Parameter determination method and electronic device

Abstract

The application discloses a parameter determination method and an electronic device. The method comprises the following steps: obtaining a first parameter optimization model for a first scene; determining a first value of a to-be-optimized parameter based on the first parameter optimization model; obtaining a first test result corresponding to the first value; the first test result comprises performance information and power consumption information of a first device; and determining an optimal value of the to-be-optimized parameter based on the first value and the first test result. Through the application, the optimal value of the to-be-optimized parameter in different scenes can be automatically explored, and the accuracy of parameter optimization can be improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the computer field, and in particular, to a parameter determination method and an electronic device. BACKGROUND

[0002] In order to improve user experience, the parameters of a mobile phone can be optimized for different use scenarios. In the process of mobile phone optimization, how to balance performance and power consumption, and reduce power consumption on the premise of maintaining performance, is an important goal of mobile phone optimization. For example, for the use scenario of full-screen video playback of a mobile phone, the frequency of the CPU of the mobile phone can be optimized to reduce power consumption, reduce the temperature of the mobile phone, and prolong the use time of the mobile phone while ensuring that the video playback does not drop frames.

[0003] Currently, there are two methods for parameter optimization of a mobile phone: one is a manual confirmation method, that is, a certain parameter is selected through multiple tests or heuristics, but the manual confirmation method is labor-intensive and has no clear parameter test direction, and cannot accurately find the optimal value of the parameter; the other is to approximately infer the parameter value of a new product based on the parameter value of a historical product, but the chip capability and architecture of the historical product and the new product may differ greatly, and the parameter of the historical product may not be suitable for the new product, thereby leading to inaccurate parameter setting. Therefore, how to accurately optimize the parameters of a mobile phone is a problem to be solved. SUMMARY

[0004] Embodiments of the present application provide a parameter determination method and an electronic device, based on the method described in the present application, which can accurately optimize the parameters of a mobile phone.

[0005] In a first aspect, the present application provides a parameter determination method, which comprises: obtaining a first parameter optimization model for a first scenario; determining a first value of a to-be-optimized parameter based on the first parameter optimization model; obtaining a first test result corresponding to the first value; the first test result comprising first performance information and first power consumption information of a first device; and determining an optimal value of the to-be-optimized parameter based on the first value and the first test result.

[0006] Based on the method described in the first aspect, the value of the to-be-optimized parameter in the next test point is determined by using the first parameter optimization model (such as the TPE model) based on Bayesian optimization, and the optimal point is found in multiple test points according to the optimization goal of increasing the frame rate and reducing the power consumption after multiple tests (for example, the Pareto optimization algorithm), at which time the value of the to-be-optimized parameter set by the optimal point is the optimal value of the to-be-optimized parameter. In this way, not only can the optimal value of the to-be-optimized parameter in different scenarios be automatically explored, but also the accuracy of parameter optimization can be improved.

[0007] In a possible implementation, the first scenario is a scenario of full-screen video playing of the first device, the to-be-optimized parameter is a central processing unit (CPU) frequency of the first device, the performance information of the first device includes a frame rate, and the power consumption information of the first device includes a power consumption.

[0008] In a possible implementation, the first value corresponding first test result is obtained by sending the first value to the first device, and receiving the first value corresponding first test result from the first device. The first test result is obtained by setting the first value in a test environment corresponding to the first scenario. In this way, the first device can assist in executing the parameter determination method, and the accuracy of parameter optimization can be improved.

[0009] In a possible implementation, after the first value corresponding first test result is obtained, the method further includes: processing the first value by using the first parameter optimization model to obtain a first prediction result corresponding to the first value. The first prediction result includes predicted first performance information and first power consumption information of the first device. Model parameters of the first parameter optimization model are adjusted based on the first test result and the first prediction result to obtain a second parameter optimization model. A second value of the to-be-optimized parameter is determined based on the second parameter optimization model. A second test result corresponding to the second value is obtained. The second prediction result includes predicted second performance information and second power consumption information of the first device. An optimal value of the to-be-optimized parameter is determined based on the first value and the first test result, including: determining the optimal value of the to-be-optimized parameter based on the first value, the first test result, the second value and the second test result. In this way, the accuracy of the parameter optimization model can be improved.

[0010] In a possible implementation, the determining the optimal value of the parameter to be optimized based on the first value, the first test result, the second value and the second test result comprises: in a case where the number of values of the parameter to be optimized determined is equal to a preset number, determining the optimal value of the parameter to be optimized from the first value and the second value based on the first value, the first test result, the second value and the second test result; in a case where the number of values of the parameter to be optimized determined is less than the preset number, processing the second value by calling the second parameter optimization model to obtain a second prediction result corresponding to the second value; the second prediction result comprises predicted second performance information and second power consumption information of the first device; adjusting model parameters of the second parameter optimization model based on the second test result and the second prediction result to obtain a third parameter optimization model; determining a third value of the parameter to be optimized based on the third parameter optimization model; obtaining a third test result corresponding to the third value; the third prediction result comprises predicted third performance information and third power consumption information of the first device; and determining the optimal value of the parameter to be optimized based on the first value, the first test result, the second value, the second test result, the third value and the third test result. Based on this manner, the accuracy of the parameter optimization model can be further improved.

[0011] In a possible implementation, the determining the optimal value of the parameter to be optimized from the first value and the second value based on the first value, the first test result, the second value and the second test result comprises: processing the first value, the first test result, the second value and the second test result by using a Pareto optimization algorithm to obtain the optimal value of the parameter to be optimized; the optimization target in the Pareto optimization algorithm indicates increasing frame rate and reducing power consumption, and the optimization direction in the Pareto optimization algorithm indicates improving the optimization target. Based on this manner, the accuracy of the parameter optimization can be further improved by using the Pareto optimization algorithm.

[0012] In a possible implementation, the first parameter optimization model is a TPE model established based on a mixed Gaussian distribution. Based on this manner, compared with a Gaussian regression model, the accuracy of the parameter optimization can be improved.

[0013] In a possible implementation, the obtaining the first parameter optimization model for the first scene comprises: obtaining a sample value of the parameter to be optimized for the first scene and a sample test result corresponding to the sample value; processing the sample value by calling an initial parameter optimization model to obtain a sample prediction result corresponding to the sample value; determining a loss value based on the sample prediction result and the sample test result; and adjusting model parameters of the initial parameter optimization model in a direction of reducing the loss value to obtain the first parameter optimization model. Based on this manner, the accuracy of model training can be improved.

[0014] In a second aspect, the present application provides a parameter determination apparatus, which can be an electronic device, a device in an electronic device, or a device that can be used in combination with an electronic device; wherein the parameter determination apparatus can also be a chip system, and the parameter determination apparatus can execute the method performed by the electronic device in the first aspect. The functions of the parameter determination apparatus can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above functions. The units can be software and / or hardware. The operations and advantages of the parameter determination apparatus can be found in the method of the first aspect and the advantages, and the repeated parts will not be described here.

[0015] In a third aspect, the present application provides a parameter determination apparatus, which includes a processor, and when the processor invokes a computer program in a memory, the method described in the first aspect is executed.

[0016] In a fourth aspect, the present application provides a parameter determination apparatus, which includes a processor and a memory, and the processor and the memory are coupled; the processor is used to implement the method described in the first aspect.

[0017] In a fifth aspect, the present application provides a parameter determination apparatus, which includes a processor, a memory and a transceiver, and the processor and the memory are coupled; the transceiver is used to transceive data, and the processor is used to implement the method described in the first aspect.

[0018] In a sixth aspect, the present application provides an electronic device, which includes one or more processors and one or more memories. The one or more memories are coupled with the one or more processors, and the one or more memories are used to store computer program codes, and the computer program codes include computer instructions, and when the one or more processors execute the computer instructions, the electronic device executes the parameter determination method in any possible implementation manner of the first aspect.

[0019] In a seventh aspect, the present application provides a chip, which includes a processor and an interface, and the processor and the interface are coupled; the interface is used to receive or output signals, and the processor is used to execute code instructions, so that the method described in the first aspect is executed.

[0020] In an eighth aspect, the present application provides a parameter determination system, which includes an electronic device; wherein the electronic device is used to execute the method described in the first aspect.

[0021] In a ninth aspect, the present application provides a parameter determination apparatus, which includes functions or units used to execute the method described in any one of the first aspect.

[0022] In a tenth aspect, the present application provides a computer readable storage medium, which stores a computer program, the computer program comprising program instructions, when the program instructions are executed on the parameter determination apparatus, causing the parameter determination apparatus to perform the parameter determination method in any possible implementation manner of the first aspect.

