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

When the Internet of things began to be implemented and applied in the industry, Because of the speed of data generation, Variety, The huge volume of the city will affect the existing cloud technology architecture, Data processing brings unimaginable pressure and challenges.

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 method? Do we have the ability to deal with the pressure and challenges brought by Internet of things data? Internet of things1.0 What are the valuable data of the stage?

data classification

Static data and dynamic data

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

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

Whether static data or dynamic data, Internet of things1.0 The growth of stage data is linear, Not exponential, But because the Internet of things dynamic data is continuous, So the amount of data is also massive. So the Internet of things1.0 The pressure of stage data is controllable, It's not as uncountable as the propaganda, Uncontrollable.

Energy sources/ Asset attribute class/ Diagnostic class/ Signal class

In terms of the raw nature of the data, We can divide Internet of things data into energy data, Asset attribute class data, Diagnostic data, Signal data.

Energy data: Energy consumption related, Or the 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 Internet of things, One of the ultimate goals of the Internet of things is to save energy, Then get energy data, Understanding energy data, Analysis of energy data is a necessary function in the implementation of the Internet of things. Energy collection equipment is also one of the important equipment of the Internet of things.

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

Diagnostic data:
Diagnostic data refers to the data that detects the operation status of the equipment during the operation of the equipment, There are two types of diagnostic data: One is equipment operation parameters, For example, device input/ Output value, This is usually the data of traditional industrial automationOT 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
It includes new sensor technology and Internet of things communication technology. Peripheral diagnostic data is an important metadata for predictive maintenance, It also provides basis for depth control model, So diagnostic data is the data type we need to focus on.

Signal data:
Signal data or alarm data are the most popular data in the industrial field at present, Because it's intuitive, Easy to understand, crux, At the same time in the local, Remote simultaneous notification. Signal data is easy to be ignored, But it's what the Internet of things needs, It's also quick to collect, And provide 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, Combing the correct relationship between data is the effective operation of the system, The cornerstone of value generation.

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

Time relevance:
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 time, From the perspective of data world, This system is the data collection at this time. Data photography reflects the static display of the system; Time stamp is the key factor of this kind of data, Therefore, it is required that the timestamps of each data acquisition must be the same, Timestamps are missing from a lot of data at present, It is also one of the problems to be concerned and solved in the implementation of the Internet of things.

Process relevance: That is to say, the data of one point will affect the data of the second point after a certain period of time, It shows the dynamic process of the system. Process relationship between data needs model to provide, And make corrections in the implementation.

Timeliness of data

The timeliness of data refers to the time from the generation of data to its removal, Data timeliness is determined by the implementation and deployment of the system. Data can be used more than once or can be cleared after being used once. In general, 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. Data needed for edge deployment is usually timely, But edge storage, The computing power is weak, so it can't be preserved for a long time; Remote data is usually historical data presentation, Calculation and analysis, Simultaneous cloud space, Strong scalability of calculation, Therefore, the timeliness of data is long.

Real time of data is also a part of data timeliness, Real time and data deployment location, The importance of data and how it is transmitted are related.

Data centric Internet of things <> Design methodology

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

How to collect data from, How to preprocess data, How to transmit data and how to apply various technologies of the Internet of things.

Design idea with data flow as the core

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

Production with data/ Consumption as the core of design ideas

production/ The design idea with consumption as the core mostly focuses on cloud business, In particular, integrate the existing decentralized subsystems as the goal or the collaborative operation of upstream and downstream of the industrial chain, Most of the data of the Internet of things are based on the key data indicator type or the starting data role.

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

2) The following is the design of integrated management system

Understanding the Internet of things <> What is the data, And can propose corresponding Internet of things technology
, Mastering valuable data is the first step at the technical level of Internet of things solutions. It is also the final concrete unit of Internet of things technology——“ Valuable data”.