Method for storing massive industrial equipment monitoring data by means of non-relation database

Abstract

The invention relates to a method for storing massive industrial equipment monitoring data by means of a non-relation database, and belongs to the technical field of computer database management. At first, a plurality of unstructured data management systems form a non-relation database cluster of a distributed structure, the massive industrial equipment monitoring data are read from a relation database, the read monitoring data are stored into multiple client machines in a form with text files are separated by commas, a non-relation database storage mode is constructed, and the text files separated by commas and on the client machines are led into the non-relation database cluster. According to the method, the massive equipment monitoring data can be effectively stored, rapid data inquiring and analyzing are supported, enterprises can inquire and analyze the massive monitoring data, information having commercial value in the monitoring data is excavated, and finally the purpose of providing references for designing, manufacturing, marketing, using, maintaining and other aspects of industrial production is realized.

Description

technical field [0001] The invention relates to a method for storing massive industrial equipment monitoring data by using a non-relational database, which belongs to the technical field of computer database management. Background technique [0002] With the rapid development of the manufacturing industry, more and more sensors are applied to industrial production lines and construction machinery equipment in the process of enterprise informatization for large-scale equipment, thereby generating a large amount of industrial equipment monitoring data. These data are usually generated by a large number of sensors on construction machinery and equipment, and are sent to the service platform through the mobile network in real time and stored in the central database. By storing and analyzing these data, enterprises can mine information with commercial value, which has reference significance for the life cycle of industrial production, such as design, manufacturing, sales, use and...

AI Technical Summary

Problems solved by technology

[0005] There are the following problems with the database partitioning technology: First, through the data partitioning technology, only a small part of the massive monitoring data can be stored in the online database, usually the data of the last few months, and the online analytical processing (OLAP) application It can only process these data; secondly, the traditional relational database needs a fixed model to describe the data, so it is difficult to adapt to the changeable characteristics of the monitoring data model; thirdly, the traditional database is difficult to expand horizontally, and the demand for capacity expansion can only be It is realized through downtime maintenance and data migration, which has high time and financial costs; finally, traditional relational databases are difficult to meet the needs of high concurrent read and write, and have high requirements for hardware performance. When the amount of data reaches tens of billions, the historical Large-scale data query operations will be difficult to complete, small-scale query operations will also become slow, and the performance of concurrent data writing will be difficult to meet the demand

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