Building Digital Twin Visualization for Predictive Update Recommendations
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Solution Overview
Problem
Existing building management systems face challenges in efficiently processing and adapting to changing circumstances due to time-consuming algorithm development and lack of flexibility, with users struggling to conceptualize data outputs in relation to physical building components.
Innovation Solution
Implementing a digital twin system that includes a virtual representation of a building with an AI-driven interrelationship between entities and data points, generating predictive inferences and recommendations for future updates, displayed graphically within the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional analytic and control algorithms are developed and deployed in building management systems, then the system can process building data, but the development and deployment process becomes time-consuming and requires significant software development effort
Solution Approach 1:
The patent creates a digital twin - a virtual copy of the building that includes all physical components, data points, and relationships. This digital replica allows analysts to develop and test algorithms in the virtual environment without affecting the actual building, significantly reducing development time and enabling parallel development of multiple algorithm scenarios
Solution Approach 2:
The system pre-establishes the complete digital twin structure including all entities, data points, and interrelationships before algorithm development begins. This preliminary setup provides a ready-to-use framework that eliminates the need for repeated system configuration and allows immediate algorithm deployment and testing
2Productivity
If traditional analytic algorithms are used in building management systems, then data processing can be performed, but the system lacks flexibility to adapt to changing circumstances in the building
Solution Approach 1:
The digital twin is designed as a dynamic system where the virtual model continuously mirrors the actual building state. When physical building components are added, modified, or removed, the corresponding digital representations are automatically updated, allowing algorithms to adapt to changing building conditions without requiring system reconfiguration
Solution Approach 2:
The system establishes continuous feedback loops between the physical building and its digital twin through bidirectional data flow. Sensors in the building feed real-time data to the digital twin, and algorithm recommendations from the digital twin can be implemented in the physical system, creating an adaptive cycle that continuously improves system responsiveness to changing conditions
3Loss of information
If analytic and control algorithms generate output data in building management systems, then building insights can be obtained, but the output data becomes hard for users to conceptualize and relate to physical building components
Solution Approach 1:
The digital twin maintains precise correspondence between virtual data points and physical building components throughout the system. When algorithms generate insights about any building element, users can directly view the results in the context of the visualized digital twin, immediately understanding which physical component the data refers to without requiring separate documentation or interpretation
Solution Approach 2:
The system transforms abstract algorithmic output into spatially contextualized visual information within the digital twin environment. By rendering data insights as overlays on the visualized building model, the system adds a spatial dimension to the data, making it intuitive for users to locate and understand insights in relation to the physical building layout
Data Source
AI summary
A building system of a building operates to store a digital twin of the building in the one or more storage devices, wherein the digital twin further includes an artificial intelligence configured to generate a plurality of inference values of a data point for a plurality of future times, the data point related to the building by the interrelationship of the digital twin. The building system operates to generate a recommendation based on the plurality of inference values for the data point for the plurality of future times, the recommendation recommending making one or more updates to the building, and cause a graphic representation of the building to display an indication of the recommendation.