TW202325990A - Electronic system and method of dynamically adjusting fan speed - Google Patents

Abstract

A sample noise signal is measured when a fan operates at a predetermined speed in an anechoic environment. An overall environmental noise signal is measured when the fan operates at the predetermined speed in an echoic environment. A probability distribution value associated with the similarity between the sample noise signal and the overall environmental noise signal is acquired by analyzing the sample noise signal and the overall environmental noise signal. The speed of the fan is then adjusted based on the relationship between the probability distribution value and a threshold value.

Description

Electronic system and method for dynamically adjusting fan speed

The present invention relates to an electronic system and method for dynamically adjusting fan speed, in particular to an electronic system and method for dynamically adjusting fan speed according to the current environmental sound characteristics.

In the modern information society, computer systems have become an indispensable information tool for most people. In order to avoid power reduction or damage to components due to overheating, computer systems generally use fans to provide heat dissipation to dissipate the heat generated inside the device or suck in cold air from the outside of the device. The speed and static pressure of the fan determine the air flow of the fan. The noise when the fan is running is approximately proportional to the fifth root of its speed. The faster the speed, the stronger the heat dissipation capability, but the greater the noise.

In quiet environment applications, the fan will usually run at the lowest speed. Generally speaking, most users can accept that the fan noise is more than 3dB lower than the ambient noise, so the previous technology will control the fan speed according to the ambient sound signal power received by the receiver: when the overall power is greater than the critical value, the allowable fan noise is lower When the overall power is lower than the critical value, the allowable fan noise is lower, so the fan speed will be reduced to avoid affecting the user's perception. However, the premise of the operation of the prior art is to assume that the noise signal of the fan corresponds to the influence of the receiver as a fixed power, but in fact, when the fan is installed on a mobile device, it may move continuously during operation, and in different environments The noise signal of the fan is constantly changing relative to the influence of the receiver, especially when it is close to the wall and causes the sound wave to reflect.

Therefore, there is a need for an electronic system and method that can dynamically adjust the fan speed according to the characteristics of the current ambient sound, thereby taking heat dissipation and noise reduction into consideration.

The invention provides an electronic system for dynamically adjusting the fan speed, which includes a fan, a sound receiving device and a controller. The fan operates according to a fan control signal to provide heat dissipation. The radio device is used to detect a first sample noise signal when the fan operates at a first rotational speed in a silent environment, and to detect a noise signal when the fan operates at the first rotational speed in a noisy environment Generated noise to provide a first overall ambient noise signal for the electronic system. The controller is used to provide the fan control signal according to a mode signal, analyze the first sample noise signal and the first overall ambient noise signal to obtain a correlation between the first sample noise signal and the first overall ambient noise signal A first probability distribution of the degree of similarity between them, and dynamically adjust the rotational speed of the fan according to the magnitude relationship between the first probability distribution and a first critical value.

The present invention also provides a method for dynamically adjusting the speed of a fan, which includes detecting a first sample noise signal when a fan in an electronic system operates at a first speed in a silent environment; in a noisy environment Detecting the noise generated when the fan operates at the first speed to provide a first overall environmental noise signal of the electronic system; analyzing the first sample noise signal and the first overall environmental noise signal to obtain a correlation A first probability distribution of similarity between the first sample noise signal and the first overall ambient noise signal; and dynamically adjusting the fan speed according to the magnitude relationship between the first probability distribution and a first critical value .

FIG. 1 is a functional block diagram of an electronic system 100 that dynamically adjusts the fan speed according to the characteristics of the current ambient sound in an embodiment of the present invention. The electronic system 100 includes a processor 10 , a fan 20 , a radio device 30 , a controller 40 , and a memory unit 50 .

The processor 10 can be a central processing unit (central processing unit, CPU) or a graphics processing unit (graphics processing unit, GPU), which is a key computing engine in the electronic system 100, responsible for executing instructions and programs required by the operating system , is also the main source of waste heat in the electronic system 100 .

