CN115529795A - Method, device and storage medium for assessing closedness of server channel in computer room - Google Patents

Abstract

The disclosure provides a method, a device and a storage medium for evaluating the channel closure condition of a machine room server, and relates to the field of energy consumption optimization of a data center. The method comprises the following steps: acquiring cold channel temperature data corresponding to a server channel to be evaluated; predicting hot channel temperature data corresponding to the cold channel temperature data according to a hot channel temperature prediction model, wherein the hot channel temperature prediction model is obtained by performing regression function fitting according to the hot channel data and the cold channel data corresponding to the channels with known sealing conditions; and evaluating the closing condition of the server channel to be evaluated according to the predicted hot channel temperature data. The method and the device can timely identify the engineering fault which causes the refrigeration energy consumption of the machine room, avoid energy consumption caused by the closed condition of the channel, and improve the working efficiency of refrigeration equipment.

Description

Method, device and storage medium for assessing closedness of server channel in computer room

technical field

The present disclosure relates to the field of data center energy consumption optimization, and in particular, to a method, device and storage medium for evaluating the closed condition of a server channel in a computer room.

Background technique

The heat dissipation of electronic equipment in the data center has always been the key to its development. The heat dissipation and cooling of the computer room is one of the basic conditions to ensure the safe and reliable operation of the data center. It is usually necessary to install precision air conditioners in the computer room. For the data center, when the equipment in the computer room is running at "full load", there will be a large amount of heat generated. Isolation and sealing of cold and hot aisles is one of the measures for energy saving and emission reduction in the computer room. And air leakage problems, and then form a reverse pressure difference, affecting the efficiency of refrigeration equipment.

Contents of the invention

A technical problem to be solved in the present disclosure is to provide a method, device and storage medium for evaluating the closedness of server channels in a computer room, which can timely identify engineering failures that cause cooling energy consumption in the computer room, and avoid energy consumption caused by closed channels.

According to one aspect of the present disclosure, a method for evaluating the closure of a server channel in a computer room is proposed, including: obtaining the cold channel temperature data corresponding to the server channel to be evaluated; predicting the hot channel temperature data corresponding to the cold channel temperature data according to the hot channel temperature prediction model , wherein, the hot aisle temperature prediction model is obtained by performing regression function fitting on the hot aisle data and cold aisle data corresponding to the aisle known to be closed;

In some embodiments, evaluating the closure of the server channel to be evaluated includes: if the predicted hot aisle temperature data is greater than or equal to the actual measured hot channel temperature data of the server channel to be evaluated, then determining that there is an air leakage situation in the server channel to be evaluated; and If the predicted hot aisle temperature data is smaller than the actually measured hot aisle temperature data of the server aisle to be evaluated, it is determined that there is no air leakage in the server aisle to be evaluated.

In some embodiments, the regression function is constructed based on inverse differential pressure data, pressure fluctuation data, cold aisle data, hot aisle data, and server power data.

In some embodiments, the parameters of the regression function include a turbulent velocity coefficient, a pressure fluctuation coefficient, and a cabinet heat generation coefficient.

In some embodiments, the output parameters of the computer room air conditioner are controlled based on the predicted hot aisle temperature data.

According to another aspect of the present disclosure, a device for evaluating the closedness of a server channel in a computer room is also proposed, including: a data acquisition module configured to obtain cold aisle temperature data corresponding to the server channel to be evaluated; a temperature prediction module configured to The hot aisle temperature prediction model predicts the hot aisle temperature data corresponding to the cold aisle temperature data, wherein the hot aisle temperature prediction model is obtained by performing regression function fitting on the hot aisle data and cold aisle data corresponding to the aisle known to be closed; and the closed aisle The evaluation module is configured to evaluate the sealing condition of the server aisle to be evaluated according to the predicted temperature data of the hot aisle.

In some embodiments, the closed evaluation module is configured to determine that there is air leakage in the server channel to be evaluated if the predicted hot channel temperature data is greater than or equal to the actual measured hot channel temperature data of the server channel to be evaluated; and if the predicted thermal channel If the aisle temperature data is smaller than the actual measured hot aisle temperature data of the server aisle to be evaluated, it is determined that there is no air leakage in the server aisle to be evaluated.

