Fan rotating speed obtaining method, server heat dissipation method, device and equipment

Abstract

The invention discloses a fan rotating speed obtaining method, a server heat dissipation method, a device and equipment, and relates to the technical field of servers. In the method, on one hand, when an operation system is in a normal operation state, computing power resources of the operation system are rich, and the operation efficiency is improved; therefore, the operating system can predict the temperature of each position of the server in the next time period in a mode of operating the model and control the fan rotating speed of the server based on the predicted temperature, so that the fan rotating speed can be ensured to be matched with the temperature of each position, the heat dissipation capability of the server is improved, and the service life of the server is prolonged. The problem that in some technologies, the heat dissipation effect of servers is not good is solved. And on the other hand, when the operating system is abnormal, the substrate management control system determines the rotating speed of the fan based on simple control logic, so that the temperature rising speed of the server can be reduced, and the influence of high temperature on the server can be reduced as much as possible.

Description

Technical Field

[0001] This application relates to the technical field of servers, and particularly to a method for obtaining fan speed, a server heat dissipation method, a device, and a device. Background Art

[0002] The good heat dissipation ability of a server can ensure the full play of the server's performance. If the heat dissipation is not good, on the one hand, it may reduce the working performance of each component inside the server. For example, components such as chips may experience frequency reduction problems due to excessive temperature, which may lead to a decrease in the operation speed. On the other hand, the electronic components inside the server may malfunction due to excessive temperature, which may lead to problems such as the server crashing, freezing, or even hardware damage, making various online services relying on the server unable to be processed in a timely manner.

[0003] Currently, in some heat dissipation technologies, the server temperature is usually monitored regularly, and then based on the server temperature, the fan speed inside the server is controlled. For example, when the server temperature is high, the fan can be controlled to run at a high speed to dissipate heat from the server in a timely manner. When the server temperature is low, the fan can be controlled to run at a low speed to reduce the power consumption of the fan. In these technologies, the control of the fan speed has hysteresis, and the heat dissipation effect is not good. Summary of the Invention

[0004] This application provides a method for obtaining fan speed, a server heat dissipation method, a fan speed acquisition device, a server heat dissipation device, an electronic device, and a computer-readable storage medium to at least solve the problem of poor heat dissipation effect of the server in related technologies.

[0005] This application provides a method for obtaining fan speed, including: Receiving the current operation information of the server sent by the heat dissipation controller, where the current operation information includes the fan speed, server power, and temperature at at least one position of the server at multiple time points during the current period; Based on the current operation information, determining the first correlation between the temperature at each position of the server and the fan speed, and determining the second correlation between the temperature at each position of the server and the server power; Inputting the current operation information, the first correlation, and the second correlation into a temperature prediction model to obtain the target temperature of each position of the server in the next period, and obtaining the fan speed in the server based on the target temperature.

[0006] This application also provides a server heat dissipation method, including: Obtain the current operating information of the server, where the current operating information includes the fan speeds, server power, and temperatures at at least one location at multiple time points within the current period of the server; If the operating system in the server is in a normal operating state, send the current operating information to the operating system, obtain the first fan speed returned by the operating system, and control the fan in the server to dissipate heat from the server according to the first fan speed, where the first fan speed is obtained by the operating system based on the above fan speed obtaining method; If the operating system is not in a normal operating state and the baseboard management control system in the server is in a normal operating state, send the temperature in the current operating information to the baseboard management control system, obtain the second fan speed returned by the baseboard management control system, and control the fan in the server to dissipate heat from the server according to the second fan speed.

[0007] This application also provides a fan speed obtaining device, including: An information receiving module, configured to receive the current operating information of the server sent by a heat dissipation controller, where the current operating information includes the fan speeds, server power, and temperatures at at least one location at multiple time points within the current period of the server; A relevance determination module, configured to determine a first relevance between the temperatures at each location of the server and the fan speeds based on the current operating information, and determine a second relevance between the temperatures at each location of the server and the server power; A speed determination module, configured to input the current operating information, the first relevance, and the second relevance into a temperature prediction model, obtain the target temperatures at each location of the server in the next period, and obtain the fan speeds in the server based on the target temperatures.

[0008] This application also provides a server heat dissipation device, including: An information obtaining module, configured to obtain the current operating information of the server, where the current operating information includes the fan speeds, server power, and temperatures at at least one location at multiple time points within the current period of the server; A first fan control module, configured to, if the operating system in the server is in a normal operating state, send the current operating information to the operating system, obtain the first fan speed returned by the operating system, and control the fan in the server to dissipate heat from the server according to the first fan speed, where the first fan speed is obtained by the operating system based on the above fan speed obtaining method; A second fan control module, configured to, if the operating system is not in a normal operating state and the baseboard management control system in the server is in a normal operating state, send the temperature in the current operating information to the baseboard management control system, obtain a second fan speed returned by the baseboard management control system, and control the fan in the server to dissipate heat from the server according to the second fan speed.

[0009] This application also provides an electronic device, including: a memory, configured to store a computer program; a processor, configured to implement the steps of the above-mentioned fan speed acquisition method or server heat dissipation method when executing the computer program.

