Regulation and control method and device for cooling fan in relatively closed space and cooling system

Abstract

The invention relates to the artificial intelligence technology, and discloses a regulation and control method and device for cooling fans in a relatively closed space and a cooling system.The regulation and control method comprises the steps that electronic devices in the relatively closed space are controlled to operate in different power consumption modes, and a plurality of cooling fans are controlled to operate in cooperation with parameter combinations of different rotating speeds and rotating directions; temperature data of the electronic device and wind noise data outside the relatively closed space are collected; inputting related collected data into the artificial intelligence model for training, and generating corresponding optimal parameter combinations between the cooling fans when the electronic device is in different power consumption modes; and when the electronic device is actually applied, cooling the electronic device by using the corresponding optimal parameter combination between the cooling fans according to the current power consumption mode of the electronic device. The invention further discloses a computer readable storage medium. The invention aims to reduce the generated wind noise as much as possible while cooling the electronic device in the relatively closed space by using the cooling fan.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a control method, a control device, a heat dissipation system, and a computer-readable storage medium for a heat dissipation fan in a relatively closed space. Background Art

[0002] With the rapid development of electronic technology, the integration and power density of electronic devices are constantly improving, and heat dissipation has become one of the key factors restricting the performance and life of electronic equipment. Especially in relatively closed spaces, such as server rooms, data centers, vehicle-mounted electronic equipment, industrial control equipment, computers, etc., the heat dissipation problem of electronic devices is particularly prominent.

[0003] As a common heat dissipation method, cooling fans are often used to dissipate heat and cool down electronic devices in relatively confined spaces. At present, cooling fans usually rely on the operation and control of fixed setting parameters. Although this solution can meet the heat dissipation needs to a certain extent, it is difficult to cope with the dynamic heat dissipation needs of electronic devices under different power consumption modes. In addition, due to the airtightness of the space and the limitation of airflow, it is easy to cause high operating noise, affecting the equipment operating environment and user experience.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present application is to provide a control method, control device, heat dissipation system and computer-readable storage medium for a cooling fan in a relatively closed space, aiming to use a cooling fan to cool down electronic devices in a relatively closed space, and while ensuring the cooling effect, reduce the wind noise generated thereby as much as possible.

[0006] To achieve the above object, the present application provides a method for controlling a cooling fan in a relatively closed space, comprising the following steps:

[0007] Control electronic devices in a relatively confined space to operate in different power consumption modes, and in each power consumption mode, control multiple cooling fans to operate with different speed and direction parameter combinations;

[0008] When multiple cooling fans are running with different parameter combinations, the temperature data of electronic components and the wind noise data outside a relatively closed space are collected;

[0009] The relevant collected data is input into the artificial intelligence model for training, so that the artificial intelligence model learns the influence of different parameter combinations between cooling fans on temperature data and wind noise data under different power consumption modes of electronic devices; wherein, the training goal of the artificial intelligence model is set to optimize the wind noise data under the premise that the temperature data meets the cooling target under different power consumption modes of the electronic device, and obtain the optimal parameter combination of the rotation speed and direction between multiple cooling fans;

[0010] Based on the training results of the artificial intelligence model, the optimal parameter combination between the cooling fans of electronic devices in different power consumption modes is generated;

[0011] When the electronic device is actually used, the electronic device is cooled using an optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic device.

[0012] To achieve the above-mentioned purpose, the present application also provides a control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the method for controlling a cooling fan in a relatively closed space as described above are implemented.

[0013] To achieve the above objectives, the present application also provides a cooling system, including a control device and multiple cooling fans; each cooling fan establishes a communication connection with the control device and is controlled by the control device; wherein the control device is the control device as described above.

[0014] To achieve the above objectives, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for controlling a cooling fan in a relatively closed space are implemented as described above.

[0015] The control method, control device, cooling system and computer-readable storage medium for cooling fans in a relatively confined space provided by the present application are based on artificial intelligence model learning and optimization, dynamically adjust the speed and direction of multiple cooling fans according to the actual power consumption mode of the electronic device, select the optimal fan parameter combination, and realize intelligent cooling management of different power consumption modes of the electronic device. Under the premise of ensuring the cooling effect, the wind noise data is optimized as much as possible, the operating noise is reduced, and the best balance between cooling effect and noise control is achieved, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the steps of a method for controlling a heat dissipation fan in a relatively closed space in one embodiment of the present application;

[0017] Figure 2Schematic diagram of the internal structure of a control device according to an embodiment of the present application.

[0018] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0020] In addition, if the description of "first", "second", etc. is involved in this application, it is only used for descriptive purposes (such as for distinguishing the same or similar features), and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0021] Reference Figure 1 In one embodiment, a method for controlling a cooling fan in a relatively closed space includes:

[0022] Step S10, controlling the electronic devices in the relatively confined space to operate in different power consumption modes, and in each power consumption mode, controlling a plurality of cooling fans to operate with different speed and direction parameter combinations;

[0023] Step S20, when multiple cooling fans are operated in combination with different parameters, collecting temperature data of electronic components and wind noise data outside the relatively closed space;

[0024] Step S30, inputting the relevant collected data into the artificial intelligence model for training, so that the artificial intelligence model learns the influence of different parameter combinations between cooling fans on temperature data and wind noise data under different power consumption modes of the electronic device; wherein, the training goal of the artificial intelligence model is set to optimize the wind noise data on the premise that the temperature data meets the cooling target under different power consumption modes of the electronic device, and obtain the optimal parameter combination of the rotation speed and the direction of rotation between the multiple cooling fans;

[0025] Step S40: generating an optimal parameter combination corresponding to the cooling fans of the electronic device in different power consumption modes according to the training result of the artificial intelligence model;

[0026] Step S50: When the electronic device is actually used, the electronic device is cooled using the optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic device.

[0027] In this embodiment, relatively closed spaces refer to those spaces that are physically closed to a certain extent. These spaces usually have limited air circulation paths, and heat exchange is mainly carried out through designated ventilation channels (such as fans, vents, etc.). The environmental conditions in such spaces are relatively stable, but due to space limitations and the fixed nature of airflow paths, heat distribution and transfer are relatively complex.

[0028] For example, electronic equipment compartments in cars, airplanes or ships usually rely on onboard fans and ventilation systems to dissipate heat; PLC (Programmable Automation Controller) cabinets use fans inside the cabinet to dissipate heat; personal computer cases or laptops have compact internal structures and rely on built-in fans to dissipate heat.

[0029] Electronic devices refer to electronic devices or components that operate in a relatively confined space and generate heat under different workloads. The power consumption and heat generation of these devices usually vary with their operating mode and load level.

