Method and device for generating airflow organization quasi-state field in machine room and terminal equipment

Abstract

The application is suitable for the technical field of data center monitoring, and provides a method and device for generating a computer room airflow organization quasi-state field and terminal equipment, the method comprising: acquiring layout information and state parameters corresponding to the computer room at a current time; inputting the layout information and state parameters corresponding to the computer room at the current time into a first preset mathematical model to obtain predicted airflow data corresponding to each cabinet measuring point at the current time; the predicted airflow data comprises predicted temperature data, predicted speed data and / or predicted pressure data; and generating a computer room airflow organization quasi-state field corresponding to the current time based on the predicted airflow data corresponding to each cabinet measuring point at the current time. Through the above scheme, the computer room airflow organization quasi-state field can be generated without configuring intensive sensors, thereby simplifying the generation process of the computer room airflow organization.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data center monitoring, and particularly relates to a method and device for generating a room airflow organization quasi-state field and a terminal device. BACKGROUND

[0002] Due to the uninterrupted operation of a data center throughout the year, the safe and stable operation of each system is increasingly required, and the real-time monitoring demand of a user for a data center temperature control system and airflow organization is increasingly clear.

[0003] At present, the method for generating a data center airflow organization mainly relies on temperature and humidity sensors to collect data. In order to ensure the accuracy of the airflow organization field, a large number of sensors are configured, and the installation and system configuration are relatively complicated, requiring high professional skills of the installation personnel and high overall cost. SUMMARY

[0004] Therefore, the embodiments of the present application provide a method and device for generating a room airflow organization quasi-state field and a terminal device to solve the problem that the method for generating a data center airflow organization in the prior art is complicated.

[0005] A first aspect of the embodiments of the present application provides a method for generating a room airflow organization quasi-state field, comprising:

[0006] obtaining layout information and state parameters corresponding to the room at a current time; the state parameters comprising a wind volume difference value and a load value corresponding to each cabinet measuring point in the room; the wind volume difference value being a difference between a required wind volume and an actual wind volume;

[0007] inputting the layout information and state parameters corresponding to the room at the current time into a first preset mathematical model to obtain predicted airflow data corresponding to each cabinet measuring point at the current time; the predicted airflow data comprising predicted temperature data, predicted speed data and / or predicted pressure data; the first preset mathematical model being trained by layout information, state parameters and actual airflow data of the room in a historical period;

[0008] generating a room airflow organization quasi-state field corresponding to the current time based on the predicted airflow data corresponding to each cabinet measuring point at the current time.

[0009] A second aspect of the embodiments of the present application provides a device for generating a room airflow organization quasi-state field, comprising:

[0010] a data acquisition module configured to obtain layout information and state parameters corresponding to the room at a current time; the state parameters comprising a wind volume difference value and a load value corresponding to each cabinet measuring point in the room; the wind volume difference value being a difference between a required wind volume and an actual wind volume;

[0011] a prediction data calculation module configured to input the layout information and the state parameters of the machine room at the current time into a first preset mathematical model to obtain predicted airflow data corresponding to each cabinet measuring point at the current time, wherein the predicted airflow data comprises predicted temperature data, predicted speed data and / or predicted pressure data, and the first preset mathematical model is trained by the layout information, the state parameters and actual airflow data of the machine room at a historical time period;

[0012] a airflow organization quasi-field generation module configured to generate a machine room airflow organization quasi-field corresponding to the current time based on the predicted airflow data corresponding to each cabinet measuring point at the current time.

[0013] A third aspect of the embodiment of the present application provides a terminal device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method for generating a machine room airflow organization quasi-field when executing the computer program.

[0014] A fourth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method for generating a machine room airflow organization quasi-field when executed by a processor.

[0015] Compared with the prior art, the embodiment of the present application has the following beneficial effects: first, the layout information and the state parameters of the machine room at the current time are obtained; then, the layout information and the state parameters of the machine room at the current time are input into a first preset mathematical model to obtain predicted airflow data corresponding to each cabinet measuring point at the current time, wherein the predicted airflow data comprises predicted temperature data, predicted speed data and / or predicted pressure data; finally, a machine room airflow organization quasi-field corresponding to the current time is generated based on the predicted airflow data corresponding to each cabinet measuring point at the current time. Through the above scheme, the machine room airflow organization quasi-field can be generated without configuring intensive sensors, thereby simplifying the generation process of the machine room airflow organization. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments and / or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 is the implementation flowchart of the method for generating a machine room airflow organization quasi-field provided by the embodiment of the present application;

[0018] Figure 2This is a schematic diagram of the device for generating a simulated airflow field in a computer room according to an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0021] To illustrate the technical solution described in this invention, specific embodiments are described below.

