IoT Gas Pipeline Sampling Parameters for Targeted Fault Detection
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Solution Overview
Problem
Current gas pipeline sampling methods rely on manual operations, which are time-consuming, labor-intensive, and prone to errors, affecting the accuracy of data assessment and pipeline safety.
Innovation Solution
An IoT system comprising a government safety monitoring and management platform that determines sampling parameters based on pipeline sensing data to identify abnormal pipelines and regions, activating specific sampling devices for targeted data collection and issuing fault detection commands for manual inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operation is used for gas pipeline sampling, then device complexity is reduced, but productivity decreases and measurement precision deteriorates
Solution Approach 1:
The system enables automatic sampling parameter determination where the sensing network platform autonomously analyzes pipeline sensing data, identifies abnormal regions, and generates sampling parameters without requiring manual intervention. The platform self-adjusts sampling strategies based on real-time data, achieving high productivity while managing complexity through automation.
Solution Approach 2:
The patent replaces manual mechanical sampling operations with an automated information-processing system. The government safety monitoring and management platform uses computational algorithms to analyze sensing data, determine abnormal pipelines, and generate sampling parameters electronically, substituting human manual operations with automated digital processes.
2Measurement precision
If manual operation is used for gas pipeline sampling, then device complexity is reduced, but measurement precision deteriorates due to human factors
Solution Approach 1:
The system continuously monitors pipeline sensing data through the sensing network platform, compares actual data against standard values, and uses this feedback to dynamically determine sampling parameters. The feedback mechanism ensures high measurement precision by automatically adjusting sampling strategies based on real-time pipeline conditions and identified abnormalities.
Solution Approach 2:
The patent eliminates human factors affecting measurement precision by replacing manual sampling operations with automated computational algorithms. The government safety monitoring and management platform uses objective data analysis and algorithmic decision-making to determine sampling parameters, ensuring consistent and accurate measurement results without human intervention.
3Reliability
If comprehensive pipeline monitoring is implemented, then reliability improves, but loss of energy increases
Solution Approach 1:
The system implements targeted monitoring by first analyzing pipeline sensing data to identify specific abnormal regions and pipelines requiring attention. Rather than uniformly monitoring all pipelines equally, the platform concentrates resources on local areas with detected abnormalities, improving fault detection reliability while reducing overall energy consumption through selective monitoring.
Solution Approach 2:
The patent applies partial monitoring action by determining sampling parameters based on identified abnormal regions rather than conducting comprehensive sampling of the entire pipeline network. This selective approach maintains sufficient reliability for fault detection while minimizing energy consumption by focusing monitoring efforts only where needed.
Data Source
AI summary
Disclosed are an IoT system, a method, and a storage medium for generating a sampling parameter for a gas pipeline. The method is executed by a government safety monitoring and management platform, and includes: obtaining pipeline sensing data; determining the sampling parameter based on the pipeline sensing data; and in response to obtaining at least one piece of sampling data from at least one sampling device, determining a fault detection command based on the sampling data, and sending the fault detection command to a gas maintenance object platform to schedule a manual inspection. The IoT system includes the government safety monitoring and management platform, a government safety monitoring sensing network platform, a government safety monitoring object platform, a gas company sensing network platform, a gas equipment object platform, and the gas maintenance object platform. The method may be executed by reading computer instructions stored in a non-transitory computer-readable storage medium.