[0010] This application also provides a computer-readable storage medium, in which a computer program is stored, and wherein the computer program implements the steps of the above-mentioned fan speed acquisition method or server heat dissipation method when executed by a processor.

[0011] In the technical solutions of some embodiments of this application, on the one hand, when the operating system is in a normal operating state, since the computing power resources of the operating system are relatively rich, the operating system can predict the temperature at each position of the server in the next time period by running a model, and control the fan speed of the server based on the predicted temperature. In this way, it can be ensured that the fan speed matches the temperature at each position, thereby improving the heat dissipation ability of the server. On the other hand, when the operating system is abnormal, the baseboard management control system determines the fan speed based on a simple control logic, which can reduce the temperature rise speed of the server, thereby minimizing the impact of high temperature on the server. Description of the Drawings

[0012] To more clearly illustrate the embodiments of this application, the following will briefly introduce the drawings required for the embodiments. Obviously, the drawings in the following description are only some embodiments of this application, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0013] Figure 1 A module schematic diagram of a server heat dissipation system in some technologies; Figure 2 A flowchart of a fan speed acquisition method provided by some embodiments of this application; Figure 3 A module schematic diagram of a temperature prediction model provided by some embodiments of this application; Figure 4 A flowchart of a server heat dissipation method provided by some embodiments of this application; Figure 5 For Figure 4Schematic diagram of module interaction of server heat dissipation method in Figure 6 Schematic diagram of modules of a fan speed acquisition device provided in some embodiments of the present application; Figure 7 Schematic diagram of modules of a server heat dissipation device provided in some embodiments of the present application; Figure 8 Schematic diagram of modules of an electronic device provided in some embodiments of the present application. Detailed implementation manners

[0014] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0015] It should be noted that in the description of the present application, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. The terms "first", "second", etc. in the present application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0016] To enable those skilled in the art of the present technology to better understand the solution of the present application, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0017] Referring to Figure 1 , which is a schematic diagram of modules of a server heat dissipation system in some technologies. Figure 1 The server heat dissipation system in includes a baseboard management control system, a heat dissipation controller, multiple temperature sensors, and fans. Among them, the temperature sensors can be deployed in the key thermal-sensitive areas of the server to sense the temperatures of these key thermal-sensitive areas. For example, temperature sensors can be deployed around components such as a central processing unit (CPU), a memory module, a hard disk drive, a power supply unit, etc., and at the positions of the air inlets and outlets of the chassis. The fans are used to accelerate the air circulation inside the server to achieve server heat dissipation. The deployment positions of the fans can correspond one by one to the deployment positions of the temperature sensors, or may not correspond one by one.

[0018] The baseboard management control system runs in the Baseboard Management Controller (BMC) and is used for out-of-band management of the server. Specifically, the baseboard management controller can be physically connected to temperature sensors. Based on this physical connection, the baseboard management control system can obtain the temperatures at different positions of the server from each temperature sensor at preset intervals. Since the hardware resources (such as storage resources) in the baseboard management controller are limited, after obtaining the temperatures, the baseboard management control system can determine the fan speed based on some simple control logic. For example, the fan speed can be determined based on a pre-established mapping relationship between temperature and fan speed. In this mapping relationship, the temperature is proportional to the fan speed. In this way, when the temperature at a certain position of the server is high, the fan speed at this position or near this position can be high. Thus, heat dissipation can be carried out in a timely manner at this position. On the contrary, when the temperature at this position is low, the fan speed at this position or near this position can be low. Thus, the power consumption of the fan can be reduced.

[0019] The heat dissipation controller is a Complex Programmable Logic Device (CPLD). The baseboard management controller is connected to the heat dissipation controller through I2C (Inter-Integrated Circuit, two-wire serial bus) lines and WDT (Watchdog Timer) lines. Each fan is connected to the heat dissipation controller through PWM (Pulse Width Modulation Signal) lines and TACH (TachometerSignal) lines respectively.

[0020] Based on the I2C lines, the baseboard management control system can send the determined fan speed to the heat dissipation controller. The heat dissipation controller can convert the fan speed into a PWM signal and send the PWM signal to the fan through the PWM lines. In this way, the fan speed can be controlled. During the rotation of the fan, the TACH signal can be returned to the heat dissipation controller through the TACH lines. The TACH signal represents the current speed of the fan. Based on the TACH signal, on the one hand, the heat dissipation controller can determine whether the fan is rotating at the planned speed, and on the other hand, the heat dissipation controller can count the fan speed and return the statistical result to the baseboard management control system through the I2C lines.

[0021] Furthermore, during the normal operation of the baseboard management control system, a WDT signal can be sent to the thermal controller through the WDT circuit. The WDT signal can be a periodically changing signal. If the WDT signal does not change periodically for a long time, the thermal controller can determine that the baseboard management control system or the baseboard management controller is abnormal. At this time, the thermal controller can take over the control right of the fan speed. Specifically, a pre-set target fan speed can be stored in the thermal controller. The target fan speed can be the maximum speed of each fan. When the baseboard management control system or the baseboard management controller is abnormal, since the thermal controller cannot obtain the temperatures at various positions inside the server, it can control the speeds of each fan according to the pre-set target fan speed. At this time, although each fan consumes relatively more power, it can at least ensure that the server can dissipate heat normally.

