Gas Gate Station IoT Early Warning for Pipeline Anomalies
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing gas pipeline networks require significant manpower for monitoring, which is inefficient and can lead to delayed warnings due to the high correlation of the network, posing operational risks and health hazards from noise pollution.
Innovation Solution
A smart gas safety management platform using an IoT system that includes a smart gas user platform, service platform, sensor network platform, and pipeline network device object platform, utilizing machine learning models to predict operational data and issue timely early warnings based on monitoring data and historical data, reducing personnel involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring of gas pipeline network is implemented, then monitoring coverage can be achieved, but significant manpower cost is required and monitoring efficiency is low
Solution Approach 1:
The system enables automated self-monitoring of the gas pipeline network through IoT devices, sensors, and machine learning models that automatically detect anomalies and issue warnings without human intervention, eliminating the need for manual monitoring while maintaining comprehensive coverage
Solution Approach 2:
The patent replaces manual mechanical monitoring with an automated electronic system comprising IoT devices, sensors, data transmission modules, and machine learning algorithms that automatically analyze pipeline data and generate warnings, substituting human labor with intelligent automation
2Reliability
If manual monitoring is used, then operational control is maintained, but response time to hazards is delayed due to high correlation of the network
Solution Approach 1:
The system performs preliminary detection and analysis of potential hazards using machine learning models that identify abnormal patterns in pipeline data before they escalate into serious issues, enabling early warnings and preventive actions to be taken in advance
Solution Approach 2:
The system implements real-time feedback mechanisms where sensors continuously monitor pipeline parameters, data is transmitted to the platform, machine learning models analyze the data and generate warnings, which are then immediately communicated to relevant personnel, creating a closed-loop rapid response system
3Productivity
If gas gate station operates continuously, then pipeline network supply is maintained, but noise pollution affects staff health
Solution Approach 1:
The automated monitoring system enables the gas gate station to operate autonomously without continuous human presence, using IoT devices and algorithms to self-monitor and self-diagnose pipeline conditions, maintaining supply continuity while eliminating staff exposure to noise pollution
Solution Approach 2:
The system extracts and removes the need for human staff from the noisy gas gate station environment by implementing fully automated monitoring and control functions, separating human operators from the harmful noise exposure while maintaining operational continuity
Data Source
AI summary
Disclosed is monitoring and early warning method for gas gate station and IoT system thereof. The method comprises: obtaining a downstream user feature and historical warning data corresponding to the gas gate station; determining, based on the downstream user feature and the historical warning data and in combination with operation data of the gas gate station, an associated node related to the gas gate station in a preset time period; obtaining monitoring data of the associated node; determining a pipeline network monitoring feature based on pipeline network monitoring data; determining ideal operation data of the gas gate station at a future time through a prediction model, the gate station operation data, and the pipeline network monitoring feature; and issuing an early warning notification in response to a difference between gate station monitoring data and the ideal operation data exceeding a threshold.