In some embodiments, the regression function is constructed based on inverse differential pressure data, pressure fluctuation data, cold aisle data, hot aisle data, and server power data.

In some embodiments, the parameters of the regression function include a turbulent velocity coefficient, a pressure fluctuation coefficient, and a cabinet heat generation coefficient.

In some embodiments, the air conditioner control module is configured to control output parameters of the computer room air conditioner according to the predicted hot aisle temperature data.

According to another aspect of the present disclosure, there is also proposed a device for evaluating the channel closure of a server in a computer room, including: a memory; and a processor coupled to the memory, the processor is configured to perform the above-mentioned evaluation based on instructions stored in the memory The method for the closed situation of the server channel in the computer room.

According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is also proposed, on which computer program instructions are stored, and when the instructions are executed by a processor, the above method for evaluating channel closure of a server in a computer room is realized.

In the embodiment of the present disclosure, the hot aisle temperature prediction model is used to predict the temperature of the hot aisle of the server to be evaluated. The closed condition of the server channel whose temperature is to be evaluated can timely identify the engineering faults that lead to the cooling energy consumption of the computer room, avoid energy consumption caused by the closed channel, and improve the working efficiency of the refrigeration equipment.

Other features of the present disclosure and advantages thereof will become apparent through the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings.

Description of drawings

The accompanying drawings, which constitute a part of this specification, illustrate the embodiments of the disclosure and together with the description serve to explain the principles of the disclosure.

The present disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

FIG. 1 is a schematic flow diagram of some embodiments of the method for evaluating the channel closure of a server room in a computer room according to the present disclosure;

FIG. 2 is a schematic flowchart of another embodiment of the method for evaluating the channel closure of a server room in a computer room according to the present disclosure;

Fig. 3 is a structural schematic diagram of some embodiments of the device for evaluating the closed condition of the server channel in the computer room of the present disclosure;

FIG. 4 is a schematic structural diagram of another embodiment of the device for evaluating the closedness of the channel of the server in the computer room of the present disclosure; and

Fig. 5 is a schematic structural diagram of other embodiments of the device for evaluating the closed condition of a server channel in a computer room according to the present disclosure.

detailed description

Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that relative arrangements of components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

At the same time, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

The following description of at least one exemplary embodiment is merely illustrative in nature and in no way intended as any limitation of the disclosure, its application or uses.

Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of the description.

In all examples shown and discussed herein, any specific values should be construed as exemplary only, and not as limitations. Therefore, other examples of the exemplary embodiment may have different values.

It should be noted that like numerals and letters denote like items in the following figures, therefore, once an item is defined in one figure, it does not require further discussion in subsequent figures.

In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

The air conditioners in the real computer room jointly cool the cabinets in the computer room. Since the airflow in the computer room is a turbulent flow model, the analysis is complicated, and it is difficult to predict the temperature of the hot and cold aisles of the server from the perspective of aerodynamic analysis. Therefore, necessary and efficient methods are needed to determine the temperature of the server. In order to make full use of each air conditioner and avoid the energy consumption caused by the closed passage.

Fig. 1 is a schematic flow chart of some embodiments of the method for evaluating the channel closure of a server in a computer room according to the present disclosure.

In step 110, the cold aisle temperature data corresponding to the server aisle to be evaluated is obtained.

In some embodiments, the data of three temperature measurement points on the front and rear doors of the cabinet may be acquired respectively.

In step 120, according to the hot aisle temperature prediction model, predict the hot aisle temperature data corresponding to the cold aisle temperature data, wherein, the hot aisle temperature prediction model performs regression function simulation according to the hot aisle data and cold aisle data corresponding to the aisle known to be closed. Get it.

In some embodiments, cold aisle data and hot aisle data corresponding to known aisles with good sealing conditions are obtained, wherein the sealing conditions are good, that is, the air leakage is less than a threshold. Use the cold aisle data and hot aisle data to train the hot aisle temperature prediction model.

In some embodiments, the regression function is constructed based on inverse differential pressure data, pressure fluctuation data, cold aisle data, hot aisle data, and server power data. The parameters of the regression function include the turbulent velocity coefficient, the pressure fluctuation coefficient and the cabinet heating coefficient.