[0022] With the rapid development and wide application of artificial intelligence, cloud computing, big data, etc., the operating power of servers is getting higher and higher. When the operating power of the server increases, the speed at which heat is generated during its operation also becomes faster and faster, that is, the temperatures at various positions inside the server may rise to very high temperatures in a short period of time. In Figure 1 the shown solution, the fan speed is controlled based on the temperatures at various positions of the server. For example, after the temperature has risen to 50 degrees Celsius, the fan speed is then controlled according to the fan speed corresponding to 50 degrees Celsius. This control method of the fan speed has hysteresis. When the temperature inside the server rises rapidly, it is unable to effectively dissipate heat from the server, that is, the heat dissipation effect of the server is not good. For example, at time point 1, the baseboard management control system detects that the temperature inside the server is 50 degrees Celsius. After simple logical calculation, it determines that the fan speed corresponding to 50 degrees Celsius is 15 revolutions per second, and sends this fan speed to the thermal controller. The thermal controller generates a PWM signal according to the fan speed of 15 revolutions per second, and at time point 2 after time point 1, adjusts the fan speed to 15 revolutions per second. However, due to the relatively fast rising speed of the temperature inside the server, therefore, at time point 2, the temperature inside the server may have risen to 60 degrees Celsius, but the fan is rotating at the fan speed corresponding to 50 degrees Celsius, so there will be a problem of poor heat dissipation effect of the server.

[0023] In view of this, the present application first provides a method for obtaining the fan speed, which can predict the temperatures at various positions of the server in a future period of time. In this way, based on the predicted temperatures, the fan speed can be controlled in advance, so that the fan speed matches the temperature inside the server, thereby improving the heat dissipation capacity of the server. For example, taking the above time point 1 and time point 2 as examples for illustration. Assume that at time point 1, it is predicted that at time point 2, the temperature inside the server will rise to 60 degrees Celsius. Then, at time point 1, the target fan speed corresponding to 60 degrees Celsius (such as 20 revolutions per second) can be determined, and a PWM signal can be generated according to the target fan speed. In this way, at time point 2, the fan can be rotated according to the target fan speed, so that the fan speed matches 60 degrees Celsius, thereby greatly improving the heat dissipation capacity of the server.

[0024] Specifically, considering that excessive computing power resources may be consumed during the temperature prediction process and the operating system has sufficient computing power resources, therefore, the program code of the operating system can be updated so that it can execute the method for obtaining the fan speed of the present application during operation. Since the operating system mainly runs in the central processing unit of the server, the method of the present application can be applied to the operating system or to the central processing unit running the operating system. Referring to Figure 2 , which is a schematic flowchart of the method for obtaining the fan speed provided by some embodiments of the present application.

[0025] Step S201, receive the current operating information of the server sent by the heat dissipation controller, where the current operating information includes the fan speeds, the server power, and the temperatures at at least one position of the server at multiple time points during the current period.

[0026] Specifically, the current period refers to the period of a specified duration before the current time point. For example, when the specified duration is 10 minutes, if the current time point is 10:45, the current period is the period between 10:35 and 10:45; if the current time point is 10:46, the current period is the period between 10:36 and 10:46.

[0027] In this embodiment, the heat dissipation controller can be directly connected to the temperature sensor. The heat dissipation controller can collect the fan speed, server power, and temperatures at various positions every preset time interval (for example, every 10 seconds). In this way, the fan speed, server power, and temperatures at various positions at multiple time points can be obtained. Based on the collected data, the heat dissipation controller can use the fan speed, server power, and temperatures at various positions collected at multiple time points during the current period as the current operating information and send it to the operating system. For example, assuming the current period is from 10:35 to 10:45, the heat dissipation controller can use the fan speed, server power, and temperatures at various positions collected at the following time points as the current operating information and send it to the operating system: The fan speeds S11, S12, ……, S1m of each fan, server power P1, and temperatures T11, T12, ……, T1n at various positions collected at 10:35:10; The fan speeds S21, S22, ……, S2m of each fan, server power P2, and temperatures T21, T22, ……, T2n at various positions collected at 10:35:20; …… The fan speeds St1, St2, ……, Stm of each fan, server power Pt, and temperatures Tt1, Tt2, ……, Ttn at various positions collected at 10:45:00.

[0028] Step S202: Based on the current operating information, determine the first correlation degree between the temperature and the fan speed at each position of the server, and determine the second correlation degree between the temperature and the server power at each position of the server.

[0029] Specifically, the first correlation degree is used to characterize the magnitude of the correlation between the temperature at each position and the fan speed of each fan. The larger the first correlation degree, the greater the correlation. For the temperature at any position A and the fan speed of any fan F, if there is a large correlation between the temperature at position A and the fan speed of fan F, then when the fan speed of fan F changes, the temperature at position A will also change accordingly. Conversely, if there is no large correlation between the temperature at position A and fan F, then when the fan speed of fan F changes, the temperature at position A will not change or will change less. For example, assume that the first correlation degree between the temperature at position A1 and the fan speed of fan F1 is 0.3, which means that there is no large correlation between the temperature at position A1 and the fan speed of fan F1, that is, when the fan speed of fan F1 changes, the temperature at position A1 will not change or will change less. Another example, assume that the first correlation degree between the temperature at position A1 and the fan speed of fan F2 is 0.8, which means that there is a large correlation between the temperature at position A1 and the fan speed of fan F2, that is, when the fan speed of fan F2 changes, the temperature at position A1 will also change accordingly.

