A control method and electronic device

Abstract

The application discloses a control method and an electronic device. The control method comprises the following steps: determining a current state parameter of at least one application process running on the electronic device; determining a cooling control strategy and a noise reduction control strategy matched with the current state parameter; and executing the cooling control strategy and the noise reduction control strategy, wherein the cooling control strategy is used for controlling the operation of a cooling device, and the noise reduction control strategy is used for reducing noise caused by the operation of the cooling device.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more particularly to a control method and electronic device. Background Technology

[0002] Electronic devices (such as desktop computers and laptops) have fans that spin when they are cooling down. The faster the fan spins, the louder the noise will be. Summary of the Invention

[0003] In view of the above, this application provides a control method and an electronic device, the specific solutions of which are as follows:

[0004] A control method, comprising:

[0005] Determine the current state parameters of at least one application process running on an electronic device;

[0006] Determine a cooling control strategy and a noise reduction control strategy that match the current state parameters;

[0007] The cooling control strategy and the noise reduction control strategy are executed. The cooling control strategy is used to control the operation of the cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

[0008] Furthermore, determining the cooling control strategy and noise reduction control strategy that match the current state parameters includes:

[0009] Determine the current operating parameters of the target device in the electronic device;

[0010] Determine a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process.

[0011] Furthermore, determining a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process includes:

[0012] The current operating scenario of the electronic device is determined based on at least one of the current operating parameters of the target device in the electronic device and the current status parameters of the at least one application process.

[0013] Determine the cooling control strategy and noise reduction control strategy that match the current operating scenario.

[0014] Furthermore, determining the current operating scenario of the electronic device based on at least one of the current operating parameters of the target device in the electronic device and the current status parameters of the at least one application process includes at least one of the following:

[0015] The target model is input with at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process to obtain the current operating scenario of the electronic device output by the target model.

[0016] Based on the pre-defined correspondence between the operating parameters of the target device in the electronic device and the operating scenario, and the correspondence between the status parameters of the application process in the electronic device and the operating scenario, the current operating scenario is determined.

[0017] Furthermore, determining the cooling control strategy and noise reduction control strategy that match the current state parameters includes:

[0018] Predict the temperature data of the electronic device operating according to the current state parameters based on the historical operating data of the electronic device;

[0019] If it is determined that the predicted temperature of the electronic device reaches the target threshold after the current state parameters of the electronic device have been running for a first period of time, a cooling control strategy and a noise reduction control strategy corresponding to the current predicted state are determined. The current predicted state is that the predicted temperature of the electronic device reaches the target threshold after the current state parameters of the electronic device have been running for a first period of time.

[0020] Furthermore, determining the current operating parameters of the target device in the electronic device includes at least one of the following:

[0021] Determine the hardware processing information of multiple hardware modules in the electronic device used to perform data processing functions;

[0022] Determine the memory usage data in the electronic device;

[0023] Determine the noise information during the operation of the cooling device in the electronic device.

[0024] Furthermore, determining a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process includes:

[0025] The cooling control strategy is determined based on at least one of the hardware processing information, memory usage data and current status parameters in the current operating parameters.

[0026] The noise reduction control strategy is determined based on the noise information during the operation of the cooling device and the cooling control strategy.

[0027] Furthermore, determining a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process includes:

[0028] Determine the historical hardware processing information of the hardware module and the historical noise information during the operation of the cooling device within the second time period prior to the current moment;

[0029] Hardware data change information is determined based on the hardware processing information of the hardware module and the historical hardware processing information, and noise change trend is determined based on the historical noise information and the noise information during the operation of the cooling device.

[0030] The cooling control strategy and the noise reduction control strategy are determined based on at least one of the current operating parameters, the current status parameters, hardware data change information, and noise change trend.

[0031] Furthermore, the noise reduction control strategy is implemented, including:

[0032] The target sound wave is determined as a sound wave whose phase is opposite to the sound pressure waveform of the noise information in the current operating parameters;

[0033] The target sound wave is output based on the noise reduction control strategy.

[0034] An electronic device, comprising:

[0035] Cooling devices are used to reduce the temperature of the electronic equipment;

[0036] A processor is configured to determine the current state parameters of at least one application process running on an electronic device; determine a cooling control strategy and a noise reduction control strategy that match the current state parameters; and execute the cooling control strategy and the noise reduction control strategy, wherein the cooling control strategy is used to control the operation of a cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of a control method disclosed in an embodiment of this application;

[0039] Figure 2 This is a flowchart of a control method disclosed in an embodiment of this application;

[0040] Figure 3 This is an example diagram illustrating the correspondence between the operating scenario and the cooling control strategy and noise reduction control strategy disclosed in this embodiment of the application.

[0041] Figure 4 This is a flowchart of a control method disclosed in an embodiment of this application;

[0042] Figure 5 This is a flowchart of a control method disclosed in an embodiment of this application;

[0043] Figure 6 This is a schematic diagram of the structure of a control system disclosed in an embodiment of this application;

[0044] Figure 7 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0045] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0046] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0047] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0048] This application discloses a control method, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0049] Step S11: Determine the current state parameters of at least one application process running on the electronic device;

[0050] Step S12: Determine the cooling control strategy and noise reduction control strategy that match the current state parameters;

[0051] Step S13: Execute the cooling control strategy and the noise reduction control strategy. The cooling control strategy is used to control the operation of the cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

[0052] Electronic devices (such as desktop computers and laptops) use fans to dissipate heat. The faster the fan spins, the louder the noise becomes. This is especially true when electronic devices are small in size, but their computing power is increasing, leading to higher power consumption. To ensure adequate heat dissipation within a limited space, high-speed fans are needed, which generates significant noise.

[0053] In addition, for heat dissipation of electronic devices, multiple temperature sensors are usually integrated on the motherboard to monitor the temperature of various components on the motherboard (such as the central processing unit CPU, graphics processing unit GPU, motherboard chips and other hardware). Based on the temperature data provided by the temperature sensors, the fan speed is adjusted to achieve the purpose of cooling. However, with this approach, the fan is usually controlled to accelerate when the temperature value of a certain component provided by the temperature sensor exceeds a preset threshold in order to achieve the purpose of heat dissipation. Heat dissipation only occurs after the temperature rises, which may lead to the problem of untimely cooling.

[0054] Based on this, in this solution, during the operation of the electronic device, the current state parameters of at least one application process running on the electronic device are monitored. Based on the current state parameters of at least one application process, a cooling control strategy and a noise reduction control strategy are determined. This allows for the prediction of potential cooling needs and possible noise generation during the cooling process based on the current state parameters of at least one application process running on the electronic device. Consequently, heat dissipation begins before the temperature of the electronic device rises, ensuring timely cooling. Furthermore, since heat dissipation begins before the temperature rises, the noise generated during the heat dissipation process will be relatively small. Based on this, a noise reduction control strategy is determined, enabling more accurate and timely noise reduction based on the current state parameters generated during the cooling process.

