A method for cooling equipment and electronic equipment
By employing temperature prediction models and dynamic frequency reduction strategies in electronic devices, the problem of overheating in electronic devices has been solved, achieving fine-grained temperature control and performance stability, and improving the user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANYSMART TECH CO LTD
- Filing Date
- 2026-01-13
- Publication Date
- 2026-06-02
Smart Images
Figure CN122131885A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of electronic equipment technology, specifically to a device cooling method and electronic equipment. Background Technology
[0002] With the development of technology, electronic devices are becoming increasingly diverse, and the tasks they need to handle are also increasing, including high-density data transmission and complex computation. However, high-density data transmission and complex computation can lead to a significant increase in the power consumption and temperature of electronic devices, causing overheating and potentially affecting their performance and user experience. Summary of the Invention
[0003] This application provides a device cooling method and an electronic device to reduce the temperature of the electronic device in a timely manner, ensure the performance of the electronic device, and improve the user experience.
[0004] On one hand, embodiments of this application provide a device cooling method applied to electronic devices, the method comprising: During the operation of the hardware module of the electronic device, temperature prediction is performed based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object; wherein, the historical temperature data includes temperature data within a preset time period prior to the current time; the target object includes the electronic device and / or the hardware module; Based on the predicted temperature of the target object and the scene in which the target object is located, the target object is subjected to cooling treatment.
[0005] In some embodiments, the step of predicting the temperature based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object includes: Based on the exponential smoothing method, a weighted average is performed on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object; wherein, the exponential smoothing method is used to perform a weighted average on the historical temperature data; In some embodiments, the step of predicting the temperature based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object includes: Temperature prediction is performed based on the historical temperature data of the hardware module and the scene in which the target object is located, to obtain the predicted temperature corresponding to the target object.
[0006] In some embodiments, the step of predicting the temperature based on the historical temperature data of the hardware module and the scene in which the target object is located, to obtain the predicted temperature corresponding to the target object, includes: Based on the exponential smoothing method, the historical temperature data and the scene type value corresponding to the scene are weighted to obtain the predicted temperature corresponding to the target object; wherein, the scene type value is different for different scenes, and the scene type value is related to the data processing volume corresponding to the scene.
[0007] In some embodiments, the step of cooling the target object based on the predicted temperature corresponding to the target object and in conjunction with the scene in which the target object is located includes: If the predicted temperature corresponding to the target object is greater than the temperature threshold corresponding to the target object, the frequency reduction magnitude corresponding to the target object is determined according to the configuration strategy and / or processing task corresponding to the scene in which the target object is located; Based on the frequency reduction amplitude corresponding to the target object, the target object is subjected to cooling treatment.
[0008] In some embodiments, determining the frequency reduction magnitude corresponding to the target object based on a configuration strategy corresponding to the scenario in which the target object is located includes: When the configuration strategy indicates that performance should be guaranteed, the frequency reduction magnitude corresponding to the target object is determined as the first magnitude; Under the condition that the configuration strategy indicates that stability is guaranteed, the frequency reduction magnitude corresponding to the target object is determined as the second magnitude; wherein, the first magnitude is smaller than the second magnitude.
[0009] In some embodiments, determining the frequency reduction magnitude corresponding to the target object based on the configuration strategy and processing task corresponding to the scenario in which the target object is located includes: When the configuration strategy indicates performance protection and the processing task belongs to a preset high-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the third magnitude. When the configuration strategy indicates performance protection and the processing task belongs to a preset low-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the fourth magnitude. If the configuration strategy ensures stability and the processing task is a preset high-processing-volume task, the frequency reduction level corresponding to the target object is determined to be the fifth level. If the configuration strategy ensures stability and the processing task is a preset low-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the sixth magnitude. The fourth amplitude is greater than the third amplitude, and the sixth amplitude is greater than the fifth amplitude.
[0010] In some embodiments, the step of cooling the target object based on the predicted temperature corresponding to the target object and in conjunction with the scene in which the target object is located includes: Based on the predicted temperature of the target object, combined with the scene in which the target object is located and the cooling device corresponding to the target object, the target object is cooled down.
