Expert question and answer auxiliary interaction method and system based on large water affair model

By using an expert question-and-answer system based on a large water management model, the problems of insufficient context awareness and data silos in smart water management systems have been solved, enabling accurate understanding of user intent and efficient decision support.

CN121745321APending Publication Date: 2026-03-27SEQUOIA LIBRA TECH GRP CO LTD
5 Cites 3 Cited by

Patent Information

Application Number
CN202610231378.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-27
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

In existing smart water systems, traditional question-and-answer methods lack context awareness, cannot handle the experience and qualitative language of frontline personnel, and suffer from severe data silos, resulting in low decision-making efficiency and an inability to provide end-to-end intelligent services.

Method used

An expert question-and-answer assisted interactive system based on a large water affairs model is constructed. The system constructs semantic vectors for questions through multimodal input, retrieves SCADA time-series data, static knowledge graphs and historical treatment records, performs semantic mapping and time-series feature engineering, and generates structured expert responses.

Benefits of technology

It achieves a precise understanding of user intent, breaks down data silos, provides proactive decision support constrained by expert rules, and improves decision quality and efficiency.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to an expert question and answer auxiliary interaction method and system based on a large water affair model, and relates to the technical field of intelligent water affair. The method comprises the following steps: firstly, constructing a problem semantic vector fusing a current intention and a historical background by deeply analyzing user interaction data and a historical session; then, SCADA time sequence data, a static knowledge graph and a historical processing record are retrieved from an external multi-source data stream by taking the vector as an index, and are converted into a situation feature vector reflecting the running state constraint at the current moment through semantic mapping and time sequence feature engineering. By jointly inputting a problem semantic vector and a situation feature vector into a pre-trained water affair field large model, the system can perform verification reasoning under strict working condition constraints, and encapsulates a reasoning result into a structured expert reply based on a visual template. The technical problems that in the prior art, a general model is prone to generating illusion, the retrieval result accuracy is low, and end-to-end intelligent diagnosis cannot be provided are effectively solved.
Need to check novelty before this filing date? Find Prior Art