Cloud Asset Data Mapping for Industrial Digital Twin Compatibility
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Solution Overview
Problem
The integration of asset information from various data sources in industrial digital twin systems is challenging due to incompatible data schemas and tool dependencies, making it difficult to generate digital twins efficiently and accurately.
Innovation Solution
A method and system for generating digital representations of asset information in a cloud computing environment that extracts and processes asset information from structured and unstructured data sources using predefined rules, generating a digital twin knowledge graph and dynamically mapping data to create a common data model compatible with multiple user devices, while minimizing changes during partial updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If classical data integration approach using relational database systems is used for structured asset information, then data can be organized in structured format, but the process becomes tedious and requires complex mappings between source and target schemata
Solution Approach 1:
The patent introduces an intermediary layer (data integration service with predefined rules and digital twin model) between the heterogeneous data sources and the target system. This intermediary automatically maps and transforms data from various sources into a unified digital twin representation, eliminating the need for complex manual mappings and reducing integration process complexity while maintaining structured data organization.
2Measurement precision
If human domain experts manually collate information from unstructured documents, then accurate digital twin can be built, but the process is time-consuming and heavily dependent on engineer expertise
Solution Approach 1:
The system enables self-service by automatically processing unstructured asset information through predefined rules and algorithms. The data integration service autonomously extracts, validates, and integrates information from unstructured documents without requiring manual human intervention, thereby maintaining accuracy while dramatically reducing the time required for information collation.
Solution Approach 2:
The patent implements preliminary action by pre-defining rules, validation criteria, and digital twin models before the actual data integration process. These predefined configurations enable the system to automatically and accurately process unstructured information without requiring real-time human expertise, thus reducing both time consumption and dependency on engineer experience.
3Adaptability or versatility
If each data source exposes different data schemas, then data source specific requirements can be met, but asset information from one data source is not compatible with asset information of other data sources
Solution Approach 1:
The patent implements universality by creating a unified digital twin model that can represent asset information from multiple different data sources with varying schemas. The system maintains the ability to adapt to different source formats while integrating them into a single compatible representation, thus achieving both data source versatility and seamless information integration without loss.
4Ease of manufacture
If different tools are used to handle asset information from different data sources, then each tool can be optimized for its specific data source, but there is large dependency on type of tools used
Solution Approach 1:
The patent merges multiple data handling tools and processes into a single unified data integration service. This consolidated service handles asset information from all data sources through a common interface and rule-based processing mechanism, thereby maintaining data handling efficiency while eliminating the complexity and dependency associated with using multiple different tools.
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AI summary
A method and system for generating digital representation of asset information in a cloud computing environment (100) is disclosed. The method includes extracting asset information associated with one or more assets (122A-N) from plurality of structured and unstructured data sources (120A-N). The one or more assets (122A-N) are deployed in an industrial environment (106). Further, the method includes processing the extracted asset information based on a first predefined set of rules. The method further includes generating a digital representation of the processed asset information based on plurality of user devices (122A-N). Furthermore, the method includes storing the generated representation of the processed asset information in a predefined file format in a database. The predefined file format is compatible with a format used by the plurality of user devices (122A-N).