Aerial Vehicle Task Allocation Using Dynamic Spot Pricing
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Solution Overview
Problem
Aerial vehicle delivery systems lack cost-effective pricing and scheduling methodologies for allocating aerial vehicles to user requests.
Innovation Solution
A method and system for task allocation of aerial vehicles using dynamic pricing, which involves generating a spot price for tasks, determining the expected time of completion, comparing user bids to the spot price, and scheduling task fulfillment if the bid exceeds the spot price, incorporating modules for spot price generation, expected time calculation, bidding, and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional fixed pricing is used for aerial vehicle allocation, then simplicity of pricing is maintained, but cost-effectiveness and optimization capability deteriorate
Solution Approach 1:
The patent implements dynamic pricing by continuously adjusting spot prices based on real-time supply and demand conditions, vehicle availability, and task characteristics. This transforms the static pricing model into a dynamic one that adapts to changing market conditions, thereby improving cost-effectiveness while maintaining operational simplicity through automated price generation.
Solution Approach 2:
The system changes the pricing parameter from fixed to variable by introducing spot prices that fluctuate based on multiple factors including vehicle availability, demand intensity, task urgency, and environmental conditions. This parameter transformation enables optimal pricing that reflects actual market conditions without requiring complex manual intervention.
2Device complexity
If manual allocation methods are used, then system complexity is reduced, but allocation efficiency and task fulfillment speed deteriorate
Solution Approach 1:
The patent implements a self-service allocation mechanism where the system automatically generates spot prices, receives bids from users, compares bids to spot prices, and allocates tasks to aerial vehicles without human intervention. This automated self-service approach significantly improves allocation efficiency while keeping the user interface simple and intuitive.
Solution Approach 2:
The system incorporates continuous feedback loops where allocation outcomes, bid responses, and market conditions are fed back into the spot price generation algorithm. This feedback mechanism enables the system to learn from past allocations and optimize future pricing and allocation decisions, improving efficiency without increasing perceived complexity for users.
3Productivity
If dynamic spot pricing is implemented, then cost optimization and allocation efficiency are improved, but pricing complexity and computational requirements worsen
Solution Approach 1:
The patent segments the pricing and allocation process into distinct modular components: spot price generation module, bid reception module, bid comparison module, and task allocation module. Each module handles a specific function independently, which reduces overall system complexity by breaking down the complex dynamic pricing problem into manageable, interchangeable components.
Solution Approach 2:
The system employs a universal spot price generation mechanism that handles multiple pricing scenarios and allocation decisions through a single integrated algorithm. This multi-functional approach reduces pricing complexity by providing a unified framework that adapts to different situations rather than requiring separate complex models for each scenario.
4Measurement precision
If real-time bid comparison is performed, then task allocation accuracy is improved, but processing time and computational load worsen
Solution Approach 1:
The patent performs preliminary actions by pre-generating spot prices based on current market conditions before receiving bids, and by pre-establishing comparison criteria. This allows the system to quickly compare incoming bids against pre-calculated spot prices without requiring complex real-time computations during the bid comparison phase, thereby reducing processing time while maintaining allocation accuracy.
Data Source
AI summary
The disclosure is directed to an autonomous method for dynamically providing a security policy. A method for task allocation of aerial vehicles includes: generating a spot price for a task to be performed by at least one aerial vehicle; determining an expected time for completion of the task by the at least one aerial vehicle; comparing a received bid to the spot price; and performing the task using the at least one aerial vehicle if the bid is greater than the spot price.