1274 results about "Computer-aided" patented technology

The invention relates to the technical field of computer aided design, in particular to a text-driven CAD modeling method and system based on a diffusion and visual language model.The method comprises the steps that natural language text description is obtained, and CAD semantic features of the natural language text description are extracted; carrying out geometric standardization on the CAD semantic features by adopting a fine-tuning diffusion model, and generating a CAD view image conforming to engineering specifications; carrying out fusion by adopting a fine-tuned visual language model to generate a parameterized CAD construction sequence; a three-mode alignment mechanism is adopted, and the semantic consistency of the CAD semantic features, the CAD view images and the CAD construction sequences is checked; performing verification and post-processing on the CAD construction sequence, and outputting an executable Python code or STEP file; the CAD modeling method disclosed by the invention performs explicit modeling based on flexible modal description, and has the characteristics of high geometric constraint and high usability.

The invention belongs to the technical field of geometric pretreatment in computer aided engineering (CAE), and particularly relates to a dirty geometry restoration method for a complex three-dimensional geometric model, which comprises a dirty geometry detection and restoration module based on heuristic rules and geometric topology analysis. By means of a multi-model collaborative alignment and bonding mechanism, high-precision assembly between parts is achieved through geometric feature matching and adjacent domain self-adaptive adsorption; acquiring material attributes provided by the simulation platform, and performing accurate cutting and boundary reconstruction on an intersection domain based on an intersection domain intelligent segmentation algorithm of the material attributes to ensure correct mapping of boundary conditions of a physical field; a geometric Boolean processing engine iterative learning mechanism of a general merging algorithm is integrated, and a high-quality model for simulating a grid is generated through multi-round iterative optimization. According to the method, double challenges of internal dirty geometry of a single model and derivative dirty geometry of intersection / adjacent areas among multiple bodies are solved, and key physical attributes and topological continuity of electromagnetic signal links are ensured and are completely reserved during repair.

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention relates to the technical field of computer-aided manufacturing, and provides a staged processing real-time cutting simulation method and system.The method comprises the steps that S1, initialization modeling is conducted, specifically, a blank voxel grid model, a tool body voxel grid and a triangular grid are built; s2, performing a first simulation stage of low-precision real-time cutting dominated by voxel grids, judging a space overlapping relationship between the tool body voxel grids and the blank voxel grids in real time so as to determine a voxel region to be removed, and deleting blank voxels in the overlapping region through Boolean operation; s3, in the execution process of the first simulation stage, monitoring a preset multi-dimensional switching trigger condition in real time, and when any condition or a combination of multiple conditions is met, entering a second simulation stage; and S4, a second simulation stage of high-precision real-time cutting with the triangular network as the dominant is carried out, the voxel grid intermediate workpiece is converted into a transition triangular grid, and high-precision cutting calculation is carried out based on the triangular grid. And the efficiency-precision contradiction is broken through through a staged strategy.

The invention discloses a special-shaped pipe forming compensation method and system based on digital twinning, and belongs to the technical field of computer-aided manufacturing and intelligent control. The method comprises the steps that multi-source sensing data in the special-shaped pipe forming process is obtained, and time-space fusion processing is executed; generating a time-space aligned multi-source sensing data set, carrying out dynamic calibration on the parameterized virtual digital twin, generating a calibrated virtual digital twin, carrying out simulation calculation on the forming process of the special pipe in a future time window, outputting predicted deviation data, and combining with a preset compensation strategy to generate a compensation instruction through logical judgment and calculation; and obtaining an adjustment result after the compensation instruction is applied, and updating the compensation strategy by using a reinforcement learning algorithm. According to the method, prospective simulation prediction is carried out by constructing the dynamically calibrated virtual digital twins, closed-loop optimization is carried out on a compensation strategy by utilizing a reinforcement learning algorithm, and active, high-precision and self-adaptive compensation control on the special pipe forming process can be realized.

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

The invention discloses a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention belongs to the technical field of computer aided design and structural design, and discloses a multi-scale lattice structure and a design method thereof. The design method comprises the following steps: S1, designing a cubic unit cell with eight symmetrically distributed corner nodes according to a cubic lattice structure; s2, all the lattice unit cells are zoomed, one part of the lattice unit cells are zoomed into small lattice unit cells with the size being half of the original size, and the other part of the lattice unit cells are large lattice unit cells with the original size reserved; s3, in the macroscopic lattice structure, arranging the position relation of the small lattice unit cells and the large lattice unit cells; and S4, through a computer aided design method, adjusting the macroscopic lattice structure by adopting a large and small lattice unit cell connection mode based on rod diameter transition. A multi-scale large and small lattice unit cell arrangement mode is introduced into the lattice structure, so that the lattice structure has better mechanical performance under the same compression load.

