Vehicle Allocation Device Selecting High-Learning Vehicles
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Solution Overview
Problem
Existing vehicle allocation methods do not effectively prioritize vehicles with advanced learning capabilities, leading to inefficient allocation and potential unfairness in user benefits.
Innovation Solution
A vehicle allocation device and system that selects vehicles with the highest degree of progress in learning for specific travel areas by using machine learning to analyze input and output parameters, such as air temperature and engine conditions, and communicates with vehicles and terminals to optimize allocation based on teacher data freshness and quantity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a vehicle with low degree of progress in learning is preferentially allocated, then the learning system can maintain balance across vehicles, but the user experiences fewer advantages in use
Solution Approach 1:
Instead of allocating vehicles with low learning progress (conventional approach), the patent inverts the selection criterion to allocate vehicles with high learning progress. The vehicle selector selects a vehicle having a relatively large degree of progress in learning based on teacher data, thereby providing users with better service quality while maintaining learning balance through centralized management.
2Use of energy by moving object
If the vehicle allocation device learns teacher data instead of individual vehicles, then the calculation load on vehicles is reduced, but the system requires centralized processing capability
Solution Approach 1:
The patent extracts the learning function from individual vehicles and concentrates it in the vehicle allocation device. The vehicle allocation device acquires teacher data from multiple vehicles and performs centralized learning, while individual vehicles only need to provide data and receive allocation instructions, significantly reducing their calculation load.
3Measurement precision
If the user specifies the area to be traveled, then the allocation accuracy is improved, but the user effort increases
Solution Approach 1:
The system enables self-service by allowing the vehicle allocation device to automatically estimate the area to be traveled based on the destination provided by the user. The area-to-be-traveled estimator acquires location information and determines the travel area without requiring explicit user specification, thereby maintaining allocation accuracy while reducing user effort.
Data Source
AI summary
A vehicle allocation device allocates a vehicle in response to a vehicle allocation request from a terminal of a user, and includes a vehicle selector configured to, when acquiring the vehicle allocation request, select a vehicle having a relatively large degree of progress in learning of a relation between input and output of a parameter depending on an area to be traveled by the user from a plurality of vehicles learning a relation between input and output of a parameter depending on a predetermined area, and output a vehicle allocation instruction to the selected vehicle.