A Research On Cloud Computing And Fog Computing Integration Architecture For Intelligent Energy Management Base On Elastic Stack

At present,China's total economic output and total energy consumption have been among the highest in the world for many years.At the same time,China's energy efficiency is low,and China's energy consumption per unit of GDP still has a large gap with the world average.This has led to energy shortages and energy pollution problems,and improving energy efficiency is the most realistic and direct way to solve these problems at this stage.An excellent energy management platform can help enterprise or units to strengthen control over energy use and improve energy efficiency in it's radiation parks.Data is the foundation of a platform.Currently,in the era of the Internet of Things,the way in which basic data is acquired has undergone tremendous changes.The IoT has brought about a change in the real-time and accuracy of data,and it has opened up new application prospects for these data.Many current smart platforms use the IoT + cloud computing model,which pushes data to the cloud by the collection point,and the cloud mines and analyzes the data.This mode can often achieve good results when there is no real-time demand or the platform data carrying capacity is sufficient.However,if faced with a large amount of real-time data flow,the IoT + cloud computing model can not achieve low latency response,and network bandwidth,data reliability and other issues are difficult to solve.For this thesis,we consider adding fog computing between the modes of the IoT + cloud computing,which is used to supplement these shortcomings of cloud computing.The computing node of the fog computing is close to the data edge,and can quickly receive,process,store,and forward edge data,alleviate cloud pressure and increase the effective utilization of data.For the huge difference between cloud computing and fog computing,this thesis uses Elastic Stack as a data bridge between cloud computing and fog computing.Elastic Stack securely and reliably captures data from any source,in any format,and is flexible enough for different environments for cloud computing and fog computing.The content of this thesis is to build a part of the smart energy management platform.The whole platform integrates large modules such as IoT data collection,fog computing and cloud computing,big data analysis and forecasting.The main work of this thesis is reflected in the architecture design and function implementation of the fog end,the cloud storage architecture design and the data connection between the fog and the cloud,and the deployment and use of the Elastic Stack platform.In the specific research and implementation process,this thesis uses container technology to manage the fog computing resources,use a variety of message middleware for data caching,build Elastic Stack platform for data analysis,select the use of Spring Cloud architecture to complete the development of fog computing's data applications,etc..The final result shows that the data path of the cloud hybrid architecture realized in this thesis can maintain stable connectivity and can bear considerable data flow pressure.Data filtering achieves expected functions and has strong flexibility.Data analysis uses the Elastic Stack platform to explore highdegree-of-freedom real-time data streams.This article uses a lot of new technologies and technical features and integrates them,not only can be applied to the energy management platform,but also can be extended to various data platforms.