Systems and methods for a threat-hunting development environment are disclosed herein. The systems and methods can include: executing a networked, assisted, threat-hunting environment that, via a dedicated user interface, is configured to establish data transfer pipelines between the threat-hunting environment and the at least one tenant network, the data transfer pipelines maintaining a connection between the threat-hunting environment and the at least one tenant network during an active session, via the at least one SIEM server, to allow continuous data communication; query the at least one tenant network with a query developed via an integrated code editor; receive the query result data from the at least one tenant network: analyze, the result data for a detected threat in the at least one tenant network; and based on the analyzed result data, push a subsequent query to the at least one tenant network to respond to detected threat.

The invention relates to the technical field of computer-aided anti-seismic analysis, and discloses an anti-seismic performance simulation test method and system for a concrete-filled steel tube structure, and the method comprises the steps: obtaining a parameter set of the concrete-filled steel tube structure; identifying a critical buckling mode and constructing a defect correction model; constructing a coupling damage index; constructing a numerical instability risk evaluation value based on the structure energy proportion; carrying out strong earthquake nonlinear time-history iterative solution; and outputting the anti-seismic performance index set and the coupling failure area. Compared with the prior art, the technical problem that Newton iteration divergence is prone to occurring in numerical solution under the coupling instability condition that traditional nonlinear finite element analysis is relied on for strong earthquake response calculation, especially when a steel pipe is subjected to local buckling and core concrete enters a high-pressure three-axis stress state is solved. Due to the fact that the local buckling and crushing damage coupling index and the self-adaptive time step length adjusting mechanism are constructed, the stability of the anti-seismic performance simulation result of the concrete-filled steel tube structure under the strong earthquake effect is improved.

Disclosed is a mutually generative artificial intelligence system based on multi-dimensional spatiotemporal information vector graphics, relating to the field of artificial intelligence and engineering applications. Based on a geographic information system (GIS) or a computer-aided design (CAD) platform and a data source, a multi-dimensional vector spatiotemporal large model terminal, a multi-dimensional spatiotemporal information processing agent terminal, and an intelligent information system application terminal are constructed. For multi-dimensional spatiotemporal data such as two-dimensional and three-dimensional vectors and temporal states, a multimodal spatiotemporal large model having an understanding capacity for an engineering professional knowledge system, a data processing flow, multi-dimensional vector graphics, and thematic graphics-text documents is pre-trained to achieve the mutual expression and generation of engineering multi-dimensional vector graphics and thematic graphics-text documents and to form an intelligent engineering graphic data processing application.

The invention provides an undercarriage system modeling method based on a natural language demand text, and relates to the technical field of model-based computer aided design, and the method comprises the steps: S1, constructing a SysML meta-model knowledge base and an undercarriage system model knowledge base; s2, analyzing the natural language demand text, and generating a structured demand text of the undercarriage system; s3, mapping a structured demand text entity of the undercarriage system into a node type of a semantic graph; and S4, in combination with a historical model template and real-time retrieval, generating an undercarriage system SysML model according to the semantic map obtained in the step S3, and performing undercarriage system engineering analysis through the undercarriage system SysML model, thereby improving the efficiency and quality of aviation equipment development. On the basis of a retrieval enhancement generation technology and an intention routing mechanism, the modeling efficiency and accuracy of the undercarriage system are remarkably improved; a graph neural network and a semantic vector space model are adopted to realize deep fusion and conflict resolution of cross-domain knowledge; and through an incremental learning mechanism, model continuous evolution and design experience closed-loop iteration are supported.

The invention relates to the technical field of computer aided design and BIM (Building Information Model), discloses a BIM ancient building component parameterization design method and system fused with a historical form and system, and aims to solve the problems of parameter distortion, low modeling efficiency, high rework rate and the like caused by disjunction of a historical building rule and a BIM in the prior art. The method comprises the following steps: acquiring a historical shape and system standard text data set; a component-region-geometry triple knowledge graph is constructed; outputting Bezier curve control points conforming to regional characteristics through a lightweight curvature parameter generation network; generating an initial model in the BIM; a dynamic geometric constraint engine is used for verifying whether parameters such as the roll-killing radian and curvature continuity are out of tolerance or not in real time, and visual feedback and correction suggestions are triggered; and the model is automatically updated after user confirmation. The modeling precision and efficiency are remarkably improved, the rework rate is reduced, and the requirements of ancient building protection engineering specifications are met.

