Digital Twin Process Control with Asynchronous Event Buffers
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Solution Overview
Problem
Current digital twin implementations in process domains, such as manufacturing and healthcare, face challenges in efficiently and accurately achieving process objectives for process control, as they primarily focus on data interaction and real-time data gathering rather than effective control methodologies.
Innovation Solution
The implementation of an event-driven process control system within the digital twin domain using state machine models to model the behavior of process entities, where event data is asynchronously received, processed, and used to generate external control commands for process entities through a controller service module and system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If digital twins are used to gather extensive real-time data, then data availability and access are improved, but the ability to efficiently achieve process control objectives deteriorates
Solution Approach 1:
The patent segments the monolithic digital twin system into modular controller service modules, each responsible for specific control functions. This segmentation allows independent optimization of data gathering and control execution, enabling efficient process control while maintaining comprehensive data availability through the modular architecture.
Solution Approach 2:
The patent introduces controller service modules as intermediary components between the digital twin data layer and the physical process control layer. These modules act as mediators that translate extensive real-time data into actionable control commands, bridging the gap between data availability and control efficiency.
2Manufacturing precision
If state machine models are executed in the digital twin domain, then process entity behavior modeling accuracy is improved, but system complexity increases
Solution Approach 1:
The patent uses state machine models as simplified copies or abstractions of complex process entity behaviors. These models capture essential behavioral patterns without replicating full physical complexity, enabling accurate behavior prediction while maintaining manageable system complexity through abstraction.
Solution Approach 2:
The patent manages system complexity by parameterizing state machine models with configurable variables and transitions. This allows the models to adapt to different process entities through parameter adjustment rather than structural redesign, maintaining high modeling accuracy across diverse applications while keeping the underlying system architecture simple and reusable.
Data Source
AI summary
The present invention aims at providing an approach to digital twin-based process control for efficient and accurate achievement of process objectives. Heretofore, a controller service module (18) runs an event-driven control process in a digital twin domain for control of process entities operated in a process domain. The behavior of process entities is modeled through execution of state machine models. Event data is communicated asynchronously to the controller service module (18) for storage in a process cycle buffer (26). A model-based process controller (24) reads input information in processing cycles and controls process entities by operating state machine models to reflect the input of event data. It is checked whether the operation of state machine models triggers the generation of external control commands which are then output by an outbound interface (32) to process entities for control processing.