A big data-based intelligent breeding information management system

By improving the target detection and tracking algorithm and the multi-dimensional feature fusion network, the problem of individual behavior recognition and data fusion in high-density group environments was solved, and the accurate assessment of individual health and growth status was achieved.

CN122244809APending Publication Date: 2026-06-19陕西安康玮创达信息技术有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
陕西安康玮创达信息技术有限公司
Filing Date
2026-05-25
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing smart farming systems struggle to accurately identify and track individual behavior in high-density population environments, and information from different data sources is not effectively integrated, leading to inaccurate assessments of individual health and growth status.

⚗Method used

The smart aquaculture information management system based on big data is adopted. Through improved target detection and tracking algorithms, individual identities and behaviors are identified. Combined with multi-dimensional feature engineering and feature fusion networks, data alignment and evaluation are achieved to generate health scores and growth status assessments for individuals and groups.

🎯Benefits of technology

It achieves stable identification of individual identities and continuous tracking of behaviors in highly occluded environments, breaks down data silos, constructs multimodal high-dimensional fusion features of individual status, and supports accurate assessment of individual health and growth.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention relates to the field of smart aquaculture information technology, specifically a smart aquaculture information management system based on big data. The system includes: a data acquisition module for acquiring time-series data on pen environment, individual biological behavior, and feeding consumption, as well as video monitoring stream data; a video analysis module for processing the video stream using an improved target detection and tracking algorithm to identify and track individual identities, locations, and behaviors; a feature engineering module for processing the time-series data to generate standardized environmental, behavioral, and consumption features; a feature fusion module for spatiotemporally aligning and fusing the identification and tracking information with the standardized features to generate a fused aquaculture status feature set; and a status assessment module for processing the fused feature set through deep networks to output individual and group health scores, growth status assessments, and abnormal risk warnings.
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