[0030] For example, server CPUs (Central Processing Units) and personal computer CPUs will generate a lot of heat under high load; graphics processing units (GPUs), which are mainly used for graphics rendering and computing tasks, usually generate a lot of heat under high load; server power supplies and industrial control power supplies will generate heat under high voltage and high current; FPGA (Field Programmable Gate Array) and ASIC (Application Specific Integrated Circuit) chips are used for specific computing tasks and generate significant heat under high load.

[0031] A cooling fan is a device that transfers heat from a heat source to the surrounding environment by generating airflow through rotating blades. Installing a cooling fan in a relatively confined space can enhance air flow and improve heat dissipation efficiency. The number of rotations per minute of a cooling fan directly affects the airflow speed and heat dissipation effect. In a relatively confined space, multiple cooling fans are usually set up to ensure that air flows evenly in the space. The specific number and location of cooling fans can be determined based on the size of the space, the layout of electronic devices, and the distribution of heat sources. Usually, cooling fans can be distributed on the top, side, or bottom of electronic devices to ensure air circulation.

[0032] For example, personal computer fans include CPU fans, chassis fans and graphics card fans, and multiple types of fans are used in combination to optimize heat dissipation; industrial control equipment fans usually use small, high-efficiency fans to adapt to compact equipment layouts; automotive electronic equipment fans are generally high-temperature resistant and vibration-resistant fans, suitable for use in automotive environments.

[0033] The electronic equipment and the cooling fan in a relatively closed space can be electrically connected to the corresponding control device to form a corresponding cooling system, and the control device is responsible for controlling the operation of the electronic equipment and the cooling fan.

[0034] As described in step S10, the electronic device is controlled to operate in different power consumption modes. For example, these modes may include low power consumption mode, standard mode and high performance mode, etc., to simulate different working scenarios in actual use.

[0035] Among them, in low power consumption mode, the power consumption of electronic devices is low, and it is usually used in standby state or light load operation, such as the low power consumption of laptop computers when in standby or browsing the web; standard mode is the normal working mode of electronic devices, with moderate power consumption, suitable for most daily operations, such as the power consumption of laptop computers when processing documents or watching videos; in high performance mode, the power consumption of electronic devices is higher, and it is usually used for high-load tasks, such as video editing, 3D rendering or games.

[0036] Electronic devices can be switched to different power consumption modes through hardware settings or software control. For example, different power consumption modes can be switched through BIOS settings or power management options in an operating system.

[0037] Furthermore, in each power consumption mode, multiple cooling fans are controlled to operate with different rotation speed and direction parameter combinations. These parameter combinations can be tested through multiple pre-set combinations.

[0038] In each power mode, the speed of the cooling fan is controlled. The speed can be adjusted by a PWM (Pulse Width Modulation) signal, usually in RPM (Revolutions Per Minute). For example, in low power mode, the fan speed can be set to a lower value (such as 1000RPM), while in high performance mode, the fan speed can be set to a higher value (such as 3000RPM).

[0039] The cooling fan usually has two rotation modes: forward rotation and reverse rotation. Different rotation modes can affect the direction and speed of air flow.

[0040] In each power consumption mode, test different speed and direction parameter combinations of multiple cooling fans. For example, you can test the following combinations:

[0041] Combination 1: All fans rotate forward at low speed;

[0042] Combination 2: All fans rotate forward at high speed;

[0043] Combination 3: Some fans rotate forward at a low speed, and some fans rotate reversely at a high speed;

[0044] Combination 4: Some fans are rotating at medium speed and some fans are not rotating.

[0045] Among them, a special controller or software can be used to control the speed and direction of the cooling fan. For example, an intelligent fan controller (such as NCT6798D, Nuvoton, etc.) can be used to achieve precise speed and direction control.

[0046] By controlling the operation of electronic devices in different power consumption modes and matching multiple cooling fans with different speeds and steering parameters, various cooling requirements in actual applications can be simulated. This step provides a basis for subsequent temperature and wind noise data collection and provides rich data support for the training of artificial intelligence models.

[0047] As described in step S20, when multiple cooling fans are operated with different parameter combinations, temperature sensors are used to collect temperature data of electronic components. These temperature data may include local temperatures and overall average temperatures of key components.

[0048] At the same time, wind noise data outside a relatively confined space is collected, which mainly refers to the noise generated when the cooling fan is running, and its impact on the user or the surrounding environment. A sound level meter or digital noise sensor can be used to measure the noise level when the fan is running.

[0049] In order to accurately collect these temperature data, temperature sensors can be installed at key parts of electronic devices. These sensors can be thermistors, thermocouples, or temperature monitoring circuits integrated on chips.

[0050] The electronic devices are controlled to run each power consumption mode in sequence, and in each power consumption mode, multiple cooling fans are controlled to run various parameter combinations in sequence, and the temperature data of the electronic devices collected under different parameter combinations and the wind noise data outside the relatively closed space are recorded.

[0051] Through this step, a large amount of temperature data and wind noise data about electronic devices and cooling fans under different working conditions can be obtained. These data will be used to train artificial intelligence models to learn the relationship between cooling fan parameter combinations and temperature and noise, so as to find the optimal parameter combination that achieves the corresponding cooling effect while minimizing noise.

[0052] As described in step S30, the operating parameters of the electronic device in different power consumption modes, as well as various parameter combinations, temperature data and wind noise data between the cooling fans in each power consumption mode of the electronic device can all be collected as training data related to the artificial intelligence model.

[0053] Before the relevant collected data is input into the artificial intelligence model, the collected data can be preprocessed to ensure the quality and consistency of the data. The preprocessing steps can include: data cleaning to remove outliers (for example, if the data collected by a temperature sensor at a certain moment deviates significantly from the normal range, these data need to be removed or corrected), data normalization (so that the model can better handle data of different dimensions), and data segmentation (dividing the data set into training set, validation set, and test set).

[0054] When choosing an artificial intelligence model, you can use basic network models such as multi-layer perceptron, convolutional neural network, recurrent neural network, etc. that are suitable for processing complex relational data.

[0055] The model is constructed based on the causal data division, where the power consumption mode parameters of the electronic device, the speed and direction parameters of the cooling fan are the cause data; the temperature data and wind noise data are the effect data. The input layer of the model thus divided contains the power consumption mode parameters of the electronic device, the speed and direction parameters of the cooling fan; the hidden layer of the model contains multiple layers of neurons for learning complex relationships; the output layer of the model contains temperature data and wind noise data, as well as the corresponding optimal parameter combination of the cooling fan.