[0023] In an eleventh aspect, the present application provides a computer program product, when the computer program product is executed on a computer, causing the computer to perform the parameter determination method in any possible implementation manner of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a schematic diagram of a system architecture provided by an embodiment of the present application;

[0025] Figure 2 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present application;

[0026] Figure 3 is a software structure block diagram of an electronic device provided by an embodiment of the present application;

[0027] Figure 4 is a flowchart of a parameter determination method provided by an embodiment of the present application;

[0028] Figure 5A is a flowchart of a computer device performing operations provided by an embodiment of the present application;

[0029] Figure 5B is a flowchart of a computer device fitting out a first parameter optimization model for the first scene provided by an embodiment of the present application;

[0030] Figure 5C is a flowchart of a first device (mobile phone) performing operations provided by an embodiment of the present application;

[0031] Figure 5D is a flowchart of a computer device obtaining a second value and a second test result provided by an embodiment of the present application;

[0032] Figure 5E is a schematic diagram of two cases when determining the optimal value of the to-be-optimized parameter provided by an embodiment of the present application;

[0033] Figure 5F is a flowchart of operations performed when the number of values of the to-be-optimized parameter is equal to a preset number provided by an embodiment of the present application;

[0034] Figure 5G is a flowchart of operations performed when the number of values of the to-be-optimized parameter is less than a preset number provided by an embodiment of the present application;

[0035] Figure 6 is a flow diagram of another parameter determination method provided by the embodiments of the present application;

[0036] Figure 7 is a test result diagram based on the parameter determination method provided by the embodiments of the present application;

[0037] Figure 8 is a flow diagram of another parameter determination method provided by the embodiments of the present application;

[0038] Figure 9 is a structural diagram of a display device provided by the embodiments of the present application;

[0039] Figure 10 is a structural diagram of a chip provided by the embodiments of the present application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings. In the description of the embodiments of the present application, unless otherwise specified, “ / ” represents the meaning of or, for example, A / B can represent A or B; the “and / or” in the text only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. In addition, in the description of the embodiments of the present application, “multiple” means two or more than two.

[0041] Hereinafter, the terms “first” and “second” are only used for descriptive purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with “first” and “second” can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, the meaning of “multiple” is two or more than two.

[0042] The term "user interface (UI)" in the following embodiments of the present application is a medium interface for interaction and information exchange between an application program or an operating system and a user, which realizes conversion between internal forms of information and forms acceptable by the user. The user interface is source code written in specific computer languages such as Java and extensible markup language (XML), and the interface source code is parsed and rendered on an electronic device to finally present content recognizable by the user. A commonly used form of the user interface is a graphic user interface (GUI), which refers to a user interface related to computer operation displayed in a graphical manner. It can be visual interface elements such as time, date, text, icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, Widgets and the like displayed on the display screen of the electronic device.

[0043] In order to facilitate understanding of the scheme provided by the embodiments of the present application, the related concepts involved in the embodiments of the present application are introduced as follows:

[0044] 1. Gaussian model and Bayesian optimization

[0045] The Gaussian model is to accurately quantify things by using a Gaussian probability density function (normal distribution curve), and to decompose a thing into a plurality of models formed based on the Gaussian probability density function (normal distribution curve). According to the law of large numbers and the central limit theorem: no matter what the original distribution of things is, the probability distribution of the mean value of the sampling sample tends to be Gaussian distribution as the number of samples increases. Therefore, the Gaussian model can be used to fit the distribution of any known or unknown function.

[0046] Bayesian optimization is a black-box optimization algorithm for solving the extreme value problem of a function whose expression is unknown. The algorithm predicts the probability distribution of the function value at any point according to the function values at a set of sampling points, which is realized by Gaussian process regression. According to the results of Gaussian process regression, an acquisition function is constructed to measure the degree of exploration of each point value, and the extreme value of the acquisition function is solved to determine the next sampling point. Finally, the extreme value of the set of sampling points is returned as the extreme value of the function.

[0047] In this embodiment, a model based on a Gaussian mixture distribution, such as the TPE (tree-structured parzenestimator) model, is used. The TPE model is a tree-structured Bayesian optimization model used to solve the global optimization problem of black-box functions. Its core idea is to classify known data into two categories: good data and bad data, establishing two models: a good Gaussian mixture model f(x) and a poor Gaussian mixture model g(x). The optimal value of the model should be highly probable in the good Gaussian mixture model and less probable in the poor Gaussian mixture model. Therefore, the selection of the next test point is based on... The data point with the largest value is most likely the expected optimal extreme value point, meaning the next test point can be selected from these values. Maximizing the corresponding parameter value. In this way, the TPE model can adaptively adjust the size of the parameter search space and find the global optimum in as few iterations as possible. The TPE model is a classification parameter tuning model based on a regression model, and it performs better than the Gaussian regression model in practical applications.

[0048] 2. Pareto Optimization Algorithm

[0049] Pareto optimality is a commonly used optimization algorithm, also known as the Pareto front or Pareto optimization algorithm. Its core idea is to find a set of solutions to a multi-objective optimization problem such that it is impossible to obtain a better result by improving one objective without changing the others. The principle is to transform the multi-objective optimization problem into a single-objective optimization problem by weighted summation of multiple objective functions. Specifically, assuming there are n objective functions, each with a weight coefficient, our goal is to find a set of solutions that are optimal for each objective function. These optimal solutions constitute the Pareto optimal set, also known as the Pareto front.

[0050] 3. Optimize phone parameters

[0051] To improve user experience, phone parameters can be optimized for different usage scenarios. A key objective of phone optimization is balancing performance and power consumption, reducing power consumption while maintaining performance. For example, for scenarios involving full-screen video playback, the frequency of the phone's central processing unit (CPU) can be optimized to reduce power consumption, lower phone temperature, and extend battery life while ensuring smooth video playback without frame drops.

[0052] Currently, methods for parameter tuning of a mobile phone are classified into two categories: one is a manual confirmation method, that is, a certain parameter is selected through multiple tests or heuristics, but the manual confirmation method has a large workload and no clear parameter test direction, and cannot accurately find the optimal value of the parameter; the other is to approximately infer the parameter value on a new product from the parameter value on a historical product, but the chip capability and architecture of the historical product and the new product may differ greatly, and the parameter of the historical product may not be suitable for the new product, thereby leading to inaccurate parameter setting. Therefore, how to accurately tune the parameters of a mobile phone is a problem to be solved.

[0053] Therefore, in order to accurately tune the parameters of a mobile phone, the present application provides a parameter determination method and an electronic device. In specific implementation, as shown in Figure 1 The parameter determination method mentioned above can be executed by an electronic device 100, which can be a computer device or a first device; or the parameter determination method mentioned above can also be executed by the computer device and the first device together, which is not limited here.

[0054] The computer device can be a notebook computer, an ultra-mobile personal computer (UMPC), a desktop computer, a personal digital assistant (PDA), a server, etc., but is not limited thereto; the first device can be a mobile phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a wearable electronic device (such as a smart watch) with wireless communication function, a smart vehicle, etc., but is not limited thereto. In addition, the electronic device 100 is configured with a display screen and can be installed with a preset application (APP) and the like, which is not limited here.

[0055] It should be noted that the server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution servers (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0056] The hardware structure of the electronic device 100 will be introduced below. Please refer to Figure 2 , Figure 2 is a hardware structure schematic diagram of the electronic device 100 provided by the present application embodiment.

[0057] The electronic device 100 can include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headset interface 170D, a sensor module 180, a key 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 can include a pressure sensor 180A, a gyroscope sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0058] It can be understood that the structure shown in the embodiments of the present application does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 can include more or fewer components than shown, or combine certain components, or split certain components, or different arrangement of components. The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0059] The processor 110 can include one or more processing units, for example: the processor 110 can include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units can be independent devices, or can be integrated in one or more processors.

[0060] The controller can be the nerve center and command center of the electronic device 100. The controller can generate operation control signals according to instruction operation codes and timing signals, and complete the control of fetching instructions and executing instructions.

[0061] The processor 110 can also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory can hold instructions or data that the processor 110 has just used or is using repeatedly. If the processor 110 needs to use the instructions or data again, it can be directly called from the memory. This avoids repeated access and reduces the waiting time of the processor 110, thereby improving the efficiency of the system. The processor 110 calls the instructions or data stored in the memory to make the electronic device 100 perform the parameter determination method performed by the electronic device in the following method embodiments.

