Edge Building Software Deployment for Low-Carbon Workload Allocation

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Solution Overview

Problem

Existing building management systems face challenges in efficiently managing computing workloads, optimizing machine learning model performance, and enhancing sustainability, particularly in the context of on-premises and off-premises devices, without considering environmental impacts.

Innovation Solution

A method for processing compute activities in building management systems that involves determining the most suitable on-premises or off-premises devices based on workload characteristics and device capabilities, including edge devices, and optimizing workload distribution to minimize carbon emissions and improve performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If computing workloads are processed using traditional building management systems, then basic computing tasks can be performed, but system efficiency and sustainability are compromised due to lack of intelligent workload distribution

Engineering Contradiction:

Improvecomputing workload processing efficiencyVSAvoidcarbon emissions

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system dynamically assigns computing workloads to different devices based on real-time device characteristics, workload characteristics, and environmental conditions. The workload assignment is not static but adapts continuously to optimize both processing efficiency and carbon emissions, resolving the contradiction between productivity and energy loss.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes multiple parameters simultaneously including device selection, workload partitioning ratios, and timing of processing operations. By adjusting these parameters based on device capabilities and environmental factors, the system achieves optimal balance between computing efficiency and sustainability.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If computing workloads are concentrated on single on-premises devices, then data security and response time are improved, but device overload and reduced sustainability occur

Engineering Contradiction:

Improvedata security and response timeVSAvoidsystem sustainability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system segments computing workloads into multiple parts and distributes them across different devices. Critical functions requiring fast response remain on-premises while less time-sensitive tasks can be processed elsewhere, maintaining reliability while improving overall system sustainability through balanced resource utilization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system enables multiple devices to perform computing functions based on their capabilities. Different devices can handle different types of workloads, making the system more versatile and sustainable while maintaining the ability to provide secure and timely responses when needed.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Speed

If computing resources are fully utilized on on-premises devices, then processing speed is maximized, but energy consumption and carbon footprint increase

Engineering Contradiction:

Improveprocessing speedVSAvoidcarbon footprint

Core Design Contradiction:

SpeedVSLoss of energy

Solution Approach 1:

The system introduces an intelligent workload assignment mechanism that acts as an intermediary between computing tasks and execution devices. This mediator optimizes the balance between processing speed and energy consumption by selecting appropriate devices and timing based on current conditions, resolving the contradiction between speed and carbon footprint.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Measurement precision

If machine learning models are continuously trained and updated, then model performance is improved, but computing resource consumption and operational complexity increase

Engineering Contradiction:

Improvemachine learning model performanceVSAvoidoperational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system applies partial training actions by selecting which models to retrain based on performance thresholds and operational needs. Instead of continuously training all models, it performs targeted retraining only when necessary, improving model performance while controlling operational complexity and resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12524273B2Building management systems with intelligent deployment and configuration

Publication Date: 2026.01.13 TYCO FIRE & SECURITY GMBH

AI summary

A method for testing software in an edge building device of a building, the method includes executing, by one or more processors of the edge building device, a first version of the software to perform a first set of one or more processing tasks for the edge building device, and testing, by the one or more processors of the edge building device, a second version of the software configured to perform a second set of one or more processing tasks for the edge building device, testing the second version of the software including: executing the second version of the software together with execution of the first version of the software on the edge building device, and evaluating, by the one or more processors of the edge building device or by causing a separate computing device to perform the evaluation, a performance of the second version of the software.