Containerized Edge Digital Twins for OT Data Latency Reduction
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Solution Overview
Problem
Industrial automation systems face inefficiencies due to the large amount of data communicated to cloud computing systems, where only a small portion of data attributes change, leading to increased latencies and bandwidth usage, especially when implementing digital twins for monitoring and optimization.
Innovation Solution
Implementing digital twins on edge computing devices using software containers generated by a cloud-based container orchestration system, which identifies and transmits only attribute variations, reducing data communication and enhancing network bandwidth availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all operational technology data is communicated to cloud computing systems for digital twin implementation, then comprehensive monitoring and analysis capability is improved, but data communication latency and network bandwidth consumption increase
Solution Approach 1:
The patent segments the data communication process by implementing digital twins at the edge device level, allowing local processing of operational technology data. Only attribute variations (changes) are transmitted to the cloud, rather than all raw data, thereby reducing communication latency while maintaining comprehensive monitoring capability through distributed digital twin instances
Solution Approach 2:
The patent introduces edge computing devices as intermediaries between industrial automation devices and cloud computing systems. These edge devices host local digital twins that pre-process and filter data before cloud transmission, acting as a mediator that reduces the data volume and communication latency while preserving monitoring reliability
2Loss of information
If all operational technology data attributes are transmitted to the cloud, then complete data analysis is improved, but network bandwidth usage increases
Solution Approach 1:
The patent extracts only the essential information from operational technology data - specifically attribute variations or changes - and transmits only these extracted elements to the cloud. The digital twin infrastructure maintains complete data context locally at the edge, allowing comprehensive analysis while minimizing network bandwidth consumption by transmitting only deltas rather than full data sets
Solution Approach 2:
The patent monitors and transmits only when parameter changes occur in the operational technology data. By implementing change-detection mechanisms at the edge digital twin level, the system maintains complete data awareness locally while reducing network bandwidth usage by transmitting updates only when attribute values change, rather than continuous data streams
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.