[0062] In some embodiments, the processor 110 can include one or more interfaces. The interfaces can include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0063] The charging management module 140 is configured to receive a charging input from a charger. The charger can be a wireless charger or a wired charger.

[0064] The power management module 141 is configured to connect the battery 142 and the charging management module 140 to the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140 to power the processor 110, the internal memory 121, the external memory, the display 194, the camera 193, and the wireless communication module 160, among other components. In some embodiments, the power management module 141 can also be disposed in the processor 110.

[0065] The wireless communication function of the electronic device 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor, among other components.

[0066] Antenna 1 and antenna 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in electronic device 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization of antennas. For example: antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In some other embodiments, antennas can be used in combination with tuning switches.

[0067] Mobile communication module 150 can provide a solution including 2G / 3G / 4G / 5G wireless communication applied on electronic device 100. Mobile communication module 150 can include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. Mobile communication module 150 can receive electromagnetic waves by antenna 1, and perform filtering, amplification, etc. on the received electromagnetic waves, and transmit the processed electromagnetic waves to a modem processor for demodulation. Mobile communication module 150 can also amplify signals modulated by the modem processor, and convert the amplified signals into electromagnetic waves radiated by antenna 1. In some embodiments, at least part of the functional modules of mobile communication module 150 can be arranged in processor 110. In some embodiments, at least part of the functional modules of mobile communication module 150 can be arranged in the same device as at least part of the modules of processor 110.

[0068] The modem processor can include a modulator and a demodulator. The modulator is used to modulate a low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used to demodulate a received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to a baseband processor for processing. The low-frequency baseband signal processed by the baseband processor is transmitted to an application processor.

[0069] The wireless communication module 160 can provide a solution for wireless communication including wireless local area networks (WLAN) (e.g., Wi-Fi network), Bluetooth (BT), BLE broadcasting, global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR) technology, etc. applied to the electronic device 100. The wireless communication module 160 can be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency-modulates and filters the electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 can also receive signals to be transmitted from the processor 110, frequency-modulate them, amplify them, and radiate them as electromagnetic waves via the antenna 2.

[0070] In some embodiments, the antenna 1 and the mobile communication module 150 of the electronic device 100 are coupled, and the antenna 2 and the wireless communication module 160 are coupled, so that the electronic device 100 can communicate with a network and other devices through wireless communication technology.

[0071] The electronic device 100 implements a display function through a GPU, a display screen 194, and an application processor, etc. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 110 can include one or more GPUs that execute program instructions to generate or change display information.

[0072] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. In some embodiments, the electronic device 100 can include 1 or N display screens 194, N being a positive integer greater than 1.

[0073] The electronic device 100 can implement a photographing function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor, etc. The ISP is used to process data fed back by the camera 193. The camera 193 is used to capture still images or videos. The digital signal processor is used to process digital signals, which can process not only digital image signals but also other digital signals. The video codec is used to compress or decompress digital videos. The electronic device 100 can support one or more video codecs.

[0074] The NPU is a neural-network (NN) computing processor that quickly processes input information by drawing on the structure of a biological neural network, such as the transmission mode between human brain neurons, and can also continuously self-learn.

[0075] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to implement a data storage function.

[0076] The internal memory 121 can be used to store computer executable program code, which includes instructions. The processor 110 executes various functional applications and data processing of the electronic device 100 by running the instructions stored in the internal memory 121. The internal memory 121 can include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as a sound playing function), and the like. The data storage area can store data (such as audio data) created during use of the electronic device 100, and the like. In addition, the internal memory 121 can include a high-speed random access memory, and can also include a non-volatile memory, such as a flash memory device, and the like.

[0077] The electronic device 100 can implement an audio function through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, the application processor, and the like. For example, music playing, recording, and the like.

[0078] The audio module 170 is used to convert digital audio information into an analog audio signal output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 can also be used to encode and decode an audio signal. In some embodiments, the audio module 170 can be disposed in the processor 110, or part of the functions of the audio module 170 can be disposed in the processor 110.

[0079] The speaker 170A, also known as a "loudspeaker", is used to convert an audio electrical signal into a sound signal. The receiver 170B, also known as a "earpiece", is used to convert an audio electrical signal into a sound signal. The microphone 170C, also known as a "microphone", "sound transducer", is used to convert a sound signal into an electrical signal. The earphone interface 170D is used to connect a wired earphone. The pressure sensor 180A is used to sense a pressure signal and can convert the pressure signal into an electrical signal.

[0080] In some embodiments, the pressure sensor 180A can be disposed on the display screen 194. The gyroscope sensor 180B can be used to determine the motion posture of the electronic device 100. The barometric pressure sensor 180C is used to measure the air pressure. The magnetic sensor 180D includes a Hall sensor. The acceleration sensor 180E can detect the magnitude of acceleration of the electronic device 100 in various directions (generally three axes). The distance sensor 180F is used to measure the distance. The proximity light sensor 180G can include, for example, a light emitting diode (LED) and a light detector. The ambient light sensor 180L is used to sense the ambient light brightness. The fingerprint sensor 180H is used to collect fingerprints. The temperature sensor 180J is used to detect the temperature. The touch sensor 180K, also known as a "touch panel". The touch sensor 180K can be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, also known as a "touch screen". The touch sensor 180K is used to detect touch operations acting on or near it. The bone conduction sensor 180M can obtain vibration signals. The keys 190 include power-on keys, volume keys, and the like. The motor 191 can generate a vibration prompt. The indicator 192 can be an indicator light, which can be used to indicate the charging state, the power change, and can also be used to indicate messages, missed calls, notifications, and the like. The SIM card interface 195 is used to connect the SIM card.

[0081] It can be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 can include more or fewer components than illustrated, or combine certain components, or split certain components, or different component arrangements. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

[0082] In addition, an operating system runs on the above-mentioned components. For example, an operating system such as iOS, Android, etc. The operating system of the electronic device 100 can adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. The embodiments of the present application take the Android system with a layered architecture as an example to exemplarily illustrate the software structure of the electronic device 100. It should be noted that although the embodiments of the present application are illustrated by taking the Android system as an example, the basic principles are also applicable to electronic devices of other operating systems.

[0083] Figure 3is a software structure block diagram of the electronic device 100 of the embodiment of the present application. The software structure adopts a layered architecture, which divides the software into several layers, each of which has a clear role and division of labor. The layers communicate with each other through software interfaces. In the embodiment of the present application, the operating system (taking the Android system as an example, which runs on the AP) can be divided into six layers, from top to bottom, which are application layer (application, APP), application framework layer (framework, FWK), Android runtime and system library, hardware abstraction layer (hardware abstraction layer, HAL), kernel layer and hardware layer.

[0084] Among them, the application layer can include a series of application packages. As shown in Figure 3 , the application package can include camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc. The application layer can also include system UI (systemUI), which is used to display the interface of the electronic device, such as the camera interface, etc.

[0085] The application framework layer provides application programming interface (application programming interface, API) and programming framework for the application of the application layer. The application framework layer includes some pre-defined functions. As shown in Figure 3 , the application framework layer can include window manager, content provider, view system, phone manager, resource manager, notification manager, camera service, etc., which are not limited by the embodiment of the present application.

[0086] The window manager is used to manage the window program. The window manager can obtain the size of the display screen, judge whether there is a status bar, lock the screen, intercept the screen, etc.

[0087] The content provider is used to store and obtain data, and make these data accessible to the application. The data can include video, image, audio, dialing and answering phone, browsing history and bookmark, phone book, etc.

[0088] The view system includes visual controls, such as controls for displaying text, controls for displaying pictures, etc. The view system can be used to build an application. The display interface can be composed of one or more views. For example, the display interface including the short message notification icon can include the view for displaying text and the view for displaying pictures.

[0089] The phone manager is used to provide the communication function of the electronic device 100. For example, the management of the call state (including connection, hang up, etc.).

[0090] The resource manager provides various resources for the application program, such as localized strings, icons, pictures, layout files, video files, and the like.

[0091] The notification manager enables the application program to display notification information in the status bar, which can be used to convey a message of the notification type, and can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify the completion of the download, message reminders, and the like. The notification manager can also be a notification in the form of a chart or a scroll bar text appearing in the system top status bar, such as a notification of an application program running in the background, and can also be a notification in the form of a dialog window appearing on the screen. For example, the text information is prompted in the status bar, a prompt sound is emitted, the electronic device is vibrated, the indicator light is blinked, and the like.

