Smart Gas Pipeline Inspection Scheduling for Low-Demand Windows
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing gas pipeline network inspection methods do not adequately consider the varying gas consumption patterns of different user types, leading to inefficiencies and potential disruptions in gas supply due to unsynchronized inspection times.
Innovation Solution
A smart gas pipeline network supervision system utilizing an IoT system with a smart gas pipeline network safety management platform, including a smart gas data center, sensor network, and object platform, uses machine learning to predict peak-valley consumption patterns and optimize inspection times to minimize disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gas pipeline network inspection is conducted during peak consumption periods, then inspection coverage and safety can be ensured, but gas supply disruption and user impact increase
Solution Approach 1:
The inspection schedule is made dynamic by predicting future gas consumption patterns using machine learning models and adjusting inspection times based on real-time consumption data. The system determines optimal inspection windows by analyzing historical consumption patterns and predicting future peak-valley periods, allowing flexible scheduling that adapts to changing demand conditions rather than following fixed schedules
Solution Approach 2:
The system performs preliminary analysis of historical gas consumption data to identify peak-valley patterns before scheduling inspections. By predicting future consumption patterns in advance and pre-determining optimal inspection time windows, the system prepares inspection schedules that avoid peak periods without requiring real-time reactive adjustments, thus preventing supply disruptions before they occur
2Measurement precision
If inspection frequency is increased to improve safety monitoring, then detection capability improves, but gas consumption disruption increases
Solution Approach 1:
The system changes the temporal parameter of inspection scheduling by predicting future gas consumption patterns and positioning inspections during predicted low-consumption periods. The machine learning model analyzes historical data to identify optimal time windows where inspection frequency can be increased without significantly impacting gas consumption, effectively changing when inspections occur rather than just how often
Solution Approach 2:
The system implements a feedback loop where historical inspection results and gas consumption data are continuously fed into the machine learning model to refine future predictions. The model learns from past patterns to improve its accuracy in predicting optimal inspection times, and the system adjusts schedules based on actual consumption patterns observed during and after inspections, creating a self-improving scheduling system
3Loss of energy
If inspection is scheduled during low consumption periods, then gas supply disruption is minimized, but inspection timing flexibility is reduced
Solution Approach 1:
The system provides dynamic scheduling flexibility by continuously predicting future consumption patterns and adjusting inspection windows based on real-time data. Rather than being constrained to fixed low-consumption periods, the system can adapt inspection timing to any predicted low-consumption window that arises, providing multiple flexible options for scheduling while minimizing disruption
Solution Approach 2:
The system performs preliminary prediction of future consumption patterns to identify multiple potential inspection time windows in advance. By pre-calculating optimal scheduling options based on predicted peak-valley patterns, the system provides flexibility in choosing from multiple acceptable time windows rather than being forced into a single fixed schedule, thus maintaining adaptability while minimizing disruption
Data Source
AI summary
The embodiment of the present disclosure provides a method for gas pipeline network supervision based on smart gas and an Internet of Things system, the method including: obtaining an area to be inspected; determining one or more downstream users based on the area to be inspected, obtaining gas consumption data of each of the one or more downstream users; determining, based on the gas consumption data, peak-valley features of gas consumption at a future time; determining a plurality of optional time points based on the peak-valley features of gas consumption; predicting future gas consumption features based on one or more change values of accessibility, the plurality of optional time points, and the gas consumption data through a prediction model; determining a target inspection time point based on the future gas consumption features; and generating inspection reminder instructions based on the target inspection time point.