Entity-Based Digital Twin Architecture for Integrated Building Data
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Solution Overview
Problem
Current building management systems face inefficiencies due to fragmented data management and lack of integration of real-time operational data with static building information, limiting effective decision-making and automation.
Innovation Solution
The implementation of an entity-based digital twin building management system that integrates as-built and operational data, using a centralized platform to create multi-dimensional digital twins, enabling interactive visualization, monitoring, and AI-driven management decisions across 2D, 3D, and AR environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If building management systems integrate multiple data sources and create comprehensive digital twins, then decision-making effectiveness improves, but system complexity increases
Solution Approach 1:
The system segments building management into discrete entities (rooms, equipment, systems) where each entity has its own digital twin. This segmentation allows comprehensive data integration at the entity level while maintaining manageable system complexity through modular architecture. Each entity's digital twin processes specific data types independently, then contributes to overall building insights.
Solution Approach 2:
The patent introduces a centralized platform as an intermediary layer between diverse data sources and management decision-making processes. This platform standardizes data integration, processes multiple data types (IoT sensor data, BIM data, operational data), and presents unified insights, thereby managing complexity while achieving comprehensive information integration.
2Productivity
If the system processes and analyzes data from multiple sources in real-time, then productivity improves, but use of energy increases
Solution Approach 1:
The system performs preliminary data processing and entity relationship analysis in advance, organizing data into structured digital twins before actual decision-making queries. This pre-processing reduces the computational burden during real-time operations, enabling faster decision-making with lower energy consumption during critical operations.
Solution Approach 2:
The patent applies local quality by processing and analyzing data at the entity level rather than centrally for the entire building. Each entity's digital twin processes relevant data locally, generating insights specific to that entity. This distributed processing approach reduces overall system energy consumption while maintaining high decision-making productivity through parallel processing capabilities.
Data Source
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AI summary
Systems, methods, and apparatus for building management are described. The methods, among other benefits, improve the decision-making effectiveness and efficiency of entity-based building management. An example method includes modeling and managing a building on an entity level by dividing the building and its related information into one or more entities, where a management result is presented in a two-dimensional (2D) representation, a three-dimensional (3D) representation, or an augmented reality (AR) representation. Another example method includes using a predictive artificial intelligence (AI) model to manage a building on an entity level by dividing the building and its related information into one or more entities, where the predictive AI model is a multi-input multi-output system.