一种面向羊只的多源融合饲料采食量预测方法及系统
By constructing a multi-layered prediction model and fusing multi-source data, high-precision prediction of sheep feed intake and automated optimization of process parameters were achieved, solving the problems of low prediction accuracy and reliance on human experience in existing technologies, and improving production efficiency and adaptability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INSTITUTE OF ANIMAL SCIENCES OF CHINESE ACADEMY OF AGRICULTURAL SCIENCES
- Filing Date
- 2025-12-11
- Publication Date
- 2026-07-17
AI Technical Summary
The current methods for predicting sheep feed intake have low accuracy and cannot dynamically adapt to changes in the environment and feed. Adjusting process parameters relies on human experience, resulting in low production efficiency and high costs.
A multi-layered prediction model is constructed, including a basic demand layer, an environmental correction layer, and a feed characteristic correction layer. Multi-source data are integrated to predict feed intake in real time. Accurate prediction and optimization of process parameters are achieved through a meta-causal discovery model and a multi-modal fusion hypergraph convolutional network.
It achieves high-precision feed intake prediction, meets the dynamic optimization needs of production sites, reduces human experience differences and response delays, reduces production losses, is applicable to different sheep pens and sheep breeds, and has broad industry promotion value.
Smart Images
Figure CN121504219B_ABST