Digital Twin Knowledge Transfer Between Dissimilar Machines
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Solution Overview
Problem
The direct transfer of a knowledge corpus from one machine to another is not feasible due to differences in their functionalities, input collection mechanisms, and control systems, making it challenging to apply learning from one machine to another, especially in autonomous vehicles with unique configurations.
Innovation Solution
A method and system that utilize digital twin models to compare and identify the feasibility of knowledge corpus transfer between machines, map the knowledge corpus of a source machine to the input and output systems of a target machine, and transfer the mapped knowledge corpus, even if the machines have different configurations, using a learning transfer program that includes comparing, identifying, mapping, and transferring modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If direct transfer of knowledge corpus is attempted between machines with different configurations, then transfer speed is fast, but transfer accuracy and applicability deteriorate due to functional differences
Solution Approach 1:
A digital twin model serves as an intermediary between the source machine and target machine. The digital twin abstracts the knowledge corpus into functional relationships that can be mapped across different machine configurations. This mediator enables accurate knowledge transfer by translating between different system architectures while preserving the essential functional semantics.
Solution Approach 2:
The system changes the parameter representation of knowledge from concrete machine-specific implementations to abstract functional parameters. By representing knowledge in terms of input-output relationships and functional behaviors rather than specific hardware configurations, the system enables portable and applicable knowledge transfer across diverse machine types.
2Reliability
If detailed mapping and comparison processes are implemented to ensure accurate knowledge transfer, then knowledge corpus applicability improves, but system complexity increases
Solution Approach 1:
The knowledge transfer process is segmented into distinct modules: a comparing module that analyzes digital twin models, an identifying module that determines transfer feasibility, and a mapping module that performs the actual knowledge adaptation. This segmentation allows each module to focus on a specific task, reducing overall system complexity while maintaining high transfer accuracy.
Solution Approach 2:
Instead of directly transferring complex knowledge structures between machines, the system creates a simplified copy through the digital twin model. This copy captures the essential functional relationships in a standardized format that is easier to process and map, reducing the complexity of the transfer mechanism while preserving knowledge integrity.
3Reliability
If digital twin model comparison is performed to assess transfer feasibility, then knowledge corpus applicability improves, but processing time increases
Solution Approach 1:
The comparing module performs a focused assessment of digital twin models, analyzing only the critical functional parameters necessary for knowledge transfer feasibility rather than conducting a complete exhaustive comparison. This partial action approach maintains high accuracy in feasibility determination while significantly reducing processing time.
Data Source
AI summary
A computer-implemented method for transferring a knowledge corpus from a first machine to a second machine. The method compares a digital twin model of the first machine with the second machine and identifies whether the knowledge corpus transfer is possible, based on the comparison. If the knowledge corpus transfer is possible, the method maps the knowledge corpus of the first machine with input and output systems of the second machine. If the knowledge corpus transfer is not possible, the method identifies how functionalities of the first machine and the second machine are being executed and controlled. The method then creates a hierarchical functional digital twin model of the first machine and the second machine, based on the identified functionalities. The method further transfers the mapped knowledge corpus of the first machine with the input and output systems of the second machine.