The fan 20 can have different structures depending on its type, mainly using a motor to drive the fan blades to rotate, so as to bring cooler air to the inside of the chassis and discharge hotter air inside to achieve heat dissipation. In the present invention, the fan 20 will operate according to a fan control signal S _FG provided by the controller 40. The larger the value of the fan control signal S _FG , the faster the motor speed in the fan 20 and the stronger the heat dissipation effect. Makes a lot of noise. During operation of the electronic system 100 , the fan 20 is usually the main source of noise. In one embodiment, the fan control signal S _FG can be a pulse width modulation (PWM) square wave signal, which adjusts the motor speed of the fan 20 by changing its duty cycle. In one embodiment, the fan 20 can be an axial fan or a centrifugal fan. However, the type and driving method of the fan 20 do not limit the scope of the present invention.

The sound receiving device 30 is used to pick up noise when the electronic system 100 is running, and output a corresponding noise signal to the controller 40 . In one embodiment, the sound receiving device 30 may be a digital Micro Electro Mechanical System (MEMS) microphone, which has performances such as high heat resistance, high vibration resistance and high resistance to radio frequency interference. However, the type of the sound receiving device 30 does not limit the scope of the present invention.

The controller 40 can control the operation of the processor 10 and the fan 20 according to a mode signal S _MODE . The value of the mode signal S _MODE can determine the operation mode of the fan 20 , such as operating in a performance mode, an optimal mode and a silence mode. In the high-efficiency mode, the fan 20 will operate at a higher speed, and at this time, the heat dissipation effect is stronger, but it will also generate greater noise. In the optimization mode, the speed of the fan 20 is automatically adjusted according to the temperature of the processor 10 . In the quiet mode, the fan 20 will operate at a lower speed, and the noise generated at this time is the smallest, but the heat dissipation effect is limited.

The memory unit 50 can be used to store the data required for the operation of the electronic system 100 and the data obtained during the operation. The memory unit 50 can be a random access memory (random access memory, RAM), a flash memory (flash), or various forms of hard disks. However, the implementation of the memory unit 50 does not limit the scope of the present invention.

FIG. 2 is a schematic diagram of the implementation of the electronic system 100 in the embodiment of the present invention. In the embodiment shown in Fig. 2, the electronic system 100 can be a notebook computer, the sound receiving device 30 is arranged on the upper casing and is positioned above the screen, and the processor 10, the fan 20, the controller 40 and the memory unit 50 is set in the lower case. However, the implementation of the electronic system 100 does not limit the scope of the present invention.

FIG. 3 is a flow chart of the electronic system 100 in the embodiment of the present invention when dynamically adjusting the fan speed according to the current environmental sound characteristics, which includes the following steps:

Step 310: In a silent environment, the sound receiving device 30 detects the sample noise signals S _X1 -S _XN when the fan 20 operates at N speeds SP ₁ -SP _N.

Step 320: In normal circumstances, the controller 40 provides the fan control signal S _FG according to the mode signal S _MODE .

Step 330: In a normal environment, when the fan 20 operates at the first speed SP ₁ according to the fan control signal S _FG , the audio receiving device 30 detects the overall environmental noise signal S _Y1 of the electronic system 100 .

Step 340: The controller 40 analyzes the _sample noise signal S X1 corresponding to the first rotational speed SP ₁ and the overall environmental noise signal S _Y1 to obtain the probability distribution of the similarity between the relevant sample noise signal S _X1 and the overall environmental noise signal S _Y1 P _XY1 .

Step 350: The controller 40 dynamically adjusts the speed of the fan 20 according to the relationship between the probability distribution P _XY1 and a first threshold TH1.

Step 360: In a normal environment, when the fan 20 operates at the n-th speed SP _n according to the fan control signal S _FG , the audio receiving device 30 detects the overall environmental noise signal S _Yn of the electronic system 100 .

Step 370: The controller 40 analyzes the sample noise signal S _Xn and the overall environmental noise signal S _Yn corresponding to the nth rotational speed SP _n to obtain the probability distribution of the similarity between the relevant sample noise signal S _Xn and the overall environmental noise signal S _Yn P _XYn .

Step 380: When the probability distribution P _XYn is smaller than a second threshold TH2 for more than a predetermined time, store the overall environmental noise signal S _Yn into the memory unit 50 .

