Sustainable and Intelligent Public Facility Failure Management System Based on Large Language Models
Siguo Bi, Jilong Zhang, Wei Ni

TL;DR
This paper introduces a novel LLM-based framework for managing and predicting failures in intelligent public facility devices, demonstrated in library settings to improve efficiency and reduce costs.
Contribution
The paper presents a pioneering LLM-driven management system tailored for public facilities, integrating failure prediction and cybersecurity for proactive maintenance.
Findings
Successful prototype validation in real-world libraries
Significant reduction in operational costs
Enhanced device failure prediction accuracy
Abstract
This paper presents a new Large Language Model (LLM)-based Smart Device Management framework, a pioneering approach designed to address the intricate challenges of managing intelligent devices within public facilities, with a particular emphasis on applications to libraries. Our framework leverages state-of-the-art LLMs to analyze and predict device failures, thereby enhancing operational efficiency and reliability. Through prototype validation in real-world library settings, we demonstrate the framework's practical applicability and its capacity to significantly reduce budgetary constraints on public facilities. The advanced and innovative nature of our model is evident from its successful implementation in prototype testing. We plan to extend the framework's scope to include a wider array of public facilities and to integrate it with cutting-edge cybersecurity technologies, such as…
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