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emqx-ci-robot · Red-Asuka · commit 81a75b25482c · 2025-06-05T19:05:17.000+08:00
diff --git a/README-ZH.md b/README-ZH.md
@@ -469,6 +469,7 @@ Build a reliable, efficient and industry-specific Internet of Vehicles platform
 
 ## [Industrial IoT | Unified Namespace | Sparkplug](https://www.emqx.com/zh/blog/category/industrial-iot)
 
+- [EMQX + NeuronEX：构建基于 UNS 架构的工业 AI 数据中枢](https://www.emqx.com/zh/blog/building-the-industrial-data-hub-with-emqx-and-neuronex) ([Edit](https://github.com/emqx/blog/blob/main/zh/202506/building-the-industrial-data-hub-with-emqx-and-neuronex.md))
 - [自然语言 + 工业数据：AI agent 驱动的 IIoT 数据探索实践](https://www.emqx.com/zh/blog/the-practice-of-ai-agent-driven-iiot-data-exploration) ([Edit](https://github.com/emqx/blog/blob/main/zh/202505/the-practice-of-ai-agent-driven-iiot-data-exploration.md))
 - [先进制造企业为何选择 EMQ 助力数字化升级](https://www.emqx.com/zh/blog/why-manufacturing-enterprises-choose-emq) ([Edit](https://github.com/emqx/blog/blob/main/zh/202502/why-manufacturing-enterprises-choose-emq.md))
 - [如何在离线环境下部署 EMQX ECP](https://www.emqx.com/zh/blog/how-to-deploy-emqx-ecp-in-an-offline-environment) ([Edit](https://github.com/emqx/blog/blob/main/zh/202501/how-to-deploy-emqx-ecp-in-an-offline-environment.md))
diff --git a/README.md b/README.md
@@ -7,6 +7,7 @@
 ## [MQTT Tutorials](https://www.emqx.com/en/blog/category/mqtt-protocol)
 Get to know the preferred protocol in IoT from beginner to master.
 
+- [MQTT Trends for 2025 and Beyond: Powering the Future of AI and IoT](https://www.emqx.com/en/blog/mqtt-trends-for-2025-and-beyond) ([Edit](https://github.com/emqx/blog/blob/main/en/202506/mqtt-trends-for-2025-and-beyond.md))
 - [Mastering MQTT: The Ultimate Beginner's Guide for 2025](https://www.emqx.com/en/blog/the-easiest-guide-to-getting-started-with-mqtt) ([Edit](https://github.com/emqx/blog/blob/main/en/202502/the-easiest-guide-to-getting-started-with-mqtt.md))
 - [MQTT QoS 0, 1, 2 Explained: A Quickstart Guide](https://www.emqx.com/en/blog/introduction-to-mqtt-qos) ([Edit](https://github.com/emqx/blog/blob/main/en/202408/introduction-to-mqtt-qos.md))
 - [MQTT Topics and Wildcards: A Beginner's Guide](https://www.emqx.com/en/blog/advanced-features-of-mqtt-topics) ([Edit](https://github.com/emqx/blog/blob/main/en/202407/advanced-features-of-mqtt-topics.md))
diff --git a/en/202506/mqtt-trends-for-2025-and-beyond.md b/en/202506/mqtt-trends-for-2025-and-beyond.md
@@ -0,0 +1,90 @@
+## Rethinking MQTT for a Smarter, More Connected Future
+
+The Internet of Things (IoT) is no longer on the horizon—it’s here, growing rapidly with billions of devices already connected and billions more on the way. At the center of this explosion is [MQTT](https://www.emqx.com/en/blog/the-easiest-guide-to-getting-started-with-mqtt) (Message Queuing Telemetry Transport), a protocol originally designed for lightweight, reliable messaging in constrained environments. As the IoT landscape evolves, driven by the rise of AI, real-time data demands, and global scale, MQTT is transforming from a simple telemetry tool into a critical infrastructure layer for modern intelligent systems.
+
+Here’s a look at how MQTT is changing and what trends to watch in 2025.
