Extensible Energy Data Model for Multi-Source Industrial Control
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Solution Overview
Problem
Current industrial data management systems require significant time and resources to integrate data from multiple vendors and technologies, leading to complex and costly custom data models for comprehensive analysis across multiple industry applications, limiting the ability to seamlessly share insights and manage industrial processes efficiently.
Innovation Solution
An extensible homogeneous data model that transforms and contextualizes data into a graph structure, allowing for a single platform to manage and analyze data from various industrial sources, enabling standardized security, reporting, and easy addition of new applications while reducing the need for custom models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If custom data models are created for each industry application, then data integration accuracy is improved, but device complexity and deployment time increase
Solution Approach 1:
The patent implements a universal data model framework that can be applied across multiple industry applications (oil and gas, manufacturing, utilities, etc.) without requiring custom models for each application. The framework provides standardized data structures, relationships, and metadata schemas that work universally across different domains, eliminating the need to create separate custom data models for each industry while maintaining accurate data integration.
Solution Approach 2:
The data model is segmented into modular components including standardized entities (assets, processes, measurements), relationships, and metadata elements that can be independently configured and extended. This segmentation allows the universal framework to be adapted to specific industry needs through configuration rather than custom model creation, reducing complexity while maintaining accuracy.
2Adaptability or versatility
If vendor-specific data formats are integrated, then data source compatibility is improved, but loss of time and resources for integration increase
Solution Approach 1:
The patent introduces a standardized data model framework as an intermediary layer between vendor-specific data sources and analytical applications. This framework provides standardized data structures, relationships, and metadata schemas that mediate between diverse input formats and processing requirements, enabling automatic integration without time-consuming custom development for each vendor while maintaining full compatibility.
Solution Approach 2:
The system uses configurable parameters and metadata to adapt the universal data model to different vendor formats through parameter adjustment rather than structural changes. This allows the same framework to handle multiple data sources by changing configuration parameters rather than creating custom integration logic for each vendor, significantly reducing integration time.
3Adaptability or versatility
If multiple custom data models are maintained, then application-specific requirements are met, but resource usage and operational efficiency decrease
Solution Approach 1:
The framework provides a single universal data model that serves multiple industry applications simultaneously, eliminating the need to maintain separate custom models for different applications. The standardized structures and relationships work across oil and gas, manufacturing, utilities, and other sectors, improving operational efficiency while maintaining application-specific capabilities through configurable parameters and metadata.
Solution Approach 2:
The patent merges the functionality of multiple application-specific data models into a single unified framework that combines common elements (assets, processes, measurements, relationships) into a cohesive structure. This consolidation eliminates redundant maintenance efforts and improves operational efficiency while preserving application-specific requirements through configuration options and extensibility mechanisms.
Data Source
AI summary
A system and method for monitoring and controlling energy use in an industrial process. The method includes: receiving, by a processing circuit, data describing energy use in an industrial process from one or more data sources; contextualizing, by the processing circuit, the data describing the energy use in an industrial process; generating, by the processing circuit, an energy data model based on the contextualized data; executing, by the processing circuit, the energy data model to determine key performance indicators for the energy use in an industrial process; displaying, by the processing circuit, the key performance indicators to a user; determining, by the processing circuit, if the key performance indicators are above one or more pre-determined thresholds; and taking a corrective action in response to the key performance indicators being above the one or more pre-determined thresholds.