[0022] In one embodiment, such as Figure 1 As shown, Figure 1 The implementation flow of the method for generating a simulated airflow field in a computer room provided in this embodiment is illustrated, and the process is described in detail below:

[0023] S101: Obtain the layout information and status parameters of the computer room at the current moment; the status parameters include the air volume difference and load of each rack measuring point in the computer room; the air volume difference is the difference between the required air volume and the actual air volume.

[0024] The state parameters selected in this embodiment are those that the monitoring system can acquire on its own without adding additional sensors.

[0025] Specifically, to generate a simulated airflow field for the computer room, this embodiment first automatically selects a large number of rack measurement points based on the computer room layout and a preset unit step size. After determining the rack measurement points, the relative positions of the rack air conditioners corresponding to each rack measurement point are determined based on the positions of each rack measurement point and the positions of each air conditioner in the computer room. The specific implementation process includes:

[0026] For any rack measuring point in the computer room, obtain the relative position of the rack measuring point with each air conditioner; based on the relative position of the rack measuring point with each air conditioner, calculate the relative position of the air conditioner rack corresponding to the rack measuring point.

[0027] The relative position can be a relative distance. In this embodiment, the relative distance between the cabinet measuring point and each air conditioner can be averaged to obtain the relative position of the air conditioner cabinet corresponding to the cabinet measuring point.

[0028] In addition, the actual air volume of the cabinet measuring point is the actual air volume of the air conditioner fan corresponding to the cabinet where the cabinet measuring point is located, the required air volume of the cabinet measuring point is the required air volume of the air conditioner fan corresponding to the cabinet where the cabinet measuring point is located, and the air volume difference is the difference between the required air volume and the actual air volume of the air conditioner.

[0029] In one embodiment, the state parameters further include an air conditioner supply and return air temperature difference, a total load, an environment temperature and humidity, a cabinet inlet and outlet air temperature difference, and an air conditioner running number.

[0030] The total load in this embodiment is the sum of the loads of all cabinets in the entire machine room, the environment temperature and humidity is the temperature and humidity of the overall environment inside the machine room, the air conditioner supply and return air temperature difference is the difference between the supply air temperature and the return air temperature, the cabinet inlet and outlet air temperature difference is the difference between the temperature of the cabinet inlet channel and the temperature of the outlet channel, and the air conditioner running number is the number of air conditioners in the running state at the current time. In order to improve the calculation result of the airflow organization quasi-state field, the state parameters in this embodiment can further include the load rate of each cabinet, the total load rate of the machine room, and the sum of the cooling capacities of all air conditioners in the machine room.

[0031] S102: input the layout information and the state parameters of the machine room corresponding to the current time into a first preset mathematical model to obtain predicted airflow data corresponding to each cabinet measuring point at the current time; the predicted airflow data includes predicted temperature data, predicted speed data, and / or predicted pressure data; the first preset mathematical model is trained by the layout information, the state parameters, and the actual airflow data of the machine room in a historical period.

[0032] In this embodiment, the first preset mathematical model can be obtained by polynomial fitting of the layout information, the state parameters, and the actual airflow data in the historical period. This embodiment can also establish a neural network model based on a neural network learning algorithm, use the layout information, the state parameters, and the actual airflow data in the historical period as training samples, and train the neural network model using the training samples to obtain the first preset mathematical model.

[0033] S103: generate a machine room airflow organization quasi-state field corresponding to the current time based on the predicted airflow data corresponding to each cabinet measuring point at the current time.

[0034] This embodiment can generate a machine room airflow organization quasi-state field based on the predicted airflow data corresponding to each cabinet measuring point at the current time in the CFD simulation software.

[0035] In one embodiment, the first preset mathematical model includes a preset temperature field fitting formula, a preset speed field fitting formula, and a preset pressure field fitting formula. Figure 1 The specific implementation process of S102 in the above embodiment includes:

[0036] S201: input the layout information of the machine room corresponding to the current time, the air volume difference corresponding to each cabinet measuring point and the load into the preset temperature field fitting formula, to obtain the predicted temperature data corresponding to each cabinet measuring point at the current time.