Step 390: The controller 40 judges whether the fan 20 has been operating for more than a predetermined period in a normal environment? If yes, go to step 400 ; if not, go to step 320 .

Step 400 : Update the sample noise signals S _X1 -S _XN according to all the stored overall environmental noise signals; go to step 320 .

In step 310, the fan 20 operates at different speeds in a silent environment, and the sound receiving device 30 obtains sample noise signals S _X1 -S _XN when the fan 20 operates at N speeds SP ₁ -SP _N , where N is An integer greater than 1. The rotation speed of the fan 20 will only affect the narrow-band noise frequency. The faster the rotation speed of the fan 20 , the frequency response characteristics of the broadband noise in the sample noise signals S _X1 -S _XN will remain the same, but the amplitude will increase. Therefore, when the fan 20 operates at different speeds in an audible environment, the obtained eigenvalues of the sample noise signals S _X1 -S _XN related to N speeds can be used as initial built-in samples. In one embodiment, the anechoic environment can be an anechoic room, an anechoic box, or any experimental environment without a reflection field, but the scope of the present invention is not limited.

In step 320, when the electronic system 100 operates in a normal environment, the controller 40 provides a fan control signal S _FG according to the mode signal S _MODE . As mentioned above, the value of the mode signal S _MODE can determine the operation mode of the fan 20 , for example, operate in the high-efficiency mode, the optimization mode and the quiet mode.

In step 330 , when the fan 20 operates at the first rotational speed _SP1 according to the fan control signal S _FG in a normal environment, the audio receiving device 30 detects the overall environmental noise signal S _Y1 of the electronic system 100 . When the electronic system 100 operates in different environments, the noise signal of the fan 20 corresponding to the influence of the sound receiving device 30 will constantly change, especially when the sound wave is reflected when it is close to the wall. Since the overall ambient noise signal S _Y1 may come from both the operation of the fan 20 and the background environment, the controller 40 analyzes the sample noise signal S _X1 corresponding to the first rotational speed SP ₁ and the overall ambient noise signal S _Y1 in step 340 , In order to obtain the probability distribution P _XY1 of the similarity between the sample noise signal S _X1 corresponding to the first rotational speed SP ₁ and the overall environmental noise signal S _Y1 .

In one embodiment, the controller 40 uses deep learning to compare the difference in eigenvalues between the sample noise signal S _X1 and the overall environmental noise signal S _Y1 , if the sample noise signal S corresponding to the first rotational speed SP ₁ The more similar the eigenvalues of _X1 and the overall environmental noise signal S _Y1 are, the larger the value of the probability distribution P _XY1 is. Similarly, if the characteristic values of the sample noise signal S _X1 corresponding to the first rotational speed _SP1 and the overall environmental noise signal S _Y1 are different, the value of the probability distribution P _XY1 is smaller.

In step 350 , the controller 40 dynamically adjusts the speed of the fan 20 according to the relationship between the probability distribution P _XY1 and the first threshold TH1 . When the value of the probability distribution P _XY1 is greater than the first critical value TH1, it means that the overall environmental noise signal S _Y1 mainly comes from the operation of the fan 20 (the allowable fan noise is relatively small), so the controller 40 will adjust the fan control signal S _FG to reduce the speed of the fan 20; when the value of the probability distribution P _XY1 is not greater than the first critical value TH1, it means that the overall environmental noise signal S _Y1 mainly comes from the background environment (the allowable fan noise is relatively large), so the controller 40 adjusts the fan control signal S _FG to increase the speed of the fan 20 .

After a long time of use, the sound characteristics of the noise generated by the fan 20 may change, so that the sample noise signals S _X1 -S _XN obtained in step 310 are no longer accurate. Therefore, in the steps 360-400 of the present invention, the sample noise signals S _X1 -S _XN are updated according to the operation status of the fan 20 within a predetermined period.

