Dynamic Ontology Data Operations for IoT Systems
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Solution Overview
Problem
The complexity of managing disparate data structures from various connected elements in IoT systems makes it challenging to efficiently and effectively access and analyze voluminous data, especially when tightly coupled with cloud services, limiting flexibility and requiring high dependency on data model updates.
Innovation Solution
The implementation of dynamic ontology data operations, which involve updating data models to normalize data relationships and allow independent development of system components, enabling flexible configuration and automatic adaptation to new devices, along with the generation of human-machine interfaces and control logic for devices and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data structures are tightly coupled with cloud services, then data access and analysis can be performed, but flexibility is limited and high dependency on data model updates is required
Solution Approach 1:
The patent introduces an intermediary layer between connected elements and cloud services that standardizes data access patterns. This intermediary enables flexible data querying without requiring tight coupling to specific cloud service implementations, thereby improving adaptability while reducing data model dependency complexity
Solution Approach 2:
The system segments data access functionality into independent, standardized operations that can be performed without requiring updates to the overall data model. This segmentation allows individual data access patterns to be modified independently, enhancing flexibility while maintaining manageable complexity
2Adaptability or versatility
If heterogeneous data structures from various connected elements are managed, then comprehensive data coverage is achieved, but data organization and access efficiency deteriorate
Solution Approach 1:
The patent implements universal data access patterns that work across heterogeneous data structures from different connected elements. These standardized patterns provide multi-functional access methods that maintain efficiency while supporting comprehensive data coverage across diverse device types and data formats
Solution Approach 2:
The system applies homogeneous access patterns and standardized data representation methods across heterogeneous data sources. This homogenization of access interfaces maintains efficiency by providing consistent query mechanisms while still supporting diverse underlying data structures through standardized transformation layers
3Reliability
If cloud application and on-premises systems are tightly coupled, then integrated operations are achieved, but update dependency and maintenance complexity increase
Solution Approach 1:
The patent introduces intermediary components that enable integrated operations between cloud applications and on-premises systems while decoupling their direct dependencies. This intermediary layer maintains reliable integrated functionality while allowing independent updates and maintenance of individual system components
Solution Approach 2:
The system implements dynamic coupling mechanisms that allow the degree of integration between cloud and on-premises systems to be adjusted. This dynamic approach maintains reliable integrated operations when needed while enabling flexible updates and independent maintenance by temporarily reducing coupling strength during update cycles
Data Source
AI summary
A method and system are provided for dynamically linking device data models and asset data models. A first data model link between a first one or more asset data model components of an asset data model and a first one or more device data model components of a device data model is dynamically updated. The device data model is processed to determine normalized data relationship paths utilized by an inference engine to populate a device element data set. A first determined normalized data relationship path for a first device(s) is inferred. The method and system execute an asset data model query against the asset data model comprising analyzing asset data model component metrics by querying the device element data set, based on the updated first data model link, and generate an asset data model descriptive analytics determination output, based on the execution of the asset data model query.