Digital Twin Model Adjustment via Semantic Keyword Expansion
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Solution Overview
Problem
Current digital twin modeling methods are complex and require manual intervention, struggling to accurately represent dynamic physical entities like data centers or wind turbines, and lack automated tools for real-time updates and adequate documentation handling.
Innovation Solution
An automated method and system for digital twin modeling using task keyword analysis, which extracts tasks from workflow data, expands keywords with semantic relationships, generates new tasks, and adjusts models based on execution results, enabling real-time updates and improved fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated keyword extraction and expansion is implemented, then model fidelity and real-time adaptability are improved, but system complexity and computational resources required increase
Solution Approach 1:
The patent replaces manual digital twin model updating with automated natural language processing and keyword analysis systems. The system automatically extracts tasks from workflow data, expands keywords using semantic relationships, generates new tasks, and adjusts the digital twin model without human intervention, thereby maintaining high model fidelity while reducing operational complexity.
Solution Approach 2:
The digital twin model updating system performs self-service by automatically monitoring workflow data, extracting relevant tasks through keyword analysis, and adjusting the model in real-time without requiring manual intervention. The system serves itself by continuously improving its own accuracy through automated semantic analysis and task generation.
2Manufacturing precision
If manual intervention is used in digital twin modeling, then model accuracy can be maintained, but productivity and real-time update capability deteriorate
Solution Approach 1:
The patent replaces manual digital twin modeling operations with automated natural language processing systems that extract tasks from workflow data, expand keywords semantically, generate new tasks, and adjust models in real-time. This substitution maintains model accuracy through sophisticated automated analysis while dramatically improving productivity and enabling continuous real-time updates.
Solution Approach 2:
The system enables continuous real-time updating of digital twin models by continuously monitoring workflow data, performing automated keyword extraction and expansion, and constantly adjusting the model based on new information. This continuous automated process eliminates the interruptions inherent in manual updating while maintaining high model accuracy.
3Reliability
If comprehensive documentation is maintained, then model reliability is improved, but ease of operation and maintenance deteriorate due to documentation complexity
Solution Approach 1:
The patent replaces manual documentation management with automated natural language processing that extracts tasks from workflow data and maintains documentation automatically. The system processes and structures documentation through automated keyword analysis and semantic expansion, improving model reliability while eliminating the operational burden of manual documentation maintenance.
Solution Approach 2:
The system performs self-service documentation by automatically extracting, structuring, and maintaining model documentation through workflow data analysis. The automated keyword extraction and task generation processes continuously update documentation without human intervention, ensuring reliability while maintaining ease of operation.
Data Source
AI summary
A set of tasks is extracted from workflow data of a system. A set of keywords is extracted from a task in the set of tasks. The set of keywords is expanded into an expanded set of keywords, the expanded set of keywords comprising a new keyword with a semantic relationship to a keyword in the set of keywords. A new task is generated using the expanded set of keywords. Based on a result of execution of the new task, a model of the system is adjusted, the model comprising the workflow data, the set of tasks, and the expanded set of keywords.