基于大模型及数据智能体的问答方法
By adopting a question-answering method based on large models and data agents, dynamically scheduling agent invocation strategies, and combining real-time data and domain knowledge parsing, the problem of high accuracy and high reliability in existing question-answering systems under complex application scenarios is solved, realizing an efficient and interpretable question-answering system in the medical field.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HEBEI PORT GROUP SHULIAN TECHNOLOGY (XIONGAN) CO LTD
- Filing Date
- 2025-09-12
- Publication Date
- 2026-07-17
AI Technical Summary
Existing question-answering systems struggle to meet the demands for high accuracy and reliability in complex application scenarios. They suffer from outdated knowledge updates, fixed agent combinations, and a lack of traceability mechanisms, making them ill-suited for the rapid knowledge updates in the medical field.
We adopt a question-answering method based on large models and data agents. By receiving natural language questions, we preprocess them to generate a question dataset, dynamically schedule agent calling strategies, combine real-time data acquisition, domain knowledge parsing, and logical reasoning verification to output interpretable optimized answers, and optimize agent calling strategies through feedback mechanisms.
It improved the completeness and credibility of answers to complex questions, enabled full-chain traceability, and enhanced the practicality and reliability of the question-and-answer system.
Smart Images
Figure CN121413751B_ABST