IoT Pipeline Particulate Monitoring With Targeted Inspection Control
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Solution Overview
Problem
Existing gas pipeline networks face challenges in efficiently monitoring and managing particulate matter, which can lead to pipeline failures due to high concentrations causing increased resistance, erosion, and corrosion, necessitating improved monitoring methods.
Innovation Solution
An IoT system comprising a gas company management platform, sensing network platform, and equipment object platform, which collects and analyzes particulate matter concentration data to identify areas requiring inspection, adjust equipment parameters, and generate targeted inspection work orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used for particulate matter in gas pipelines, then the monitoring coverage is limited and response time is slow, but the system complexity and cost increase when implementing comprehensive monitoring
Solution Approach 1:
The gas pipeline network is divided into multiple pipeline areas with specific monitoring devices deployed in each area. The system segments the monitoring task into regional units, each managed by a gas equipment object platform, while the gas company sensing network platform coordinates across all segments. This segmentation allows comprehensive coverage without requiring a monolithic complex system.
Solution Approach 2:
The gas company sensing network platform and gas equipment object platform are designed as multi-functional systems that handle multiple tasks: collecting concentration data from monitoring devices, generating concentration levels and markers, determining pipelines for inspection, generating work orders, and regulating equipment parameters. This multi-functionality reduces the need for separate specialized systems.
2Measurement precision
If comprehensive monitoring of all pipeline areas is implemented, then the detection accuracy improves, but the time and resources required for data processing and inspection increase
Solution Approach 1:
The system generates concentration levels and concentration level markers in advance based on collected concentration data. Pipelines requiring inspection are predetermined by analyzing concentration level differences before actual inspection occurs. This preliminary processing and prioritization reduces the time needed during actual inspection operations.
Solution Approach 2:
Instead of inspecting all pipelines uniformly, the system applies partial action by focusing inspection resources only on pipelines with high concentration level differences that require inspection. This selective approach processes only the necessary portion of data and pipelines, reducing overall processing time while maintaining detection accuracy for critical areas.
3Productivity
If real-time monitoring and automatic regulation are implemented, then the response speed to particulate matter issues improves, but the automation complexity and system requirements increase
Solution Approach 1:
The system implements feedback by continuously collecting concentration data from monitoring devices, comparing it against standards to generate concentration levels, identifying pipelines with abnormal concentration level differences, and automatically regulating equipment parameters based on this feedback loop. This structured feedback mechanism enables efficient automatic response without excessive complexity.
Solution Approach 2:
The gas equipment object platform automatically regulates equipment parameters in response to detected particulate matter issues without requiring constant human intervention. The system generates inspection work orders and adjusts equipment self-service based on the concentration level analysis, reducing manual operation complexity while maintaining high productivity.
Data Source
AI summary
The present disclosure provides a method, an Internet of things (IoT) system, and a medium for safety monitoring of a particulate matter in a smart gas pipeline network. The method includes: obtaining concentration data of a pipeline area; generating a concentration level for the pipeline area based on the concentration data and generating a concentration level marker in a preset display machinery; determining a concentration level difference based on the concentration level for the pipeline area; determining a pipeline to be inspected based on the concentration level difference and generating a marker of the pipeline to be inspected in the preset display machinery; generating a pipeline inspection instruction based on the pipeline to be inspected; generating a pipeline inspection work order; and regulating, based on an execution result of the pipeline inspection work order and/or the concentration level difference, an operating parameter of pipeline ancillary equipment in the pipeline area.