| 孟刚,李昭昶,郭慧,杨丹丹,胡斌.大数据与数字孪生驱动的智慧校园集成设计研究[J].包装工程,2023,44(18):458-466. |
| 大数据与数字孪生驱动的智慧校园集成设计研究 |
| Integrated Design and Research of Smart Campus Driven by Big Data and Digital Twins |
| 投稿时间：2023-04-22 |
| DOI：10.19554/j.cnki.1001-3563.2023.18.054 |
| 中文关键词: 智慧校园 集成设计 数字孪生驱动 大数据 |
| 英文关键词: smart campus integrated design digital twins driven big data |
| 基金项目:国家自然科学基金(61961036，62162054);江苏省社会科学规划基金(21YSD006);江苏高等教育学会“大学素质教育与数字化课程建设”专项课题(2020JDKT109);江苏省高校哲学社会科学研究重大项目(2023SJZD148);江苏省高校哲学社会科学研究项目(2023SJYB0201);广西科技基地和人才专项(桂科AD20297148);广西自然科学基金(2020JJA170007);江苏开放大学科研平台(23-KYPT-H06);苏州社会科学基金(Y2023LX013) |
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| 中文摘要: |
| 目的 为解决智慧城市集成化设计中仍存在数据孤岛与智慧决策的相关问题，因此设计了一套以智慧校园创新服务生态为导向的园区集成设计方案，为大数据与数字孪生驱动的智慧城市构建提供建设新思路。方法 研究通过建立智慧校园数据的标准信息模型，采用结构化数据为主的物联网传感器进行部署，并利用大数据与深度学习方法实现校园的智慧大脑，开发了基于Web 3D与数字孪生驱动的人机共融可视化平台，从可操作性角度制定了可执行、可落地的智慧校园设计。结果 从大数据特点切入对智慧校园的构建状况进行分析，建立智慧校园数据的标准化模型，设计了基于GRU-CNN深度学习智慧大脑的Web 3D与数字孪生驱动可视化平台。实验表明该系统具有数据可视化与智能决策功能，能提供给用户沉浸式、多维动态的人机共融交互体验。结论 利用大数据驱动的数字孪生可视化平台可以实现校区一体化运行，增强校园结构的智能化、高效化和人性化，提高使用效率。智慧校园集成平台设计同时可以推动现代化城市进一步完成数据互通互联、数据可视化、智慧服务管理新模式。为当前智慧城市的建设提供技术及理论参照。 |
| 英文摘要: |
| The work aims to design an integrated design scheme for campus-oriented innovation services, to offer new approaches to constructing a smart city driven by big data and digital twins and address the persistent issues of data silos and intelligent decision-making in smart city integration design. In this research, a standardized information model for smart campus data was established and Internet of Things (IoT) sensors primarily using structured data were deployed. By leveraging big data and deep learning methods, a smart campus brain was realized. Furthermore, a human-machine integrated visualization platform driven by Web 3D and digital twins was developed, and an operability perspective, feasible and implementable smart campus design was formulated. Starting from the characteristics of big data, the construction status of smart campuses was analyzed. A standardized model for smart campus data was established, and a Web 3D and digital twin-driven visualization platform based on GRU-CNN deep learning for the smart campus brain was designed. Experimental results demonstrated that this system possessed data visualization and intelligent decision-making capabilities, offering users an immersive, multidimensional, and dynamic human-machine integrated interactive experience. The utilization of a big data-driven digital twin visualization platform enables the integrated operation of campus facilities, enhancing the intelligence, efficiency, and user-friendliness of the campus infrastructure, thereby improving overall utilization efficiency. The design of a smart campus integration platform also facilitates the advancement of modern cities by promoting new modes of data interconnection, data visualization, and intelligent service management. It provides technological and theoretical references for the current development of smart cities. |
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