[0037] In the embodiment, the preset temperature field fitting formula can be:

[0038] T m = a n1 A + B n2 B m + c n3 C + d n4 D m + e n5 E;

[0039] wherein, T m represents the predicted temperature data corresponding to the mth cabinet measuring point at the current time, a, b, c, d and e respectively represent the coefficients of each term in the preset temperature field fitting formula, A represents the environment temperature and humidity of the machine room at the current time, B m represents the load corresponding to the mth cabinet measuring point at the current time, C represents the air volume difference at the current time, D m represents the relative position of the air conditioning cabinet of the mth cabinet measuring point at the current time, and E represents the temperature and humidity setting value.

[0040] S202: input the layout information of the machine room corresponding to the current time and the air volume difference corresponding to each cabinet measuring point into the preset speed field fitting formula, to determine the predicted speed data corresponding to each cabinet measuring point at the current time.

[0041] In the embodiment, the preset speed field fitting formula can be:

[0042] V m = F n6 C + G n7 D;

[0043] wherein, V m represents the predicted speed data corresponding to the mth cabinet measuring point at the current time, and F and G respectively represent the coefficients of each term in the preset speed field fitting formula.

[0044] S203: based on the predicted temperature data and the predicted speed data corresponding to each cabinet measuring point at the current time, calculate the predicted pressure data corresponding to each cabinet measuring point at the current time.

[0045] In one embodiment, the specific implementation process of S203 includes:

[0046] obtain the leakage rate of each cabinet measuring point in the machine room at the current time;

[0047] For any cabinet measurement point, input the predicted temperature data, predicted speed data and leakage rate corresponding to the cabinet measurement point at the current time into the preset pressure field fitting formula to obtain predicted pressure data corresponding to the cabinet measurement point at the current time.

[0048] In this embodiment, the leakage rate is used to represent the sealing performance of the cabinet, which can be directly obtained in the monitoring system.

[0049] The preset pressure field fitting formula can be:

[0050] P m = h n8 T m + i n9 V m + j n10 F m

[0051] Wherein, P m m represents predicted pressure data corresponding to the mth cabinet measurement point at the current time, h, i and j respectively represent coefficients of each term in the preset pressure field fitting formula, and F m m represents leakage rate corresponding to the mth cabinet measurement point at the current time.

[0052] In one embodiment, Figure 1 The specific implementation process of S103 includes:

[0053] S301: Obtain actual temperature data sent by a temperature sensor at a target cabinet measurement point in the machine room;

[0054] S302: Calculate a drift coefficient corresponding to the current time according to actual temperature data and predicted temperature data corresponding to the target cabinet measurement point in the machine room at the current time;

[0055] S303: Correct predicted airflow data corresponding to each cabinet measurement point according to the drift coefficient corresponding to the current time;

[0056] S304: Generate a machine room airflow organization phase field corresponding to the current time according to the corrected predicted airflow data of each cabinet measurement point.

[0057] In this embodiment, in order to improve the calculation accuracy of the predicted airflow data, temperature sensors can be arranged at some key positions of the machine room to obtain actual temperature data, and the actual temperature data and the predicted temperature data are compared to obtain the drift coefficient.

[0058] Specifically, the calculation formula of the drift coefficient can be: Wherein, k represents the drift coefficient, T s represents predicted temperature data of the target cabinet measurement point, and T o represents actual temperature data of the target cabinet measurement point.

[0059] After the offset coefficient is calculated, if the offset coefficient is greater than the preset offset coefficient threshold, the predicted air flow data of all cabinet measuring points is multiplied by the offset coefficient to obtain updated predicted air flow data, and if the offset coefficient is less than the preset offset coefficient threshold, S303 is not executed, and the preset air flow data is directly used to generate the data center air flow organization quasi-state field.

[0060] Further, the target cabinet measuring point can be multiple. When updating the predicted air flow data of any cabinet measuring point except the target cabinet measuring point, the offset coefficient calculated by the target cabinet measuring point closest to the cabinet measuring point is selected as the offset coefficient required by the cabinet measuring point, and the offset coefficient is multiplied by the initial predicted air flow data of the cabinet measuring point to obtain the updated predicted air flow data of the cabinet measuring point.

