Industrial Internet platform has greatly accelerated the integration process of IT and OT
Industrial app, for the industrial Internet platform Responsible for IT and OT (Operational Technology) managers, in the decades of industrialization and information integration, well water does not make river water: IT values ​​business processes are reasonable, OT value business execution stability. From a concrete object-oriented perspective, the difference between OT and IT is mainly to reflect the edge of the device. The world of OT follows the principles and mechanisms of physical evolution, and the development is slow: from control and focus on operations.
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However, the development of the industrial Internet, and the flow of data has become more unprecedented, stimulating people's imagination of the value of data, which greatly promoted the necessity of OT and IT integration. The application of industrial Internet is to unify the perspective of IT and OT on many levels. However, IT and OT are very different in their own needs, latitude and way of thinking. Integration is very difficult. The popularity of the industrial Internet is different from the conventional enterprise management software ERP and the execution management system MES application. It is not only the data richness and granularity of the collected data, but also the value behind the data. It can be evaluated and defined by standing in a higher strategic position.
In the 2018 report, GE pointed out that the main battlefield of true digital transformation is precisely where the IT and OT borders. In fact, GE is more inclined that the charm of OT technology will be greatly released. "IT is losing magic, and OT's baton is slowly rising." The ARC consulting team described the relationship between people, processes, technologies, and measurements in the IT and OT integration maturity model proposed in 2016. It also shows that there is a need to connect edges and clouds, and each needs to be processed. The OT protocol and data format are the places where IIoT is very popular. 
Figure 1 Fusion of device connections and IT applications
IT is going to sink, OT is going to rise, and data coming from various systems is analyzed, which makes the rise of the industrial Internet platform possible.
This has led to a significant acceleration in the development of industrial applications. The IIoT platform provides a breeding ground for fertile soil for the development of industrial app applications.
Data gravity pushes the evolution of the edge of the device's data, with a "sinking" feature. It is rarely used for real salvage. Because of the industrial data on the machine site, the biggest feature is massive and disorderly. In the centuries of industrial development, the managers of production lines have never seen them before. It has been considered for consideration in recent years.
For example, with just a single CNC machine tool, the data generated per second can reach 400M. According to the calculation of 10 units of 10 stations on a production line, if there are five production lines, then a simple factory, the data production can reach 20G per second! Think about a person, mobile phone traffic is only about 10G per month. The difference between the two is 5 million times!
The big data of the factory is often the garbage data mountain, which is mainly manifested in six major symptoms: the data is dirty (there must be a large number of algorithm cleaning, in order to have available data), the frequency is different (the frequency of the field trigger is very different), the quantity, the size ( The data has different sizes, many types (various heterogeneous data sources), and interdisciplinary relationships are complex (data mechanisms come from different fields such as "optical electromagnetism").
Most of this huge amount of data is useless and can only be left on the machine side. This is called "data gravity."
It allows a large amount of data to be discarded on the floor of the shop floor and in the air surrounding the equipment.
Data gravity, resulting in a large amount of data can not be in the cloud, and can not complete the analysis. The development of Internet of Things and computing power in recent years has promoted people's thinking about edge intelligence. Too heavy data can be processed locally. In the era of big data analysis, this task was handed over to edge computing.
The industrial Internet platform itself is a distributed computing platform that solves cloud and edge integration problems well. Connections, device management, data management, and machine learning provide a serious key to truly unlocking the analytical value of your data. This also provides great convenience for the development and deployment of industrial applications for scenario-oriented applications.
The spring of industrial app is coming The PaaS platform (Platform as a Service) in the middle of the industrial Internet platform is the most important part. The most ambitious players are currently focusing on this place. New API technologies and environmentally independent container packaging technologies enable rapid deployment and application of the platform itself. With the support of the industrial PaaS platform, the industrial APP application for the scene is also the most embarrassing field of the industrial Internet at present: the situation of thousands of troops crossing the river is coming. 
Figure 2 Industrial Internet Platform Architecture
Civilian developers (CiTIzen developers), which are non-professional software people, are emerging in large numbers. Currently, many companies maintain equipment in this category. If they can use the software environment and deploy easily, this requires a lot of light code programming. , a large number of drag-and-drop applications.
In October 2017, the industrial Internet giant, General Electric Company GE and Apple reached a cooperation, the two companies will jointly develop enterprise-class iOS applications, and launched a new Predix SDK, the focus is on the Internet of Things. GE will develop iOS apps and business partners for itself and deploy iPhones and iPads throughout the company.
There is a real story behind this app for IoT development app.
California State University’s computer science students invented an app at GE Predix’s Innovation Contest, which uses data from three different energy points (solar photovoltaic panels, cogeneration, etc.) The best amount of power can be drawn. The project received a prize of $10,000. 
Figure 3 The winner team of the GE Innovation APP competition
The GE team, which has been thinking about how power plants operate more efficiently, is inspired by TITAN because one direction is to better solve pipeline steam losses. The development team then found a camera-assisted application in the iPhone app store that would enable rapid, efficient, and low-cost thermal imaging. This made them feel that they have found ways to improve the efficiency of the power plant.
GE's app development team works with field managers at the Atlanta Power Plant. The latter accompanied them to visit the various pipelines on the site and pointed out which pipeline interfaces were likely to leak steam and cause heat loss.
With the help of anomalous fault images calibrated by experts in these fields, combined with machine learning and imaging tools, the GE development team subsequently developed an app, the tubular thermal imaging tool TITAN (abnormal alarm thermal imaging tool), which can save 50,000 per year. Dollar.
