Design And Implementation Of The Application Platform Of Smart Environmental Protection Air Pollution Data

With the rapid development and maturity of the Internet of Things technology,equipment in various fields of the environmental protection industry has successively adopted the technology to achieve the environmental quality monitoring.In the atmospheric field,it is becoming more and more intelligent to monitor the various atmospheric pollutant concentrations using the Internet of Things technology.As the air pollution monitoring equipment increased with time,the amount of data collected and reported by the equipment has largely increased.In addition,how to apply the data reasonably,analyze and predict the air quality data based on time series,mine the value behind the data,and give useful information to guide production and life,which has become an urgent problem in the field of air pollution.To solve the problems mentioned above,the main work of this paper were listed as follows:This paper designed a scheme for hierarchical storage,retrieval and sharing of air pollution data basing on the research and analysis of related technologies and the characteristics of Internet of Things data.In the data grading storage scheme,the relational database is used for real-time data storage,and the architecture separated by the master-slave database separates the frequent reading and writing operations of the real-time data;Data migration tools are used to implement data migration between heterogeneous databases on a regular basis,and redundant backup of historical data is completed to alleviate the storage pressure of real-time databases and improve system throughput.In the data retrieval sharing scheme,the co-processing mechanism of HBase database and distributed retrieval tool of ElasticSearch were applied to optimize the hierarchical indexing method of the platform distributed database,which supported multi-condition retrieval of platform data.Moreover,the platform provides WebService data interfaces to the inside and outside of the platform for data sharing,allowing users to obtain platform data according to requirements using RESTful-compliant interfaces.Based on the platform air pollution data,the air quality index prediction model was studied,and a time series prediction algorithm S-LSTM incorporating spatial information features was proposed.The K-means clustering algorithm is used to cluster the stations data,and the stations that have a great influence on the air quality condition in space are grouped together.Using the clustering results,the weights are set according to the geographical distance between the stations,and the historical pollutant concentration data of each station in the same cluster is weighted.By integrating spatial information into temporal data,the sequential data with the dual characteristics of space-time is constructed.Finally,the stacked LSTM algorithm is used to predict the air quality index of the spatial and temporal feature information fusion data series.Through experimental verification and analysis,it was concluded that the temporal sequence prediction algorithm integrated with spatial information can integrate the influence of spatial and temporal dual-dimensional features of data on the prediction results,and can effectively improve the accuracy of the prediction results in this prediction scenario.Based on the results above,the design and implementation of each functional module were completed in this paper,and further a smart environmental protection air pollution data application platform was built.With the software engineering method,the data application functions such as data hierarchical storage,data retrieval and sharing,data forecasting and early warning,data visualization and presentation were successfully introduced.Consequently,the functional and non-functional test of the platform were carried out.