Design of Campus Network Intelligent Operation and Maintenance Platform

With rapid advancements in AI and big data, AIOps has matured in network management. University campus networks, with dense access points, high user concurrency, and complex services, face challenges that limit traditional manual maintenance. To address this, we propose an LLM-based AIOps platform that collects real-time traffic and user feedback to optimize network performance. Large language models interpret natural language queries and network logs, combining monitoring metrics to classify issues and automatically trigger optimization models. The platform integrates LLM-based user perception with multi-dimensional data analysis for intelligent classification, enabling automated issue categorization and adaptive optimization. This approach enhances fault detection, performance management, and operational efficiency, providing a foundation for intelligent campus network operations while highlighting potential challenges and applications in large-scale deployments.