Underwater Cable Route Planning Using Bathymetry and Outage Data
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Solution Overview
Problem
Planning underwater cable routes is time-consuming and resource-intensive, often resulting in inefficient use of resources and potential environmental damage due to the complexity of considering physical, territorial, and regulatory constraints, as well as the challenge of identifying and repairing fault-prone areas.
Innovation Solution
A system that generates optimized underwater cable routes using bathymetry data, existing route data, and best practice rules, which includes machine learning models trained on efficiency parameters such as cable laying speed, type, outage rates, and cost, to minimize overall efficiency scores and distances, allowing for automated and efficient route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual route planning is used considering all constraints, then route reliability is improved, but planning time and human effort increase significantly
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on historical route data, bathymetry data, and outage patterns before actual route planning is needed. This pre-computed knowledge enables rapid generation of reliable routes without manual analysis during the planning phase, resolving the contradiction between thorough constraint consideration and planning time.
Solution Approach 2:
The patent replaces manual mechanical route planning with an automated machine learning system that processes bathymetry data, existing route data, and outage information to generate optimized routes. This substitution maintains high reliability through comprehensive data analysis while dramatically reducing planning time and human effort.
2Productivity
If comprehensive factor analysis is performed for route planning, then resource efficiency is improved, but complexity of the planning process increases
Solution Approach 1:
The machine learning system performs self-service by automatically analyzing multiple factors including bathymetry characteristics, cable laying speed variations, and outage patterns without requiring manual intervention. The system independently processes complex data relationships and generates optimized routes, improving resource efficiency while the automated nature reduces perceived complexity for users.
Solution Approach 2:
The system transforms complex multi-factor analysis into a streamlined process by changing parameters from manual constraint checking to machine learning model predictions. The model takes multiple input parameters (bathymetry data, existing routes, outage history) and automatically outputs optimized route parameters, resolving the contradiction between comprehensive analysis and process complexity.
3Ease of manufacture
If existing cable routes are used, then infrastructure cost is reduced, but vulnerability to outages increases
Solution Approach 1:
The system incorporates feedback from historical outage data and ship traffic patterns associated with cable faults into the route optimization process. By analyzing where outages have occurred and why, the machine learning model adjusts route recommendations to avoid previously problematic areas, enabling reuse of existing infrastructure while reducing outage susceptibility through data-driven route selection.
Data Source
AI summary
An underwater cable route planning technology is provided for automatically generating underwater cable routes using a model. In this regard, one or more processors may receive bathymetry data, and may also receive existing route data for a plurality of existing underwater cable routes. Based on the bathymetry data and the existing route data, a model for determining underwater cable routes may be generated. As such, when a request for an underwater cable route connecting a first location and a second location is received, the model may be used to generate one or more potential underwater cable routes based on the first location, the second location, and the bathymetry data.