[0056] Optionally, the training goal of the model is set to optimize the wind noise data while meeting the cooling goals of the electronic device in different power consumption modes, so as to obtain the optimal parameter combination of the rotation speed and direction of the multiple cooling fans.

[0057] Among them, temperature target: the temperature of the electronic device should be kept within a safe range, usually a set threshold (for example, in high power mode, the threshold value can be 75-85°C, in medium power mode, the threshold value can be 55-65°C, and in low power mode, the threshold value can be 35-45°C).

[0058] Among them, the wind noise target is: to reduce the wind noise data as much as possible while meeting the cooling target.

[0059] The ultimate optimization goal is to find the fan speed and direction combination that minimizes wind noise while meeting the temperature target.

[0060] Based on this, the multi-objective loss function is defined:

[0061] Loss = α × M 1 +(1-α)×M 2 ;

[0062] Among them, α is a hyperparameter that balances the weight of temperature and wind noise and needs to be adjusted through the validation set; M 1 is the temperature loss, M 2 is the wind noise loss; both the temperature loss and the wind noise loss can be calculated by using the mean square error (MSE) to calculate the difference between the predicted value and the actual value.

[0063] The preprocessed training set data is input into the selected model for multiple iterative training. During the forward propagation process of model training, the input data is calculated by the model to obtain the predicted value. The loss (such as mean square error, cross entropy loss) is calculated based on the predicted value and the actual value. The gradient of the loss function to the model parameters is then calculated through the back propagation algorithm to adjust the model parameters. Finally, the optimization algorithm (such as SGD, Adam) is used to update the model parameters to minimize the loss function.

[0064] Among them, model hyperparameter tuning can try different speed and direction combinations within the predefined fan parameter range, calculate the corresponding temperature and wind noise prediction values, and select the optimal combination; or randomly select fan parameter combinations, calculate prediction values, and select the optimal combination; or use the Bayesian optimization method to gradually search for the optimal fan parameter combination.

[0065] Furthermore, the performance of the model is evaluated using the validation set data. The evaluation indicators can be mean square error, root mean square error (RMSE), R 2 Score, etc. The performance gap between the training set and the validation set is used to determine whether the model is overfitting or underfitting. If the training set performance is very good, but the validation set performance is poor, it indicates that the model is overfitting; if both the training set and the validation set performance are poor, it indicates that the model is underfitting.

[0066] Among them, regularization terms (such as L1 and L2 regularization) can be added to prevent overfitting; or, training can be stopped early when the performance of the validation set no longer improves to prevent overfitting.

[0067] Finally, the model is evaluated using the test set data to ensure that the model has good generalization ability on unseen data and the performance indicators of the model on the test set are recorded.

[0068] In this way, through the training of the artificial intelligence model, the optimization of the cooling fan parameter combination in different power consumption modes is achieved. This step not only improves the cooling efficiency, but also reduces wind noise and improves the user experience. At the same time, the introduction of the artificial intelligence model makes the entire system adaptive and intelligent, and can better cope with complex working environments and dynamically changing needs.

[0069] It should be understood that if the hardware conditions related to the deployment environment of the cooling system are sufficient to support model data collection and training, the model can be trained and deployed directly on the control device of the cooling system; if the hardware conditions related to the deployment environment of the cooling system are not sufficient to support model data collection and training, model data collection and training can be carried out during the production design process of the relevant products before leaving the factory, and finally the trained model can be deployed to the control device of the cooling system.

[0070] As described in step S40, the trained model is used to predict the optimal parameter combination of the cooling fan, that is, to predict the optimal temperature data and wind noise data that can be achieved by the electronic device under different power consumption modes (that is, the lowest wind noise that can be achieved under the premise that the temperature data meets the cooling target of the electronic device under the corresponding power consumption mode), and then a reverse query is performed to find the optimal parameter combination of speed and direction among multiple cooling fans that can achieve optimal heat dissipation and wind noise control.

[0071] In the model application, the comprehensive loss of each parameter combination can be compared, and the parameter combination with the smallest loss can be selected as the optimal parameter combination. It is ensured that the selected optimal parameter combination has the smallest wind noise while meeting the temperature target.

[0072] Optionally, for each power consumption mode, a list containing optimal parameter combinations is generated. Each entry in the list may include: power consumption mode, rotation speed of each cooling fan, direction of each cooling fan, predicted temperature, and predicted wind noise.

[0073] Then, the generated optimal parameter combination list is stored in a database or configuration file so as to be called in actual application.

[0074] For example, the generated optimal parameter combination example is shown in the following table (I):

[0075] Table (I):

[0076]

[0077] In addition, in low power mode, the speed of some fans can be reduced or stopped to save energy and reduce wind noise. By precisely controlling the speed and direction of each fan, wind noise can be minimized while ensuring the heat dissipation effect.

[0078] It should be noted that the cooling fans may have different directions and speeds, mainly to optimize the cooling effect while minimizing wind noise. In some complex cooling systems, fans with different directions can generate multi-directional airflow, which helps to distribute heat more evenly; fans with different directions can generate eddy effects and increase air turbulence, which helps to more effectively remove heat from the surface of the equipment, especially in high heat load areas.

[0079] If all fans run at the same speed and direction, resonance may occur, resulting in noise superposition and increasing the overall wind noise; by adjusting the speed and direction of different fans, the resonance effect can be reduced, thereby reducing wind noise. Of course, fans with different speeds and directions produce different sound waveforms, and these waveforms can be mixed to reduce the noise of a single frequency, making the overall sound softer.

[0080] This also explains why, under the premise that the temperature data meets the cooling target, reducing the speed as much as possible does not necessarily achieve the effect of reducing wind noise. This is because the relationship between wind noise and fan speed is not just a simple linear relationship, but also involves the design, material, structure of the fan blades, and the fluid mechanics of air flow. Even at the same speed, fans of different designs and configurations may produce very different wind noise. The fan may also resonate at a certain speed, causing vibration and noise amplification. Simply reducing the speed may not avoid these resonance points, but may make the fan perform worse in certain speed ranges. By fine-tuning the speed and direction of different cooling fans, airflow can be optimized and turbulence and noise can be reduced.

[0081] That is, by properly configuring the speed and direction of each fan, a noise cancellation effect can be achieved, thereby reducing the overall noise level. Fans with different speeds and directions produce different sound waveforms, which may produce an interference effect when mixed, causing noise of certain frequencies to be weakened, thereby reducing the overall noise level.

