摘 要：
本研究针对智慧校园交通流优化，提出融合计算机视觉、物联网与深度学习的多源数据处理框架，构建预测与调度模型，并通过校园场景验证其工程可行性。随着人工智能、大数据与物联网技术发展，智慧交通系统作用凸显，校园交通流的复杂动态性需多模态人工智能技术提升管理效能。本文整合多源异构数据，构建多层级协同优化架构，提出基于深度学习的数据全生命周期处理与系统协同优化方法，借鉴锅炉燃烧诊断、轨道交通客服、油藏智能化等多模态融合经验，优化校园交通流数据融合策略。通过智能交通调度系统实现精细化调整，工程验证显示模型在车辆延误与油耗控制上优势显著，为校园交通系统提供解决方案，也为城市交通智能化升级提供参考路径。
关键词：多模态人工智能；智慧校园；交通流优化
Abstract:
This study proposes a multi-source data processing framework integrating computer vision, Internet of Things and deep learning for the optimization of traffic flow in smart campuses, builds a prediction and scheduling model, and verifies its engineering feasibility through campus scenarios. With the development of artificial intelligence, big data and Internet of Things technologies, the role of smart transportation systems has become increasingly prominent. The complex and dynamic nature of campus traffic flow requires multimodal artificial intelligence technology to enhance management efficiency. This paper integrates multi-source heterogeneous data, builds a multi-level collaborative optimization architecture, and proposes a data full life cycle processing and system collaborative optimization method based on deep learning. It draws on the multi-modal fusion experience of boiler combustion diagnosis, rail transit customer service, and reservoir intelligence, and optimizes the campus traffic flow data fusion strategy. Through the intelligent transportation dispatching system, precise adjustment is achieved. Engineering verification shows that the model has significant advantages in vehicle delay and fuel consumption control, providing a solution for the campus transportation system and also offering a reference path for the intelligent upgrade of urban transportation
Keywords: Multimodal artificial intelligence; Smart campus; Traffic flow optimization
--
