今日下载这份补充白皮书！
这篇白皮书解释了边缘AI和物理AI如何帮助城市超越被动监测。 显示城市基础设施如何能感知、理性
你会学到什么：
——从智慧城市向认知城市的转变如何取决于那些原因和作用的基础设施，而不是只进行监测的基础设施。
- 为什么边缘AI支持低纬度决定，将敏感数据保存在城市边界内，并减少对集中式云基础设施的依赖。
- 计算机视觉、多式联运、基因和代理系统如何合作支持公共安全、流动和基础设施应用。
- 分层边缘架构如何分配横跨远边缘、区域和云层的监测、规划和行动任务，以及硬件和软件的耐久性、安全和互操作性要求。
点击“ LOOK INSIDE ” 现在下载 PDF。
进去看看
IEEE Spectrum和威利自豪地带给你这份由Intel赞助的白皮书。
更多信息
智能城市部署在历史上一直依赖于摄像头，传感器，和仪表板，这些仪表板可以捕捉数据而不及时提供洞察力。 Edge AI通过让推论接近生成数据的地方来改变这一点。 这支持更快的决策，更强大的数据主权，以及比只云设计更低的运行成本。 该指南记录了从自动化和感知到预测、代理协调以及物理AI的发展过程，在这些过程中，城市系统不断察觉到条件、原因，并在跨越远端传感器、区域基础设施和云层的分层边缘结构中发挥作用。 它还概述了生产级硬件和软件必须满足的耐久性、安全和互操作性要求，以支持城市规模的公共安全、流动性和基础设施使用。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Download this complimentary White Paper today!
This white paper explains how edge AI and physical AI help cities move beyond passive monitoring. It shows how urban infrastructure can sense, reason, and act on real-time conditions across public safety, mobility, and infrastructure management.
What you will learn about:
- How the shift from smart cities to cognitive cities depends on infrastructure that reasons and acts, rather than infrastructure that only monitors.
- Why edge AI supports low-latency decisions, keeps sensitive data within city boundaries, and reduces dependency on centralized cloud infrastructure.
- How computer vision, multimodal sensing, and generative and agentic systems work together to support public safety, mobility, and infrastructure applications.
- How a tiered edge architecture assigns monitoring, planning, and action tasks across the far edge, regional, and cloud layers, and what durability, security, and interoperability requirements this places on hardware and software.
Click ‘LOOK INSIDE’ to Download the PDF Now.
LOOK INSIDE
IEEE Spectrum and Wiley are proud to bring you this white paper, sponsored by Intel.
More Information
Smart city deployments have historically relied on cameras, sensors, and dashboards that capture data without delivering timely insight. Edge AI changes this by letting inference happen close to where data is generated. This supports faster decisions, stronger data sovereignty, and lower operating costs than cloud-only designs. This guide traces the progression from automation and perception to prediction, agentic coordination, and physical AI, where urban systems continuously perceive conditions, reason about them, and act within a tiered edge architecture spanning far-edge sensors, regional infrastructure, and the cloud. It also outlines the durability, security, and interoperability requirements that production-grade edge hardware and software must meet to support public safety, mobility, and infrastructure use cases at city scale.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.