In some embodiments, the regression function can be constructed with reference to the three basic law equations of the fluid and the air flow control equation. The three basic law equations of fluid include mass conservation equation, momentum conservation equation and energy conservation equation, and the air flow control equations include turbulent kinetic energy equation and diffusion equation. Since the model follows the three basic laws of fluid and the turbulent transport equation, it theoretically explains the law of temperature change in the hot zone and provides a scientific reference for the construction of the regression function.

In step 130, based on the predicted hot aisle temperature data, the enclosure condition of the server aisle to be evaluated is evaluated.

In some embodiments, if the predicted hot aisle temperature data is greater than or equal to the actual measured hot aisle temperature data of the server aisle to be evaluated, it is determined that there is air leakage in the server aisle to be evaluated; and if the predicted hot aisle temperature data is smaller than the actual measurement If the hot aisle temperature data of the server aisle to be evaluated is used, it is determined that there is no air leakage in the server aisle to be evaluated. That is, if the real temperature of the hot aisle is found to be high, it means that there is no problem with the aisle. If the real temperature of the hot aisle is found to be low, it means that there is air leakage in the aisle, so that the hot and cold aisle is closed.

In the above-mentioned embodiment, the hot aisle temperature prediction model is used to predict the temperature of the hot aisle of the server to be evaluated, wherein the hot aisle temperature prediction model is determined by fitting the regression function with the data of the hot and cold aisles known to be well closed, and then according to the predicted The temperature is subject to evaluation of the closure of the server channel, which can promptly identify engineering failures that lead to cooling energy consumption in the computer room, and avoid energy consumption due to channel closure.

Fig. 2 is a schematic flowchart of another embodiment of the method for evaluating the channel closure of a server in a computer room according to the present disclosure.

In step 210, a regression function of the hot aisle temperature prediction model is constructed based on the inverse pressure difference data, pressure fluctuation data, cold aisle data, hot aisle data and server power data.

其中，T_hot(t)为热通道数据，T_cold(t-1)为冷通道数据，μ为速度矢量，P为流体压强，Ym为在可压缩湍流中，过度的扩散产生的波动，G_b为浮力产生的湍流动能，

为逆压差数据的湍流速度，Power为服务器功率，反映服务器的发热量。α为压强波动系数、β为湍流速度系数、γ为机柜发热量系数，其中，α、β、γ为未知量。In some embodiments, the regression function is, for example,

Among them, T _hot (t) is the hot channel data, T _cold (t-1) is the cold channel data, μ is the velocity vector, P is the fluid pressure, Ym is the fluctuation caused by excessive diffusion in the compressible turbulent flow, G _b is the turbulent kinetic energy generated by buoyancy,

is the turbulent velocity of the inverse pressure difference data, and Power is the power of the server, which reflects the calorific value of the server. α is the pressure fluctuation coefficient, β is the turbulent velocity coefficient, and γ is the cabinet heating coefficient, among which α, β, and γ are unknown quantities.

其中&_μ为常量。Since the data center generally adopts the form of closed cold and hot aisles, the air supply method is downward air supply. When the cold air flows through the electronic equipment, it cools the equipment, and the hot air is discharged outside through the return air outlet arranged indoors. The cold air mixes directly with the heat from the equipment and is sent outside. Acquire inverse differential pressure data and obtain turbulent velocity

Where & _μ is a constant.

In step 220, the cold aisle data and the hot aisle data corresponding to the aisles known to be well sealed are obtained.

In some embodiments, the temperature data at the same time is acquired through real-time monitoring.

In step 230, the obtained cold aisle data and hot aisle data are used to fit the regression function to obtain a hot aisle temperature prediction model.

In some embodiments, there may be inconsistencies in the hot aisle temperature prediction models of the hot and cold aisles of different servers. Therefore, the model needs to be verified so that the model can be applied to the server aisle to be evaluated. During the running process, iteratively updates by comparing data to enhance the actual operability.

In step 240, cold aisle temperature data corresponding to the server aisle to be evaluated is collected.

At step 250, the hot aisle temperature data corresponding to the cold aisle temperature data is predicted according to the hot aisle temperature prediction model.