In step 360, in a general environment, when the fan 20 is operating at the nth speed SP _n according to the fan control signal S _FG , the audio receiving device 30 will detect the overall environmental noise signal S _Yn of the electronic system 100, where n is greater than 1 and An integer not greater than N. In step 370, the controller 40 analyzes the sample noise signal S Xn corresponding to the nth rotational speed SP _n and the overall environmental noise signal S _{Yn to obtain the similarity between the relevant sample noise signal S Xn and} the overall environmental noise signal S _Yn The probability distribution P _XYn .

In one embodiment, the controller 40 will use deep learning to compare the feature value difference between the sample noise signal S _Xn and the overall environmental noise signal S _Yn , if the sample noise signal S _Xn corresponding to the nth rotational speed SP _n and the overall environment The more similar the eigenvalues of the noise signal S _{Yn are} , the larger the value of the probability distribution P _XYn is. Similarly, if the characteristic values of the sample noise signal S _Xn corresponding to the nth rotational speed SP _n are different from the overall environmental noise signal S _Yn , the value of the probability distribution P _XYn is smaller.

In step 380, when the controller 40 determines that the value of the probability distribution P _XYn is not less than the second critical value TH2, it means that the sample noise signals S _X1 -S _XN obtained in step 310 can still accurately reflect the operating characteristics of the fan 20. will not record the overall environmental noise signal S _Yn corresponding to the nth speed SP _n ; when the controller 40 determines that the value of the probability distribution P _XYn is less than the second critical value TH2 and does not exceed the predetermined time, it means that it may be a short-term external noise influence, and the sample noise signals S _X1 -S _XN obtained in step 310 can still accurately reflect the operating characteristics of the fan 20, and will not record the overall environmental noise signal S _Yn corresponding to the nth speed SP _n at this time; when the controller 40 When the value of the probability distribution P _XYn is determined to be less than the second critical value TH2 for more than a predetermined time, it means that the sample noise signals S _X1 -S _XN obtained in step 310 can no longer accurately reflect the operating characteristics of the fan 20, and the corresponding nth rotation speed will be recorded at this time The overall ambient noise signal S _Yn of SP _n .

As mentioned above, the rotation speed of the fan 20 only affects the narrow-band noise frequency. The faster the rotation speed of the fan 20 , the frequency response characteristic of the broadband noise in the sample noise signals S _X1 -S _XN remains unchanged, but the amplitude increases. Therefore, in the present invention, the second threshold TH2 is much smaller than the first threshold TH1.

In the present invention, the first rotation speed SP ₁ corresponds to the quiet mode of the fan 20 , and the nth rotation speed SP _n corresponds to the high-efficiency mode or the optimization mode of the fan 20 . That is, the value of the first rotational speed _SP1 is smaller than the value of the nth rotational speed _SPn .

In step 390, the controller 40 determines whether the fan 20 has been operating for more than a predetermined period in a normal environment. When the fan 20 has not been operated for more than a predetermined period in a general environment, steps 360-380 may be executed multiple times, and multiple overall environmental noise signals are stored. When the fan 20 operates in a normal environment for more than a predetermined period, the sound characteristics of the noise generated by the fan 20 may change. At this time, in step 400, the sample noise signal S will be updated according to all the stored overall environmental noise signals. _X1 -S _XN , so that the updated sample noise signals S _X1 -S _XN can accurately reflect the operation characteristics of the fan 20 after the operation exceeds a predetermined period.

To sum up, the present invention uses deep learning to determine the probability that the current overall environmental noise signal is caused by the fan, and then adjusts the fan speed accordingly. Therefore, the present invention can provide an electronic system and method for dynamically adjusting the rotation speed of the fan according to the characteristics of the current ambient sound, thereby taking into account environmental changes, heat dissipation, and noise reduction at the same time. The above descriptions are only preferred embodiments of the present invention, and all equivalent changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope of the present invention.

10: Processor 20: Fan 30: Radio device 40: Controller 50: Memory unit 100: Electronic system 310-400: Step S _FG : Fan control signal S _MODE : Mode signal

FIG. 1 is a functional block diagram of an electronic system that dynamically adjusts the fan speed according to the characteristics of the current ambient sound in an embodiment of the present invention. Fig. 2 is a schematic diagram of the implementation of the electronic system in the embodiment of the present invention. FIG. 3 is a flow chart of the electronic system dynamically adjusting the fan speed according to the current environmental sound characteristics in the embodiment of the present invention.

