Building Energy Consumption Data Index Method in Cloud Computing Environment

摘要

In order to represent the building energy consumption data in the existing relational database system, the traditional method needs to update and store the building location information frequently. This takes up a large amount of resources and drops the performance sharply, resulting in low efficiency of query. In order to overcome these problems, an index method based on hbstr tree in the cloud computing environment is proposed to model the spatial location of buildings and the time attribute of building energy consumption data. Through abstract methods, the frequently updated location and time information can be represented in a static way. On this basis, the building energy consumption data is updated and divided, and the existing relational database is used for storage and processing. The spatial-temporal characteristics of building energy consumption data are fully considered for data compression to obtain feature points, and the maximum and minimum distance method is used to select the initial clustering center. At the same time, combining the advantages of spatiotemporal R-tree, b * tree, and hash table, the index is constructed to realize the effective index of building energy consumption data. The experimental results show that the proposed method can ensure the efficient query of building energy consumption data in large-scale and multi concurrent numbers, and the query accuracy can meet the actual needs.