Hybrid Edge-Cloud Building Control for Adaptive Space Management
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Solution Overview
Problem
Building management systems (BMS) often fail to account for various factors affecting space characteristics like temperature, humidity, and occupancy, leading to inefficient control of equipment such as HVAC systems, lighting, and security systems.
Innovation Solution
A hybrid edge/cloud processing system where edge controllers analyze data locally and, if conditions are not met, request cloud controllers to analyze additional information from other spaces or domains, allowing for adaptive control schemes to be generated and implemented, incorporating neural networks for data analysis and policy compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all data analysis is performed by edge controllers using only local data, then response time is fast and system simplicity is maintained, but control accuracy and adaptability deteriorate due to lack of external information
Solution Approach 1:
The system divides control functions into two segments: edge controllers handle local real-time control using local data, while cloud controllers provide supplementary analysis using external data. This segmentation allows each component to operate within its optimal complexity range while achieving overall high accuracy.
Solution Approach 2:
The edge controller acts as an intermediary between local edge devices and remote cloud controllers. It receives local data, determines when external analysis is needed, requests cloud assistance, and integrates cloud recommendations with local control decisions, thereby achieving high accuracy without direct cloud-edge complexity.
2Measurement precision
If cloud controllers analyze all data with external information, then control accuracy and adaptability improve, but response time increases due to communication delays
Solution Approach 1:
The edge controller performs partial data analysis locally using available local data, and only requests cloud assistance when local analysis is insufficient or external information is needed. This partial action approach achieves high accuracy without always incurring cloud communication delays.
Solution Approach 2:
The edge controller preliminarily analyzes local data and determines whether cloud assistance is needed before initiating cloud communication. This preliminary assessment prevents unnecessary cloud requests and reduces overall response time while maintaining accuracy when cloud data is actually beneficial.
3Adaptability or versatility
If edge controllers use only local data for control decisions, then system simplicity is maintained and communication overhead is reduced, but adaptability to external conditions deteriorates
Solution Approach 1:
The edge controller is designed with multi-functionality: it can operate independently using local data for simple cases, and can also integrate cloud-provided external information for complex cases requiring higher adaptability. This universal design achieves high adaptability without requiring permanently complex control schemes.
Solution Approach 2:
The control scheme dynamically adjusts its complexity based on needs: using simple local-only control when sufficient, and transitioning to hybrid cloud-edge control when adaptability is required. This dynamic approach achieves high adaptability without permanently increasing system complexity.
4Productivity
If hybrid edge-cloud processing is implemented, then intelligence and efficiency improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The edge controller autonomously determines when cloud assistance is needed based on local data assessment, without requiring complex centralized scheduling or manual configuration. This self-service capability simplifies implementation by eliminating the need for complex system-wide coordination mechanisms.
Solution Approach 2:
The system changes operational parameters dynamically: switching between local-only and hybrid processing modes based on data characteristics and performance needs. This parameter-based flexibility achieves high operational efficiency without requiring permanently complex system architecture.
Data Source
AI summary
A method includes receiving, by an edge controller, data relating to a first space. The edge controller controls operation of an edge device affecting a characteristic of the first space and that is associated with a first building equipment domain. The method further includes analyzing, by the edge controller, the data to determine whether the data satisfies a condition. If the condition is satisfied, the edge controller controls operation of the edge device using the data. If the condition is not satisfied, the edge controller (a) transmits a request to a cloud controller to analyze the data based on information obtained by the cloud controller regarding at least one of a second space or a second building equipment domain, (b) receives a response to the request from the cloud controller, and (c) controls operation of the edge device using the response from the cloud controller.