[0061] In an embodiment, the specific implementation process of S303 includes:

[0062] Step one: correcting the first preset mathematical model according to the corresponding offset coefficient at the current time, and inputting the layout information and state parameters of the data center at the current time into the corrected first preset mathematical model to obtain corrected predicted air flow data;

[0063] Step two: updating the offset coefficient according to the actual temperature data and the corrected predicted temperature data corresponding to the target cabinet measuring point, and returning the updated offset coefficient to step one to repeat steps one to two until the actual temperature data and the corrected predicted temperature data corresponding to the target cabinet measuring point are equal, and outputting the predicted air flow data corresponding to each cabinet measuring point at the current time calculated based on the final first preset mathematical model.

[0064] As can be seen from the above embodiment, the embodiment is different from the temperature field air flow organization generation method of the traditional temperature and humidity sensor distribution scheme of the current data center. It can efficiently respond to the air flow organization change and monitoring of the data center system, and can simultaneously display the temperature, speed and pressure quasi-state field in real time, and has the beneficial effects of strong visibility and more rich display content. In addition, the sensor configuration and deployment quantity provided by the embodiment is greatly reduced, which can not only be quickly deployed, but also can reduce the cost, and the embodiment can continuously improve the accuracy of the first preset data model by correcting several target cabinet measuring points in real time, and has stronger reliability.

[0065] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0066] As shown in the formula (1), the first preset mathematical model is a function of the temperature field, the velocity field and the pressure field, and the temperature field is a function of the temperature and humidity sensor distribution scheme of the data center. Figure 2 The first preset mathematical model is a function of the temperature field, the velocity field and the pressure field, and the temperature field is a function of the temperature and humidity sensor distribution scheme of the data center.Figure 2 A structural schematic diagram of a device for generating a machine room airflow organization pseudo-state field according to an embodiment of the present application is shown. For ease of understanding, only the corresponding part of the method for generating the machine room airflow organization pseudo-state field is provided in this embodiment, which includes:

[0067] The data acquisition module 110 is configured to acquire layout information and state parameters corresponding to the machine room at the current time. The state parameters include air volume difference values and load values of each cabinet measuring point in the machine room. The air volume difference value is the difference between the required air volume and the actual air volume.

[0068] The prediction data calculation module 120 is configured to input the layout information and the state parameters corresponding to the machine room at the current time into a first preset mathematical model to obtain predicted airflow data of each cabinet measuring point at the current time. The predicted airflow data includes predicted temperature data, predicted speed data and / or predicted pressure data. The first preset mathematical model is trained by the layout information, the state parameters and the actual airflow data of the machine room in a historical period.

[0069] The airflow organization pseudo-state field generation module 130 is configured to generate a machine room airflow organization pseudo-state field corresponding to the current time based on the predicted airflow data of each cabinet measuring point at the current time.

[0070] In one embodiment, the first preset mathematical model includes a preset temperature field fitting formula, a preset speed field fitting formula and a preset pressure field fitting formula. The prediction data calculation module 120 includes:

[0071] The temperature prediction unit is configured to input the layout information corresponding to the machine room at the current time, the air volume difference values and the load values of each cabinet measuring point into the preset temperature field fitting formula to obtain the predicted temperature data of each cabinet measuring point at the current time.

[0072] The speed prediction unit is configured to input the layout information corresponding to the machine room at the current time and the air volume difference values of each cabinet measuring point into the preset speed field fitting formula to determine the predicted speed data of each cabinet measuring point at the current time.

[0073] The pressure prediction unit is configured to calculate the predicted pressure data of each cabinet measuring point at the current time based on the predicted temperature data and the predicted speed data of each cabinet measuring point at the current time.

[0074] In one embodiment, the airflow organization pseudo-state field generation module 130 includes:

[0075] The actual temperature data acquisition unit is configured to acquire actual temperature data sent by a temperature sensor at a target cabinet measuring point in the machine room.

[0076] an offset coefficient calculation unit configured to calculate an offset coefficient corresponding to the current time according to the actual temperature data and the predicted temperature data of the target cabinet measurement point in the computer room at the current time;

[0077] a predicted data correction unit configured to correct the predicted airflow data corresponding to each cabinet measurement point according to the offset coefficient corresponding to the current time;

[0078] an airflow organization quasi-field generation unit configured to generate a computer room airflow organization quasi-field corresponding to the current time according to the corrected predicted airflow data of each cabinet measurement point.

