A Method and System for Digital Assessment and Feedback of Chicken Activity Based on Smart Terminals

By deploying smart motion ankle bracelets and machine vision monitoring units in chicken coops, and combining edge computing and multimodal fusion causal inference models to generate executable instructions, the problems of high hardware cost, insufficient accuracy and large latency in chicken activity assessment are solved, achieving efficient and intelligent real-time monitoring and feedback.

CN122314239APending Publication Date: 2026-06-30PINGNAN GUGU FOREST ECOLOGICAL AGRICULTURE TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
PINGNAN GUGU FOREST ECOLOGICAL AGRICULTURE TECHNOLOGY CO LTD
Filing Date
2026-03-25
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies for assessing chicken activity levels suffer from problems such as high hardware costs, insufficient accuracy, difficulty in interpreting results, large delays, low levels of intelligence, and insufficient adaptability, making it difficult to meet the real-time monitoring needs of high-density farms.

Method used

A non-uniform sampling strategy is adopted to deploy intelligent motion ankle bracelets and machine vision monitoring units. Data preprocessing is combined with edge computing, analysis is performed through a multimodal fusion causal inference model, and executable instructions are generated using a dynamic routing strategy to form a closed-loop feedback mechanism, thereby achieving end-to-end automation and intelligence.

Benefits of technology

It reduces hardware costs, improves monitoring accuracy and interpretability, ensures low-latency response and adaptive capabilities, and enhances human-machine collaboration efficiency and system intelligence.

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Abstract

This invention discloses a method and system for digital assessment and feedback of chicken activity based on smart terminals, belonging to the field of smart farming technology. The steps include: deploying a hybrid sensing network including smart motion ankle bracelets, machine vision monitoring units, and environmental sensors within the chicken coop using a non-uniform sampling strategy to collect data; transmitting the processed data to the cloud for analysis using a multimodal fusion assessment model integrating a causal inference module; generating specific instruction sequences based on the health risk index and diagnostic report, and distributing them to various types of on-site interactive terminals through a dynamic routing strategy to drive on-site intervention operations; and capturing feedback from on-site personnel via the interactive terminals regarding the intervention results. Thus, through an innovative non-uniform sampling sensing strategy and an interpretable multimodal fusion causal inference model, the entire process from data collection to precise intervention is automated and intelligent.
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