AI Convoy Control Using Digital Twins for Following Vessels
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Solution Overview
Problem
Existing convoy management systems for waterborne vessels rely excessively on natural water sources, lack real-time data integration, and predictive capabilities, leading to inefficiencies, increased collision risks, and resource wastage due to outdated technologies.
Innovation Solution
A control system utilizing artificial intelligence models with digital twins of lead and following vessels, and the waterbody, to simulate missions, generate control data, and adjust operations dynamically based on real-time environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing convoy management systems rely on natural water sources for operation, then the systems can maintain basic navigation functions, but water availability becomes a limiting factor that reduces reliability and operational continuity
Solution Approach 1:
The patent extracts the dependency on natural water sources by implementing artificial recharge systems that draw water from alternative sources (seas, oceans, lakes) and transport it to the canal system, thereby separating operational reliability from local water availability
Solution Approach 2:
The patent introduces intermediate water storage facilities and artificial recharge mechanisms as mediators between alternative water sources and the canal navigation system, enabling continuous operation independent of natural water source availability
2Device complexity
If outdated technologies are used for convoy management, then system complexity is reduced, but the ability to integrate real-time data and predict maritime conditions deteriorates, increasing collision risks
Solution Approach 1:
The patent implements a unified convoy management system that performs multiple functions (real-time data integration, predictive analytics, route optimization, collision avoidance) through a single integrated platform, managing complexity while enhancing reliability
Solution Approach 2:
The patent incorporates continuous feedback loops that collect real-time data from vessels and maritime environment, process it through predictive models, and adjust navigation recommendations dynamically, enabling proactive collision avoidance
3Measurement precision
If real-time data integration and predictive capabilities are implemented, then navigation precision and safety are enhanced, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex predictive system into modular components (data collection modules, processing modules, prediction modules, execution modules) that can be independently developed, tested, and maintained, reducing overall system complexity while maintaining precision
Solution Approach 2:
The patent performs preliminary data processing and predictive analysis before critical navigation decisions are required, pre-computing risk assessments and route optimizations to reduce real-time computational burden
4Productivity
If dynamic route adjustment and schedule creation are implemented, then operational efficiency is improved, but the ability to account for water depth variations and tidal changes requires more advanced monitoring
Solution Approach 1:
The patent implements continuous monitoring of water depth, tidal changes, and environmental parameters throughout the canal system, maintaining constant data flow that enables dynamic route adjustment without interruption to navigation operations
Solution Approach 2:
The patent replaces traditional mechanical depth measurement methods with advanced sensor systems and predictive modeling that continuously track water levels and predict changes, enabling more precise and responsive operational adjustments
Data Source
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AI summary
Disclosed is a method for generating control data for automated, computerized control of following vessel (204, 306) in convoy of waterborne vessels sailing in body of water using an artificial intelligence model, method comprising: providing first digital twin (DT) of lead vessel (202, 302) and second DT of following vessel, providing third DT of body of water, providing mission specific information for lead vessel, simulating mission using mission specific information, AI model, and one of following: first DT (304) of lead vessel, second DT of following vessel, third DT of body of water, executing first part of mission, generating control data based on simulation and execution of first part of mission, calculating correlation factor between simulation and execution of first part of mission, and when correlation factor is higher than predetermined value, then using generated control data and second DT of following vessel for controlling following vessel.