the field of knowledge graph data processing, and discloses an indoor space automatic layout and rendering method and system based on a design knowledge graph, which comprises the following steps: maintaining an entity knowledge graph containing a variation path, and discretizing a continuous geometric space into an environment topology graph; performing node mapping based on subgraph isomorphism and resource topology handshake verification; in response to a mapping deadlock, triggering recursive topology rewriting along the variation path to reconstruct a query subgraph, and generating a configuration instruction; the application converts continuous geometric search into discrete graph isomorphism and attribute propagation, reduces the high-dimensional space layout calculation complexity, solves the deadlock problem under over-constrained conditions by using a recursive variation mechanism, and ensures the logical rigor and engineering compliance of the layout scheme.

The present disclosure provides a multi-geometry space adaptive visual language modeling system for astronomical simulation. A two-stage training strategy is adopted to receive external images and user queries. First, the external images are input into a multi-geometry space prompt generation module, and the spatial features P E , H , S of three spatial geometry priors of Euclidean, hyperbolic and spherical are output. The spatial features are input into a multi-modal astronomical data input and alignment module together with the user query. After vector splicing, the input is input into a geometry adaptive backbone module, and finally the corresponding numerical value and reply are output through an astronomical task intelligent inference and output module. By integrating the representations of multiple geometry spaces such as Euclidean, spherical and hyperbolic into the visual language model architecture, the defects of existing visual language models that cannot handle complex astronomical physical phenomena due to their limitation to Euclidean space are effectively solved.

This paper discloses a reconstruction method for HBIM in digital twin technology for ancient buildings. The method begins with detailed modeling, integrating a standard parametric model with an irregular triangulated network model to construct a basic geometric model. Next, digital-analog decoupling separates geometric space from semantic attribute information, generating an IFC-formatted geometric model. The method then proceeds to geometric conversion, where high-, medium-, and low-risk areas are divided based on the number of vertices per unit area and their area ratio. High-risk areas are subdivided and low-risk areas are merged. Geometric element extraction and reconstruction algorithms are then applied to convert high- and medium-risk areas into OBJ format using the Delaunay triangulation algorithm, and finally into glTF format. Finally, attribute mapping is performed, using component unique identifiers to associate attribute data with the glTF-formatted geometric model and establishing an update mechanism to maintain synchronization. This completes the HBIM reconstruction, laying a solid foundation for the digital preservation and application of ancient buildings.

A construction method of spatio-temporal neural operator on Riemannian manifolds for complex geometries is disclosed, and relates to the field of machine learning technology, the method comprises the following steps: S1, according to a geometric space of an input spatio-temporal function and an output spatio-temporal function, solving a set of Laplacian eigenfunctions as basis functions, and then constructing a spatial dimension encoding module and a spatial dimension decoding module ; S2, solving a set of Fourier basis functions, and then constructing a temporal dimension encoding module and a temporal dimension decoding module −1; and S3, constructing a Laplace-Fourier nested kernel integration module, and constructing a spatio-temporal neural operator on Riemannian manifolds for complex geometries by serially connecting a plurality of kernel integration modules. The present disclosure adopts the construction method of spatio-temporal neural operator on Riemannian manifolds for complex geometries based on the above steps, and the mapping between two spatio-temporal functions defined on complex geometries is represented by constructing a parameterized model.

The invention of the origami pyramid with the ability to connect in five faces and repeatability in geometric space in the form of cubes and n-dimensional prisms in basic sciences, mathematics, and spatial geometry topics is an effective step towards knowing the future of geometric space, implementing for the first time in industrial design. This origami pyramid uses cubes connecting volumes in the form of cubes and prisms with the help of continuous and combined symmetry in the geometric space. In such a way that a cube divides into 27 small cubes, the first cubic symmetry is formed in the center of the edge of the pyramid, the center of spatial symmetry of the cube.

The invention discloses a mechanical arm generalization operation method and system based on mixed geometric representation pre-training, and belongs to the field of mechanical arm generalization operation. The method comprises the steps that a 3D-MOV data set containing an object, a fine-grained scene and desktop-level 3D point cloud is constructed, and a mixed geometric representation encoder is trained based on the pre-training; and after mapping to the hyperbolic space optimization learning, returning to the Euclidean space to complete self-supervised reconstruction. The scene observation point cloud is rendered into an RGB image, the RGB image is subjected to double-path distribution after being processed, strategy network fusion information is subjected to fine adjustment, and the action of the mechanical arm is predicted. Hyperbolic geometric representation is introduced into visual self-supervision pre-training based on 3D multi-view angles, 3D multi-view representation pre-training based on mixed geometric space is used for mechanical arm operation, the next operation action is effectively predicted, and the generalization operation capacity of a mechanical arm in different scenes is greatly enhanced.

The invention is suitable for the technical field of three-dimensional computer vision, and provides an anti-disturbance three-dimensional object semantic segmentation geometric enhancement method. Firstly, normal vector and curvature calculation of discrete point clouds is converted into a statistical optimization problem, a normal vector of a neighborhood is solved, then an initial curvature is calculated in combination with a characteristic value, sparsity is introduced to smoothly correct a curvature error of a sparse neighborhood, geometric space information of the point clouds is accurately captured, and the geometric understanding ability of disturbance point clouds is enhanced. Then, the obtained normal vector and curvature information are embedded into scalar features, the scalar features are expanded into independent space vectors through self-adaptive feature vectorization operation, vector rotation is achieved through Rodrigues rotation operation, point cloud disturbance is accurately modeled in combination with a relative direction refinement mechanism, and finally robust direction consistency representation is extracted. According to the invention, the anti-disturbance capability of the three-dimensional object semantic segmentation method is effectively improved, so that the three-dimensional object semantic segmentation method can still maintain a stable and accurate segmentation effect in a complex application scene.

Systems and methods for persistence of memory on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. A PCM with cognitive manifold performs cognition on a cognitive manifold in a continuous, differentiable, cognitive manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. A mechanism inspired by magnetohydrodynamics is provided for coupling of typed spaces where thoughts on a cognitive manifold are structured as typed entities.

The invention discloses a network security knowledge graph embedding method and device based on a mixed space, and the method comprises the following steps: S1, mapping entities and relationships of a network security knowledge graph into a hyperbolic space and a hyperspherical space at the same time; s2, respectively mapping points in the hyperbolic space and points in the hyperspherical space to a tangent space through logarithm mapping, and mapping vectors in the tangent space back to corresponding geometric spaces through exponential mapping; s3, the hyperbolic space embedding and the hyperspherical space embedding are switched to a tangent space through logarithmic mapping, the geometric information embedded in the tangent space and the geometric information in the Euclidean space are fused, and the fused geometric information is returned to a target geometric space through exponential mapping; and S4, carrying out weighted fusion on the geometric information in different geometric spaces by using an attention mechanism. According to the method, the capability of capturing complex relations and multi-layer structures of the atlas is enhanced.

The invention discloses a multi-entity grid division method, electronic equipment and a storage medium, which are used for converting a target entity in a geometric space into a grid with a hexahedron as a main parameter space. According to the method and the device, the target entity is divided into a plurality of fruit entities with simple shapes, the contact surfaces among the multiple entities are automatically identified and extracted through multi-entity collaborative division by utilizing geometric feature matching and topological relation analysis, and the parameters capable of being swept are automatically defined; and manual selection of entities for Boolean operation for target surface extraction or one-by-one scanning is not needed. By adopting the connode algorithm provided by the invention, the connode constraint is applied in the grid division stage, and the connode grid is generated, so that the error accumulation caused by first division and then merging in the traditional method is avoided.