The invention discloses a deformation compensation method for a 3D printing model of an oral implanting guide plate, and belongs to the technical field of computer-aided design and manufacturing. The method comprises the steps that original three-dimensional model data of the oral implanting guide plate is obtained; performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data, and generating a dynamic stress evolution field for describing the internal stress distribution and change of the model in the virtual printing process; based on the dynamic stress evolution field, generating an adaptive compensation transformation matrix for correcting geometric deformation; and performing compensation transformation on the original three-dimensional model data by using the self-adaptive compensation transformation matrix to generate high-fidelity compensation model data. A self-adaptive compensation transformation matrix is generated through dynamic hierarchical stress accumulation analysis to preprocess three-dimensional model data, complex deformation in the printing process can be accurately predicted and compensated, and therefore the forming precision of a printed piece is remarkably improved.

The invention belongs to the technical field of computer aided process design (CAPP), and particularly relates to a body-based shaft part fixture scheme automatic generation method. The method specifically comprises the following steps: (1) constructing a shaft part clamp scheme generation body; (2) establishing a shaft part clamp scheme to generate an SWRL inference rule; (3) extracting relevant feature constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, and carrying out inference selection and adjustment on a shaft part fixture scheme. Constructing an ontology reasoning knowledge framework by utilizing ontology pair shaft part clamp scheme domain knowledge, and reasoning implicit knowledge according to dominant domain knowledge; and reasoning an optimal clamp scheme according to the feature constraint condition of the geometric product in combination with a semantic network rule language rule base. According to the method, a consistent knowledge description framework generated by the shaft part clamp scheme can be provided, so that a computer can automatically select a proper clamp scheme and the like according to various constraints of geometric product parts, and a quick and effective method is provided for intelligent selection of the shaft part clamp scheme.

The invention relates to the technical field of computer-aided modeling, in particular to a heating and ventilation equipment energy efficiency optimization method and system based on digital twinning, and the method comprises the steps: obtaining a real-time operation data set, and constructing a mechanism model; obtaining a model prediction data set by using the mechanism model, and training a data reconstruction model; inputting the real-time operation data set into the data reconstruction model to obtain a reconstruction error, and taking the reconstruction error as a state deviation data set; dynamically correcting an adjustable model parameter of the mechanism model based on the state deviation data set, and generating a dynamic calibration digital twinborn model; and finally, executing a multi-target collaborative optimization algorithm based on the dynamic calibration digital twin model, and generating an energy efficiency optimization control instruction. According to the method, the state deviation caused by the performance degradation of the physical equipment is quantified by utilizing the data reconstruction model, and the mechanism model is dynamically corrected by taking the state deviation as feedback, so that the problem that a static model is disjointed from a physical entity state is solved, and high-precision dynamic simulation and energy efficiency collaborative optimization are realized.

The invention relates to the technical field of computer-aided planning, and discloses a territorial space planning decision-making method and system based on multi-dimensional influence assessment, and the method sequentially comprises the steps: multi-source data fusion and problem diagnosis, multi-dimensional influence quantitative assessment, scheme collaborative optimization, and scheme comprehensive screening and decision-making. The system corresponds to the method. According to the method, through multi-source data fusion processing, the data reliability is improved, and a solid foundation is provided for subsequent problem identification; according to a quantitative index and a GIS spatial analysis technology, key problem region identification is more targeted, and the defect of generalization adjustment of a traditional qualitative method is avoided; through traffic, environment and municipal dimension parallel evaluation, the efficiency bottleneck of serial evaluation is avoided, and the evaluation period is shortened; a Pareto candidate scheme set generated through a multi-objective evolutionary algorithm is combined with a comprehensive screening mechanism to ensure that a final scheme is balanced between performance requirements and constraint conditions, and the feasibility of planning landing is enhanced.

