Synthetic Training Data for AI Building Risk Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing building management systems face challenges in accurately identifying and managing low-frequency risks, particularly in HVAC-R systems, due to limitations in data processing and the inability of current AI systems to generate precise, relevant data for specific conditions, leading to inefficiencies in fault detection and equipment servicing.
Innovation Solution
The implementation of machine learning models, including LLMs and generative AI systems, that process unstructured data to generate accurate outputs, integrate expert feedback, and automate data validation, enabling real-time risk identification and equipment servicing through a conversational interface and data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used in building management systems, then system complexity is reduced, but the ability to identify low-frequency risks and measurement precision deteriorates
Solution Approach 1:
The patent introduces synthetic data generation as an intermediary component between traditional data processing and AI model training. This intermediary layer creates realistic training scenarios for low-frequency risks without requiring actual historical data, thereby improving risk identification accuracy while avoiding the complexity of collecting and processing vast amounts of real-world data.
Solution Approach 2:
The system performs preliminary actions by pre-generating synthetic training data that covers rare and low-frequency risk scenarios before actual risk detection is needed. This allows the AI models to be pre-trained on comprehensive risk scenarios, improving their ability to identify low-frequency risks when deployed in real building management operations.
2Measurement precision
If more training data is collected for AI model configuration, then model accuracy improves, but data processing time and resource requirements increase
Solution Approach 1:
The patent applies copying by generating synthetic replicas of real-world building operation scenarios, including rare failure modes and risk conditions. Instead of collecting and processing extensive real data over long periods, the system creates artificial copies of training scenarios that capture the essential characteristics of low-frequency risks, thereby achieving model accuracy without the time cost of real data collection.
Solution Approach 2:
The synthetic data generation system changes parameters by systematically varying operating conditions, failure modes, and risk scenarios in generated training data. This allows comprehensive coverage of rare events through parameter variation rather than requiring extensive temporal data collection, reducing processing time while maintaining model accuracy.
3Adaptability or versatility
If comprehensive risk scenarios are covered in training data, then adaptability of the system improves, but the complexity of data generation and management increases
Solution Approach 1:
The patent implements universality by designing a synthetic data generation system that can produce multiple types of training scenarios across different building systems, equipment types, and risk categories using a unified framework. This single system serves multiple functions: generating operational data, failure scenarios, risk conditions, and recovery scenarios, thereby achieving comprehensive risk scenario coverage without proportionally increasing data generation complexity.
Data Source
AI summary
Systems and methods are disclosed relating to generating synthetic training data for artificial intelligence-based event detection and/or risk monitoring. A system can include one or more processors configured to receive a prompt identifying one or more characteristics of a training data image. The one or more processors can generate, using at least one machine learning model, the training data image based on the one or more characteristics. The one or more processors can provide the training data image as input to the at least one machine learning model to configure the at least one machine learning model using the training data image.