Spatial Movement Analysis for User Device Power Management
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Solution Overview
Problem
Employees traveling for work often face issues with user devices not functioning due to inadequate charging, leading to battery degradation, frequent replacements, and productivity losses, as well as device damage from frequent travel, resulting in operational costs and downtime.
Innovation Solution
A system that assesses spatial movement behavior using machine learning to assign a mobility factor, allowing for the reconfiguration of user devices to optimize power consumption and reduce wear, including switching to power-saving modes or restricting connectivity based on travel patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user devices are provided to personnel for work operations, then productivity is improved, but battery degradation and device damage occur due to inadequate charging and frequent travel
Solution Approach 1:
The system proactively assesses spatial movement behavior and predicts charging needs before battery depletion occurs. By analyzing travel patterns and location data, the system preemptively notifies users of upcoming charging requirements, allowing them to prepare and charge devices before critical low-battery situations arise, thus preventing productivity interruptions.
Solution Approach 2:
The system continuously monitors device location, movement patterns, and battery status, then provides real-time feedback to users about charging needs. This feedback loop enables users to make informed decisions about device charging timing and location, optimizing both device reliability and productivity by preventing unexpected device failures.
2Adaptability or versatility
If personnel travel to various locations to carry out operations, then work flexibility is improved, but charging availability decreases leading to inadequate charging
Solution Approach 1:
The system analyzes historical and real-time spatial movement data to predict future locations and identify charging opportunities before the user arrives. By notifying users in advance of upcoming charging locations along their travel route, the system enables them to plan charging stops proactively, maintaining device functionality despite frequent travel to various work locations.
Solution Approach 2:
The system autonomously monitors user travel patterns and automatically identifies optimal charging opportunities without requiring user input. It generates proactive notifications that guide users to charging locations based on their movement behavior, enabling users to self-manage device charging efficiently while maintaining work flexibility across multiple locations.
3Productivity
If user devices are used frequently during travel, then productivity is maintained, but device damage and hard disk damage increase
Solution Approach 1:
The system predicts device vulnerability periods based on travel patterns and proactively notifies users of upcoming charging and safe periods. By providing advance warning of when devices will be stationary and charged, users can plan important work operations during these protected periods, reducing the risk of damage during vulnerable travel states while maintaining productivity.
4Reliability
If battery replacement is performed frequently due to degradation, then device functionality is restored, but operational costs and downtime increase
Solution Approach 1:
The system continuously monitors battery health and usage patterns, predicting degradation trends before critical failure occurs. By providing early warnings of impending battery issues and identifying optimal replacement timing based on travel patterns, the system enables proactive battery replacement during convenient periods rather than reactive replacement during critical failures, minimizing downtime and operational disruption.
Data Source
AI summary
The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.