[0055] Specifically, during the operation of an electronic device, the current state parameters of at least one application process running on the electronic device are monitored and analyzed.

[0056] It can monitor the status parameters of every application process running on an electronic device to ensure comprehensive monitoring of the application processes running on the electronic device and avoid situations where the temperature rises due to the operation of a certain application process but is not detected in time; or, it can monitor only the status parameters of some application processes running on the electronic device, that is, pre-determine a monitoring table, which includes information on application processes that may cause the temperature of the electronic device to rise, and only monitor the application processes included in the monitoring table to avoid the problem of increasing the amount of data due to monitoring all application processes.

[0057] The current status parameters of a determined application process may include: the name of the application process, the CPU and memory resources consumed by the application process, and other information.

[0058] Analyzing the current status parameters allows for the sorting of the current status parameters of each application process running on the monitored electronic device. This enables the selection of a target number of application processes with higher resource utilization than others. Higher resource utilization increases the risk of overheating the electronic device. Cooling and noise reduction control strategies are determined solely based on the current status parameters of the selected application processes with higher resource utilization. For example, from 22 application processes running on the monitored electronic device, 10 application processes can be selected, choosing the 10 with the highest resource utilization among the 22 processes.

[0059] The current status parameters of each application process running on the monitored electronic device are sorted. This can be done by sorting the CPU resources consumed by each application process from high to low, or by sorting the memory resources consumed by each application process from high to low, or by combining the CPU and memory resources consumed by each application process and then sorting them from high to low.

[0060] Alternatively, the current status parameters of application processes running on the monitored electronic device can be selected from those with resource utilization rates higher than a specific value (e.g., a first value). The matching cooling and noise reduction control strategies can then be determined solely based on the current status parameters of these application processes. This specific value can be determined through historical data analysis to identify when an application process's resource utilization rate is higher than this value, and lower, generally indicating no need for cooling. Alternatively, the specific value can be determined based on the device's settings.

[0061] Alternatively, after monitoring the current status parameters of at least one application process running on the electronic device, instead of selecting the application process with the highest resource utilization, the matching cooling control strategy and noise reduction control strategy can be determined directly based on the current status parameters of each monitored application process.

[0062] Alternatively, the resource utilization rates of the current status parameters of each application process running on the monitored electronic device can be summed. Based on the sum, a cooling control strategy and a noise reduction control strategy matching the current operation of the electronic device can be determined. If the sum is greater than a certain fixed value (e.g., a second value), a cooling device (e.g., a fan) can be activated. After the cooling device is activated, a noise reduction module is activated to reduce the noise generated during the operation of the cooling device.

[0063] The cooling control strategy may include at least one of the following: the time when the cooling device starts running, the time when the cooling device accelerates, the duration of the cooling device's operation, the time when the cooling module stops running, the time when the noise reduction module stops accelerating, whether the cooling device runs at a constant speed, the rotation speed of the cooling device when running at a constant speed, whether the cooling device accelerates, and the acceleration of the cooling device when accelerating.

[0064] The noise reduction control strategy may include at least one of the following: the time when the noise reduction module starts running, the time when the noise reduction module stops running, the duration of the noise reduction module's operation, whether the noise reduction module operates at high power consumption, and whether the noise reduction module operates at low power consumption.

[0065] The specific content of the cooling control strategy and noise reduction control strategy needs to be determined based on the current state parameters of at least one application process running on the electronic device. For example, if the CPU utilization of an application process is less than 30%, the cooling control strategy can be: keep the cooling device inactive, or keep the cooling device running at low speed. The noise reduction control strategy is: keep the noise reduction module inactive. Or, if the CPU utilization of an application process is greater than 70%, the cooling control strategy can be: after the CPU utilization is greater than 70% for 2 seconds, the cooling device starts running, accelerates to 500 RPM / s, and stops running after the CPU utilization is less than 50% for 5 seconds. The noise reduction control strategy is: run synchronously with the cooling device, operate at high power, and stop synchronously with the noise reduction device.

[0066] After determining the matching cooling and noise reduction control strategies, these strategies are executed. The cooling control strategy controls the operation of the cooling device, ensuring that the temperature of the electronic device has not yet risen when the current state parameters of the application process meet the conditions. This ensures that cooling begins before the temperature of the electronic device increases, preventing temperature rise and avoiding impact on the operation of the application within the electronic device. The noise reduction control strategy controls the operation of the noise reduction module, reducing the noise generated during the operation of the cooling device, improving the response speed of cooling and noise reduction, and preventing sudden noise bursts caused by temporary high-speed operation of the cooling device. This allows for relatively smooth noise changes during the operation of the cooling device, improving the user experience.

[0067] The control method disclosed in this embodiment can determine the current state parameters of at least one application process running on an electronic device, so as to determine the cooling control strategy and noise reduction control strategy of the electronic device based on the current state parameters. After executing the cooling control strategy and noise reduction control strategy, the temperature of the electronic device can be reduced by the operation of the cooling device. At the same time, the noise caused by the operation of the cooling device can be reduced based on the noise reduction control strategy. This solution can accurately and proactively dissipate heat from the electronic device based on the real-time state of the application process running on the electronic device, and effectively reduce the noise generated during the heat dissipation process. In addition, this solution directly monitors the state parameters of the application process, rather than directly monitoring the temperature value, so that potential heat dissipation needs can be identified in advance based on the state parameters before the hardware temperature of the electronic device rises, and noise reduction needs exist when heat dissipation needs exist. This allows for early prediction and intervention, making the control more precise and timely, improving the response speed of heat dissipation and noise reduction during the heat dissipation process, and enhancing the user experience.

[0068] This embodiment discloses a control method, the flowchart of which is shown below. Figure 2 As shown, it includes:

[0069] Step S21: Determine the current state parameters of at least one application process running on the electronic device;

[0070] Step S22: Determine the current operating parameters of the target device in the electronic device;

[0071] Step S23: Determine the current operating scenario of the electronic device based on at least one of the current operating parameters of the target device in the electronic device and the current status parameters of at least one application process;

[0072] Step S24: Determine the cooling control strategy and noise reduction control strategy that match the current operating scenario;

[0073] Step S25: Execute the cooling control strategy and the noise reduction control strategy. The cooling control strategy is used to control the operation of the cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

[0074] After determining the current state parameters of at least one application process running on an electronic device, matching cooling and noise reduction strategies can be determined based on these parameters. These strategies are then executed, controlling the operation of cooling devices through the cooling control strategy and reducing noise generated by the cooling devices through the noise reduction strategy. This allows for real-time monitoring of the application processes running on the electronic device, predicting temperature rise in advance, and providing precise and proactive heat dissipation while effectively reducing noise generated during heat dissipation. Furthermore, while controlling the cooling devices to dissipate heat, the noise reduction strategy is simultaneously activated to further reduce noise generated during heat dissipation, improving the user experience.

