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Data as value is a highly respected concept in the field of computer science . Data, no matter how much, comes down to big data , Data analysis is becoming more and more popular , Capital is also flocking to companies with big data labels . Data is being evaluated like a flowing digital currency , Pursue and worship .

When the Internet of things began to land and apply in the industry , Because of the speed of data generation , The variety , The huge size of the city will affect the existing cloud technology architecture , Data processing methods bring pressure and challenges beyond imagination .

Faced with such complex data , Legendary “ Data is value ” Will be “ Valuable data ” This rational understanding is broken .

What are the characteristics of Internet of things data ? Is there a reasonable classification ? Do we have the ability to cope with the pressure and challenges brought about by the data of the Internet of things ? Internet of things 1.0 What are the valuable data of the stage ?

data classification

Static data and dynamic data

In terms of data changes alone , Internet of things data can be divided into static data and dynamic data , Static data is mostly label class , Address data ,RFID
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The data generated are mostly static data , Static data are mostly structured , Relational database storage ; Dynamic data is data with time series , The characteristic of dynamic data of Internet of things is that each data has one-to-one correspondence with time , And this relationship is particularly important in data processing , This kind of data storage is usually stored in time series database .

Static data will increase with the increase of sensors , The number of control equipment increases ; Dynamic data not only depends on the number of devices , The number of sensors increases , It will increase with time .


Whether static data or dynamic data , In the Internet of things 1.0 The growth of stage data is linear , It's not exponential , But because the dynamic data of the Internet of things is continuous , So the amount of data is huge . So the Internet of things 1.0 The pressure of stage data is controllable , It's not as innumerable as it is advertised , Uncontrollable .

Energy / Asset attribute class / Diagnosis / Signal class

In terms of the original characteristics of the data , We can divide the data of Internet of things into energy data , Asset attribute data , Diagnostic data , Signal data .


Energy data : Energy consumption related , Or the relevant data needed to calculate energy consumption, such as current , Voltage , Power factor , frequency , Harmonics and so on . Energy data is the most critical data type of the Internet of things , One of the ultimate goals of the Internet of things is to save energy , So get energy data , Understanding energy data , Analysis of energy data is a necessary function in the implementation of Internet of things . Energy acquisition equipment is also one of the important devices of the Internet of things .

Asset attribute data :
Asset data usually refers to hardware asset data, such as equipment specifications , Parameters, etc , Location information of the device , Subordination between devices, etc . Asset data is mainly used for asset management , Asset management is a very important function of industrial Internet of things, and even can be studied as an independent system , Because it can interact with ERP system ,MES system , Logistics and other almost all the system docking .

Diagnostic data :
Diagnostic data refers to the data that detects the running state of equipment during the operation of equipment , There are two types of diagnostic data : One is equipment operation parameters , For example, device input / Output value , This is usually the traditional industrial automation data OT Technical related data ; The other is peripheral diagnostic data , For example, the surface temperature of the equipment , Equipment noise , Equipment vibration, etc , It is worth mentioning that peripheral diagnosis is
Internet of things technology
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It includes new sensor technology and Internet of things communication technology . Peripheral diagnostic data is an important metadata of predictive maintenance , It also provides the basis for depth control model , Therefore, diagnostic data is the data type that we need to focus on .

Signal data :
Signal data or alarm data is the most popular data in the industrial field , Because it's intuitive , Easy to understand , crux , At the same time, local , Remote simultaneous notification . Signal data is easy to be ignored , But it's what the Internet of things needs , It can also be collected quickly , And provides one of the important reference value data for the Internet of things system .

Correlation between data

The relationship between data is the relationship between different data , The relationship between data has the most direct impact on understanding the operation of the whole system , Sorting out the correct relationship between data is the effective operation of the system , The cornerstone of value .

The correlation between data can be analyzed from the following aspects :

Time relevance :
That is, data photography at the same time , Data is generated by the system at the same time , It reflects the state of the system at this moment , From the perspective of data world , This system is the data collection of this moment . Data photography reflects the static display of the system ; Time stamp is a key factor in this kind of data , Therefore, the time stamp of each data acquisition must be the same , A lot of the current time stamp is missing , It is also one of the problems that need to be paid attention to and solved in the implementation of the Internet of things .

Process relevance : That is, the data of one point will affect the generation of the data of the second point after a certain period of time , It reflects the system dynamic process display . The process relationship between data needs to be provided by the model , And it is revised in the implementation .

Timeliness of data


The timeliness of data refers to the time from data generation to data clearing , Data timeliness is determined by the implementation and deployment of the system . Data can be used multiple times, or it can be cleared after being used once . Generally speaking, whether remote deployment data or edge deployment data affects the timeliness of data , Generally, the data timeliness of edge deployment is short , Long time effectiveness of remote data . The data needed for edge deployment is usually timely , But edge storage , It can't be preserved for a long time because of its weak computing power ; Remote data is usually historical data presentation , Computational analysis , Cloud space at the same time , The calculation is flexible , Therefore, the timeliness of data is long .

The real-time of data is also a part of data timeliness , Real time and where data is deployed , The importance of data and the way it is transmitted are relevant .



Data centric Internet of things <http://iot.ofweek.com/> Design methodology

The purpose of summarizing all kinds of data and characteristics in the Internet of things or abstracting its generality is to better select different Internet of things technologies
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Go fast , Effective implementation . Data centric Internet of things solutions and even business models will be the mainstream design ideas of the Internet of things .

The following is how the data is collected , How to preprocess data , How to transfer data and other different angles to analyze how to apply various technologies of the Internet of things .

Design ideas with data flow direction as the core

Design from data flow, including : data acquisition , Data aggregation , data transmission , Data persistence , Data display , data processing , Data correction , Data consumption .



Data production / The core of design is consumption


production / The design idea with consumption as the core focuses on cloud business , In particular, the integration of existing decentralized subsystems as the target or the upstream and downstream collaborative operation of the industrial chain , The data of the Internet of things is mainly based on the type of key data indicators or as the starting data role .

1) The following is the design idea of data flow of supply chain .



2) The following is the design of integrated management system



Understanding the Internet of things <http://iot.ofweek.com/> What is the data , And can put forward the corresponding Internet of things technology
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, Mastering valuable data is the first step at the technical level of the IOT solution . It is also the final concrete unit of Internet of things technology ——“ Valuable data ”.