[0079] In one embodiment, the predicted data correction unit is specifically configured to:

[0080] Step one: correct the first preset mathematical model according to the offset coefficient corresponding to the current time, and input the layout information and the state parameter of the computer room corresponding to the current time into the corrected first preset mathematical model to obtain corrected predicted airflow data;

[0081] Step two: update the offset coefficient according to the actual temperature data and the corrected predicted temperature data of the target cabinet measurement point, and return the updated offset coefficient to step one to repeat steps one to two until the actual temperature data and the corrected predicted temperature data of the target cabinet measurement point are equal, and output the predicted airflow data of each cabinet measurement point at the current time calculated based on the final first preset mathematical model.

[0082] In one embodiment, the pressure prediction unit comprises:

[0083] obtain the leakage rate of each cabinet measurement point in the computer room at the current time;

[0084] For any cabinet measurement point, input the predicted temperature data, the predicted speed data, and the leakage rate of the cabinet measurement point at the current time into a preset pressure field fitting formula to obtain predicted pressure data of the cabinet measurement point at the current time.

[0085] In one embodiment, the state parameter further comprises an air conditioner supply and return air temperature difference, a cabinet inlet and outlet air temperature difference, and an air conditioner running number.

[0086] In one embodiment, the layout information comprises an air conditioner cabinet relative position corresponding to each cabinet measurement point; and the data acquisition module 110 is specifically configured to:

[0087] For any cabinet measurement point in the computer room, obtain the relative position of the cabinet measurement point and each air conditioner; and based on the relative position of the cabinet measurement point and each air conditioner, calculate the air conditioner cabinet relative position corresponding to the cabinet measurement point.

[0088] As can be seen from the above embodiments, this embodiment first obtains the layout information and state parameters of the computer room at the current moment; then, it inputs the layout information and state parameters of the computer room at the current moment into a first preset mathematical model to obtain the predicted airflow data corresponding to each rack measuring point at the current moment; the predicted airflow data includes predicted temperature data, predicted velocity data, and predicted pressure data; finally, it generates a simulated airflow organization field for the computer room at the current moment based on the predicted airflow data corresponding to each rack measuring point at the current moment. Through the above scheme, this embodiment can generate a simulated airflow organization field for the computer room without configuring dense sensors, thereby simplifying the generation process of the computer room airflow organization.

[0089] Figure 3 This is a schematic diagram of a terminal device provided in an embodiment of the present invention. Figure 3 As shown, the terminal device 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, it implements the steps in the above-described embodiments of the methods for generating simulated airflow fields in computer rooms, for example... Figure 1 Steps 101 to 103 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of modules 110 to 130 are shown.

[0090] The computer program 32 can be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 32 in the terminal device 3.

[0091] The terminal device 3 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of terminal device 3 and does not constitute a limitation on terminal device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0092] The processor 30 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0093] The memory 31 can be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3. The memory 31 can also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 31 can also include both the internal storage unit and the external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the terminal device. The memory 31 can also be used to temporarily store data that has been output or is to be output.

[0094] It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the above-described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the purpose of mutual distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0095] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0096] Those skilled in the art can appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware, or in a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0097] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other manners. For example, the described apparatus / terminal device embodiments are merely schematic, and the division of the modules or units can be different, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0098] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0099] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0100] The integrated module / unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0101] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for generating a simulated field for airflow organization in a computer room, characterized in that, include: Obtain the layout information and status parameters of the computer room at the current moment; the status parameters include the air volume difference and load of each rack measuring point in the computer room; the air volume difference is the difference between the required air volume and the actual air volume; The layout information and status parameters of the computer room at the current moment are input into the first preset mathematical model to obtain the predicted airflow data corresponding to each rack measuring point at the current moment; the predicted airflow data includes predicted temperature data, predicted velocity data and / or predicted pressure data; the first preset mathematical model is trained by the layout information, status parameters and actual airflow data of the computer room in historical periods; Generate the simulated airflow organization field of the computer room at the current moment based on the predicted airflow data of each rack measuring point at the current moment. The first preset mathematical model includes a preset temperature field fitting formula, a preset velocity field fitting formula, and a preset pressure field fitting formula; The step of inputting the layout information and status parameters of the computer room at the current moment into the first preset mathematical model to obtain the predicted airflow data corresponding to each rack measuring point at the current moment includes: Input the layout information of the computer room at the current moment, the air volume difference and load of each rack measuring point into the preset temperature field fitting formula to obtain the predicted temperature data of each rack measuring point at the current moment. Input the layout information of the computer room at the current moment and the air volume difference of each rack measuring point into the preset velocity field fitting formula to determine the predicted velocity data of each rack measuring point at the current moment. Based on the predicted temperature and speed data of each rack measuring point at the current moment, the predicted pressure data of each rack measuring point at the current moment is calculated.

