Building Data Batch Metrics Using Space Hierarchy Siblings
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Solution Overview
Problem
In large building management systems (BMS) with complex hierarchies, the computation of metrics across multiple spaces and time aggregations becomes cumbersome, leading to slow user interfaces, delayed key performance indicator calculations, and difficulties in applying business rules.
Innovation Solution
A BMS that includes a space hierarchy database to store sibling relationships among points, a batch metrics engine to aggregate data samples from sibling points, and a controller to adjust building equipment operations based on calculated aggregate metrics, facilitating efficient metric calculation and user interface updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional metric computation methods are used in large BMS with complex hierarchies, then complete metric calculation across all spaces and time aggregations is achieved, but computational load increases and processing speed decreases
Solution Approach 1:
The patent segments the complex hierarchy into parent spaces and child spaces, allowing metrics to be calculated and stored at each level independently. The batch metrics engine processes metrics hierarchically, computing child space metrics first, then aggregating them to parent spaces. This segmentation reduces the computational complexity by breaking down the overall calculation into smaller, manageable segments that can be processed in batches.
Solution Approach 2:
The patent implements preliminary action by pre-calculating and storing metrics in the metrics database before they are requested. The batch metrics engine continuously computes metrics for all spaces and time aggregations in advance, storing results in the metrics database. When user interfaces or business rules need metric values, they are retrieved directly from the database without performing complex real-time calculations, thus improving response speed and reducing computational load during operation.
2Reliability
If real-time metric calculation is performed for all spaces and time aggregations, then up-to-date metrics are available, but user interface responsiveness deteriorates due to computational overhead
Solution Approach 1:
The batch metrics engine performs preliminary calculation of all metrics for every space and time aggregation in advance, storing the results in the metrics database. This ensures that when user interfaces request metric data, the values are already computed and immediately available, eliminating real-time computational delays and ensuring both reliability and responsiveness.
Solution Approach 2:
The system creates copies of metric data at different hierarchical levels (parent spaces and child spaces) and stores them in the metrics database. Instead of calculating metrics on-demand from raw data, the system retrieves pre-computed copies, which significantly reduces the time required for user interface operations while maintaining data reliability.
3Loss of information
If metrics are calculated for every space and time aggregation level, then comprehensive analytics are achieved, but processing time increases
Solution Approach 1:
The patent segments the metric calculation process by space hierarchy (parent and child spaces) and time aggregation levels. The batch metrics engine processes each segment independently, calculating metrics for child spaces first, then aggregating to parent spaces. This segmentation allows comprehensive metric coverage across all levels while reducing overall processing time through parallel batch processing of discrete segments.
Solution Approach 2:
The system performs preliminary calculation of comprehensive metrics for all spaces and time aggregations in advance through batch processing. By computing all required metrics beforehand and storing them in the metrics database, the system ensures complete metric availability without incurring processing delays during actual use, as all calculations are completed in advance batches.
4Measurement precision
If traditional computation methods are used, then accurate aggregate metrics are calculated, but system performance deteriorates under complex hierarchies
Solution Approach 1:
The patent segments the complex hierarchy into manageable parent-child space relationships, allowing the batch metrics engine to process accurate aggregate calculations in smaller batches. By breaking down the overall aggregation into sequential segments (child spaces first, then parent spaces), the system maintains measurement precision through systematic processing while improving system performance through reduced computational overhead at each step.
Solution Approach 2:
The batch metrics engine performs preliminary accurate calculation of aggregate metrics for all spaces and stores them in the metrics database. By pre-computing the exact aggregate values in advance through systematic batch processing, the system ensures measurement precision is maintained while avoiding the performance deterioration that would occur during real-time operations with complex hierarchies.
Data Source
AI summary
A building management system includes a plurality of meters configured to provide data samples of a plurality of points relating to a building and a space hierarchy database configured to store a sibling relationship for each of the points. Each sibling relationship identifies two or more of the points as sibling points. The building management system includes a batch metrics engine configured to receive a first data sample of a first point, access the sibling relationship for the first point to identify one or more sibling points of the first point, aggregate the first data sample with data samples of the sibling points to generate a batch, and calculate an aggregate metric using the first data sample and the other data samples in the batch. The building management system also includes a controller configured to adjust an operation of building equipment based on the aggregate metric.