Method for finely monitoring water level of each region of reservoir based on neural network

Abstract

A method for finely monitoring the water level of each region of a reservoir based on a neural network comprises the following steps: firstly, performing regional division on a water collection part of the reservoir to obtain different water collection regions of the reservoir, then collecting historical data of the water collection regions of the reservoir, and then constructing a neural network model for each type of water yield of each region of the reservoir; the neural network models are trained; after a water level mathematical model is constructed according to the reservoir water collection region, prediction is conducted according to data collected in real time, predicted data are input into the mathematical model to be solved, the water surface change and the water level height of each region at each fine graining moment in the future are obtained, and an alarm is given when the water level is larger than a set threshold value. According to the method, the water surface change and the water level height can be more accurately described, and the water level difference in a small time period is captured, so that refined monitoring and alarming are realized.

Description

technical field [0001] The invention relates to the field of monitoring of water conservancy reservoirs, in particular to a neural network-based refined monitoring method for water levels in various regions of the reservoir. Background technique [0002] Reservoir water level monitoring is a decisive factor in hydraulic dispatching, which can reflect the flood peak adjustment ability of hydraulic engineering, and its accuracy and timeliness directly affect the accuracy of flood control dispatching. [0003] Most of the monitoring methods in the prior art adopt manual monitoring or physical equipment monitoring, such as optical fiber temperature sensors and piezoresistive water level gauges. These methods can determine the real-time height of the reservoir water level to a certain extent, but they are not forward-looking. The prior art "CN108917876A" uses neural network to predict by collecting images. The disadvantage of this method is that it needs to collect and process a...

AI Technical Summary

Problems solved by technology

The prior art "CN108917876A" uses the neural network to make predictions by collecting images. The disadvantage of this method is that it needs to collect and process a large number of waterline images, which makes the processing efficiency low

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