[0075] Specifically, determining the cooling control strategy and noise reduction control strategy that match the current operating scenario can be achieved by: determining the current operating scenario of the electronic device based on the current state parameters of at least one application process running on the electronic device, and determining the cooling control strategy and noise reduction control strategy that match the current operating scenario.

[0076] Different application processes running on an electronic device have different state parameters, which correspond to different operating scenarios. For example, if there are many application processes running on an electronic device (such as application processes for document editing tools, application processes for browsers, etc.), but each application process consumes relatively low CPU and memory resources, then it can be determined that the current situation is a normal office scenario. On the other hand, if a small number of application processes running on an electronic device consume high CPU resources, then it can be determined that the current situation is a large-scale game scenario.

[0077] Furthermore, in the control method disclosed in this embodiment, when determining the current state parameters of at least one application process running on the electronic device, the current operating parameters of the target device in the electronic device can also be determined, so as to determine the current operating scenario of the electronic device based on the current state parameters and the current operating parameters, and determine the cooling control strategy and noise reduction control strategy that match the current operating scenario.

[0078] The target device in the electronic device may include at least one of the following: a central processing unit (CPU), a graphics processing unit (GPU), an embedded neural network processor (NPU), memory, etc. Of course, the target device may also include: a sound pickup device, such as a microphone, for collecting noise generated during the operation of the cooling device.

[0079] Accordingly, determining the current operating parameters of the target device in the electronic device may include at least one of the following: determining the hardware processing information of multiple hardware modules (CPU, GPU, NPU, etc.) in the electronic device used to perform data processing functions; determining the memory usage data in the electronic device; determining the noise information during the operation of the cooling device in the electronic device.

[0080] This explanation will take hardware modules including CPU, GPU, and NPU as an example:

[0081] The hardware processing information of the hardware module may include: static information of the hardware and dynamic information during hardware operation.

[0082] Static hardware information can include: CPU static information, GPU static information, and NPU static information. CPU static information can include: CPU model, brand, architecture, number of cores, number of threads, base frequency, cache size, etc., which can be obtained through CPUid (CPU Identification, a dedicated instruction for a specific CPU architecture). GPU static information can include: GPU model, video memory capacity, video memory type, core frequency, number of stream processors, etc., which can be obtained through IDXGIAdapter (a programming interface in a specific graphics framework). NPU static information can include: NPU model, supported AI computing power types (such as INT8, FP16, etc.), peak computing power (TOPS), manufacturer-specific capabilities, etc., which can be obtained through DXCore (hardware discovery and management library).

[0083] The dynamic information of hardware refers to the utilization rate of hardware modules (CPU, GPU, NPU, etc.) during operation. Among them, CPU utilization rate can include kernel-level utilization rate and global utilization rate. Kernel-level utilization rate is the utilization rate of each core in the CPU, while global utilization rate is the average utilization rate of all cores in the CPU. If only the global utilization rate of the CPU is monitored, its heat dissipation requirements will be seriously underestimated. For example, a single-threaded heavy task (such as some games) may cause one core of the CPU to be 100% fully loaded and the temperature to soar, but its global utilization rate is not high. Therefore, by monitoring the utilization rate of each core, the monitoring of heat dissipation requirements can be improved.

[0084] In addition, GPU and NPU utilization can be determined by separately collecting data on the load of the GPU and NPU on three typical tasks: 3D graphics processing, video decoding, and AI acceleration algorithms. This means identifying the 3D load, video decoding load, and AI accelerator load. By using different load types, the real-time status of these dedicated processors under different tasks can be more accurately determined, providing high-quality input data for subsequent cooling and noise reduction control. Specifically, 3D load measures the workload of the GPU in processing 3D graphics rendering tasks (such as games and 3D modeling software); video decoding load measures the workload of the GPU in decoding video streams (such as video playback and video conferencing); and AI acceleration load measures the workload of the NPU (or the dedicated AI core in the GPU) in performing artificial intelligence inference / computation tasks. In this embodiment, the utilization rates of GPU and NPU are precisely divided so that different combinations of utilization rates can directly correspond to different scenarios. For example, high 3D load and low video decoding load correspond to scenarios such as gaming or professional graphics, in which case the heat generation is relatively high. On the other hand, low 3D load and high video decoding load correspond to scenarios such as high-definition video playback or video calls, in which case the heat generation is relatively low.

[0085] In addition, the memory usage data in electronic devices can be specifically defined as: current memory load and remaining capacity. A high memory load (low remaining capacity) may require some data to be transferred from memory to virtual memory on the hard drive. At this time, the memory management unit needs to frequently exchange data between physical memory and hard drive, which will increase the CPU's computational load and may lead to an increase in temperature. In addition, frequent read and write operations will significantly increase power consumption and temperature.

[0086] Secondly, the noise generated by the cooling devices of electronic equipment during operation can be collected by external sound-collecting devices on the electronic equipment to record the waveform, frequency, and decibel value of the noise, so as to determine the noise reduction control strategy based on this.

[0087] When determining at least one of the current operating parameters of a target device in an electronic device, the current operating scenario of the electronic device can be determined based on at least one of the current operating parameters and the current state parameters. Different combinations of current operating parameters and current state parameters can correspond to different operating scenarios. The current operating scenario can be determined based on a model or based on a pre-defined correspondence.

[0088] Specifically, when determining the current operating scenario based on the model, at least one of the current operating parameters of the target device in the electronic device and at least one current state parameter of an application process can be input into the target model to obtain the current operating scenario of the electronic device output by the target model.

[0089] First, the target model can be trained. After the target model is trained, the parameters of the running scenario (at least one of the current running parameters and the current state parameters) that need to be determined are input into the target model to obtain the current running scenario predicted by the target model based on the parameters.

[0090] During the training of the target model, the data can be labeled first, that is, the determined parameters are taken as the items to be labeled, and the corresponding scene categories are labeled for these parameters. The scene categories may include: AI inference, games, video playback, video editing and rendering, general office work, and mixed multi-load.

[0091] The annotation method can be specifically as follows: For the AI ​​inference scenario, its typical characteristics are high NPU utilization and high memory usage. That is, when the NPU utilization is greater than the first value and the memory usage is greater than the second value, the scenario corresponding to the current combination of these parameters (i.e., the running parameters and state parameters determined in this instance, specifically a certain instance in the history) is labeled as an AI inference scenario. Similarly, for the game scenario, its typical characteristics are high GPU utilization and high CPU load. That is, when the GPU utilization is greater than the third value and the CPU load is greater than the fourth value, the scenario corresponding to the current combination of these parameters is labeled as a game scenario. Furthermore, for the video playback scenario, its CPU and GPU utilization are low, accompanied by a stable audio module activity state. That is, when the CPU utilization is lower than the fifth value and the GPU utilization is lower than the sixth value, and the electronic device... If the audio module is in a stable audio output state, then the scenario corresponding to the current parameter combination is labeled as a video playback scenario. Similarly, for the video editing and rendering scenario, where CPU and GPU utilization are high and accompanied by periodic fluctuations in computational load (i.e., CPU utilization is greater than the seventh value, GPU utilization is greater than the eighth value, and computational load changes periodically), then the scenario corresponding to the current parameter combination can be labeled as a video playback scenario. For a typical office scenario, where the utilization of each hardware component is lower than the corresponding value, and multiple lightweight processes are active simultaneously (e.g., browsers, document editing tools), then the scenario corresponding to the current parameter combination is labeled as a typical office scenario. Furthermore, for a multi-load mixed scenario, which typically involves the overlap of features from two or more scenarios, the scenario corresponding to the currently determined parameter combination is labeled as a multi-load mixed scenario.

