Non-standard format database query method and system based on time granularity classifier

By using a deep learning network based on a time-granularity classifier, the problem of low query efficiency in non-standard format databases is solved, and data querying is accelerated and standardized, meeting the real-time analysis needs of enterprises.

CN116010476BActive Publication Date: 2026-02-06CHINA COAL TECH GRP INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202211649040.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-12-21
Publication Date
2026-02-06
Estimated Expiration
2042-12-21

AI Technical Summary

Technical Problem

The inefficiency of querying non-standard format data in existing databases results in slow query speeds, failing to meet the real-time data analysis needs of enterprises.

Method used

A deep learning network based on a time-granularity classifier is used. By obtaining the field matrix of the table to be queried and performing word embedding vector transformation, the deep learning network classifier is used to perform time-granularity classification, and temporary columns are created for data querying.

Benefits of technology

It improves the efficiency of data querying in non-standard format databases and increases the speed of data querying and comparison.

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Abstract

The application provides a non-standard format database query method and system based on a time granularity classifier. The method comprises the following steps: obtaining a field matrix corresponding to a table to be queried; inputting the field matrix into a pre-constructed deep learning network classifier to obtain a time granularity classification result of the table to be queried; creating a temporary column according to the time granularity classification result of the table to be queried, and performing data query based on the temporary column. The technical scheme provided by the application improves the efficiency of the non-standard format database in data query.
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Citation Information

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