3D Digital Twin Simulation Scenario Generation
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Solution Overview
Problem
Existing technologies struggle to create realistic simulation scenarios using 3D digital twins, as machine-learning models often combine images from training datasets without understanding how objects interact, resulting in unrealistic components such as misshapen fingertips or inaccurate object combinations.
Innovation Solution
A computer-implemented method that receives a request for generating a simulation scenario with contextual information, selects 3D digital twins corresponding to assets, entities, and environments, initializes and spatially relates these twins, and controls their behavior based on the contextual information, thereby generating a realistic simulation scenario.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-learning models combine images from training datasets to create scenarios, then the scenario generation process is automated and efficient, but the resulting scenarios contain unrealistic components such as misshapen fingertips or inaccurate object combinations
Solution Approach 1:
The patent introduces 3D digital twins as an intermediary between the machine-learning model and the final scenario visualization. Instead of directly combining 2D images, the system uses 3D digital twins that accurately represent physical objects with correct geometries, materials, and spatial relationships. This intermediary layer ensures realistic object combinations and interactions while maintaining automated scenario generation efficiency.
Solution Approach 2:
The patent creates accurate 3D copies (digital twins) of physical objects that preserve their true geometries, physical properties, and spatial relationships. These digital twins serve as faithful replicas that can be manipulated in virtual scenarios without the distortions and inaccuracies that occur when combining 2D images from training datasets.
2Manufacturing precision
If 3D digital twins are used to create realistic scenarios, then the realism and spatial accuracy of the scenario improve, but the complexity of selecting and initializing multiple digital twins increases
Solution Approach 1:
The patent implements self-service mechanisms where the simulation system automatically selects appropriate 3D digital twins from a library based on the scenario requirements, and automatically initializes their spatial relationships and behaviors. The system uses sensor data and contextual information to autonomously configure the digital twins without requiring manual intervention, thereby reducing operational complexity while maintaining high spatial accuracy.
Solution Approach 2:
The patent creates a universal 3D digital twin library that can serve multiple scenario generation needs. Each digital twin is designed to be multi-functional, representing a physical object that can be placed in various contexts and configurations. This universal approach reduces the need to create and manage separate digital models for each specific scenario, thereby reducing system complexity.
3Reliability
If contextual information is used to control digital twin behavior, then the relevance and accuracy of the simulation improve, but the processing requirements and computational load increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most relevant contextual information for each digital twin rather than analyzing all available data. The system identifies and processes only the critical sensor data and contextual factors that directly impact the behavior and spatial relationships of specific digital twins, thereby reducing computational energy consumption while maintaining high prediction accuracy.
4Manufacturing precision
If virtual sensors generate digital media from simulation scenarios, then the realism and contextual relevance of the generated media improve, but the time and computational resources required for generation increase
Solution Approach 1:
The patent implements preliminary action by pre-configuring virtual sensors and their parameters before the simulation scenario begins. The virtual sensors are pre-positioned, pre-oriented, and pre-configured with appropriate capture settings based on the expected scenario requirements. This preliminary setup allows the virtual sensors to efficiently generate realistic digital media during the simulation without requiring complex real-time processing decisions, thereby reducing media generation time while maintaining high realism.
Data Source
AI summary
A computer-implemented method includes receiving a request for generating a simulation scenario or digital media that includes one or more entities and one or more assets in an environment, the request including contextual information. The method further includes selecting three-dimensional (3D) digital twins based on the contextual information, wherein one or more of the 3D digital twins correspond to the assets, one or more of the 3D digital twins correspond to the one or more entities, and one of the 3D digital twins corresponds to the environment. The method further includes generating the simulation scenario that initializes the 3D digital twins and establishes a spatial relationship between the 3D digital twins based on the contextual information. The method further includes controlling behavior of the 3D digital twins in the simulation scenario based on the contextual information.