Stem crop growth real-time data management method

Abstract

The invention discloses a stem crop growth real-time data management method. When stem crop real-time data is created, if corresponding stem crop growth analysis data and stem crop growth environmentdata do not exist in the stem crop real-time data set, stem crop growth analysis data and stem crop growth environment data are created in the stem crop real-time data set; when the real-time data ofthe stem crops is inquired, the real-time data of the stem crops is directly inquired from the real-time data set of the stem crops; when the stem crop real-time data is updated, stem crop growth analysis data, stem crop growth environment data and synchronization time in the stem crop real-time data set are firstly updated, and then whether abnormal data exists in the updated stem crop growth analysis data and stem crop growth environment data or not is judged; and finally, the abnormal data is written into a database, and an early warning is given. And when the stem crop real-time data is deleted, the stem crop real-time data is directly deleted from the stem crop real-time data set.

Description

technical field [0001] The invention relates to a data management method, in particular to a real-time data management method for stem crop growth. Background technique [0002] The current real-time data management methods for stem crops are all based on regularly reporting the real-time data of stem crops to the host computer according to the reporting frequency, and the real-time data of stem crops is the latest data reported. In order to ensure the timeliness and accuracy of the reported data, the frequency of reporting is required to be high, resulting in frequent data reporting, a huge amount of data storage, and frequent interactions between the host computer and the database. At the same time, real-time data is usually used for stem crop growth warnings, etc. Scenarios, applications, and queries are relatively frequent, and frequent database queries also put a huge performance pressure on the database. At the same time, the timeliness and query efficiency of data are...

AI Technical Summary

Problems solved by technology

In order to ensure the timeliness and accuracy of the reported data, the frequency of reporting is required to be high, resulting in frequent data reporting, a huge amount of data storage, and frequent interactions between the host computer and the database. At the same time, real-time data is usually used for stem crop growth warnings, etc. Scenarios, applications, and queries are relatively frequent, and frequent database queries also put a huge performance pressure on the database. At the same time, the timeliness and query efficiency of data are high, and the real-time data stored in the database cannot well meet the observation needs.

[0003] Therefore, the current data management methods all have the following shortcomings: the lack of effective optimization and filtering of the reported data, and the huge amount of data stored in the database; frequent writing to the database increases the performance pressure of the database; frequent real-time Data query, resulting in high pressure on database query, etc.

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