Building Equipment Analysis Components for Precise AI Servicing
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Solution Overview
Problem
Current building management systems face challenges in generating precise data for service operations, as existing AI models struggle to accurately respond to specific equipment conditions, requiring manual input adjustments and limited by computational resources and data complexity.
Innovation Solution
Implementing a method that uses generative artificial intelligence models to receive subject matter expert knowledge, create analysis components, and prompt AI networks for automated actions, leveraging techniques like k-means clustering and convolutional neural networks to process data from various sources, including unstructured formats, for precise equipment servicing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AI models are used to generate data for service operations, then the system can process equipment data, but the precision and accuracy of responses to specific equipment conditions deteriorate
Solution Approach 1:
The patent segments the AI processing into distinct components: a generative AI model that creates synthetic equipment data, a separate analysis module that processes this data, and a service operation generation module. This segmentation allows each component to be optimized independently, improving precision without proportionally increasing overall system complexity.
Solution Approach 2:
The patent introduces an intermediary generative AI model that acts as a mediator between raw equipment data and the analysis system. This intermediary transforms complex, unstructured equipment data into structured, standardized formats that are easier to analyze with precision, thereby improving measurement precision while managing the complexity through abstraction.
2Measurement precision
If manual input adjustments are made to improve AI model responses, then the accuracy of equipment condition analysis improves, but the time and labor required deteriorate
Solution Approach 1:
The system implements self-service through automated generative AI models that autonomously generate and process equipment condition data without requiring manual input adjustments. The generative model automatically adapts to specific equipment conditions and generates appropriate service operations, eliminating the need for manual intervention while maintaining high accuracy.
Solution Approach 2:
The patent applies preliminary action by pre-training the generative AI model with extensive equipment data and service knowledge before deployment. This preliminary preparation enables the model to accurately respond to specific equipment conditions without requiring manual adjustments during actual service operations, thereby reducing time loss while maintaining precision.
3Measurement precision
If more computational resources are allocated to process complex equipment data, then the accuracy of service operation generation improves, but the computational resource consumption deteriorates
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting the complexity and resource allocation of the generative AI model based on the specific equipment condition being analyzed. For routine conditions, simpler processing parameters are used, while complex anomalies trigger more intensive computational resources, thereby improving accuracy only when necessary and reducing overall computational resource consumption.
4Measurement precision
If the AI model is prompted with detailed analysis components, then the precision of service recommendations improves, but the complexity of data processing increases
Solution Approach 1:
The patent segments the detailed analysis components into modular, standardized prompts that are generated automatically by the generative AI model. These segmented prompts cover specific aspects such as equipment condition, historical data, and service recommendations, allowing the system to achieve high precision through structured, manageable components rather than monolithic complex processing.
Data Source
AI summary
A method for servicing building equipment using generative artificial intelligence models includes creating a set of analysis components that describe expected behaviors of the building equipment, combining multiple analysis components that satisfy a similarity criterion to form a concise set of analysis components, prompting a generative artificial intelligence model using the concise set of analysis components, and performing an automated action for servicing the building equipment based on the response of the generative artificial intelligence model.