[0092] The camera service is a core process module of the camera framework, which mainly provides API interface functions to the application program layer, and calls the camera hardware abstraction layer downwardly through the HIDL (hardware interface definition language).

[0093] The Android runtime includes a core library and a virtual machine. The Android runtime is responsible for the scheduling and management of the Android system.

[0094] The core library includes two parts: one part is the function function called by the java language, and the other part is the core library of Android.

[0095] The application program layer and the application program framework layer run in the virtual machine. The virtual machine executes the java file of the application program layer and the application program framework layer into a binary file. The virtual machine is used to perform the management of the object life cycle, the management of the stack, the management of the thread, the management of the security and the exception, and the garbage collection, and the like.

[0096] The system library can include multiple functional modules. For example: the surface manager, the media library, the three-dimensional graphics processing library (for example: OpenGL ES), the 2D graphics engine (for example: SGL), and the like.

[0097] The surface manager is used to manage the display subsystem, and provides the fusion of 2D and 3D layers for multiple application programs.

[0098] The media library supports multiple commonly used audio, video format playback and recording, and static image files, and the like. The media library can support multiple audio and video coding formats.

[0099] A three-dimensional graphics processing library is used to implement three-dimensional graphics drawing, image rendering, compositing, and layer processing.

[0100] A 2D graphics engine is a drawing engine for 2D drawing.

[0101] A hardware abstraction layer is an interface layer between an operating system kernel and hardware circuits, and its purpose is to abstract hardware. It hides the hardware interface details of a specific platform and can provide a virtual hardware platform for an operating system. The hardware abstraction layer is a wrapper for Linux kernel drivers, providing an interface upward and shielding the implementation details of the low-level hardware. As shown in Figure 3 The hardware abstraction layer can include a Wi-Fi HAL, an audio HAL, a camera HAL, and the like.

[0102] The kernel layer is a layer between hardware and software, is the core of an operating system, is the first layer of software expansion based on hardware, provides the most basic functions of an operating system, is the basis of operating system work, and is responsible for managing system processes, memory, device drivers, files, and network systems, and determines system performance and stability. The kernel layer can include display drivers, audio drivers, camera drivers, sensor drivers, and the like. Among them, the camera driver is the driving layer of the camera device, and is mainly responsible for interacting with the hardware.

[0103] The hardware layer includes a display, a camera, a sensor, and the like.

[0104] Based on the above, a parameter determination method provided by the embodiments of the present application is further described in detail. As shown in Figure 4 The parameter determination method includes the following steps S401-S406. Figure 4 The method execution subject shown in the method can be the computer device and the first device mentioned above. Alternatively, Figure 4 The method execution subject shown in the method can be a chip in the computer device and a chip in the first device, and the embodiments of the present application are not limited thereto. Figure 4 The computer device and the first device are taken as an example to illustrate the method execution subject.

[0105] S401, the computer device acquires a first parameter optimization model for a first scene.

[0106] In the embodiments of the present application, the first scenario can be various use scenarios of the first device. Taking the first device as a mobile phone, the first scenario can be a scenario of full-screen video playing of the mobile phone, a scenario of webpage browsing of the mobile phone, a scenario of background music playing of the mobile phone, or a composite scenario of video calling and video playing, which is not limited herein. For different use scenarios of the first device, each use scenario has a corresponding to-be-optimized parameter, and the to-be-optimized parameter is adapted and optimized to ensure the device performance and reduce power consumption. Similarly, each use scenario also has a first parameter optimization model for the to-be-optimized parameter, which is beneficial to improve the accuracy and pertinence of subsequent parameter optimization.

[0107] Taking the first scenario as a scenario of full-screen video playing of the mobile phone, for the scenario of full-screen video playing of the mobile phone, the to-be-optimized parameter can be the CPU frequency of the mobile phone. Therefore, the computer device needs to obtain the first parameter optimization model for the CPU frequency of the mobile phone in the scenario of full-screen video playing of the mobile phone.

[0108] In a possible implementation, the first parameter optimization model can be a TPE model established based on a mixed Gaussian distribution. It can be understood that, in order to simulate and optimize the real parameter model distribution, a specific parameter adjustment model needs to be selected to approximate it. A direct idea is to use a Gaussian model to approximate the real data distribution as much as possible, and at this time, the problem is a regression problem, that is, to find a Gaussian regression equation of known data. However, the regression problem is difficult to solve: first, it is difficult to select a loss function for the regression problem, for example, the predicted value is 0.1, the real value is 0.2, the loss is calculated as 0.1 using the absolute value loss function, and the loss is calculated as 0.01 using the square loss function, and it is difficult to determine which loss function is better; second, the regression problem is sensitive to outliers and errors, and because the loss of outliers is large, it will have a great impact on the regression model. Therefore, the present application can adopt a model established based on a mixed Gaussian distribution, such as a TPE model (a model of Bayesian optimization based on a tree structure). The TPE model is a classification parameter adjustment model established based on a regression model, and performs better than the Gaussian regression model in actual use, and is more beneficial to improve the accuracy of parameter optimization.

[0109] For example, Figure 5AAs shown in FIG. 1, in step 1, the computer device first establishes a first parameter optimization model for the first scenario. The specific implementation process can be: first, determine an initial parameter optimization model (for example, an initial TPE model), and determine hyperparameters (for example, learning rate, etc.); then, determine the parameter to be optimized (for example, the CPU frequency of the mobile phone), the parameter range of the parameter to be optimized, and the loss function; then, randomly select N groups of sample values of the parameter to be optimized under the first scenario (N is a positive integer), and send the N groups of sample values to the first device for testing respectively to obtain the sample test results corresponding to each group of sample values; finally, fit the first parameter optimization model for the first scenario using the N groups of sample values and the corresponding sample test results.

[0110] Specifically, as shown in FIG. 1, the specific implementation manner in which the computer device fits the first parameter optimization model for the first scenario (which can be understood as the computer device obtaining the first parameter optimization model for the first scenario) can include the following steps s11-s14. Figure 5B

[0111] s11, the computer device obtains the sample value of the parameter to be optimized for the first scenario and the sample test result corresponding to the sample value.

[0112] In a specific implementation, the computer device randomly selects N groups of sample values of the parameter to be optimized under the first scenario, and sends the N groups of sample values to the first device respectively; wherein N is a positive integer. The first device first initializes the test environment, adjusts the test environment to the test environment corresponding to the first scenario; then sets the parameter to be optimized in the first device according to the sample value sent by the computer device; collects the data under the parameter setting to calculate the sample test result corresponding to the sample value; and sends the sample test result corresponding to each group of sample values to the computer device. The sample test result includes sample performance information and sample power consumption information of the first device.

[0113] Taking the first device as a mobile phone and the first scenario as a mobile phone full-screen video playing scenario as an example, the parameter to be optimized is the CPU frequency of the mobile phone. The computer device randomly selects 10 groups of sample values of the CPU frequency of the mobile phone under the mobile phone full-screen video playing scenario (for example, a group of sample values can be 1017600, 940800, 1843200, representing the frequencies of 3 CPU cores in the mobile phone), and sends the 10 groups of sample values to the mobile phone respectively.

[0114] As shown in FIG. 1, the mobile phone first initializes the test environment, sets the test environment corresponding to the mobile phone full-screen video playing scenario, and the specific process includes: Figure 5C

[0115] (1) Determine the model, version, software version, etc. of the mobile phone. ​​

[0116] (2) Close the thread that affects the CPU frequency of the mobile phone.

[0117] (3) Turn on the flight mode, turn off the automatic light adjustment, set the volume, etc.

[0118] (4) Prepare the test scene, such as full-screen video playback, open the data collector.

[0119] Then, the mobile phone will set the CPU frequency (i.e. the parameter to be optimized) of the mobile phone according to the sample value sent by the computer device in the test environment corresponding to the scene of full-screen video playback, collect the data under the setting of the parameter using the data collector, and store it in the preset folder; further, calculate the sample test result corresponding to the sample value, i.e. the sample performance information (such as frames per second (FPS)) and sample power consumption information (such as power consumption) of the mobile phone, using the collected data; finally, send the sample test result corresponding to the sample value to the computer device, and the computer device can form a set of training samples by combining the sample value and the sample test result corresponding to the sample value, i.e. 10 sets of training samples, which can be used for training the first parameter optimization model in the future.

[0120] s12, the computer device invokes the initial parameter optimization model to process the sample value, and obtains the sample prediction result corresponding to the sample value.

