Research On Water Quality Monitoring And Early Warning System Of Aquaculture Industry Based On Internet Of Things Technology

At present,the scale of aquaculture in China is the first in the world,but due to the lack of time from traditional natural capture to aquaculture of aquatic products,the lack of information means to assist,resulting in the lack of effective monitoring of aquaculture water quality data and early warning of the deterioration of key water quality factors.In order to realize the real-time monitoring of water quality data and the prediction of key water quality factors,this project innovatively combines the Internet of Things technology with the prediction algorithm,and studies the water quality monitoring and early warning system of the aquaculture industry based on the Internet of Things and the prediction algorithm.The details are as follows:(1)The use of Internet of Things technology for real-time monitoring of water quality factors.In view of the current aquaculture practitioners’ judgment of water quality mainly relying on naked eye observation,empirical judgment and other issues,the research system of this project uses the Internet of Things technology to realize the remote real-time monitoring of four basic water quality factor data,namely water temperature,p H value,turbidity,and dissolved oxygen in water.The water quality monitoring and early warning system designed by the project can effectively solve the problem that the current water quality data cannot be intuitively obtained.(2)Use prediction algorithms to predict dissolved oxygen of key water quality factors.In view of the current situation that the key water quality factors of the aquaculture industry are concerned about the inability to predict dissolved oxygen,this project proposes the use of long-term short-term memory neural network LSTM for the prediction of dissolved oxygen in water,and at the same time innovatively proposes to use the correlation between various water quality factors to predict dissolved oxygen in water,by permutation and combination of four water quality factors monitored,combined to predict dissolved oxygen data,and use MAE,RMSE and other evaluation indicators to evaluate the results of the combination prediction of various factors.The combination of factors with the best prediction effect DO+p H was screened and applied to the design of the early warning system,which solved the problem of difficult prediction of dissolved oxygen data.(3)The use of photovoltaic power supply system solves the problem of outdoor operation and power supply of Internet of Things data acquisition equipment.Aiming at the problem that the Internet of Things acquisition equipment works for a long time in the outdoor power supply difficulty,the project designed the corresponding photovoltaic power supply control system.At the same time,the project designed the object of the photovoltaic charge controller,which solved the problem of outdoor operation and power supply of the Internet of Things data acquisition equipment.At last,for the large amount of data collected by the Io T collection devices cannot be stored and processed directly on the on-board devices,this paper uses 4G Cat1 module to send the data to the remote server,and achieves the direct access to the server by the communication module through fixed IP and intranet penetration.The local server and Mongo DB database are used for data processing,and the visual database management tool Navicat is used at the server terminal for data management.After experimental testing shows that the system studied in this paper can solve the problem of online monitoring of water quality and early warning of dissolved oxygen in water bodies in the farming industry to a certain extent.