IoT Pipeline Pressure Control for Urban Burst Prevention
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Solution Overview
Problem
Traditional monitoring methods for city water supply pipeline networks are susceptible to interference and have high misjudgment rates, failing to provide accurate warnings and timely emergency measures for pipe bursts.
Innovation Solution
Implementing an IoT large model-based system for city lifeline pipeline burst prevention, which includes obtaining fluid pressure data, determining pipe burst probability, and controlling target valves to mitigate risks through intelligent analysis and automated valve operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pressure gauges and flow meters are used for monitoring, then the monitoring system is simple to implement, but the misjudgment rate is high and accuracy is poor
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor nodes distributed throughout the pipeline network. Each node independently monitors local parameters (pressure, temperature, vibration, acoustic signals) and transmits data to a central analysis system. This segmentation allows comprehensive monitoring without requiring a single complex centralized system, thereby improving detection accuracy while managing system complexity through modular deployment.
Solution Approach 2:
Multiple types of sensors (pressure sensors, temperature sensors, vibration sensors, acoustic sensors) are merged into an integrated monitoring system that collects and analyzes multiple parameters simultaneously. This multi-parameter fusion approach significantly improves pipe burst detection accuracy by cross-validating signals and reducing false positives, while the unified data processing platform manages the complexity of integrating diverse sensor types.
2Measurement precision
If single indicator monitoring (pressure drop) is used, then the monitoring method is simple, but the ability to distinguish between pipe bursting and leakage is insufficient
Solution Approach 1:
The monitoring system transitions from single-dimensional pressure drop detection to multi-dimensional parameter monitoring. In addition to pressure, the system monitors temperature changes, vibration patterns, acoustic signals, and flow rate variations. This dimensional expansion enables differentiation between pipe bursts (which produce characteristic vibration and acoustic signatures) and leaks (which show different patterns), significantly improving diagnostic accuracy without requiring a proportional increase in monitoring points.
Solution Approach 2:
The system implements real-time feedback analysis where sensor data is continuously monitored, analyzed, and used to adjust monitoring strategies. When abnormal patterns are detected, the system can trigger additional measurements, alert operators, or automatically adjust valve positions to isolate affected sections. This feedback mechanism enables accurate distinction between burst and leakage events while optimizing the use of monitoring resources.
3Reliability
If comprehensive real-time monitoring is implemented, then the effectiveness and timeliness of burst prevention is improved, but the manpower cost increases
Solution Approach 1:
The monitoring system incorporates automated data collection, analysis, and alert generation capabilities that operate without continuous human intervention. Sensors automatically monitor pipeline parameters, the system self-diagnoses abnormal conditions using predefined algorithms, and generates alerts only when actual threats are detected. This self-service approach maintains high reliability for burst prevention while minimizing the need for manual monitoring personnel, improving overall manpower efficiency.
Solution Approach 2:
Manual monitoring and response actions are replaced with automated electronic systems. The monitoring platform uses computer algorithms to analyze sensor data, identify burst risks, and trigger automated responses such as valve closure or pump adjustment. This substitution of mechanical/manual operations with automated electronic control maintains comprehensive real-time monitoring effectiveness while dramatically reducing manpower requirements and operational costs.
Data Source
AI summary
The present disclosure relates to a method and system for city lifeline pipeline burst prevention based on an IoT large model. The method includes: obtaining fluid pressure data; determining a pipe burst probability of a pipeline network node based on the fluid pressure data; and determining a target valve and a valve opening degree of the target valve based on the pipe burst probability, and controlling the target valve to the valve opening degree.