Intelligent query method for relational database based on machine learning

By collecting and dynamically aligning multimodal stream data in real time, a dynamic psychological state map is constructed, which solves the temporal semantic changes and contextual dependencies of multimodal psychological dialogue data, realizes continuous understanding and personalized analysis of users' psychological states, and improves query accuracy.

CN121560908APending Publication Date: 2026-02-24LUSHAN COLLEGE OF GUANGXI UNIV OF SCI & TECH
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Patent Information

Application Number
CN202511508258.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-21
Publication Date
2026-02-24

AI Technical Summary

Technical Problem

Existing database query technologies based on static knowledge graphs and rule-based reasoning struggle to handle the temporal semantic changes in multimodal psychological dialogue data and the contextual dependencies of users' natural language queries, resulting in intelligent query results deviating from users' true psychological states and consultation needs.

Method used

By collecting multimodal stream data in real time, aligning different modal signals using a dynamic time warping algorithm, constructing a dynamic psychological state map, modeling the evolution of psychological states using a time-series graph neural network and neural ordinary differential equation mechanism, and generating personalized analysis reports by combining a causal discovery algorithm and a neural symbolic reasoning engine.

Benefits of technology

It effectively solves the problems of timing deviation and context association of multimodal signals, improves the accuracy and personalization of queries in psychological dialogue scenarios, and realizes continuous understanding and personalized analysis of users' psychological states.

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Abstract

The invention relates to the technical field of data processing, in particular to a relational database intelligent query method based on machine learning, which comprises the following steps of: processing multi-modal flow data through time sequence alignment, generating a unified semantic representation vector, constructing a dynamic psychological state map, and modeling a psychological state evolution track by utilizing a neural common differential equation mechanism. After user query is received, historical dialogue nodes are retrieved from the graph, enhanced query intention representation is generated, the enhanced query intention representation is converted into an execution plan through a neural symbol inference engine, and a graph neural network is adopted to predict execution cost. And finally, a personalized analysis report is generated by combining a causal discovery algorithm, and system adaptive optimization is realized through feedback signals. According to the method, the problems of inconsistent time sequence semantics and strong context dependency of the multi-modal psychological data are effectively solved, and the query accuracy and the personalized level in a psychological dialogue scene are improved.
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