1007 results about "ModelSim" patented technology

The present disclosure provides a Boolean formula unsatisfiability core prediction method based on hypergraph modeling. A CNF formula generated by a software and hardware formal verification problem coding in the field of electronic design automation is received from the outside, input into a hypergraph representation learning model, and modeling for solving a SAT problem corresponding to the software and hardware formal verification problem is obtained. The model is constructed in the following manner: first, SAT problem representation based on hypergraph is performed. Specifically, the CNF formula is modeled as a clause-literal hypergraph by using a SAT problem modeling method based on hypergraph and a message passing method based on hypergraph, and a clause correlation graph is further constructed to obtain problem representation. Then, a polarity-aware variable decomposition method is applied to the clause correlation graph, and the problem representation is structured and modeled. The hypergraph representation learning model is trained by using a training method based on polarity-aware consistency constraints, and is integrated with a SAT problem solver, so that the model can be used to solve the problems that the existing graph neural network-based SAT learning method has in the aspects of polarity modeling and high-order structure expression.

The present application relates to the technical field of engineering drawing, and particularly relates to a three-dimensional model generation method, device, equipment and storage medium. The method loads a two-dimensional drawing through a building three-dimensional modeling software, identifies layer names of all layers in the two-dimensional drawing, and determines a component type of a component corresponding to each layer according to each layer name; determines a corresponding drawing identification mode and a corresponding information analysis mode according to the component type; extracts a drawing feature group corresponding to the component from a corresponding layer according to the drawing identification mode; analyzes the drawing feature group according to the information analysis mode to obtain modeling information corresponding to the component; and performs three-dimensional modeling based on the modeling information corresponding to each component to obtain a three-dimensional model corresponding to the two-dimensional drawing, thereby solving the problem of low accuracy in converting a two-dimensional drawing into a three-dimensional model in the prior art.

This invention discloses a production line modeling and real-time data-driven quality deviation prediction method for process inspection, comprising: employing a forward causal reasoning mechanism to calculate the transfer function step by step along the path based on the overall causal relationship layer, obtaining the expected value and deviation probability prediction of the target process quality index; periodically adaptively optimizing the parameters of the causal relationship layer model based on the cause node list and historical data loop, using machine learning algorithms to enhance parameter accuracy, and determining the optimized parameter set; updating the digital model structure by optimizing the parameter set, incorporating enhanced causal relationships during subsequent real-time data acquisition, and obtaining a more accurate transfer function representation; re-performing causal reasoning and deviation prediction based on the accurate transfer function representation, judging the overall production line quality status, and obtaining the final deviation control basis.

This application provides a method for establishing cross-magnification CD measurement value correction during OPC modeling, comprising: obtaining the actual CD measurement value of CD-SEM at a first magnification; inputting the actual CD measurement value into a trained cross-magnification measurement value conversion model to obtain the equivalent CD measurement value at a second magnification; wherein, the cross-magnification measurement value conversion model is constructed based on a mapping function between the equivalent CD measurement value and the area ratio of the area of ​​the graphic to be measured to the field of view area of ​​the CD-SEM at the corresponding first magnification, the magnification ratio of the second magnification to the first magnification, and the actual CD measurement value. The technical solution of this application addresses the problem that existing technologies lack effective cross-magnification measurement data calibration schemes, resulting in the ineffective utilization of a large amount of low-magnification measurement data, and the difficulty in including large-size graphics in the modeling scope due to measurement limitations.

The application discloses a kind of reconstruction guided multimodal CAD retrieval method and system, it is related to CAD model retrieval technical field.The method includes: obtaining text description corresponding with CAD model, rendering image and the CAD modeling sequence for representing the modeling process of CAD model;Text description, rendering image and CAD modeling sequence are extracted and fused processing feature, and multiple modal fusion features are obtained;Based on multiple modal fusion features, the reconstruction of CAD modeling sequence is carried out, and the reconstruction result is obtained, and guide constraint is constructed using reconstruction result, and the joint training of multimodal CAD retrieval model is carried out, and the training of multimodal CAD retrieval model is completed;Query information to be retrieved is obtained and matched with candidate CAD modeling sequence, and CAD modeling sequence retrieval result is output.Thereby it can improve the utilization sufficiency of retrieval information and editable reuse feature representation ability, and enhance the distinguishing degree of similar CAD model.

