Narrow Results

This study addresses the critical need for advanced intelligent platforms to facilitate alumni network interaction, aligning with the evolving landscape of digital collaboration within higher education. Traditional methods for alumni network construction often face limitations in scalability, adaptability, and dynamic reconfiguration, which hinder effective engagement, data exchange, and real-time communication. To address these challenges, we present an innovative Intelligent Alumni Network Platform (IANP) framework. Built on adaptive modularity principles, the platform integrates novel methods such as dynamic load balancing, fault-tolerant scheduling, and latency-aware optimization, which enhance its ability to adapt to changing network conditions and user demands. Central to our approach is the Adaptive Modularity Network (AMN) and the Intelligent Network Synergy (INS) strategy, which collectively enable seamless, efficient interaction across a heterogeneous network of alumni nodes, ensuring optimal resource utilization and improved user experience. Experimental results demonstrate substantial improvements in network performance, including a 40% reduction in communication latency, enhanced task distribution efficiency, and superior resilience against operational disruptions. By combining theoretical rigor and practical utility, this work sets a foundation for next-generation intelligent alumni interaction systems, contributing significantly to the digital transformation of alumni engagement and offering a sustainable framework for long-term community building.

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of $\mathrm{\text{Flume}}+\mathrm{\text{Kafka}}+\mathrm{\text{Storm}}$ of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.