[0121] In specific implementation, the initial parameter optimization model here can be an initial TPE model. The computer device can take the sample value as the input of the initial parameter optimization model, and output the sample prediction result corresponding to the sample value, i.e. the predicted sample performance information (such as the predicted FPS) and sample power consumption information (such as the predicted power consumption) of the mobile phone.

[0122] s13, the computer device determines the loss value based on the sample prediction result and the sample test result.

[0123] In specific implementation, the computer device can calculate the sample prediction result and the sample test result using the set loss function to obtain the loss value. The loss function here can be a cross-entropy loss function, a mean square error loss function, an exponential loss function, a square loss function, etc., which are not limited here.

[0124] s14, the computer device adjusts the model parameters of the initial parameter optimization model in the direction of reducing the loss value, and obtains the first parameter optimization model.

[0125] In a specific implementation, the computer device can adjust the model parameters of the initial parameter optimization model in a direction of reducing the loss value by using a stochastic gradient descent method, an adaptive gradient algorithm, a momentum method, or the like, and obtains the first parameter optimization model after training of all training samples is completed.

[0126] S402, the computer device determines a first value of the parameter to be optimized based on the first parameter optimization model.

[0127] In the embodiments of the present application, as shown in Figure 5A , the computer device further determines the selection value of the next test point, i.e., the first value of the parameter to be optimized, by using the first parameter optimization model.

[0128] Optionally, the first parameter optimization model includes a first sub-model and a second sub-model, and when the computer device determines the first value of the parameter to be optimized based on the first parameter optimization model, the specific implementation can be that the first value of the parameter to be optimized is calculated based on the first sub-model and the second sub-model.

[0129] It can be understood that, taking the first parameter optimization model as the TPE model, the core idea of the TPE model is to divide the known data values into two categories, good data and bad data, to establish two models, a good Gaussian mixture model f(x) and a bad Gaussian mixture model g(x). The optimal value of the model should be in the good Gaussian mixture model with a high probability and in the bad Gaussian mixture model with a low probability. Therefore, the selection of the next test point is based on the value of , and the data point with the maximum value is most likely to be the expected optimal extreme point. It can be understood that, the next test point can be selected when the value is maximized, i.e., the first value of the parameter to be optimized.

[0130] S403, the computer device sends the first value to the first device. Correspondingly, the first device receives the first value.

[0131] S404, the first device sets the first value in the test environment corresponding to the first scene, and collects a first test result corresponding to the first value; the first test result includes first performance information and first power consumption information of the first device.

[0132] S405, the first device sends the first test result corresponding to the first value to the computer device. Correspondingly, the computer device receives the first test result corresponding to the first value from the first device.

[0133] In the embodiments of the present application, as shown in Figure 5A , the computer device can further send the determined first value of the parameter to be optimized to the first device for testing.

[0134] In a possible implementation, the first scenario is a scenario of full-screen video playing of the first device, the to-be-optimized parameter is a CPU frequency of the first device, and the performance information of the first device includes a frame rate, and the power consumption information of the first device includes a power consumption.

[0135] It can be understood that, taking the first device being a mobile phone and the first scenario being a scenario of full-screen video playing of the mobile phone as an example, after the computer device determines the first value of the to-be-optimized parameter, the computer device sends the first value of the to-be-optimized parameter to the mobile phone. The mobile phone sets the CPU frequency (that is, the to-be-optimized parameter) of the mobile phone according to the first value sent by the computer device in the test environment corresponding to the scenario of full-screen video playing of the mobile phone mentioned above, and collects data under the parameter setting by using a data collector and stores the data in a preset folder. Further, the mobile phone calculates the first test result corresponding to the first value by using the collected data, that is, the first performance information (for example, FPS) and the first power consumption information (for example, power consumption) of the mobile phone, and finally sends the first test result corresponding to the first value to the computer device.

[0136] S406, the computer device determines an optimal value of the to-be-optimized parameter based on the first value and the first test result.

[0137] In the embodiment of the application, after the computer device receives the first test result corresponding to the first value, the computer device can regard the first value and the first test result as a first test point. For example, the first test point indicates that, in the first test, the CPU frequency of the mobile phone is set to 1007600, 947300, and 1373500 (representing the frequencies of three CPU cores in the mobile phone, that is, the first value), the FPS of the mobile phone in the scenario of full-screen video playing under this set of parameter settings is 0.4608, and the power consumption (power consumption) is 583.2502 (that is, the first test result). Subsequently, the computer device can take increasing the FPS and reducing the power consumption as an optimization target, and find an optimal point in these test points. The value of the to-be-optimized parameter set corresponding to the optimal point is the optimal value of the to-be-optimized parameter.

[0138] In a possible implementation, it is assumed that the number (that is, a preset number) of values of the to-be-optimized parameter required in the test is set in advance as M (M is an integer greater than 1), that is, the mobile phone needs to be tested M times to obtain M test points. Then, after the computer device finds the first test point, the computer device further finds a second test point. That is, as shown in FIG. 6, after the computer device obtains the first test result corresponding to the first value, the computer device further performs steps s21-s25. Figure 5D

[0139] ​s21, the computer device invokes the first parameter optimization model to process the first value, and obtains a first prediction result corresponding to the first value; the first prediction result includes predicted first performance information and first power consumption information of the first device.

[0140] In a specific implementation, as shown in Figure 5A the second step, the computer device can optimize the first parameter optimization model according to the returned test result, and obtain a second parameter optimization model. Specifically, the computer device can group the first value and the first test result corresponding to the first value into a new set of training samples, which is used to optimize the first parameter optimization model. At this time, the computer device takes the first value as the input of the first parameter optimization model, and outputs the first prediction result corresponding to the first value, which can include, for example, the predicted first performance information (such as the predicted FPS) and the first power consumption information (such as the predicted power consumption) of the mobile phone.

[0141] s22, the computer device adjusts the model parameters of the first parameter optimization model based on the first test result and the first prediction result, and obtains a second parameter optimization model.

[0142] In a specific implementation, the computer device can calculate the first prediction result and the first test result by using a set loss function, and obtain a first loss value. The loss function herein can be a cross-entropy loss function, a mean square error loss function, an exponential loss function, a square loss function, etc., which are not limited herein. Then, the model parameters of the first parameter optimization model are adjusted in the direction of reducing the first loss value by using a stochastic gradient descent method, an adaptive gradient algorithm, a momentum method, etc., and a second parameter optimization model is obtained.

[0143] s23, the computer device determines a second value of the to-be-optimized parameter based on the second parameter optimization model.

[0144] In a specific implementation, as shown in Figure 5A the third step, the computer device determines the value of the to-be-optimized parameter in the next test, i.e., the second value of the to-be-optimized parameter, according to the second parameter optimization model. Taking the second parameter optimization model as a TPE model, the second value of the to-be-optimized parameter in the next test point can also be determined according to the maximum value of the corresponding parameter.

[0145] s24, the computer device obtains a second test result corresponding to the second value.

[0146] In a specific implementation, the second test result includes second performance information and second power consumption information of the first device. After the computer device determines the second value of the to-be-optimized parameter, the computer device sends the second value of the to-be-optimized parameter to the mobile phone again for testing. The mobile phone sets the CPU frequency (i.e., the to-be-optimized parameter) of the mobile phone according to the second value sent by the computer device in the test environment corresponding to the scenario of the aforementioned full-screen video playing of the mobile phone (for a specific reference, see Figure 5C ), and collects data under the parameter setting by using the data collector and stores the data into a preset folder. Further, the second test result corresponding to the second value, i.e., the second performance information (such as FPS) and the second power consumption information (such as power consumption) of the mobile phone, is calculated by using the collected data. Finally, the computer device is sent the second test result corresponding to the second value.

[0147] s25, the computer device determines the optimal value of the to-be-optimized parameter based on the first value, the first test result, the second value, and the second test result.

[0148] It can be understood that, after the computer device receives the second test result corresponding to the second value, the computer device can regard the second value and the second test result as a second test point. Subsequently, the computer device can find an optimal point in the first test point and the second test point by taking increasing FPS and reducing power consumption as an optimization target. The value of the to-be-optimized parameter corresponding to the optimal point is the optimal value of the to-be-optimized parameter.

[0149] In a possible implementation, when the computer device determines the optimal value of the to-be-optimized parameter based on the first value, the first test result, the second value, and the second test result, the specific implementation can include the following two cases, as shown in Figure 5E .

