Server-Based Fleet Energy Tracking With State-Based Estimation
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Solution Overview
Problem
The challenge of accurately monitoring and optimizing energy consumption across fleets of electronic devices, such as printers and barcode scanners, is complicated by varying operational states and locations, leading to difficulties in assessing and mitigating their carbon footprint.
Innovation Solution
A server-based system that collects operational data from client devices, applies energy consumption estimation mechanisms based on device definitions, and transmits updated configuration settings to optimize energy use while minimizing performance impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If energy consumption is monitored and optimized across diverse electronic device fleets, then carbon footprint is reduced, but device complexity and measurement difficulty increase due to varying operational states and locations
Solution Approach 1:
The system segments the fleet management into distinct components: energy consumption definition storage, operational data collection, estimation mechanism application, and configuration setting transmission. Each component handles specific aspects of energy optimization, making the overall complex system manageable through modular organization of functions.
Solution Approach 2:
The server acts as an intermediary between client devices and the energy management system. It receives operational data from devices, applies estimation mechanisms to calculate energy consumption, and transmits optimized configuration settings back to devices, thereby mediating the complexity of energy optimization across diverse device fleets.
2Loss of information
If energy consumption is tracked across diverse device fleets, then energy usage visibility is improved, but measurement precision is compromised due to varying operational states
Solution Approach 1:
The system applies different estimation mechanisms based on the operational state parameters of each device. By changing the estimation approach according to the device's operational state (e.g., idle, active, sleep modes), the system maintains measurement precision across diverse operational conditions while achieving comprehensive energy consumption visibility.
Solution Approach 2:
The energy estimation system is dynamic, adapting its measurement and estimation approaches based on the operational state of each device. This dynamic adjustment allows the system to maintain precision for each device state while providing organization-wide energy consumption visibility through centralized collection and analysis.
3Use of energy by moving object
If configuration settings are updated to optimize energy use, then energy consumption is reduced, but device performance may be impacted
Solution Approach 1:
The system implements feedback by transmitting updated configuration settings to client devices based on energy consumption analysis, then monitoring operational data to assess the impact of these changes. This closed-loop feedback mechanism allows optimization of energy consumption while monitoring for performance degradation, enabling iterative refinement of energy-saving configurations.
Solution Approach 2:
The system applies partial optimization through configuration setting updates, implementing energy-saving measures selectively rather than universally. By applying optimization to specific operational states or device types where impact is minimal, the system reduces energy consumption while maintaining device performance for critical functions.
Data Source
AI summary
A method in a server includes: storing, in a memory of the server, an energy consumption definition corresponding to a client device, the energy consumption definition including: (i) a plurality of operational states of the client device, and (ii) an estimation mechanism for each operational state; receiving, from the client device, operational data including an active one of the operational states at the client device, and a time period associated with the active operational state; generating, based on the operational data and the estimation mechanism corresponding to the active operational state, an estimated energy consumption for the client device; in response to generating the estimated energy consumption, obtaining an updated configuration setting for the client device; and transmitting the updated configuration setting to the client device to alter energy consumption at the client device.