The application provides a class-level 6D pose estimation method based on geometric perception key points, and relates to the technical field of pose estimation. On the basis of fusing RGB image and point cloud features, a dynamic key point proposal module is designed to generate class-consistent and geometric-adaptive key points to enhance the adaptability to intra-class deformation and weak texture targets. Secondly, a spatial geometry attention module is introduced to model the spatial structure relationship between key points to optimize the key point distribution. Finally, through the geometric perception reconstruction and scale estimation module, point cloud reconstruction and scale regression are simultaneously realized under the condition of no CAD model prior, and end-to-end optimization is carried out by using multi-task loss. The experimental results on REAL275, CAMERA25 and HouseCat6D datasets show that the method of the application is superior to existing methods in IoU and pose accuracy indexes, and has stronger generalization and robustness in real complex scenes.

The application discloses a complex specification multi-modal understanding method and system based on YOLO two-stage segmentation, comprising the following steps: performing pre-processing on an input specification image; inputting the pre-processed image into a coarse-grained detection stage of an improved YOLOv8 network to generate a coarse-grained candidate region set; inputting the coarse-grained candidate region set into a fine-grained segmentation network based on an improved YOLOv8 feature extraction structure; constructing a layout hierarchical relationship structure based on an accurate layout segmentation result; according to the layout hierarchical relationship structure, performing multi-modal feature extraction on the accurate layout segmentation result, and calculating a matching weight; inputting a graphic-text semantic alignment result and the layout hierarchical relationship structure into a logical structure modeling module together to construct a layout element graph; and generating a knowledge graph semantic output result according to a structured logical reasoning result. The application realizes automatic structured understanding of a complex specification by adopting YOLO two-stage segmentation and multi-modal semantic alignment.

This invention relates to the field of digital simulation technology, specifically to a method for three-dimensional visualization modeling of natural resources based on digital twins. The method includes: constructing a discrete computational map based on a real-world three-dimensional model of the target area and geographic information system (GIS) data; projecting the design base of the building information model (BIM) onto the discrete computational map to determine design boundary nodes; performing constraint propagation calculations on the discrete computational map using the design boundary nodes as propagation starting points to determine the cumulative compliance consumption and cumulative horizontal projection distance of each node in the discrete computational map; determining the topology type of each node based on its cumulative compliance consumption, cumulative horizontal projection distance, original terrain elevation, and preset engineering slope parameters; and geometrically reconstructing the discrete computational map based on the topology type of each node to generate a digital twin model that integrates the BIM and the real-world three-dimensional model. This invention simultaneously achieves automatic compliance avoidance and structural closure generation of the slope model.

This application relates to the field of dynamic modeling technology, specifically to a multiphysics coupling modeling method for a dual-stator synchronous generator. The method includes: simulating and modeling the multiphysics of the dual-stator synchronous generator and discretizing the mesh; analyzing the magnetic pull force, simulated temperature, and simulated stress per unit area for each discretized element; determining the distribution differences of magnetic pull force, simulated temperature, and simulated stress among the elements to obtain the spatial distribution deviation and multiphysics difference of each element, thereby obtaining the distribution anomaly degree of each element; calculating the synergistic influence degree of each element to obtain the anomaly significance of each element; constructing four-dimensional feature points for each element using the anomaly significance, magnetic pull force per unit area, simulated temperature, and simulated stress; and obtaining characteristic outlier elements in the finite element physical model based on these features, thereby achieving structural optimization of the dual-stator synchronous generator. This application can improve modeling accuracy and optimize the structure of the dual-stator synchronous generator.

