AI Cable Mapping via Fiber Sensing and GPS Synchronization
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Solution Overview
Problem
Current systems lack an efficient method for automatically determining and mapping the location of field-deployed fiber optic cables, relying on outdated information and requiring significant labor and costs for telecommunications carriers and cable providers.
Innovation Solution
An AI-driven cable mapping system integrating fiber optic sensing technology with vehicle-assisted methods and environmental landmarks, utilizing supervised learning algorithms and deep neural networks to autonomously map fiber optic cables by synchronizing GPS data with distributed fiber optic sensing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to locate and map fiber optic cables, then deployment information can be obtained from construction maps and notes, but the information is outdated and requires significant labor and cost
Solution Approach 1:
The patent replaces manual mechanical surveying methods with an automated system using distributed acoustic sensing (DAS) technology and machine learning algorithms. The DAS system uses fiber optic cables as sensors to detect acoustic signatures of vehicles passing above them, automatically determining cable locations without manual intervention, thereby improving both accuracy and productivity
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw DAS sensor data and final cable location mapping. The model processes acoustic signals, identifies patterns corresponding to vehicle traffic near cables, and automatically generates updated location maps, eliminating the need for direct manual surveying while maintaining high accuracy
2Productivity
If no automated system is used, then labor costs are high, but implementing an automated system requires integration of multiple technologies
Solution Approach 1:
The patent makes the fiber optic cable serve multiple functions: it acts as both the communication transmission medium and the sensing element for location detection. This multi-functionality eliminates the need for separate sensing equipment, reducing system complexity while maintaining high productivity
Solution Approach 2:
The system uses existing infrastructure (deployed fiber optic cables and vehicle traffic) to automatically update cable location information. The cables essentially survey themselves by detecting acoustic signatures from vehicles passing near them, eliminating the need for dedicated surveying equipment or personnel while improving productivity
3Loss of information
If outdated construction maps are used, then initial mapping can be done, but the information becomes non-up-to-date and inaccurate
Solution Approach 1:
The patent implements continuous monitoring using the DAS system that operates ongoing to detect vehicle acoustic signatures and update cable location information in real-time. This continuous action ensures information remains current without requiring periodic manual resurveying, maintaining both information currency and automation
Solution Approach 2:
The system establishes a feedback loop where DAS sensors continuously monitor acoustic signatures, the machine learning model processes this data to detect changes in cable locations, and the system automatically updates location maps. This closed-loop feedback ensures information remains current and accurate over time
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate and efficient mapping of fiber optic cables with reduced labor costs, achieving high accuracy and enabling easy integration into GIS systems, with mapping errors reduced from 3.4% to 0.4% when using multiple field reference points.
Implementation Method 1
a distributed acoustic sensing system that detects vehicle traffic patterns
Implementation Method 2
a vehicle carries a Global Positioning System (GPS) device and drives along a roadway thereby following the fiber optic cable route
Data Source
AI summary
An AI-driven cable mapping system that employs distributed fiber optic sensing (DFOS) fiber sensing and machine learning that provides autonomous determination of fiber optic cable location and mapping of same. Designed Al algorithms operating within our inventive systems and methods provide an easy solution for cable mapping in a GIS system; automatically maps using landmarks and manhole locations; and employs a supervised learning algorithm. A vehicle-assist operation is employed wherein a vehicle carries a Global Positioning System (GPS) device and drives along a roadway thereby following the fiber optic cable route; data paring that provides further significant locational information wherein time synchronizes between the DFOS system and vehicle GPS device from which we automatically pair the data of fiber length from traffic trajectories and GPS coordinates by time series.