[0082] It is precisely because the generation of wind noise is complex and is affected by factors such as the characteristics of the cooling fan itself, its operation, and the spatial layout of a relatively confined space that it is difficult to exhaustively calculate all wind noise generation situations by manpower alone. By applying artificial intelligence models, we can explore the influence of parameter combinations between cooling fans in a specific relatively confined space on wind noise. There is no need to deeply understand the physical mechanism of wind noise generation in a specific relatively confined space. We only need to find out which parameter combinations have the least effect on wind noise, thereby simplifying the complexity of the problem. That is, through artificial intelligence models, the optimal combination can be quickly screened out from a large number of possible parameter combinations without the need for detailed physical simulation of each combination.

[0083] Of course, this artificial intelligence model can be extended to various relatively confined space environments in the application of the control method of cooling fans in relatively confined spaces. It is only necessary to collect relevant data for model training and updating for specific relatively confined spaces based on the training model. The key is to train and update the model for different space environments in a data-driven way, so as to achieve balanced control of temperature and wind noise.

[0084] As described in step S50, when the electronic device is actually used, the electronic device is cooled using the optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic device.

[0085] Optionally, the power consumption of the electronic device is monitored in real time, and the current power consumption mode (such as low power consumption mode, medium power consumption mode, high power consumption mode, etc.) is determined according to the power consumption.

[0086] According to the currently monitored power consumption mode, the corresponding optimal parameter combination is called from the database, and each cooling fan is controlled to work according to the called parameter combination to achieve the best cooling effect.

[0087] In this way, while ensuring that the electronic devices can be effectively cooled in different power consumption modes, the wind noise generated by the cooling fan can be reduced as much as possible.

[0088] In one embodiment, based on artificial intelligence model learning and optimization, the speed and direction of multiple cooling fans are dynamically adjusted according to the actual power consumption mode of the electronic device, and the optimal fan parameter combination is selected to realize intelligent heat dissipation management of different power consumption modes of the electronic device. Under the premise of ensuring the heat dissipation effect, the wind noise data is optimized as much as possible to reduce the operating noise, and the best balance between the heat dissipation effect and noise control is achieved, thereby improving the user experience. It is suitable for heat dissipation and noise reduction processing of various electronic devices in relatively confined spaces (such as data centers, vehicle-mounted electronics, personal computers, etc.), and has broad application prospects.

[0089] In one embodiment, based on the above embodiment, an electric control valve is provided at the air guide port of the relatively closed space, and the opening and closing of the air guide port can be controlled based on the electric control valve;

[0090] When multiple cooling fans are running with different parameter combinations, the opening and closing of the air ducts are adjusted, and the temperature data of electronic components and the wind noise data outside a relatively closed space are collected;

[0091] The relevant collected data is input into the artificial intelligence model for training, so that the artificial intelligence model can learn the influence of different parameter combinations between cooling fans and different opening and closing conditions of air ducts under different power consumption modes of electronic devices on temperature data and wind noise data; wherein the training results of the artificial intelligence model include the corresponding relationship between the optimal parameter combination of the speed and direction of the cooling fans and the opening and closing conditions of the air ducts;

[0092] When the electronic device is actually used, the optimal parameter combination between the cooling fans is used according to the current power consumption mode of the electronic device, and the air guide port is opened and closed accordingly to cool the electronic device.

[0093] In this embodiment, an electric control valve is provided at the air guide port in a relatively closed space. The electric control valve can control the opening and closing of the air guide port, thereby adjusting the path and speed of air flow. The air guide port can be adjusted to have a variety of opening and closing conditions based on the electric control valve, such as fully closed, partially open, fully open, opening direction, etc.

[0094] Optionally, in the process of collecting relevant training data for the artificial intelligence model, when controlling multiple cooling fans to operate with different parameter combinations, the opening and closing conditions of the air guide ports are adjusted at the same time.

[0095] Optionally, in each power consumption mode, in addition to setting different speeds and directions of the cooling fans, the opening and closing conditions of the air guide ports can also be adjusted in combination. For example, the air guide ports can be set to 25% open, 50% open, 75% open, and fully open, and the opening direction can be forward, backward, left, right, or center.

[0096] Then, under each combination of cooling fan parameters and air duct opening and closing conditions, the temperature data of the electronic components and the wind noise data outside the relatively closed space are collected.

[0097] All collected data is input into the artificial intelligence model for training. The goal of the training is to optimize the wind noise data and find the best match between the cooling fan parameter combination and the opening and closing of the air duct while ensuring that the temperature of the electronic components meets the cooling target under different power consumption modes.

[0098] The goal of the training model is to minimize the wind noise level while ensuring that the temperature of the electronic components is controlled within a safe range. This goal can be achieved through multi-objective optimization algorithms such as Pareto optimization.

[0099] Then, based on the training results of the model, the optimal parameter combination of the cooling fan parameter combination and the opening and closing of the air duct in each power consumption mode is generated. That is, the model will output the optimal combination of the speed and direction of the cooling fan and the opening and closing of the air duct in each power consumption mode. A corresponding relationship table between the power consumption mode and the cooling fan parameter combination and the opening and closing of the air duct is established so that it can be quickly called in actual applications.

[0100] When the electronic device is actually running, the pre-calculated optimal parameter combination is used according to its current power consumption mode, and the opening and closing of the air duct is adjusted accordingly to achieve the best heat dissipation effect and the lowest noise level.

[0101] It should be noted that the heat generated by electronic devices varies greatly in different power output modes. For example, in some low-load conditions, excessive air flow may not be necessary and will increase wind noise and energy consumption. Therefore, in low-power mode, only a small amount of cooling air may be needed to effectively dissipate heat. At this time, appropriately reducing the opening and closing of the air guide can reduce wind noise and energy consumption.

[0102] In actual applications, there is a trade-off between wind noise and heat dissipation. An overly large air vent opening may cause excessive wind noise, especially when the fan is running at high speed. By controlling the opening and closing of the air vent, the wind noise can be reduced as much as possible while meeting the heat dissipation requirements, thus improving the user experience.

[0103] Through the above expansion plan, the system can not only control the operation of the cooling fan more precisely, but also further optimize the air flow path by adjusting the opening and closing of the air guide port, achieving better heat dissipation effect and lower noise level. This will significantly improve the performance of electronic equipment and user experience.

[0104] In one embodiment, based on the above embodiment, the artificial intelligence model further analyzes the influence of the operation time of each cooling fan speed increment on the rising curve of wind noise data under each parameter combination, and obtains the instantaneous speed increment corresponding to each optimal parameter combination, and the interval control time of the instantaneous speed increment; wherein, on the premise that the slope of the rising curve of wind noise data meets the preset value, the maximum instantaneous speed increment is selected; the interval control time is related to the jump time of wind noise data;

[0105] When the electronic device is actually used, according to the current power consumption mode of the electronic device, in the process of using the optimal parameter combination corresponding to the cooling fans, based on the instantaneous increase in speed, the speed of each cooling fan is increased for an interval adjustment time, and then the speed of each cooling fan is reduced back to the original value, waiting for the arrival of the next interval adjustment time;

[0106] Among them, the interval length between the two interval adjustments is related to the recovery time of the wind noise data after speed reduction.