[0011] On the other hand, embodiments of this application also provide a device cooling apparatus for use in electronic devices, the apparatus comprising: A temperature prediction module is configured to predict the temperature of a target object based on historical temperature data of the hardware module during operation of the hardware module of the electronic device; wherein the historical temperature data includes temperature data within a preset time period prior to the current time; and the target object includes the electronic device and / or the hardware module. The cooling module is configured to cool the target object based on the predicted temperature of the target object and the scene in which the target object is located.
[0012] On the other hand, embodiments of this application also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps: During the operation of the hardware module of the electronic device, temperature prediction is performed based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object; wherein, the historical temperature data includes temperature data within a preset time period prior to the current time; the target object includes the electronic device and / or the hardware module; Based on the predicted temperature of the target object and the scene in which the target object is located, the target object is subjected to cooling treatment.
[0013] On the other hand, embodiments of this application also provide a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps in any of the device cooling methods provided in embodiments of this application.
[0014] On the other hand, embodiments of this application also provide a computer-readable storage medium storing a computer program or instructions thereon, including a computer program or instructions that, when executed by a processor, implement the steps in any of the device cooling methods provided in embodiments of this application.
[0015] The device cooling method provided in this application acquires historical temperature data of the hardware module during its operation. This historical temperature data is used for temperature prediction to obtain the predicted temperature of the target object. This advance prediction of the target object's temperature allows for timely cooling based on the predicted temperature and the target object's environment. This ensures timely and effective cooling, preventing overheating risks due to delayed cooling and ensuring the electronic device's normal operation and performance, thus improving the user experience. Furthermore, the cooling method is tailored to the specific scenario, enabling flexible cooling and avoiding excessive performance sacrifices or failure to promptly suppress temperature increases caused by fixed cooling strategies. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating a device cooling method provided in an embodiment of this application. Figure 1 ; Figure 2 This is a device cooling process provided in the embodiments of this application; Figure 3 This is a flowchart illustrating a device cooling method provided in an embodiment of this application. Figure 2 ; Figure 4 This is a schematic diagram of the structure of a device cooling system provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the steps and operations described below can also be implemented in hardware.
[0020] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. The various components, modules, engines, and services described herein can be considered as implementations on the computing system. While the apparatus and methods described herein are preferably implemented in software, they can also be implemented in hardware, both of which are within the scope of this invention.
[0021] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when an element is “connected” or “coupled” to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein may include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.
[0022] With the rapid development of communication technology, hardware modules, such as communication modules, are increasingly used in various electronic devices, especially in the fields of the Internet of Things, mobile terminals, and edge computing. However, high-density data transmission and complex computing tasks lead to a significant increase in the power consumption and temperature of communication modules.
[0023] In some embodiments, thermal management solutions typically employ threshold-triggered mechanisms, such as frequency reduction upon reaching a certain temperature threshold. This is a passive response, unable to predict temperature trends, leading to frequent performance fluctuations or overheating risks caused by delayed responses, thus limiting the effectiveness of cooling. Furthermore, cooling typically employs fixed strategies, resulting in poor adaptability to dynamic loads. In sudden high-load scenarios (such as video streaming), fixed frequency reduction strategies may excessively sacrifice performance or fail to suppress temperature rise in a timely manner.
[0024] Therefore, to address the aforementioned issues, this application provides a collaborative thermal management solution. Electronic devices predict temperature changes in advance by forecasting temperature trends. When the predicted temperature is high, they directly implement cooling measures, achieving precise control over the timing of cooling and avoiding performance fluctuations or hardware damage caused by delayed responses. Furthermore, the electronic devices can dynamically adjust the frequency reduction amplitude based on user-configured strategy preferences and current load conditions to flexibly adapt to different scenario requirements, ensuring both cooling flexibility and precise control, and avoiding excessive performance sacrifice or failure to promptly suppress temperature increases due to fixed cooling strategies.