[0030] Similarly, the second correlation degree is used to characterize the magnitude of the correlation between the temperature at each position and the server power. The larger the second correlation degree, the greater the correlation. For the temperature at any position A, if there is a large correlation between the temperature at position A and the server power, it means that when the server power changes, the temperature at position A will also change accordingly. Conversely, if there is no large correlation between the temperature at position A and the server power, it means that when the server power changes, the temperature at position A will not change or will change less. For example, assume that the second correlation degree between the temperature at position A1 and the server power is 0.2, which means that there is no large correlation between the temperature at position A1 and the server power, that is, when the server power changes, the temperature at position A1 will not change or will change less. Another example, assume that the second correlation degree between the temperature at position A2 and the server power is 0.9, which means that there is a large correlation between the temperature at position A2 and the server power, that is, when the server power changes, the temperature at position A will also change accordingly.

[0031] In this embodiment, the first correlation degree between the temperature at each position and the fan speed of each fan can be calculated based on Expression (1).

[0032]

[0033] Among them, represents the first correlation degree between the temperature at the i-th position of the server and the fan speed of the j-th fan, and p represents the number of time points within the current time period. represents the fan speed of the j-th fan at the k-th time point in the current period, represents the average fan speed of the j-th fan within the current period, represents the temperature at the k-th time point in the current period at the i-th position of the server, represents the average temperature at the i-th position of the server within the current period.

[0034] Moreover, based on Expression (2), the second correlation degree between the temperature at each position and the server power can be calculated.

[0035]

[0036] Among them, regarding 、 、 , please refer to the description of Expression (1), which will not be elaborated here. represents the second correlation degree between the temperature at the i-th position of the server and the server power, represents the server power at the k-th time point in the current period, represents the average server power in the current period.

[0037] Step S203: Input the current running information, the first correlation degree, and the second correlation degree into the temperature prediction model to obtain the target temperature at each position of the server in the next period, and based on the target temperature, obtain the fan speed in the server.

[0038] Specifically, similar to the current period, the next period refers to the period of a specified duration after the current time point. For example, when the specified duration is 10 minutes, if the current time point is 10:45, then the next period is the period between 10:45 and 10:55; if the current time point is 10:46, then the next period is the period between 10:46 and 10:56.

[0039] Specifically, the next period can also be divided into multiple time points. The temperature prediction model can output the predicted temperature at each position of the server at each time point in the next period. Furthermore, the fan speed at each time point can be determined in sequence according to the order of the time points, and the fan can be controlled according to the fan speed corresponding to each time point before each time point. For example, assuming that the next period is between 10:46 and 10:56, the temperature prediction model can output the following content: The predicted temperature T11 of position A1 at 10:46:10, the predicted temperature T12 at 10:46:20, ……, the predicted temperature T1p at 10:56:00; The predicted temperatures T21 at 10:46:10 for location A2, T22 at 10:46:20, …, T2p at 10:56:00; …; The predicted temperatures Tn1 at 10:46:10 for location An, Tn2 at 10:46:20, …, Tnp at 10:56:00.

[0040] Furthermore, based on the predicted temperature T11 at 10:46:10 for location A1, T21 at 10:46:10 for location A2, …, Tn1 at 10:46:10 for location An, it can be determined that the fan speed of fan F1 at 10:46:10 is 10 revolutions per second, the fan speed of fan F2 at 10:46:10 is 15 revolutions per second, …, the fan speed of fan Fm at 10:46:10 is 20 revolutions per second; Based on the predicted temperature T12 at 10:46:20 for location A1, T22 at 10:46:20 for location A2, …, Tn2 at 10:46:20 for location An, it can be determined that the fan speed of fan F1 at 10:46:20 is 11 revolutions per second, the fan speed of fan F2 at 10:46:20 is 13 revolutions per second, …, the fan speed of fan Fm at 10:46:20 is 22 revolutions per second.

[0041] Furthermore, at the k-th time point of the next time period (such as 10:46:10), the fan can be controlled according to the determined fan speed at the (k + 1)-th time point (such as 10:46:20). In this way, at the (k + 1)-th time point, the fan speed will match the temperature at the (k + 1)-th time point, thus greatly improving the heat dissipation capacity of the server.

[0042] Of course, it can be understood that the temperature prediction model can also output the average temperature of each location of the server in the next time period. In this way, near the end of the current time period, the fan speed can be determined according to the average temperature of the next time period, and before entering the next time period, the fan can be controlled according to the determined fan speed. In this way, when entering the next time period, the fan speed will match the temperature at each location.