310-400: Steps

Claims (10)

An electronic system for dynamically adjusting fan speed, which includes: A fan is used to operate according to a fan control signal to provide heat dissipation; A radio device for: detecting a first sample noise signal when the fan is operating at a first speed in an audible environment; and detecting the noise generated by the fan operating at the first rotational speed in a noisy environment to provide a first overall ambient noise signal of the electronic system; a controller for: providing the fan control signal according to a mode signal; analyzing the first sample noise signal and the first global ambient noise signal to obtain a first probability distribution related to the degree of similarity between the first sample noise signal and the first global ambient noise signal; and The rotational speed of the fan is dynamically adjusted according to the magnitude relationship between the first probability distribution and a first critical value. The electronic system as claimed in claim 1, wherein the controller is additionally used for: When it is determined that the value of the first probability distribution is greater than the first critical value, adjusting the fan control signal to reduce the value of the first rotational speed; and When it is determined that the value of the first probability distribution is not greater than the first critical value, the fan control signal is adjusted to increase the value of the first rotational speed. The electronic system as described in claim 1, wherein: The radio is also used for: detecting a second sample noise signal of the fan operating at a second rotational speed in the audible environment; and detecting the noise generated by the fan operating at the second rotational speed in the loud environment to provide a second overall ambient noise signal of the electronic system; The controller is also used to: analyzing the second sample noise signal and the second overall ambient noise signal to obtain a second probability distribution related to the degree of similarity between the second sample noise signal and the second overall ambient noise signal; storing the second overall ambient noise signal when it is determined that the second probability distribution is less than a second critical value for more than a predetermined time; and updating the first sample noise signal according to the second overall ambient noise signal when it is determined that the fan has been operating in the loud environment for more than a predetermined period of time; and The second rotational speed is greater than the first rotational speed. The electronic system as described in claim 3, wherein the controller uses deep learning (deep learning) to analyze the first sample noise signal and the first overall environmental noise signal to obtain the first probability distribution, and analyze The second sample noise signal and the second overall environmental noise signal are used to obtain the second probability distribution. The electronic system as described in claim 3, further comprising a memory unit for storing the first sample noise signal, the first overall ambient noise signal, the second sample noise signal, and the second overall ambient noise signal. A method for dynamically adjusting fan speed, comprising: Detecting a first sample noise signal when a fan in an electronic system operates at a first rotational speed in an audible environment; detecting the noise generated by the fan operating at the first rotational speed in a noisy environment to provide a first overall ambient noise signal of the electronic system; analyzing the first sample noise signal and the first global ambient noise signal to obtain a first probability distribution related to the degree of similarity between the first sample noise signal and the first global ambient noise signal; and The rotational speed of the fan is dynamically adjusted according to the magnitude relationship between the first probability distribution and a first critical value. The method as described in claim item 6, which further includes: Deep learning is used to analyze the first sample noise signal and the first overall environmental noise signal to obtain the first probability distribution. The method as described in claim item 6, which further includes: When it is determined that the value of the first probability distribution is greater than the first critical value, lowering the value of the first rotational speed; and When it is determined that the value of the first probability distribution is not greater than the first critical value, the value of the first rotational speed is increased. The method as described in claim item 6, which further includes: detecting a second sample noise signal when the fan operates at a second rotational speed in the silent environment; detecting the noise generated by the fan operating at the second rotational speed in the loud environment to provide a second overall ambient noise signal of the electronic system; analyzing the second sample noise signal and the second overall ambient noise signal to obtain a second probability distribution related to the degree of similarity between the second sample noise signal and the second overall ambient noise signal; storing the second overall ambient noise signal when it is determined that the second probability distribution is less than a second critical value for more than a predetermined time; and When it is determined that the fan has been operating in the noisy environment for more than a predetermined period, the first sample noise signal is updated according to the second overall ambient noise signal, wherein the second rotational speed is greater than the first rotational speed. The method as described in Claim 9, which further includes: Deep learning is used to analyze the second sample noise signal and the second overall environmental noise signal to obtain the second probability distribution.