Building Automation Semantic Modeling for Automated Analytics Setup
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Solution Overview
Problem
Building automation systems require automated configuration of analytics to efficiently process large amounts of data from tens of thousands of sensors and devices across millions of buildings, which is currently hindered by the manual and costly creation of semantic models.
Innovation Solution
A system and method for automatically deriving semantic models for devices in building automation systems using context discovery algorithms, processing device information to identify properties, and generating property role filters and parameter values, enabling automated analytics configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of semantic models is used, then model accuracy and precision are improved, but configuration time and cost increase significantly
Solution Approach 1:
The system enables automated self-configuration of semantic models by having the building automation system automatically discover devices, extract properties, and generate semantic models without requiring manual human intervention. The system serves itself by autonomously populating the semantic model database with device information and relationships.
Solution Approach 2:
The system performs preliminary actions by pre-defining property role filters and parameter values that can be automatically applied during the semantic model generation process. This allows the system to prepare configuration templates in advance that guide the automated model creation, ensuring accuracy without manual intervention.
2Productivity
If automated configuration is implemented, then productivity and speed are improved, but the complexity of the configuration system increases
Solution Approach 1:
The automated configuration system is divided into distinct functional modules: device discovery module, property extraction module, semantic model generation module, and validation module. Each module handles a specific aspect of the configuration process, making the overall complex system manageable through clear separation of concerns while maintaining high productivity.
Solution Approach 2:
The system introduces an intermediary semantic model database that acts as a bridge between raw device data and analytics applications. This intermediary layer standardizes the data representation and provides a uniform interface for automated configuration, reducing the apparent complexity for end users while enabling high-speed automated processing.
3Manufacturing precision
If comprehensive property filtering is applied, then analytics configuration precision is improved, but processing time increases
Solution Approach 1:
Property role filters and parameter values are pre-defined and stored in the semantic model database before analytics configuration is needed. This preliminary preparation allows the system to quickly retrieve and apply appropriate filters during automated configuration, maintaining high precision without adding processing time during actual analytics deployment.
Solution Approach 2:
The system applies different levels of filtering precision to different properties based on their specific requirements. Critical properties receive more rigorous filtering and validation, while less critical properties use simpler filtering rules. This localized approach to quality control maintains overall configuration precision while minimizing unnecessary processing time.
Data Source
AI summary
An approach and system incorporating obtaining a semantic model for a device, identifying properties from the semantic model, processing the properties, obtaining a property role, identifying a property role filter that correlates to the property role, and generating parameter values associated with the property role filter.