[0107] In this embodiment, in certain high power consumption modes, the heat dissipation demand will increase dramatically, and the fan speed may need to be increased instantaneously to quickly cool down. The instantaneous increase in speed will cause a rapid increase in wind noise data. In this case, the artificial intelligence model can be further used to analyze the impact of the operation time after the fan speed increase on the wind noise data rising curve under different parameter combinations, thereby determining the change trend of the wind noise data.

[0108] Then, under the premise that the slope of the wind noise data rising curve meets the preset value (that is, the rate of wind noise data rising is within an acceptable range), the maximum speed instantaneous increment is selected. This can maximize the heat dissipation efficiency while avoiding the excessive increase of wind noise data.

[0109] The time required for the wind noise data to jump from the value before the speed increase to the extreme value of noise fluctuation acceptable to the human ear (that is, once the extreme value is exceeded, the noise change can be clearly felt), is called the jump duration. When the noise level suddenly rises above a certain threshold, the human ear will clearly feel the change in noise, which may cause discomfort or distraction. The extreme value of noise fluctuation refers to the maximum acceptable fluctuation range of the noise level without causing obvious discomfort to the user.

[0110] Among them, the interval control duration is related to the jump duration of the wind noise data. It is necessary to ensure that the interval control duration ends before the wind noise data rises to the extreme value of the noise fluctuation. The interval control duration can be set to be less than the jump duration of the wind noise data.

[0111] Based on the analysis results of the artificial intelligence model, the instantaneous increase in speed corresponding to each fan speed in each optimal parameter combination is obtained. These values ​​are optimized parameters based on the power consumption mode and the rising curve of wind noise data.

[0112] When electronic devices are actually used, the optimal parameter combination of the cooling fan is used according to the current power consumption mode. In the high power consumption mode that requires rapid cooling, the speed of each cooling fan is increased intermittently according to the instantaneous increase in speed. After the speed is increased, the fan needs to run at a high speed for a period of time (i.e., the interval control time) to complete the heat dissipation task. After the interval control time is over, the speed of each cooling fan is reduced back to the original value to reduce the wind noise data and return to a lower noise level.

[0113] After the speed drops back to the original value, it takes a certain amount of time for the wind noise data to return to normal levels. The interval between the two interval adjustments is related to the recovery time of the wind noise data. For example, if the recovery time of the wind noise data is 15 seconds, the interval can be set to 15 seconds to ensure that the wind noise data has returned to normal levels before the next speed increase.

[0114] On the basis of an embodiment, a corresponding instantaneous speed increment is provided for each optimal parameter combination, and when necessary, the heat dissipation efficiency can be quickly improved by the instantaneous speed increment to ensure that the temperature of the electronic device is within a safe range. At the same time, by reducing the speed back to the original speed after the interval control period ends, long-term high-speed operation is avoided, and the continuous increase of wind noise data is reduced. Moreover, the interval time is related to the recovery time of the wind noise data, ensuring that the next speed increase is performed only after the wind noise data returns to a normal level, thereby achieving smooth noise control.

[0115] In this way, the fan speed increment, interval control duration and interval duration can be dynamically adjusted according to the requirements of different power consumption modes to achieve intelligent heat dissipation management.

[0116] Example of actual application scenario: When electronic devices are running in high-performance mode, power consumption increases sharply and the temperature rises rapidly. The control process starts the optimal parameter combination to control multiple cooling fans for cooling according to the current power consumption mode. On this basis, the fan speed is intermittently increased based on the instantaneous increase in speed to ensure rapid cooling while avoiding large fluctuations in wind noise data.

[0117] In one embodiment, the dynamic control strategy of the cooling fan is optimized by further analyzing the impact of the operation time of the fan speed increment on the rising curve of the wind noise data. By setting the instantaneous speed increment, interval control time and interval time, it is possible to quickly respond to the cooling demand in the high power consumption mode while effectively controlling the rise and recovery of the wind noise data. This method not only improves the intelligence level of the cooling system, but also further optimizes the user experience.

[0118] In one embodiment, based on the above embodiment, when the electronic device is actually used, after the step of using the optimal parameter combination corresponding to the cooling fans to cool the electronic device according to the current power consumption mode of the electronic device, the step further includes:

[0119] After the cooling process is executed for a preset time, if it is detected that the current temperature of the electronic device exceeds the warning temperature, the speed of the cooling fan is gradually increased until the current temperature drops below the warning temperature;

[0120] Feedback the speed of the cooling fan at each stage, the operation status of the associated electronic components, the temperature change data and the optimal parameter combination used in the current power consumption mode to the artificial intelligence model to update the artificial intelligence model;

[0121] Regenerate the optimal parameter combination based on the updated AI model.

[0122] In this embodiment, when the electronic device is actually used, the system uses the optimal parameter combination between the corresponding cooling fans to perform cooling according to the current power consumption mode. This operation needs to be performed for a period of time (ie, "preset duration") to ensure stable cooling effect.

[0123] Among them, the preset time length can be set accordingly according to actual heat dissipation requirements, such as setting it to 30 seconds to 1 minute.

[0124] Optionally, after the cooling process is performed for a preset time, the system continuously monitors the current temperature of the electronic device and determines whether the temperature exceeds the warning temperature.

[0125] The warning temperature is a pre-set temperature threshold used to determine whether the electronic device is in an overheated state. The setting of the warning temperature needs to be determined based on the specifications of the electronic device, the workload characteristics, and the temperature range of safe operation.

[0126] Optionally, if it is detected that the current temperature of the electronic device exceeds the warning temperature, it means that the initial cooling process fails to fully meet the heat dissipation demand, and therefore the heat dissipation capacity needs to be further enhanced.

[0127] Optionally, when the temperature exceeds the warning temperature, the system gradually increases the speed of the cooling fan. "Gradually" here means that the fan speed is increased in stages, rather than a large increase all at once. This can avoid the wind noise impact and unnecessary energy consumption caused by a sudden increase in fan speed.

[0128] Optionally, the speed increase amplitude and frequency can be optimized according to the balance between the system's heat dissipation requirements and wind noise control. For example, the speed increase amplitude can be 5% or 10% each time, and a period of time (such as 10 seconds) is allowed after each increase before the next adjustment.

