Design And Implementation Of A Water Quality Prediction And Monitoring Platform

The vast majority of China has neglected the treatment of discharged sewage and discharged industrial wastewater into lakes and rivers at will,and water quality monitoring has become increasingly important.The research focus of this thesis is to build a big data platform that can monitor and predict water quality data.First of all,in this paper,a kriging interpolation algorithm and an inverse distance weighting algorithm are used to predict the current water quality turbidity parameters with relatively low precision.TR parameter is optimized,and more accurate TR parameter is selected,which improves the accuracy of the prediction algorithm.The experimental results show that the top-relevant-based water quality prediction method has higher prediction accuracy and stability than the kriging interpolation and inverse distance weighted interpolation algorithms.Compared with the kriging interpolation and inverse distance weighted algorithms,the average absolute errors are respectively reduced by 1.07 and 1.31.Based on the water quality parameter prediction algorithm based on Top-Relevant,this paper establishes a model of the water quality monitoring big data platform by conducting demand analysis and business analysis of water quality monitoring in accordance with the requirements of the management application of the big data platform.The function design required by the platform realizes the design of the table structure of the database.In this study,a data platform for water quality monitoring and prediction based on the SSM framework was established,and data visualization display function,collection device map distribution function,water quality fixed-point collection data function,unmanned ship trajectory display function,and multiple water quality monitoring index display functions were realized.Display functions of inversion of water quality turbidity parameters and analysis and prediction of water quality parameters.