Building Operation Rule Extraction Using Contextual Pattern Correlation
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Solution Overview
Problem
Existing building management systems lack automated methods for correlating context to patterns and extracting complex, context-sensitive operation rules, relying heavily on manual processes and human intervention, which increases integration costs and reduces efficiency in optimizing building operations.
Innovation Solution
A system and method that automatically identify patterns in operational data, correlate contextual attributes with these patterns, and derive operational rules using a pattern extractor, context correlator, and rule deriver, enabling the extraction of complex, context-sensitive building operation rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to analyze building operational data and derive component operation rules, then flexibility and adaptability are maintained, but integration costs increase and efficiency decreases
Solution Approach 1:
The system enables automated self-analysis of building operational data through pattern extractors and context correlators that automatically derive operation rules without manual intervention, allowing the building management system to self-optimize its operations while reducing integration costs and improving efficiency
Solution Approach 2:
Manual analytical processes are replaced with automated computational systems including pattern extractors, context correlators, and rule derivers that process building operational data electronically, substituting human labor with automated mechanical and computational systems to reduce costs and improve efficiency
2Productivity
If automated pattern recognition is implemented, then efficiency and energy optimization improve, but system complexity and difficulty of detecting patterns increase
Solution Approach 1:
Context correlators serve as intermediaries between raw operational data and pattern recognition algorithms, preprocessing and contextualizing data to make patterns more detectable while enabling automated energy optimization without requiring direct complex pattern detection in raw data
Solution Approach 2:
The automated system segments the complex task of pattern detection into distinct modular components including pattern extractors that identify specific patterns, context correlators that add contextual meaning, and rule derivers that create optimization rules, making each component's detection task simpler and more manageable
3Reliability
If complex context-sensitive rules are extracted automatically, then building operation optimization improves, but computational complexity and processing requirements increase
Solution Approach 1:
The complex rule extraction process is segmented into distinct automated modules including pattern extractors that identify operational patterns, context correlators that associate patterns with contextual attributes, and rule derivers that generate optimization rules, reducing computational complexity by breaking down the overall task into manageable segments
Solution Approach 2:
The system performs automated self-optimization by automatically extracting context-sensitive operation rules from building operational data without requiring external computational resources or manual intervention, improving reliability while managing computational complexity through autonomous operation
Data Source
AI summary
A method for generating an operational rule associated with a building management system includes identifying, with a processing device, a first pattern associated with a series of operational observations corresponding to a property of the building management system, correlating a first contextual attribute with the first pattern, and deriving the operational rule at least in part based on the first pattern and the first contextual attribute.