基于百炼平台的宠物健康检测AI助手：深度多模态分析与早期预警系统
Pet Health Detection AI Assistant Based on BaiLian Platform: Deep Multi-modal Analysis and Early Warning System
王小雨 1诸葛斌1
作者信息
- 1. 浙江工商大学信息与电子工程学院，杭州 310018
- 折叠
摘要
随着宠物家庭化趋势的加剧，宠物健康管理已成为数亿养宠家庭的刚需。然而，宠物具有隐藏疾病的本能，且传统监测手段存在连续性差、专业门槛高等痛点。本文提出并实现了一种基于阿里云百炼大模型服务平台的"AI宠物健康早期预警与行为分析助手"。该系统创新性地采用了"轻量化实时检测+多模态大模型深度推理"的双流分析架构，结合检索增强生成（RAG）技术，实现了对宠物异常行为（如弓背、跛行、过度舔舐）的精准识别与病理关联分析。系统通过钉钉平台进行集成部署，为用户提供实时的健康预警与量化分析报告。实验结果表明，该系统在复杂行为识别上的准确率达到92%以上，显著优于传统计算机视觉方案，为智能宠物医疗领域提供了一种高效、低成本的闭环解决方案。
Abstract
With the intensification of the trend of pet domestication, pet health management has become a necessity for hundreds of millions of pet-owning families. However, pets have the instinct to hide diseases, and traditional monitoring methods have drawbacks such as poor continuity and high professional thresholds. This paper proposes and implements an "AI Pet Health Early Warning and Behavior Analysis Assistant" based on Alibaba Cloud\'s Bailian Large Model Service Platform. The system innovatively adopts a dual-stream analysis architecture of "lightweight real-time detection + multi-modal large model deep reasoning", combined with the Retrieval-Augmented Generation (RAG) technology, to accurately identify abnormal behaviors of pets (such as arched back, limping, excessive licking) and conduct pathological correlation analysis. The system is integrated and deployed through the DingTalk platform, providing users with real-time health warnings and quantitative analysis reports. Experimental results show that the system achieves an accuracy rate of over 92% in complex behavior recognition, significantly outperforming traditional computer vision solutions, and provides an efficient and low-cost closed-loop solution for the field of intelligent pet healthcare.关键词
宠物健康/百炼平台/多模态大模型/RAG/行为识别/钉钉智能体/Key words
Pet health/Baolian platform/Multi-modal large model/RAG/Behavior recognition/Dingding intelligent agent/引用本文复制引用
王小雨,诸葛斌.基于百炼平台的宠物健康检测AI助手：深度多模态分析与早期预警系统[EB/OL].(2026-01-13)[2026-01-18].http://www.paper.edu.cn/releasepaper/content/202601-15.学科分类
基础医学/计算技术、计算机技术
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