Building Equipment Data Standardization Using Generative AI
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Solution Overview
Problem
Existing building management systems face challenges in generating precise and timely data for equipment servicing due to the difficulty in identifying appropriate response actions and sequences, especially with unstructured data from various sources, leading to inefficiencies in service operations.
Innovation Solution
Implementing a building management system that utilizes generative AI models, such as LLMs and transformer-based neural networks, to process unstructured data from technicians, standardize service reports, and generate structured data compliant with technical documents, enhancing data accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If unstructured data from technicians is processed manually, then data accuracy may be maintained through human judgment, but data generation efficiency and standardization are significantly reduced
Solution Approach 1:
The system enables automated self-service processing of service reports through generative AI models that automatically extract, standardize, and generate structured data from unstructured technician inputs, eliminating the need for manual data processing while maintaining consistency through predefined standardization rules
Solution Approach 2:
The generative AI model transforms data from unstructured format to structured format by changing the parameters of data organization, applying standardization rules that convert variable-length free-text inputs into fixed-schema outputs with standardized terminology and formatting
2Productivity
If generative AI models are used to process unstructured data, then data standardization and efficiency are improved, but system complexity increases
Solution Approach 1:
The generative AI model serves as an intermediary component between unstructured technician inputs and structured data requirements, handling the complexity of data transformation internally while presenting a simple interface to users and standardized outputs to downstream systems
Solution Approach 2:
The system replaces manual mechanical data processing operations with automated generative AI processing, substituting human cognitive operations with algorithmic transformations that handle extraction, standardization, and generation tasks through machine learning models
3Manufacturing precision
If manual processing of service reports is used, then system complexity is kept low, but data accuracy and consistency across different technicians are reduced
Solution Approach 1:
The generative AI model provides universal processing capabilities that handle multiple data types and formats from different technicians through a single standardized process, ensuring consistent application of standardization rules across all service reports regardless of source or format
Data Source
AI summary
A method includes receiving, by one or more processors, unstructured building equipment data characterizing one or more operations, specifications, or designs of building equipment. The unstructured building equipment data may not conform to a predetermined format or may conform to a plurality of different predetermined formats. The method may include extracting, by the one or more processors, a set of standards from one or more data sources associated with the building equipment. The method may include automatically generating, by the one or more processors using a generative AI model, structured building equipment data in the predetermined format for use in monitoring or controlling the building equipment. Generating the structured building equipment data may include standardizing the unstructured building equipment data to comply with the set of standards extracted from the one or more data sources associated with the building equipment.