Water pollution early warning method, system and equipment based on deep learning and storage medium

Abstract

The invention discloses a water pollution early warning method, system and device based on deep learning and a storage medium, and the method comprises the steps: setting a plurality of monitoring points in a river channel, and obtaining the water quality data information of each monitoring point; extracting a plurality of pollution characteristic indexes from the water quality data information; constructing an RNN feature fusion model, and inputting the pollution feature indexes into the RNN feature fusion model to generate fusion feature indexes; constructing a water quality monitoring convolutional neural network model, and training the convolutional neural network model; converting the fusion feature index into a fusion feature vector, inputting the fusion feature vector into the trained water quality monitoring convolutional neural network model, and outputting a probability value corresponding to the fusion feature vector; comparing a difference value between the binary probability value and a preset threshold value to judge whether the water quality of the corresponding monitoring point is polluted or not; and the monitoring point module which monitors the water quality pollution carries out early warning. The water pollution monitoring efficiency can be effectively improved, and early warning can be performed in time.

Description

technical field [0001] The invention relates to the technical field of deep learning and environmental pollution control, in particular to a water pollution monitoring and early warning method, system, equipment and storage medium based on deep learning. Background technique [0002] Water pollution monitoring is an important part of comprehensive environmental management. With the rapid development of my country's economy, industrial and domestic water consumption has also increased sharply. The shortage of water resources and water pollution have become major problems faced by my country's economic and social development. [0003] Traditional water pollution monitoring and early warning methods mainly include laboratory monitoring, remote sensing monitoring, biological monitoring, etc. The models used include time series models, regression analysis models, and grey system theoretical models. However, traditional water pollution monitoring and early warning methods often onl...

AI Technical Summary

Problems solved by technology

[0003] Traditional water pollution monitoring and early warning methods mainly include laboratory monitoring, remote sensing monitoring, biological monitoring, etc. The models used include time series models, regression analysis models, and gray system theoretical models. However, traditional water pollution monitoring and early warning methods often only focus on the data itself characteristics, without fully considering the interrelationship between data, the prediction accuracy is generally not high, the operation is cumbersome and highly dependent on manual labor, and it is difficult to accurately predict and monitor the water quality parameters of the water environment

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