The invention discloses a non-standard goods shelf intelligent design method and system based on a parameterized model, and relates to the technical field of computer aided design, and the method comprises the following steps: collecting parameters, and carrying out abnormal value elimination and dimension unified processing to generate a standardized demand parameter set; defining core structure parameters and establishing a component association rule, and constructing a parameterized basic model through three-dimensional modeling; the storage rack structure is optimized by combining storage mechanics and material mechanics characteristics, stress concentration is reduced, and deformation under rated load is controlled; a simulation environment is built for statics and dynamics simulation, and a simulation test report is output; screening an optimal parameter combination to generate a detailed design scheme; and receiving user feedback, adjusting parameters and material selection in combination with production feasibility and cost budget, and repeating optimization simulation steps to generate a final scheme. Efficient design of the non-standard goods shelf is achieved through parametric modeling and intelligent optimization, the structure is safe and reliable, the material utilization rate is high, and the design quality and efficiency are remarkably improved.

The invention relates to the technical field of computer aided design, in particular to a method for designing a high-temperature and high-toughness titanium alloy based on machine learning and a preparation method. The method comprises the following steps: acquiring components, a heat treatment process and corresponding tensile property data of a titanium alloy, constructing a titanium alloy data set and defining an exploration space; cleaning the titanium alloy data set, dividing the titanium alloy data set into a training set and a verification set, and normalizing the training set and the verification set; training the titanium alloy tensile property prediction model through a machine learning model and a training set, and evaluating the model through a cross validation method and a validation set; the components and heat treatment process parameters of the high-temperature and high-toughness titanium alloy are obtained through the trained prediction model, the genetic algorithm and the exploration space; according to the components of the high-temperature and high-toughness titanium alloy, the high-temperature and high-toughness titanium alloy is prepared through electric arc melting, and heat treatment is conducted according to heat treatment process parameters of the high-temperature and high-toughness titanium alloy. According to the method, collaborative intelligent design of titanium alloy components and the process is achieved, and the development efficiency is remarkably improved.

The invention belongs to the technical field of underground engineering and computer-aided engineering analysis, and particularly relates to an anti-seismic safety collaborative analysis method for multi-modal data of proximity tunnel engineering. By integrating engineering geology, structural design, construction monitoring and multi-level seismic oscillation data, a three-dimensional numerical model fusing geology and structural information is constructed. Firstly, refined simulation of the whole construction process and the operation static load is carried out, and a reference mechanical state is obtained; carrying out multi-task condition earthquake dynamic time history analysis on the basis, dynamically capturing dynamic response of surrounding rock and a structure, carrying out collaborative interpretation on static and dynamic analysis results, emphatically analyzing spatio-temporal evolution and connectivity risks of a tunnel anchor and an adjacent highway tunnel surrounding rock plastic zone, and extracting a plurality of quantitative collaborative indexes; and an interactive analysis report integrating a three-dimensional visual scene, a multi-dimensional comparison chart and a rule base-based safety interpretation conclusion is automatically generated, and systematic decision support is provided for seismic design, construction control and safety evaluation of proximity projects.

An exemplary system includes a memory storing instructions and a processor communicatively coupled to the memory. The processor may be configured to execute the instructions to: detect an intent of a user of a computer-assisted surgical system to use a robotic instrument attached to the computer-assisted surgical system to interact with a target object while the target object is located in a surgical space; determine a pose of the target object in the surgical space; and perform, based on the detected intent of the user to interact with the target object and the determined pose of the target object in the surgical space, an operation with respect to the target object.

The invention relates to the cross technical field of computer-aided engineering and audio equipment design, discloses a circuit design simulation-based disc player design optimization method and system, and aims to solve the problem that the existing disc player design depends on physical prototype trial-manufacture and subjective listening evaluation and lacks multi-physical domain co-simulation capability. The method comprises the following steps: constructing a unified multi-physical domain model comprising a mechanical transducer, an electromechanical transducer and an electronic circuit sub-model; bidirectional coupling co-simulation based on the time sequence is executed, and an output electric signal is generated; extracting objective audio performance indexes such as total harmonic distortion and signal-to-noise ratio; and carrying out multi-objective optimization on the key design parameters by utilizing a genetic algorithm, and outputting a Pareto optimal scheme. The system comprises a model construction module, a co-simulation module, a performance index extraction module and an optimization module. Through high-fidelity full-link simulation and automatic optimization, the design efficiency and the audio fidelity are remarkably improved, and the research and development cost is reduced.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The invention discloses a multi-task graph neural network fusion point cloud-attribute adjacency graph information-based processing feature identification method and system, and relates to the technical field of computer aided design, and the method comprises the steps: obtaining a three-dimensional CAD model data set which comprises a plurality of processing features and is provided with a feature tag; preprocessing the model in the data set to obtain a model attribute adjacency graph, a surface point cloud and a corresponding feature vector; learning the model data by using a multi-task graph neural network, realizing three tasks of semantic segmentation, instance segmentation and bottom surface segmentation, and comprehensively obtaining a processing feature recognition result; and inputting a part model of which the machining features need to be recognized into the trained recognition model, and vectorizing part model information to obtain a machining feature recognition result. According to the invention, the accuracy of processing feature recognition is improved.

