Data query method and system based on Key-Value data blocks

Abstract

The invention brings forward a data query method and system based on Key-Value data blocks. The method comprises following steps: selecting dimensions of data aggregation based on attributes of perception data, clustering perception data of the same attribute dimension in common, saving perception data to a same data block and utilizing selected data dimensions as attribute information corresponding to data blocks; adopting Key-Value data format to save and manage data in the data blocks; and saving and managing storage positions and corresponding attribute information by adopting a relational database. The data query method and system based on Key-Value data blocks has following beneficial effects: a deluge of perception data on the Internet of Things can be read and written in an efficient manner without using a great number of computers; and the data block is easy to expand according to business scale.

Description

technical field [0001] The invention relates to the technical fields of the Internet of Things, databases and cloud computing, in particular to a data query method and system based on Key-Value data blocks. Background technique [0002] The Internet of Things has a wide range of applications in various fields such as smart cities, intelligent transportation, industrial monitoring, and food traceability, and has developed rapidly in recent years. The Internet of Things uses radio frequency identification, infrared sensors, global positioning systems, laser scanners, gas sensors and other information sensing devices to connect any item with the network according to the agreed protocol for information exchange and communication to achieve intelligence. A network that identifies, locates, tracks, monitors, and manages. [0003] A characteristic of the Internet of Things is the wide application of various sensing technologies. Various types of sensors are deployed on the Intern...

AI Technical Summary

Problems solved by technology

This type of database improves data storage scale and query efficiency by reducing consistency constraints, and greatly reduces the complexity of system horizontal expansion, but it cannot satisfy structured and complex queries and statistics for massive data, requiring distributed server clusters. Large number of machines and high maintenance costs (site, energy)

[0007] In short, in the face of massive IoT sensing data, relational databases have low read and write efficiency, complex expansion, and high technical requirements; although non-relational databases represented by NoSQL improve efficiency by reducing consistency constraints, the processing Hundreds of thousands or even tens of thousands of computers are used for massive data, which is too heavy for many applications and does not support structured and complex queries

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