[0092] After the data is labeled, the model is trained through supervised learning. The gradient boosting tree model (XGBoost) can be used to train the model on the labeled data to obtain the target model. Using this target model, when the determined current running parameters and current state parameters are input into the target model, the target model can output the current running scenario that matches the current running parameters and current state parameters. Of course, it is also possible to predict the current running scenario using only the current running parameters, or only the current state parameters.

[0093] By predicting the current operating scenario through the target model, the model can simultaneously analyze multi-dimensional input information and extract complex relationships between the information. This effectively ensures the accuracy and efficiency of the current operating scenario prediction. Furthermore, based on the current operating scenario, the model determines the matching cooling control strategy and noise reduction control strategy, effectively ensuring that the cooling control strategy and noise reduction control strategy can be executed in advance and smoothly before the temperature rises.

[0094] In addition, the method of determining the current operating scenario based on a pre-defined correspondence can be specifically as follows: the current operating scenario is determined based on the pre-defined correspondence between the operating parameters of the target device in the electronic device and the operating scenario, and the correspondence between the status parameters of the application process in the electronic device and the operating scenario.

[0095] Different operating scenarios and different parameter combinations can be pre-defined. Once the current operating parameters and current status parameters are determined, the operating scenario corresponding to the current operating parameters and current status parameters can be determined by querying the corresponding relationship, and that operating scenario can be identified as the current operating scenario.

[0096] For example: If we predetermine that NPU utilization is greater than a first value and memory usage is greater than a second value, we identify the corresponding scenario as an AI inference scenario and establish a correspondence between parameters and AI inference scenarios. If we predetermine that GPU utilization is greater than a third value and CPU load is greater than a fourth value, we identify the corresponding scenario as a game scenario and establish a correspondence between parameters and game scenarios. After obtaining the current running parameters and current status parameters, we determine whether there is a situation where NPU utilization is greater than the first value and memory usage is greater than the second value. If so, the current running scenario is identified as an AI inference scenario. If not, we continue to determine whether there is a situation where GPU utilization is greater than the third value and CPU load is greater than the fourth value. If so, the current running scenario is identified as a game scenario. If not, we continue to analyze whether the current running parameters and current status parameters conform to other correspondences in order to determine the current running scenario based on the predetermined correspondences.

[0097] Once the current operating scenario is determined, the corresponding cooling control strategy and noise reduction control strategy can be determined based on the pre-established correspondence between the operating scenario and the cooling control strategy and noise reduction control strategy.

[0098] like Figure 3 The diagram illustrates an example of the correspondence between the operating scenario and the cooling control strategy and noise reduction control strategy disclosed in this embodiment. In an AI inference scenario, the cooling control strategy can be specifically defined as: early acceleration and rapid deceleration to avoid noise fluctuations caused by frequent changes. Early acceleration refers to controlling the cooling device to start accelerating early, while rapid deceleration refers to quickly reducing the speed of the cooling device (e.g., a fan). Since AI inference tasks are typically short in duration but high in load, allowing the cooling device to rotate quickly can lower the temperature within a time frame. After the inference task is completed, the cooling device does not require a high speed and can reduce its speed. Correspondingly, the noise reduction control strategy can be specifically defined as: rapid access to the noise reduction module, but it can be set to a "short-term no-response zone." Noise reduction begins after a certain time, meaning that the noise reduction module starts running as soon as the cooling device is activated. AI inference tasks typically end after a period of time and are then inferred again after another period. To avoid frequent switching of the noise reduction module, a no-response zone can be set, meaning the noise reduction module is always on, but does not respond for a certain period after completing a task.

[0099] For gaming or video playback scenarios, the cooling control strategy can be to smoothly adjust the rotation speed of the cooling device based on the load. This smooth adjustment ensures that the cooling device's rotation speed is not too high during gameplay or video playback, thus avoiding excessive noise during operation. Correspondingly, the noise reduction control strategy involves the noise reduction module running continuously to maintain a quiet environment, preventing any impact on audio output during gameplay or video playback. The difference between gaming and video playback scenarios is that the cooling control strategy in gaming only involves smoothly adjusting the cooling device's rotation speed based on the load, while the cooling control strategy in video playback prioritizes low-speed operation. The smooth adjustment of the cooling device's rotation speed is because electronic devices typically do not generate high temperatures during video playback; therefore, the cooling device can prioritize low-speed operation.

[0100] For video editing / rendering scenarios, the cooling control strategy is to run the cooling devices (such as fans) at high speed in advance and maintain full speed for extended periods, focusing on temperature control and efficiency. Since electronic devices typically run at high temperatures in video editing / rendering scenarios, it is necessary to run the cooling devices in advance and keep them running at high speed to avoid overheating during operation. Correspondingly, the noise reduction control strategy is to have the noise reduction module work continuously to reduce the noise generated by the cooling devices during operation.

[0101] For a typical office setting, the cooling control strategy is as follows: low system (i.e., electronic device system) utilization and quiet operation are prioritized. In a typical office setting, electronic devices typically do not reach high temperatures, so cooling devices are usually not used and can be kept silent. When a certain temperature increase is detected, the rotation speed of the cooling devices can be appropriately increased. Correspondingly, the noise reduction control strategy is as follows: the noise reduction module is turned off or operates at low power. Since the cooling devices prioritize quiet operation, they will not generate significant noise during operation. Therefore, the noise reduction module can be turned off or operate at low power.

[0102] In mixed-load scenarios, the cooling control strategy can be weighted processing, prioritizing system stability (i.e., the electronic device's system) to prevent overheating or crashes. This means that in mixed-load scenarios, a cooling control strategy corresponding to a single scenario is not directly selected. Instead, different weights are assigned to different cooling control strategies based on the type, real-time intensity, and importance of each concurrent load, ultimately calculating a cooling control strategy suitable for the current mixed-load scenario. The weight allocation can be related to the load type; different types of loads have different weights. For example, game rendering or AI inference tasks that may cause extremely high instantaneous temperature rises will have a higher weight than tasks like video playback. Additionally, the weight allocation is related to real-time resource usage; for example, a task with high CPU, GPU, or memory usage will have a higher weight. Furthermore, the weight allocation is related to process status / user interaction; for example, processes running in the foreground and directly interacting with the user usually have a higher weight than processes running entirely in the background. Correspondingly, the noise reduction control strategy can be: moderate operation of the noise reduction module.