+
+## Laying the Foundation: Protocol and Transport Evolution
+
+### Smarter Transport with MQTT over QUIC
+
+While MQTT has traditionally run over TCP, its limitations in mobile and unstable networks are becoming more apparent. MQTT over QUIC offers a faster, more resilient alternative, using UDP to improve connection setup times and reduce latency. It’s particularly valuable for applications like connected vehicles or remote industrial deployments. EMQX is the first broker to support this transport method, with standardization efforts ongoing through the OASIS MQTT Technical Committee.
+
+### What’s Next for the Protocol: MQTT 5.1 and Beyond
+
+[MQTT 5.0](https://www.emqx.com/en/blog/introduction-to-mqtt-5) introduced features like topic aliases, session expiry, and shared subscriptions. Future enhancements aim to improve performance and control, including subscription filters for more targeted message delivery and batch publishing to reduce transmission overhead. These changes are being actively shaped by vendor implementations and community input, with additional focus on MQTT-SN for ultra-constrained devices.
+
+## Scaling for Speed: Real-Time Messaging and Streaming
+
+### Introducing MQTT/RT
+
+MQTT/RT proposes a real-time messaging layer designed for latency-sensitive use cases like robotics, autonomous systems, and industrial automation. It supports peer-to-peer architectures and diverse transports such as UDP and shared memory, making it a compelling option when traditional broker models become bottlenecks.
+
+### Bringing Streaming Capabilities to MQTT
+
+Many IoT systems today rely on Kafka to handle high-throughput data. MQTT Streams aims to simplify that architecture by integrating similar capabilities, such as message replay, persistence, and deduplication, directly into [MQTT brokers](https://www.emqx.com/en/blog/the-ultimate-guide-to-mqtt-broker-comparison). This consolidation could reduce infrastructure complexity without sacrificing performance.
+
+### Reliable File Transfers Over MQTT
+
+Standard MQTT isn’t ideal for large files like firmware updates or diagnostic logs. Extensions like those from EMQX enable chunked, resumable transfers using the existing MQTT framework. This approach avoids the need for separate tools like FTP or HTTP, simplifying the overall system architecture.
+
+## Enabling Smarter Systems: MQTT and AI Integration
+
+### Connecting AI Models with MCP over MQTT
+
+The Model Context Protocol (MCP) helps standardize how AI models interact with other systems. Running [MCP over MQTT](https://www.emqx.com/en/blog/mcp-over-mqtt) allows low-power and intermittently connected devices to communicate with AI services in real time. EMQ is already building this into its MQTTX client, including natural language interfaces that let users control devices through AI agents.
+
+### MQTT as the Communication Backbone for AI
+
+As AI becomes more embedded in industrial and consumer systems, MQTT plays a central role. It delivers sensor data for predictive maintenance, supports inter-device coordination in robotics, connects distributed AI models at the edge, and enables real-time data flow for digital twin simulations.
+
+## Preparing for Scale: MQTT in Complex Ecosystems
+
+### Serverless MQTT for Agile Deployments
+
+Platforms like EMQX Cloud Serverless make it easy to spin up MQTT services without managing infrastructure. This model is ideal for fast-moving projects, pilot programs, and small teams that need to prototype quickly and scale as needed.
+
+### Supporting Multiple Users with Multi-Tenancy
+
+Multi-tenant MQTT deployments allow different applications or users to share a broker while keeping data secure and organized. This reduces overhead and simplifies operations for large-scale platforms.
+
+### Global MQTT Networks through Geo-Distribution
+
+Distributed MQTT clusters can serve clients around the world with low latency and high availability. EMQX’s Cluster Linking feature synchronizes data across regions, supporting real-time use cases like connected vehicles and global manufacturing systems.
+
+### Unifying Industrial Data with UNS and Sparkplug
+
+In industrial environments, Unified Namespace (UNS) has become a popular architecture for structuring OT and IT data. MQTT brokers often serve as the foundation for these systems. Sparkplug 3.0 adds standardization to the mix, defining payload formats and device state protocols to support true interoperability.
+
+### Integrating with Enterprise Systems
+
+MQTT is increasingly connected to enterprise platforms like Apache Kafka and AMQP-based tools such as RabbitMQ. These integrations create flexible, end-to-end pipelines that support real-time data processing, event-driven workflows, and long-term analytics.
