Edge Digital Twins for Low-Latency Industrial Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial automation systems face challenges in implementing digital twins due to limited computing resources at device level, leading to increased data communication and latency when using cloud-based solutions, which can inhibit performance and efficiency.
Innovation Solution
Implementing digital twins on edge computing devices using software containers generated by a cloud-based container orchestration system, which identifies attribute variations and transmits only these changes to the cloud, reducing data communication and enhancing local processing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital twins are implemented on cloud-based solutions, then centralized data processing and management are improved, but data communication latency and network bandwidth consumption increase
Solution Approach 1:
The patent segments the digital twin system into edge-based components and cloud-based components. Edge computing devices execute containers locally to process data in real-time, while cloud systems handle centralized management and long-term analytics. This segmentation allows critical time-sensitive operations to occur locally, reducing communication latency while maintaining centralized oversight.
Solution Approach 2:
The patent introduces edge computing devices as intermediaries between industrial automation devices and cloud systems. These edge devices run containerized digital twins that preprocess and filter data locally, sending only essential information to the cloud. This intermediary layer reduces network bandwidth consumption and communication latency while maintaining centralized data processing capabilities.
2Reliability
If digital twins are implemented on cloud-based solutions, then centralized management is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent extracts data processing functions from the cloud and places them in containerized environments on edge computing devices. By taking out the computationally intensive digital twin operations from the cloud and executing them locally at the edge, the system reduces network bandwidth consumption for data transmission while cloud systems continue to provide centralized management and coordination.
3Productivity
If digital twins are implemented at device level, then real-time processing is improved, but computing resource requirements increase
Solution Approach 1:
The patent uses containerization to create lightweight, portable copies of digital twin environments on edge computing devices. Instead of requiring full digital twin implementations on resource-constrained industrial devices, the system deploys standardized container images that can be executed on edge hardware with adequate resources, achieving real-time processing without overburdening original devices.
Solution Approach 2:
The patent implements dynamic container orchestration that can scale and adjust resource allocation based on operational needs. Containers can be dynamically started, stopped, and configured on edge devices, allowing the system to optimize computing resource usage according to real-time processing requirements while maintaining flexibility in resource management.
Data Source
AI summary
An industrial automation system may include a first computing device that may receive operational technology (OT) data from industrial automation devices of an industrial automation system, determine identities of the industrial automation devices based on the OT data, determine that the OT data includes data attributes having variations as compared to additional OT data, transmit the identifiers and the data attributes to a second computing device in response to determining that the data attributions have the variations, receive containers including updated digital representations of the industrial automation devices from the second computing device, execute the containers to output additional data attributes, and send commands to the industrial automation devices to modify processes based the additional data attributes.