Area building energy consumption platform data storing and query method

Abstract

The invention relates to an area building energy consumption platform data storing and query method which are used for uniformly storing and querying energy consumption data of a plurality of buildings. The method includes a data storage process and a data query process. The data storage process includes: starting up a monitoring thread for each building respectively; reading building energy consumption data in a data message queue through the monitoring threads, wherein the building energy consumption data includes building ownership and data uploading time, the monitoring threads write the building energy consumption data in a database file corresponding to a duration of the building ownership and data uploading time. The data query process includes: accessing and reading the corresponding database file according to the building ownership and the data uploading time in a query request, and then acquiring desired energy consumption data. Compared with the prior art, the method is high in utilization of hardware resources, is fast in initialization speed, is high in platform operation efficiency, and facilitates expansion of a building energy consumption platform.

Description

technical field [0001] The invention relates to a data storage and query method, in particular to a data storage and query method for a regional building energy consumption platform. Background technique [0002] In the regional building energy consumption monitoring platform, the storage and access of building energy consumption is one of the important technologies. Because it is a platform-level application, the energy consumption data of hundreds or even thousands of buildings are aggregated to the platform. After a period of years, the amount of data accumulated is quite amazing. The traditional approach is to use commercial relational databases to store and access large amounts of data, such as sql server and oracle database clusters, which can basically meet the application requirements of a certain scale, but the storage expansion cost of the system platform is very high, and the maintenance is quite expensive. Complicated, not suitable for platform applications with...

AI Technical Summary

Problems solved by technology

The traditional approach is to use commercial relational databases to store and access large amounts of data, such as sql server and oracle database clusters, which can basically meet the application requirements of a certain scale, but the storage expansion cost of the system platform is very high, and the maintenance is also quite expensive. Complicated, not suitable for platform applications with rapidly expanding platform scale, and currently popular big data platforms, such as hadoop, can meet the needs of big data storage, the storage cost is not high, but it is not suitable for online real-time query data, and the quality of maintenance personnel is high; There are also some small relational databases of free software that cannot meet the platform requirements for large data storage and efficient access.

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