[0025] The following section will detail the implementation process of the aforementioned thermal management collaborative scheme, which is essentially the equipment cooling method. This implementation process can be executed by electronic devices. For example... Figure 1 The implementation process may include: S101. During the operation of the hardware module of the electronic device, the electronic device performs temperature prediction based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object.
[0026] Historical temperature data includes temperature data for a preset time period prior to the current time. Target objects include electronic devices and / or hardware modules.
[0027] In this embodiment, during the operation of the electronic device—that is, during the operation of its various hardware modules (such as the central processing unit (CPU), graphics processing unit (GPU), baseband, and chips)—the electronic device can acquire temperature data of each hardware module in real time or periodically. For each hardware module, the electronic device can use the temperature data of that hardware module within a preset time period (such as 30 minutes) prior to the current moment as the historical temperature data of that hardware module. After obtaining the historical temperature data of each hardware module, temperature prediction can be performed based on the historical temperature data of each hardware module to achieve temperature trend prediction, predict temperature changes in advance, and obtain the predicted temperature corresponding to the target object.
[0028] In one scenario, the target object can be a hardware module. For each hardware module, the electronic device can predict its temperature based on its historical temperature data to obtain the predicted temperature for that hardware module, thereby enabling temperature prediction for each hardware module.
[0029] In another scenario, the target object can be the electronic device itself. The electronic device can predict its temperature based on historical temperature data from multiple hardware modules to obtain a predicted temperature for itself, thereby achieving overall temperature prediction for the electronic device. Optionally, the electronic device can first predict the predicted temperatures of each of the multiple hardware modules, and then calculate a weighted average to obtain the predicted temperature for the electronic device. Of course, this is only one example of how to determine the predicted temperature of the electronic device; other methods can also be used, and this application does not limit this approach.
[0030] In some embodiments, the process of acquiring the temperature data of the above-mentioned hardware module may include: such as Figure 2 As shown, sensor drivers (such as Qualcomm) in the data acquisition layer of electronic devices TM The sensor driver can read temperature data (including temperature) from various hardware modules in real time or periodically, and then send this temperature data, i.e., the historical temperature data of the hardware modules, to the kernel through the kernel interface layer. This is achieved through the thermal zone framework, which connects the sensor data (historical temperature data) to the kernel. The kernel then sends the historical temperature data of the hardware modules to the target process in user space (or, alternatively, user space) via the file system interface (System File System, sysfs) or the socket interface (netlink).
[0031] Sysfs is a virtual file system that exports the hierarchy and attributes of kernel objects such as devices, drivers, and modules to user space in the form of files and directories.
[0032] Netlink is an IPC mechanism for communication between the Linux kernel and user-space processes, providing bidirectional communication capabilities between user processes and kernel modules.
[0033] Thermal Subsystem: Provides a method for exposing thermal sensors via sysfs and defining thermal configurations based on the sensors.
[0034] In some embodiments, the electronic device may construct a real-time temperature prediction model for temperature prediction. For example, this temperature prediction model may be implemented using exponential smoothing. The electronic device may use exponential smoothing to perform a weighted average of historical temperature data from hardware modules to obtain the predicted temperature corresponding to the target object.
[0035] Exponential smoothing is used to perform a weighted average of the historical temperature data. Specifically, exponential smoothing is a classic time series forecasting method that uses exponentially decreasing weights to perform a weighted average of historical data, making it suitable for short-term trend forecasting.
[0036] Optionally, the more recent the historical temperature data is, the greater its weight.
[0037] For example, an electronic device uses exponential smoothing to perform a weighted average of the temperature data from its hardware modules to obtain the predicted temperature for that module. For instance, if the current time is 10:00, the hardware module's temperature data includes data from the 30 minutes preceding the current time, specifically from 9:30 to 10:00. The temperature data from 9:30 to 9:40 corresponds to weight 1, the data from 9:41 to 9:50 corresponds to weight 2, and the data from 9:51 to 10:00 corresponds to weight 3. Weight 3 is greater than weight 2, and weight 2 is greater than weight 1. Then, the electronic device performs a weighted average of the temperature data and its corresponding weights to obtain the predicted temperature for that hardware module.
