Fiber Path Fragment Grid for Shared-Risk Link Failure Detection
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Solution Overview
Problem
Identifying shared risk link groups (SRLGs) that share a common segment and can fail simultaneously in fiber optic networks is computationally intensive and not amenable to parallel processing, posing challenges in preventing network failures and downtime.
Innovation Solution
A method to efficiently identify SRLGs by dividing fiber paths into fragments, applying geographic tolerance, and using rectangular equidistant projection to create a global grid for parallel processing, thereby identifying overlapping segments and cumulative lengths of overlap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive analysis of KMZ data is performed to identify overlapping fiber routes, then detection precision of SRLG single points of failure is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the continuous KMZ fiber route data into discrete path fragments with defined start and end coordinates. Each fragment is assigned a unique identifier and spatial bounds, transforming the continuous geographic data into manageable discrete units that can be processed efficiently in parallel without requiring comprehensive analysis of the entire dataset at once.
Solution Approach 2:
The patent introduces a spatial grid dimension by projecting fiber path fragments onto a rectangular equidistant projection grid. This adds a spatial indexing dimension that enables efficient overlap detection through grid cell comparison rather than direct pairwise fragment comparison, reducing computational complexity from O(n²) to approximately O(n log n) or O(n) depending on fragment distribution.
2Measurement precision
If traditional sequential processing methods are used to analyze fiber routes, then detection accuracy is maintained, but processing speed and productivity decrease
Solution Approach 1:
The patent divides the fiber route analysis into independent path fragment units that can be processed separately. Each fragment's spatial bounds are pre-calculated, enabling independent processing of multiple fragments simultaneously through parallel computing operations while maintaining accurate overlap detection through systematic comparison of fragment identifiers and spatial coordinates.
Solution Approach 2:
The patent introduces spatial grid cells as intermediary structures that mediate between raw fiber path data and overlap detection results. Fragments are assigned to grid cells based on their spatial bounds, and overlap detection is performed by comparing fragments within the same or adjacent grid cells, significantly reducing the number of comparisons needed compared to direct pairwise analysis.
3Reliability
If geographic tolerance is applied to define overlapping paths, then detection robustness is improved, but measurement precision may be reduced due to tolerance threshold
Solution Approach 1:
The patent applies geographic tolerance locally at the fragment level rather than globally across the entire fiber route. Each path fragment is evaluated against the tolerance threshold independently when comparing with other fragments, allowing the system to maintain precision for small deviations while robustly identifying overlaps that exceed the tolerance. This local application of tolerance preserves measurement precision for critical small-scale variations.
Data Source
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AI summary
Rectangular equidistant projection is applied to coordinates for path fragments of a plurality of fiber optic routes. A bounding box is generated for each path fragment. For each cell of a grid of cells where its bounding box overlaps with a bounding box created by the cell, each path fragment is placed into the cell of the global grid. For any cell with more than one path fragment, each path fragment inside the cell is compared to every other path fragment in that cell. For any path fragment with overlapping bounding boxes, the length of the overlap is saved. A table of cumulative overlap distances between path fragments is generated to identify overlapping segments and cumulative lengths of the overlap distances.