[0103] The cooling device's rotation speed can be adjusted by integrating the motherboard manufacturer's SDK. The interface function allows specifying the cooling device's number and inputting a percentage to control its rotation speed. When the processor (e.g., CPU) utilization is below 50%, the cooling device's rotation speed increases at a low rate; when the processor utilization reaches 75%, the rotation speed increases at a moderate rate; and when the processor utilization reaches 90%, the rotation speed increases at a high rate.

[0104] The control method disclosed in this embodiment determines the current operating parameters of the target device in the electronic device and the current state parameters of at least one application process running on the electronic device. Based on at least one of the current operating parameters and current state parameters, the current operating scenario is determined, achieving precise perception of the operating scenario. This allows for the determination of matching cooling and noise reduction control strategies to achieve the optimal dynamic balance between heat dissipation efficiency and noise control, thereby improving the user experience. In this embodiment, the current operating parameters and current state parameters can be used to anticipate user intent and the load the device will bear. This allows for the prediction of whether the electronic device will generate heat and require cooling even when cooling is not currently needed, enabling the early execution of cooling control and noise reduction control due to cooling. Furthermore, this embodiment determines the current operating scenario based on the current operating parameters and current state parameters and matches different cooling and noise reduction control strategies to different scenarios, achieving scenario-driven control to ensure a good user experience in different scenarios.

[0105] This embodiment discloses a control method, the flowchart of which is shown below. Figure 4 As shown, it includes:

[0106] Step S41: Determine the current state parameters of at least one application process running on the electronic device;

[0107] Step S42: Based on historical data of electronic equipment operation, predict the temperature data of electronic equipment operating according to current state parameters;

[0108] Step S43: If it is determined that the predicted temperature of the electronic device reaches the target threshold after the current state parameters of the electronic device have been running for a first time, determine the cooling control strategy and noise reduction control strategy corresponding to the current predicted state. The current predicted state is that the predicted temperature of the electronic device reaches the target threshold after the current state parameters of the electronic device have been running for a first time.

[0109] Step S44: Execute the cooling control strategy and the noise reduction control strategy. The cooling control strategy is used to control the operation of the cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

[0110] After determining the current state parameters of at least one application process running on an electronic device, matching cooling and noise reduction strategies can be determined based on these parameters. These strategies are then executed, controlling the operation of cooling devices through the cooling control strategy and reducing noise generated by the cooling devices through the noise reduction strategy. This allows for real-time monitoring of the application processes running on the electronic device, predicting temperature rise in advance, and providing precise and proactive heat dissipation while effectively reducing noise generated during heat dissipation. Furthermore, while controlling the cooling devices to dissipate heat, the noise reduction strategy is simultaneously activated to further reduce noise generated during heat dissipation, improving the user experience.

[0111] Specifically, determining the cooling control strategy and noise reduction control strategy that match the current state parameters can be done as follows: based on the historical data of the electronic device's operation, the temperature data of the electronic device operating according to the current state parameters is predicted; if it is determined that the predicted temperature of the electronic device reaches the target threshold after the electronic device has been operating for a first period of time, the cooling control strategy and noise reduction control strategy corresponding to the current predicted state are determined, where the current predicted state is that the predicted temperature reaches the target threshold after the electronic device has been operating for a first period of time.

[0112] During the operation of electronic devices, operational data is stored to obtain historical records. These records contain state parameters, temperature data, and corresponding times during the operation of the electronic devices. For example, the historical records may show that an electronic device is running an application process with a CPU consumption value of 'a', a temperature of 'b', and a time of 'c'. Furthermore, the historical records also show the CPU consumption value and temperature data of the application process for a period of time after time 'c'. By comparing the state parameters of the electronic device recorded in the historical records, subsequent temperature predictions can be made.

[0113] Therefore, after obtaining the current state parameters, the subsequent temperature data can be predicted by combining the historical data that matches the current state parameters, so as to determine the corresponding cooling control strategy and noise reduction control strategy.

[0114] For example, if the currently running applications on an electronic device include video conferencing software (process 1) and presentation software (process 2), then process 1 has a medium-high CPU utilization, a high GPU video encoding load, and utilizes the camera and microphone, while process 2 has a low GPU rendering load. Based on this, analyzing historical data on the temperature rise of the electronic device in a video conferencing + presentation scenario leads to the conclusion that if the current parameters are maintained, the CPU package temperature is expected to reach 85 degrees Celsius (predicted temperature) after 30 seconds (the first duration), while the target threshold is 82 degrees Celsius. Since the first duration exceeds the target threshold, a cooling control strategy and a noise reduction control strategy can be determined. The cooling control strategy could be to gradually and linearly increase the cooling device's rotation speed from the current 2000 RPM to 4500 RPM over the next 25 seconds, preventing the CPU package temperature from reaching 82 degrees Celsius after 30 seconds. Correspondingly, the noise reduction control strategy could be to start the noise reduction module synchronously with the cooling device, allowing the noise reduction module to run continuously to maintain quiet operation.

[0115] Furthermore, the control method disclosed in this embodiment determines the current state parameters of at least one application process running on the electronic device, and can also determine the current operating parameters of the target device in the electronic device, so as to predict the temperature data of the electronic device running according to the current state parameters and the current operating parameters based on the historical data of the electronic device. If it is determined that the predicted temperature of the electronic device reaches the target threshold after running according to the current state parameters and the current operating parameters for a first period of time, a cooling control strategy and a noise reduction control strategy corresponding to the current predicted state are determined.

[0116] When predicting the temperature of electronic devices, the current state parameters of at least one application process on the electronic device are referenced, as well as the current operating parameters of the target device in the electronic device. This ensures the accuracy of the temperature prediction, thereby ensuring the accuracy of the determined cooling control strategy and noise reduction control strategy.

[0117] The control method disclosed in this embodiment predicts the temperature of the electronic device within a certain period of time based on the current state parameters of at least one application process running on the electronic device and historical data. When the predicted temperature reaches the target threshold, a matching cooling control strategy and noise reduction control strategy are determined. By predicting the temperature of the electronic device in the future, cooling is performed in advance before the temperature rises, avoiding the impact of the electronic device's temperature rise on the operation of the application. In addition, when it is predicted that heat dissipation is needed, the noise reduction control strategy is determined and executed simultaneously, reducing the impact of noise generated by the operation of cooling devices during the cooling process and improving the user experience.

[0118] This embodiment discloses a control method, the flowchart of which is shown below. Figure 5As shown, it includes:

[0119] Step S51: Determine the current state parameters of at least one application process running on the electronic device, and determine the current operating parameters of the target device in the electronic device. The current operating parameters include at least one of the following: hardware processing information of multiple hardware modules in the electronic device used to perform data processing functions, memory usage data in the electronic device, and noise information during the operation of the cooling device in the electronic device.