[0038] It should be noted that the method described above, which uses exponential smoothing to calculate a weighted average of historical temperature data to obtain the predicted temperature of the target object, is only one possible way for electronic devices to predict temperature based on the historical temperature data of hardware modules. Electronic devices can also predict temperature in other ways, such as by inputting the historical temperature data of hardware modules into a temperature prediction model, which can then directly output the predicted temperature of the target object.
[0039] In other embodiments, the electronic device can also utilize the scene in which the target object is located during temperature prediction to ensure the accuracy of the temperature prediction. The electronic device can perform temperature prediction based on historical temperature data from the hardware module and the scene in which the target object is located, to obtain the predicted temperature corresponding to the target object.
[0040] For example, based on exponential smoothing, electronic devices weight historical temperature data and scene type values corresponding to different scenes to obtain the predicted temperature of the target object; where different scenes correspond to different scene type values, and the scene type value is related to the amount of data processing corresponding to the scene.
[0041] The weighted process of averaging historical temperature data and scene type values can include the following: the electronic device can first perform a weighted average of the historical temperature data to obtain an initial predicted temperature. Then, the electronic device can perform a weighted sum of the initial predicted temperature and the scene type values to obtain the predicted temperature corresponding to the target object. Alternatively, the electronic device can perform a weighted average of the historical temperature data and the scene type values to obtain the predicted temperature corresponding to the target object.
[0042] The process of weighted averaging of historical temperature data by electronic devices can be referred to above and will not be repeated here.
[0043] It should be noted that the method described above, which uses exponential smoothing to weight historical temperature data and scene type values to obtain the predicted temperature of the target object, is only one possible implementation of electronic devices predicting temperature based on the historical temperature data of the hardware module and the scene in which the target object is located. Electronic devices can also predict temperature in other ways. For example, they can input the historical temperature data of the hardware module and the scene type value of the scene in which the target object is located into a temperature prediction model, which can then directly output the predicted temperature of the target object.
[0044] Understandably, the amount of data processing corresponding to a scene affects the power consumption of electronic devices. For example, the greater the amount of data processing, the greater the power consumption, and thus the faster the temperature rises. Therefore, the greater the amount of data processing, the larger the scene type value can be, and the greater its contribution to the predicted temperature of the final target object. Simply put, the scene type value is positively correlated with the amount of data processing corresponding to the scene.
[0045] In some embodiments, the temperature prediction described above may be performed by a prediction model layer of the electronic device. For example... Figure 2 As shown, the target process in the user space described above can perform lightweight temperature prediction using exponential smoothing and historical temperature data from the hardware module.
[0046] S102. The electronic device performs cooling treatment on the target object based on the predicted temperature of the target object and the scene in which the target object is located.
[0047] In this embodiment, the electronic device can perform targeted cooling on the target object based on the predicted temperature of the target object and the scenario in which the target object is being processed, so as to flexibly adapt to different scenario requirements, ensure the flexibility of cooling, and avoid excessive performance sacrifice due to excessive cooling or insufficient cooling that cannot suppress temperature rise.
[0048] Optionally, the aforementioned cooling process may include frequency reduction. Correspondingly, such as... Figure 3 As shown, the implementation process of S102 above may include: S10. The electronic device determines whether the predicted temperature of the target object is greater than the temperature threshold corresponding to the target object.
[0049] In this embodiment of the application, if the predicted temperature of the target object is greater than the temperature threshold of the target object, it indicates that the future temperature of the target object will be high and there is a risk of overheating. Therefore, the electronic device can determine the frequency reduction range so as to reduce the frequency of the target object by using the frequency reduction range, such as reducing the CPU frequency. That is, the electronic device can execute S20.
