Networked Digital Twins for Autonomous Collaborative Decisions
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Solution Overview
Problem
Existing digital twin technologies face challenges with isolated operation, lack of interconnectivity, inefficient real-time data handling, and reliance on human intervention for decision-making, limiting their effectiveness in complex environments.
Innovation Solution
A system and method for networked digital twins with autonomous collaborative decision-making, integrating AI and Complex Event Processing for real-time data analysis, enabling secure and efficient data exchange, and reducing human intervention through a scalable and modular architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital twin systems operate in isolation focusing on individual assets, then system simplicity is maintained, but interconnectivity and collaborative capability are limited
Solution Approach 1:
The system segments digital twins into autonomous individual units that maintain their own operational simplicity while being part of a larger network. Each digital twin operates independently with its own data processing and decision-making capabilities, yet can collaborate with others through standardized communication protocols, thus achieving interconnectivity without sacrificing individual simplicity
Solution Approach 2:
The patent implements universal communication protocols and standardized data exchange formats that enable digital twins to interact across different platforms and applications. This multi-functionality allows the same digital twin architecture to serve multiple purposes - individual asset monitoring, collaborative decision-making, and integration with various external systems - thereby achieving versatility without proportionally increasing complexity
2Extent of automation
If digital twin systems process real-time data with basic predictive analytics, then responsiveness is improved, but autonomous decision-making capability is limited
Solution Approach 1:
The system performs preliminary data processing, filtering, and feature extraction at the edge devices and individual digital twins before data reaches central processing systems. This preliminary action reduces the complexity of subsequent autonomous decision-making by pre-processing data into actionable insights, enabling faster and more autonomous decisions without overwhelming system complexity
Solution Approach 2:
The patent introduces intermediary layers including event processing engines and decision support systems that mediate between raw data and autonomous decision-making. These intermediaries translate complex data streams into structured events and recommendations, enabling autonomous decision-making capability while managing processing complexity through layered architecture
3Productivity
If digital twin systems rely on human intervention for critical decisions, then system simplicity is maintained, but operational efficiency is reduced
Solution Approach 1:
The system enables digital twins to make autonomous decisions for routine operational tasks without human intervention. Each digital twin is equipped with decision-making algorithms that allow it to self-manage its operations, schedule maintenance, and respond to events autonomously, thereby improving operational efficiency while maintaining manageable system complexity through automation of routine tasks
4Speed
If digital twin systems integrate AI and Complex Event Processing for real-time analysis, then decision-making speed is improved, but data processing complexity increases
Solution Approach 1:
The system implements periodic event processing where complex event processing is triggered by specific events or time intervals rather than continuously analyzing all data streams. This periodic action maintains fast decision-making speed by focusing computational resources on relevant events while reducing overall processing complexity by avoiding continuous full-system analysis
Data Source
AI summary
The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an AI Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises AI-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.