Extensible Industrial Data Model for Cross-Application Synchronization
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Solution Overview
Problem
Existing data management systems in industrial processes require significant time and resources to integrate and analyze data across multiple industry applications, leading to complex and costly custom models that fail to synchronize data relationships effectively.
Innovation Solution
An extensible homogeneous data model is implemented, which organizes industrial data into a graph structure, allowing seamless integration and extension across various industry applications, reducing the need for separate models and enhancing data synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate custom models are created for each industry application, then specific application requirements are met, but system complexity and development cost increase significantly
Solution Approach 1:
The patent implements a universal data model framework that can serve multiple industry applications simultaneously. The contextualized data model with entities, attributes, and relationships provides a common foundation that can be extended to different applications (manufacturing, healthcare, finance, etc.) without creating separate models for each, thereby reducing overall system complexity while maintaining application-specific adaptability through configuration rather than structural duplication
Solution Approach 2:
The data model is segmented into contextualized entities, attributes, and relationships that can be independently configured and extended. This segmentation allows the system to maintain a core universal structure while enabling application-specific customizations through modular additions, preventing the need for completely separate models for each application
2Adaptability or versatility
If multiple separate models are developed for different applications, then each application's specific needs are addressed, but development time and resources increase
Solution Approach 1:
The patent establishes a pre-configured universal data model framework with defined entities, attributes, and relationships that can be directly applied to multiple industry applications. This preliminary structure eliminates the need to build models from scratch for each application, significantly reducing development time while maintaining the ability to address application-specific requirements through configuration and extension
Solution Approach 2:
The universal data model framework is designed to serve multiple industry applications simultaneously through a common structure that can be configured for different domains, reducing development time by reusing the same foundational model across applications rather than creating separate models for each
3Adaptability or versatility
If custom models are created for each application, then application-specific data requirements are met, but data synchronization and relationship management become difficult
Solution Approach 1:
The patent implements a universal contextualized data model that provides a common framework for data representation across multiple applications. This unified model ensures consistent data relationships and synchronization mechanisms are maintained across all applications, while still allowing application-specific data requirements to be met through configuration of entities and attributes within the same model structure
Solution Approach 2:
The contextualized data model acts as an intermediary layer between different applications and the underlying data sources. This intermediary framework standardizes data relationships and synchronization protocols, making it easier to maintain data consistency across multiple applications while still accommodating application-specific data handling requirements
4Adaptability or versatility
If separate models are used for different applications, then each application's unique requirements are satisfied, but resource consumption increases
Solution Approach 1:
The patent implements a universal data model framework that can serve multiple applications simultaneously, eliminating the need to maintain separate model structures for each application. This consolidation reduces computational resource consumption by sharing the same model framework, processing logic, and data relationships across applications, while still allowing application-specific customizations through configuration rather than structural duplication
Data Source
AI summary
A system and method for monitoring and controlling an industrial process using a data model extensible to different industry applications. The system is configured to: receive data describing the industrial process from one or more data sources in a first format; contextualize and transform the data by determining one or more tags for the data, the one or more tags comprising context information describing characteristics of the entities involved in the industrial process; generate a data model describing the industrial process based on the one or more tags; receive a first indication from a user indicating a first industry application to which the data model will be applied; and extend the data model to include a second plurality of nodes representing entities associated with the first industry application and a second plurality of edges connecting the second plurality of nodes and describing relationships between the second plurality of nodes.