China Institute of Water Resources and Hydropower Research

China Three Gorges Corporation

Beijing Youyi Union Building Materials Co., Ltd.

Xiaohui Lei

Hao Wang

Chao Wang

Huichao Dai

Zhao Zhang

Heng Yang

Zhaohui Yang

Yongnan Zhu

Lingzhong Kong

Assembled non-slip floor mat

A method for predicting and controlling a water level of a series water conveyance canal on the basis of a fuzzy neural network is disclosed. The method includes: performing the relationship between a sluice opening degree and an open canal control water level by means of a fuzzy neural network, and constructing an upstream water level controller of a coupled predictive control algorithm; solving an optimal control rate of the upstream water level controller using a gradient optimization algorithm on the basis of a control target of the upstream water level controller; and generating a control strategy by collecting actually measured water level change information and multiplying the actually measured water level change information by the optimal control rate on the basis of the solved optimal control rate, thereby fulfilling the object of predicting and controlling the water level.

Method for predicting and controlling awater level of a series water conveyance canal on a basis of a fuzzy neural network

A real-time abnormity-diagnosing and interpolation method for water regime-monitoring data relates to the technical field of monitoring water regime. This method includes the following steps: acquiring water regime-monitoring data, drawing a box plot, recognizing and diagnosing abnormal data in real time based on the box plot, performing grey correlation analysis on other variables related to a predictor variable, building a BP neural network model and making training, applying the BP neural network model to predict water regime-monitoring data in real time, and performing abnormity diagnosis and data interpolation. Adopting this method, we can effectively enhance predicting and monitoring the water regime-monitoring data in real time, and diagnose abnormal data and make interpolations in time, thereby improving the reliability of data, objectively reflecting water regime changes, and effectively guiding engineering scheduling.

Real-time abnormity-diagnosing and interpolation method for water regime-monitoring data

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Beijing, China

Xiaohui Lei