Mobile Object Brokerage System Correcting Remaining Life
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Solution Overview
Problem
Existing mobile object brokerage systems do not account for the remaining life of vehicles, leading to situations where the vehicle's life expires before the user's expected usable period.
Innovation Solution
A mobile object brokerage system that includes a desired-remaining-life condition recognition section, a candidate-mobile-object remaining-life recognition section, a remaining-life consumption degree recognition section, a corrected-remaining-life calculation section, and a transaction target mobile-object selection section, which together enable the system to match users with vehicles that meet their desired remaining life conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional brokerage systems match users with mobile objects based on basic information, then transaction efficiency is improved, but the reliability of matching is worsened because remaining life is not considered
Solution Approach 1:
The system performs preliminary recognition of remaining life for candidate mobile objects before the transaction matching process. The remaining life recognition section evaluates the remaining life of each candidate mobile object in advance, and this information is then used by the transaction target selection section to make reliable matching decisions that satisfy user requirements.
Solution Approach 2:
The system incorporates feedback loops where the remaining life consumption degree recognition section continuously monitors actual usage patterns and compares them with predicted consumption degrees. This feedback is used to refine and correct remaining life predictions, improving the reliability of matching over time through iterative optimization.
2Measurement precision
If the system calculates corrected remaining life using detailed use history information, then the precision of remaining life prediction is improved, but the device complexity is worsened
Solution Approach 1:
The system segments the complex remaining life prediction task into distinct functional sections: remaining life recognition section, remaining life consumption degree recognition section, and corrected remaining life calculation section. Each section handles a specific aspect of the analysis, processing different types of information independently before integrating results, which manages complexity while maintaining precision.
Solution Approach 2:
The remaining life consumption degree acts as an intermediary parameter that bridges raw use history information and final remaining life predictions. This intermediate metric simplifies the relationship between complex usage patterns and remaining life calculations, making the system more manageable while preserving prediction accuracy.
3Adaptability or versatility
If the system recognizes remaining life consumption degree based on actual use history, then the adaptability to user-specific usage patterns is improved, but the loss of time for data processing is worsened
Solution Approach 1:
The system implements a tiered approach where it first performs basic remaining life recognition without detailed consumption degree analysis for quick initial assessments. When higher precision is needed or time permits, it then performs the more time-consuming remaining life consumption degree recognition based on actual use history, balancing speed and adaptability.
Data Source
AI summary
A mobile object brokerage system includes a desired-remaining-life condition recognition section configured to recognize a desired-remaining-life condition of a user for a mobile object, a candidate-mobile-object remaining-life recognition section, a remaining-life consumption degree recognition section configured to recognize a remaining-life consumption degree on the basis of use history information about a mobile object by the user, a corrected-remaining-life calculation section configured to calculate a corrected remaining life obtained by correcting the remaining life of each of the plurality of candidate mobile objects with the remaining-life consumption degree, and a transaction target mobile-object selection section configured to select, as a target for a predetermined transaction with the user U, the candidate mobile object having the corrected remaining life that meets the desired-remaining-life condition.