Autonomous and semantic optimization approach for real-time performance management in a built environment
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Solution Overview
Problem
Conventional optimization methods for built environments, such as energy management in sports facilities, require expert intervention and are not scalable or autonomous, failing to leverage vast data for optimal performance due to reliance on facility-specific information and pre-determined objectives.
Innovation Solution
A semantic-driven performance management system that utilizes ontologies, machine learning models, and natural language inputs to enable non-expert users to optimize built environments in real-time, eliminating the need for expert intervention by interpreting user objectives and automating optimization processes through semantic interpretation and prediction services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional optimization methods are used, then facility-specific optimization can be achieved, but expert intervention is required and the system is not scalable
Solution Approach 1:
The system enables autonomous self-service optimization by automatically interpreting natural language objectives, selecting appropriate optimization algorithms, and executing optimization without requiring expert intervention. The semantic interpretation service autonomously translates user goals into technical optimization parameters, and the system self-configures the optimization process based on the built environment data and constraints.
Solution Approach 2:
The patent implements a universal optimization platform that can handle multiple types of built environments (buildings, sports facilities, industrial complexes) and multiple optimization objectives (energy efficiency, cost reduction, performance improvement) through a single integrated system. The semantic domain model and algorithm selection mechanism provide universality across different applications.
2Adaptability or versatility
If conventional optimization methods are used, then pre-determined objectives can be optimized, but the system cannot adapt to dynamic changes or leverage vast data
Solution Approach 1:
The system implements dynamic adaptability by continuously monitoring built environment data, automatically adjusting optimization parameters in real-time, and adapting to changing conditions such as occupancy patterns, weather conditions, and system performance variations. The semantic interpretation service dynamically translates evolving user objectives into updated optimization targets.
Solution Approach 2:
The system incorporates feedback mechanisms where optimization results and system performance data are continuously fed back into the semantic interpretation and algorithm selection processes. This enables the system to learn from past optimization outcomes, refine its understanding of objectives, and improve future optimization decisions based on actual system responses.
3Measurement precision
If expert intervention is required, then optimization can be performed, but the process is time-consuming and not autonomous
Solution Approach 1:
The system performs preliminary actions by pre-configuring semantic domain models, pre-training machine learning algorithms for algorithm selection, and pre-establishing optimization frameworks for different built environment types. This preliminary preparation enables rapid response to user objectives without requiring time-consuming expert analysis at the time of optimization.
Solution Approach 2:
The patent replaces the mechanical process of expert human analysis and decision-making with automated semantic interpretation services and machine learning-based algorithm selection. Natural language processing substitutes for expert understanding, and automated algorithm selection replaces expert judgment, dramatically reducing the time required while maintaining optimization precision.
4Reliability
If facility-specific information is used, then local optimization can be achieved, but the system lacks scalability and requires reconfiguration for each facility
Solution Approach 1:
The system achieves universality through a standardized semantic domain model framework that can represent diverse built environments using common ontological structures. The platform maintains scalability by using generic optimization algorithms that can be applied across different facility types while adapting to facility-specific characteristics through automatic parameter configuration.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for semantically contextualizing structured and unstructured data, extracting and making sense of domain knowledge, and performing semantic-driven optimization to provide generic, scalable, autonomous and real-time performance management within a built environment domain.