AI智慧城市应用-夜雨聆风

AI智慧城市应用

预计到 2050 年，全球城市人口将实现翻倍增长，约 25 亿人可能在本世纪中叶新增至城市地区。这一趋势将显著推动对更加可持续的城市规划和公共服务的需求。为应对这一挑战，全球各地的城市正逐步引入数字孪生技术和 AI 智能体，用于城市规划情景分析以及基于数据的运营决策。

然而，构建一座城市级的数字孪生，并在其中测试智慧城市 AI 智能体，本身是一项复杂且资源密集型的工作，面临着诸多技术与运营层面的挑战。

为了解决这些难题，NVIDIA（英伟达）今日正式发布了 NVIDIA Omniverse 智慧城市 AI 蓝图（Blueprint for Smart City AI）。该参考框架整合了 NVIDIA Omniverse、Cosmos、NeMo 和 Metropolis 等平台，旨在将物理 AI（Physical AI）的优势拓展至整个城市及其关键基础设施。

借助这一蓝图，开发者可以构建具备仿真能力（SimReady）的高逼真城市数字孪生模型，在虚拟环境中开发和测试 AI 智能体，从而实现对城市运行的监测、优化与智能管理。

包括 XXII、AVES Reality、Akila、Blyncsy、Bentley、Cesium、K2K、Linker Vision、Milestone Systems、Nebius、SNCF Gares & Connexions、Trimble 以及 Younite AI 在内的多家领先企业，已成为首批采用该蓝图的用户。

Urban populations are expected to double by 2050, which means around 2.5 billion people could be added to urban areas by the middle of the century, driving the need for more sustainable urban planning and public services. Cities across the globe are turning to digital twins and AI agents for urban planning scenario analysis and data-driven operational decisions. Building a digital twin of a city and testing smart city AI agents within it, however, is a complex and resource-intensive endeavor, fraught with technical and operational challenges. To address those challenges, NVIDIA today announced the NVIDIA Omniverse Blueprint for smart city AI, a reference framework that combines the NVIDIA Omniverse, Cosmos, NeMo and Metropolis platforms to bring the benefits of physical AI to entire cities and their critical infrastructure. Using the blueprint, developers can build simulation-ready, or SimReady, photorealistic digital twins of cities to build and test AI agents that can help monitor and optimize city operations. Leading companies including XXII, AVES Reality, Akila, Blyncsy, Bentley, Cesium, K2K, Linker Vision, Milestone Systems, Nebius, SNCF Gares&Connexions, Trimble and Younite AI are among the first to use the new blueprint.

NVIDIA Omniverse 智慧城市 AI 蓝图

NVIDIA Omniverse 智慧城市 AI 蓝图 提供了一整套完整的软件技术栈，用于在物理精度极高的城市数字孪生环境中，加速 AI 智能体的开发、训练与测试。该蓝图包括以下核心组件：

NVIDIA Omniverse：用于构建物理真实的城市级数字孪生，并运行大规模城市仿真。
NVIDIA Cosmos：用于大规模生成合成数据，支持 AI 模型的后训练（post-training）。
NVIDIA NeMo：用于高质量数据整理，并基于这些数据训练和微调视觉语言模型（VLM）及大语言模型（LLM）。
NVIDIA Metropolis：基于 NVIDIA 视频搜索与摘要（VSS）AI 蓝图，构建并部署视频分析 AI 智能体，帮助处理海量视频数据，并提供关键洞察以优化业务流程。

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蓝图工作流程（Blueprint Workflow）

智慧城市 AI 蓝图的工作流程分为三个关键步骤：

构建 SimReady 数字孪生开发者使用 Omniverse 和 Cosmos，基于航空影像、卫星数据或地图数据，构建位置与设施的 SimReady（可仿真）数字孪生模型。
训练与微调 AI 模型利用 NVIDIA TAO 和 NeMo Curator 对计算机视觉模型、视觉语言模型（VLM）等进行训练和微调，从而提升视觉 AI 应用场景中的识别精度。
部署实时 AI 智能体使用 Metropolis VSS 蓝图，将定制化模型部署为实时 AI 智能体，对摄像头与传感器数据进行告警、摘要与查询分析。

NVIDIA Omniverse Blueprint for Smart City AI The NVIDIA Omniverse Blueprint for smart city AI provides the complete software stack needed to accelerate the development and testing of AI agents in physically accurate digital twins of cities. It includes: NVIDIA Omniverse to build physically accurate digital twins and run simulations at city scale. NVIDIA Cosmos to generate synthetic data at scale for post-training AI models. NVIDIA NeMo to curate high-quality data and use that data to train and fine-tune vision language models (VLMs) and large language models. NVIDIA Metropolis to build and deploy video analytics AI agents based on the NVIDIA AI Blueprint for video search and summarization (VSS), helping process vast amounts of video data and provide critical insights to optimize business processes. The blueprint workflow comprises three key steps. First, developers create a SimReady digital twin of locations and facilities using aerial, satellite or map data with Omniverse and Cosmos. Second, they can train and fine-tune AI models, like computer vision models and VLMs, using NVIDIA TAO and NeMo Curator to improve accuracy for vision AI use cases.

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NVIDIA 合作伙伴生态系统赋能全球智慧城市

智慧城市 AI 蓝图支持庞大的合作伙伴生态系统，使其能够通过统一的工作流，结合 NVIDIA 技术与自身解决方案，构建并激活面向智慧城市应用的数字孪生。

案例：SNCF Gares & Connexions

SNCF Gares & Connexions 负责运营法国及摩纳哥境内 3,000 座火车站。该机构已部署数字孪生和 AI 智能体，用于：

实时运营监控
应急响应仿真
基础设施升级与改造规划

这使得每个车站都能够分析能源与用水等运营数据，并实现：

预测性维护
自动化报告生成
符合 GDPR 的视频分析，用于事故检测与人群管理

实际成效

在 Omniverse、Metropolis 以及生态伙伴 Akila 和 XXII 解决方案的支持下，SNCF Gares & Connexions 在摩纳哥蒙特卡洛站和马赛站部署的物理 AI 系统取得了显著成效：

预防性维护按时完成率：100%
停机时间与问题响应时间减少：50%
能源消耗降低：20%

Finally, real-time AI agents powered by these customized models are deployed to alert, summarize and query camera and sensor data using the Metropolis VSS blueprint. NVIDIA Partner Ecosystem Powers Smart Cities Worldwide The blueprint for smart city AI enables a large ecosystem of partners to use a single workflow to build and activate digital twins for smart city use cases, tapping into a combination of NVIDIA’s technologies and their own. SNCF Gares&Connexions, which operates a network of 3,000 train stations across France and Monaco, has deployed a digital twin and AI agents to enable real-time operational monitoring, emergency response simulations and infrastructure upgrade planning. This helps each station analyze operational data such as energy and water use, and enables predictive maintenance capabilities, automated reporting and GDPR-compliant video analytics for incident detection and crowd management. Powered by Omniverse, Metropolis and solutions from ecosystem partners Akila and XXII, SNCF Gares&Connexions’ physical AI deployment at the Monaco-Monte-Carlo and Marseille stations has helped SNCF Gares&Connexions achieve a 100% on-time preventive maintenance completion rate, a 50% reduction in downtime and issue response time, and a 20% reduction in energy consumption.