[0120] Step S52: Determine the historical hardware processing information of the hardware module and the historical noise information during the operation of the cooling device within the second time period before the current moment;

[0121] Step S53: Determine hardware data change information based on hardware processing information and historical hardware processing information of hardware modules, and determine noise change trend based on historical noise information and noise information during the operation of cooling devices.

[0122] Step S54: Determine the cooling control strategy and the noise reduction control strategy based on at least one of the current operating parameters, current status parameters, hardware data change information and noise change trend.

[0123] Step S55: Execute the cooling control strategy and the noise reduction control strategy. The cooling control strategy is used to control the operation of the cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

[0124] After determining the current state parameters of at least one application process running on an electronic device, matching cooling and noise reduction strategies can be determined based on these parameters. These strategies are then executed, controlling the operation of cooling devices through the cooling control strategy and reducing noise generated by the cooling devices through the noise reduction strategy. This allows for real-time monitoring of the application processes running on the electronic device, predicting temperature rise in advance, and providing precise and proactive heat dissipation while effectively reducing noise generated during heat dissipation. Furthermore, while controlling the cooling devices to dissipate heat, the noise reduction strategy is simultaneously activated to further reduce noise generated during heat dissipation, improving the user experience.

[0125] Specifically, determining the cooling control strategy and the noise reduction control strategy can involve: determining the historical hardware processing information of the hardware module and the historical noise information during the operation of the cooling device within the second time period prior to the current moment; determining the hardware data change information based on the hardware processing information of the hardware module and the historical hardware processing information; determining the noise change trend based on the historical noise information and the noise information during the operation of the cooling device; and determining the cooling control strategy and the noise reduction control strategy based on at least one of the current operating parameters, the current state parameters, the hardware data change information, and the noise change trend.

[0126] Determine the current status parameters of at least one application process running on the electronic device, such as the name of the application process and the CPU or memory resources consumed by the application process; determine the current operating parameters of the target device in the electronic device, such as the hardware processing information of the hardware module, memory usage data, and noise information during the operation of the cooling device.

[0127] After obtaining the current status parameters and current operating parameters, features can be extracted from the obtained parameters to determine the cooling control strategy and noise reduction control strategy based on the extracted features. The extracted features may include: hardware and status features, as well as noise features. If it is necessary to determine the cooling control strategy and noise reduction control strategy based on the current operating scenario, scenario features can also be extracted. Scenario features may include: information on a certain number of software processes selected from high to low based on CPU and memory resource usage, such as: process name and CPU and memory resource information consumed by the process.

[0128] In addition, noise characteristics can be specifically defined as: the spectral characteristics and peak decibels of the noise generated by the cooling device during operation, and can also include: the noise change trend; hardware and status characteristics can be specifically defined as: hardware module information and utilization rate, temperature, power consumption, etc., and can also include: hardware data change information, which can be specifically defined as: load fluctuation speed.

[0129] Among these, the noise change trend and hardware data change information need to be analyzed from a set of continuous data, rather than being directly extracted from the current state parameters and current operating parameters.

[0130] Regarding noise variation trends, after determining that the current operating parameters include the noise information of the cooling device during current operation, it is necessary to obtain the historical noise information of the cooling device during operation within a certain time period before the current time (e.g., the second time period). For example, if the current time is 14:30 and the second time period is 30 minutes, then it is necessary to obtain the historical noise information of the cooling device during the period from 14:00 to 14:30, and analyze the historical noise information during this period to determine its noise variation trend, such as whether the noise during this period is smooth, whether the noise change between adjacent times exceeds a certain threshold, whether the noise during this period is smoothly decreasing, and whether the noise during this period is smoothly increasing, etc.

[0131] To determine the noise change trend based on historical noise information and noise information under the current operating scenario, a curve can be generated directly from the historical noise information and the noise information under the current operating scenario. This curve can clearly show the noise change trend within this period. Alternatively, the historical noise information and the noise information under the current operating scenario can be input into the model, and the model can determine the noise change trend within this period. Alternatively, the incremental change within a certain time interval can be calculated, and the rate feature can be extracted. That is, the rate of change is equal to the current value minus the previous value divided by the time interval, which can give the noise change trend.

[0132] In addition, regarding hardware data change information, after determining the hardware processing information in the current operating parameters, it is necessary to obtain the historical hardware processing information of the hardware module within a certain time period before the current moment (e.g., a second time period). For example, if the current moment is 14:30 and the second time period is 30 minutes, then it is necessary to obtain the historical hardware processing information of the hardware module (e.g., at least one of the CPU, GPU, or NPU) during the period from 14:00 to 14:30, and analyze the historical hardware processing information within this period to determine the corresponding hardware data change information, such as whether the hardware data change within this period is smooth, and whether the hardware data change at adjacent moments exceeds a certain threshold. Of course, the trend of hardware data change information can be clearly shown through curve graphs, determined through models, or calculated; no specific limitations are made here.

[0133] After determining the hardware data change information and noise change trend, the hardware data change information and noise change trend are combined with the obtained current operating parameters and current state parameters to determine the cooling control strategy and noise reduction control strategy (or, the hardware data change information and noise change trend are combined with the extracted features to determine the cooling control strategy and noise reduction control strategy). This enables the determination of the current temperature of the electronic equipment and the noise level during the operation of the cooling device. It can also predict whether the temperature has an upward trend, a downward trend or a stable trend based on the noise change trend and hardware data change trend, so as to perform predictive cooling and noise reduction control, improve the response rate, and avoid problems such as insufficient cooling or noise reduction effect, or excessive cooling or noise reduction, thus achieving precise control of cooling and noise reduction.

[0134] When determining the cooling and noise reduction control strategies, they can be based solely on the current operating parameters, the current state parameters, hardware data changes and noise trends, or a combination of these. The specific parameter(s) used for determination depends on the currently available parameters. If only the current state parameters are available, the cooling and noise reduction strategies are determined based solely on them. If only the current operating parameters are available, the cooling and noise reduction strategies are determined based solely on them. If the current operating parameters, current state parameters, hardware data changes, and noise trends are all available, the cooling and noise reduction strategies are determined collectively based on this information. This ensures the timeliness of the determination of the cooling and noise reduction strategies, enabling timely cooling and noise reduction control of the electronic equipment. The more information used to determine the cooling and noise reduction strategies, the more precise the cooling and noise reduction control can be.

[0135] In addition, when determining the cooling control strategy and the noise reduction control strategy, the cooling control strategy can be determined based on at least one of the hardware processing information, memory usage data and current status parameters in the current operating parameters. The noise reduction control strategy, on the other hand, needs to be determined based on the noise information during the operation of the cooling device and the cooling control strategy together, so as to ensure that the noise during the operation of the cooling device can be effectively reduced when the noise reduction control strategy is used.

