Cloud Edge Gateway Configuration for Contextualized Industrial Data
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Solution Overview
Problem
The process of extracting operational and diagnostic data from industrial devices for use in visualization and analytic systems is complicated, requiring cooperation between IT and OT experts, and much of the data is uncontextualized, necessitating external applications to define its meaning.
Innovation Solution
A cloud-based Edge as a Service (EaaS) system that allows users to centrally configure and manage edge devices, using edge gateways to collect, contextualize, and egress data to external applications, leveraging device profiles to automatically discover and map relevant data tags to information models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data extraction methods are used from industrial devices, then data can be collected for visualization and analytic systems, but the process becomes complicated requiring cooperative effort between IT and OT experts
Solution Approach 1:
The patent introduces an edge gateway as an intermediary component that sits between industrial devices and higher-level applications. This gateway automatically discovers data tags from devices, maps them to information models, and contextualizes the data, eliminating the need for direct complex configuration between IT and OT systems. The gateway acts as a mediator that handles the complexity internally while presenting simplified interfaces to users.
Solution Approach 2:
The edge gateway implements self-service capabilities through automated device profile matching, automatic data tag discovery, and automated information model mapping. When a device is connected, the gateway automatically identifies the device type, retrieves appropriate profiles, discovers relevant data tags, and maps them to contextualized information models without requiring manual configuration by experts, thereby reducing dependency on cooperative IT-OT effort.
2Quantity of substance
If uncontextualized data is collected from industrial devices, then data volume is maximized, but the data requires external applications to define its meaning
Solution Approach 1:
The edge gateway performs preliminary contextualization of data before it reaches external applications. By automatically mapping raw data tags to pre-defined information models that contain semantic meaning and context, the gateway prepares data in advance for consumption. This preliminary action ensures data is already contextualized when it arrives at visualization or analytic systems, eliminating the need for those applications to independently define data meaning.
Solution Approach 2:
The system transforms raw uncontextualized data parameters into contextualized parameters through the information model mapping process. Data tags with simple numeric values are transformed into structured objects with semantic meaning, units, data types, and contextual relationships defined by the information models. This parameter transformation maintains data volume while enriching data with contextual information.
3Adaptability or versatility
If manual configuration of edge devices is performed, then data mappings can be customized, but the time and effort required for configuring increases significantly
Solution Approach 1:
The system performs preliminary configuration work by maintaining a library of pre-defined device profiles and information models that contain standard data mappings and contextualizations for common industrial devices. When a device is connected, the gateway automatically matches it to the appropriate profile and applies pre-configured mappings, dramatically reducing configuration time while maintaining adaptability through the ability to customize mappings when needed.
Solution Approach 2:
The edge gateway implements universal configuration capabilities that work across multiple device types through standardized device profiles and information models. A single gateway instance can handle various device types by automatically selecting appropriate profiles, providing universal adaptability without requiring device-specific manual configuration for each type. This multi-functionality allows the same system to serve diverse industrial devices efficiently.
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.