Transportation Digital Twin for Mobile Element Position Updates
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Solution Overview
Problem
Transportation systems face challenges in capturing and utilizing subject matter expertise, leading to inefficiencies in maintenance prediction and operational agility, particularly due to the loss of knowledge when subject matter experts leave the workforce, and unexplored uses of sensor data for improving system uptime and responsiveness.
Innovation Solution
A digital twin system for transportation systems that includes a datastore and processors to create and update digital twins of mobile elements, such as workers and vehicles, using sensor data to optimize their positions and paths, and integrate AI and machine learning for predictive maintenance and route optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subject matter experts are relied upon for maintenance prediction and operational guidance, then expertise quality is improved, but knowledge loss occurs when experts leave the workforce
Solution Approach 1:
The patent creates digital twin copies of physical transportation assets that encapsulate operational data, maintenance history, and performance characteristics. These digital replicas serve as persistent knowledge repositories that remain available regardless of personnel changes, effectively copying critical expertise into machine-readable formats that can be continuously updated and queried.
Solution Approach 2:
The system performs preliminary data collection and analysis by continuously monitoring transportation assets and building comprehensive digital twins before failures or operational issues occur. This proactive approach captures expertise and operational patterns in advance, enabling predictive maintenance and operational optimization without requiring real-time expert intervention.
2Quantity of substance
If traditional sensor data collection is used in transportation systems, then data is captured, but unexplored uses of the data limit operational improvements
Solution Approach 1:
The digital twin platform serves multiple functions simultaneously: it monitors asset health, predicts maintenance needs, optimizes operational parameters, captures subject matter expertise, and enables scenario analysis. This multi-functional approach extracts maximum value from the same sensor data across different operational contexts, transforming raw data into diverse actionable insights.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from physical assets updates the digital twins, which then generate insights that feed back into operational decisions. This closed-loop approach enables real-time optimization of maintenance schedules, operational parameters, and resource allocation based on actual asset performance and condition.
3Loss of information
If real-time tracking of mobile elements is implemented, then operational visibility is improved, but system complexity increases
Solution Approach 1:
The patent merges the tracking of multiple mobile elements into a unified digital twin platform that consolidates position data, operational status, and maintenance information for all assets in one system. This integrated approach reduces complexity by eliminating siloed tracking systems and enabling centralized management of fleet-wide data through a common interface and analysis engine.
Data Source
AI summary
A system for representing attributes in a transportation system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin including real-world-element digital twins embedded therein. The transportation system digital twin corresponds to a transportation system. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the transportation system. The real-world-element digital twins include mobile-element digital twins. Each mobile-element digital twin provides a digital twin of a respective mobile element within the real-world elements. The one or more processors are configured to, for each mobile element, determine, in response to an occurrence of a triggering condition, a position of the mobile element, and update, in response to determining the position of the mobile element, the mobile-element digital twin corresponding to the mobile element to reflect the position of the mobile element.