Network Graph Representation of Physically Connected Sensor Pipelines
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Solution Overview
Problem
Existing network connectivity determination methods fail to accurately represent the physical and logical connections among sensors in pipeline networks, limiting advanced analytical capabilities and decision-making due to unknown interconnectivity and changes in network configuration.
Innovation Solution
A method that selects sensors, searches for measurement patterns in time-varying data signals, identifies candidate sensors with matching patterns, constructs possible graph sub-structures, determines feasible graph structures based on spatial placement, and iteratively generates a network graph representation by selecting different sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network connectivity determination methods are used, then the system is simple to operate, but the accuracy of network connection representation is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual network mapping methods with signal processing and pattern recognition techniques. By analyzing time-varying data signals from sensors and identifying measurement patterns, the system automatically determines network connectivity without requiring manual intervention or complex physical inspection, thereby achieving high accuracy while maintaining operational simplicity.
Solution Approach 2:
The network system performs self-diagnosis and self-mapping by having sensors continuously monitor and report their own states. The measurement patterns from each sensor serve as fingerprints that automatically identify connections to neighboring components, eliminating the need for external surveyors or complex testing equipment while maintaining high precision in network representation.
2Difficulty of detecting and measuring
If manual network mapping methods are used, then the system complexity is low, but the ability to detect and measure network connections is insufficient
Solution Approach 1:
The patent substitutes manual detection methods with automated signal analysis. Each sensor's time-varying data signal contains embedded information about its connections; by processing these signals and identifying measurement patterns, the system automatically detects and measures network connections with high capability, replacing complex manual surveying equipment with intelligent software-based detection.
Solution Approach 2:
The measurement patterns extracted from sensor data serve multiple functions: they identify direct connections, determine connection orientation, and validate network topology simultaneously. This multi-functional approach enhances detection capability without requiring separate specialized tools for each type of measurement, thereby managing system complexity efficiently.
3Measurement precision
If detailed network topology analysis is performed, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The system continuously collects and pre-processes time-varying data signals from all sensors during normal operation, maintaining a ready pool of measurement patterns. When network analysis is needed, this pre-prepared data enables rapid precision topology identification without requiring time-consuming real-time measurements or detailed sequential analysis, thus reducing analysis time while maintaining high precision.
Solution Approach 2:
The patent analyzes measurement patterns from a selected sensor and its immediate candidate connections first, rather than performing exhaustive analysis of the entire network. This partial action approach achieves sufficient precision for local connectivity determination quickly, and only expands analysis scope if higher-level topology details are needed, thereby optimizing the balance between precision and processing time.
Data Source
AI summary
A method and system to generate a network graph representation of a physically connected network include selecting a selected sensor among a plurality of sensors arranged along the physically connected network, searching a time-varying data signal from the selected sensor for measurement patterns; identifying candidate sensors among the plurality of sensors that are candidates for being directly connected with the selected sensor based on each of the candidate sensors outputting a respective candidate time-varying data signal with candidate patterns that match the measurement patterns of the time-varying data signal, and constructing possible graph sub-structures including the selected sensor and the candidate sensors. Determining a feasible graph sub-structure is based on the possible graph sub-structure and spatial placement of each of the selected sensor and the candidate sensors, and generating the network graph representation is by iteratively determining the feasible graph sub-structure by selecting a different sensor as the selected sensor.