In step 260 , it is judged whether the predicted hot aisle temperature data is smaller than the actual measured data, if yes, execute step 270 , otherwise, execute step 280 .

In step 270, it is determined that there is no air leakage in the channel of the server to be evaluated.

In step 280, it is determined that there is air leakage in the channel of the server to be evaluated.

In the above-mentioned embodiment, by constructing a regression function, using the cold and hot aisle data corresponding to the well-closed aisle for linear fitting, the hot aisle temperature prediction model is obtained, and the hot aisle temperature prediction model is used to predict the temperature of the hot aisle, according to The temperature of the hot aisle evaluates whether there is air leakage in the aisle, makes full use of each air conditioner, avoids energy consumption due to the closed aisle, and achieves the purpose of efficient use of resources.

In some embodiments, the output parameters of the computer room air conditioner are controlled according to the predicted temperature data of the hot aisle, for example, the operating frequency and wind speed of the air conditioner compressor are controlled. It is corrected through guidance, so as to achieve the goal of optimizing energy saving of data center computer room air conditioners.

FIG. 3 is a schematic structural diagram of some embodiments of the device for evaluating the closedness of server channel in a computer room according to the present disclosure, the device includes a data collection module 310 , a temperature prediction module 320 and a closed evaluation module 330 .

The data acquisition module 310 is configured to acquire cold aisle temperature data corresponding to the server aisle to be evaluated.

In some embodiments, the data acquisition module 310 is further configured to acquire cold aisle data and hot aisle data corresponding to aisles known to be well sealed, as well as inverse pressure difference data, pressure fluctuation data and server power data.

The temperature prediction module 320 is configured to predict the hot aisle temperature data corresponding to the cold aisle temperature data according to the hot aisle temperature prediction model, wherein the hot aisle temperature prediction model is based on the hot aisle data and cold aisle data corresponding to the aisle with known closed conditions. The regression function is fitted.

In some embodiments, as shown in FIG. 4 , the device further includes a model training module 410 configured to construct a hot aisle temperature prediction based on inverse pressure difference data, pressure fluctuation data, cold aisle data, hot aisle data, and server power data The regression function of the model, the parameters of the regression function include the turbulence velocity coefficient, the pressure fluctuation coefficient and the cabinet heating coefficient, and the hot aisle temperature prediction model is trained by using the hot aisle data and cold aisle data corresponding to the aisle with known closed conditions.

In some embodiments, the regression function is, for example,

The enclosure evaluation module 330 is configured to evaluate the enclosure condition of the server aisle to be evaluated according to the predicted hot aisle temperature data.

If the predicted hot aisle temperature data is greater than or equal to the actual measured hot aisle temperature data of the server channel to be evaluated, it is determined that there is air leakage in the server channel to be evaluated; If the hot aisle temperature data is used, it is determined that there is no air leakage in the server aisle to be evaluated.

In the above embodiment, the hot aisle temperature prediction model is used to predict the temperature of the hot aisle of the server to be evaluated, and then according to the predicted temperature. The closed situation causes energy consumption.

In some other embodiments of the present disclosure, an air conditioner control module 420 is further included, configured to control the output parameters of the computer room air conditioner according to the predicted hot aisle temperature data. Make full use of air-conditioning resources, effectively eliminate the energy consumption loss caused by ignoring the closure of hot and cold aisles of servers, and maximize resources.

Fig. 5 is a schematic structural diagram of other embodiments of the device for evaluating the closed condition of a server channel in a computer room according to the present disclosure. The device 500 includes a memory 510 and a processor 520 . Wherein: the memory 510 may be a disk, a flash memory or any other non-volatile storage medium. The memory is used to store the instructions in the above embodiments. Processor 520 is coupled to memory 510 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 520 is used to execute instructions stored in the memory.

In some embodiments, the processor 520 is coupled to the memory 510 through a BUS 530 . The device 500 can also be connected to an external storage system 550 through a storage interface 540 to call external data, and can also be connected to a network or another computer system (not shown) through a network interface 560 . No more detailed introduction here.

