AI Federated Data Layer for Faster Digital Twin Integration
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Solution Overview
Problem
The process of creating a digital twin for product lifecycle management is time-consuming and costly, requiring skilled consultants, and is not affordable by smaller enterprises due to the high cost and time involved in data review and integration across disparate data sources with varying formats and properties.
Innovation Solution
An AI-assisted system uses machine-learning models to identify, map, and recognize data types, relationships, and patterns across disparate data sources, creating a federated data model that enables seamless data integration and querying, without physically moving data, thereby facilitating the creation of digital twins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual data review and integration methods are used, then data integration accuracy and quality are improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical data review processes with automated machine learning models and AI algorithms. The system automatically ingests, validates, enriches, and integrates data from multiple sources without human intervention, substituting the mechanical work of consultants with computational automation while maintaining integration quality through trained models.
Solution Approach 2:
The data integration system performs self-service by automatically reviewing, validating, and integrating data from multiple sources without requiring external consultant intervention. The machine learning models autonomously handle data quality assessment, pattern recognition, and integration decisions, enabling the system to serve itself rather than relying on external expertise.
2Reliability
If skilled consultants perform data review and integration, then data model quality and coherence are improved, but cost increases making it unaffordable for smaller enterprises
Solution Approach 1:
The patent substitutes expensive human consultant services with automated machine learning systems. The AI models perform data review, validation, and integration tasks that previously required skilled professionals, dramatically reducing costs while maintaining or improving data model quality through consistent, scalable automation.
Solution Approach 2:
The system changes the fundamental parameter of who performs data integration from human consultants to machine learning algorithms. This parameter change transforms the cost structure from high-cost expert services to lower-cost automated processing, making data integration accessible to enterprises of all sizes while preserving quality through trained models.
3Stability of the object's composition
If data is physically moved and integrated from disparate sources, then data coherence is improved, but data movement cost and complexity increase
Solution Approach 1:
The patent introduces an intermediary layer of machine learning models and data integration platforms that mediate between disparate data sources and the target data model. Instead of physically moving and directly integrating data, the AI systems act as intermediaries that ingest, validate, enrich, and transform data from multiple sources into a coherent unified model, reducing direct data movement complexity.
Solution Approach 2:
The system creates copies and representations of data from disparate sources within the unified data model without requiring physical movement or direct integration of source systems. The machine learning models generate copies of relevant data elements and relationships, assembling them into a coherent whole while leaving source systems intact, thereby reducing integration complexity.
Data Source
AI summary
Artificial Intelligence-assisted building/execution of federated data layer for enterprise engineering: A system trains at least one machine-learning model to identify information about industrial assets from training data, then map the information to a federated data model. The system retrieves information about data from an application in an industrial asset. The at least one machine-learning model identifies types of the data, relationships between the data, and patterns of the data, from the information and based on data types, data relationships, and data patterns in the federated data model. The at least one machine-learning model maps the types of the data, the relationships between the data, and the patterns of the data to the federated data model. The system identifies knowledge about the types of the data, the relationships between the data, and/or the patterns of the data in the federated data model, in response to a query about data.