A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Method for positioning leakage pipe section in urban sewage pipe network
The invention discloses a method for positioning a leakage pipe section in an urban sewage pipe network, and the method comprises the following steps: 1, carrying out the machine learning training, and obtaining a pipeline leakage model and a region leakage model; 2, pipe network data, regional environment data and historical damage data of a target pipe network are collected, and the pipeline leakage model and the regional leakage model are verified; 3, establishing an integrated model according to the pipeline leakage model and the regional leakage model; 4, comparing an actual measurement result with a result given by the integrated model; and 5, feeding back the result of the integrated model, if the result of the integrated model is wrong, adding the related data of the detection area to the machine learning training in the step 1 for optimization, and executing again until the result given by the integrated model meets the requirement. According to the invention, defects in the prior art can be overcome, pipe network degradation and leakage risk prediction can be carried out, and low-cost pipeline leakage detection can be realized.
本发明公开了城市污水管网中漏损管段的定位方法，包括如下步骤：1、进行机器学习训练，得到管道漏损模型和区域漏损模型；2、收集目标管网的管网数据、地区环境数据和历史破损数据，对管道漏损模型和区域漏损模型进行验证；3、根据管道漏损模型和区域漏损模型建立集成模型；4、将实测结果与集成模型给出的结果进行对比；5、反馈集成模型的结果，如集成模型的结果错误，则将检测区域的相关数据添加到步骤1的机器学习训练中进行优化，并重新执行直至集成模型给出的结果符合要求。本发明能够克服现有技术存在的缺陷而进行管网劣化与漏损风险预测以及实现低成本下管道漏损检测。
Method for positioning leakage pipe section in urban sewage pipe network
The invention discloses a method for positioning a leakage pipe section in an urban sewage pipe network, and the method comprises the following steps: 1, carrying out the machine learning training, and obtaining a pipeline leakage model and a region leakage model; 2, pipe network data, regional environment data and historical damage data of a target pipe network are collected, and the pipeline leakage model and the regional leakage model are verified; 3, establishing an integrated model according to the pipeline leakage model and the regional leakage model; 4, comparing an actual measurement result with a result given by the integrated model; and 5, feeding back the result of the integrated model, if the result of the integrated model is wrong, adding the related data of the detection area to the machine learning training in the step 1 for optimization, and executing again until the result given by the integrated model meets the requirement. According to the invention, defects in the prior art can be overcome, pipe network degradation and leakage risk prediction can be carried out, and low-cost pipeline leakage detection can be realized.
本发明公开了城市污水管网中漏损管段的定位方法，包括如下步骤：1、进行机器学习训练，得到管道漏损模型和区域漏损模型；2、收集目标管网的管网数据、地区环境数据和历史破损数据，对管道漏损模型和区域漏损模型进行验证；3、根据管道漏损模型和区域漏损模型建立集成模型；4、将实测结果与集成模型给出的结果进行对比；5、反馈集成模型的结果，如集成模型的结果错误，则将检测区域的相关数据添加到步骤1的机器学习训练中进行优化，并重新执行直至集成模型给出的结果符合要求。本发明能够克服现有技术存在的缺陷而进行管网劣化与漏损风险预测以及实现低成本下管道漏损检测。
Method for positioning leakage pipe section in urban sewage pipe network
城市污水管网中漏损管段的定位方法
ZHAO GANG (author) / JIANG MING (author) / ZHAO GUOZHI (author) / TANG JIANGUO (author) / MAO YUNFENG (author)
2024-05-14
Patent
Electronic Resource
Chinese
Waterproof leakage repairing construction method for underground sewage pipe network
| European Patent Office | 2020
| European Patent Office | 2023