[0129] The goal of gradually increasing the fan speed is to reduce the current temperature of the electronic device to below the warning temperature. Once the temperature of the electronic device drops below the warning temperature, the system stops further increasing the fan speed and maintains the current speed to maintain temperature stability.

[0130] When the temperature exceeds the warning temperature and the fan speed is gradually increased, the system records the fan speed at each stage. These speed data reflect the fan operation status under different heat dissipation requirements.

[0131] Optionally, the fan speed at each stage is associated with the following data:

[0132] Operation of electronic devices: may include current power consumption mode, load status, operating time, etc.;

[0133] Temperature change data: may include the extent to which the temperature exceeds the warning temperature, the speed at which the temperature drops, the final stable temperature value, etc.;

[0134] Optimal parameter combination used in the current power consumption mode: records the optimal fan parameter combination used for cooling in the current power consumption mode.

[0135] The above-mentioned correlation data is fed back to the artificial intelligence model. These data provide the model with dynamic cooling effects and fan control behaviors in actual operation, which helps the model further optimize its learning and prediction capabilities.

[0136] The AI ​​model is updated and trained based on the latest feedback data. By analyzing the relationship between fan speed, temperature changes, and operating conditions, the model can learn more cooling strategies, especially how to respond when the temperature exceeds the warning temperature. The goal of the updated model is to further optimize the dynamic adjustment strategy of fan speed while meeting the cooling requirements, and optimize the balance between heat dissipation and wind noise.

[0137] Based on the updated AI model, the optimal fan parameter combination for electronic devices in different power consumption modes is regenerated. The new parameter combination will more accurately match the actual cooling requirements and optimize the dynamic control strategy of the fan.

[0138] In future practical applications, the system will use the updated optimal parameter combination for cooling to ensure that electronic devices can achieve the best heat dissipation effect under different working conditions.

[0139] In this way, the dynamics and responsiveness of heat dissipation control are enhanced through the temperature warning mechanism and dynamic fan speed adjustment. At the same time, by feeding back real-time data to the artificial intelligence model, the updated model can more intelligently generate the optimal parameter combination to optimize the heat dissipation effect and wind noise control. This solution not only improves the reliability and intelligence level of the heat dissipation system, but also provides strong support for the stable operation of electronic devices in complex working environments.

[0140] In one embodiment, based on the above embodiment, when the electronic device is actually used, after the step of using the optimal parameter combination corresponding to the cooling fans to cool the electronic device according to the current power consumption mode of the electronic device, the step further includes:

[0141] After the cooling process is performed and the current temperature of the electronic device is controlled below the warning temperature, the current temperature of the electronic device is continuously monitored and updated, and the maintenance time of the current rotation speed is recorded;

[0142] If the current temperature is detected to be rising, the speed of the cooling fan is gradually increased until the rising trend is curbed and the current temperature is controlled below the warning temperature;

[0143] Feedback the speed and duration of each stage of the cooling fan, the operation status of the associated electronic components, the temperature change data and the optimal parameter combination used in the current power consumption mode to the artificial intelligence model to update the artificial intelligence model;

[0144] Based on the updated artificial intelligence model, the optimal parameter combination of the time-sharing control strategy with multiple speed ranges is generated.

[0145] In this embodiment, when the electronic device is actually used, the system uses the corresponding fan optimal parameter combination to perform cooling processing according to the current power consumption mode, and ensures that the current temperature of the electronic device is controlled below the warning temperature.

[0146] After the cooling process is executed, the system continuously monitors the current temperature of the electronic components to detect any temperature change trend in time. At the same time, the system records the maintenance time of the current fan speed, that is, the time the fan runs continuously at a certain speed, which helps to analyze the stability and continuity of the cooling effect at a specific speed.

[0147] If it is monitored that the current temperature of the electronic device shows an increasing trend, that is, the temperature continues to rise over a period of time, it means that the current heat dissipation effect may not be sufficient to meet the current heat dissipation needs.

[0148] To curb the temperature rise trend, the system gradually increases the speed of the cooling fan. This gradual increase can avoid sudden increases in wind noise and energy consumption, while effectively enhancing the heat dissipation capacity. The speed is adjusted until the temperature rise trend is curbed and the current temperature is controlled below the warning temperature again.

[0149] The fan speed at each stage and its corresponding maintenance duration are recorded, and these data are associated with the following information:

[0150] Operation of electronic devices: This may include power consumption mode, load conditions, operating time, etc.

[0151] Temperature change data: can record the temperature change trend, heating rate, cooling rate, etc.

[0152] The optimal parameter combination used in the current power consumption mode: that is, the fan parameter combination used in the current power consumption mode of the electronic device, which is also the optimal parameter combination recommended by the previous artificial intelligence model.

[0153] The collected data is fed back to the AI ​​model for further training and optimization. The AI ​​model learns from these real-time data to optimize its ability to predict temperature trends and improves the fan speed control strategy to more effectively cope with temperature fluctuations.

[0154] At this stage, the AI ​​model has received feedback data from the actual operation process, including the duration of the fan at different speeds, the operation of electronic components, temperature change data, and the optimal parameter combination used at the time. This data is used as a training set for the model to help the model learn how to more effectively control the fan speed under different conditions to cope with temperature changes.

[0155] The training goal of the model at this stage is to learn how to dynamically adjust the fan speed under different time and temperature conditions to achieve the best heat dissipation effect and the lowest wind noise level. This involves predicting the temperature change trend and adjusting the fan speed in advance accordingly.

[0156] Based on the trained model, a time-sharing, multi-speed control strategy can be generated. This strategy takes into account the time series and temperature changes, divides the entire operation cycle into multiple time periods, and sets different fan speeds in each time period.

[0157] Time is divided into different segments based on the usage patterns and historical data of electronic devices. In each time period, the fan speed is set accordingly based on the temperature change trend predicted by the model. If the temperature is predicted to rise, the speed is increased in advance; if the temperature is predicted to drop, the speed is appropriately reduced to save energy and reduce noise.

[0158] Finally, based on the learning results of the artificial intelligence model, an optimal parameter combination containing multiple time periods and corresponding fan speeds is generated. This combination needs to meet the following conditions:

[0159] (1) Temperature control: Ensure that the temperature of electronic devices remains within a safe range and does not exceed the warning temperature at all times;

[0160] (2) Energy consumption and noise optimization: While ensuring temperature control, minimize the fan's energy consumption and noise to improve user experience.

[0161] The generated optimal parameter combination will be implemented in actual applications and its effect will be continuously monitored. By collecting actual operation data, it is evaluated whether the temperature control meets expectations and whether the wind noise and energy consumption are optimized. Based on the evaluation results, the model parameters can be further adjusted and the control strategy can be iteratively optimized.