[0150] Case one: the number of values of the to-be-optimized parameter determined by the computer device is equal to a preset number. For case one, as shown in Figure 5F , the following step s31 needs to be performed.

[0151] s31, the computer device determines the optimal value of the to-be-optimized parameter from the first value and the second value based on the first value, the first test result, the second value, and the second test result.

[0152] In a specific implementation, if the value of the to-be-optimized parameter determined by the computer device has reached the preset number M, i.e., the number of required test points has reached M, the optimal point can be found in the existing test points at this time. The value of the to-be-optimized parameter corresponding to the optimal point is the optimal value of the to-be-optimized parameter.

[0153] For example, it is assumed that the preset number of values of the to-be-optimized parameter required in the test is 2 (i.e., M is 2), that is, 2 test points are required, and the computer device has obtained 2 test points. Therefore, the computer device determines that the number of values of the to-be-optimized parameter is equal to the preset number. Then, the computer device can directly find the optimal point in the first test point (including the first value and the first test result) and the second test point (including the second value and the second test result), and the value of the to-be-optimized parameter corresponding to the optimal point is the optimal value of the to-be-optimized parameter.

[0154] Optionally, when the computer device determines the optimal value of the to-be-optimized parameter from the first value and the second value based on the first value, the first test result, the second value, and the second test result, the implementation manner can be: processing the first value, the first test result, the second value, and the second test result by using a Pareto optimization algorithm to obtain the optimal value of the to-be-optimized parameter; the optimization target in the Pareto optimization algorithm indicates increasing the frame rate and reducing the power consumption, and the optimization direction in the Pareto optimization algorithm indicates improving the optimization target.

[0155] It can be understood that, in order to optimize the performance and power consumption of the mobile phone at the same time, it is necessary to first quantify the performance and power consumption and define the advantages and disadvantages. For the performance, the frame rate FPS (0<FPS≤120) of the mobile phone can be used for quantification. In the use scenario of the mobile phone of the user, the higher the frame rate, the smoother the picture of the mobile phone, and the better the performance, so the optimization direction is max(FPS). For the power consumption, the power consumption power (power>0) of the mobile phone is used for quantification. The lower the power consumption, the better the power consumption optimization, and the longer the use time of the mobile phone, so the optimization direction is min(power). After quantifying the performance and power consumption of the mobile phone by using FPS and power, it is necessary to consider how to define the optimization target to optimize FPS and power at the same time. The present application performs multi-objective optimization on FPS and power, and the Pareto optimization algorithm can be used for optimization. The optimization target p=(max(FPS), min(power)) (i.e., indicating increasing the frame rate and reducing the power consumption), and the optimization direction is max(p) (i.e., indicating improving the optimization target).

[0156] Case two: the number of values of the to-be-optimized parameter determined by the computer device is less than the preset number. For case two, as shown in Figure 5G the following steps s41-s45 need to be performed.

[0157] It can be understood that if the value of the to-be-optimized parameter determined by the current computer does not reach the preset number M, that is, the required test points do not reach M, then the value of the to-be-optimized parameter still needs to be determined through loop iteration until the preset number is reached. After reaching the preset number, the optimal point is found in the M test points, and the value of the to-be-optimized parameter corresponding to the optimal point is the optimal value of the to-be-optimized parameter.

[0158] In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model.

[0159] In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model.

[0160] In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model.

[0161] In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model.

[0162] In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model.

[0163] In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model. In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model.

[0164] In the specific implementation, the computer device can determine the third value of the to-be-optimized parameter by using the third parameter optimization model, for example, the third value of the to-be-optimized parameter is determined by maximizing the expected improvement function of the third parameter optimization model.

[0165] In a specific implementation, the third test result includes third performance information and third power consumption information of the first device. For example, referring to the description of step s24, when the computer device determines the third value of the to-be-optimized parameter, the computer device sends the third value of the to-be-optimized parameter to the mobile phone for testing again; the mobile phone sets the CPU frequency (i.e., the to-be-optimized parameter) according to the third value sent by the computer device in the test environment corresponding to the scenario of full-screen video playing of the mobile phone (for details, refer to the description of the test environment corresponding to the scenario of full-screen video playing of the mobile phone), and collects data under the parameter setting by using the data collector and stores the data in a preset folder; further, the third test result corresponding to the third value is calculated by using the collected data, that is, the third performance information (for example, FPS) and the third power consumption information (for example, power consumption) of the mobile phone; and finally, the computer device sends the third test result corresponding to the third value to the computer device. Figure 5C ) under the test environment corresponding to the scenario of full-screen video playing of the mobile phone (for details, refer to the description of the test environment corresponding to the scenario of full-screen video playing of the mobile phone), and collects data under the parameter setting by using the data collector and stores the data in a preset folder; further, the third test result corresponding to the third value is calculated by using the collected data, that is, the third performance information (for example, FPS) and the third power consumption information (for example, power consumption) of the mobile phone; and finally, the computer device sends the third test result corresponding to the third value to the computer device.

[0166] s45, the computer device determines the optimal value of the to-be-optimized parameter based on the first value, the first test result, the second value, the second test result, the third value, and the third test result.

[0167] In a specific implementation, after the computer device receives the third test result corresponding to the third value, the computer device can regard the third value and the third test result as a third test point. At this time, whether to continue to determine the test point in a loop iteration manner is determined according to whether the number of determined values of the to-be-optimized parameter reaches a preset number. When the number of determined values of the to-be-optimized parameter reaches the preset number, the loop iteration is ended, that is, the optimization target of increasing FPS and reducing power consumption is directly found in the first test point (including the first value and the first test result), the second test point (including the second value and the second test result), and the third test point (including the third value and the third test result), and the value of the to-be-optimized parameter corresponding to the optimal point is the optimal value of the to-be-optimized parameter; when the number of determined values of the to-be-optimized parameter is less than the preset number, the loop iteration is continued to determine the test point until the number of determined values of the to-be-optimized parameter reaches the preset number.

[0168] In addition, the loss value and the preset number can also be combined to determine whether to continue the loop iteration. For example, when the number of determined values of the to-be-optimized parameter reaches the preset number and the third loss value is less than a preset value, the loop iteration is ended, that is, the optimization target of increasing FPS and reducing power consumption is directly found in the first test point, the second test point, and the third test point, and the value of the to-be-optimized parameter corresponding to the optimal point is the optimal value of the to-be-optimized parameter; when the number of determined values of the to-be-optimized parameter is less than the preset number, or the third loss value is greater than or equal to the preset value, the loop iteration is continued to determine the test point until the number of determined values of the to-be-optimized parameter reaches the preset number.

[0169] It should be noted that the preset quantity here can be determined based on the first scenario, or it can be considered that the preset quantity is related to the first scenario. For example, when the first scenario is a full-screen video playback scenario on a mobile phone, the preset quantity can be 300; when the first scenario is a web page browsing scenario on a mobile phone, the preset quantity can be 200; when the first scenario is a combined scenario of video call and video playback, the preset quantity can be 400.

[0170] In general, such as Figure 6 As shown, taking a mobile phone as the first device and a full-screen video playback scenario as the first example, the specific implementation process of this parameter determination method can be as follows: The computer device establishes a first parameter optimization model, determines the first value of the parameter to be tuned (i.e., the frequency of the mobile phone CPU), and sends it to the first device; the first device initializes the test environment, sets the frequency of the mobile phone CPU according to the first value sent by the computer device, collects data under the parameter setting, calculates the first test result corresponding to the first value using the collected data, and sends the first test result to the computer device; the computer device optimizes the first parameter optimization model based on the first test result returned by the mobile phone to obtain a second parameter optimization model; then, based on the second parameter optimization model, it determines the value of the parameter to be tuned (i.e., the frequency of the mobile phone CPU) for the next test. Through continuous iteration, when the number of values ​​of the parameter to be tuned reaches a preset number, increasing the frame rate and reducing power consumption can be used as optimization goals. The Pareto optimality algorithm is used to find the optimal point in these test points. At this point, the value of the parameter to be tuned corresponding to the optimal point is the optimal value of the parameter to be tuned. Finally, the optimal value of the parameter to be tuned can be used to verify power consumption on a test board, thereby achieving parameter tuning. In this way, we can not only automatically explore the optimal values ​​of parameters to be tuned in different scenarios, but also improve the accuracy of parameter tuning.

