A method and device for intelligently generating big data on heating of cabinet equipment

Abstract

The invention discloses a method and device for intelligently generating big data on heating of cabinet equipment, which belongs to the field of environmental protection and energy saving. The device disclosed in the invention includes a heating command device, a dynamic heating control device and a thermal state acquisition device. The heating command device can be used according to test requirements. , to generate a specific cabinet equipment heating command and send it to the dynamic heating control device. The dynamic heat generation control device dynamically adjusts the operating load of the internal components (such as CPU, GPU, and hard disk) of the equipment (server, etc.) in the control cabinet according to the instructions, thereby generating corresponding heat generation. The heating state acquisition device collects the dynamic heating data of the cabinet; the built-in dynamic heating control device of the present invention can intelligently and accurately adjust the operating load changes of the equipment in the cabinet in real time according to the test requirements, so as to accurately control the external heating changes of the cabinet. It can be seen that the present invention It can intelligently generate real dynamic big data of cabinet equipment heating.

Description

technical field [0001] The present invention relates to the field of environmental protection and energy saving, in particular to a method and device for intelligently generating large data on heating of cabinet equipment. Background technique [0002] In an IDC computer room, a reasonable cooling solution has a significant impact on the cost of using the computer room. There are two objectives of cooling scheme optimization: stable operation of servers and reduction of cooling power consumption. An excellent cooling solution can ensure the long-term stable operation of the server, avoid economic losses caused by various failures, and reduce the consumption of additional power resources for cooling. At present, only the three major operators consume more than 100 billion yuan of electricity for computer room cooling every year. How to optimize the cooling scheme and reduce power consumption has important social value. [0003] When designing the cooling scheme, it is neces...

AI Technical Summary

Problems solved by technology

[0005] 1. Data is difficult to change dynamically

The thermocouple power-on method can only generate heat at fixed power (such as 1KW, 2KW) intervals, and the heat balance time is long (>15 minutes), resulting in a relatively fixed change in heat generation, so the generated cabinet heat generation data also shows a fixed interval

However, when the servers in the actual cabinet are running, the combination of different servers and the business characteristics of different time periods will cause the heat generated to be dynamic. The heat generation data generated by the fixed heat generation simulation cannot accurately explain the actual situation of the cabinet.

[0006] 2. Insufficient precision and accuracy

Since the resistance wire heating increases according to the fixed power, and the gear interval is relatively large (at least 1kw or more), and it is impossible to automatically control multiple devices, the temperature range that causes the entire cabinet to heat is relatively rough, and the fluctuation of data is often above 3-5 degrees , it is impossible to accurately simulate the actual change of the cabinet temperature field of 1-2 degrees Celsius, which does not conform to the real situation of the actual cabinet heating

[0007] Obviously, there is a large deviation between the traditional thermocouple power-on method and the real environment, and it is impossible to accurately generate large heating data similar to real cabinets

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