How IoT data is organized
- The production and manufacturing Internet of Things application of industrial enterprises is generally called the Internet of Things in the workshop or the Internet of Manufacturing Things . By using technologies such as RFID, sensors, wireless network communications, GPS positioning, and audio and video systems, the manufacturing plan and manufacturing resources "human, machine, material, Linking information such as law and environment , so as to intelligently identify, locate, track, monitor and manage the five major manufacturing resources, so as to meet the management requirements of enterprise command and dispatch, environmental monitoring and other aspects.
- The five major manufacturing resources are divided into static attributes and dynamic attributes. For example, the static attributes of a machine tool can be divided into management information (equipment code, equipment name, equipment classification, etc.), static parameters (working environment, feed speed, cutting parameters, etc.) , Dynamic parameters (machine tool status, lathe complete rate, lathe load rate, maintenance records, etc.).
- Static attributes are not affected by the production process and have been determined before the production process begins. They are constant data in the workshop site management, but these data are not fixed forever, and they can be adjusted by the user after the production process ends; dynamic data It is data that is always changing, and most of the IoT data in the workshop are dynamic data.
IoT Data Management Technology
The Internet of Things in the workshop is a typical complex information system, involving all aspects of data management, including: data quality control, data fusion and integration, complex event processing, data storage and processing, and security access control .
1. Data Quality Control
- The data quality of the Internet of Things can be measured by three indicators: accuracy, confidence and completeness .
- In terms of improving radio frequency identification and sensor network data quality control, it is mainly used to clear over-read and mis-read data and fill in missed-read data. Data cleaning usually uses the method of probability statistics and time-space correlation.
2. Data fusion and integration
The polymorphism of data objects in the IoT data space is manifested in multi-type, heterogeneous and no unified mode , so:
(1) It is necessary to build a unified data model for the workshop and express data in a unified way;
(2) Based on the unified data model, study how to map and transform heterogeneous data into a unified data framework;
(3) The data sources in the Internet of Things are distributed, autonomous and independent. In the process of data integration, it is sometimes necessary to automatically discover relevant data sources;
(4) To record the source of the data, so as to realize the traceability of the data;
(5) Workshop manufacturing resources are constantly changing, and this change will have an impact on data consistency, version and model updates, etc. It is necessary to be able to record the process of data evolution.
3. Complex event processing
In a typical IoT application, the upper layer system is responsible for monitoring the status and behavior of each object, and controlling it to make an intelligent response and complete the corresponding behavior according to the established program. The behavior of objects is usually expressed in the form of events.
4. Security Access Control
Due to the openness of the Internet of Things, private information can be deduced using complex reasoning techniques. How to protect the privacy of sensory data has become a thorny issue. But at the same time, the heterogeneity and mobility of objects in the Internet of Things increase the complexity of privacy protection.