[0136] The determination of the cooling control strategy is based on a lot of information to ensure that the electronic equipment can be cooled in a timely and effective manner. The noise reduction control strategy, on the other hand, only targets the noise of the cooling device. Therefore, the noise reduction control strategy can be determined directly based on the cooling control strategy and the noise information of the current cooling device.

[0137] The control method disclosed in this embodiment, after determining the current state parameters of at least one application process running on the electronic device and the current operating parameters of the target device, includes at least one of the following: hardware processing information of the hardware module, memory usage data, and noise information during the operation of the cooling device. Hardware data change information can be determined based on historical hardware processing information, and noise change trends can be determined based on historical noise information. This allows for the determination of cooling and noise reduction control strategies based on at least one of the following: hardware data change information, noise change trends, current operating parameters, and current state parameters. This enables the dynamic adaptability of the control strategy formulation, allowing for predictive cooling and noise reduction, avoiding problems such as response lag or overcompensation in cooling and noise reduction. Furthermore, analysis based on multiple data (i.e., at least one of hardware data change information, noise change trends, current operating parameters, and current state parameters) ensures that the determined control strategy can adapt to the current complex scenario, guaranteeing the accuracy and timeliness of cooling and noise reduction control.

[0138] Furthermore, in the control method disclosed in this embodiment, a cooling control strategy and a noise reduction control strategy are executed. Specifically, for the execution of the cooling control strategy, the cooling device can be directly controlled according to the cooling control strategy, so that it can run according to the data such as the running time, acceleration time, and deceleration time of the cooling device in the cooling control strategy to achieve the cooling effect. Correspondingly, for the execution of the noise reduction control strategy, the noise reduction module needs to be controlled to execute according to the noise reduction control strategy.

[0139] Specifically, the target sound wave is determined, which is a sound wave whose phase is opposite to the sound pressure waveform of the noise information in the current operating parameters; the target sound wave is output based on the noise reduction control strategy.

[0140] The noise reduction module may include: an audio acquisition device (i.e., a sound pickup device, such as a microphone), an audio output device (such as a speaker), and a system interface. The system interface is used to receive noise reduction control strategies. Additionally, the noise reduction module includes a noise reduction algorithm that generates a target sound wave based on the noise reduction control strategy and outputs this target sound wave through the audio output device. The target sound wave is a sound wave with a phase opposite to the sound pressure waveform of the noise information in the current operating parameters (i.e., the noise information during the operation of the cooling device). The noise reduction algorithm can analyze the waveform of the noise during the operation of the cooling device in real time and generate an inverse signal. Outputting this inverse signal through the audio output device can cancel at least part of the noise during the operation of the cooling device, thereby reducing the user's perception of noise.

[0141] In addition, the audio acquisition device of the noise reduction module can also collect the noise generated during the operation of the cooling device in real time, so as to generate a noise reduction control strategy using the collected noise and the current cooling control strategy.

[0142] It should be noted that the noise reduction module can also be specifically a physical noise reduction module, which means that by adding physical materials, the sound wave energy is absorbed or blocked to prevent noise from propagating.

[0143] The physical materials can specifically include acoustic foam, silicone pads, sealing strips, sound-absorbing cotton, etc. These physical materials can be placed around the cooling devices inside electronic devices or on the inside of the electronic device casing to ensure noise absorption.

[0144] In the control method disclosed in this embodiment, noise reduction is achieved by using a target sound wave with a phase opposite to the sound pressure waveform of the noise information during the operation of the cooling device. When two sound waves with the same amplitude and opposite phase are superimposed in space, destructive interference will occur, thereby significantly reducing or even eliminating noise in a specific area (such as near the user's ear). This is an efficient direct cancellation method triggered by acoustic principles, which achieves accurate and active cancellation of noise information, thereby effectively reducing the noise generated by the operation of the cooling device without affecting the heat dissipation performance, and significantly improving the user's auditory comfort.

[0145] This embodiment discloses a control system, the schematic diagram of which is shown below. Figure 6 As shown, it includes:

[0146] The first determining unit 61, the second determining unit 62, and the execution unit 63.

[0147] The first determining unit 61 is used to determine the current state parameters of at least one application process running on the electronic device;

[0148] The second determining unit 62 is used to determine the cooling control strategy and noise reduction control strategy that match the current state parameters;

[0149] The execution unit 63 is used to execute the cooling control strategy and the noise reduction control strategy. The cooling control strategy is used to control the operation of the cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

[0150] The control system disclosed in this embodiment is implemented based on the control method disclosed in the above embodiments, and will not be described again here.

[0151] The control system disclosed in this embodiment can determine the current state parameters of at least one application process running on an electronic device. Based on these parameters, it can determine a cooling control strategy and a noise reduction control strategy for the electronic device. After executing these strategies, the temperature of the electronic device can be reduced by the operation of its cooling devices. Simultaneously, the noise generated by the operation of the cooling devices can be reduced based on the noise reduction control strategy. This solution can accurately and proactively dissipate heat from the electronic device based on the real-time state of the application process running on it, while effectively reducing noise generated during the heat dissipation process. Furthermore, by directly monitoring the state parameters of the application process, rather than directly monitoring the temperature value, this solution can identify potential heat dissipation needs before the hardware temperature of the electronic device rises, and identify noise reduction needs that exist when heat dissipation is required. This allows for early prediction and intervention, making the control more precise and timely, improving the response speed of heat dissipation and noise reduction during the heat dissipation process, and enhancing the user experience.

[0152] This embodiment discloses an electronic device, the structural schematic diagram of which is shown below. Figure 7 As shown, it includes:

[0153] Cooling device 71 and processor 72.

[0154] Among them, the cooling device 71 is used to reduce the temperature of electronic equipment;

[0155] The processor 72 is used to determine the current state parameters of at least one application process running on the electronic device; determine a cooling control strategy and a noise reduction control strategy that match the current state parameters; and execute the cooling control strategy and the noise reduction control strategy, wherein the cooling control strategy is used to control the operation of the cooling device and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

[0156] Furthermore, the processor is used for:

[0157] Determine the current operating parameters of the target device in the electronic device;

[0158] Determine a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of at least one application process.

[0159] Furthermore, the processor is used for:

[0160] Based on at least one of the current operating parameters of the target device in the electronic device and at least one current state parameter of at least one application process, determine the current operating scenario of the electronic device; determine the cooling control strategy and noise reduction control strategy that match the current operating scenario.

[0161] Furthermore, the processor is used for:

[0162] Input at least one of the current operating parameters of the target device in the electronic device and at least one current state parameter of the application process into the target model to obtain the current operating scenario of the electronic device output by the target model;

[0163] Based on the pre-defined correspondence between the operating parameters of the target device in the electronic device and the operating scenario, and the correspondence between the status parameters of the application process in the electronic device and the operating scenario, the current operating scenario is determined.

