Cloud Edge Gateway Modeling for Industrial Data Contextualization
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Solution Overview
Problem
The process of extracting operational and diagnostic data from industrial devices for use in visualization, analytic, or predictive maintenance systems is complex, requiring collaboration between IT and OT experts, and often involves uncontextualized, unstructured data that requires external applications to define meaning.
Innovation Solution
A cloud-based system that configures and manages distributed edge devices through a centralized interface, using edge gateways to collect device data from industrial devices, contextualize it based on information models, and store the modeled data in cloud storage allocations assigned to industrial enterprises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data extraction methods are used from industrial devices, then data can be collected for analysis systems, but the process becomes complicated requiring cooperative effort between IT and OT experts
Solution Approach 1:
The patent introduces an intermediary data extraction system that acts as a mediator between industrial devices and analysis systems. This intermediary automatically extracts and contextualizes data from devices using standardized protocols, eliminating the need for direct complex collaboration between IT and OT experts while maintaining data reliability.
Solution Approach 2:
The system enables self-service data extraction where the extraction mechanism automatically identifies, collects, and contextualizes data from industrial devices without requiring manual expert intervention. The system autonomously handles data routing, formatting, and delivery to appropriate analysis systems.
2Ease of manufacture
If uncontextualized unstructured data is stored, then data storage is simplified, but external applications cannot understand the meaning of the data
Solution Approach 1:
The system performs preliminary contextualization of data during the extraction process itself. Before data is stored, it is automatically enriched with contextual information such as device metadata, operational parameters, and semantic annotations. This preliminary action ensures data meaning is preserved without complicating storage operations.
3Adaptability or versatility
If manual configuration and expert collaboration are required, then data extraction can be customized, but the process time and resource requirements increase
Solution Approach 1:
The system allows dynamic adjustment of extraction parameters and contextualization rules without requiring manual reconfiguration. Users can modify data extraction behavior by changing parameters such as data sources, filtering criteria, and contextualization templates, enabling adaptability while maintaining automated operation that reduces configuration time.
Data Source
AI summary
A cloud-based edge-as-as-service (EaaS) system allows edge gateways to be easily configured and deployed on the cloud for collection, contextualization, and egress of industrial data to downstream applications, including analytic applications, work order management systems, or visualization systems. The EaaS system uses predefined device profiles to automatically discover relevant data items on plant floor devices and present these data items to a user. Model configuration interfaces served by the EaaS system allow the user to map selected data items to predefined models for organizing or contextualizing the selected data, and for egressing the contextualized data to the target applications. These model configurations can be deployed on the cloud platform as edge gateways that use the resulting information models to collect and contextualize relevant items of device data during runtime and to export the modeled data to the target application.