[0050] If the predicted temperature of the target object is less than or equal to the temperature threshold corresponding to the target object, it indicates that the probability of the target object having a high temperature in the future is low, and the probability of overheating is also low. Therefore, there is no need to cool down the target object, but temperature prediction can continue, such as obtaining the historical temperature data of the hardware module for use in temperature prediction, i.e., executing S40.
[0051] S20. The electronic device determines the frequency reduction magnitude corresponding to the target object based on the configuration strategy and / or processing task corresponding to the scenario in which the target object is located.
[0052] S30: Electronic devices reduce the frequency of the target object based on the frequency reduction magnitude.
[0053] S40: The electronic device acquires new historical temperature data from the hardware module and returns it to S101 above.
[0054] The configuration strategy mentioned above can be configured by the user according to their preferences.
[0055] In this embodiment of the application, when the predicted temperature of the target object is greater than the temperature threshold of the target object, the electronic device can determine the frequency reduction range according to the configuration strategy and the processing task (i.e. the current load) to achieve fine control of the frequency reduction range.
[0056] The following section will introduce the process of determining the frequency reduction range by combining several possible implementation methods.
[0057] In one possible implementation, the frequency reduction magnitude for the target object is determined based on the configuration strategy corresponding to the scenario in which the target object exists. For example, if the configuration strategy indicates that performance is guaranteed, meaning that performance takes precedence and the frequency reduction magnitude cannot be too large, the electronic device can determine the frequency reduction magnitude corresponding to the target object as the first magnitude.
[0058] When the configuration strategy indicates that stability is guaranteed, it means that stability takes priority. In other words, the frequency reduction can be larger. Therefore, the electronic device can determine the frequency reduction corresponding to the target object as the second reduction; where the first reduction is smaller than the second reduction.
[0059] In another possible implementation, the frequency reduction magnitude for the target object is determined based on the configuration strategy and processing task corresponding to the scenario in which the target object is located.
[0060] When the configuration strategy indicates that performance should be guaranteed, and the task being processed is a preset high-processing-volume task, the frequency reduction range corresponding to the target object is determined to be the third range. When the configuration strategy indicates that performance should be guaranteed, and the task being processed is a preset low-processing-volume task, the frequency reduction level corresponding to the target object is determined to be the fourth level. If the configuration strategy ensures stability and the task being processed is a preset high-volume task, the frequency reduction level corresponding to the target object is determined to be the fifth level. If the configuration strategy ensures stability and the processing task is a preset low-processing-volume task, the frequency reduction level corresponding to the target object is determined to be the sixth level. In this embodiment, the aforementioned high-processing-volume tasks have high performance requirements, such as video streaming and gaming tasks. Therefore, the frequency reduction cannot be too large, otherwise it will affect the normal operation of the tasks. The aforementioned low-processing-volume tasks have lower performance requirements, such as news browsing tasks. Therefore, the frequency reduction can be larger, otherwise it will affect the normal operation of the tasks. Accordingly, the fourth reduction is greater than the third reduction, and the sixth reduction is greater than the fifth reduction.
[0061] Optionally, since ensuring stability has lower performance requirements while ensuring performance has higher performance requirements, the sixth amplitude can be greater than the fourth amplitude. Furthermore, the fifth amplitude can be greater than or less than the fourth amplitude; this application does not limit it.
[0062] In another implementation, the electronic device determines the frequency reduction level corresponding to the target object based on the processing task corresponding to the scene in which the target object is located. If the processing task is a preset high-processing-volume task, the frequency reduction level corresponding to the target object is determined to be the seventh level. When processing a task that is a preset low-processing-volume task, the frequency reduction amplitude corresponding to the target object is determined to be the eighth amplitude. The eighth amplitude is greater than the seventh amplitude.
[0063] Under the premise of ensuring stability by configuring the strategy, the frequency reduction magnitude corresponding to the target object is determined as the second magnitude; among them, the eighth magnitude is smaller than the second magnitude.
