Digital Twin Correction via Constraint Optimization
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Solution Overview
Problem
Inaccuracies in digital twins of physical structures, such as floorplans, can negatively impact their usability in downstream applications due to precision issues and user errors, necessitating a method for correction.
Innovation Solution
Employing optimization methods like gradient descent to correct digital twins by translating constraints into a cost function, which identifies and applies necessary modifications to structural properties, utilizing a device with a depth sensor, processor, and memory to create and optimize the digital twin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optimization methods are used to correct digital twins, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual correction processes with automated optimization algorithms (gradient descent, constraint solving). The system substitutes human judgment and manual adjustment with computational methods that automatically optimize digital twin parameters by minimizing cost functions and satisfying constraints, thereby improving precision while managing complexity through automation.
Solution Approach 2:
The system changes parameters of the digital twin model by optimizing structural properties through constraint-based solving. The optimization process adjusts parameters such as wall positions, dimensions, and orientations to satisfy geometric constraints and minimize errors, thereby improving manufacturing precision through systematic parameter optimization.
2Measurement precision
If constraint-based optimization is applied, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining constraints and cost functions before the optimization process. Geometric constraints, boundary conditions, and objective functions are established in advance, allowing the optimization algorithm to efficiently converge to accurate solutions without requiring extensive iterative adjustments during the correction phase.
Solution Approach 2:
The patent creates a computational model (digital twin) that copies the physical structure's geometric and structural properties. This virtual copy allows for rapid optimization and correction without affecting the actual physical structure, enabling multiple iterations of precision improvement in silico before applying changes, thereby reducing overall correction time.
Data Source
AI summary
Various embodiments relate to a method, apparatus, and machine-readable storage medium including one or more of the following: creating a reference into the digital twin that accesses a structural property stored in the digital twin; identifying a constraint for the physical structure associated with the reference; creating a cost function from the reference and the constraint; and optimizing a value for structural property using the cost function, whereby the structural property stored in the digital twin is corrected.