[0043] In summary, in the technical solutions of some embodiments of the present application, based on the current operating information of the server, the first correlation between the temperature and the fan speed at each position of the server can be determined, and the second correlation between the temperature and the server power at each position of the server can be determined. Furthermore, based on the current operating information, the first correlation, and the second correlation, the target temperature at each position of the server in the next time period can be predicted. In this way, the fan speed in the server can be controlled in advance according to the target temperature, so that the fan speed in the server can match the temperature. Thus, the heat dissipation ability of the server can be greatly improved, and the problem of poor heat dissipation effect of the server caused by the lag of fan control in some technologies can be solved.

[0044] Referring to Figure 3 , which is a schematic diagram of the modules of the temperature prediction model provided by some embodiments of the present application. Figure 3 In [the figure], the temperature prediction model includes an input layer, a Long Short-Term Memory (LSTM) network layer, a self-attention layer, a fully connected layer, and an output layer. After the current operating information, the first correlation, and the second correlation are input into the temperature prediction model through the input layer, the LSTM network layer can capture the long-term dependencies in the input data, and the self-attention layer can dynamically capture the importance of different parts of the input data. In this way, the prediction accuracy can be greatly improved. The fully connected layer can integrate the information output by the LSTM network layer and the self-attention layer and obtain the predicted temperature at each position of the server. The predicted temperature can be output from the output layer.

[0045] Furthermore, it can be understood that during the operation of the server, its operating environment, operating state, etc. are all dynamically changing. Correspondingly, the correlation between the temperature and the fan speed and the server power at each position of the server should also be dynamically changing. For example, in the early stage of server use, the correlation between the temperature at position A1 and the server power may be small, but in the middle stage of server use, the correlation between the temperature at position A1 and the server power will gradually increase. Therefore, it is necessary to fine-tune and train the temperature prediction model at different stages of server use so that the temperature prediction model can capture the latest changes and characteristics of the server operating state, thereby ensuring the prediction accuracy.

[0046] Specifically, in some embodiments, after the server is started and run each time, the fan speed, server power, and temperature at at least one location of the server can be obtained at multiple time points within the first preset duration, and the temperature prediction model can be fine-tuned and trained based on the fan speed, server power, and temperature at at least one location of the server at multiple time points within the first preset duration. The temperature prediction model after fine-tuning and training is used to predict the target temperature of each location of the server in the next time period based on the current operating information of the server. For example, if the server is restarted at 10 o'clock, the heat dissipation controller can obtain the fan speed, server power, and temperature at at least one location of the server every preset duration (such as every 10 seconds) between 10 o'clock and 10:30, and send the obtained data to the operating system. In this way, the operating system can fine-tune and train the temperature prediction model based on the obtained data, so that the inference logic of the temperature prediction model can match the current operating environment and operating status of the server, thereby greatly improving the model prediction accuracy.

[0047] Further, in some embodiments, the temperature prediction model can include a first temperature prediction model and a second temperature prediction model. In the current time period, the current operating information, the first relevance, and the second relevance can be input into the first temperature prediction model to obtain the target temperature of each location of the server in the next time period. At the same time, the second temperature prediction model can be fine-tuned and trained based on the fan speed, server power, and temperature at at least one location of the server at multiple time points before the current time period. After the current time period, the second temperature prediction model can be used for temperature prediction, and the first temperature prediction model can be fine-tuned and trained based on the fan speed, server power, and temperature at at least one location of the server at multiple time points within the current time period. In this way, during the operation of the server, the two temperature prediction models can be alternately fine-tuned and trained based on the current operating information of the server, thereby further ensuring the prediction accuracy of the temperature prediction model.

[0048] Specifically, after the server is started and run each time, the first temperature prediction model and the second temperature prediction model can be initially fine-tuned and trained simultaneously based on the fan speed, server power, and temperature at at least one location at multiple time points within the first preset duration. After the initial fine-tuning training is completed, the first temperature prediction model and the second temperature prediction model can be alternately micro-trained, and the first temperature prediction model and the second temperature prediction model can be alternately used for temperature prediction. For example, within the first time period after the initial fine-tuning training, the first temperature prediction model is used for temperature prediction, and the second temperature prediction model is fine-tuned and trained based on the operation information of the server within the first time period. Within the second time period, the second temperature prediction model is used for temperature prediction, and the first temperature prediction model is fine-tuned and trained based on the operation information of the server within the second time period. Within the third time period, the first temperature prediction model is used for temperature prediction, and the second temperature prediction model is fine-tuned and trained based on the operation information of the server within the third time period. And so on. In this way, the inference logic of the temperature prediction model can be highly consistent with the operating environment, operating state, etc. of the server, thereby greatly improving the prediction accuracy.

[0049] In some embodiments, determining the first correlation between the temperature and the fan speed at each location of the server and determining the second correlation between the temperature and the server power at each location of the server based on the current operation information in step S202 may include: Normalize the fan speed, server power, and temperature at each location in the current operation information respectively; Based on the normalized fan speed and the temperature at each location, determine the first correlation, and based on the normalized server power and the temperature at each location, determine the second correlation.

[0050] Specifically, the fan speed, server power, and temperature at each location in the current operation information can be normalized respectively based on expression (3).