[0064] In some embodiments, the cooling process described above may further include controlling a cooling device. An electronic device (such as the policy execution layer of an electronic device) performs cooling on the target object based on the predicted temperature of the target object, combined with the scene in which the target object is located and the corresponding cooling device. When an available cooling device (such as a fan) exists for the target object, the electronic device can activate the cooling device or increase its speed, and determine the frequency reduction amplitude to utilize the frequency reduction amplitude for frequency reduction, thereby lowering the core temperature. The process for determining the frequency reduction amplitude can be referred to above and will not be repeated here.
[0065] In the embodiments of this application, as described above Figure 2 As shown, the target process in user space can execute cooling strategies to proactively reduce temperature and avoid overheating risks caused by frequent performance fluctuations or delayed responses. Furthermore, even under sudden high load scenarios, it can reasonably reduce its frequency to avoid excessive performance sacrifice or failure to suppress temperature rise in time. For example, electronic devices (such as the target process) can combine cpufreq or device management interfaces (such as Qualcomm...) TM Methods such as device management interfaces can be used to dynamically reduce the frequency of a target object, combined with a cooling device to actively reduce core temperature. In essence, if the target object is an electronic device, the frequency of one or more hardware modules within that device can be reduced. If the target object is a hardware module, the frequency of that hardware module can be reduced directly.
[0066] cpufreq is a power management framework in the Linux kernel that is responsible for changing the CPU frequency.
[0067] In this embodiment, by integrating the Linux kernel's thermal zone framework with sensor drivers, a real-time temperature prediction model is constructed, and the power consumption and frequency reduction strategy is dynamically optimized to achieve advance prediction of the temperature of hardware modules, thereby ensuring timely cooling.
[0068] To facilitate better implementation of the equipment cooling method provided in this application, this application also provides an equipment cooling device based on the above-described equipment cooling method. The meanings of the terms used are the same as in the above-described equipment cooling method, and specific implementation details can be found in the descriptions in the method embodiments.
[0069] Figure 4 This is a schematic diagram of a device cooling system provided in an embodiment of this application. Please refer to [link / reference]. Figure 4The cooling device has the functionality to implement the method example described above for the electronic device. This functionality can be implemented in hardware or by hardware executing corresponding software. The cooling device can be one of the electronic devices described above (such as mobile phones, tablets, computers, etc., devices with data processing capabilities), or it can be installed within the electronic device. Figure 4 As shown, the cooling device 400 may include a temperature prediction module 401 and a cooling module 402.
[0070] Temperature prediction module 401 is configured to predict the temperature of a target object based on historical temperature data of the hardware module during operation of the hardware module of the electronic device; wherein the historical temperature data includes temperature data within a preset time period prior to the current time; and the target object includes the electronic device and / or the hardware module. The cooling module 402 is configured to perform cooling processing on the target object based on the predicted temperature corresponding to the target object and the scene in which the target object is located.
[0071] In one example, the temperature prediction module 401 is specifically configured as follows: Based on the exponential smoothing method, the historical temperature data of the hardware module is weighted and averaged to obtain the predicted temperature corresponding to the target object; wherein, the exponential smoothing method is used to perform weighted averaging on the historical temperature data.
[0072] In one example, the temperature prediction module 401 is specifically configured as follows: Temperature prediction is performed based on the historical temperature data of the hardware module and the scene in which the target object is located, to obtain the predicted temperature corresponding to the target object.
[0073] In one example, the temperature prediction module 401 is specifically configured as follows: Based on the exponential smoothing method, the historical temperature data and the scene type value corresponding to the scene are weighted to obtain the predicted temperature corresponding to the target object; wherein, the scene type value is different for different scenes, and the scene type value is related to the data processing volume corresponding to the scene.
[0074] In one example, the cooling module 402 is specifically configured as follows: If the predicted temperature corresponding to the target object is greater than the temperature threshold corresponding to the target object, the frequency reduction magnitude corresponding to the target object is determined according to the configuration strategy and / or processing task corresponding to the scene in which the target object is located; Based on the frequency reduction amplitude corresponding to the target object, the target object is subjected to cooling treatment.
