Building Digital Twin Graph Modeling for BACnet IoT Integration
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Solution Overview
Problem
Current building automation systems face challenges in accurately modeling many-to-many relationships between physical entities in commercial buildings, limited by parent-child relationship models and centralized architectures that are prone to bottlenecks and complexity, and lack effective integration of IoT devices across different communication protocols and manufacturers.
Innovation Solution
A contextually-aware digital twin system utilizing a graph database representation that decouples devices and controllers, enabling accurate modeling of relationships and integration of IoT devices through a label property graph and actor model, with automatic discovery and normalization of devices and data, and edge-based analytics for real-time control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a parent-child relationship model is used to model building entities, then the system structure is simple to implement, but it cannot accurately represent many-to-many relationships between physical entities
Solution Approach 1:
The patent segments the building automation system into multiple independent graph components (devices, controllers, assets, spaces, floors, building) that can represent many-to-many relationships. Each entity type is a separate node category in the graph database, allowing flexible relationships without hierarchical constraints.
Solution Approach 2:
The patent introduces an intermediary graph database structure that mediates between physical entities and their relationships. The graph database acts as a mediator layer that can represent complex many-to-many relationships without requiring direct hierarchical connections between all entities.
2Device complexity
If a centralized architecture is used for building automation systems, then data management is simplified, but the system becomes prone to bottlenecks and complexity
Solution Approach 1:
The patent divides the centralized system into distributed edge computing nodes that process data locally. Each edge device runs analytics and control functions independently, segmenting the computational load and eliminating the single-point bottleneck of centralized architecture.
Solution Approach 2:
The patent adds a spatial dimension to the architecture by distributing computing resources across multiple physical locations (edge devices at different building sites). This transforms the system from a single-dimensional centralized model to a multi-dimensional distributed network, improving performance through parallel processing.
3Adaptability or versatility
If IoT devices from different manufacturers and protocols are integrated, then system versatility is improved, but integration complexity and data normalization difficulty increase
Solution Approach 1:
The patent introduces a protocol translation layer and standardized data model as intermediaries between diverse IoT devices and the core system. The graph database uses universal node types and relationship patterns that can represent any device type, acting as a mediator that normalizes data from different protocols without requiring complex point-to-point integration.
Solution Approach 2:
The patent creates a universal device representation model where all IoT devices are mapped to common graph node types (device, controller, asset) regardless of manufacturer or protocol. This universal model allows any device to be integrated through standardized relationship patterns, eliminating the need for device-specific integration logic.
4Measurement precision
If detailed tracking of all building entities is implemented, then analytics precision is improved, but network load and data management burden increase
Solution Approach 1:
The patent performs preliminary data processing and filtering at the edge devices before transmitting data to the cloud. Analytics are computed locally where possible, and only essential aggregated data is transmitted over the network, reducing network load while maintaining analytics precision through pre-computed insights.
Solution Approach 2:
The patent extracts and processes critical analytics functions at the edge, removing them from the centralized cloud system. This extraction of computation from communication reduces network traffic to only essential data exchanges, maintaining precision through local processing while minimizing network energy consumption.
Data Source
AI summary
IoT devices within a commercial real-estate or residential building environment may be connected through networks, such as a Building Automation and Control network (BACnet). Systems and methods according to this disclosure provide automatic discovery of IoT devices and relationships in commercial real-estate and residential buildings and integration of the BACnet devices into the digital twin of the building. In some implementations, an IoT gateway is configured to translate the communication received from the BACnet to an IoT cloud platform and configured to normalize the data across the different security platforms into a consistent format which enables integration and interoperability of the different building system platforms that may otherwise be operating in isolation from each other. Systems and methods according to the present disclosure provide edge based analytics and control of IoT BACnet devices based on defining conditions and rules, and provide integration of multiple building systems in the context of commercial real-estate and residential buildings.