Optical Fiber Route Identification Using DFOS and AI Feedback
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Solution Overview
Problem
Existing optical fiber cable management systems lack efficient methods to locate and identify precise fiber routes, especially in outdated or missing layout maps, leading to inefficient maintenance processes.
Innovation Solution
Employ distributed fiber optic sensing (DFOS) technology combined with generative AI-based algorithms to analyze optical fiber routing and cable diversity, using route condition matched engines for interactive feedback and precise location data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If field technicians rely on paper records or memory to locate optical fibers, then the process can be performed with simple tools, but the time required and efficiency are severely degraded
Solution Approach 1:
The patent replaces manual mechanical methods (paper records, physical inspection) with an automated optical sensing system. The DFOS system uses optical backscatter signals to automatically detect and map fiber routes, eliminating the need for technicians to manually trace fibers through paper records or physical inspection, thereby dramatically improving efficiency and reducing time loss.
Solution Approach 2:
The system enables the fiber network itself to provide location information through intrinsic optical backscatter signals. The fibers automatically generate detectable signals that reveal their own routing information without requiring external manual intervention, allowing the system to self-identify its configuration and location.
2Measurement precision
If technicians disconnect and check each fiber at every circuit, then accurate fiber identification can be achieved, but the maintenance process becomes extremely inefficient
Solution Approach 1:
The patent replaces the mechanical process of disconnecting and physically checking each fiber with an automated optical sensing system. The DFOS system continuously monitors optical backscatter signals to automatically identify and track fiber routes through multiple circuits, maintaining 100% identification accuracy while eliminating the need for time-consuming manual disconnection and inspection procedures.
3Reliability
If optical fiber layout maps are updated frequently to maintain accuracy, then current fiber routes can be accurately represented, but the cost and complexity of map management increases
Solution Approach 1:
The system replaces manual map updating processes with an automated self-updating system. The DFOS system continuously monitors optical backscatter signals and automatically detects changes in fiber routing, generating and updating digital layout maps in real-time without requiring manual intervention, thereby maintaining high reliability while eliminating management complexity.
Solution Approach 2:
The system implements continuous feedback through real-time monitoring of optical backscatter signals. When fiber routes change or new circuits are added, the system automatically detects these changes through signal analysis and updates the layout maps accordingly, ensuring maps remain accurate without manual updates and reducing management complexity through automated feedback loops.
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
Provides real-time, wide coverage, and scalable identification of fiber routes with minimal field efforts, ensuring accurate and efficient cable management.
Implementation Method 1
a DFOS system sends optical pulses through an optical fiber cable
Implementation Method 2
analyze DVS sensing data from different optical fibers located within the same cable
Data Source
AI summary
Disclosed are systems and methods that analyze DFOS sensing data from different optical fibers located within the same cable, and discern how many of the different optical fibers are routed in different directions, their distances, or the circuits through which they pass. When combined with cable survey techniques, our systems and methods according to aspects of the present disclosure rapidly identify optical fiber cable diversity. Our inventive systems and methods according to aspects of the present disclosure employ a route condition matched engine and interactive feedback between field technicians and DFOS systems. These aspects are coupled with interactive-AI-based algorithms to solve what we have called, a cable diversity puzzle quickly, with minimum field efforts.