[0051]

[0052] Taking the temperature at the i-th location of the server as an example. represents the original temperature at the i-th location at the k-th time point, represents the lowest temperature at the i-th location within the current time period, represents the highest temperature at the i-th location within the current time period, and r represents the normalized temperature at the i-th location at the k-th time point.

[0053] After normalization processing, all data can be converted to the same scale, reducing the problem of numerical instability caused by too large a scale difference.

[0054] Refer to in combination Figure 4 and Figure 5 . Figure 4 The flowchart of the server heat dissipation method provided for some embodiments of this application. Figure 5 For Figure 4 the schematic diagram of module interaction of the server heat dissipation method in Figure 4 The server heat dissipation method in Figure 5 can be applied to the heat dissipation controller in Figure 5 In Figure 1 the heat dissipation controller is connected to the temperature sensor, the fan, the baseboard management controller and the central processing unit. Among them, the connection lines between the heat dissipation controller and the temperature sensor, the fan, and the baseboard management controller are similar to Figure 1 and will not be elaborated here. The baseboard management controller is used to run the baseboard management control system, and the central processing unit is used to run the operating system. The central processing unit is connected to the heat dissipation controller through SMBUS (System Management Bus) lines and WDT lines. Among them, based on the SMBUS lines, data can be transmitted between the operating system and the heat dissipation controller; based on the WDT lines, the heat dissipation controller can detect whether the operating system is in a normal running state, and the relevant principle is similar to that of the WDT lines in

[0055] Based on Figure 5 the structural relationship of Figure 4 the server heat dissipation method in can include the following steps:

[0056] Specifically, since the heat dissipation controller is directly connected to the temperature sensor, based on the temperature sensor, the temperatures at multiple time points at various positions of the server can be obtained. Also, since the heat dissipation controller is directly connected to the fan, based on the connection line between the heat dissipation controller and the fan, the fan speeds at multiple time points in the current period can be obtained.

[0057] Furthermore, as the out-of-band management system of the server, the baseboard management control system usually stores the server power at each time point, so the heat dissipation controller can obtain the server power at multiple time points in the current period from the baseboard management control system.

[0058] Step S402: If the operating system in the server is in a normal running state, send the current running information to the operating system, obtain the first fan speed returned by the operating system, and control the fan in the server to dissipate heat from the server according to the first fan speed. The first fan speed is obtained by the operating system based on the above fan speed acquisition method.

[0059] Specifically, since the computing power resources of the operating system are relatively rich, the operating system can run a temperature prediction model to predict the temperature of each position of the server in the next time period, and generate a fan speed based on the temperature in the next time period. In this way, it can ensure that the fan speed matches the temperature at each position of the server, thereby improving the heat dissipation capacity of the server. For the relevant principle, refer to Figure 2 the relevant description, which will not be elaborated here.

[0060] Step S403: If the operating system is not in a normal running state and the baseboard management control system in the server is in a normal running state, send the temperature in the current running information to the baseboard management control system, obtain the second fan speed returned by the baseboard management control system, and control the fan in the server to dissipate heat from the server according to the second fan speed.

[0061] Specifically, when the operating system is abnormal, after sending the temperature in the current running information to the baseboard management control system, since the computing power resources of the baseboard management controller are limited, the baseboard management control system can determine the fan speed based on a simple control logic. Based on Figure 1 the relevant description, although the fan speed determined by the baseboard management control system has hysteresis, it can at least ensure the heat dissipation of the server as much as possible, thereby reducing the temperature rise rate of the server.

[0062] Based on the above description, in the technical solutions of some embodiments of the present application, on the one hand, when the operating system is in a normal running state, since the computing power resources of the operating system are relatively rich, the operating system can predict the temperature of each position of the server in the next time period by running a model, and control the fan speed of the server based on the predicted temperature. In this way, it can ensure that the fan speed matches the temperature at each position, thereby improving the heat dissipation capacity of the server. On the other hand, when the operating system is abnormal, the baseboard management control system determines the fan speed based on a simple control logic, which can reduce the temperature rise rate of the server, thereby minimizing the impact of high temperature on the server as much as possible.

[0063] In some embodiments, if both the operating system and the baseboard management control system are not in a normal running state, control the fan in the server to dissipate heat from the server according to a preset third fan speed. The third fan speed is not related to the current running information. For the relevant principle, refer to Figure 1 this, which will not be elaborated here.

[0064] In some embodiments, when the operating system in the server is in a normal running state, it is determined whether the running duration after the server is started reaches a first preset duration. If not, sending the current running information to the operating system is suspended, and the temperature in the current running information is sent to the baseboard management control system, and the fan in the server is controlled to dissipate heat from the server according to the second fan speed returned by the baseboard management control system. Specifically, as Figure 2 described in the related content, to improve the prediction accuracy of the temperature prediction model, the temperature prediction model needs to be fine-tuned and trained within the first preset duration after the server is started. Therefore, within this period of time, the baseboard management control system can control the fan speed, so as to reduce the temperature rising speed of the server. After the fine-tuning and training of the temperature prediction model are completed, the fan speed of the server can be accurately controlled based on the temperature prediction model, thereby improving the heat dissipation ability of the server.

