Master-Slave ATM Power Management via Dynamic State Control
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Solution Overview
Problem
Existing ATMs and similar computing devices consume high amounts of energy continuously, leading to increased costs and CO2 emissions, as they lack the ability to manage energy usage based on peak times, usage, or geographical location.
Innovation Solution
A computer-implemented method where a master computing device communicates with multiple slave ATMs to determine which ATMs to put into standby or off mode, utilizing data on their status and capability, and employing machine learning to optimize power reduction while ensuring customer services are maintained.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ATMs are kept continuously powered on to ensure customer service availability, then service reliability is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the power state of ATMs based on real-time demand conditions. ATMs transition between active and standby states according to usage patterns, ensuring service availability when needed while reducing power consumption during low-demand periods. The master computing device continuously monitors and reconfigures the operational state of each ATM in the network.
Solution Approach 2:
The system performs preliminary actions by pre-positioning ATMs in standby mode during periods of low expected usage, while maintaining the capability to quickly activate them when demand increases. Usage pattern analysis enables the system to anticipate demand and adjust power states proactively rather than reactively.
2Adaptability or versatility
If multiple ATMs are kept active across an estate to serve customers, then service coverage is improved, but total power consumption increases
Solution Approach 1:
The system merges the control functions of multiple ATMs under a single master computing device that manages the entire network. By consolidating control and using centralized intelligence, the system can coordinate ATM states across the estate, ensuring adequate service coverage while optimizing total power consumption through shared decision-making.
Solution Approach 2:
The system applies partial action by keeping only the necessary number of ATMs active based on actual demand, rather than maintaining all ATMs in a continuous active state. The master computing device determines the optimal subset of ATMs to keep active, allowing the system to provide sufficient service coverage with minimal power consumption.
3Ease of operation
If ATMs operate without centralized control to maintain independence, then operational autonomy is improved, but energy management efficiency deteriorates
Solution Approach 1:
The master computing device acts as an intermediary between individual ATMs and the central control system. Each ATM maintains its operational independence while communicating with the master device, which coordinates power state decisions across the network. This intermediary approach preserves ATM autonomy while enabling centralized energy management optimization.
Solution Approach 2:
The system implements feedback loops where ATMs report their operational status and usage data to the master computing device, which then adjusts power state commands accordingly. This continuous feedback mechanism enables energy-efficient decision-making while maintaining the operational autonomy of individual ATMs, as each ATM responds to directed commands rather than operating in complete isolation.
Data Source
AI summary
A method, computing device, computing system and computer program which receive, by at least one first computing device of a plurality of computing devices, and from a master computing device in communication with each of the plurality of computing devices, at least one command for changing a state of at least one user interface device of the first computing device; and responsive to receiving the command, change a state of at least one said user interface device from a first state with a first power usage to a second state with a second power usage that is less than the first power usage, thereby reducing the power usage of the first computing device.