关键词: 智能洗衣机, 知识图谱, 检索增强生成, 大语言模型, 个性化洗涤

Abstract: With the rising consumer demand for sophisticated garment care, the pre-set programs and parameters of traditional washing machines are struggling to meet the needs of complex and diverse personalized scenarios. To strike a balance between efficient stain removal and delicate fabric care, a hybrid reasoning architecture leveraging Knowledge Graph-enhanced Retrieval-Augmented Generation (RAG) was proposed. The method involves constructing a domain-specific knowledge graph for laundry care, which encompasses multi-dimensional knowledge such as fabric materials and colors, stain types, and washing-care techniques. The architecture performs parallel queries on the knowledge graph and retrieval via the RAG framework to acquire both structured causal knowledge and unstructured contextual information. Following a knowledge fusion stage, a Large Language Model (LLM) generates precise washing parameters along with natural language recommendations. Experimental results demonstrate that the proposed architecture significantly outperforms baseline methods in both parameter accuracy and safety. The hybrid intelligence paradigm effectively addresses the challenges of trustworthiness and interpretability for AI in complex decision-making scenarios, advancing smart appliances into an era of "expert-level decision" generative AI.

Key words: Smart washing machine, Knowledge graph, Retrieval-augmented generation (RAG), Large language model (LLM), Personalized washing