Dynamic Emergency Device Deployment in Smart Gas Networks
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Solution Overview
Problem
Current emergency gas supply systems are inefficient in dynamically deploying devices to meet peak gas demand, often resulting in insufficient gas supply during peak consumption periods, leading to user complaints and potential safety hazards.
Innovation Solution
An IoT system for smart gas pipeline networks that determines gas emergency regions and dynamically deploys emergency devices based on real-time supply and demand data, using sensors and a safety management platform to generate movement instructions for emergency devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If emergency devices are deployed only after gas supply fault occurs, then device deployment responds to actual emergencies, but gas supply restoration takes too long to meet peak demand
Solution Approach 1:
The system performs preliminary actions by predicting gas emergency regions based on pipeline network data, user consumption patterns, and historical fault information before actual emergencies occur. Emergency devices are pre-positioned in these predicted high-risk regions, so when peak demand or actual faults occur, the devices are already in place or nearby, dramatically reducing the response time and ensuring timely gas supply restoration.
Solution Approach 2:
The system implements dynamic deployment by continuously updating the locations of emergency devices and recalculating predicted emergency regions based on real-time pipeline data, seasonal consumption patterns, and changing user demands. The deployment scheme is not static but adapts dynamically, with devices being moved to new locations as needed to follow the shifting risk profile, ensuring optimal positioning at all times.
2Reliability
If multiple emergency devices are distributed throughout the network, then coverage is improved, but device management and tracking complexity increases
Solution Approach 1:
The system incorporates feedback mechanisms where the location and status of each emergency device are continuously monitored and reported back to the safety management platform. This real-time feedback enables the system to track multiple distributed devices, assess their availability, and automatically adjust deployment schemes based on current device positions and predicted emergency regions, making management of multiple devices systematic and efficient.
Solution Approach 2:
The safety management platform serves multiple functions: it predicts emergency regions, tracks device locations, generates deployment schemes, issues movement instructions, and monitors device status. This multi-functional universal platform consolidates all management tasks for distributed emergency devices into a single integrated system, reducing overall management complexity despite the large number of devices.
3Speed
If emergency devices are pre-positioned in predicted emergency regions, then response time is reduced, but device location flexibility decreases
Solution Approach 1:
The system maintains flexibility through dynamic recalculation of predicted emergency regions based on real-time pipeline data, seasonal consumption patterns, and changing user demands. Devices are not permanently fixed but are dynamically repositioned as the predicted emergency regions shift, allowing the system to adapt to changing conditions while maintaining fast response times through continuous optimal positioning.
Data Source
AI summary
The present disclosure provides a method and an Internet of Things (IoT) system for dynamic allocating emergency devices of smart gas. The method comprises determining gas supply and demand features based on node data and downstream user features of a gas pipeline network; determining a plurality of gas emergency regions based on the gas supply and demand features; determining physical distances between the plurality of gas emergency regions and time intervals required for an emergency response based on the plurality of gas emergency regions; determining weighted distances based on the physical distances, the time intervals, and weighted weights; determining a dynamic deployment scheme for a plurality of emergency devices based on the weighted distances and device data of the plurality of emergency devices; and generating a movement instruction based on the dynamic deployment scheme, and sending the movement instruction to the plurality of emergency devices.