Artificial intelligence real-time prediction method and device

By configuring the AI ​​engine based on SQL statements and real-time AI metadata, the problem of insufficient usability of existing real-time AI prediction solutions is solved, and AI models from different modeling environments are seamlessly integrated, improving the usability and maintainability of real-time prediction.

CN116955385BActive Publication Date: 2026-06-19CHINA MOBILE INFORMATION TECHNOLOGY CO LTD +1
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Patent Information

Application Number
CN202210347798.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-04-01
Publication Date
2026-06-19
Estimated Expiration
2042-04-01

AI Technical Summary

Technical Problem

Existing real-time AI prediction solutions require code modification, testing, compilation, packaging, and deployment when business requirements change, resulting in insufficient usability and maintainability.

Method used

By obtaining physical plans and real-time AI metadata based on SQL statements, configuring the AI ​​engine, and using the configured AI engine to make predictions on the data to be predicted, the code development of programming languages ​​is avoided, and seamless integration of AI models in different modeling environments is supported.

Benefits of technology

It improves the usability and maintainability of real-time AI prediction solutions, reduces the cumbersome process of code modification and deployment, and enables data stream preprocessing and real-time prediction of AI models.

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Abstract

This invention provides an AI real-time prediction method and apparatus. The method includes: obtaining a physical plan based on an AI-related SQL statement; obtaining a configured AI engine based on the physical plan and real-time AI metadata; using the configured AI engine to predict data to be predicted and obtaining prediction results; the amount of data to be predicted is dynamically adjusted according to data backlog. This invention seamlessly integrates an AI framework based on SQL statements, supports AI models trained in different modeling environments, avoids AI models failing to execute due to environmental differences, and eliminates the need for code development using programming languages ​​such as Java and Python. It achieves data stream preprocessing and real-time AI model prediction, improving the usability of real-time AI prediction solutions.
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