Abstract: A high-efficiency solar multi-layer evaporative seawater desalination device includes a transparent housing. In the device, wedge-shaped recesses are formed on an outer wall of the transparent housing; during seawater desalination, seawater is injected into evaporation trays and sunlight is irradiated on the evaporation trays through the transparent housing; the transparent housing is made of a transparent plastic material, such that sunlight can directly enter the transparent housing and increase a temperature therein to speed up the evaporation; the wedge-shaped recesses are on a shady surface; during desalination, seawater in the evaporation trays absorbs heat and evaporates to form water vapor, such that there is a given temperature difference between the water vapor and the outside; and a lower part of the wedge-shaped recess can serve as a condensation surface, the water vapor condenses on the transparent housing or at the wedge-shaped recess.

Abstract: 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.

Abstract: A high-efficiency solar multi-layer evaporative seawater desalination device includes a transparent housing. In the device, wedge-shaped recesses are formed on an outer wall of the transparent housing; during seawater desalination, seawater is injected into evaporation trays and sunlight is irradiated on the evaporation trays through the transparent housing; the transparent housing is made of a transparent plastic material, such that sunlight can directly enter the transparent housing and increase a temperature therein to speed up the evaporation; the wedge-shaped recesses are on a shady surface; during desalination, seawater in the evaporation trays absorbs heat and evaporates to form water vapor, such that there is a given temperature difference between the water vapor and the outside; and a lower part of the wedge-shaped recess can serve as a condensation surface, the water vapor condenses on the transparent housing or at the wedge-shaped recess.

Abstract: 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.

Abstract: Disclosed in the present invention is a method for forecasting runoff under influence of an upstream reservoir group by utilizing forecasting errors. The method comprises: collecting data; establishing a regulation and storage influence quantity estimation model by utilizing a known hydrological model and a KNN model according to the collected data; driving the hydrological model by combining the collected data to predict a future runoff volume; obtaining a forecast error in a previous time period; obtaining a future regulation and storage influence quantity estimated value according to the forecast error in the previous time period in combination with the regulation and storage influence quantity estimation model; and superposing the future runoff volume and the future regulation and storage influence quantity estimated value to obtain a runoff forecast value in a future time period.

Abstract: 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.