什么是工业物联网？

Industrial IoT (IIoT) is a subset of the Internet of Things (IoT) revolution that refers to the application of IoT principles, technology, and approaches, specifically in industry, manufacturing, energy and similar sectors. For all industries, IIoT ultimately aims first at gathering and analyzing data from factory sensors and devices, and then secondly to make intelligent responses based on data-driven insights. Automated real-time responses can be implemented to significantly streamline performance.

IIoT concepts are similar to other IoT concepts, in particular the networking together of numerous small devices, sensors, instruments, and actuators, to create the “internet of things”, a convergence of networking and device technology. However, IIoT differs from common IoT examples like smart homes, in both the degree and scale of technologies that are connected. Smart home sensors can monitor temperature, and send mobile device notifications in emergencies. Comparatively, in larger industrial settings, IIoT may orchestrate the operations and interactions of tens of thousands of devices, sensors, and robots. This difference requires more complex implementation methods, including using IIoT platforms, sophisticated device management software, and custom integrated automation tools.  

Industrial applications have become the perfect testing ground for upcoming IoT technologies, including the following:

• Digital Twins — Digital twins is an innovative way using computer modeling, the digital twin, to predict behaviors and anticipate problems through the life of a real world object, process, or ecosystem. Simulations using real-world data can be applied to the digital twin model and reveal predictive insights, which can be used in pre-designs, or to monitor existing machine performance, and for example, suggest maintenance schedules to maximize ROI.
• Electronic Logging Device (ELD) — IIoT extends beyond factory settings, demonstrated by vehicle sensors like electronic logging devices (ELD) used in commercial fleets. ELDs connect a series of on-vehicle sensors to monitor trip mileage, time, gas usage, and even brake deterioration. Firstly, these devices help replace error-prone paper logs, and secondly capture essential vehicle, trip, and driving data that can be used to optimize fleet operations.
• Intelligent Edge — “The Edge” is a frame of reference where compute and storage resources are positioned in relation to the data generated by a IoT and mobile devices. The general IoT analytics model is that when data is generated from sensors it is communicated to a central server for processing and insights. Today, sensors are generating huge volumes of data, but require an almost instantaneous computation response to that data. In many instances, a central server, even if located on-premises in the next building, may introduce lag times that hamper performance. To solve this problem, computational and storage resources are placed closer to the sensor devices, or closer to “the edge”. For example, on-board computers work with networked GPS systems in self-driving trucks to not only control the vehicle in the moment, but to coordinate with others on the road.  Now, sensor data can be processed instantly where the data is created, at an intelligent edge.
• Radio-Frequency Identification (RFID) — RFID technology significantly enables the tracking and tagging of items with identification and detailed object information. RFID is a commonly known technology, with a long existence of over 70 years, but as the technologies in the RFID ecosystem have become cheaper, standard, and widely adopted, it has become a way for entire value chains to integrate and provide greater efficiencies, and innovative new services such as omnichannel customer engagements. 

IoT is an acronym for Internet of Things, and refers to appliances, sensors, and otherwise common devices, like thermostats, lights, heating and cooling, that have been network enabled. Further, these devices can communicate amongst themselves, machine-to-machine (M2M), to perform simple tasks, like adjusting lighting to conserve energy. IoT fits most consumer models, and aims to add some layer of “intelligence” to common “dumb” devices.

Industrial Internet of Things (IIoT) refers to IoT devices and machines within industries like manufacturing, energy, or transportation. In the industrial context, robots, vehicles, and industrial lines can all be IIoT enabled. Unlike consumer IoT, more sophisticated IIoT orchestrates device automations to ensure that production matches real-time demands.

The table below illustrates a comparison of common aspects shared by both IIoT and IoT.

Some confusion develops around the industrial internet of things (IIoT) and manufacturing execution systems. MES applications have been in use since the 1990s, and in the wave of modern industrial smart technology adoption, IIoT has become synonymous with industrial automation, and Industry 4.0, spurring the notion that MES has been replaced. To clarify, the Industrial Internet of Things (IIoT) and manufacturing execution systems (MES) work together to provide a complete picture of a manufacturer’s floor operations.

For many business benefits, data and its analysis has been recognized as a fundamental requirement in modern manufacturing. For instance, to perform tasks like root cause analysis, large quantities of data are needed. IIoT gathers much of this data, like contextual details from devices, sensors, machines and controllers. The MES combines the data from IIoT with its own context, like customer details, orders, products, recipes, billings, etc., to complete the total picture of operations within the wall of the factory.

While both IIoT and MES collect data, the MES, in conjunction with team members, owns analysis and determination of situational changes in the ongoing manufacturing process. This means, installing just an IIoT will provide a communications backbone between and among devices, but it will not include the analytical brain to compile the context.