IoT Gas Gate Station Pressure Regulation Using Flow Prediction
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Solution Overview
Problem
Existing gas distribution systems face challenges in accurately and timely regulating pressure at gas gate stations due to delayed feedback and fluctuations in downstream gas consumption, leading to instability in gas pressure.
Innovation Solution
A method and system utilizing an intelligent gas management platform with machine learning models to predict gas gate station flow based on gas terminal information, enabling precise pressure regulation by aggregating and adjusting gas gate station pressures according to user distribution and consumption patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pressure regulation is performed based on downstream feedback, then gas pressure stability can be maintained, but regulation delay occurs causing instability
Solution Approach 1:
The system performs preliminary action by predicting future gas consumption patterns and proactively adjusting gas gate station pressures before actual demand fluctuations occur. The machine learning model analyzes historical consumption data, user types, and distribution features to forecast demand, enabling advance pressure regulation that eliminates delays associated with reactive feedback-based control.
2Measurement precision
If pressure regulation is performed manually or based on simple feedback, then system complexity remains low, but regulation accuracy is insufficient causing greater pressure fluctuations
Solution Approach 1:
The system introduces an intelligent gas management platform as an intermediary between gas consumption data and pressure regulation control. This platform incorporates machine learning models that process multiple input features (user types, consumption patterns, distribution characteristics) to generate accurate pressure regulation predictions, thereby achieving high regulation accuracy while managing complexity through a dedicated intermediate system.
Solution Approach 2:
The system applies parameter changes by utilizing multiple input parameters (user type distribution, consumption patterns, temporal features) to dynamically adjust gas gate station pressures. The machine learning model transforms these parameters into optimized pressure settings, enabling precise regulation that adapts to changing conditions without requiring complex manual intervention.
3Loss of information
If gas terminal information is collected from all users, then comprehensive data for prediction is obtained, but data processing complexity and time increase
Solution Approach 1:
The system extracts and utilizes only the most relevant features from gas terminal information, such as user type distribution, consumption patterns, and key temporal characteristics. Rather than processing all raw data, the machine learning model focuses on extracting meaningful features that drive pressure regulation decisions, thereby reducing data processing complexity while maintaining prediction accuracy.
Data Source
AI summary
A method and system of determining a pressure regulation scheme at an intelligent gas gate station based on an Internet of Things are provided. The method is performed by at least one processor of an intelligent gas management platform, and the intelligent gas management platform includes an intelligent customer service management sub-platform, an intelligent operation management sub-platform, and an intelligent gas data center. The method includes: obtaining, by a processor of the intelligent gas data center, gas terminal information from an intelligent gas object platform through an intelligent gas sensor network platform; predicting, by a processor of the intelligent operation management sub-platform, a gas gate station flow by analyzing the gas terminal information based on a flow model; determining, by the processor of the intelligent operation management sub-platform, gas gate station pressure; obtaining a pressure sum; and determining the pressure regulation scheme based on the pressure sum and a total pressure threshold.