2. The method for generating a simulated airflow field in a computer room as described in claim 1, characterized in that, The process of generating a simulated airflow field for the data center based on the predicted airflow data of each rack measuring point at the current moment includes: Acquire the actual temperature data sent by the temperature sensor at the measuring point of the target rack in the computer room; Calculate the offset coefficient corresponding to the target rack measurement point in the computer room at the current moment based on the actual temperature data and predicted temperature data at the current moment. The predicted airflow data for each rack measuring point is corrected based on the offset coefficient at the current moment. The predicted airflow data from each rack measurement point is used to generate a simulated airflow field for the current moment in the computer room.

3. The method for generating a simulated airflow field in a computer room as described in claim 2, characterized in that, The step of correcting the predicted airflow data corresponding to each rack measuring point based on the offset coefficient at the current moment includes: Step 1: Correct the first preset mathematical model according to the offset coefficient at the current moment, and input the layout information and status parameters of the computer room at the current moment into the corrected first preset mathematical model to obtain the corrected predicted airflow data; Step 2: Based on the actual temperature data and the corrected predicted temperature data corresponding to the target cabinet measuring point, update the offset coefficient and return the updated offset coefficient to Step 1. Repeat Step 1 to Step 2 until the actual temperature data and the corrected predicted temperature data corresponding to the target cabinet measuring point are equal. Output the predicted airflow data corresponding to each cabinet measuring point at the current moment, calculated based on the final first preset mathematical model.

4. The method for generating a simulated airflow field in a computer room as described in claim 1, characterized in that, The calculation of predicted pressure data for each rack measuring point based on the predicted temperature and predicted speed data at the current moment includes: Obtain the leakage rate of each rack measuring point in the computer room at the current moment; For any cabinet measuring point, the predicted temperature data, predicted velocity data, and leakage rate corresponding to the measuring point at the current moment are input into the preset pressure field fitting formula to obtain the predicted pressure data corresponding to the measuring point at the current moment.

5. The method for generating a simulated airflow field in a computer room as described in claim 1, characterized in that, The status parameters also include the temperature difference between the air conditioner's supply and return air, the temperature difference between the cabinet's inlet and outlet air, and the number of air conditioners in operation.

6. The method for generating a simulated airflow field in a computer room as described in claim 1, characterized in that, The layout information includes the relative positions of the air conditioning cabinets corresponding to each cabinet measuring point; obtaining the layout information of the computer room at the current moment includes: For any rack measuring point in the computer room, obtain the relative position of the rack measuring point with each air conditioner; based on the relative position of the rack measuring point with each air conditioner, calculate the relative position of the air conditioner rack corresponding to the rack measuring point.

7. A device for generating a simulated airflow field in a computer room, characterized in that, include: The data acquisition module is used to acquire the layout information and status parameters of the computer room at the current moment; the status parameters include the air volume difference and load of each rack measuring point in the computer room; the air volume difference is the difference between the required air volume and the actual air volume; The prediction data calculation module is used to input the layout information and status parameters of the computer room at the current moment into a first preset mathematical model to obtain the predicted airflow data corresponding to each rack measuring point at the current moment; the predicted airflow data includes predicted temperature data, predicted speed data and / or predicted pressure data; the first preset mathematical model is trained by the layout information, status parameters and actual airflow data of the computer room in historical periods; The airflow organization mimicry field generation module is used to generate the current data center airflow organization mimicry field based on the predicted airflow data of each rack measuring point at the current moment. The first preset mathematical model includes a preset temperature field fitting formula, a preset velocity field fitting formula, and a preset pressure field fitting formula; The prediction data calculation module includes: The temperature prediction unit is used to input the layout information of the computer room at the current moment, the air volume difference and load of each rack measuring point into the preset temperature field fitting formula to obtain the predicted temperature data of each rack measuring point at the current moment. The speed prediction unit is used to input the layout information of the computer room at the current moment and the air volume difference corresponding to each rack measuring point into the preset speed field fitting formula to determine the predicted speed data corresponding to each rack measuring point at the current moment. The pressure prediction unit is used to calculate the predicted pressure data corresponding to each rack measuring point at the current moment based on the predicted temperature data and predicted speed data corresponding to each rack measuring point at the current moment.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

Images