Ontological Context Model for Automation Data Retrieval
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Solution Overview
Problem
Automation systems generate vast amounts of sensor data, but conventional data analytics struggle to make sense of this data due to lack of context, such as sensor location, product identifier, or maintenance tasks, making it difficult to optimize and enhance the systems effectively.
Innovation Solution
A control apparatus with a database that stores time series data and event data, using an ontological context model to contextualize queries and output data in a semantic format, allowing for the retrieval of semantically relevant information from automation systems, including programmable logic controllers with integrated memory for historian and event data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional data analytics methods are used to analyze sensor data, then data processing is simple and straightforward, but the data lacks context and cannot provide meaningful insights into automation system effectiveness
Solution Approach 1:
An ontological context model is introduced as an intermediary layer between the automation system components and the data analytics system. This model serves as a mediator that enriches raw sensor data with contextual information about system components, their relationships, and operational parameters, enabling meaningful data interpretation without requiring complex analytics infrastructure
Solution Approach 2:
Contextual information is prepared and structured in advance through the ontological context model before data analysis occurs. The model pre-defines relationships between system components, data sources, and relevant parameters, so that when sensor data is collected, the contextual framework is already in place to provide immediate meaning without requiring complex real-time processing
2Reliability
If the automation system stores and processes contextualized data using an ontological context model, then data insights and system optimization are enhanced, but the device complexity and data processing requirements increase
Solution Approach 1:
The control apparatus is designed to perform multiple functions: it acts as both the automation control system and the data contextualization engine. The same control apparatus that manages automation processes also stores the ontological context model and performs data enrichment, eliminating the need for separate complex analytics infrastructure and reducing overall system complexity while improving data reliability
Data Source
AI summary
A control apparatus of an automation system, the control apparatus includes a database adapted to store time series data in a historian data source and adapted to store events derived from the time series data based on event detection rules in an event data source, wherein a semantic data or event query received by the control apparatus is mapped to a corresponding data source of the database to retrieve the queried data or event which are contextualized using an ontological context model of the automation system stored in the database and output by control apparatus in a semantic format is provided.