Hierachical building performance dashboard with key performance indicators alongside relevant service cases
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Solution Overview
Problem
Building management systems face overwhelming data volumes, making it difficult for operators to effectively monitor and identify performance issues within building systems, as existing solutions lack intuitive methods for drilling down into specific components and service cases impacting Key Performance Indicators (KPIs).
Innovation Solution
A hierarchical building performance dashboard is provided, allowing users to interactively display KPIs and related service cases, enabling users to drill down from high-level KPIs to identify components and service cases causing underperformance, with a processor-configured system to receive and analyze operational data, display hierarchical dashboard levels, and filter service cases based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If building management systems display all operational data, then complete information is provided, but the data volume overwhelms operators and makes monitoring difficult
Solution Approach 1:
The dashboard is segmented into multiple hierarchical levels (building level, system level, component level) that divide the overwhelming data into manageable portions. Operators can view aggregated KPIs at the building level and drill down to specific systems or components only when needed, preventing information overload while maintaining access to complete data.
Solution Approach 2:
The system extracts and highlights only the most relevant information (KPIs and associated service cases) at each hierarchical level, separating critical performance indicators from the full operational data set. This extraction allows operators to focus on key issues without being overwhelmed by comprehensive data.
2Difficulty of detecting and measuring
If the dashboard displays detailed service cases, then root cause identification is improved, but the complexity of the dashboard increases
Solution Approach 1:
The dashboard implements a nested hierarchical structure where building-level KPIs contain system-level details, which in turn contain component-level information and service cases. This nesting allows the dashboard to maintain a simple overall structure while providing access to detailed fault information through controlled drilling down, reducing perceived complexity while improving detection capability.
Solution Approach 2:
The system adds a hierarchical dimension to the dashboard organization, arranging information across multiple levels (building → system → component → service case) rather than presenting all data in a single flat view. This dimensional organization allows detailed service case information to be accessed systematically without overwhelming the interface.
3Measurement precision
If the system provides comprehensive monitoring of all building components, then performance tracking is improved, but the time required to analyze data increases
Solution Approach 1:
The system performs preliminary aggregation and analysis of operational data to generate KPIs and identify associated service cases before presenting information to operators. By pre-processing data to highlight only relevant performance issues and their root causes, the system maintains precise performance measurement while significantly reducing the time operators need to spend analyzing raw data.
Data Source
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AI summary
A dashboard having a plurality of selectable hierarchical dashboard levels is displayed, where a higher dashboard level of the dashboard displays a Key Performance Indicator (KPI) that represents an aggregation of a plurality of related KP's at a next lower dashboard level. The dashboard displays service cases that are related to one or more of the building system components of the building. The service cases displayed at the next lower dashboard level are identified as having a negative impact on at least one of the plurality of related KP's displayed at the next lower dashboard level and the service cases displayed on the higher dashboard level represent an aggregation of the service cases displayed at the next lower dashboard level.