AI Building Configuration Data for BMS Retrofit Accuracy
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Solution Overview
Problem
The configuration management of building management systems (BMS) during modernization or retrofitting is complex, time-consuming, and prone to errors, leading to potential malfunctions due to different data structures and capabilities among various BMS providers, necessitating a more efficient method for generating accurate system configuration data.
Innovation Solution
A method utilizing an AI processing component, particularly based on machine learning and large language models, processes building data and instruction data to generate configuration data for BMS or other systems, accurately reflecting the technical relationships and setup of building devices, enabling precise system configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration methods are used for BMS modernization or retrofitting, then configuration data can be obtained, but the process becomes complex, time-consuming, and prone to errors
Solution Approach 1:
The patent replaces manual configuration processes with an AI-based automated system that uses machine learning and large language models to generate configuration data. This substitution of mechanical/manual operations with intelligent automation directly reduces time consumption while improving accuracy by eliminating human error in configuration tasks.
Solution Approach 2:
The AI processing component autonomously generates configuration data by processing building data and instruction data without requiring manual intervention. The system self-configures by automatically understanding building layouts, device relationships, and technical requirements, thereby reducing both time and potential errors associated with manual configuration.
2Adaptability or versatility
If different BMS providers use different data structures and capabilities, then each system can be optimized for its specific function, but configuration management becomes more complex and error-prone
Solution Approach 1:
The AI processing component is designed to handle multiple BMS provider data structures and capabilities through a universal processing framework. By using large language models trained on diverse BMS data formats, the system can adapt to different providers while maintaining consistent configuration output, thereby reducing complexity despite varying input formats.
Solution Approach 2:
The patent introduces an AI-based intermediary layer that sits between different BMS providers and the configuration management process. This intermediary automatically translates and harmonizes different data structures into a unified configuration format, reducing complexity while maintaining compatibility with various providers through intelligent data transformation.
3Measurement precision
If comprehensive building data is processed to ensure accurate configuration, then system reliability improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by pre-training large language models on extensive building data and configuration patterns before actual configuration generation. This preliminary action enables the AI to quickly and accurately process specific building cases without requiring exhaustive analysis each time, thereby maintaining high accuracy while improving generation speed.
Solution Approach 2:
The patent dynamically adjusts processing parameters of the AI model based on the complexity and size of the building data. By changing parameters such as model depth, processing granularity, and inference settings, the system optimizes the balance between processing comprehensive data for accuracy and maintaining productive generation speeds for different scenarios.
Data Source
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AI summary
The disclosure relates to a method (100) for obtaining configuration data (66) indicative of a system configuration of a system (22, 70) for one or more buildings (10), the method (100) comprising: - obtaining building data (63) of the one or more buildings (10); - obtaining instruction data (65) comprising one or more instructions for processing the building data (63) by an Al processing component (44) for obtaining the configuration data (66); - providing the building data (63) and the instruction data (65) for processing by the Al processing component (44); and - obtaining the configuration data (66) based on the building data (63) and the instruction data (65) from the processing by the Al processing component (44).