UAV Fleet Distribution via Performance Metrics
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Solution Overview
Problem
Aerial transport service providers face challenges in efficiently distributing unmanned aerial vehicles (UAVs) across a large geographic area to meet changing demand, as existing methods do not effectively account for location-specific demand fluctuations and item provider performance metrics.
Innovation Solution
An aerial transport service provider system dynamically distributes UAVs amongst multiple deployment stations based on performance metrics, such as past usage, loading time, and customer feedback, to prioritize item providers with better performance, even if they offer lower monetary bids, ensuring efficient allocation and long-term market growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If UAVs are distributed based solely on monetary bids from item providers, then revenue is maximized, but service quality and customer retention deteriorate
Solution Approach 1:
The patent changes the allocation parameter from purely monetary bid amount to a composite metric that includes performance metrics (delivery success rate, customer feedback, loading time). This transforms the single-parameter bid-based allocation into a multi-parameter evaluation system, resolving the contradiction by weighing both revenue potential and service quality factors.
Solution Approach 2:
The system implements feedback loops where item providers' past performance metrics are continuously monitored and fed back into the allocation decision process. Providers with better historical performance receive preferential treatment in future allocations, creating a self-reinforcing mechanism that rewards quality service and improves overall system reliability while maintaining revenue generation.
2Ease of operation
If UAV capacity is allocated uniformly across all item providers, then fairness is maintained, but efficiency and responsiveness to high-demand providers deteriorate
Solution Approach 1:
The patent applies local quality by differentiating allocation rules based on each item provider's specific performance characteristics and demand patterns. Instead of uniform allocation, the system tailors capacity distribution to each provider's needs and historical performance, allowing high-efficiency providers to receive more capacity while maintaining fair treatment through transparent, metric-based criteria.
Solution Approach 2:
The allocation system transitions from static uniform distribution to dynamic allocation that adapts based on real-time and historical performance data. The capacity distribution automatically adjusts as item providers' performance metrics change, ensuring both fairness through consistent criteria and efficiency through responsive allocation to high-performing providers.
3Reliability
If more UAVs are deployed to high-demand locations, then demand satisfaction improves, but overall fleet utilization and redistribution efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-positioning UAVs at deployment stations based on predicted demand patterns and item provider performance metrics. Rather than reactively moving UAVs after demand arises, the system anticipates high-demand periods and locations, proactively allocating capacity in advance to ensure demand satisfaction while maintaining optimal fleet distribution.
Solution Approach 2:
The patent implements periodic redistribution of UAVs based on cyclical demand patterns and performance metric updates. The system regularly reassesses allocation needs and redistributes capacity across the fleet in scheduled intervals, ensuring that high-demand locations receive adequate UAVs while preventing over-concentration that would reduce overall fleet utilization efficiency.
Data Source
Figure 1A
Figure 1B~1C
Figure 1D
AI summary
Disclosed herein are methods and systems that can help an aerial transport service provider (ATSP) determine how to distribute and redistribute unmanned aerial vehicles (UAVs) amongst a plurality of UAV deployment stations located throughout a geographic area. In accordance with example embodiments, the ATSP system can take one or more performance metrics for item providers into account when determining how much UAV transport capacity to allocate to different item providers for a given time period. The ATSP can then determine how to distribute UAVs amongst different UAV nests in advance of and/or during the given time period, such that each item provider's allocated UAV transport capacity is available from the UAV nest or nest(s) that serve each item provider during the given time period.