Data Virtualization for Dynamic Industrial Asset Hierarchies
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Solution Overview
Problem
Existing database management systems fail to integrate real-time and relational data sources effectively, leading to incomplete or inaccurate digital twins of asset groups, particularly in complex industrial environments like oil and gas, where millions of real-time data points require efficient handling and dynamic hierarchies to understand complex relationships between datasets.
Innovation Solution
A digital platform with an open architecture that connects to corporate and real-time databases, employs exception-based surveillance, workflow management, and visual reporting tools, enabling no-code configuration for integrated asset management, and uses data virtualization to provide a single version-of-the-truth across multiple disciplines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional database integration methods are used to connect multiple data sources, then data integration capability is improved, but system complexity and coding requirements increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (data virtualization platform) between the user and multiple scattered database sources. This intermediary handles the complexity of integrating real-time databases, relational databases, and other data sources, allowing users to access integrated data without directly managing the complexity of multiple connections and protocols.
Solution Approach 2:
The system provides a universal platform that can connect to and integrate multiple types of data sources (real-time databases, relational databases, external systems) through a single interface. This multi-functional approach eliminates the need for separate integration solutions for each data source type, reducing overall system complexity.
2Ease of manufacture
If static hierarchy methods are used to organize asset data, then implementation simplicity is improved, but accuracy and completeness of digital twin representation deteriorate
Solution Approach 1:
The patent implements dynamic hierarchies that automatically adapt to the actual relationships between assets and data sources. Unlike static hierarchies, the system can dynamically adjust the structure based on real-time data flow and relationships, ensuring accurate representation of complex asset connections while maintaining ease of implementation through automated configuration.
Solution Approach 2:
The system uses nested hierarchy structures where assets can belong to multiple levels and types of hierarchies simultaneously (e.g., an asset can be part of both a process hierarchy and a spatial hierarchy). This nested approach allows comprehensive representation of complex relationships without requiring complex implementation, as the system automatically manages the nested structures.
3Device complexity
If manual data tagging and integration methods are used, then system simplicity is improved, but productivity and efficiency in handling big data deteriorate
Solution Approach 1:
The system implements automated data discovery and integration capabilities that eliminate the need for manual tagging. The platform automatically discovers data sources, establishes connections, and integrates data streams without requiring manual intervention, thereby maintaining simplicity while dramatically improving productivity in handling millions of real-time data points.
Solution Approach 2:
The system performs preliminary actions by pre-configuring integration templates and data models that can be automatically applied to new data sources. This preliminary setup enables rapid integration of new assets and data sources without requiring time-consuming manual configuration, thus maintaining simplicity while enhancing processing efficiency.
4Ease of operation
If traditional data integration approaches are used, then ease of implementation is improved, but ability to provide holistic overview and single version-of-truth deteriorates
Solution Approach 1:
The patent merges data from multiple scattered sources into a unified view, combining real-time data, historical data, and data from different systems into a single version-of-truth. This merging approach maintains ease of implementation through automated consolidation while ensuring data completeness by integrating all relevant data sources into a holistic overview.
Data Source
AI summary
The present invention relates to the field of digitally accessing, modeling and optimizing scattered ‘big-data’ from various assets having complex and dynamic physical relationships. The present disclosure also relates to a system and method for supporting version-of-the-truth from multiple data sources and for multiple disciplines. It helps organizations that possess various physical assets that are been utilized by multiple user groups and through a period of time to optimize operations, conduct surveillance and manage complex business processes related to their valuable assets and equipment. The invention is built with an open architecture to connect to various corporate databases combining real-time and relational data. The system employs exception-based surveillance, sophisticated queries, and no-code methods to automatically detect deviations in asset performance from optimal conditions, and flag them to the right users, at the right time, and in the right way. The present invention also allows monitoring the status and performance of equipment and facilities by presenting its findings through intuitive dashboards and automated workflows. In addition, a mobile version of the present invention is developed to help users execute day to day work as well as manage and maintain equipment and assets more efficiently.