Research On Environmental Data Center Platform

With the rapid development of the economy,human beings' efforts to develop nature have continued to increase,the ecological environment has been severely damaged,and environmental pollution has become increasingly serious.The timely management and control of environmental pollution problems is an urgent matter.Monitoring and prevention of quality problems has become the focus of research by many experts and scholars,and it is also a key auxiliary tool required by relevant departments.Traditional environmental data collection work cannot mine and analyze the non-linear and multivariate data types in the data.This article uses big data analysis technology to design and implement an environmental data center platform.The collected data is stored,classified,analyzed,and displayed so that users can intuitively understand the state of environmental pollution and provide scientific decision support for environmental protection departments..The specific work is as follows:(1)Propose the ozone concentration prediction model of td-LSTM.This model uses time series delay correlation algorithm to improve the LSTM model,fully tap the potential connection of data between monitoring stations,and compare with the conventional LSTM model to effectively improve the prediction accuracy.(2)Introduce the related technologies and development framework involved in the system,design an environmental data center platform,and analyze the system goals,requirements,system architecture design,database design,interface design,etc.(3)Introduce the realization and interface of some functions of the system and show the relevant user interface.The system is strictly tested to verify the reliability and effectiveness of the system.The operation results show that the environmental data center platform can collect,process and display the data analysis results in time,realize the sharing of data resources,significantly improve the detection efficiency of environmental protection departments,and provide strong support for related decisions.