Sustainable and Intelligent Public Facility Failure Management System Based on Large Language Models

Abstract: This paper presents a new 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 Internet of Things (IoT) security and machine learning algorithms for threat detection and response. This will result in a comprehensive and proactive maintenance system that not only bolsters the security of intelligent devices but also utilizes machine learning for automated analysis and real-time threat mitigation. By incorporating these advanced cybersecurity elements, our framework will be well-positioned to tackle the dynamic challenges of modern public infrastructure, ensuring robust protection against potential threats and enabling facilities to anticipate and prevent failures, leading to substantial cost savings and enhanced service quality.

• The paper introduces an LLM-based framework that semantically integrates diverse data for proactive smart device monitoring in public facilities.
• The paper employs localized processing to secure sensitive data and reduce privacy risks while managing intelligent devices.
• The paper validates its prototype in a library setting, demonstrating effective failure prediction and cost-efficient maintenance strategies.

Sustainable and Intelligent Public Facility Failure Management System Based on LLMs

The paper discusses the development of a LLM-based framework aimed at managing intelligent devices in public facilities, specifically focusing on libraries. This framework aims to improve operational efficiency and device reliability by preemptively identifying and mitigating failures. The authors showcase their framework's implementation through prototype validation, underscoring its potential to alleviate financial constraints associated with managing public facilities.

Framework Overview

The proposed LLM-based framework takes advantage of advanced natural language processing capabilities to handle the complex challenge of managing diverse smart devices within public domains. It incorporates cutting-edge machine learning models to analyze and predict potential failures in device operation. The framework's real-world application was tested within library environments, where its efficacy in handling device management tasks was evaluated. The promising results of this study suggest that expanding the application of this framework to other types of public facilities could result in widespread benefits, guiding improved public service delivery and sustainable management practices.

Key Contributions

1. Semantic Data Integration: The framework is designed to semantically integrate heterogeneous data from multiple intelligent devices. By doing so, it enables comprehensive monitoring and management of device health, thus enhancing user experience and operational continuity in public spaces.
2. Localized Data and Security: By employing LLMs locally, the framework ensures that both data and models are maintained on-site, thereby significantly reducing the risk of data breaches. This is a vital consideration in maintaining privacy and security when handling sensitive information, especially within public infrastructure.
3. Failure Prediction and Prevention: The framework leverages localized knowledge bases derived from device manuals, offering tailored services for fault prevention. These preventive capabilities are a distinct advantage, allowing for cost-effective maintenance and resource allocation strategies in the management of public infrastructure.

Experimental Validation

Prototype testing, performed in a library setting, was a vital component of the research, aimed at validating the framework's potential impact. The deployment of the framework involved managing various smart devices such as self-service borrowing and returning machines, smart bookshelves, and surveillance cameras using heterogeneous data for better fault diagnosis. Through semantic fusion of multimodal data sources, the LLM was able to analyze potential failure points and bolster device reliability and efficiency.

Implications and Future Directions

This research indicates several significant implications for both theory and practice. From a theoretical perspective, the study enriches the understanding of how LLMs can be effectively leveraged in practical infrastructure management. Practically, the framework could significantly reduce operational costs and improve service delivery quality across public facilities. Looking ahead, future development could integrate cybersecurity measures to fortify the framework's resilience against potential threats. Furthermore, the extension of this framework to broader public facility networks can serve as a foundation for smarter, more integrated public infrastructure environments.

In conclusion, the research exemplifies how innovative AI applications can address traditional challenges in public infrastructure management, setting a course for ongoing improvements in this critical domain.