Smart Gas Pipeline IoT System for Predictive Maintenance Material Allocation
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Solution Overview
Problem
The challenge in the field of gas pipeline network maintenance is predicting future faults to rationally allocate and reserve maintenance materials, as various types of faults require specific materials and timely scheduling, which existing methods fail to address effectively.
Innovation Solution
A smart gas pipeline network IoT system is implemented, comprising a user platform, service platform, safety management platform, sensor network platform, and object platform, which predicts fault probabilities based on pipeline features and historical data to determine the demand for maintenance materials, enabling efficient scheduling and allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If maintenance materials are reserved in advance for all possible faults, then the response speed to faults is improved, but the cost and complexity of material management increases
Solution Approach 1:
The system performs preliminary actions by predicting potential faults before they occur using historical data and machine learning models. It calculates the probability of faults at different pipeline locations and pre-arranges maintenance materials based on these predictions, rather than preparing for all possible faults indiscriminately. This resolves the contradiction by enabling targeted advance preparation that improves response speed without proportionally increasing management complexity.
Solution Approach 2:
The system changes the parameter of material reservation from a static, comprehensive approach to a dynamic, probability-based approach. By using fault probability values as a parameter, the system adjusts material allocation levels according to the predicted likelihood of faults at different locations, optimizing the balance between response readiness and management complexity.
2Measurement precision
If maintenance materials are allocated based on comprehensive fault analysis, then the accuracy of material allocation is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary fault probability calculations and material allocation planning in advance, using historical maintenance data and pipeline operation data to build predictive models. This pre-processing enables accurate material allocation decisions to be made quickly when actual maintenance needs arise, resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The system uses historical maintenance data and operational patterns as copies or templates to predict future maintenance needs. By analyzing past fault patterns and material consumption data, the system creates predictive models that can quickly estimate future material requirements without performing comprehensive real-time analysis, thus maintaining accuracy while reducing time consumption.
3Measurement precision
If the system monitors and analyzes all pipeline data in real-time, then the prediction accuracy of faults is improved, but the computational load and system complexity increase
Solution Approach 1:
The system extracts only the most relevant features from pipeline data for fault prediction, such as pressure variations, flow rate changes, and temperature anomalies. By selecting and focusing on key indicators rather than processing all available data, the system maintains high prediction accuracy while reducing computational load and simplifying the system architecture.
Solution Approach 2:
The system segments the pipeline network into multiple sections or zones, each monitored and analyzed independently. This segmentation allows the system to manage complexity by dividing the overall monitoring task into smaller, more manageable units while still providing comprehensive coverage and accurate fault prediction across the entire network.
Data Source
AI summary
Disclosed is a method for predicting demand for maintenance materials of a smart gas pipeline network. The method comprises: obtaining a pipeline network feature of a gas pipeline network; predicting fault probabilities of one or more point positions of the gas pipeline network based on the pipeline network feature, the fault probabilities including probabilities of one or more preset fault types of faults occurring at the point positions; determining sub-demand for the maintenance materials of each point position based on the fault probability of each point position of the one or more point positions, the sub-demand being obtained based on historical maintenance data of the gas pipeline network; determining the demand for the maintenance materials based on the sub-demand of each point position of the gas pipeline network; and transmitting the demand for the maintenance materials to the smart gas user platform based on the smart gas service platform.