Context-Dependent Information Modeling for Data-Limited Diagnostics
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Solution Overview
Problem
Current automation systems face challenges in generating context-dependent information due to disconnected information models, lack of semantic interoperability, and absence of required contexts, particularly in industrial automation devices where real data sources are not available, limiting precise diagnostics and decision-making.
Innovation Solution
A method to generate estimated context-dependent information by correlating data elements from various sources using context IDs and semantic rule engines, deriving new contexts based on existing knowledge and relationships without real data sources, and providing these as information models through OPC UA servers and ontologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If semantic rule engines and OPC UA are used as data sources, then semantic interoperability and machine-readable description are improved, but the ability to provide estimated or approximate information when real data is unavailable remains insufficient
Solution Approach 1:
The patent introduces a semantic rule engine as an intermediary component that mediates between raw data sources and the information needs of users. This mediator can generate estimated or approximate information by applying semantic rules when direct measurements are unavailable, thus resolving the contradiction between maintaining semantic interoperability and providing adaptability for estimated information.
Solution Approach 2:
The system changes the parameter state of information by generating estimated values with associated confidence levels or probability ranges. Instead of providing only binary available/unavailable data, the system transforms data into a spectrum of certainty, allowing users to understand the reliability of estimated information while maintaining semantic consistency.
2Measurement precision
If multiple data sources are correlated using context IDs, then diagnostic precision is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of multi-source data correlation by introducing context IDs as modular identifiers. Each data source is associated with specific context IDs, allowing the system to break down the correlation process into manageable units that can be independently processed and recombined, thus reducing overall system complexity while maintaining diagnostic precision.
Solution Approach 2:
The context ID serves as a universal identifier that can be applied across multiple different data sources and contexts. This multi-functional identifier system allows the same mechanism to handle various types of correlations (location, time, device state) without requiring separate complex systems for each, thereby improving diagnostic precision without proportionally increasing system complexity.
3Measurement precision
If context information is modeled for all automation devices, then diagnostic accuracy is improved, but data availability and storage requirements increase
Solution Approach 1:
The system performs preliminary action by pre-defining context ID structures and semantic rules that can be reused across multiple devices and scenarios. Instead of modeling all possible context information for every device from scratch, the system prepares reusable templates and rules in advance, reducing the actual data storage and processing requirements while maintaining diagnostic accuracy.
Solution Approach 2:
The patent employs copying by reusing context ID models, semantic rules, and correlation patterns across different automation devices and scenarios. Once a context model is created for one device type, it can be copied and adapted for similar devices, significantly reducing the total data storage requirements while maintaining consistent diagnostic accuracy across the system.
Data Source
AI summary
The invention relates to a method for generating items of context-dependent information for informing a user about a status of a technical system, wherein the items of context-dependent information of the technical system are provided by various data sources. In order to obtain items of information about a new context of the technical system without real data sources, it is proposed that estimated context-dependent items of information of a new context are generated by correlating the data elements of the various data sources and without knowledge of real values of the new context, but based on known and experience-related values, and provided to the user.