Semantic Tagging for Building Automation Data Retrieval
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Solution Overview
Problem
Existing building automation systems (BAS) face challenges in efficiently managing and tagging data points, particularly due to the difficulty in manually identifying and applying semantic tags to entities like equipment, spaces, and events, which limits their functionality and requires significant user effort.
Innovation Solution
The method involves automatically suggesting and tagging data points with semantic descriptions, using context information and keyword-based suggestions to facilitate quick identification and querying of tagged data points, enabling the generation of user interface elements for real-time trend data display and automatic embedding within the user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging of data points with semantic descriptions is implemented, then data retrieval efficiency is improved, but user effort and time consumption increase significantly
Solution Approach 1:
The system automatically generates semantic tag suggestions for data points without requiring manual user input. The processor analyzes the data structure and autonomously proposes appropriate semantic descriptions, allowing the system to serve itself rather than relying on user effort for tagging.
Solution Approach 2:
The system performs preliminary tagging by generating semantic tag suggestions before the user needs to query the data. This advance preparation of semantic descriptions enables efficient data retrieval when needed, eliminating the need for users to manually tag data at the moment of need.
2Adaptability or versatility
If comprehensive semantic tagging is applied to all data points, then data functionality is enhanced, but system complexity increases
Solution Approach 1:
The system applies semantic tagging selectively to specific data points based on their individual characteristics and context within the data structure, rather than uniformly tagging all data. This localized approach enhances functionality where needed while avoiding unnecessary complexity in other areas.
Solution Approach 2:
The semantic tagging system serves multiple functions: it improves data retrieval efficiency, enables better data analysis, supports various query types, and enhances user understanding of building automation data. This multi-functionality justifies the system complexity by delivering diverse benefits from a single tagging mechanism.
3Productivity
If automatic suggestion and embedding of user interface elements is implemented, then operational efficiency is improved, but processing requirements increase
Solution Approach 1:
The system generates user interface element suggestions in advance based on the semantic tags and data structure analysis, so that when users need to interact with the data, the interface elements are already prepared and can be automatically embedded, improving operational efficiency without requiring intensive processing at the moment of user interaction.
Data Source
AI summary
A method of retrieving data using metadata tags including identifying, by a processing circuit from a data structure, a digital representation of a device deployed within a space, tagging, by the processing circuit, a data point associated with the digital representation of the device with a semantic description having a tag schema, receiving, by the processing circuit, a query including a partial string referencing the tag schema, identifying, by the processing circuit, the semantic description from a plurality of semantic descriptions based on the partial string of the query and the tag schema, retrieving, by the processing circuit, one or more tagged data points by querying the data structure using the semantic description, and automatically performing an operation using the one or more tagged data points.