3BSE004737R1 PU513V1 ABB双通道RTA 板

　　自动化专家预测，到 2020 年，我们将拥有超过 300 亿个相互连接的对象。这种趋势已经随着越来越多的设备、机器、结构、汽车和其他物体而开始。然而，这种倍增的物联网导致数据量显着增加。由于延迟等问题，在处理大量数据时，更好的分析一直是一个挑战。到目前为止，原始数据被转储到云端进行进一步检查，导致更高的延迟和数据碎片。然而，随着越来越多的公司在将数据传输到云之前从源头分析和分割数据，这种趋势正在发生变化。

　　公司现在意识到，如果他们希望通过在云上存储非结构化数据来获得商业价值和模式，他们最终将花费大量资源而产出很少。边缘计算平台，如 CISCO、ARM、英特尔和微软等科技的产品 Fog Computing 已经确定，纯云模式使物联网不可持续，尽管潜力爆炸式增长。雾在云和端点设备和网关之间添加了元素层次结构，以应对与延迟、网络带宽、可靠性和安全性相关的挑战。

　　可扩展的分析方法可以通过解决企业不同级别存在的问题来帮助您防止数据过载。例如，本地维护分析可以使用设备级数据来生成有关关键设备和机器健康状况的实时警报。这可以帮助您更快地实施决策，并且更接近时间至关重要的流程。

　　同样，在控制器和车间服务器等边缘设备中实施的机器级或工厂级分析可用于优化机器、流程和工厂。企业级分析将工厂车间信息与商业智能相结合。这可以帮助您提高跨多个站点的运营效率或合规性工作。

　　以下是我司【主营产品】，有需要可以发来帮您对比下价格哦!

　　主营：世界品牌的PLC 、DCS 系统备件 模块

　　①Allen-Bradley(美国AB)系列产品》

　　②Schneider(施耐德电气)系列产品》

　　③General electric(通用电气)系列产品》

　　④Westinghouse(美国西屋)系列产品》

　　⑤SIEMENS(西门子系列产品)》

　　⑥销售ABB Robots. FANUC Robots、YASKAWA Robots、KUKA Robots、Mitsubishi Robots、OTC Robots、Panasonic Robots、MOTOMAN Robots。

　　⑦estinghouse(西屋)： OVATION系统、WDPF系统、MAX1000系统备件。

　　⑧Invensys Foxboro(福克斯波罗)：I/A Series系统，FBM(现场输入/输出模块)顺序控制、梯形逻辑控制、事故追忆处理、数模转换、输入/输出信号处理、数据通信及处理等。Invensys Triconex: 冗余容错控制系统、基于三重模件冗余(TMR)结构的现代化的容错控制器。

　　⑨Siemens(西门子)：Siemens MOORE， Siemens Simatic C1，Siemens数控系统等。

　　⑩Bosch Rexroth(博世力士乐)：Indramat，I/O模块,PLC控制器,驱动模块等。

　　◆Motorola(摩托)：MVME 162、MVME 167、MVME1772、MVME177等系列。

　　PLC模块，可编程控制器，CPU模块，IO模块，DO模块，AI模块，DI模块，网通信模块，

　　以太网模块，运动控制模块，模拟量输入模块，模拟量输出模块，数字输入模块，数字输出

　　模块，冗余模块，电源模块，继电器输出模块，继电器输入模块，处理器模块。

　　我们的优势是：全新原装，，供给一年质保!本公司所有产品都经过严格检测，欢迎询价，收购。只需您有诚心，本公司将会给你供给一个比同行优势的价格，共同拿下单子。

　　Automation experts predict that by 2020, we will have over 30 billion inter connected objects. And the trend has already begun with more and more devices, machines, structures, automobiles and other objects. This multiplying network of things however is resulting in a significant increase in the volume of data. Better analytics have always been a challenge while handling large amounts of data due to problems such as latency. Until now, raw data was being dumped into the Cloud for further examination, leading to higher latency and data fragmentation. However, this trend is changing as more companies are now analyzing and fragmenting data at the source before transferring it to the Cloud.

　　Companies are now realizing that if they are expecting to get business value and patterns out of storing unstructured data on Cloud, they will end up spending huge amounts of resources with little output. Edge Computing platforms such as Fog Computing, a product of tech giants such as CISCO, ARM, Intel and Microsoft, has identified that a Cloud-only model has made IoT unsustainable, although exploding with potential. Fog adds a hierarchy of elements between Cloud and end-point devices and gateways to meet the challenges related to latency, network bandwidth, reliability and security.

　　A scalable analytics approach can help you prevent data overload by solving problems that exist at different levels of your enterprise. As examples, local maintenance analytics can use device-level data to produce real-time alerts about critical device and machine health. This can help you implement decisions faster and closer to the process, where time is critical.

　　Similarly, machine-level or plant-level analytics implemented in Edge devices such as controllers and plant-floor servers can be used to optimize machines, processes and plants. And enterprise-level analytics integrate plant-floor information with business intelligence. This can help you improve your operational productivity or compliance efforts across several sites.