The price is so small. The college students of the innovation team only received a bonus of $10,000. GE's development team used just six to develop such an amazing industrial app.
This is the charm of digital combination innovation, just like the dance of high school graduates, the air is filled with sparks that can be matched at any time.
To meet the new combination of digitization, all the elements are served as much as possible. The data thus released can easily become the basic material for the scene application. By means of a loosely coupled, multi-party callable resource, the Industrial App reassembles various data, encapsulates it into modules that can be executed or invoked through informatization and knowledge processing.
Yunhua Fengzheng Sheng On the application of the cloud, the scene intention is often obvious, and the demand changes quickly. In order to adapt to this rapid change, it is necessary to micro-service, which is why micro-services are very popular in recent years. Microservices can provide a variety of app developers with resource pool calls that satisfy the scenario application, so it is becoming a new trend. Kingdee, who is bent on transforming into the cloud, is said to have had hundreds of microservices.
At this year's Hannover Fair in Germany, sensor manufacturer Sick introduced a programmable sensor and a software system to help field workers build AppSpace for sensor applications. This has greatly changed the traditional perception that the sensor only has a switching signal. Software-defined hardware that has been armed to the teeth at the very end of the device. And Sick also launched AppSpace's programming community, which is designed to promote those who are on the scene, and can also become App developers, to achieve a variety of flexible features, is very attractive.
The industrial Internet platform is moving towards civilians, and non-IT professionals can easily get started with industrial app applications. This is a huge improvement, an open flood of knowledge that roars on the industrial Internet platform and re-emerges into exciting scene applications.
Data ladder to promote OT and IT data fusion
IT consists of technologies, systems, and applications that manage business data and support management processes. Usually reported to the CIO, these managed applications include ERP, MES, EAM, WMS, and more. OT consists of technologies, systems, and applications that manage production assets and maintain smooth operations. Usually reported to the COO, managed applications include PLC, PCD, SCADA, SIS, data history, and gateway systems.
The convergence of these data means overcoming the gravity of the data and completing the floor-to-ceiling migration.
These data have three channels for direct access to the ceiling: sensors with communication capabilities, gateways and PLCs. For brown fields, it is difficult to use PLC/IPC because there are many difficulties in reprogramming the PLC; at this time, gateway integration is generally adopted. For the green field, that is, by using the PLC/IPC of the various interfaces, including the OPC UA protocol, there are many free options; for some brown factories, the sensor is also suitable. However, this cannot be scaled up. The cost of such sensors with communication capabilities is still too high. 
Figure 4 data ladder
The data has to be completed from the equipment level floor to the corporate ceiling. Three levels need to be passed, the first level is the device connection; the second level is data gravity recognition, the relevant data is analyzed; the third level is to establish a personal-oriented app.
For the industrial Internet platform, the most needed is to build such a "data ladder" to complete the data up and down, so as to enrich the application of the industrial Internet platform.
In response to this situation, Yike Electronics adopted an IoT Hub idea to implement a data solution from the edge to the cloud through the troika. First, the IoT Hub edge of the connected large stomach king completes the data acquisition of various devices, especially the PLC collection; the second is to build the ThingsWise big data analysis software at the edge layer to realize real-time analysis of the data; The edge analysis and calculation of the data is completed. Finally, through the application side of the industrial APP rapid generation tool WorkBench, the "civilian programmer" (ciTIzen developer) can quickly generate app programs and adapt to various mobiles by means of views and drag and drop with minimal code. operating system.
This enables a “trinity†enabling platform for device connectivity, edge analysis and app applications, enabling the industrial Internet platform to handle a wide range of devices and data for scenario applications.
For example, in the case of a pure steam generator in Bosch, large equipment often requires multiple local HMIs, and some operations require manual operations (such as conversion, material re-storage, etc.). 
Figure 5 provides Bosch with IoT Hub solution
In the IoT Hub's Trinity solution, real-time data of process variables are collected by OPC UA and transmitted over WiFi, and then various KPI information is analyzed on the mobile side. This allows machine faults to be fed back in time, while saving a fixed HMI and eliminating multiple switching.
At the 2nd World Intelligence Conference in Tianjin in May, Zhang Xin, the general manager of Yike Electronics, gave a speech on the theme of “Encouraging the Industrial Internet Platform to Enable Cloud Manufacturing†and mentioned: “Yike’s philosophy is to build a 'Data ladder', the data of the edge layer is sent to the cloud, and the data is analyzed and processed by the industrial Internet platform at the Paas layer; the data application is displayed at the Saas layer by providing innovative tools such as industrial APP.†Such an enabling platform The core IoT Hub is like a “data ladderâ€, making data capabilities truly a strategic resource advantage.
A good industrial Internet application requires the needs and descriptions of industry experts, which must be solved independently by the company – and to a large extent, this is also a key factor in a company's Know-how. Based on this, asset modeling can be done through an outsourcing team or a full-time programmer. The remaining equipment connections, data analysis, and the generation of industrial APPs are all places where data ladders can be used.
Xiaoji Industrial Internet platform has greatly accelerated the integration process of IT and OT. The data begins to break away from the data gravity of the device, like a bead, rolling around. With the help of data ladders on the Industrial Internet platform, some are processed in-place by edge calculation and analysis, and some are raised to the IT layer, which are part of business decisions.
In the gaze of machines and people, the data from top to bottom creates a digital age of technical division of labor. 