Dynamic Battery Scheduling for Mobile Base Stations During Power Outages
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Solution Overview
Problem
Existing mobile phone service optimization techniques during power failures do not account for user movement or usage rates of individual base stations, leading to inefficiencies in battery scheduling and prolonged waiting times for call acceptance.
Innovation Solution
A method that identifies affected base stations, calculates initial user numbers, generates user location and call probability models, schedules battery power operation, monitors user movement and calls, and updates these models to optimize battery power usage and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If battery power operation is scheduled based on static load information exchange between adjacent base stations, then power consumption is reduced, but user movement and individual base station usage rates are not accounted for leading to prolonged waiting times
Solution Approach 1:
The patent implements dynamic battery scheduling by continuously monitoring user movement patterns and base station usage rates, then adjusting battery power allocation in real-time. This transforms the static scheduling approach into a dynamic system that adapts to changing conditions, resolving the contradiction between energy conservation and service responsiveness.
Solution Approach 2:
The system establishes a feedback loop where user movement information and call acceptance data are continuously collected and used to update battery scheduling decisions. This feedback mechanism enables the system to respond to actual usage patterns, preventing both energy waste and service degradation.
2Productivity
If transmission power is adjusted based on network congestion state, then network load is balanced, but long-term battery scheduling capability is lost
Solution Approach 1:
The patent performs preliminary battery scheduling by predicting future user movement patterns and usage rates, then pre-configuring battery power allocation strategies. This allows the system to prepare for upcoming demand changes rather than merely reacting to current conditions, maintaining both service efficiency and battery duration.
3Adaptability or versatility
If load information is exchanged between adjacent base stations, then network optimization is achieved, but user-specific usage rates and movement patterns are not considered
Solution Approach 1:
The patent applies local quality by tailoring battery scheduling decisions to individual base station characteristics and local user patterns rather than using uniform regional scheduling. Each base station's battery operation is optimized based on its specific usage rate and the movement patterns of users in its coverage area.
Data Source
AI summary
Optimization of mobile telecommunications service during a power outage at one or more base stations, wherein optimization includes identifying one or more of a plurality of base stations to which non-emergency electrical power has been interrupted; determining an initial number of users in areas corresponding to the one or more of the plurality of base stations; generating a user location probability model and a user call probability model; scheduling initial battery power operation for the plurality of base stations; monitoring user calls and user movement after the battery power operation has started; updating the user location probability model and the user call probability model based on the monitoring; and updating battery power operation scheduling for the plurality of base stations.