Virtual Twin Engine for Adaptive Event-Discrete Process Control
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Solution Overview
Problem
Current real-time process control methods for manufacturing environments, particularly in distributed event-discrete systems, lack flexibility and adaptability due to rigid hierarchical tree models that require complete recoding for changes or additions, leading to inefficiencies as complexity increases.
Innovation Solution
Implementing a virtual twin engine with an executable modeling software kernel that uses meta models to represent subsystems, partitions systems into clusters, and configures routing topologies for message exchange, allowing for dynamic modification and real-time operation of digital twins to mimic and control physical processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If hierarchical tree models are used for process control, then system structure is clearly defined, but flexibility and adaptability deteriorate requiring complete recoding for changes
Solution Approach 1:
The system is divided into independent subsystems that can be individually modified without affecting the entire system. Each subsystem is represented as a separate node in the hierarchical tree model, allowing localized changes while maintaining overall system structure.
Solution Approach 2:
The hierarchical tree model is transformed from a static structure to a dynamic one where nodes and connections can be modified in real-time. The system allows adding, removing, or modifying subsystems during operation without requiring complete recoding, enabling adaptive process control.
2Ease of manufacture
If hierarchical tree models are used for process control, then system organization is established, but device complexity increases with system complexity
Solution Approach 1:
A universal modeling language and set of standard node types are implemented that can represent various subsystems and their interactions. This standardized approach simplifies the modeling process even as system complexity increases, as new subsystems can be integrated using the same framework and conventions.
Solution Approach 2:
The hierarchical tree model allows nested representation of subsystems at multiple levels of abstraction. Complex systems are organized into parent-child relationships where sub-subsystems can be nested within subsystems, creating a manageable hierarchical structure that reduces overall model complexity.
3Ease of operation
If rigid hierarchical models are used, then initial system setup is straightforward, but real-time modification capability is lost
Solution Approach 1:
The system is pre-configured with a flexible hierarchical tree model framework and standardized node types that enable both easy initial setup and future modifications. The modeling language is designed from the outset to support dynamic changes, allowing subsystems to be added or modified without disrupting existing operations.
Solution Approach 2:
The system automatically handles model updates and reconfiguration when changes are made to subsystems. The virtual twin engine continuously adapts the process control model in real-time based on system changes, eliminating the need for manual recoding and enabling self-updating capabilities.
Data Source
AI summary
Real time process control using digital twins. In more detail, the present disclosure relates to the field of modeling distributed event-discrete systems using digital twins and subsequent use of the models for real time control of distributed even-discrete systems. There is provided a virtual twin engine for control of a distributed even-discrete system in real-time. The virtual twin engine has installed at least one executable modeling software kernel which runs subsystem use models in relation to subsystem clusters of the distributed event-discrete system. Also, the virtual twin engine operates the at least one digital twin in a passive manner through real time access to the modeling software kernel modeling the subsystem use model of the at least one digital twin.