This invention relates to an optimization method and system for preventing coking in waste incinerators based on CFD and solid-phase combustion coupled simulation, belonging to the field of solid waste energy utilization and combustion optimization technology. The technical solution is as follows: S1. Full-process coupled numerical modeling and baseline operating condition simulation; S2. Coking risk diagnosis and dominant cause analysis; S3. Two-level sequential collaborative optimization adjustment; S4. Numerical verification and engineering implementation of the optimization scheme; S5. Adaptive operation and closed-loop feedback control. This invention has the following beneficial effects: breakthroughs in the realism and reliability of numerical simulation, greatly improving the accuracy of diagnostic results and the reliability of optimization guidance; the control strategy possesses high innovation and synergy, the technical solution is specific, quantifiable, and highly operable, with extremely significant industrial application effects, outstanding economic benefits, improved boiler operating efficiency and overall economic benefits, and greatly enhanced equipment operating safety; and it forms a complete technical system that is replicable and scalable.

The application provides a pre-training language model construction method, system and device, relates to the fields of artificial intelligence technology and semantic processing, and mainly comprises the following steps: based on a knowledge graph, a subgraph with a triple as a node unit is constructed; based on entity and relation description text, both are encoded through a conventional pre-training language model to obtain entity representation vectors and relation representation vectors; for the entity representation vectors and the relation representation vectors corresponding to the subgraph, modeling relation information is carried out based on a graph self-attention neural network, and the parameters of the graph neural network and the entity vectors are updated through a prediction link task; the parameters of the model are updated based on a contrast learning technology; and the parameters of the model are fine-tuned for a specific task. The scheme overcomes the problem that entity in the equipment field is sparse and a conventional pre-training language model cannot sufficiently learn entity semantics, and the updated pre-training language model has better understanding and reasoning capabilities in the application of the equipment field.

The application relates to the technical field of knowledge engineering, and discloses a production and manufacturing digital twin modeling method based on a knowledge graph, which comprises the following steps: performing data fusion and model construction on original multi-source data, obtaining multi-source fusion data, and generating a digital twin architecture; performing ontological design and semantic modeling on the multi-source fusion data, and generating a manufacturing knowledge graph; wherein the manufacturing knowledge graph is represented by triplets, and the triplets comprise entities-relation-entities / attributes; performing entity modeling on the manufacturing knowledge graph, and obtaining a knowledge-driven geometric model; and performing reasoning based on the manufacturing knowledge graph and historical data, and obtaining a knowledge-driven decision model. The method can construct a digital twin model with knowledge-driven prediction and decision-making capabilities, and provides a high-robustness and sustainable-evolution digital twin modeling method for intelligent manufacturing, smart factories and other fields. The application also discloses a production and manufacturing digital twin modeling device based on the knowledge graph and an electronic device.

This invention discloses a virtual simulation experiment method and system based on multi-engine collaboration. The method is as follows: S1, the front-end interaction layer receives the simulation experiment task and parses the experiment mode classification; selects the corresponding learning mode according to the learning requirements, and then transmits the task data to the engine layer; S2, in the engine layer, based on the preset equipment association engine module, obtains the equipment required for the experiment through the equipment library information; and determines whether it is compliant; if compliant, at least two simulation engine modules are dynamically matched and linked through the collaborative engine association mapping mechanism, and then the results are calculated by the multi-disciplinary computing engine module and output in a unified data format; S3, the information collector receives the calculation results and scores or renders the experimental results. This method uses unified modeling and engine-based encapsulation to realize intelligent reasoning and animation rendering of virtual experimental scenarios in multiple disciplines such as chemistry and physics, thereby achieving an open, interactive, and verifiable virtual simulation experiment experience.

A transmission system bearing interface modeling method includes the steps of: establishing a three-dimensional (3D) model of a bearing interface, and performing grid generation for a fluid domain of the bearing interface within the 3D model; determining simulation operating conditions, modeling the fluid domain using a volume of fluid (VOF) model, and capturing phase interfaces in multiphase flow within a gear transmission; modeling a solid domain of the bearing interface using a thermal network model, and discretizing the solid domain into a plurality of temperature nodes; calculating frictional heat generation of a gear pair to obtain heat sources under meshing conditions, and applying the heat sources to the thermal network model for simulation; and packaging the modeled bearing interface into an analysis system to calculate a heat source of the bearing interface. Reference can be provided for temperature variation and multiphase flow behavior in gear transmissions, improving research efficiency.

