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城市排水管网动态监测预警及冒溢预测

Dynamic Monitoring and Early Warning of Urban Drainage Network and Overflow Prediction
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摘要 针对我国排水管网运行状态监测能力较弱,无法及时发现并预防排水事故的问题,建立了城市排水管网在线监测预警系统。首先,依托布设的压力及超声液位计,对排水管网液位进行实时在线监测;其次,根据液位监测数据、管网地理信息数据与拓扑数据进行分析,研究上下游排水管网水位与井中液位的关系、井中液位与冒溢的关系以及降雨量与未来液位的关系;系统最终实现了城市排水管网液位的多级动态预警、冒溢预测等功能,以期为排水管网状态的全面感知、液位异常的实时监测提供技术支持。 An online monitoring and early warning system for urban drainage network was established to address the issues of the inadequate monitoring capabilities of China's drainage network operation,enabling timely detection and prevention of drainage accidents.Firstly,the liquid level of the drainage network was monitored online in real-time using pressure and ultrasonic level meters.Secondly,the relationships between upstream and downstream drainage pipe network water levels,well liquid levels and overflow,and rainfall and future liquid levels were investigated based on the analysis of liquid level monitoring data,pipe network geographic information data,and topological data of the drainage network.The system ultimately achieved the functionalities of multi-stage dynamic warning and overflow prediction for the liquid level in urban drainage networks,aiming to provide technical support for comprehensive perception of drainage network status and real-time monitoring of liquid level anomalies.
作者 俞焰 王莹璐 赵启涵 YU Yan;WANG Ying-lu;ZHAO Qi-han(PowerChina Huadong Engineering Corporation Limited,Hangzhou 311100,China;PowerChina Huadong Engineering<Shenzhen>Corporation Limited,Shenzhen 518101,China)
出处 《中国给水排水》 CAS CSCD 北大核心 2024年第17期123-130,共8页 China Water & Wastewater
关键词 城市排水管网 动态预警 长短记忆神经网络(LSTM) 冒溢预测 urban drainage network dynamic warning long-short term memory(LSTM)neural network overflow prediction
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