China Institute of Water Resources and Hydropower Research

China Three Gorges Corporation

Beijing Baidu Netcom Science Technology Co., Ltd

Zhaohui Yang

Hao Wang

Chao Wang

Huichao Dai

Zhao Zhang

Heng Yang

Xiaohui Lei

Yongnan Zhu

Lin Wang

Xiao Li

Wanli Shi

Xuecen Shen

Guiyu Yang

Juncang Tian

Lingzhong Kong

Linghao Pu

Chuansheng Jia

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

The present disclosure provides a method and apparatus for a route navigation, which relate to the technical fields of computer vision, image processing, augmented reality and the like. An implementation solution is: performing an augmented reality navigation on a current route based on real-time acquired GPS information of a terminal; acquiring SLAM (simultaneous localization and mapping) coordinates of the terminal; converting, in response to determining that the GPS information meets a preset positioning difference condition, the SLAM coordinates into calculated geographic coordinates; and performing an augmented reality navigation on the current route based on the calculated geographic coordinates.

Method and apparatus for route navigation, electronic device, computer readable medium

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

A method for forecasting an agricultural irrigation water requirement includes: selecting a baseline period and crops each with a sown area larger than a set proportion as typical crops; calculating an irrigation water quota of the typical crop during a growth stage according to meteorological information; obtaining ten-day rainfall in a water-saving area from weather stations; and calculating effective rainfall in a sown region of the typical crop based on the ten-day rainfall; calculating an irrigation water requirement per unit area of the typical crop; and determining whether the irrigation water requirement per unit area of at least one typical crop is greater than an average of actual irrigation water usage per unit area measured in consecutive years, if yes, correcting the irrigation water requirement per unit area of all typical crops according to actual irrigation water usage; otherwise calculating a total water requirement for a next year.

Method for forecasting an agricultural irrigation water requirement

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Beijing, China

Zhaohui Yang