Digital Twin Refinement via Neural Clustering and Feedback
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Solution Overview
Problem
Current digital twin technologies face challenges in effectively predicting and optimizing physical entity enhancements, as they often rely on outdated data and lack robust mechanisms for iterative feedback and real-world improvement.
Innovation Solution
A method involving neural network-based data clustering and machine learning models to generate and refine digital twins, allowing for the analysis of performance data to identify issues and revert to previous versions, while utilizing state transition version graphs for optimal digital twin selection and implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital twin technologies rely on outdated data and lack iterative feedback mechanisms, then data currency and optimization capability deteriorate, but system complexity and implementation cost increase
Solution Approach 1:
The patent implements a feedback mechanism where performance data from the physical entity is continuously collected and fed back to update the digital twin. This closed-loop system allows the digital twin to learn from actual performance and improve prediction accuracy over time without requiring complete system redesign
Solution Approach 2:
The patent performs preliminary actions by generating multiple candidate digital twins using neural network-based data clustering before actual deployment. This allows prediction and optimization to be tested in advance, selecting the best candidate before implementation, thereby improving reliability without proportionally increasing complexity
2Measurement precision
If multiple digital twin versions are generated and tested, then prediction accuracy improves, but computational time and resources increase
Solution Approach 1:
The patent generates multiple digital twin versions through neural network-based data clustering, creating more candidate models than strictly necessary. This excessive action ensures that the best performing twin is selected, improving measurement precision while the selection process filters out inferior candidates efficiently
Solution Approach 2:
The patent segments the digital twin generation process into distinct phases: data clustering to create multiple candidates, performance analysis to evaluate each candidate, and selection of the optimal version. This segmentation allows parallel processing of candidate evaluation, reducing overall computational time despite generating multiple versions
3Adaptability or versatility
If digital twin changes are implemented without validation, then adaptability improves, but system stability deteriorates
Solution Approach 1:
The patent performs preliminary validation by analyzing performance data of candidate digital twins before implementing changes to the physical entity. This advance testing ensures that only optimized and validated changes are deployed, improving adaptability while maintaining system stability through careful pre-screening
Solution Approach 2:
The patent implements a feedback loop where performance data from the physical entity is used to validate digital twin changes before implementation. This closed-loop validation process ensures that adaptability improvements do not compromise system stability, as changes are only accepted when performance analysis confirms their benefit
Data Source
AI summary
A method, computer system, and a computer program product for digital twin usage are provided. A first digital twin and performance data of the first digital twin are input into a first machine learning model to produce a second digital twin. The first machine learning model performs neural network-based data clustering. The first and second digital twins digitally represent a first physical entity. The second digital twin includes one or more changes from the first digital twin. Performance data of the second digital twin is analyzed. In response to the analysis indicating a problem with the second digital twin, implementation of the second digital twin is revoked and the first digital twin is reimplemented