Cloud Edge Gateway Modeling for Industrial Data Contextualization
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Solution Overview
Problem
Industrial automation systems face challenges in efficiently collecting and contextualizing data from industrial devices, requiring cooperative efforts between IT and OT experts, and often deal with unstructured data that needs external interpretation.
Innovation Solution
A cloud-based Edge as a Service (EaaS) system that includes an edge gateway component to discover and retrieve relevant data items from industrial devices, a user interface for data selection and mapping to information models, and an egress component to export modeled data to external applications, enabling centralized configuration and management of edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data extraction is performed using traditional methods requiring cooperative effort between IT and OT experts, then data can be correctly interpreted and understood, but the process becomes complicated and time-consuming
Solution Approach 1:
The system enables automated data extraction and interpretation through AI/ML models that independently analyze device data without requiring manual intervention from IT or OT experts. The contextualization engine automatically enriches raw data with relevant information, and the generation engine creates actionable insights, allowing the system to serve itself rather than relying on expert cooperation for each data extraction task.
Solution Approach 2:
The system performs preliminary actions by pre-configuring data extraction rules, contextualization parameters, and analysis models in advance. Device profiles and data models are established beforehand, enabling the system to automatically process incoming data according to pre-defined frameworks, thus eliminating the need for real-time expert intervention while maintaining accurate interpretation.
2Quantity of substance
If all available device data is collected and made available to external applications, then data completeness is improved, but data usability deteriorates due to unstructured and uncontextualized nature
Solution Approach 1:
The system introduces an intermediary layer between raw device data and external applications. The contextualization engine acts as a mediator that enriches unstructured device data with contextual information from multiple sources, while the generation engine transforms this contextualized data into structured, actionable insights. This intermediary processing layer maintains data completeness while dramatically improving usability for external applications.
Solution Approach 2:
The system changes the parameters of device data by transforming raw numerical values into contextualized information with meaningful attributes. The AI/ML models analyze data patterns and restructure information according to predefined data models, changing the organizational parameters and semantic meaning of the data to make it directly usable by external applications without loss of completeness.
3Measurement precision
If manual configuration of data collection and analysis is performed, then data accuracy is maintained, but system complexity increases and scalability is reduced
Solution Approach 1:
The system implements universal data processing frameworks that can handle multiple device types and data formats through standardized profiles and models. The same contextualization engine and generation engine can process data from diverse industrial devices by applying appropriate pre-configured rules and AI/ML models, maintaining data accuracy across different contexts without requiring separate manual configuration for each device type, thus reducing overall system complexity.
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.