Multi-dimensional indexing for subterranean pipe network data
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Solution Overview
Problem
Current pipe inspection systems fail to adequately correlate and analyze multi-dimensional data sets, leading to ambiguous and incomplete representations of complex pipe networks, which results in lost information and inaccurate predictive analyses due to insufficient precision and lack of error modeling.
Innovation Solution
The implementation of multi-dimensional indexing (MDI) that cross-references and correlates spatial, temporal, contextual, environmental, feature, and uncertainty data sets, allowing for comprehensive analysis and prediction of pipe conditions, and recommending maintenance and repair actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional linear footage indexing is used to reference sensor data in pipe networks, then the system is simple to implement, but the data becomes ambiguous and meaningless in complex interconnected networks
Solution Approach 1:
The patent transitions from one-dimensional linear footage indexing to multi-dimensional indexing that incorporates spatial coordinates (x, y, z), temporal information, and contextual data. This dimensional expansion resolves the ambiguity problem by providing multiple reference frames for locating sensor data in complex pipe networks, allowing precise identification of positions even at branch points and junctions where linear footage alone is insufficient.
2Quantity of substance
If multiple sensor data streams are collected during inspection, then comprehensive information is gathered, but the data streams become difficult to correlate and analyze
Solution Approach 1:
The patent merges multiple sensor data streams (imaging sensors, lasers, sonar, environmental sensors) into a unified multi-dimensional index structure. All sensors reference their data to the same spatio-temporal framework, enabling automatic correlation without complex post-processing. The unified index allows any data type to be cross-referenced with any other type using the same spatial and temporal keys, dramatically simplifying data analysis.
Solution Approach 2:
The multi-dimensional index structure serves as an intermediary between multiple sensor data streams. Instead of directly correlating disparate sensor outputs, the system uses the unified index as a mediator that translates and aligns all data to a common reference frame, making correlation and analysis straightforward through consistent spatial-temporal keying.
3Quantity of substance
If data is collected at different times from multiple inspection runs, then more complete pipe condition information is obtained, but the data cannot be accurately compared or analyzed
Solution Approach 1:
The patent performs preliminary spatial registration and temporal alignment of data from multiple inspection runs before analysis. By pre-processing the data to establish consistent spatial references and temporal relationships across different inspection times, the system enables accurate comparison and change detection. This preliminary organization prevents the accumulation of spatial and temporal errors that would otherwise make multi-temporal analysis impossible.
Data Source
AI summary
Systems, methods and devices for indexing, archiving, analyzing and reporting pipe and other void network data. Specifically, multi-dimensional indexing and correlation of spatial, temporal, feature, environmental, uncertainty and/or context-based data is synchronized, indexed and analyzed across a wide variety of pipe networks at various times. The present invention preferably includes data represented at several different levels of reference including: referenced to the sensor with which it was collected; referenced to the robot or platform upon which the sensor is attached; and the world. The structure and functionality of the system provides for extensive querying, trouble-shooting and predictive analysis for pipe networks.