[0164] Furthermore, the processor is used for:

[0165] Based on historical data of electronic device operation, the temperature data of electronic device operation according to current state parameters is predicted; if it is determined that the predicted temperature of electronic device will reach the target threshold after the electronic device operates according to the current state parameters for a first period of time, the cooling control strategy and noise reduction control strategy corresponding to the current predicted state are determined. The current predicted state is that the predicted temperature will reach the target threshold after the electronic device operates for a first period of time.

[0166] Furthermore, the processor is used for:

[0167] Determine the hardware processing information of multiple hardware modules in an electronic device that perform data processing functions;

[0168] Determine the memory usage data in electronic devices;

[0169] Determine the noise information during the operation of cooling devices in electronic devices.

[0170] Furthermore, the processor is used for:

[0171] A cooling control strategy is determined based on at least one of the following: hardware processing information, memory usage data, and current status parameters in the current operating parameters; a noise reduction control strategy is determined based on the noise information during the operation of the cooling device and the cooling control strategy.

[0172] Furthermore, the processor is used for:

[0173] Determine the historical hardware processing information of the hardware module and the historical noise information during the operation of the cooling device within the second time period prior to the current moment; determine the hardware data change information based on the hardware processing information of the hardware module and the historical hardware processing information, and determine the noise change trend based on the historical noise information and the noise information during the operation of the cooling device; determine the cooling control strategy and the noise reduction control strategy based on at least one of the current operating parameters, current state parameters, hardware data change information and noise change trend.

[0174] Furthermore, the processor is used for:

[0175] The target sound wave is determined as a sound wave whose phase is opposite to the sound pressure waveform of the noise information in the current operating parameters; the target sound wave is output based on the noise reduction control strategy.

[0176] The electronic device disclosed in this embodiment is implemented based on the control method disclosed in the above embodiments, and will not be described again here.

[0177] The electronic device disclosed in this embodiment can determine the current state parameters of at least one application process running on the electronic device, so as to determine the cooling control strategy and noise reduction control strategy of the electronic device based on the current state parameters. After executing the cooling control strategy and noise reduction control strategy, the temperature of the electronic device can be reduced by the operation of the cooling device. At the same time, the noise caused by the operation of the cooling device can be reduced based on the noise reduction control strategy. This solution can accurately and proactively dissipate heat from the electronic device based on the real-time state of the application process running on the electronic device, and effectively reduce the noise generated during the heat dissipation process. In addition, this solution directly monitors the state parameters of the application process, rather than directly monitoring the temperature value, so that potential heat dissipation needs can be identified in advance based on the state parameters before the hardware temperature of the electronic device rises, and noise reduction needs exist when heat dissipation needs exist. This allows for early prediction and intervention, making the control more precise and timely, improving the response speed of heat dissipation and noise reduction during the heat dissipation process, and enhancing the user experience.

[0178] This application embodiment also provides a readable storage medium on which a computer program is stored. The computer program is loaded and executed by a processor to implement the steps of the above control method. The specific implementation process can be referred to the description of the corresponding part of the above embodiment, and will not be repeated in this embodiment.

[0179] This application also proposes a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. The 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 control method or control system described above. Specific implementation processes can be referred to the descriptions of the corresponding embodiments above, and will not be repeated here.

[0180] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0181] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0182] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0183] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

Claims

1. A control method, comprising: Determine the current state parameters of at least one application process running on an electronic device; Determine a cooling control strategy and a noise reduction control strategy that match the current state parameters; The cooling control strategy and the noise reduction control strategy are executed. The cooling control strategy is used to control the operation of the cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

2. The method according to claim 1, wherein determining the cooling control strategy and noise reduction control strategy matching the current state parameters includes: Determine the current operating parameters of the target device in the electronic device; Determine a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process.

3. The method according to claim 2, wherein determining a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process includes: The current operating scenario of the electronic device is determined based on at least one of the current operating parameters of the target device in the electronic device and the current status parameters of the at least one application process. Determine the cooling control strategy and noise reduction control strategy that match the current operating scenario.

4. The method according to claim 3, wherein determining the current operating scenario of the electronic device based on at least one of the current operating parameters of the target device in the electronic device and the current status parameters of the at least one application process includes at least one of the following: The target model is input with at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process to obtain the current operating scenario of the electronic device output by the target model. Based on the pre-defined correspondence between the operating parameters of the target device in the electronic device and the operating scenario, and the correspondence between the status parameters of the application process in the electronic device and the operating scenario, the current operating scenario is determined.

5. The method according to claim 1, wherein determining the cooling control strategy and noise reduction control strategy matching the current state parameters includes: Predict the temperature data of the electronic device operating according to the current state parameters based on the historical operating data of the electronic device; If it is determined that the predicted temperature of the electronic device reaches the target threshold after the current state parameters of the electronic device have been running for a first period of time, a cooling control strategy and a noise reduction control strategy corresponding to the current predicted state are determined. The current predicted state is that the predicted temperature of the electronic device reaches the target threshold after the current state parameters of the electronic device have been running for a first period of time.

6. The method according to claim 2, wherein determining the current operating parameters of the target device in the electronic device includes at least one of the following: Determine the hardware processing information of multiple hardware modules in the electronic device used to perform data processing functions; Determine the memory usage data in the electronic device; Determine the noise information during the operation of the cooling device in the electronic device.

7. The method according to claim 6, wherein determining a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process includes: The cooling control strategy is determined based on at least one of the hardware processing information, memory usage data and current status parameters in the current operating parameters. The noise reduction control strategy is determined based on the noise information during the operation of the cooling device and the cooling control strategy.

8. The method according to claim 6, wherein determining a cooling control strategy and a noise reduction control strategy that match at least one of the current operating parameters of the target device in the electronic device and the current state parameters of the at least one application process includes: Determine the historical hardware processing information of the hardware module and the historical noise information during the operation of the cooling device within the second time period prior to the current moment; Hardware data change information is determined based on the hardware processing information of the hardware module and the historical hardware processing information, and noise change trend is determined based on the historical noise information and the noise information during the operation of the cooling device. The cooling control strategy and the noise reduction control strategy are determined based on at least one of the current operating parameters, the current status parameters, hardware data change information, and noise change trend.

9. The method according to claim 2, comprising executing the noise reduction control strategy, including: The target sound wave is determined, wherein the target sound wave is a sound wave whose phase is opposite to the sound pressure waveform of the noise information in the current operating parameters; The target sound wave is output based on the noise reduction control strategy.

10. An electronic device, comprising: Cooling devices are used to reduce the temperature of the electronic equipment; A processor is configured to determine the current state parameters of at least one application process running on an electronic device; determine a cooling control strategy and a noise reduction control strategy that match the current state parameters; and execute the cooling control strategy and the noise reduction control strategy, wherein the cooling control strategy is used to control the operation of a cooling device, and the noise reduction control strategy is used to reduce the noise caused by the operation of the cooling device.

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