家电科技 ›› 2025, Vol. 0 ›› Issue (zk): 380-384.doi: 10.19784/j.cnki.issn1672-0172.2025.99.079

• 第三部分 健康适老与智能 • 上一篇    下一篇

基于多智能体下的健康适老家电智能联动应用与研究

陈长运1,2,3,4, 尹德帅1,2,3,4, 栾琳1,2,3,4, 张轲1,2,3,4, 刘刚1,2,3,4   

  1. 1.青岛海尔科技有限公司 山东青岛 266100;
    2.数字家庭网络国家工程研究中心 山东青岛 266100;
    3.山东省智慧家庭人工智能与自然交互研究重点实验室 山东青岛 266100;
    4.青岛市智慧家庭交互与控制工程研究中心 山东青岛 266100
  • 发布日期:2025-12-30
  • 作者简介:陈长运,本科学历。研究方向:智慧家庭。地址:山东省青岛市崂山区海尔信息科技园。E-mail:chenchangyun@haier.com。

Application and research of smart home appliance integration for elderly healthcare based on multi-agent system

CHEN Changyun1,2,3,4, YIN Deshuai1,2,3,4, LUAN Lin1,2,3,4, ZHANG Ke1,2,3,4, LIU Gang1,2,3,4   

  1. 1. Qingdao Haier Technology Co., Ltd. Qingdao 266100;
    2. National Engineering Research Center of Digital Home Networking Qingdao 266100;
    3. Shandong Key Laboratory of Artificial Intelligence and Natural Interaction in Smart Home Qingdao 266100;
    4. Qingdao Engineering Research Center of Smart Home Interaction and Control Qingdao 266100
  • Published:2025-12-30

摘要: 随着全球人口老龄化加剧,老年人健康管理需求日益迫切。传统健康监测系统依赖可穿戴设备或单点传感器,存在依从性低、数据孤立和用户体验差等问题。基于此,提出了一种智慧家庭环境下的AI多智能体健康管理系统,利用智能家电(如冰箱、床垫、坐便器)作为行为感知节点,实时采集老年人行为数据(如开关频次、压力分布、如厕规律)。系统采用LangGraph框架协调多智能体任务,融合健康监测、设备控制和行为理解模块,通过专家规则与机器学习方法分析健康趋势。通过“家电即传感器”理念和人性化推送机制,显著提升了老年用户体验,解决了适老化交互和隐私保护问题,为智能家居与健康养老融合提供了创新方案。

关键词: 智慧家庭, AI多智能体, 老年人健康, 智能家电, 健康数据分析, LangGraph框架, 行为识别, 健康评估

Abstract: With the intensification of global population aging, the demand for elderly healthcare is increasingly urgent. Traditional health monitoring systems rely on wearable devices or single-point sensors, facing issues such as low compliance, data isolation, and poor user experience. Proposes an AI-driven multi-agent healthcare system in smart home environments, utilizing smart appliances (e.g., refrigerators, mattresses, toilets) as behavior-sensing nodes to collect real-time behavioral data (e.g., usage frequency, pressure distribution, toileting patterns). The system employs the LangGraph framework for multi-agent task coordination, integrating modules for health monitoring, device control, and behavior understanding, while analyzing health trends through expert rules and machine learning. By leveraging the “appliance-as-sensor” concept and user-friendly notification mechanisms, this system significantly enhances elderly user experience and addresses accessibility and privacy concerns, offering a novel solution for integrating smart homes with elderly care.

Key words: Smart home, AI multi-agent, Elderly health, Smart appliances, Health data analysis, LangGraph framework, Behavior recognition, Health assessment

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