Regional Gas Pipeline Opening Scheme Using ML Demand Forecasting
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Solution Overview
Problem
The increasing complexity and management costs of gas pipeline networks due to urbanization necessitate a more efficient method for determining optimal opening schemes, as existing approaches lack precision and scalability.
Innovation Solution
A smart gas management platform utilizing a machine learning-based gas demand degree prediction model, integrated with an Internet of Things system, to assess region features and determine gas demand, classify regions, and formulate pipeline network opening schemes based on preset conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the gas pipeline network system expands to cover more regions due to urbanization, then the service coverage and utility are improved, but the management complexity and management costs increase
Solution Approach 1:
The patent segments the gas pipeline network into multiple regional units, each with its own feature parameters (population, area, building types, etc.). The machine learning model processes each region independently to determine opening schemes, transforming a complex system-wide problem into manageable regional decisions. This segmentation allows the network to expand coverage while maintaining manageable complexity through localized analysis.
Solution Approach 2:
The patent transforms qualitative management decisions into quantitative parameter analysis. By defining specific region features (population density, building area, road network characteristics) and using machine learning to process these parameters, the system converts complex management judgments into data-driven decisions based on measurable parameters, thereby reducing management complexity.
2Measurement precision
If traditional methods are used to determine gas pipeline opening schemes, then implementation is straightforward, but precision and accuracy in predicting gas demand are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual methods of determining pipeline opening schemes with a machine learning-based intelligent system. The model automatically processes region feature parameters and predicts gas demand with high precision, substituting human judgment and experience-based decisions with data-driven algorithms, thereby improving accuracy while accepting increased system complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from actual gas usage data and region feature changes. By comparing predicted gas demand with actual consumption patterns, the model refines its predictions over time, improving measurement precision through iterative feedback while maintaining a manageable level of system complexity.
3Productivity
If manual assessment methods are used to evaluate region features and determine opening schemes, then system simplicity is maintained, but productivity and efficiency of management operations decrease
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically evaluates region features and determines opening schemes without requiring extensive manual intervention. The system autonomously processes input parameters, performs predictions, and generates recommendations, significantly improving management productivity while the automated nature helps contain complexity through standardized algorithms.
Solution Approach 2:
The system performs preliminary analysis of region features before making opening scheme decisions. By pre-processing and evaluating multiple region parameters in advance, the machine learning model prepares comprehensive assessments that accelerate the decision-making process, thereby improving management efficiency while the preparatory work is handled systematically to manage complexity.
Data Source
AI summary
The present disclosure provides a method for determining a gas pipeline network opening scheme based on smart gas, which is performed by a smart gas management platform. The smart gas management platform comprises a smart user service management sub-platform, a smart operation management sub-platform and a smart gas data center. The method comprises: obtaining, by the smart gas data center, a region feature of each region within a target range through a smart gas sensing network platform; determining, by the smart operation management sub-platform, a gas demand degree of the each region based on the region feature of the each region; determining, by the smart operation management sub-platform, a region as a first-class region based on the gas demand degree in the region meeting a preset condition, and determining the gas pipeline network opening scheme for the region.