In this embodiment, the data instruction is stored in the memory, and the instruction is processed by the processor, so that the channel closure can be detected in time, and energy consumption caused by the channel closure can be avoided.

In some other embodiments, a computer-readable storage medium stores computer program instructions thereon, and when the instructions are executed by a processor, the steps of the methods in the above-mentioned embodiments are implemented. Those skilled in the art should understand that the embodiments of the present disclosure may be provided as methods, apparatuses, or computer program products. Accordingly, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein. .

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each procedure and/or block in the flowchart and/or block diagram and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

So far, the present disclosure has been described in detail. Certain details known in the art have not been described in order to avoid obscuring the concept of the present disclosure. Based on the above description, those skilled in the art can fully understand how to implement the technical solutions disclosed herein.

Although some specific embodiments of the present disclosure have been described in detail through examples, those skilled in the art should understand that the above examples are for illustration only, rather than limiting the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims (12)

1. A method for evaluating the channel closure condition of a machine room server comprises the following steps:

acquiring cold channel temperature data corresponding to a server channel to be evaluated;

predicting hot channel temperature data corresponding to the cold channel temperature data according to a hot channel temperature prediction model, wherein the hot channel temperature prediction model is obtained by performing regression function fitting according to the hot channel data and the cold channel data corresponding to the channels with known sealing conditions; and

and evaluating the closing condition of the server channel to be evaluated according to the predicted hot channel temperature data.

2. The method of claim 1, wherein evaluating the closure of the server channel under evaluation comprises:

if the predicted hot channel temperature data is larger than or equal to the actually measured hot channel temperature data of the server channel to be evaluated, determining that the server channel to be evaluated has an air leakage condition; and

and if the predicted hot channel temperature data is smaller than the actually measured hot channel temperature data of the server channel to be evaluated, determining that the server channel to be evaluated has no air leakage condition.

3. The method of claim 1, wherein,

the regression function is constructed based on the inverse pressure difference data, the pressure fluctuation data, the cold channel data, the hot channel data, and the server power data.

4. The method of claim 3, wherein,

the parameters of the regression function comprise a turbulent flow velocity coefficient, a pressure fluctuation coefficient and a cabinet heat productivity coefficient.

5. The method of any of claims 1 to 4, further comprising:

and controlling the output parameters of the air conditioner of the machine room according to the predicted hot channel temperature data.

6. An apparatus for evaluating the closure of a machine room server channel, comprising:

the data acquisition module is configured to acquire cold channel temperature data corresponding to a server channel to be evaluated;

the temperature prediction module is configured to predict hot channel temperature data corresponding to the cold channel temperature data according to a hot channel temperature prediction model, wherein the hot channel temperature prediction model is obtained by performing regression function fitting according to the hot channel data and the cold channel data corresponding to the channel with known sealing condition; and

and the sealing evaluation module is configured to evaluate the sealing condition of the server channel to be evaluated according to the predicted hot channel temperature data.

7. The apparatus of claim 6, wherein,

the closed evaluation module is configured to determine that the server channel to be evaluated has an air leakage condition if the predicted hot channel temperature data is greater than or equal to actually measured hot channel temperature data of the server channel to be evaluated; and if the predicted hot channel temperature data is smaller than the actually measured hot channel temperature data of the server channel to be evaluated, determining that the server channel to be evaluated does not have the air leakage condition.

8. The apparatus of claim 6, wherein,

the regression function is constructed based on the inverse pressure difference data, the pressure fluctuation data, the cold channel data, the hot channel data, and the server power data.

9. The apparatus of claim 7, wherein,

the parameters of the regression function comprise a turbulent flow velocity coefficient, a pressure fluctuation coefficient and a cabinet heat productivity coefficient.

10. The apparatus of any of claims 6 to 9, further comprising:

and the air conditioner control module is configured to control the output parameters of the air conditioner in the machine room according to the predicted hot channel temperature data.

11. An apparatus for evaluating the closing condition of a machine room server channel, comprising:

a memory; and

a processor coupled to the memory, the processor configured to perform the method of assessing machine room server channel closure of any of claims 1 to 5 based on instructions stored in the memory.

12. A non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of assessing machine room server channel closure as claimed in any one of claims 1 to 5.

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