A method and system for cold and hot storage of power grid GIS data under cloud architecture

Abstract

In the present invention, after pre-judging the temperature of the data to be stored, the data to be stored is stored in different storage areas according to the judgment result, and the inverted index is constructed by constructing a Map / Reduce function. It can improve the efficiency of data calling and storage, and provide fast positioning means for later search. The method includes: firstly judging the degree of coldness of a plurality of data to be stored, and then classifying the data to be stored by the subject keyword of each data to be stored in the plurality of data to be measured after the judgment of the degree of coldness and heat, classifying the data to be stored, The data to be stored is stored in a distributed storage based on hot and cold degrees, and finally the data to be stored that has been distributed is stored using the Map / Reduce model to achieve the construction of an inverted index.

Description

technical field [0001] The invention relates to the field of cloud architecture storage, and more particularly, to a method and system for cold and hot storage of power grid GIS data under a cloud architecture. Background technique [0002] Cloud architecture provides efficient and fast computing and storage functions for massive data. On this basis, a data management system with high scalability is established, which can provide users with features such as good data availability, data uniformity and security. Using cloud architecture for data storage management can effectively take advantage of various advantages of cloud technology to provide better services to users. [0003] However, the huge amount of data is accompanied by problems such as storage, search and retrieval of valid data. When the power grid geographic information system is stored on the cloud architecture, in the face of massive data, how to store it quickly and accurately is a practical problem that the ...

AI Technical Summary

Problems solved by technology

However, the NBC algorithm sets up attribute independence when classifying, which is not true in practical applications. When the data correlation is large, the accuracy and work efficiency of the NBC algorithm are reduced; the KNN algorithm needs to edit samples, so in The amount of calculation for classification is large, which affects the classification efficiency; the artificial neural network requires a large number of parameter settings, and the learning time is slow, which will reduce the credibility of the classification output results

Images