[0162] In this way, the AI ​​model can learn and adapt to the heat dissipation requirements of electronic devices under different working conditions, generate dynamic and time-sharing fan speed control strategies, and thus achieve more intelligent and efficient heat dissipation management.

[0163] Based on the updated artificial intelligence model, the optimal parameter combination of the time-sharing control strategy with multiple speeds is generated. Different fan speeds are set according to different time stages and temperature change trends to achieve more refined temperature control and energy consumption management.

[0164] In future practical applications, the system will adopt a new optimal parameter combination to adjust the fan speed in stages according to the time series and temperature changes to keep the temperature of electronic components within a safe range while minimizing wind noise and energy consumption.

[0165] In one embodiment, by continuously monitoring the temperature changes of electronic devices and dynamically adjusting the fan speed according to the temperature trend, more intelligent and refined heat dissipation management is achieved. By feeding back the actual operation data to the artificial intelligence model and continuously optimizing the fan control strategy, the system can maintain the best heat dissipation effect and the lowest wind noise level under different working conditions, thereby improving the reliability of electronic devices and user experience.

[0166] In one embodiment, based on the above embodiment, after the step of gradually increasing the speed of the cooling fan if the current temperature is monitored to be increasing, until the temperature increase trend is curbed and the current temperature is controlled below the warning temperature, the step further includes:

[0167] If the current temperature is detected to be lower than the temperature value when the temperature is rising, the speed of the cooling fan is reduced;

[0168] If the current temperature does not show a rising trend after the speed of the cooling fan is reduced, the current speed is maintained;

[0169] If the current temperature shows an upward trend after the speed of the cooling fan is reduced, restore the previous speed.

[0170] In this embodiment, when it is monitored that the current temperature of the electronic device shows a rising trend, the system will gradually increase the speed of the cooling fan until the rising trend is curbed and the temperature is controlled below the warning temperature.

[0171] If after increasing the fan speed, the system detects that the current temperature is lower than the temperature value during the temperature increase trend, the system will gradually reduce the fan speed to save energy and reduce noise.

[0172] After reducing the fan speed, the system will continue to monitor the temperature changes to see if the temperature begins to rise.

[0173] Optionally, if the temperature does not show a rising trend, the current fan speed is maintained.

[0174] Optionally, if the temperature again shows an upward trend, the previous rotation speed is restored to ensure that the temperature does not exceed the warning range.

[0175] By dynamically adjusting fan speed in this way, the system can manage temperature more effectively, optimizing both energy usage and user experience.

[0176] Of course, if the current temperature does not show a rising trend after the speed of the cooling fan is reduced, then while maintaining the current speed, the relevant data of this speed reduction phase can also be fed back to the artificial intelligence model together with the speed and maintenance duration of the cooling fan in previous stages, the operation status of related electronic devices, temperature change data and the optimal parameter combination used in the current power consumption mode to update the artificial intelligence model; based on the updated artificial intelligence model, the optimal parameter combination of the time-sharing control strategy with multi-speed range is generated.

[0177] This process not only ensures the optimization of fan speed when the temperature is not rising, but also further improves the intelligence level of the system and the efficiency of heat dissipation management through data feedback and model updates. Through continuous optimization, the system can achieve the best heat dissipation effect and wind noise balance management under different operating conditions.

[0178] In one embodiment, based on the above embodiment, the method for controlling the cooling fan in a relatively closed space further includes:

[0179] When electronic devices are actually used, if the temperature control and wind noise balance strategy is selected, the electronic devices are cooled using the optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic devices;

[0180] Alternatively, when the electronic device is actually used, if the cooling priority strategy is selected, the rotation speed of each cooling fan is adjusted in real time according to the current temperature of the electronic device.

[0181] In this embodiment, the system may provide a relevant setting interface, allowing the user to select different cooling strategies according to actual needs, including a temperature control wind noise balance strategy and a cooling priority strategy.

[0182] Among them, under the temperature control and wind noise balance strategy, the system's goal is to meet the cooling needs of electronic devices while minimizing the wind noise generated when the cooling fan is running; under the cooling priority strategy, the system's primary goal is to ensure that the temperature of electronic devices is always kept within a safe range, and the optimization of wind noise is secondary.

[0183] Optionally, when the electronic device is actually used, its current power consumption mode is detected in real time.

[0184] Optionally, if the temperature control wind noise balance strategy is selected, the optimal parameter combination corresponding to the current power consumption mode is called according to the training results of the artificial intelligence model, which can minimize wind noise while meeting the cooling target. While meeting the cooling requirements, wind noise is optimized as much as possible to improve user experience.

[0185] Optionally, if the cooling priority strategy is selected, the speed of each cooling fan is dynamically adjusted according to the current temperature. If the temperature approaches or exceeds the safe range, the system will quickly increase the speed of the cooling fan to enhance the cooling effect; if the temperature drops to the safe range, the system can gradually reduce the fan speed to reduce energy consumption and wind noise. By dynamically adjusting the speed of the cooling fan, the temperature of the electronic device is always controlled within the safe range. In this way, the temperature of the electronic device is prioritized to be within the safe range, providing higher cooling efficiency.

[0186] In addition, a control device is also provided in an embodiment of the present application. The internal architecture of the control device can be as follows: Figure 2 As shown, it includes a processor, a memory, a communication interface and an input interface connected by a system bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database is used to store data called by the computer program. The communication interface is used to communicate data with an external terminal. The input interface is used to receive a signal input by an external device. When the computer program is executed by the processor, a method for regulating a cooling fan in a relatively confined space as described in the above embodiment is implemented.

[0187] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the present application scheme, and does not constitute a limitation on the control device to which the present application scheme is applied. For example, in some optional embodiments, the control device may also include an output interface (not shown in the figure), and the output interface is also connected to the system bus and is used to output corresponding signals to the peripheral device.

[0188] In addition, the present application also proposes a heat dissipation system, which includes a control device and multiple heat dissipation fans; each heat dissipation fan establishes a communication connection with the control device and is controlled by the control device; the specific structure of the control device refers to the above-mentioned embodiment. Since the heat dissipation system adopts all the technical solutions of all the above-mentioned embodiments, it has at least all the technical effects brought by the technical solutions of the above-mentioned embodiments, which will not be repeated here one by one.

