Automated Asset Tagging for Similar Equipment in BMS and SCADA
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Solution Overview
Problem
Current building management systems (BMS) and Supervisory Control and Data Acquisition (SCADA) systems face challenges in managing complex environments due to their large size and dynamic changes, requiring labor-intensive manual semantic tagging of assets, which hinders efficient energy management and operational control.
Innovation Solution
A computer tool and method for automated grouping of assets in BMS and SCADA systems using AI algorithms to identify and tag similar equipment based on textual and numerical attributes, simplifying the asset tagging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual semantic tagging of assets is performed in BMS and SCADA systems, then asset management accuracy is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables self-service automated tagging by utilizing existing asset data, relationships, and operational patterns within the BMS/SCADA system to automatically generate and assign semantic tags without requiring manual intervention for each asset
Solution Approach 2:
The system creates copies of tagging patterns from previously tagged assets or template assets and applies them to similar assets through automated matching algorithms, eliminating the need to manually tag each asset individually
2Measurement precision
If manual semantic tagging of assets is performed in BMS and SCADA systems, then asset management accuracy is improved, but operational costs increase
Solution Approach 1:
The system leverages existing data infrastructure and relationships within the BMS/SCADA environment to perform tagging automatically, eliminating the need for external consulting services or specialized manual tagging operations
Solution Approach 2:
The automated tagging system serves multiple functions simultaneously: it tags assets, categorizes them, establishes relationships between assets, and enables various analytical applications, providing high value through a single integrated solution
3Adaptability or versatility
If the BMS and SCADA systems are expanded to manage larger facilities, then system capability is improved, but complexity of asset management increases
Solution Approach 1:
The system automatically segments assets into meaningful groups and categories based on their functional characteristics, relationships, and operational patterns, transforming the complex inventory into organized, manageable segments that scale with facility size
Solution Approach 2:
The automated tagging system acts as an intermediary layer between the raw asset data and the user interface, providing structured organization and semantic meaning that simplifies how users interact with and manage large numbers of assets
4Adaptability or versatility
If dynamic changes are made to the BMS and SCADA systems, then system adaptability is improved, but monitoring and understanding system impact becomes more difficult
Solution Approach 1:
The system continuously monitors asset relationships and tagging structures, providing feedback when changes occur that help users understand the impact of additions, modifications, or removals on the overall asset management framework
Solution Approach 2:
The system pre-establishes tagging structures, relationships, and categorization frameworks before changes occur, enabling automatic adaptation and impact assessment when new assets are added or existing assets are modified
Data Source
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AI summary
System and method for grouping like equipment in an AIC system. A textual label of the reference equipment is embedded in a first numeral value and textual attributes associated with each point associated with the reference equipment are embedded in a second numerical value. A textual label of at least one candidate equipment is embedded in a third numerical value and textual attributes associated with each point associated with the at least one candidate equipment are embedded in a fourth numerical value. The first and third numerical values are compared to one another to determine if there is a sufficient level of similarity. Responsive to determining there is a sufficient level of similarity between the first and third numerical values, the second and fourth numerical values are compared to one another to determine if there is a sufficient level of similarity. Group the reference equipment with the at least one candidate equipment, responsive to determining a sufficient level of similarity between the third and fourth numerical values.