Natural Gas Pipe Network Maintenance Timing Using Graph Neural Networks
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Solution Overview
Problem
Current natural gas pipe network management relies heavily on manual inspections, making it difficult to detect issues in a timely manner, leading to inefficient maintenance and potential serious losses due to undetected damage.
Innovation Solution
A method and system utilizing a Graph Neural Network model to predict maintenance times by extracting feature information from running time and gas leakage data, incorporating historical maintenance locations and environmental information to prioritize maintenance processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual inspection is used for pipe network management, then operational simplicity is maintained, but detection timeliness and maintenance efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated intelligent system that uses sensors, data processing algorithms, and predictive models to detect pipe network issues automatically, thereby improving detection timeliness while reducing manual operational burden
Solution Approach 2:
The system enables the pipe network to monitor and report its own status through embedded sensors and automated detection mechanisms, allowing the infrastructure to self-diagnose issues without requiring continuous manual inspection
2Device complexity
If manual inspection is used for pipe network management, then system complexity is reduced, but maintenance efficiency deteriorates
Solution Approach 1:
The patent divides the pipe network into segmented monitoring zones with distributed sensors and detection points, allowing parallel processing of multiple inspection tasks simultaneously, which improves maintenance efficiency while managing system complexity through modular architecture
Solution Approach 2:
The system performs preliminary detection and prediction of potential failures before they occur, allowing maintenance to be scheduled proactively rather than reactively, thereby improving maintenance efficiency by preventing catastrophic failures and optimizing resource allocation
3Measurement precision
If predictive maintenance is implemented using advanced models, then maintenance accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces intermediate data processing layers including feature extraction modules, data normalization components, and predictive algorithms that act as mediators between raw sensor data and maintenance decisions, improving prediction accuracy while managing complexity through layered architecture
Solution Approach 2:
The system employs universal data processing frameworks and multi-functional predictive models that can handle various types of sensor data and failure modes using the same underlying architecture, improving accuracy across different scenarios without proportionally increasing complexity
Data Source
AI summary
The present disclosure provides a method and a system for determining a maintenance time of a pipe network of natural gas. The method may comprise: obtaining pipe network information of natural gas in at least one area, the pipe network information including a running time of the system and gas leakage information of the pipe network; extracting feature information based on the running time and the gas leakage information; generating a pipe network maintenance value through a maintenance value prediction model based on pipe network maintenance information and pipe network environment information, the pipe network maintenance value reflecting a priority of pipe network maintenance processing; and predicting the maintenance time of the pipe network based on the feature information and the pipe network maintenance value using a maintenance time prediction model, the maintenance time prediction model being a machine learning model.