Industrial Data Model Versioning for Adaptive Asset Context
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Solution Overview
Problem
Industrial asset data integration and contextualization are hindered by the lack of effective data modeling tools that can handle complex assets and routine changes, leading to brittle models and inefficient data analysis, especially in asset-intensive industries.
Innovation Solution
A scalable, event-driven platform for integrating and contextualizing industrial enterprise data, using a flexible agent architecture to connect IT and OT systems, providing standardized models, and enabling hierarchical relationships between assets, along with governance controls for data lineage and version control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If spreadsheet-based data integration is used to model asset data, then initial data integration can be achieved, but the model becomes brittle and cannot adapt to routine changes in physical equipment and operating conditions
Solution Approach 1:
The patent implements a dynamic data modeling system that automatically adapts to changes in physical equipment and operating conditions. The system uses event-driven architecture to detect changes in real-time and automatically updates data models without requiring manual intervention, transforming the static spreadsheet approach into a dynamic system that evolves with the assets it models.
Solution Approach 2:
The system performs self-updating through automated change detection and model regeneration. When changes are detected in equipment or operating conditions, the system automatically retrieves updated data, regenerates the data model, and validates changes without human intervention, enabling the model to serve itself in maintaining accuracy and adaptability.
2Measurement precision
If manual expert effort is used to create contextualization for asset data, then accurate data context can be established, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent replaces the mechanical process of manual expert analysis with an automated computational system. The system uses event-driven architecture, machine learning algorithms, and automated reasoning to perform contextualization tasks that previously required human experts, dramatically reducing time while maintaining or improving accuracy through consistent application of contextual rules.
Solution Approach 2:
The system introduces an automated intermediary layer between raw asset data and analytical applications. This intermediary automatically generates and maintains data context, semantic relationships, and metadata, serving as a bridge that eliminates the need for manual expert intervention while ensuring accurate contextualization is always available to downstream applications.
3Quantity of substance
If comprehensive data integration from multiple sources is implemented, then complete asset data can be consolidated, but the complexity of integrating and maintaining data from 100+ sources becomes unmanageable
Solution Approach 1:
The patent segments the complex integration task into manageable components by organizing data around event types and asset hierarchies. Instead of integrating all 100+ sources simultaneously, the system divides integration into discrete event streams and asset-level contexts, making the complexity tractable through modular organization and event-driven processing.
Solution Approach 2:
The system implements a universal data model that handles multiple data sources and types through a common framework. The event-driven architecture and standardized asset models serve as universal interfaces that can accommodate diverse data sources without requiring separate integration logic for each source, reducing overall system complexity while maintaining comprehensive data integration.
Data Source
AI summary
A system for managing industrial enterprise (IE) data models may include a storage device storing instructions and IE data models. Each of the IE data models may include references to artifacts and a directed graph having nodes and edges. Each of the nodes may represent a respective one of the artifacts. Each of the edges may indicate a relationship between a respective pair of the artifacts. The system may also include a processing device operable to execute the instructions to perform operations including managing dataflow through a particular one of the data models, wherein the edges of the directed graph of the particular data model define a direction of propagation of data through the artifacts of the particular data model, and providing version control functionality for the plurality of data models and for the artifacts referenced by any of the plurality of data models.