This invention relates to the field of computer graphics processing and 3D modeling technology, specifically to an automated intelligent generation system for 3D modeling of shoe exteriors. The system includes: data acquisition and streaming, where an image acquisition terminal generates and sends four-channel image data using a high-precision sensor array; feature reconstruction and representation, where a topological semantic reconstruction server extracts semantic features and constructs an initial geometric representation using an implicit function based on pixel alignment and a decoder to predict the symbolic distance function; topology optimization and segmentation, where topological streamline optimization and component semantic segmentation are performed based on the curvature field to generate a structured 3D model with independent component structures; and interaction and local updates, where an interactive terminal responds to user editing operations on independent component structures, sending local topology update commands, and the server performs parametric recalculation of the local mesh based on the commands. This invention solves the problem of the separation between geometric structure and semantic information, achieving real-time and refined model updates.

The present application relates to the field of shell-and-tube heat exchanger, in particular to a kind of multi-physical field coupling performance comprehensive simulation evaluation system of shell-and-tube heat exchanger, system includes: analytical modeling module;For integrating obtaining semantic parameterization geometric information library;Flow field geometric model repair and parameterization construction module;For completing the topological reconstruction of initial flow field geometric model and obtaining zero defect parameterization three-dimensional flow field model;Cross-domain grid collaborative generation module;For integrating forming cross-grid model set;Multi-physical field simulation solving and evaluation module;For forming multi-physical field simulation original data set, simulation evaluation is carried out.The present application will flow field geometric model repair and parameterization construction module be converted into three-dimensional voxel grid by voxelization discrete to model, avoid model geometric feature distortion by fairing processing etc., solve the problem that traditional model repair means is single, topological distortion, defect identification is not accurate, guarantee the geometric accuracy of subsequent simulation calculation from source.

This invention discloses a PCB characteristic impedance simulation optimization method and system based on 3D modeling, relating to the field of high-speed intelligent circuit technology. The method involves acquiring measured material parameters and process deviations of the PCB to generate twin parameter vectors; constructing a local 3D subdomain model; concatenating the local 3D subdomain model with the manufacturing twin dataset to obtain multi-dimensional physical feature vectors, which are then processed to obtain a sequence of key influencing factors; processing the key influencing factor sequence using an impedance prediction proxy model to obtain the time-domain reflection impedance curve and the corresponding coordinates of 3D spatial impedance abrupt change points; adjusting the coordinates of the 3D spatial impedance abrupt change points based on an impedance reshaping strategy to obtain optimal 3D compensation parameters; reconstructing the 3D simulation model using the optimal 3D compensation parameters to obtain updated PCB manufacturing data; and outputting impedance control parameters through a target verification rule engine to accurately locate the coordinates of impedance abrupt change points in 3D space, thereby improving design efficiency and quality.

The present invention sets forth techniques for predicting motion in a 3D model. The disclosed techniques include receiving one or more Gaussian primitives representing a 3D scene and receiving one or more control inputs describing one or more primary motions associated with one or more objects. The techniques also include generating, via a first trained machine learning model, a dynamic state based on the one or more control inputs, and generating, via a second trained machine learning model, one or more deformed Gaussian primitives based on the dynamic state and the one or more Gaussian primitives. The techniques further include generating a 2D representation of the 3D scene, and generating an output sequence based on the 2D representation, wherein the output sequence depicts both the primary motions associated with the one or more objects and one or more secondary motions associated with the one or more objects.

This application discloses a method, system, device, and computer medium for generating 3D models of urban rail transit, relating to the field of digital twin modeling and 3D visualization technology for urban rail transit. The method involves acquiring diverse and heterogeneous data of urban rail lines, including 2D design drawings and derived data; processing the diverse and heterogeneous data to generate a basic dataset for the urban rail lines; acquiring user-defined configuration data for the urban rail lines; integrating the basic dataset and the user-defined configuration data to obtain a target dataset for the urban rail lines; identifying 3D model elements matching the target dataset for the urban rail lines from a pre-set model library; and rendering the 3D model elements according to the target dataset to generate a 3D model of the urban rail lines. This method ensures that the generated 3D urban rail models meet user needs, provides diversity and flexibility in model construction, improves model scalability, reduces the degree of human intervention, and enhances the efficiency of urban rail modeling.