+
+## Powering the Edge: Real-Time Processing Where It Matters Most
+
+Edge computing reduces latency and bandwidth use by processing data closer to the source. MQTT complements this by serving as the local messaging layer between devices, gateways, and the cloud. Together, they enable real-time automation, edge AI, and system resilience even when cloud connectivity is limited.
+
+Bidirectional communication is especially important in edge scenarios, allowing not just data collection but also commands, model updates, and remote firmware delivery.
+
+## How to Prepare: Strategic Recommendations for 2025
+
+- Adopt MQTT 5.0 to access the full suite of modern features
+- Evaluate MQTT over QUIC for mobile or unreliable networks
+- Build edge strategies that include local MQTT brokers
+- Experiment with MCP over MQTT for AI and natural language interfaces
+- Explore serverless and distributed options for agility and scale
+- Monitor protocol developments from the OASIS MQTT Technical Committee
+- Apply multi-layered security practices across your MQTT ecosystem
+
+## MQTT’s Role in the Next Generation of Connected Systems
+
+MQTT is no longer just a lightweight protocol for telemetry. It is evolving into a foundational layer for intelligent, real-time, and scalable systems across IoT, AI, and edge computing. The organizations that invest in these capabilities now will be better equipped to lead the next era of innovation in connected technology.
+
+
+
+<section class="promotion">
+    <div>
+        Talk to an Expert
+    </div>
+    <a href="https://www.emqx.com/en/contact?product=solutions" class="button is-gradient">Contact Us →</a>
+</section>
diff --git a/zh/202506/building-the-industrial-data-hub-with-emqx-and-neuronex.md b/zh/202506/building-the-industrial-data-hub-with-emqx-and-neuronex.md
@@ -0,0 +1,92 @@
+## **引言**
+
+工业 4.0 时代，数据已成为驱动智能制造的核心引擎。AI，特别是 LLM，凭借其强大的自然语言理解、模式识别和决策辅助能力，为工业领域带来了前所未有的想象空间——从预测性维护到智能排产，从质量精准追溯到人机自然交互。然而，这些美好的愿景都高度依赖于高质量、易于访问且充满上下文的实时数据。
+
+现实中，工业数据往往散落在不同的设备、控制系统和信息系统中，遵循着各异的协议标准，形成了难以逾越的数据孤岛。这些数据若未经有效整合与梳理，对于 AI 模型而言，无异于一堆难以解读的乱码。**EMQ 通过构建基于统一命名空间（UNS）架构的工业 AI 数据中枢**，连接工业设备、工业应用、AI 与大模型，实现设备、产线、工厂和云端系统的无缝数据融通，为 AI 驱动的智能制造注入强大动力。
