Litmus Automation > 实例探究 > 边缘到云数据平台的工作原理

边缘到云数据平台的工作原理

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 How an Edge-to-Cloud Data Platform Works - IoT ONE Case Study
技术
  • 分析与建模 - 边缘分析
  • 平台即服务 (PaaS) - 边缘计算平台
适用功能
  • 离散制造
  • 流程制造
用例
  • 边缘计算与边缘智能
服务
  • 系统集成
挑战

在 Litmus,客户面临的最大挑战是访问他们为机器学习和分析模型提供动力所需的数据。大型制造商来到 Litmus 寻求以最快的方式连接到他们的资产并将数据发送到云。公司不仅需要将数据发送到云端以创建机器学习模型,还需要使用统一的边缘到云平台将这些模型部署回边缘。

解决方案

统一的边缘到云端数据连接平台弥合了边缘工业设备与云端大数据和机器学习系统之间的差距。该平台部署在边缘计算设备上,旨在收集数据并将其结构化为通用数据格式。统一的边缘到云平台充当数据代理,支持边缘应用和分析,以及云中的大数据和机器学习。

Litmus 被部署为工业资产旁边的边缘计算平台——从源头收集和规范化数据。即用型数据在边缘进行管理以进行本地分析,并发送到云和大数据系统以进行更复杂的处理。数据模型被部署回边缘以完成循环。

运营影响
  • [Data Management - Data Integration]

    Device Connectivity - Litmus Edge offers rapid data connectivity to all modern and legacy industrial systems with just a few clicks – enabling data collection and structuring data in a ready-to-use format by any edge or enterprise application.

    Edge Analytics - Get immediate value at the edge with pre-built data visualizations and analytics for common KPIs to realize rapid time-to-value. Run data models created in the cloud back at the edge for closed-loop edge-to-cloud operations.

    Application Deployment - Host and access public or private applications in a centralized repository with the ability to rapidly and securely deploy and run applications at the edge. Stream normalized and structured data to any pre-built or custom application.

    Data Integration - Immediately feed valuable and ready-to-use data to any cloud or enterprise application to achieve a complete data picture from OT to IT. Activate machine learning and advanced analytics use cases with bi-directional integration from edge to cloud.

  • [Data Management - Data Syncing]

    Edge-to-Cloud Deployments - A vast majority of companies are standardizing on one of these major cloud platforms – Microsoft Azure, Google Cloud, AWS, or Cloudera. Litmus has built great relationships and pre-integrated Litmus Edge with these and other vendors.

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