Fusion Of Multi-source Data Of Water Environment Management System Research

Recently,in order to solve the increasingly serious problem of water environmental pollution,the water environmental management system established by various organizations relying on their own databases has caused the phenomenon of isolated data islands due to the independence of databases,which is very unfavorable to the analysis and utilization of water environmental data.Therefore,a watershed water environment management system is built by various advanced information science and technology such as machine learning,big data,data mining,database and other science and technology.The system extracts useful information and integrates data from huge,complex and changeable water environment data,realizes the intelligent digitalization of water environment big data,which is conducive to data analysis and prediction,and provides favorable support for various departments to formulate effective protection measures to improve water environment pollution,and greatly enhances the efficiency of various tasks.The main contents are as follows.(1)The water environment management system designed and implemented in this paper has a series of functions such as report center,multi-dimensional analysis,pollution source archives,water environment topics,etc.The main technologies are water environment data collection,data processing,information extraction,data storage and data visualization analysis.In the following contents,the system requirements analysis,interface design,database design and so on are introduced in detail.In order to detect environmental data,the data processing is carried out by water environment management system designed and implemented independently in Shandong Province.Finally,the prediction and analysis of surface water quality factors is carried out by water quality prediction model.(2)A multi-factor water quality prediction model based on Long Short-Term Memory Neural Network(LSTM)neural network is designed and implemented.For the sake of improving the accuracy of model prediction,it is necessary to denoise the data.K-Means noise reduction method is to calculate the cosine similarity of data vectors.According to the similarity degree,whether it is noise or not is judged and the corresponding removal operation is carried out.Finally,in order to predict the data,the LSTM neural network is adopted,and the optimization algorithm of the network is’ Adam ’algorithm,and then the weight value of the model is adjusted according to the optimization results of the algorithm.The prediction results show that the new model has better prediction effect.(3)System function realization and test.Realize the system code,and deploy the system in the prepared test environment,and then carry out strict test and analysis on the system and optimize it continuously,so that the function and performance can be continuously improved to meet the needs of users.The water environment management systema improves the sharing ability of water quality data,and inhibits the phenomenon of data islands to a certain extent,which is of great significance to water environment protection and decision-making.