[0189] In addition, the present application also proposes a computer-readable storage medium, the computer-readable storage medium includes a computer program, and when the computer program is executed by a processor, the steps of the method for controlling a cooling fan in a relatively closed space as described in the above embodiment are implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0190] In summary, the control method, control device, cooling system and computer-readable storage medium for cooling fans in a relatively confined space provided in the embodiments of the present application are based on artificial intelligence model learning and optimization, dynamically adjust the speed and direction of multiple cooling fans according to the actual power consumption mode of the electronic device, select the optimal fan parameter combination, realize intelligent cooling management of different power consumption modes of the electronic device, and optimize the wind noise data as much as possible to reduce the operating noise while ensuring the cooling effect, so as to achieve the best balance between cooling effect and noise control, thereby improving the user experience. It is suitable for heat dissipation and noise reduction processing of various electronic devices in relatively confined spaces (such as data centers, vehicle-mounted electronics, personal computers, etc.), and has broad application prospects.

[0191] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0192] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0193] The above description is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for controlling a cooling fan in a relatively confined space, characterized in that: include: Control electronic devices in a relatively confined space to operate in different power consumption modes, and in each power consumption mode, control multiple cooling fans to operate with different speed and direction parameter combinations; When multiple cooling fans are running with different parameter combinations, the temperature data of electronic components and the wind noise data outside a relatively closed space are collected; The relevant collected data is input into the artificial intelligence model for training, so that the artificial intelligence model learns the influence of different parameter combinations between cooling fans on temperature data and wind noise data under different power consumption modes of electronic devices; wherein, the training goal of the artificial intelligence model is set to optimize the wind noise data under the premise that the temperature data meets the cooling target under different power consumption modes of the electronic device, and obtain the optimal parameter combination of the rotation speed and direction between multiple cooling fans; Based on the training results of the artificial intelligence model, the optimal parameter combination between the cooling fans of electronic devices in different power consumption modes is generated; When the electronic device is actually used, the electronic device is cooled using an optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic device.

2. The method for controlling a cooling fan in a relatively closed space according to claim 1, characterized in that: An electric control valve is provided at the air guide port of the relatively closed space, and the opening and closing of the air guide port can be controlled based on the electric control valve; When multiple cooling fans are running with different parameter combinations, the opening and closing of the air ducts are adjusted, and the temperature data of electronic components and the wind noise data outside a relatively closed space are collected; The relevant collected data is input into the artificial intelligence model for training, so that the artificial intelligence model can learn the influence of different parameter combinations between cooling fans and different opening and closing conditions of air ducts under different power consumption modes of electronic devices on temperature data and wind noise data; wherein the training results of the artificial intelligence model include the corresponding relationship between the optimal parameter combination of the speed and direction of the cooling fans and the opening and closing conditions of the air ducts; When the electronic device is actually used, the optimal parameter combination between the cooling fans is used according to the current power consumption mode of the electronic device, and the air guide port is opened and closed accordingly to cool the electronic device.

3. The method for controlling a cooling fan in a relatively closed space according to claim 1, characterized in that: The artificial intelligence model also analyzes the influence of the operation time of each cooling fan speed increment on the rising curve of wind noise data under each parameter combination, and obtains the instantaneous speed increment corresponding to each optimal parameter combination, and the interval control time of the instantaneous speed increment; wherein, on the premise that the slope of the rising curve of wind noise data meets the preset value, the maximum instantaneous speed increment is selected; the interval control time is related to the jump time of wind noise data; When the electronic device is actually used, according to the current power consumption mode of the electronic device, in the process of using the optimal parameter combination corresponding to the cooling fans, based on the instantaneous increase in speed, the speed of each cooling fan is increased for an interval adjustment time, and then the speed of each cooling fan is reduced back to the original value, waiting for the arrival of the next interval adjustment time; Among them, the interval length between the two interval adjustments is related to the recovery time of the wind noise data after speed reduction.

4. The method for controlling a cooling fan in a relatively closed space according to any one of claims 1 to 3, characterized in that: When the electronic device is actually used, after the step of cooling the electronic device using the optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic device, the step further includes: After the cooling process is executed for a preset time, if it is detected that the current temperature of the electronic device exceeds the warning temperature, the speed of the cooling fan is gradually increased until the current temperature drops below the warning temperature; Feedback the speed of the cooling fan at each stage, the operation status of the associated electronic components, the temperature change data and the optimal parameter combination used in the current power consumption mode to the artificial intelligence model to update the artificial intelligence model; Regenerate the optimal parameter combination based on the updated AI model.

5. The method for controlling a cooling fan in a relatively closed space according to any one of claims 1 to 3, characterized in that: When the electronic device is actually used, after the step of cooling the electronic device using the optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic device, the step further includes: After the cooling process is performed and the current temperature of the electronic device is controlled below the warning temperature, the current temperature of the electronic device is continuously monitored and updated, and the maintenance time of the current rotation speed is recorded; If the current temperature is detected to be rising, the speed of the cooling fan is gradually increased until the rising trend is curbed and the current temperature is controlled below the warning temperature; Feedback the speed and duration of each stage of the cooling fan, the operation status of the associated electronic components, the temperature change data and the optimal parameter combination used in the current power consumption mode to the artificial intelligence model to update the artificial intelligence model; Based on the updated artificial intelligence model, the optimal parameter combination of the time-sharing control strategy with multiple speed ranges is generated.

6. The method for controlling a cooling fan in a relatively closed space as claimed in claim 5, characterized in that: If the current temperature is monitored to be increasing, the rotation speed of the cooling fan is gradually increased until the temperature increase trend is curbed and the current temperature is controlled below the warning temperature, the step further includes: If the current temperature is detected to be lower than the temperature value when the temperature is rising, the speed of the cooling fan is reduced; If the current temperature does not show a rising trend after the speed of the cooling fan is reduced, the current speed is maintained; If the current temperature shows an upward trend after the speed of the cooling fan is reduced, restore the previous speed.

7. The method for controlling a cooling fan in a relatively closed space according to claim 1, characterized in that: The method for controlling the heat dissipation fan in the relatively closed space also includes: When electronic devices are actually used, if the temperature control and wind noise balance strategy is selected, the electronic devices are cooled using the optimal parameter combination corresponding to the cooling fans according to the current power consumption mode of the electronic devices; Alternatively, when the electronic device is actually used, if the cooling priority strategy is selected, the rotation speed of each cooling fan is adjusted in real time according to the current temperature of the electronic device.

8. A control device, characterized in that: The control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method for controlling a cooling fan in a relatively closed space as described in any one of claims 1 to 7 are implemented.

9. A heat dissipation system, characterized in that: It includes a control device and a plurality of cooling fans; each cooling fan establishes a communication connection with the control device and is controlled by the control device; Wherein, the control device is the control device as claimed in claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for controlling a cooling fan in a relatively closed space as described in any one of claims 1 to 7 are implemented.

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