Dynamic Transport Assessment System for Vehicle Utilization
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Solution Overview
Problem
Current transportation systems lack an efficient method to dynamically assess and improve the performance level of vehicles based on real-time usage and maintenance needs, leading to suboptimal utilization and value retention.
Innovation Solution
A system that utilizes sensors and blockchain technology to collect data on vehicle performance, dynamically revise performance levels based on current use, and determine next uses such as leasing or selling, while providing actionable insights for maintenance and upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static assessment methods are used for vehicle performance, then the system is simple to operate, but the vehicle utilization and value retention are suboptimal
Solution Approach 1:
The patent implements a dynamic performance level assessment system that continuously updates vehicle performance levels based on real-time sensor data, usage patterns, and maintenance history. The performance level is not static but evolves over time through dynamic revision, allowing the system to adapt to changing vehicle conditions and provide up-to-date valuation for leasing or selling decisions.
Solution Approach 2:
The system incorporates feedback mechanisms where sensor data from vehicle operation, maintenance records, and usage patterns are continuously fed back into the performance level calculation. This feedback loop enables the system to learn from historical data and improve assessment accuracy over time, directly enhancing vehicle utilization and value retention through data-driven decisions.
2Measurement precision
If real-time data collection and dynamic revision are implemented, then the performance level accuracy is improved, but the data processing requirements and system complexity increase
Solution Approach 1:
The assessment system is segmented into multiple independent components: sensor data collection modules, performance level calculation engines, maintenance schedule generators, and decision support systems. Each component handles specific data processing tasks independently, reducing the complexity of the overall system while maintaining high measurement precision through specialized processing in each segment.
Solution Approach 2:
The patent introduces intermediary processing layers that aggregate and pre-process sensor data before feeding it into the performance level calculation system. These intermediaries filter, validate, and organize raw data from multiple sensors, reducing the computational burden on the core assessment engine while maintaining data accuracy and enabling scalable system architecture.
3Reliability
If maintenance schedules are optimized based on actual usage data, then the vehicle retention value is improved, but the requirement for continuous monitoring and data analysis increases
Solution Approach 1:
The system enables self-service maintenance scheduling where the vehicle's own sensor data and usage patterns automatically trigger maintenance recommendations without requiring external intervention. The performance level assessment system continuously monitors vehicle conditions and autonomously generates optimized maintenance schedules based on actual usage, reducing the need for manual monitoring while improving retention value through timely, data-driven maintenance decisions.
Data Source
AI summary
An example operation includes one or more of receiving, by a server, characteristics related to a transport within a time period, wherein the characteristics are related to a condition of the transport, an operation behavior of the transport and an upkeep of the transport, providing, by the server, one or more actions to perform by the transport to increase a level of the characteristics, and responsive to the level of the characteristics being increased, determining, by the server, a next use of the transport after the time period.