Building Management Data Tagging With Feedback-Guided Validation
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Solution Overview
Problem
The initial commissioning of new buildings and spaces within building management systems is time-intensive and costly, involving significant manual effort and potential for input errors due to the complexity of configuring and validating data for digital representations of physical spaces and equipment.
Innovation Solution
A system utilizing processing circuits with machine learning models to automatically tag and validate data points associated with building entities, such as equipment and spaces, by extracting context data, performing signal analysis, and receiving user feedback to improve tag accuracy and reduce manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual commissioning processes are used to configure and validate data for building management systems, then data accuracy can be ensured through human review, but the process becomes time-intensive and costly
Solution Approach 1:
The system performs preliminary automated tagging of data points with suggested tags before manual review, pre-processing the data in a way that reduces the subsequent manual validation workload while maintaining accuracy standards
Solution Approach 2:
The system implements feedback loops where user corrections to suggested tags are captured and used to continuously improve the machine learning model, enhancing both speed and accuracy over time through iterative learning from manual review feedback
2Reliability
If manual commissioning processes are used to configure building management systems, then complex configurations can be carefully validated, but significant manual effort and potential input errors occur
Solution Approach 1:
The system performs self-service through automated machine learning models that independently tag data points and validate configurations without requiring extensive manual intervention, reducing both effort and potential human errors while maintaining reliability
Solution Approach 2:
The patent replaces manual mechanical processes of data tagging and validation with automated machine learning algorithms and signal analysis, substituting human cognitive effort with computational processes that reduce errors and improve consistency
3Productivity
If automated machine learning models are used to tag data points, then commissioning speed increases, but initial model training and setup require significant time and resources
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with historical building data before deployment, and by automatically tagging data points with suggested tags immediately upon ingestion, reducing the perceived setup time through advance preparation
Solution Approach 2:
The system utilizes parameter changes by leveraging historical data parameters and confidence metric thresholds that can be adjusted to optimize the balance between automated tagging speed and accuracy, allowing flexible tuning of model performance without retraining
4Measurement precision
If all data points are manually reviewed for tagging accuracy, then tag validity is ensured, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system applies local quality by differentiating the level of review required for different data points based on confidence metrics, applying rigorous validation only where needed rather than uniformly to all data points, thus improving efficiency while maintaining accuracy where critical
Solution Approach 2:
The system performs partial action by automatically tagging data points with suggested tags and confidence metrics without requiring full manual review of each point, applying human validation selectively based on confidence thresholds rather than exhaustively to all data
Data Source
AI summary
A system for commissioning a model, comprising one or more processing circuits configured to identify a first plurality of data points in the building, automatically tag at least a portion of the first plurality of data points with one or more first tags using context data extracted from and/or associated with the data points, the one or more entities comprising one or more of building equipment, building spaces, people, or events, identify at least one of the first plurality of data points for manual review and generate one or more suggested tags for the at least one data point, receive feedback from the manual review, and receive a second plurality of data points in the building and automatically tag at least a portion of the second plurality of data points with one or more second tags using the feedback from the manual review.