[0075] In one example, the cooling module 402 is further configured as follows: When the configuration strategy indicates that performance should be guaranteed, the frequency reduction magnitude corresponding to the target object is determined as the first magnitude; Under the condition that the configuration strategy indicates that stability is guaranteed, the frequency reduction magnitude corresponding to the target object is determined as the second magnitude; wherein, the first magnitude is smaller than the second magnitude.
[0076] In one example, the cooling module 402 is further configured as follows: When the configuration strategy indicates performance protection and the processing task belongs to a preset high-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the third magnitude. When the configuration strategy indicates performance protection and the processing task belongs to a preset low-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the fourth magnitude. If the configuration strategy ensures stability and the processing task is a preset high-processing-volume task, the frequency reduction level corresponding to the target object is determined to be the fifth level. If the configuration strategy ensures stability and the processing task is a preset low-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the sixth magnitude. The fourth amplitude is greater than the third amplitude, and the sixth amplitude is greater than the fifth amplitude.
[0077] In one example, the cooling module 402 is specifically configured as follows: Based on the predicted temperature of the target object, combined with the scene in which the target object is located and the cooling device corresponding to the target object, the target object is cooled down.
[0078] Figure 5 This is a schematic diagram of the structure of an electronic device provided in some embodiments of this application. Figure 5 The dashed line in the text indicates that the unit or module is optional. Figure 5 The electronic device 700 can be used to implement the methods described in the above method embodiments.
[0079] Electronic device 700 may include one or more processors 710. The processor 710 can support the electronic device 700 in implementing the methods described in the preceding method embodiments. The processor 710 can be a general-purpose processor or a special-purpose processor. For example, the processor can be a Central Processing Unit (CPU). Alternatively, the processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.
[0080] The electronic device 700 may also include one or more memories 720. Computer programs are stored on the memories 720. The memories 720 may be independent of the processor 710 or integrated into the processor 710.
[0081] The electronic device 700 may also include a transceiver 730. The processor 710 can communicate with other devices or chips via the transceiver 730. For example, the processor 710 can send and receive data with other devices or chips via the transceiver 730.
[0082] The computer program in memory 720 can be executed by processor 710, causing processor 710 to perform the following steps: During the operation of the hardware module of the electronic device, temperature prediction is performed based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object; wherein, the historical temperature data includes temperature data within a preset time period prior to the current time; the target object includes the electronic device and / or the hardware module; Based on the predicted temperature of the target object and the scene in which the target object is located, the target object is subjected to cooling treatment.
[0083] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.
[0084] Therefore, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, the computer program being loaded by a processor to execute the steps described in the above-described method embodiments of this application. For example, the computer program, when loaded by a processor, can execute the following steps: During the operation of the hardware module of the electronic device, temperature prediction is performed based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object; wherein, the historical temperature data includes temperature data within a preset time period prior to the current time; the target object includes the electronic device and / or the hardware module; Based on the predicted temperature of the target object and the scene in which the target object is located, the target object is subjected to cooling treatment.
[0085] For example, the aforementioned electronic device may be a computer, server, or other device with data processing capabilities.
[0086] For details on the implementation of each of the above operations / steps, please refer to the previous examples, which will not be repeated here.
[0087] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0088] Since the computer program stored in the computer-readable storage medium can execute the steps in any of the above method embodiments provided in the embodiments of this application, the beneficial effects that the methods described in any of the above method embodiments can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.
[0089] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations of the above embodiments.
[0090] The above provides a detailed description of a device cooling method and electronic device provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for cooling equipment, characterized in that, Applied to electronic devices, the method includes: During the operation of the hardware module of the electronic device, temperature prediction is performed based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object; wherein, the historical temperature data includes temperature data within a preset time period prior to the current time; the target object includes the electronic device and / or the hardware module; Based on the predicted temperature of the target object and the scene in which the target object is located, the target object is subjected to cooling treatment.
