Building Automation Metadata Tagging With User-Guided Auto Tags
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Solution Overview
Problem
Conventional building management systems (BMS) face inefficiencies in entity classification due to tedious manual tagging processes and lack of user control over automatically added metadata, which can lead to duplicate tags and complex tag dictionary construction.
Innovation Solution
A method for automatically tagging entities in a BMS using a processing circuit that identifies entities, determines associated tags based on a system library, and omits duplicates, allowing for user interface-driven additions and conversions between tag syntax formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual direct tagging is used to classify entities in BMS, then user control over tag selection is improved, but the time required for tagging increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating implied tags based on entity properties and relationships before user review. The tag dictionary pre-processes entity data to suggest appropriate tags, reducing the time users would otherwise spend manually selecting tags while preserving user control for final approval.
2Loss of time
If automatic implied tagging is used to classify entities in BMS, then the time required for tagging is reduced, but user control over tag selection is lost
Solution Approach 1:
The system implements feedback by presenting automatically generated implied tags to users for review and approval. Users can accept, modify, or reject the suggested tags, ensuring they maintain control over the final tagging while benefiting from the time-saving automatic generation process. The system learns from user corrections to improve future automatic tagging suggestions.
3Measurement precision
If a comprehensive tag dictionary is constructed to improve tagging accuracy, then tagging precision is improved, but the complexity of the system increases
Solution Approach 1:
The tag dictionary is segmented into multiple organized sections including entity-type mappings, relationship definitions, and hierarchical categories. This segmentation allows the system to manage complexity by breaking down the comprehensive tag dictionary into manageable, modular components that can be independently maintained and queried, while still providing high tagging precision through the collective coverage of all segments.
4Measurement precision
If manual tagging processes are used to ensure accurate entity classification, then tagging precision is improved, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically analyzing entity properties, relationships, and contextual data to generate appropriate tags without requiring manual intervention for each entity. The intelligent tagging engine autonomously queries the tag dictionary, evaluates matching criteria, and assigns tags based on predefined rules and machine learning algorithms, maintaining precision while dramatically increasing classification speed and productivity.
Data Source
AI summary
A method for tagging entities in a building automation system (BAS), the method including identifying, by a processing circuit, a first entity of one or more entities in a system library in response to receiving an indication to add the one or more entities to the BAS, wherein the system library includes a number of relationships between a number of tags and a number of entities. The method further including determining, by the processing circuit, one or more tags associated with the first entity based on the system library, determining, by the processing circuit, a tag type for each of the one or more tags based on a tag dictionary, and adding, by the processing circuit, the one or more tags to the first entity based on the tag type of each of the one or more tags.