+
+本文将深入探讨 EMQ 如何通过其基于统一命名空间（UNS）架构的工业 AI 数据中枢解决方案，利用 EMQX Platform 平台和轻量化工业边缘网关软件 NeuronEX，构建从数据采集、处理到智能决策的完整闭环，为 AI 驱动的智能制造提供实时、可靠的数据支撑，打通 IT 与 OT 的智能桥梁。
+
+## **统一命名空间 (UNS)：工业数据的通用语言与单一事实来源**
+
+我们在[之前的文章](https://www.emqx.com/zh/blog/unified-namespace-next-generation-data-fabric-for-iiot)中已详细阐述了 UNS 的核心理念。简单来说，UNS 不仅仅是一种命名规范，更是一种革命性的数据架构思想，它旨在：
+
+1. **构建单一事实来源 (Single Source of Truth)：**将企业内所有实时的、有价值的数据和事件汇聚于一个统一的、结构化的命名空间下。
+2. **赋予数据语义与上下文：**按照业务逻辑（如 `企业/区域/工厂/产线/设备组/设备/测点`）对数据进行分层组织，使每个数据点都携带其业务含义和上下文信息。
+3. **实现事件驱动与实时流动：**通常基于 [MQTT](https://www.emqx.com/zh/blog/the-easiest-guide-to-getting-started-with-mqtt) 等发布/订阅协议，确保数据按需、高效、实时地在生产者和消费者之间流动。
+4. **彻底解耦数据生产者与消费者：**无论是底层的 PLC、传感器，还是上层的 MES、ERP、BI 或 AI 应用，都可以独立地接入 UNS，轻松发布或订阅数据，实现高度的灵活性和可扩展性。
+
+## **为何 UNS 是 AI 时代构建工业数据中枢的关键？**
+
+在 **AI 驱动的工业智能化浪潮**下，传统的点对点集成或单纯的数据湖方案，在满足 AI 应用，特别是大型语言模型（LLM）对海量、高质量、实时且富有上下文的数据需求时，往往显得力不从心。而 UNS 则凭借其独特优势，成为构建工业数据中枢的基石：
+
+1. **丰富的上下文信息：**UNS 的层级结构为每个数据点提供了明确的业务语义。LLM 等 AI 模型能够基于这些上下文信息，更准确地理解数据含义，进行有效的分析、推理和决策。
+2. **数据一致性与可靠性：**作为单一事实来源，UNS 保证了所有数据消费者获取到的是一致、准确、实时的状态数据，避免了 AI 模型因垃圾数据输入而产生错误输出。
+3. **实时数据流：**基于 MQTT 的 UNS 架构天然支持事件驱动和实时数据传输，使 AI 模型能够即时响应生产变化，实现真正的实时监控、预测和控制。
+4. **简化数据访问：**AI 开发者无需再为繁杂的工业协议和数据格式转换而烦恼。他们可以通过标准化的 MQTT 接口，依据清晰的 UNS 主题订阅所需数据，极大降低开发门槛，加速 AI 应用落地。
+5. **打通 IT 与 OT：**UNS 有效弥合了运营技术（OT）与信息技术（IT）之间的鸿沟，使来自生产现场的实时数据能够无缝、安全地流向企业 IT 系统中的 AI 平台，同时也使得 AI 的分析结果和决策指令能够顺畅地反馈到 OT 层，形成智能闭环。
+
+## **EMQX + NeuronEX：打造工业 AI 数据中枢的核心引擎**
+
+![image.png](https://assets.emqx.com/images/7e6ecdf17775685a365a4368344d828c.png)
+
+EMQ 提供的云边一体化解决方案，正是构建高效、可扩展、面向 AI 的 UNS 数据中枢的理想选择。它将全球领先的 MQTT 与 AI 一体化平台 EMQX Platform、具备边缘 AI 处理能力的工业网关 NeuronEX，以及面向制造业大模型（LLM）无缝整合，为 AI 驱动的 UNS 架构构建提供以下能力：
+
+### **NeuronEX：工业连接与边缘智能基石**
+
+- **广泛的工业连接能力：**[NeuronEX](https://www.emqx.com/zh/products/neuronex) 支持 **Modbus、OPC-UA、EtherNet/IP、S7、IEC104 等 100 多种工业协议**，能够轻松连接工厂内种类繁多的新老工业设备和系统。
+- **边缘数据处理与初步的 UNS 映射：**在数据源头，NeuronEX 即可进行数据过滤、清洗、聚合、计算，并将采集到的原始数据点（如 PLC 寄存器地址）标准化，映射到预定义的 UNS 主题结构中，赋予数据初步的上下文。
+- **边缘 AI 处理能力：**NeuronEX 可以在数据进入云端或中心 UNS 之前进行初步的智能分析和预警，减轻云端负担，实现更低延迟的响应。
+- **统一 MQTT 北向接口：**将经过处理和结构化的 UNS 数据通过 MQTT 协议安全、高效地发布到 EMQX 平台，同时支持 **MQTT Sparkplug** 规范，进一步标准化数据通信，简化工业设备管理。
