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由销售和营销副总裁Derek Thomas，艾默生机器自动化解决方案
本文是四部分系列中的三分之一，探索三个新的但相关概念，用于从大数据中导出值：小数据，边缘处理和嵌入式连接。这first column提供了所有三个主题的概述，以及第二栏focused on little data and how it differs from big data.
Many companies are tempted to tackle the big data issue in one fell swoop with mega-scale projects. A better path for most manufacturers is to start small with a little data approach to greatly simplify and speed implementation, with positive results generated in days instead of the years. Of course, one must keep in mind the ultimate goal, which is larger scale implementation, often through integration with enterprise IT systems.
用于从小数据创建值的关键启用技术是边缘计算，由传感器产生的数据由现场定位的设备分析以生成见解。此信息允许源附近的人员快速评估问题并采取适当的行动。
在过去，这种类型的边缘处理需要添加单独的工业计算设备，或连接到服务器的边缘网关，以处理数据。在任何一种情况下，解决方案都需要与现有控制器和制造网络集成。这将是由于在两个不同环境中设置和编程的复杂性而存在的问题步骤 - 由于同步要求，滞后/延迟问题，网络安全问题和其他因素导致。
But today, advances in processor technology enable edge controllers to perform two functions within a low energy use and compact form factor. The first function is real-time deterministic control using IEC 61131-3 languages, much like a traditional programmable automation controller (PAC). The second function is edge processing using advanced programming and scripting techniques. Integration effort, cost, and complexity are reduced because both functions are performed in one device.
Each part of the controller is virtually connected via OPC UA, allowing the two functions to be operate seamlessly, yet also separately such that real-time control tasks and performance are not affected by the edge applications.
任何人在PAC-based控制是熟悉的the industrial automation arena, but edge processing contained in the same device is a new concept for many. In traditional implementations, a PAC would simply collect data and then forward it to a host system for processing. This host system would typically be PC-based, and it would often be located some distance from the edge, perhaps even in a data center or the cloud, raising a number of potential issues.
处理边缘的数据而不是远程主机，通过提供本地来解决这些问题：
- 数据存储，处理和分析
- 数据记录
- 操作诊断
- 闭环优化机会
- 连接到显示时可视化，仪表板和其他HMI功能
Edge processing creates an open, flexible ecosystem with broad compatibility across all layers of automation systems. Users can create dashboards, improved analytics, and trends—all acting upon full fidelity and complete data sets—to empower local operators. Data communication and storage requirements are substantially reduced because only results are transmitted, instead of entire data sets.
With today’s PACs, customers can only write rudimentary analytics utilizing IEC 61131 languages. In contrast, edge controllers support execution of modern programing languages such as C/C++, Python, and Java. These and other advanced languages can be used to apply complex optimization algorithms or analytics to improve operations. This “outer loop” or “advise” layer runs in parallel with the underlying closed-loop control. In the event of a disruption to this “outer loop”, real-time deterministic control remains unaffected.
This functionality delivers on the potential of machine learning by allowing adjusts in real time based on actual performance data collected and analyzed at the edge. It also provides the ability to update machines/programs by staging upgrades, or sequencing them in real-time, to minimize impact.
An edge controller can thus be viewed as an end user’s on-ramp to the continuous journey of digital transformation. End users can start to solve big data challenges, one manageable little data chunk at a time, and edge controllers provide the scalability required to eventually transform entire discrete part manufacturing enterprises.
Sponsored content by Emerson
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