Industrial Asset Model Mapping for Reliable Relational Data Integration
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Solution Overview
Problem
Industrial automation systems face challenges in accurately modeling and analyzing industrial assets, leading to inefficiencies in data quality and reliability, particularly due to the lack of effective data integration and contextualization across industrial devices and systems.
Innovation Solution
A system comprising a processor and memory that matches industrial relational data to industrial devices within an industrial asset model, extracts this data, and updates the model, associating it with the device, thereby enhancing data quality and reliability through contextual metadata and real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If industrial relational data is integrated into the asset model with contextual metadata, then data accuracy and reliability improve, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary data extraction and integration system that sits between the asset model and external data sources. This intermediary component automatically extracts relational data, adds contextual metadata, and integrates it into the asset model, thereby improving data reliability while shielding the core system from the complexity of direct data integration operations
Solution Approach 2:
The patent segments the data integration process into distinct functional components: data extraction, contextual metadata generation, data validation, and model updating. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while achieving high data reliability through coordinated operation of specialized modules
2Productivity
If real-time data extraction and model updating is implemented, then operational control is enhanced, but processing time and computational resources increase
Solution Approach 1:
The patent implements periodic data extraction and model updating operations that occur at optimized intervals rather than continuously. The system determines appropriate update frequencies based on data criticality, change detection triggers, and operational requirements, thereby enhancing operational control with timely updates while minimizing unnecessary processing time and computational resource consumption
Solution Approach 2:
The patent applies partial updating strategies where only specific portions of the asset model are updated when changes occur, rather than performing full model regenerations. This selective updating approach maintains enhanced operational control for critical model elements while significantly reducing overall processing time and computational resources by focusing efforts only on affected model segments
Data Source
AI summary
Industrial automation relational data extraction, connection, and mapping (e.g., using a computerized tool) is enabled. For example, a system can comprise: a memory that stores executable components, and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: a device interface component that matches industrial relational data, accessible via an industrial asset model, to an industrial device represented in the industrial asset model, and in response to matching the industrial relational data to the industrial device, extracts the industrial relational data into the industrial asset model, and a model update component that updates the industrial asset model, resulting in an updated industrial asset model, wherein updating the industrial asset model comprises associating the industrial relational data with the industrial device.