【摘要】 为了简便有效地诊断城市中普遍存在的排水管道堵塞问题，通过设计管道装置并完成堵塞通水试验并，在线监测水位数据，寻优机器学习算法并以Python代码开发基于最优算法的管道堵塞诊断程序。结果表明：排水管道发生堵塞且流量相同时，堵塞越严重，上游水位线抬升越高；堵塞物距上游监测点越近，上游水位线抬升越高；下游水位几乎只与流量相关。通过机器学习算法比选发现，K-最近邻算法对数据集的分类展现出较高的准确率，经过网格交叉法优化后其诊断准确率在98%左右。可见，该诊断方法能够实现对管道堵塞的快速诊断，可以优化派工调度流程，降低管网运维成本。更多还原

【Abstract】 In order to easily and effectively detect the common problem of drainage pipeline blockage in cities, a device of drainage pipeline was designed and a test of blockage was completed, water level data was monitored online, after optimizing machine learning algorithms, a diagnostic program for pipeline blockage was developed based on the best algorithm in Python code. The results show that when the drainage pipeline is blocked meanwhile the flow rate is the same, the more severe the blockage is, the higher the upstream water level line rises. The closer the blockage is to the position of upstream monitoring point, the higher the upstream water level rises. The downstream water level is almost only related to the flow rate. Through comparing machine learning algorithms, it is found that the K-nearest neighbor algorithm exhibites high accuracy in classifying data set. After optimized by the grid search crossing validation method, the model accuracy is around 98%. It is concluded that this diagnostic method can realize rapid diagnosis of pipeline blockage, optimize the process of dispatch scheduling, and reduce the costs of pipeline operation and maintenance.更多还原