南水北调中线总干渠水位、流量等实时水情数据受外界扰动、测量系统误差等因素影响而产生的病态水情数据将造成调度模型计算失真，甚至导致计算失败。为此，针对上下游流量数据空间上的逻辑错误和水位数据时间序列的跳变，分别建立基于粒子群优化的水量平衡模型和指数加权滑动平均模型，对病态水情数据在空间、时间上实施横向、纵向清洗处理。以穿黄节制闸至漳河节制闸间的渠段为典型研究区间，利用模型自动识别流量倒挂点，并对该渠段涉及的12座节制闸、26处分水点的流量数据进行统一修正，实现了上下游逻辑上的合理性。同时，选取研究渠段内的闫河节制闸为代表，在48 h内运行基本稳定状态下，对每2 h的闸前水位数据序列进行分析，自动识别出跳变数据并进行合理修正。结果表明：建立的模型可自动识别病态水情数据并进行智能清洗，处理后的数据能够较好地满足输水调度分析决策的需要，因此该模型具有推广应用的价值。

The real-time hydrological data such as water level and discharge of the main canal of the Middle Route of the South-to-North Water Transfers Project are affected by external disturbances, measurement system errors and other factors. The ill-conditioned hydrological data will cause the calculation distortion of the scheduling model, and even lead to the failure of the calculation.Aimed at the logical errors in the upstream and downstream flow data space and the jump of the time series of water level data, the water balance model based on particle swarm optimization and the exponential weighted moving average model were established respectively, and the pathological water regime data was cleaned horizontally and vertically in space and time. Taken the channel section between the Yellow River controlling gate and the Zhanghe River controlling gate as a typical research interval, the flow inversion point was automatically identified by the model. The flow data of 12 controlling gates and 26 water diversion points involved in the channel section were uniformly corrected to realize the rationality of upstream and downstream logic. At the same time, the Yanhe controlling gate in the research section was selected as the representative. Under the basic stable state of operation within 48 hours, the water level data sequence in front of the gate every 2 hours was analyzed, and the jump data was automatically identified and reasonably corrected.The results showed that the model established could automatically identify the pathological water regime data and carried out intelligent cleaning. The processed data was able to better meet the needs of water transfer scheduling analysis and decision-making. So the model has the value of popularization and application.