[0065] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method.

[0066] Refer to Figure 6 , which is a schematic diagram of the modules of the fan speed acquisition device provided in some embodiments of the present application. Figure 6 In The fan speed acquisition device includes: An information receiving module 601, configured to receive the current running information of the server sent by the heat dissipation controller, where the current running information includes the fan speeds at multiple time points of the server in the current time period, the server power, and the temperature at at least one position; A relevance determination module 602, configured to determine a first relevance between the temperature at each position of the server and the fan speed, and determine a second relevance between the temperature at each position of the server and the server power based on the current running information; A rotation speed determination module 603, configured to input the current running information, the first relevance, and the second relevance into the temperature prediction model to obtain the target temperature at each position of the server in the next time period, and obtain the fan speed in the server based on the target temperature.

[0067] In some embodiments, the rotation speed determination module 603 is further configured to: After the server is started and run each time, obtain the fan speeds at multiple time points, the server power, and the temperature at at least one position of the server within the first preset duration; Based on the fan speeds, server powers, and temperatures at at least one location at multiple time points within the first preset duration of the server, the temperature prediction model is fine-tuned. After the fine-tuning training, the temperature prediction model is used to predict the target temperatures at various locations of the server in the next time period based on the current operating information of the server.

[0068] In some embodiments, the temperature prediction model includes a first temperature prediction model and a second temperature prediction model; the rotation speed determination module 603 is further configured to: Input the current operating information, the first relevance, and the second relevance into the first temperature prediction model to obtain the target temperatures at various locations of the server in the next time period; Based on the fan speeds, server powers, and temperatures at at least one location at multiple time points before the current time period, fine-tune the second temperature prediction model; After the current time period, use the second temperature prediction model for temperature prediction, and based on the fan speeds, server powers, and temperatures at at least one location at multiple time points within the current time period, fine-tune the first temperature prediction model.

[0069] In some embodiments, the relevance determination module 602 is specifically configured to: Normalize the fan speeds, server powers, and temperatures at each location in the current operating information respectively; Based on the normalized fan speeds and temperatures at each location, determine the first relevance, and based on the normalized server powers and temperatures at each location, determine the second relevance.

[0070] Refer to Figure 7 for the schematic diagram of the modules of the server heat dissipation device provided in some embodiments of the present application. Figure 7 In the information acquisition module 701 is configured to acquire the current operating information of the server, where the current operating information includes the fan speeds, server powers, and temperatures at at least one location at multiple time points within the current time period of the server; The first fan control module 702 is configured to, if the operating system in the server is in a normal operating state, send the current operating information to the operating system to obtain the first fan speed returned by the operating system, and control the fan in the server to dissipate heat from the server according to the first fan speed, where the first fan speed is obtained by the operating system based on the above fan speed acquisition method; The second fan control module 703 is configured to, if the operating system is not in a normal operating state and the baseboard management control system in the server is in a normal operating state, send the temperature in the current operating information to the baseboard management control system, obtain the second fan speed returned by the baseboard management control system, and control the fan in the server to dissipate heat from the server according to the second fan speed.

[0071] In some embodiments, the second fan control module 703 is further configured to: If neither the operating system nor the baseboard management control system is in a normal operating state, control the fan in the server to dissipate heat from the server according to a preset third fan speed, and the third fan speed is not related to the current operating information.

[0072] In some embodiments, the first fan control module 702 is further configured to: When the operating system in the server is in a normal operating state, determine whether the running duration after the server is started reaches a first preset duration. If not, pause sending the current operating information to the operating system, send the temperature in the current operating information to the baseboard management control system, and control the fan in the server to dissipate heat from the server according to the second fan speed returned by the baseboard management control system.

[0073] For the description of the features in the corresponding embodiments of the fan speed acquisition device, reference can be made to the relevant descriptions in the corresponding embodiments of the fan speed acquisition method. For the description of the features in the corresponding embodiments of the server heat dissipation device, reference can be made to the relevant descriptions in the corresponding embodiments of the server heat dissipation method, which will not be elaborated here one by one.

[0074] With reference to Figure 8 , an embodiment of the present application further provides an electronic device, including a memory 10 and a processor 20. A computer program is stored in the memory 10, and the processor 20 is configured to run the computer program to execute the steps in any of the above embodiments of the fan speed acquisition method and the server heat dissipation method.

[0075] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps in any of the above embodiments of the fan speed acquisition method and the server heat dissipation method when running.

[0076] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media such as a USB flash drive, a read-only memory (ROM for short), a random access memory (RAM for short), a mobile hard disk, a magnetic disk, or an optical disc that can store a computer program.

[0077] An embodiment of the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in any of the above fan speed acquisition methods and server heat dissipation method embodiments are implemented.

[0078] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above fan speed acquisition methods and server heat dissipation method embodiments are implemented.

