ChatSync : large-language-model-enabled spatial-temporal knowledge reasoning for production logistics synchronization

Abstract

With increasing pressure from customized demands, discrete manufacturing systems face challenges due to fluctuating resource requirements. These challenges hinder the synchronization of production logistics (PL), which is essential for coordinating resources and ensuring smooth production. Poor synchronization will result in resources waiting on each other, leading to delays and idle time. Accordingly, this paper proposes ChatSync, a framework leveraging large language model (LLM) and spatial-temporal knowledge reasoning to optimize resource allocation, delivery, and monitoring in industrial applications, particularly within the Industrial Internet of Things (IIoT) environment. First, the resource spatial-temporal graph (RSTG) is constructed by integrating real-time IIoT data and expert operational experience, enhancing the knowledge base of LLM through cross-domain knowledge fusion. Second, graph-based reasoning optimization is presented, incorporating spatial-temporal, contextual, and relational reasoning mechanisms, enabling LLM to achieve credible and responsible analysis and decision-making. Third, the PL-oriented ChatSync framework with knowledge and reasoning engines is proposed, supporting chat-based interactions for resilient resource allocation, personalized suggestion, and precise traceability. A case study in air conditioning manufacturing demonstrates that ChatSync outperforms existing benchmark methods in various PL phases, achieving a delivery punctuality rate of 91.2%.