[0171] Please see Figure 7 , Figure 7 This is a test result graph based on a parameter determination method provided in an embodiment of this application. Each point represents a test result under a set of parameter settings. For example, Figure 7 The test point marked in the image indicates that in the 229th experiment, the parameters to be optimized were the frequencies of the three CPU cores: 1017600, 940800, and 1843200. Under these parameter settings, the FPS for full-screen video playback on the phone was 0.7805, and the power consumption (i.e., battery consumption) was 464.1402. Figure 7In the middle, the test points connected by black lines are all the Pareto optimal test points. The Pareto optimality refers to that for a test result in multi-objective optimization, there is no other test result which is better than the test result in all objectives, and the test point is the Pareto optimal, i.e. a better parameter setting cannot be found to increase the FPS and reduce the power consumption. It should be noted that when there are multiple Pareto optimal test points, the user can determine a test point meeting the demand in the multiple Pareto optimal test points according to the business demand, and the value of the to-be-optimized parameter corresponding to the setting of the test point is the optimal value of the to-be-optimized parameter.

[0172] Please refer to Table 1 below, which is a table of power consumption results in a first scenario provided by the embodiment of the present application. The first scenario is a scenario of full-screen video playing of a mobile phone, a composite scenario of video call and video playing. Base is the original CPU frequency of the mobile phone, and small packet is the CPU frequency (i.e. the optimal value) optimized by the method. Since the FPS loss is the same (i.e. 0), it is not shown here. As can be seen from Table 1, in the case of the same FPS loss, for the scenario of full-screen video playing of a mobile phone, the power consumption is 288.28 mA when the original CPU frequency of the mobile phone is used, and the power consumption is 275.77 mA when the CPU frequency optimized by the method is used. Thus, the power consumption using the CPU frequency optimized by the method can achieve a benefit of 12.51 mA, and the benefit ratio is 4.3%. In the case of the same FPS loss, for the composite scenario of video call and video playing, the power consumption is 885.62 mA when the original CPU frequency of the mobile phone is used, and the power consumption is 848.51 mA when the CPU frequency optimized by the method is used. Thus, the power consumption using the CPU frequency optimized by the method can achieve a benefit of 37.11 mA, and the benefit ratio is 4.2%. Therefore, in general, the small packet optimized by the method achieves benefits in different scenarios.

[0173] Table 1

[0174]

[0175] In addition, further analysis of the revenue source is performed. First, a system on chip (SOC) power consumption breakdown table is analyzed, and it is found that the small package has revenue on the CPU small core, and the CPU large core is deteriorated. Then, an application instance (trace) is captured, and the socperf tool is used to analyze the load of the CPU different cores (clusters), and it is found that the DMIPS (a measure of embedded processor performance) of the CPU small core and the power consumption ratio are reduced, and the CPU large core is increased, and the energy efficiency ratio calculated by DMIPS / power consumption is optimized. Therefore, it is inferred that the reason for the revenue is that the CPU frequency optimization performed by the method can transfer the task previously running on the CPU small core high frequency to the CPU large core low frequency, thereby achieving optimization of the energy efficiency ratio and reducing power consumption.

[0176] It can be seen that, based on the method described Figure 4 The computer device determines the value of the to-be-optimized parameter in the next test point by adopting the first parameter optimization model (such as the TPE model) based on Bayesian optimization, and finds the optimal point (such as the Pareto optimal algorithm) in multiple test points according to the optimization target of increasing the frame rate and reducing the power consumption after multiple tests. At this time, the value of the to-be-optimized parameter corresponding to the optimal point is the optimal value of the to-be-optimized parameter. In this way, not only can the optimal value of the to-be-optimized parameter in different scenes be automatically explored, but also the accuracy of parameter optimization can be improved.

[0177] The following further describes another parameter determination method provided by the embodiments of the present application. As shown in Figure 8 The parameter determination method includes the following steps S801-S804. Figure 8 The method execution subject shown in the method can be the computer device mentioned above. Alternatively, Figure 8 The method execution subject shown in the method can be a chip in the computer device, which is not limited in the embodiments of the present application. Figure 8 The computer device is taken as an example to illustrate the method execution subject.

[0178] S801, the computer device acquires a first parameter optimization model for a first scene.

[0179] In this embodiment, the specific implementation of the computer device acquiring the first parameter optimization model for the first scenario can be referred to the description of step S401 above. The main difference is that the computer device acquires the sample value of the parameter to be tuned for the first scenario and the corresponding sample test result not from the first device, but through simulation software in the computer device. That is, the simulation software can simulate the first device (such as a mobile phone) testing the sample value to obtain the corresponding sample test result. In this way, parameter tuning can be achieved using only a computer device, which is beneficial to improving processing efficiency.

[0180] S802, The computer equipment determines the first value of the parameter to be tuned based on the first parameter optimization model.

[0181] The specific implementation of step S802 can be referred to the specific implementation of step S402 above, and will not be repeated here.

[0182] S803, The computer device obtains the first test result corresponding to the first value; the first test result includes the performance information and power consumption information of the first device.

[0183] In this embodiment, the computer device obtains the first test result corresponding to the first value not from the first device itself, but through simulation software within the computer device. That is, the simulation software can simulate the first device (such as a mobile phone) testing the first value to obtain the corresponding first test result. In this way, parameter optimization can be achieved using only a computer device, which helps improve processing efficiency.

[0184] S804. The computer device determines the optimal value of the parameter to be tuned based on the first value and the first test result.

[0185] The specific implementation of step S804 can be referred to the specific implementation of step S406 above, and will not be repeated here.

[0186] It can be seen that, based on Figure 8 The described method involves a computer device using a Bayesian-optimized first-parameter optimization model (such as the TPE model) to determine the value of the parameter to be tuned in the next test point. After multiple tests, the optimal point is found among multiple test points based on the optimization objective of increasing frame rate and reducing power consumption (e.g., Pareto optimality algorithm). The value of the parameter to be tuned corresponding to this optimal point is the optimal value of that parameter. This approach not only automatically explores the optimal value of the parameter to be tuned in different scenarios but also improves the accuracy of parameter tuning. Furthermore, parameter tuning can be achieved using only computer equipment, which helps improve processing efficiency.

[0187] Please refer to Figure 9 , Figure 9 A structural diagram of a parameter determination apparatus 900 is shown. Figure 9 The parameter determination apparatus shown can be an electronic device, a device in an electronic device, or a device that can be used with an electronic device. Figure 9 The parameter determination apparatus shown can include an acquisition unit 901 and a processing unit 902. Wherein:

[0188] The acquisition unit 901 is configured to acquire a first parameter optimization model for a first scenario.

[0189] The processing unit 902 is configured to determine a first value of a to-be-optimized parameter based on the first parameter optimization model.

[0190] The acquisition unit 901 is further configured to acquire a first test result corresponding to the first value; the first test result includes first performance information and first power consumption information of a first device.

[0191] The processing unit 902 is further configured to determine an optimal value of the to-be-optimized parameter based on the first value and the first test result.

[0192] In a possible implementation, the first scenario is a scenario of full-screen video playing of the first device, the to-be-optimized parameter is a CPU frequency of the first device, the performance information of the first device includes a frame rate, and the power consumption information of the first device includes a power consumption.

[0193] In a possible implementation, acquiring the first test result corresponding to the first value includes: sending the first value to the first device; receiving the first test result corresponding to the first value from the first device; and the first test result is collected by the first device in a test environment corresponding to the first scenario with the first value set.

[0194] In a possible implementation, after acquiring the first test result corresponding to the first value, the method further includes: processing the first value by calling the first parameter optimization model to obtain a first prediction result corresponding to the first value; the first prediction result includes predicted first performance information and first power consumption information of the first device; adjusting model parameters of the first parameter optimization model based on the first test result and the first prediction result to obtain a second parameter optimization model; determining a second value of the to-be-optimized parameter based on the second parameter optimization model; acquiring a second test result corresponding to the second value; the first prediction result includes predicted first performance information and first power consumption information of the first device; and determining an optimal value of the to-be-optimized parameter based on the first value and the first test result includes: determining the optimal value of the to-be-optimized parameter based on the first value, the first test result, the second value, and the second test result.

[0195] In a possible implementation, the determining the optimal value of the parameter to be optimized based on the first value, the first test result, the second value and the second test result comprises: in a case where the number of values of the parameter to be optimized determined is equal to a preset number, determining the optimal value of the parameter to be optimized from the first value and the second value based on the first value, the first test result, the second value and the second test result; in a case where the number of values of the parameter to be optimized determined is less than the preset number, calling the second parameter optimization model to process the second value to obtain a second prediction result corresponding to the second value; the second prediction result comprises predicted second performance information and second power consumption information of the first device; adjusting a model parameter of the second parameter optimization model based on the second test result and the second prediction result to obtain a third parameter optimization model; determining a third value of the parameter to be optimized based on the third parameter optimization model; obtaining a third test result corresponding to the third value; the third prediction result comprises predicted third performance information and third power consumption information of the first device; and determining the optimal value of the parameter to be optimized based on the first value, the first test result, the second value, the second test result, the third value and the third test result.

