Key-Value data block based data management method and system

Abstract

The invention proposes a Key-Value data block based data management method and system. The method comprises: according to attributes of perception data, selecting the dimension of data aggregation, clustering perception data with general characters, namely, same attribute dimension, intensively storing the perception data in a same data block, and taking the selected dimension of data aggregation as attribute information corresponding to the data block; storing and managing the data in the data block in a Key-Value data format; and storing and managing a storage position of the data block and the attribute information corresponding to the data block by adopting a relational database. According to the data management method and system, massive internet-of-things perception data can be efficiently read and written under the condition of a relatively small amount of computers, and extension can be conveniently performed 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 management method and system based on a Key-Value data block. Background technique [0002] The Internet of Things has a wide range of applications in smart cities, intelligent transportation, industrial monitoring, food traceability and other fields, and has developed rapidly in recent years. The Internet of Things connects any item with the network through information sensing equipment such as radio frequency identification, infrared sensors, global positioning systems, laser scanners, gas sensors, etc., according to the agreed protocol, and exchanges and communicates information to achieve intelligence. A network that is identified, located, tracked, monitored and managed. [0003] A feature of IoT is the extensive application of various sensing technologies. Various types of sensors are deployed on the IoT, and each sens...

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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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