Ship Course Guidance via AIS Clustering and Network Generation
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Solution Overview
Problem
Conventional ship course guidance systems are costly due to sensor installation and maintenance, and they fail to provide optimal routes considering ship specifications, leading to unreliable navigation and inefficient fuel usage.
Innovation Solution
A course guidance method that utilizes AIS data to extract location information, plot ship locations on electronic navigational charts, generate ship course networks, and recommend optimal routes based on ship specifications, avoiding sensors and incorporating factors like weather and dangerous areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor-based technology is used to estimate marine environmental information and provide optimal course guidance, then route accuracy and navigation reliability are improved, but installation cost and maintenance cost increase significantly
Solution Approach 1:
The patent uses AIS data (a copy of ship position information) instead of direct sensor measurements to estimate marine environmental information. By processing copied data from multiple ships' AIS transmissions, the system achieves reliable course guidance without requiring expensive physical sensors on each vessel
Solution Approach 2:
The patent introduces a server-based processing system that acts as an intermediary between AIS data and course guidance recommendations. This intermediary collects, processes, and analyzes AIS data from multiple sources to generate optimal routes, eliminating the need for expensive onboard sensors while maintaining navigation reliability
2Device complexity
If conventional AIS data is used for ship location identification, then data collection cost is reduced, but data reliability deteriorates due to user entry errors
Solution Approach 1:
The patent applies feedback mechanisms by continuously collecting AIS data from multiple ships and using statistical analysis to identify and correct erroneous information. The system compares data across multiple sources and uses feedback loops to improve data accuracy over time, maintaining reliability while using low-cost AIS data
Solution Approach 2:
The patent merges AIS data from multiple ships and sources to compensate for individual data errors. By combining data from multiple vessels traveling similar routes, the system creates a more reliable dataset that overcomes the limitations of single-ship AIS data quality
3Productivity
If the shortest course method is used for course guidance, then movement distance is reduced, but ship specification considerations are insufficient leading to suboptimal fuel efficiency
Solution Approach 1:
The patent applies local quality by providing customized course recommendations tailored to each ship's specific characteristics (size, type, speed, fuel consumption rate). Instead of a universal shortest route, the system adjusts the optimal path based on the local qualities of individual vessels, improving fuel efficiency for each ship type
Solution Approach 2:
The patent makes the course guidance dynamic by continuously adjusting recommendations based on real-time ship specifications and conditions. The system adapts to changing ship parameters and environmental conditions, providing dynamically optimized routes rather than static shortest paths
Data Source
AI summary
Disclosed herein is a course guidance method for the efficient sailing of a ship. The course guidance method for the efficient sailing of a ship is performed by a ship's course guidance system, and may include: a per-ship location information extraction step of extracting location information for each ship from collected Auto Identification System (AIS) data; a ship location plotting step of plotting the location of the ship based on the location information on an electronic navigational chart; a clustering step of clustering points located within a predetermined area among a plurality of points plotted on the electronic navigational chart; a course network generation step of generating ship course networks using the clustered points; and a recommended course acquisition step of acquiring a recommended course for each ship based on the generated ship course networks.