Abstract
In order to enhance the load balance in the big data storage process and improve the storage efficiency, an intelligent classification method of low occupancy big data based on grid index is studied. A low occupancy big data classification platform was built, the infrastructure layer was designed using grid technology, grid basic services were provided through grid system management nodes and grid public service nodes, and grid application services were provided using local resource servers and enterprise grid application services. Based on each server node in the infrastructure layer, the basic management layer provides load forecasting, image backup, and other functional services. The application interface layer includes the interfaces required for the connection between the platform and each server node, and the advanced access layer provides the human-computer interaction interface for the operation of the platform. Finally, based on the obtained main structure, the depth confidence network is constructed by stacking several RBM layers, the new samples are expanded by adding adjacent values to obtain the mean value, and the depth confidence network is used to classify them. The experimental results show that the load of different virtual machines in the low occupancy big data storage process is less than 40%, and the load of each virtual machine is basically the same, indicating that this method can enhance the load balance in the data storage process and improve the storage efficiency.
Highlights
In the process of operation and development, enterprise networks will accumulate a large number of low occupancy big data [1, 2]
Because occupant behavior is the main source of uncertainty in energy management, ignoring it will usually lead to energy waste caused by overheating and undercooling, as well as discomfort caused by insufficient heat and ventilation services
This paper studies the low occupancy big data classification method based on grid technology and uses grid technology to integrate low occupancy big data and realize high-quality storage of low occupancy big data
Summary
In the process of operation and development, enterprise networks will accumulate a large number of low occupancy big data [1, 2]. E framework uses integrated classification algorithm to extract three forms of Scientific Programming occupancy information It creates a data interface connecting the energy management system and the network physical system and realizes automatic occupancy detection and interpretation by assembling multiple weak classifiers for WiFi signals. Experimental and simulation results show that the proposed model can save about 26.4% of refrigeration and ventilation energy consumption with appropriate classifiers and occupancy data types. This method does not conduct in-depth research on data classification methods. E experimental results show that the classification accuracy of the feature set is more than 86% regardless of the script This method only classifies image data and has high limitations. This paper studies the low occupancy big data classification method based on grid technology and uses grid technology to integrate low occupancy big data and realize high-quality storage of low occupancy big data
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