[0196] In a possible implementation, the determining the optimal value of the parameter to be optimized from the first value and the second value based on the first value, the first test result, the second value and the second test result comprises: processing the first value, the first test result, the second value and the second test result by using a Pareto optimization algorithm to obtain the optimal value of the parameter to be optimized; an optimization target in the Pareto optimization algorithm indicates increasing frame rate and reducing power consumption, and an optimization direction in the Pareto optimization algorithm indicates improving the optimization target.

[0197] In a possible implementation, the first parameter optimization model is a TPE model established based on a mixed Gaussian distribution.

[0198] In a possible implementation, the obtaining the first parameter optimization model for the first scene comprises: obtaining a sample value of the parameter to be optimized for the first scene and a sample test result corresponding to the sample value; calling an initial parameter optimization model to process the sample value to obtain a sample prediction result corresponding to the sample value; determining a loss value based on the sample prediction result and the sample test result; and adjusting a model parameter of the initial parameter optimization model in a direction of reducing the loss value to obtain the first parameter optimization model.

[0199] For the case where the parameter determination apparatus is a chip or a chip system, refer to the structural schematic diagram of the chip shown in Figure 10 . Figure 10The chip 1000 shown includes a processor 1001, an interface 1002. Optionally, a memory 1003 can also be included. Among them, the number of processors 1001 can be one or more, and the number of interfaces 1002 can be multiple.

[0200] For the case where the chip is used to implement the electronic device in the embodiments of the present application:

[0201] The interface 1002 is configured to receive or output a signal.

[0202] The processor 1001 is configured to perform a data processing operation of the electronic device.

[0203] It can be understood that some optional features in the embodiments of the present application can be implemented independently in some scenarios without relying on other features, such as the scheme currently based on, to solve the corresponding technical problems and achieve the corresponding effects. Also, in some scenarios, it can be combined with other features according to the needs. Accordingly, the parameter determination apparatus given in the embodiments of the present application can also correspondingly implement these features or functions, and will not be described here.

[0204] It should be understood that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method embodiments can be completed by integrated logic circuits or software instructions in the processor. The processor can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0205] It can be appreciated that the memory in the embodiments of the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, but not limitation, many forms of RAM can be used, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM) and direct RAMBUS RAM (DRAM). It should be noted that the memory of the system and method described herein is intended to include, but not limited to, these and any other suitable types of memory.

[0206] The present application also provides a parameter determination system, comprising an electronic device; wherein the electronic device is configured to execute the method performed by the electronic device in any of the method embodiments.

[0207] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program comprises program instructions, and when the program instructions are executed on an electronic device, the functions of any of the method embodiments are realized.

[0208] The present application also provides a computer program product, which, when executed on a computer, causes the computer to realize the functions of any of the method embodiments.

[0209] As used in the specification and the appended claims, the singular forms "a," "an" and "the" include plural referents unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated items, in the absence of terms such as "only one" or "cannot be present together." As used in the examples, the term "when" can be interpreted to mean "if" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "in response to determining" or "if it is detected (that a stated condition or event)" can be interpreted to mean "if determining" or "in response to determining" or "upon detecting (that a stated condition or event)" or "in response to detecting (that a stated condition or event)," depending on the context.

[0210] As described above, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, the embodiments can be implemented in the form of a computer program product including one or more computer instructions. When the computer instructions are loaded into and executed by a computer, all or part of the procedures or functions described in the embodiments are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, such as from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center through a wired (such as a coaxial cable, an optical fiber, a digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available medium can be a tangible medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a high-density digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc. A person of ordinary skill in the art can understand that all or part of the procedures in the above-described embodiments can be implemented by a computer program instructing the relevant hardware, and the program can be stored in a computer-readable storage medium, and the program can include the procedures of the above-described embodiments when executed.

[0211] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A parameter determination method characterized by, The method comprises: obtaining a first parameter optimization model for a first scene; the first parameter optimization model comprises a first sub-model and a second sub-model; the first scene is a scene of full-screen video playing of a first device; determining a first value of a to-be-optimized parameter based on the first sub-model and the second sub-model, the first value being a parameter value corresponding to a maximized ratio between the first sub-model and the second sub-model; the to-be-optimized parameter being a central processing unit (CPU) frequency of the first device; wherein the first sub-model is a good Gaussian mixture model, and the second sub-model is a poor Gaussian mixture model; sending the first value to the first device; receiving a first test result corresponding to the first value from the first device; the first test result is obtained by setting the first value in a test environment corresponding to the first scene by the first device; the first test result comprises a first frame rate and a first power consumption of the first device; processing the first value by calling the first parameter optimization model to obtain a first prediction result corresponding to the first value; the first prediction result comprises predicted first performance information and first power consumption information of the first device; adjusting model parameters of the first parameter optimization model based on the first test result and the first prediction result to obtain a second parameter optimization model; the second parameter optimization model comprises a third sub-model and a fourth sub-model; determining a second value of the to-be-optimized parameter based on the third sub-model and the fourth sub-model, the second value being a parameter value corresponding to a maximized ratio between the third sub-model and the fourth sub-model; wherein the third sub-model is a good Gaussian mixture model, and the fourth sub-model is a poor Gaussian mixture model; obtaining a second test result corresponding to the second value; the second test result comprises a second frame rate and a second power consumption of the first device; in a case where a quantity of determined values of the to-be-optimized parameter is equal to a preset quantity M, processing the M to-be-optimized parameter values and test results corresponding to each of the M to-be-optimized parameter values by using a Pareto optimization algorithm to obtain an optimal value of the to-be-optimized parameter; an optimization objective in the Pareto optimization algorithm indicates increasing the frame rate and reducing the power consumption, and an optimization direction in the Pareto optimization algorithm indicates improving the optimization objective; M is an integer greater than 1.

2. The method of claim 1, wherein, The method further comprises: in a case where a quantity of determined values of the to-be-optimized parameter is less than a preset quantity, processing the second value by calling the second parameter optimization model to obtain a second prediction result corresponding to the second value; the second prediction result comprises predicted second performance information and second power consumption information of the first device; adjusting model parameters of the second parameter optimization model based on the second test result and the second prediction result to obtain a third parameter optimization model; determining a third value of the to-be-optimized parameter based on the third parameter optimization model; obtaining a third test result corresponding to the third value; the third test result comprises third performance information and third power consumption information of the first device; Determine an optimal value of the parameter to be optimized based on the first value, the first test result, the second value, the second test result, the third value, and the third test result.

3. The method according to claim 1 or 2, characterized in that, The first parameter optimization model is a TPE model established based on a mixed Gaussian distribution.

4. The method according to claim 1 or 2, characterized in that, The first parameter optimization model for the first scene is obtained by: Obtaining sample values of the parameter to be optimized for the first scene and sample test results corresponding to the sample values; Calling an initial parameter optimization model to process the sample values to obtain sample prediction results corresponding to the sample values; Determining a loss value based on the sample prediction results and the sample test results; Adjusting model parameters of the initial parameter optimization model in a direction of reducing the loss value to obtain a first parameter optimization model.

5. An electronic device, comprising: Comprise: One or more processors, one or more memories; wherein the one or more memories are coupled with the one or more processors, the one or more memories are used to store computer program codes, the computer program codes comprise computer instructions, when the one or more processors execute the computer instructions, make the electronic device execute the method in any one of claims 1-4.

6. A parameter determination system characterized by comprising: Comprise an electronic device; wherein the electronic device is used to execute the method in any one of claims 1-4.

7. A chip, characterized by Comprise a processor and an interface, the processor and the interface are coupled; the interface is used to receive or output signals, the processor is used to execute code instructions, so that the method in any one of claims 1-4 is executed.

8. A computer storage medium, characterized in that The computer storage medium stores a computer program, the computer program comprises program instructions, when the program instructions run on the electronic device, make the electronic device execute the method in any one of claims 1-4.

9. A computer program product, characterised in that, When the computer program product runs on the computer, make the computer execute the method in any one of claims 1-4. When the computer program product runs on the computer, make the computer execute the method in any one of claims 1-4.

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