This article tells everyone about the data collection...
Achieving Industry 4.0 requires a high degree of industrialization and automation, which is a long journey. Industrial big data is the key to the future advantages of industry in global market competition. Whether it is German Industry 4.0, American Industrial Internet or "Made in China 2025", the implementation of manufacturing innovation strategies in various countries is based on the collection and analysis of industrial big data and the worry-free environment for future manufacturing systems. Regardless of the extent to which smart manufacturing is developed, data collection is the most practical and high-frequency demand in production and a prerequisite for Industry 4.0.
Digital factories are not equal to unmanned factories. Product configuration and manufacturing processes are more complex and changeable, and more human participation is required. In digital factories, workers are more likely to handle abnormal situations and adjust equipment. However, data collection has always been a traditional pain point that plagues all manufacturing plants. There are many types of automation equipment brands, different manufacturers and data interfaces, foreign manufacturers have limited local support, and different procurement years. Even if the production shutdown data is automatically collected, it does not mean that the entire manufacturing process data is obtained. As long as there are other human participation links, these data are incomplete.
Industrial data collection type
Internet data mainly comes from network equipment such as Internet users and servers, mainly a large amount of text data, social data and multimedia data, etc., and industrial data mainly comes from machine equipment data, industrial informatization data and industry chain related data.
In terms of the types of data collection, not only the basic data should be covered, but also the semi-structured user behavior data, network social relationship data, text or audio type user opinion and feedback data, devices and sensors collected. Periodic data, Internet data obtained by web crawlers, and more and more potentially meaningful data in the future. Mainly include the following:
1. Massive Key-Value data. Today, with the rapid development of sensor technology, industrial sensors of different types including photoelectric, thermal, gas, force, magnetic, acoustic, and humidity are widely used in the field, and many times the data of machinery and equipment are summarized. Only a large amount of industrial data can be analyzed with the accuracy of ms. Therefore, the characteristic of this part of the data is that each data content is very small, but the frequency is extremely high.
2. Document data. Including engineering drawings, simulation data, designed CAD drawings, etc., as well as a large number of traditional engineering documents.
3. Informational data. The data generated by the industrial information system is generally stored in the form of a database, and this part of the data is best collected.
4. Interface data. Interface type data provided by industrial automation or information systems that have been built, including txt format, JSON format, XML format, etc.
5. Video data. There will be a lot of video surveillance equipment on the industrial site, which will generate a lot of video data.
6. Image data. Including pictures taken by various types of image equipment on the industrial site (for example, equipment and environmental information pictures taken by inspectors with handheld devices).
7. Audio data. Including voice and sound information (for example, the operator's call, the volume of equipment operation, etc.).
8. Other data. For example, remote sensing telemetry information, three-dimensional elevation information, etc.
Data collection method
Traditional data collection methods include manual entry, questionnaires, and telephone follow-up. After the era of big data, a prominent change is that the method of data collection has taken a qualitative leap. The breakthrough in the data collection method described below directly changes Big data application scenarios.
1. Sensor
The sensor is a detection device that can sense the measured information and can convert the detected information into electrical signals or other required forms of information output according to a certain rule to meet the transmission, processing, storage, Requirements for display, recording and control. There are usually many sensor nodes in the production workshop, which monitor the entire production process 24 hours. When an abnormality is found, it can be quickly fed back to the upper computer. It can be regarded as a sensory acceptance system for data collection, which belongs to the bottom link of data collection.
The main characteristic of the sensor in the process of collecting data is the relationship between its input and output.
Its static characteristic reflects the input and output relationship of the sensor when the measured value is in a stable state, which means that when the input is constant or changes very slowly, this relationship is called the static characteristic. We always hope that the input and output of the sensor form a unique contrast relationship, preferably a linear relationship.
Under normal circumstances, the input and output will not meet the required linear relationship. At the same time, due to the influence of hysteresis, creep and other factors, the uniqueness of the input and output relationship cannot be achieved. Therefore, we cannot ignore the external influences in the factory. The degree of influence depends on the sensor itself, which can be suppressed by the improvement of the sensor itself, and sometimes the external conditions can also be restricted.
2. RFID technology
RFID (Radio Frequency Identification, Radio Frequency Identification) technology is a non-contact automatic identification technology that automatically identifies target objects and obtains relevant data information through radio frequency signals. Use radio frequency for non-contact two-way communication to achieve identification and exchange data. RFID technology can identify high-speed moving objects and can recognize multiple tags at the same time, and the operation is quick and convenient.