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

This invention discloses a planning method and system for a hydrogen-containing integrated energy system. The method includes: constructing equipment instance models of various energy types in the system by configuring parameters; wherein the equipment includes electrolyzer equipment; constructing a load model in the system by configuring the load demand of the area to be planned, the load demand supporting configuration at multiple time scales; obtaining the energy price model of the area to be planned and completing the energy price configuration; constructing a system topology by graphically associating the equipment instance models, load models, and energy price models through a visual configuration interface, and configuring constraint rules on the system topology; wherein the constraint rules include start-up and shutdown optimization constraints for the electrolyzer; transforming the system topology with configured constraint rules into a mixed-integer linear programming model and performing optimization to obtain the configuration scheme and operation strategy of each device in the system. This invention solves the problems of insufficient characterization of equipment operating losses and low modeling efficiency in existing planning methods by introducing refined electrolyzer start-up and shutdown constraints and a visual modeling process, thus achieving efficient and accurate planning for hydrogen-containing integrated energy systems.

The application relates to the field of computer calculation, in particular to a training method and a calculation method of a multi-physical field coupling calculation model, and solves the technical problems that in the prior art, a simulation model is difficult to realize real-time coupling and high-precision simulation of multiple physical fields, and the technical problems of large consumption of calculation resources and low efficiency. A training method of a multi-physical field coupling calculation model provided in the application firstly represents a mapping relationship between multiple physical fields as multiple PINO models, each PINO model corresponding to a mapping relationship between a group of physical fields; then a corresponding pre-trained PINO model is called to construct and train a multi-physical-multi-scale neural network, so that the physical fields correct each other in dynamic interaction, gradually approach a self-consistent coupling solution, and the modeling difficulty of complex system simulation is reduced. Since the physical law is used as a priori, excellent generalization ability is still shown when data is scarce or working conditions change, and the dependence on a large amount of high-fidelity training data is reduced.

Disclosed herein is a web-based videoconference system that allows for video avatars to navigate within a virtual environment. Various methods for efficient modeling, rendering, and shading are disclosed herein.

This invention discloses a SysML-based visual modeling workflow configuration method, comprising the following steps: creating system design stages and sub-stages through a visual interactive interface, and setting operation items for each sub-stage; establishing a dynamic association mapping relationship between operation items and system modeling language model elements based on the semantic association method of the system modeling language, and converting it into an independent configuration file; automatically analyzing the upstream and downstream dependencies of operation items based on the configuration file; monitoring the execution status of each operation item in real time, and dynamically updating the execution status of sub-stages and stages according to the status; automatically providing recommended views and model elements for operation items, and generating design deliverables; automatically performing semantic association analysis of data conflicts; and recording the modification history of model elements for traceability management of design results. This invention provides an efficient, flexible, and traceable modeling workflow configuration method, significantly improving the accuracy and collaboration efficiency in the system design process.

The application relates to the technical field of artificial intelligence, in particular to a power grid CIME model attribute graph modeling method and system, computer equipment and a medium; the method comprises the following steps: analyzing a power grid CIME format file, extracting electrical equipment entities and connection relationships, and constructing a static attribute graph; fusing dynamic operation attributes in real-time operation data into the attributes of corresponding graph nodes in the static attribute graph to form a dynamic power grid attribute graph; based on a graph-text multi-dimensional semantic decoupling projection strategy, the dynamic power grid attribute graph is projected into a structured text sequence; the structured text sequence is packaged into a prompt word and input into a large language model to execute a power grid cognitive reasoning task. In this way, the technical problem of insufficient real-time reasoning capability of a large model in the application scenario of the existing technology facing large language model cognition is solved, and the accuracy, efficiency and real-time performance of the large language model for the power grid cognitive reasoning task are improved.