机电工程技术2026，Vol.55Issue(2)：179-184,6.DOI:10.3969/j.issn.1009-9492.2026.02.030
基于Raspberry Pi的智能猫砂盆健康监测系统设计
Design of Intelligent Litter Box Health Monitoring System Based on Raspberry Pi
摘要
Abstract
With the growth of the pet-keeping demand,existing intelligent cat litter boxes have limitations in monitoring the health of cats.Aiming to this,a health monitoring system for intelligent cat litter boxes is designed.The system uses a Raspberry Pi 4B as the main control chip,integrates multiple hardware modules such as air quality monitoring,automatic cleaning,and weighing,applies the YOLOv9 algorithm for excrement identification,and enables data viewing and remote control through a mobile phone App.After testing,the air quality monitoring system of the system shows extremely high accuracy.Compared with manual measurement,the average error is less than 5%.After detecting that the ammonia concentration exceeds the standard,the average response time is only 1.5 s.After the automatic cleaning program is activated,the ammonia concentration is reduced by an average of 75%.The efficiency test of the automatic cleaning function shows that the cleaning frequency of the intelligent cat litter box is 50%higher than that of a conventional cat litter box,and the cleanliness after cleaning is higher,90%of cats show a clear preference for it.The system realizes precise monitoring of the health status of cats and intelligent management of the cat litter box,providing convenience for pet owners and enhancing the attention to the health of cats.However,there is still room for further optimization,such as improving image recognition algorithms,expanding air quality monitoring categories,and adding health data analysis features to the App.关键词
智能猫砂盆/健康监测系统/YOLOv9算法/空气质量监测/树莓派Key words
intelligent litter box/health monitoring system/YOLOv9 algorithm/air quality monitoring/Raspberry Pi分类
信息技术与安全科学引用本文复制引用
陈伟全,黄铠铭,吴杰,施杰文,陈正岚,梁国良..基于Raspberry Pi的智能猫砂盆健康监测系统设计[J].机电工程技术,2026,55(2):179-184,6.基金项目
2023年省大学生创新训练项目(S202313656018X) （S202313656018X）
2023年度广东省普通高校青年创新人才类项目(自然科学)(2023KQNCX137) （自然科学）
广东省普通高校工程技术中心资助项目(2023GCZX008) （2023GCZX008）