+
+### **EMQX Platform：UNS 数据中枢与 AI 集成核心**
+
+- **UNS 核心消息中枢：**作为高性能、高可用的分布式 MQTT 平台，EMQX 承载整个 UNS 的数据交换，是所有数据生产者（如 NeuronEX、其他设备/系统）和数据消费者（如 AI 平台、MES、SCADA、数据库）的连接中心。
+- **海量连接与高吞吐保障：**EMQX 的先进架构能够轻松应对工业场景下数百万级的并发连接和每秒数百万条消息的高吞吐需求，充分满足 AI 应用对海量实时数据的需求。
+- **内置规则引擎：**EMQX 的规则引擎能够对 UNS 数据流进行实时处理、转换、丰富（例如，结合外部数据库信息为数据添加更多维度上下文），并能将数据无缝桥接到 Kafka、各类时序数据库、数据湖等，为 AI 模型训练和推理提供可靠的数据源。
+- **AI/大模型能力集成：**EMQX 平台设计上考虑了与 AI 及大模型的协同，能够作为关键的数据管道，将结构化的 UNS 数据高效、低延迟地提供给 AI 分析平台或 LLM 服务，并接收其分析结果，实现“认知数据管道”。
+- **精细化访问控制与安全保障：**确保数据在传输和存储过程中的安全，并允许对 UNS 中的数据进行细粒度的访问权限控制，保障企业核心数据的安全。
+- **支持 MQTT Sparkplug：**作为 Sparkplug Primary Host Application 的理想选择，进一步推动工业数据标准化。
+
+## **UNS + LLM 应用场景：开启工业智能新范式**
+
+基于 EMQX 与 NeuronEX 构建的 AI + UNS 数据中枢，能够显著增强工业智能，特别是与 LLM 结合，可以催生众多创新应用场景，为新一代智能工厂提供认知数据管道和 AI 增强的工业运营能力：
+
+### **预测性维护**
+
+- **UNS：**NeuronEX 采集设备数据，结合历史运行数据、工况参数等，统一发布到 UNS 相关主题。
+- **AI/LLM：**云端异常检测模型对数据进行分析，LLM 辅助进行故障诊断解释、**剩余寿命（RUL）预测**，并根据 AI 诊断结果**自动触发 MES 维护工单**。
+
+### **智能生产排产**
+
+- **UNS：**实时聚合各产线 OEE 数据、设备状态、订单优先级、物料库存等信息到 UNS。
+- **AI/LLM：**LLM 结合实时产能分析，理解订单约束，辅助进行**动态排产优化**，甚至自动触发 AGV 补料请求以**减少线边库存**。
+
+### **数字孪生**
+
+- **UNS：**NeuronEX 和 EMQX 保证**低延迟数据传输**，实现物理实体与数字孪生体的**虚实精确同步**。
+- **AI/LLM：**LLM 可以用于自然语言查询孪生体状态，解释仿真优化结果；或结合 AR 设备，通过 UNS 主题实时获取设备数据，支持 **AR 远程协助运维**。
+
+### **自然语言交互与智能助手**
+
+- **UNS：**提供设备实时数据及及存储的历史数据
+- **AI/LLM：**基于运维人员自然语言提问（例如：“查询 A 产线当前所有设备的运行状态和报警信息”），获取 UNS 中的设备实时及历史数据，对问题进行智能解释，并提供初步的排障建议。
+
+### **多模态数据集成与 LLM 协同**
+
+- **UNS**：提供实时传感器读数、设备信号等实时生产数据
+- **AI/LLM**：结合知识库（如设备手册、维护记录、工艺参数）、视觉检测图片等多模态数据，利用 LLM 驱动的分析能力，实现更精准的设备异常检测、主动解决方案生成和认知决策。
+
+## **结语**
+
+在工业数字化和智能化转型的浪潮中，数据是无可争议的核心驱动力。然而，原始数据本身并不能直接创造价值，只有当数据被有效地组织、赋予清晰的上下文、并能实时、安全地流动时，其潜能才能被真正释放，为智能应用提供坚实基础。
+
+统一命名空间（UNS）为应对工业数据挑战提供了先进且优雅的架构范式。基于 EMQX Platform 和 NeuronEX 构建的稳健、可扩展、真正面向 AI 的 UNS 数据中枢，通过 NeuronEX 在边缘端实现异构数据的便捷接入、标准化处理和初步的 UNS 映射，再经由 EMQX 在云端或数据中心进行高效的数据路由、集成、流处理和智能分析赋能，为工业领域提供了一个清晰、实时、充满上下文的数据底座。它将真正释放工业数据的潜能，加速企业迈向更高阶的智能制造。
+
+
+
+<section class="promotion">
+    <div>
+        咨询 EMQ 技术专家
+    </div>
+    <a href="https://www.emqx.com/zh/contact?product=solutions" class="button is-gradient">联系我们 →</a>
+</section>
