When NLP meets the Industrial Internet of Things

Image by Stefan Keller from Pixabay




01 Artificial Intelligence and Internet of Things


We are in an era of artificial intelligence, IoT, and 5G. In this regard, Mr. Wu Jun once made a very vivid description: the future society will be a super-intelligent organism. If we correspond to people, then artificial intelligence is the brain and IoT is the nervous system.

The huge number of sensors and devices in IoT play the role of many sensory cells, and the developing 5G is equivalent to peripheral nerves. The Internet of Things connects things to the network through smart sensors, computer recognition technology, and communication technology, and realizes the collaboration between things to complete tasks. In the process of collaboration between all things, a large amount of data will be produced, and this precisely provides a new catalyst for the current explosion of artificial intelligence that relies on big data algorithms.

02 Natural Language Processing and Industrial Internet of Things


Industrial Internet of Things (IIoT) refers to the application of Internet of Things technology in industrial environments, especially in the instrumentation and control of sensors and equipment involving cloud computing.

With the development of cloud computing and machine learning technology, the IIoT industry will realize a brand-new operating model and create new revenue and business models. Natural language processing (NLP) technology aims to study the understanding, processing, and application of human language information through computer equipment. It is the most classic and most challenging part of artificial intelligence research.

In recent years, the industry has begun to use machine-to-machine communication to achieve wireless automation control. Typical application scenarios include smart grid, smart manufacturing, preventive and predictive maintenance, etc. IIoT is trying to empower businesses through NLP technology and will be widely used in tasks such as system control, task tracking, and information retrieval. Let's take a look at the current collision of NLP and IIoT.

03 Natural Language Processing Technology in Industrial Internet of Things


a. Intelligent system control


At present, the central control system of IoT devices based on voice interaction is a new direction of the industrial Internet of Things. Voice interaction can bring many benefits to the application environment of the Internet of Things, including rapid execution of complex operation tasks and replacement of operation tasks with touch risks.

On the one hand, voice interaction is generally considered to be the most penetrating way of interaction, especially when dealing with complex work order management operations. Traditional operations often require many steps, which are cumbersome and inefficient, while voice interaction can take one step. Reach.

On the other hand, in the context of construction operations, it is still often necessary for operators to go to the site to perform operations in person, which inevitably poses hidden dangers of construction risks. At this time, non-contact voice interaction shows irreplaceable advantages.

To realize the voice interactive application described above is inseparable from advanced intelligent technical support. And the most core part of this is undoubtedly the intelligent question answering system based on NLP technology. Through deep learning algorithms, combined with natural speech understanding models, dialogue management, speech recognition, and other technical modules, the function of voice interaction is realized. Connect semantic understanding capabilities to industrial Internet equipment to realize remote voice control and intelligent control of equipment.

This technology is different from the command control based on voice recognition. It allows the device to truly understand the user and deeply understand the user's needs, thereby skipping tedious steps and realizing flexible and intelligent system control. In addition, the voice control module can be connected to the machine translation module to realize the work of translating text content in different languages. In the IIoT scenario, it can serve cross-language text processing and voice command operations.

The Honeywell-BPS building control service launched by Honeywell is a representative application case. This service provides customers with an open building IoT platform. Collect massive building operation data, and connect various electronic systems to realize equipment intercommunication. Combining voice interaction technology and natural language understanding technology to achieve contactless voice central control capabilities, thereby improving building operation efficiency and reducing operating costs.

b. Industrial production operations


Industrial production and operation is another IIoT scenario that has been successfully entered by NLP. This involves the collection of information and data and the application of data knowledge.

The sensing devices at the collection layer of the Internet of Things are used as social network resources. Based on the interactive Internet of Things service framework implemented by NLP, an independent interactive platform is created for the Internet of Things applications, realizing natural language interaction between users and Internet of Things devices Data query, command set, regular reporting, and other functions.

NLP technology can make the machine have the ability to read and understand like a human brain, quickly refine and present the key knowledge points in the text, and this ability can be widely used in projects with large amounts of data and text resources. Mining information from massive industrial document data, realizing engineering task tracking, and optimizing the production decision-making process.

Industrial production knowledge data can also create a visual knowledge network by refining knowledge information. Machine learning can enhance this function, and further process and analyze the retrieved information, clarify the relevance of the data, so as to quickly and efficiently find abnormal situations. Help operators to complete specific tasks efficiently and accurately, and optimize the mode of human-computer collaboration.

Information retrieval and knowledge graphs are key technologies to realize the above scenarios. Information retrieval is the main way for users to query and obtain information, as well as a method and means to find information. Popular information retrieval models include Boolean models, vector space models, probability models, and language models to find unstructured content that meets information needs from the data set.

The knowledge graph can effectively organize and correlate the knowledge information in the system, so as to realize the mutual connection and communication between information and data. Build a graph database for text data containing professional technology and knowledge, so as to realize complex retrieval functions and intelligent decision-making assistance functions. Improve the retrieval quality of text information through the graph database, which can be used in public energy management monitoring, manufacturing decision-making assistance, and knowledge-based intelligent question and answer scenarios.

Speaking of the operation of intelligent IIoT, we have to mention the industrial brain launched by Alibaba Cloud. Ali Industrial Brain provides customers with a complete set of intelligent services from industrial manufacturing to production and marketing. In the production process, Ali Industrial Brain uses a combination of NLP algorithms and industrial knowledge maps to record the status of various equipment and overall production process data and provides functions such as intelligent equipment health management and real-time recommendation of process parameters to improve the efficiency of industrial production.

04 Summary


The Internet of Things not only connects electronic devices but also connects each of us. Imagine a world where devices and humans can work together. All kinds of devices will be able to understand our questions, feel our needs and provide relevant responses. However, the demand in this area is still very vague in the industrial scene.

At present, the capabilities and application depth of many technologies are far from reaching the level of industrialization, which leads to the immature application of technologies in many scenarios. Although NLP technology has achieved good performance in improving IIoT work efficiency and safety performance, most scenarios are still not rigid requirements.

In the foreseeable future, we believe that both artificial intelligence and NLP technologies will continue to develop. At that time, technology will provide people with more credible and effective services. Combining computer vision technology and RFID radio frequency identification technology, under IIoT, more and more rigid requirements will be met.

NLP technology will empower the IIoT industry more comprehensively. Possible service forms include remote central control, voice interaction, and operation judgment. All kinds of IoT devices will be more efficient to connect with humans, and at the same time better to connect with each other, and ultimately provide users with better products and services, and develop in the direction of high convenience and low power consumption.