2. The method according to claim 1, characterized in that, The step of predicting the temperature based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object includes: Based on the exponential smoothing method, the historical temperature data of the hardware module is weighted and averaged to obtain the predicted temperature corresponding to the target object; wherein, the exponential smoothing method is used to perform weighted averaging on the historical temperature data.
3. The method according to claim 1, characterized in that, The step of predicting the temperature based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object includes: Temperature prediction is performed based on the historical temperature data of the hardware module and the scene in which the target object is located, to obtain the predicted temperature corresponding to the target object.
4. The method according to claim 3, characterized in that, The step of predicting the temperature based on the historical temperature data of the hardware module and the scene in which the target object is located, to obtain the predicted temperature corresponding to the target object, includes: Based on the exponential smoothing method, the historical temperature data and the scene type value corresponding to the scene are weighted to obtain the predicted temperature corresponding to the target object; wherein, the scene type value is different for different scenes, and the scene type value is related to the data processing volume corresponding to the scene.
5. The method according to any one of claims 1 to 4, characterized in that, The step of cooling the target object based on the predicted temperature corresponding to the target object and in conjunction with the scene in which the target object is located includes: If the predicted temperature corresponding to the target object is greater than the temperature threshold corresponding to the target object, the frequency reduction magnitude corresponding to the target object is determined according to the configuration strategy and / or processing task corresponding to the scene in which the target object is located; Based on the frequency reduction amplitude corresponding to the target object, the target object is subjected to cooling treatment.
6. The method according to claim 5, characterized in that, The step of determining the frequency reduction magnitude corresponding to the target object based on the configuration strategy corresponding to the scenario in which the target object is located includes: When the configuration strategy indicates that performance should be guaranteed, the frequency reduction magnitude corresponding to the target object is determined as the first magnitude; Under the condition that the configuration strategy indicates that stability is guaranteed, the frequency reduction magnitude corresponding to the target object is determined as the second magnitude; wherein, the first magnitude is smaller than the second magnitude.
7. The method according to claim 5, characterized in that, The step of determining the frequency reduction magnitude corresponding to the target object based on the configuration strategy and processing task corresponding to the scenario in which the target object is located includes: When the configuration strategy indicates performance protection and the processing task is a preset high-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the third magnitude. When the configuration strategy indicates performance protection and the processing task belongs to a preset low-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the fourth magnitude. If the configuration strategy ensures stability and the processing task is a preset high-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the fifth magnitude. If the configuration strategy ensures stability and the processing task is a preset low-processing-volume task, the frequency reduction magnitude corresponding to the target object is determined to be the sixth magnitude. The fourth amplitude is greater than the third amplitude, and the sixth amplitude is greater than the fifth amplitude.
8. The method according to any one of claims 1 to 4, characterized in that, The step of cooling the target object based on the predicted temperature corresponding to the target object and in conjunction with the scene in which the target object is located includes: Based on the predicted temperature of the target object, combined with the scene in which the target object is located and the cooling device corresponding to the target object, the target object is cooled down.
9. A device for cooling equipment, characterized in that, Applied to electronic devices, the device includes: A temperature prediction module is configured to predict the temperature of a target object based on historical temperature data of the hardware module during operation of the hardware module of the electronic device; wherein the historical temperature data includes temperature data within a preset time period prior to the current time; and the target object includes the electronic device and / or the hardware module. The cooling module is configured to cool the target object based on the predicted temperature of the target object and the scene in which the target object is located.
10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores computer programs or instructions, and when the computer programs or instructions are executed by the processor, the processor causes the processor to perform the following steps: During the operation of the hardware module of the electronic device, temperature prediction is performed based on the historical temperature data of the hardware module to obtain the predicted temperature corresponding to the target object; wherein, the historical temperature data includes temperature data within a preset time period prior to the current time; the target object includes the electronic device and / or the hardware module; Based on the predicted temperature of the target object and the scene in which the target object is located, the target object is subjected to cooling treatment.
11. A computer-readable storage medium, characterized in that, It stores a computer program or instructions that, when executed by a processor, implement the steps of the device cooling method as described in any one of claims 1 to 8.