The rapid development of urbanization has led to a continuous rise in number of elevators. This has led to elevator failures from time to time. At present, although there are some studies on elevator fault diagnosis, they are more or less limited by the lack of data to make the research more superficial. For such complex special equipment as elevator, it is difficult to obtain reliable and sufficient data to train the fault diagnosis model. To address this issue, this paper first establishes a numerical model of vertical vibration for elevators with three degrees of freedom. The obtained motion equations are then used as constraints to acquire simulated vibration data through PINNs. Next, the proposed e-RGCN is employed for elevator fault diagnosis. Finally, experimental validation shows that the fault diagnosis accuracy with the participation of digital twins exceeds 90%, and the accuracy of the proposed model reaches 96.61%, significantly higher than that of other comparative models.

Large language models are rapidly advancing the field of artificial intelligence, with current research focusing primarily on traditional natural language understanding tasks, such as question answering and information extraction. However, in knowledge-intensive domains, such as intangible cultural heritage, hallucination problems due to insufficient domain knowledge persist. To address this, we present ChatTf, a knowledge graph-enhanced intelligent Q&A system, exemplified by Chinese traditional folklore, aimed at reducing factuality hallucinations in this domain. Specifically, we constructed the Traditional Folklore Ontology (TFOnto) and proposed the Zero-shot Traditional Folklore Triplet Extraction (ZFTE) framework. Driven by TFOnto, ZFTE builds a Traditional Folklore Knowledge Graph (TFKG). We then proposed a dual-stage Retrieval-Augmented Generation framework (TFKG-RAG) based on TFKG to provide traditional folklore knowledge to large language models, mitigating factuality hallucinations in folklore Q&A tasks. In the experimental phase, ChatTf achieved an accuracy of 96.7% on a self-built TFCQD test set, outperforming several state-of-the-art baseline methods. This demonstrates the accuracy and reliability of folklore domain question answering.

Knowledge graphs (KGs) are a crucial foundation for intelligent systems performing advanced tasks such as personalized recommendations and question answering. However, many existing KGs are incomplete because of missing triples. Recent studies have shown that link prediction based on paths can be a reliable approach. Path-based methods mainly use path patterns within the KG to solve the puzzle of missing triples. These patterns, defined by the paths connecting two entities, are used to predict the relation between them. However, these methods are not perfect; relying solely on the relation between entities or their embedding information is ineffective in link prediction. To address the above challenges, we propose a new KG link prediction method leveraging the path and graph context. For a given candidate relation and two entities, the type hierarchy of entities is used for disambiguation while preserving the entities’ neighborhood information in the graph. Our method then encodes the path information using a bi-long short-term memory (LSTM), integrates the path patterns with the attention mechanism, and finally predicts whether the entities can be linked via the candidate relations. Experiments on three standard datasets demonstrate the effectiveness of our method.