Building Automation SRI Scoring With Semantic Model Assessment
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Solution Overview
Problem
Current methods for assessing a building's Smart Readiness Indicator (SRI) require manual inspection by experts, which is time-consuming and inefficient, especially for determining the automation and autonomy capabilities of buildings, as they need to manually inspect documents and systems to compute the SRI scores.
Innovation Solution
An automated method that gathers information from building automation systems and data sources, creates a semantic model, applies rules to compute SRI scores, and provides a guided user interface to ensure accurate and timely computation of automation and autonomy capabilities, integrating the results into higher-level systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection by experts is used to assess SRI, then accuracy of assessment can be maintained, but time consumption and effort increase significantly
Solution Approach 1:
The patent introduces an automated assessment system that acts as an intermediary between the building automation system data and the SRI calculation. This system automatically collects data from building systems, creates semantic models, and computes SRI scores without requiring manual expert inspection, thereby reducing time consumption while maintaining assessment accuracy through structured automated processes
Solution Approach 2:
The patent replaces the manual mechanical process of expert inspection with an automated computational system. The system uses software-based semantic modeling and automated rule application to substitute the manual assessment process, eliminating the need for physical inspection while maintaining assessment rigor through systematic automated evaluation
2Reliability
If manual assessment methods are used, then detailed expert judgment can be applied, but productivity and efficiency decrease
Solution Approach 1:
The patent enables the building automation system to self-assess its own SRI by automatically collecting data from its own components, creating semantic models of its operations, and computing its own capability scores. This self-service approach eliminates the need for external expert assessment while maintaining reliability through systematic automated evaluation of the building's actual operational data
Solution Approach 2:
The patent implements a feedback mechanism where the automated assessment system continuously monitors building automation system performance, compares actual operations against SRI criteria, and generates capability scores that provide feedback on the building's smart readiness. This enables ongoing productivity improvements through iterative assessment and identification of optimization opportunities
3Productivity
If automated computation is implemented, then time and productivity improve, but system complexity increases
Solution Approach 1:
The patent segments the automated assessment system into distinct modular components: data collection modules that gather information from specific building systems, semantic model creation modules that structure the data, rule application modules that evaluate against SRI criteria, and scoring modules that compute final results. This segmentation reduces overall system complexity by making each component independent and manageable while maintaining high productivity through coordinated operation of the modules
4Measurement precision
If comprehensive data collection from multiple sources is performed, then assessment accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent implements a universal semantic model framework that can handle multiple data sources and formats through a single standardized structure. The semantic model uses standardized ontologies and data schemas that can represent information from diverse building automation systems, weather data sources, and occupancy sensors uniformly, thereby improving data accuracy through comprehensive collection while reducing processing complexity through standardized universal data representation
Data Source
AI summary
A method for automatically computing the automation capabilities of a building that permits the automatic computation of the capabilities of a building with respect to in terms of automation and autonomy additionally guiding a Smart Readiness Indicator (SRI) score assessment for non-digitally available information (e.g., documents/schemas/plans) that leads to complete SRI computation for existing buildings, wherein the method facilitates automatic computation of scores for at least a subset of the services such that a system can periodically recompute the performance of a large number of buildings and can offer the actual status of a fleet of buildings in terms of automation and autonomy, where the guide for experts helps to compute the final SRI scores of buildings by helping to find the information for the missing scores that result in a significant reduction in the time needed for getting a clear picture of the building performance.