Smart Campus Integrated Platform Development

瑞尔森大学的研究人员和FuseForward解决方案集团正在合作，为瑞尔森大学校园开发云托管的数字双胞胎(DT)。这个Digital Twin将作为所有校园数据的模拟和可视化环境，将校园建筑、支持物理基础设施和移动系统整合到一个公共界面中。这将使校园管理人员和其他最终用户能够a)观察校园的当前和历史行为，b)识别不正常的行为或潜在问题，以及c)执行可行性和假设分析，以测试新的校园战略，如应急响应、能源管理等。 开发这种DT需要几项新技术： 1)集成了智能建筑、智能基础设施、智能交通和遥感四个领域的广泛数据的综合数据结构，为智能城市创建了可扩展的方法 2)识别传感器的最佳位置以进行数据收集的新技术，并将来自相邻传感器的数据拼接在一起，以创建校园的无缝“视图” 3)新的遥感技术，使用无人驾驶飞行器(无人机)、人载设备和车载设备收集校园物理信息(室内和室外)并使用它创建3D模型。传感器网络将创建 4)机器学习算法，使用传感器和其他校园系统数据近乎实时地对事件进行分类和预测。 5)开发一种大型、多学科的DT发展方法，使用Ryerson校区进行演示，这可以作为在城市或社区范围内发展DT的中间步骤。 该项目产生的知识将共同开发一套应用程序，这些应用程序可以集成到一个通用解决方案中，以优化能源效率、维护行人的安全环境、识别和标记潜在的危险或安全威胁、识别设备故障或建筑结构退化，以及其他类似的应用程序，以在保护公共安全和环境的同时降低建筑运营成本。

Ryerson University researchers and FuseForward Solutions Group are collaborating to develop an cloud-hosted Digital Twin (DT) for the Ryerson University campus. This Digital Twin will serve as a simulation and visualization environment for all campus data, combining the campus buildings, supporting physical infrastructure, and mobility systems into a common interface. This will allow campus managers and other end-users to a) observe the current and historical behavior of the campus, b) identify irregular behavior or potential issues, and c) perform feasiblity and "what-if" analyses to test new campus strategies, such as emergency response, energy management, etc. Several new technologies will be required to develop this DT: 1) A comprehensive data structure that integrates a breadth of data from four domains: Smart Buildings, Smart Infrastructure, Smart Transportation, and Remote Sensing to create a scalable approach for Smart Cities 2) New technologies to identify the optimal locations of sensors for data gathering and to stitch together data from adjacent sensors to create a seamless 'view' of the campus 3) New remote sensing technologies using unmanned aerial vehicles (drones), person-mounted equipment, and vehicle-mounted equipment to gather campus physical information (both indoor and outdoor) and use it to create 3D models. sensor networks to create 4) Machine learning algorithms to classify and predict events, using sensor and other campus system data in near-real-time. 5) Development of an approach to large, multidisciplinary DT development, demonstrated using the Ryerson Campus, that can serve as an intermediate step to developing DTs at the city or neighbourhood scale. Together, the knowledge generated from this project will develop a suite of applications that can be integrated into a common solution to optimize energy efficiency, maintain a safe environment for pedestrians, identify and flag potential hazards or security threats, identify equipment malfunction or building structural degradation, and other similar applications to reduce building operational costs while protecting public safety and the environment.