Storage and management method for vector big data in water conservancy space

Abstract

The invention discloses a storage and management method for vector big data in water conservancy space. The method comprises the steps that step 1, enabling a computer to express a water conservancy space vector data set; step 2 performing parallel conversion of vector data; step 3, performing spatial data division based on the calculation amount evaluation model; step 4, constructing a distributed spatial grid R tree index; and step 5, carrying out vector data distributed memory parallel retrieval. According to the method, the bottleneck problem that spatial data and attribute data are separately treated when the spatial data and the attribute data are stored through a traditional relational database is solved. The balance of the distributed calculation amount is fully considered. The problems of low construction speed and low retrieval efficiency of the traditional space vector data index are solved.

Description

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AI Technical Summary

Benefits of technology

This patented technology describes a way to store and retrieve massive amounts of gamma rays (γray) data efficiently by utilizing its properties such as multiple dimensions or spectral content. It also suggests converting them into a special type called a cube format with specific features like fast building time and easy accessibility. By combining these techniques together, this system allows for better organization and analysis of both numerical and categorized datasets.

Problems solved by technology

This patents describes various ways to store and retrieve water conservative spatial datasets without compromising its quality level and content distribution capabilities. Current approaches involve storing them individually instead of integrating into one larger repository. These techniques require significant effort on both hardware/retrovisor systems and software tools like Apache®, Xboxescript™, Big Data Indexer, Quatty Functionality Analysis Tool(QFT), Spatial Object Representation Tensor Fusion Technique (SORMFIT). Additionally, these conventional methods often result in poor performance due to varying degrees of similarity across multiple dimensions and lack flexibility towards handling changes over time.

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