Edge Device Self-Configuration for Context-Aware Building Control
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Solution Overview
Problem
Existing building management systems face challenges in accurately generating timely and relevant data due to limitations in language models, such as incorrect, imprecise, or irrelevant outputs, computational constraints, and lack of transparency, especially when processing unstructured data.
Innovation Solution
Implementing machine learning models, including language models trained with building knowledge graphs, to process unstructured data and generate accurate outputs by leveraging causal/semantic associations, and integrating automated and expert-based thresholds for improved data quality management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If language models are used to process unstructured data in building management systems, then data generation capability is improved, but computational demands and resource consumption increase
Solution Approach 1:
The patent segments the building management system into multiple edge devices distributed throughout the building. Each edge device independently processes unstructured data from local sensors and devices, eliminating the need for centralized processing of all data. This segmentation reduces the computational burden on any single device while maintaining overall data generation capability across the distributed system.
Solution Approach 2:
The patent introduces a scan agent as an intermediary component that operates on edge devices. The scan agent automatically discovers building components, retrieves their data structures, and configures them without requiring intensive centralized computation. This intermediary layer handles the complex processing tasks locally, reducing the computational demands on the overall system while maintaining high productivity in data generation and device configuration.
2Loss of time
If automated configuration is implemented using machine learning models, then device setup time is reduced, but system complexity increases
Solution Approach 1:
The patent implements self-service through automated scan agents that autonomously discover building components, retrieve their data structures from the building management system, and configure them without human intervention. The machine learning model automatically determines appropriate configuration parameters based on device type and location, eliminating manual setup time while keeping the system architecture relatively simple through rule-based automation rather than complex centralized control.
Solution Approach 2:
The system performs preliminary actions by pre-configuring device templates and data structures in advance. When a new device is detected, the scan agent retrieves pre-defined configuration templates and automatically applies them, significantly reducing setup time. This preliminary preparation of configuration data structures simplifies the overall system complexity by avoiding the need for complex real-time decision-making algorithms.
3Measurement precision
If context-aware configuration is implemented using scan agents and machine learning, then configuration accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by enabling each edge device to independently determine its own configuration based on its specific context (location, device type, building structure). The scan agent retrieves location-specific data structures and applies context-aware configuration parameters tailored to each device's environment. This localized configuration approach improves accuracy without requiring complex centralized control, as each device operates autonomously with context-specific parameters.
Solution Approach 2:
The system improves configuration accuracy by dynamically changing configuration parameters based on context information retrieved by the scan agent. The machine learning model adjusts parameters such as device thresholds, communication protocols, and operational settings based on the specific building context and device characteristics. These parameter changes are made locally at each edge device, improving configuration precision while maintaining relatively simple device architecture through parameter adaptation rather than structural complexity.
Data Source
AI summary
Systems and methods are disclosed for edge devices in building management systems that can perform automatic self-organization and configuration operations, including but not limited to in command and control contexts. For example, the edge devices can include a scan agent to detect context information relating to where the devices are located with respect to the building and/or other components in the building. A machine learning model can use the context information, along with current configuration or capability information of the device, to identify any software/firmware/application packages to install on the device to customize the device for effective operation relative to the building/other components in the building.