A system includes proximal and distal structures and a processor system. The processor system determines a first commanded motion of the proximal structure; determine a second commanded motion for joint(s) of the distal structure or an imaging device, wherein when the first and second commanded motions are performed in conjunction, a field of view of the imaging device is maintained relative to a workspace; and in response to determining that driving the joint(s) in accordance with the second commanded motion would cause the distal structure or the imaging device to violate a constraint: determines an alternate commanded motion for the joint(s) to maintain a defined geometric relationship between a first geometric feature fixed relative to the imaging device and a second geometric feature fixed relative to the workspace, drives the proximal structure in accordance with the first commanded motion, and drives the joint(s) in accordance with the alternate commanded motion.

The invention relates to the technical field of computer-aided simulation model construction, and discloses a computer-aided engine torque model simulation construction method, which comprises the following steps of: constructing and training a model cluster and learning foreign person models with different principles; the method comprises the following steps: constructing a vector representing a model cluster divergence mode, inputting a health state representation model, and outputting a preliminary health state; and when the internal heights of the model clusters are consistent and the overall prediction deviates from the prediction of the foreign person model, the preliminary health state is corrected, a divergence mode in the model is associated with the physical health state of the engine, the physical health state of the physical entity is sensed in a pure software mode, and a built-in heterogeneous model verification mechanism is used for verifying the physical health state of the engine. In addition, integral cognition blind spots caused by training data source pollution can be recognized and avoided, and the reliability of health state perception is improved.

The invention belongs to the field of computer aided design systems, and relates to a CAD multi-modal data set automatic construction method, which comprises the following steps of: firstly, rendering a three-dimensional CAD model to be marked into a standardized multi-view two-dimensional image set through a data processing system, enhancing a multi-modal large model by utilizing a keyword vector and a low-rank adapter, and constructing a multi-modal data set; the method comprises the following steps of: extracting a geometric label from an image as a hard constraint, generating a text description of a natural language, then verifying a labeling result through a self-consistent strategy, and finally associating the multi-view two-dimensional image, the geometric label and the text description with an original model to construct a high-quality CAD multi-modal image-text pair data set. According to the method, visual feature extraction and language description generation are decoupled into two subtasks with clear purposes, so that the labeling consistency and the recognition precision are improved, and low-cost automatic construction of large-scale CAD data is realized.

A computer-assisted system includes a repositionable structure configured to support an instrument and a control unit. The control unit is configured to: while a remote center of motion (RCM) is set at a first position, receive a command to move the repositionable structure with a commanded motion; determine whether driving the plurality of joints to move the repositionable structure in accordance with the commanded motion while maintaining the remote center of motion RCM at the first position would violate a limit of the repositionable structure; and, in response: determine an alternative motion of the plurality of joints based on the commanded motion and the first position, wherein driving the plurality of joints in accordance with the alternative motion would not violate the limit, and would move the RCM to a second position different from the first position, and drive the plurality of joints in accordance with the alternative motion.

The invention provides a three-dimensional part retrieval method based on graph similarity search, relates to the field of computer graphics, and solves the technical problems that in the prior art, geometric and design semantic information of CAD cannot be fully utilized, and a topological relation is difficult to capture and display, so that the retrieval efficiency is low. The method comprises the following steps: acquiring part three-dimensional data of a computer-aided design (CAD) model, and constructing a training data set; constructing an edge-surface connection diagram based on the three-dimensional data of the part; calculating a graph editing distance (GED) matrix of all edge surface connection graphs in the training data set as a supervision signal; based on the GED matrix, training a sorting model, mapping an edge-surface connection graph to a hidden space, and constructing a part vector database according to an output graph-level embedding vector; the sorting model is constructed based on a graph attention network; and inputting a to-be-queried CAD part into the trained sorting model to obtain a feature vector, carrying out nearest neighbor search in the part vector database, and returning a similar part result.