结合多模态大模型的题目解析讲解生成方法及系统

By combining multimodal large models to standardize questions and generate personalized solution paths, the problem that existing problem-solving tools cannot meet users' personalized needs is solved, thereby improving learning effectiveness and tool usability.

CN121743487BActive Publication Date: 2026-07-17JIANGSU HAOHAN INFORMATION TECH +1

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JIANGSU HAOHAN INFORMATION TECH
Filing Date
2026-02-26
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing problem-solving tools lack a deep understanding of users' individual needs and knowledge levels, resulting in an inability to provide tailored solutions, which affects users' learning outcomes and the practicality of the tools.

Method used

By combining a multimodal large model, the system standardizes multimodal input questions, separates structured question information, generates multiple initial solution paths, selects target solution paths based on user learning profiles, performs recursive knowledge point reinforcement, generates a reliable solution chain without blind spots, and provides cross-modal targeted rendering output analysis and explanation according to user needs.

Benefits of technology

It enables the generation of personalized problem-solving paths, enhances learning interactivity and efficiency, meets the personalized needs of different users, and improves learning outcomes.

✦ Generated by Eureka AI based on patent content.

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Abstract

本发明提供了结合多模态大模型的题目解析讲解生成方法及系统,涉及多模态学习技术领域,方法包括:对多模态输入题目进行标准化处理,得到标准化题目数据;分离结构化题目信息进行深度特征提取,驱动解题思路库的三级知识结构联动匹配,生成多个初始解题路径;进行知识盲区覆盖筛选,定位目标解题路径;进行递归式知识点补强,生成无盲区可信解题链;进行分步骤解析逻辑推导,输出目标解析逻辑框架;根据实时讲解模态选择,进行跨模态定向渲染,交付输出多模态解析讲解流。本发明解决了现有技术缺乏对用户的个性化需求和知识掌握水平的深度理解,无法为用户提供量身定制的解答,影响了用户学习效果和工具实用性的技术问题。
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