Multi-Round Pipeline Inspection Data Alignment
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Solution Overview
Problem
Existing methods for aligning multi-round internal inspection data of long-distance pipelines face challenges such as low alignment success rates, misalignment, and inefficiency due to variations in inspection equipment, environmental changes, and differing definitions of pipeline features, leading to significant alignment deviations and reliability issues.
Innovation Solution
A method and apparatus for multi-round alignment of gas long-distance pipeline magnetic flux leakage internal inspection data using a feature point database to identify primary and secondary feature points, applying the longest common subsequence algorithm, and nonlinear alignment functions to achieve accurate and efficient alignment, with clustering based on industry standards to improve reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing alignment methods using uniform mileage deviation mean function and deviation threshold are applied, then the alignment process is simplified, but alignment accuracy deteriorates significantly for different pipeline distances
Solution Approach 1:
The patent applies local quality by dividing the pipeline into multiple segments based on feature points (valves, elbows, welds), and applying different alignment parameters (mileage deviation mean function and deviation threshold) to each segment. This allows each segment to be aligned with locally optimized parameters, improving overall alignment accuracy while maintaining operational simplicity through automated segment-based processing.
2Productivity
If overall linear stretching adjustment using mileage ratios is applied, then the alignment process is efficient, but alignment deviations occur due to non-uniform equipment mileage counting
Solution Approach 1:
The patent segments the pipeline into multiple sections based on feature points, transforming the single overall linear stretching adjustment into multiple localized alignment operations. Each segment is aligned independently using feature point matching, which eliminates the cumulative errors of non-uniform mileage counting while maintaining efficiency through automated batch processing of segments.
Solution Approach 2:
The patent changes the alignment parameters from uniform mileage ratios to segment-specific parameters including local mileage deviation mean functions and deviation thresholds. This parameter adaptation allows each segment to be aligned according to its specific characteristics, improving accuracy without sacrificing efficiency.
3Measurement precision
If manual alignment is performed to achieve accurate alignment, then alignment accuracy is improved, but the work amount and time required increase significantly
Solution Approach 1:
The patent implements self-service alignment by automating the feature point identification, segment division, and alignment parameter calculation processes. The system automatically extracts feature points from inspection data, divides the pipeline into segments, calculates optimal alignment parameters for each segment, and performs the alignment without manual intervention, achieving both high accuracy and efficiency.
Solution Approach 2:
The patent transforms the alignment process from manual parameter adjustment to automated parameter calculation by using algorithms to compute mileage deviation mean functions and deviation thresholds based on inspection data characteristics, eliminating time-consuming manual work while maintaining accuracy.
4Ease of operation
If feature points such as valves and elbows are used for alignment, then the alignment process is straightforward, but alignment success rate is low due to large mileage segments covering many welds and defects
Solution Approach 1:
The patent uses feature points (valves, elbows, welds) to divide the pipeline into multiple small segments, transforming the alignment task from matching large mileage segments to matching numerous small segments. This segmentation increases the number of alignment checkpoints, improving alignment success rate and reliability while maintaining operational simplicity through automated feature point-based segment division.
Data Source
AI summary
Disclosed are a method and an apparatus for multi-round alignment of gas long-distance pipeline magnetic flux leakage internal inspection data. The method includes: identifying the primary feature point data of two sets of internal inspection data, dividing the pipeline into multiple primary pipeline sections, identifying the secondary feature points in the primary pipeline sections, aligning the secondary feature points using a segment splitting algorithm, re-aligning unaligned secondary feature points, and aligning remaining feature points and defects in the secondary pipeline sections.