[0079] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0080] The above has introduced in detail a fan speed acquisition method, a server heat dissipation method, a device, a device, and a storage medium provided by this application. Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for obtaining the rotational speed of a fan, characterized in that, The method includes: Receiving the current operation information of the server sent by the heat dissipation controller, where the current operation information includes the fan speeds, server power, and temperatures at at least one location at multiple time points within the current time period of the server; Based on the current operation information, determining a first correlation degree between the temperatures at each location of the server and the fan speeds, and determining a second correlation degree between the temperatures at each location of the server and the server power; Inputting the current operation information, the first correlation degree, and the second correlation degree into a temperature prediction model to obtain the target temperatures at each location of the server in the next time period, and based on the target temperatures, obtaining the fan speeds in the server.

2. The method according to claim 1, wherein The method further includes: After the server is started and run each time, obtaining the fan speeds, server power, and temperatures at at least one location at multiple time points within a first preset time period of the server; Based on the fan speeds, server power, and temperatures at at least one location at multiple time points within the first preset time period of the server, performing fine-tuning training on the temperature prediction model, and after being fine-tuned and trained, the temperature prediction model is used to predict the target temperatures at each location of the server in the next time period based on the current operation information of the server.

3. The method according to claim 2, characterized in that, The temperature prediction model includes a first temperature prediction model and a second temperature prediction model; the method further includes: Inputting the current operation information, the first correlation degree, and the second correlation degree into the first temperature prediction model to obtain the target temperatures at each location of the server in the next time period; Based on the fan speeds, server power, and temperatures at at least one location at multiple time points before the current time period, performing fine-tuning training on the second temperature prediction model; After the current time period, using the second temperature prediction model for temperature prediction, and based on the fan speeds, server power, and temperatures at at least one location at multiple time points within the current time period, performing fine-tuning training on the first temperature prediction model.

4. The method according to claim 1, characterized in that, The determining the first correlation degree between the temperatures at each location of the server and the fan speeds, and determining the second correlation degree between the temperatures at each location of the server and the server power based on the current operation information includes: Respectively performing normalization processing on the fan speeds, server power, and temperatures at each location in the current operation information; Based on the normalized fan speeds and temperatures at each location, determining the first correlation degree, and based on the normalized server power and temperatures at each location, determining the second correlation degree.

5. A server heat dissipation method, characterized in that, Applied to the heat dissipation controller, the method includes: Obtaining the current operation information of the server, where the current operation information includes the fan speeds, server power, and temperatures at at least one location at multiple time points within the current time period of the server; If the operating system in the server is in a normal operating state, the current operating information is sent to the operating system to obtain the first fan speed returned by the operating system, and the fan in the server is controlled according to the first fan speed to dissipate heat from the server. The first fan speed is obtained by the operating system based on the fan speed obtaining method described in any one of claims 1 to 4; If the operating system is not in a normal operating state and the baseboard management control system in the server is in a normal operating state, the temperature in the current operating information is sent to the baseboard management control system to obtain the second fan speed returned by the baseboard management control system, and the fan in the server is controlled according to the second fan speed to dissipate heat from the server.

6. The method according to claim 5, wherein The method further includes: If both the operating system and the baseboard management control system are not in a normal operating state, the fan in the server is controlled to dissipate heat from the server according to a preset third fan speed, and the third fan speed is not related to the current operating information.

7. The method according to claim 5, characterized in that The method further includes: When the operating system in the server is in a normal operating state, it is determined whether the running duration after the server is started reaches a first preset duration. If not, sending the current operating information to the operating system is suspended, the temperature in the current operating information is sent to the baseboard management control system, and the fan in the server is controlled according to the second fan speed returned by the baseboard management control system to dissipate heat from the server.

8. A fan rotation speed acquisition device, characterized in that, The device includes: An information receiving module, configured to receive the current operating information of the server sent by a heat dissipation controller, where the current operating information includes the fan speeds, server power, and temperatures at at least one location of the server at multiple time points within the current time period; A relevance determination module, configured to determine a first relevance between the temperatures at each location of the server and the fan speeds based on the current operating information, and determine a second relevance between the temperatures at each location of the server and the server power; A speed determination module, configured to input the current operating information, the first relevance, and the second relevance into a temperature prediction model to obtain the target temperatures at each location of the server in the next time period, and obtain the fan speeds in the server based on the target temperatures.

9. A server heat dissipation device, characterized in that, The device includes: An information acquisition module, configured to acquire the current operating information of the server, where the current operating information includes the fan speeds, server power, and temperatures at at least one location of the server at multiple time points within the current time period; A first fan control module, configured to, if the operating system in the server is in a normal operating state, send the current operating information to the operating system to obtain the first fan speed returned by the operating system, and control the fan in the server according to the first fan speed to dissipate heat from the server. The first fan speed is obtained by the operating system based on the fan speed obtaining method described in any one of claims 1 to 4; A second fan control module, configured to, if the operating system is not in a normal operating state and the baseboard management control system in the server is in a normal operating state, send the temperature in the current operating information to the baseboard management control system, obtain a second fan speed returned by the baseboard management control system, and control the fan in the server to dissipate heat from the server according to the second fan speed.

10. An electronic device, characterized in that, Comprising: A memory, configured to store a computer program; A processor, configured to implement the steps of the fan speed acquisition method according to any one of claims 1 to 4, or implement the steps of the server heat dissipation method according to any one of claims 5 to 7 when executing the computer program.

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