Context-Aware Digital Twin Model Subset Creation
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Solution Overview
Problem
Existing digital twin models often require all possible sensor feeds, leading to increased data volume and processing demands, which is inefficient and unnecessary in every contextual situation, as each situation requires a different set of sensor feeds.
Innovation Solution
A system and method to build a subset of digital twin models based on contextual needs, using only the required sensor feeds for a specific activity and surrounding, with a processor monitoring changes and adjusting sensor data collection to optimize model creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible sensor feeds are used to create digital twin models, then the model completeness and accuracy are improved, but the data volume and processing demands increase significantly
Solution Approach 1:
The patent extracts and uses only the necessary sensor feeds required for specific contextual situations rather than all possible sensor feeds. The system dynamically determines which sensors are needed based on the current context and activity, extracting only the relevant data subset to reduce overall data volume while maintaining model accuracy for the specific situation.
Solution Approach 2:
The patent applies local quality by making different parts of the data collection system have different properties based on contextual needs. Different contextual situations require different sensor feeds, so the system configures data collection locally optimized for each specific context rather than uniformly collecting all sensor data everywhere.
2Adaptability or versatility
If all possible sensor feeds are collected for digital twin models, then the model comprehensiveness is improved, but the processing complexity and resource demands increase
Solution Approach 1:
The patent makes the sensor feed selection dynamic rather than static. The system continuously monitors contextual changes and adjusts which sensor feeds are active based on current needs. This dynamic adaptation allows the system to maintain model comprehensiveness for relevant contexts while reducing processing complexity by deactivating unnecessary sensors.
Solution Approach 2:
The patent applies partial action by collecting only the necessary subset of sensor data required for specific contextual situations rather than all possible sensor feeds. This partial data collection is sufficient for the given context and reduces processing complexity while maintaining adequate model comprehensiveness.
3Measurement precision
If continuous monitoring of all sensors is performed, then the real-time accuracy is improved, but the energy consumption and processing load increase
Solution Approach 1:
The patent implements periodic action by monitoring sensor feeds at different intervals based on their relevance to the current contextual situation. Critical sensors may be monitored continuously while less critical sensors are monitored periodically or only when specific conditions are met, reducing overall energy consumption while maintaining real-time accuracy for important parameters.
Data Source
AI summary
In an approach for building a subset of a digital twin model, a processor monitors a digital twin model of a machine for one or more changes to the machine. Responsive to detecting a change to the machine, a processor analyzes one or more aspects of the machine, wherein the one or more aspects of the machine include a contextual situation of the machine and one or more activities performed by the machine. Responsive to determining the contextual situation of the machine has changed, a processor analyzes one or more sensors and a first set of data generated by the one or more sensors. A processor determines the one or more sensors are generating a required type of data and a required amount of data to create a subset of the digital twin model. A processor creates the subset of the digital twin model incorporating the change detected.