Building Automation Component Mapping With Knowledge Graphs
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Solution Overview
Problem
Building automation systems face challenges in efficiently identifying and mapping components from different manufacturers, due to varying naming protocols, leading to complexity and human error in manual mapping processes, which are time-consuming and prone to errors.
Innovation Solution
A context generation system that uses learning algorithms to recognize patterns in name data and telemetry data, enabling the identification of equipment types, roles, and relationships independently of specific naming protocols, and creates a knowledge graph to establish uniform names and relationships between components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping processes are used to identify and map building automation system components from different manufacturers, then flexibility in handling various naming protocols is maintained, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The system automatically identifies and maps building automation system components by having the components self-identify through their data points and communication protocols. The platform autonomously creates the knowledge graph without human intervention, eliminating manual mapping errors and time consumption while maintaining manufacturer diversity.
2Adaptability or versatility
If components from different manufacturers are integrated without standardized naming protocols, then manufacturer diversity and system flexibility are preserved, but system complexity and difficulty in establishing relationships between components increase
Solution Approach 1:
The patent introduces a neutral intermediary platform that acts as a mediator between diverse manufacturer components. This platform translates various manufacturer-specific naming protocols into a universal language, creating a knowledge graph that standardizes relationships without requiring changes to the original manufacturer components, thus reducing system complexity while preserving compatibility.
Solution Approach 2:
The system creates a universal knowledge graph structure that can accommodate and translate multiple manufacturer-specific protocols simultaneously. This universal layer enables different manufacturers' components to be integrated into a single standardized framework, reducing overall system complexity while maintaining the ability to handle diverse naming conventions.
3Loss of information
If manual identification and mapping of building automation components is performed, then detailed understanding of each component can be achieved, but labor resources and human effort are significantly consumed
Solution Approach 1:
The patent replaces the manual mechanical process of component identification and mapping with an automated computational system. Machine learning algorithms and automated data extraction techniques substitute human analysts, enabling the system to process and understand component relationships at scale without consuming human labor resources, while maintaining comprehensive understanding of the building automation system.
Data Source
AI summary
Systems and methods for establishing relationships between building automation system components and controlling building automation system components. Data for a building automation system components may be received from the building automation system components and one or more models may be applied to the received data to determine types of the building automation system components and relationships between building automation system components. Once the types of building automation system components have been determined or identified, uniform names may be applied to the building automation system components. The received data may include, among other data, naming data and telemetry data from the building automation system components.