When working, the RFID reader sends out a pulse signal of a certain frequency through the antenna. When the RFID tag enters the magnetic field, it uses the energy obtained by the induced current to send the product information (Passive Tag, passive tag or passive) stored in the chip. Tag), or actively send a signal of a certain frequency (Active Tag, active tag or active tag).
The reader demodulates and decodes the received signal and sends it to the background main system for related processing; the main system judges the legality of the card according to the logical operation, makes corresponding processing and control according to different settings, and issues command signals to control Actuator action.
RFID technology solves the problem of the automatic connection of item information and the Internet. Combined with the subsequent big data mining work, it can exert its powerful power.
Difficulties in data acquisition technology
In today's manufacturing industry, data collection is a difficult point. The production data collection of many enterprises mainly relies on the traditional manual operation method, which is prone to human record errors and low efficiency during the collection process.
Although some companies have introduced relevant technical means and applied data collection systems, due to the reasons of the system and the companies have not selected the data collection system that is most suitable for themselves, they cannot achieve real-time, accurate and extended management of information collection. , Information faults appeared in each unit.
The technical difficulties mainly include the following aspects:
1. The amount of data is huge. In the face of different amounts of data, any system requires completely different technical difficulties.
If the data is simply collected, it may be relatively easy to complete, but it needs to be processed after the collection, because the specification and cleaning of the data must be considered, because a large amount of industrial data is "dirty" data, and direct storage cannot be used for analysis. Before, it had to be processed to process massive amounts of data, which technically increased the difficulty.
2. The protocol of industrial data is not standard. Internet data collection is generally our common HTTP and other protocols, but in the industrial field, there will be various types of industrial protocols such as ModBus, OPC, CAN, ControlNet, DeviceNet, Profibus, Zigbee, etc., and various automation equipment manufacturers and integrators also I will develop various private industrial protocols on my own, resulting in great difficulty in the interconnection of industrial protocols.
Many developers encountered the biggest problems when implementing comprehensive automation and other projects on the industrial site. They faced many industrial agreements in time and could not effectively analyze and collect them.
3. The bandwidth required for video transmission is huge. Since traditional industrial informatization is carried out on-site data collection, video data transmission is mainly carried out in the local area network, so bandwidth is not a major problem.
However, with the popularization of cloud computing technology and the rise of public cloud, big data requires a lot of computing resources and storage resources, so the gradual migration of industrial data to public cloud has become the trend. However, an industrial enterprise may have dozens of videos, and a large-scale enterprise will have hundreds of videos. How to transfer such a large number of video files to the cloud through the Internet is a huge challenge for developers.
4. It is difficult to collect the original system. In the implementation of big data projects in industrial enterprises, data collection is often not directed at sensors or PLCs, but at the acquisition of computer data of the automation systems that have been deployed.
The deployment level of these automation systems varies greatly. Most systems do not have data interfaces, and a large number of documents are missing. A large number of field systems do not have basic setting data such as point tables, making it difficult to collect data for this part.
5. Insufficient security considerations. The original industrial systems are all running on the local area network, and the security issue is not the focus of consideration.
Once the most core production capacity in the industry needs to be dispatched through the cloud, without full consideration of security, the loss is inaccessible. In 2015, industrial enterprises affected by cyber security incidents accounted for 30%, and as many as 20% of enterprises were down due to viruses. The Department of Homeland Security’s Industrial Control System Network Emergency Response Team (ICS-CERT) alone received 295 attacks against critical infrastructure.
Industrial data collection solution case
Solution 1: Industrial field data collection system of Internet of Things
This project belongs to a type of terminal sensor system of the Internet of Things. Through the wireless module installed on the machine, the PLC working information of the specified machine is collected and uploaded to the host. The host processes the data and uploads it to the cloud server. Users can use it on mobile phones, tablets, and computers. View machine working information and set machine working parameters to a limited extent.
Solution 2: Data collection and data transmission module for solar charging
The bottom slot of the finished product of this solution can be embedded in the industry standard din35 rail for easy installation; it comes with two digital inputs, two analog inputs, and eight IO outputs. The solution also uses a solar charging mode, integrated GPRS module, can automatically reset when disconnected, to avoid the problem of the need to send a text message to restart after the external DTU is disconnected.
Solution 3: U disk data transfer, paperless recorder solution
The paperless recorder is a new paperless recorder that uses the latest U disk data storage and data dump technology. According to user requirements, the maximum data storage capacity can be configured to 32G, which can meet the data storage requirements of any industrial site. In particular, the method of taking out the data recorded by the instrument through a USB flash drive has the advantages of large data storage, convenient and reliable use and other advantages compared with other methods such as IC cards, and is suitable for actual use on site.
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