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 predictive maintenance 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 retrieve relevant data tags and smart objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data extraction is performed using traditional methods requiring IT and OT expert cooperation, then data accuracy and contextual understanding are improved, but system complexity and implementation time increase
Solution Approach 1:
The patent introduces an edge gateway as an intermediary component that bridges IT and OT systems. The gateway contains an information model that automatically contextualizes industrial device data without requiring manual collaboration between IT and OT experts. The gateway translates raw device data into meaningful information using pre-defined relationships and contexts stored in its information model, thereby resolving the contradiction by automating the previously manual process.
Solution Approach 2:
The edge gateway performs self-service by automatically discovering, collecting, and contextualizing data from industrial devices using its embedded information model. The system autonomously identifies relevant data points, establishes contextual relationships, and makes data available to applications without requiring external expert intervention. This self-service capability eliminates the need for continuous IT-OT collaboration while maintaining high data quality.
2Quantity of substance
If all available industrial device data is collected and made available to external applications, then data completeness is improved, but data processing complexity and storage requirements increase
Solution Approach 1:
The edge gateway extracts only the most relevant data from the complete set of industrial device data based on its information model. Rather than transmitting all available data points to external applications, the gateway selectively extracts and contextualizes data that has meaningful relationships and business value. This extraction approach maintains data completeness for relevant information while reducing the overall data volume and processing complexity.
Solution Approach 2:
The information model implements local quality by assigning different levels of contextualization and processing to different data points based on their specific characteristics and relevance. Not all data receives the same level of processing - the gateway applies contextual relationships, units of measurement, and semantic meanings selectively based on the data type, device, and application requirements. This differentiated approach reduces overall processing complexity while maintaining completeness where needed.
3Measurement precision
If custom data collection and contextualization logic is developed for each application, then application-specific data accuracy is improved, but development time and cost increase
Solution Approach 1:
The edge gateway implements universality through its information model, which provides a standardized framework for data collection and contextualization that serves multiple applications simultaneously. Rather than developing custom logic for each application, the gateway creates a universal data infrastructure that can be reused across different applications and use cases. The information model's standardized relationships, data types, and contextual definitions work for various applications without requiring re-development, thereby reducing development time while maintaining accuracy.
Solution Approach 2:
The information model performs preliminary action by pre-defining data relationships, contextual meanings, and processing logic before data collection begins. The gateway is configured with an information model that establishes the framework for data contextualization in advance, eliminating the need for applications to perform complex data processing from scratch. This preliminary setup of data structures and relationships enables rapid application deployment while ensuring consistent data accuracy across multiple applications.
Data Source
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AI summary
A cloud-based edge-as-a-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.