Water level prediction method and electronic equipment
Through dynamic knowledge graphs and mixed expert network models, the hydrological characteristics of the target water level site are extracted and auxiliary prediction models are determined, which solves the problem of poor adaptability of existing water level prediction technologies and achieves higher water level prediction accuracy.
CN119358658BActive Publication Date: 2025-03-28HANGZHOU HIKVISION DIGITAL TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202411957442.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-27
- Publication Date
- 2025-03-28
- Estimated Expiration
- 2044-12-27
AI Technical Summary
Technical Problem
The existing water level prediction technology has poor adaptability to adapt to various water level influencing factors and changes, affecting the accuracy of the prediction.
Method used
The hydrological characteristics of the water level site and/or precipitation site directly connected to the target water level site are obtained through a pre-constructed dynamic knowledge graph, and the water level prediction is determined from the hybrid expert network model in conjunction with a pre-trained gated network.
Benefits of technology
This method can automatically adapt to various water level influencing factors and their changes, improving the accuracy of water level prediction.
✦ Generated by Eureka AI based on patent content.
Abstract
The disclosed embodiment provides a method and electronic device for water level prediction, which relates to the field of water level prediction technology. It includes: obtaining water level stations and / or precipitation stations directly connected to the target water level station from a pre-built dynamic knowledge graph; extracting the hydrological characteristics of the target water level station and the water level stations and / or precipitation stations directly connected to the target water level station to obtain a hydrological feature set of the target water level station; inputting the hydrological feature set into a pre-trained gated network to determine at least one auxiliary prediction model from a hybrid expert network model; inputting the hydrological feature set into at least one auxiliary prediction model and a pre-trained target neural network model to obtain a fused first target prediction result. It can automatically adapt to numerous water level influencing factors and the continuous changes of water level influencing factors, thereby improving the accuracy of water level prediction.
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