Homepage window optimization system for embedded device

Abstract

The invention relates to the technical field of equipment homepage window optimization, in particular to an embedded equipment homepage window optimization system, which is characterized in that the window optimization system and a hardware device of embedded equipment are subjected to resource interaction, an animation effect is generated and managed through an animation engine module, and a frame rate control sub-module is used for dynamically adjusting the frame rate of an animation; the rendering optimization sub-module is used for analyzing smooth parameters of the animation under different hardware conditions corresponding to the embedded device; the operation behavior of the user is detected, and the animation parameters are adjusted according to the operation behavior; meanwhile, the hardware state of the embedded equipment is monitored in real time, and animation parameters are adjusted according to the equipment state; through resource interaction and an intelligent adjustment mechanism between the animation engine module and embedded equipment hardware, the user experience can be improved, and resource use and energy consumption control can be optimized while stable operation of equipment is ensured. The method is crucial for improving the market competitiveness and the user satisfaction of the embedded equipment.

Description

Technical Field

[0001] The invention belongs to the technical field of device homepage window optimization, and relates to an embedded device homepage window optimization system. Background Art

[0002] In the context of current social development, the rapid development of the Internet of Things is well known to people. As an important part of the new generation of information technology, the Internet of Things is one of the key strategic emerging industries, which has set off the third wave of the world's information industry revolution. With the significant development of Internet of Things technology, Internet of Things devices are more intelligent and more friendly, and the demand for human-computer interaction is gradually emerging. In the era of the Internet of Things, edge devices need a friendly, intuitive, stable and diversified human-computer interaction interface display system, that is, an embedded graphics display system for edge computing, which is widely used in automotive electronics, smart homes, industrial control and other fields.

[0003] With the widespread use of embedded devices, users have higher and higher requirements for interface animation effects. However, due to the limited hardware resources of embedded devices, how to achieve smooth and diverse animation effects on these devices has become a challenge. Traditional embedded device homepage window animation effects are single, and it is easy to have a freeze phenomenon during the animation transition process, which affects the user experience and is difficult to meet the personalized and diversified needs of users. Therefore, it is particularly important to design an optimization system that can achieve flexible and diverse animation display effects. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention provides an embedded device homepage window optimization system, which realizes flexible and diverse animation display effects on the embedded end and solves the deficiencies in the prior art.

[0005] In order to achieve the above purpose and other purposes, the technical solution adopted by the present invention is as follows:

[0006] A first aspect of the present invention provides an embedded device homepage window optimization system, comprising a hardware adaptation module, an animation engine module, an adaptive adjustment module and a configuration storage module, wherein the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules;

[0007] A hardware adaptation module, used for interacting resources between the window optimization system and the hardware device of the embedded device, wherein the hardware device is divided into a processor, a memory, an image processing unit and a storage unit;

[0008] The animation engine module is used to generate and manage animation effects, including a frame rate control submodule and a rendering optimization submodule; the frame rate control submodule is used to dynamically adjust the frame rate of the animation; the rendering optimization submodule is used to analyze the smoothness parameters of the animation under different hardware conditions of the embedded device;

[0009] The adaptive adjustment module is used to automatically adjust the animation parameters according to the user's operation behavior and the state of the embedded device, and includes a user interaction detection submodule and a device state monitoring submodule; the user interaction detection submodule is used to detect the user's operation behavior through the sensor and input device included in the embedded device, and adjust the animation parameters according to the operation behavior; the device state monitoring submodule is used to monitor the hardware state of the embedded device in real time, and adjust the animation parameters according to the device state;

[0010] The configuration storage module is used to store the maximum frame rate of the embedded device, the standard required memory for the animation, and the baseline playback speed, basic resolution, and baseline detail level of the animation.

[0011] Exemplarily, the specific operation logic of the frame rate control submodule is:

[0012] Get the maximum frame rate Z of the embedded device from the configuration storage module 最大 , and synchronously collect the basic frame rate Z of the animation 基础 ;

[0013] And get the performance coefficient Z of the embedded device 性能 and the battery factor Z 电池 , the target frame rate Z of the embedded device is obtained by calculation 目标 =Z 最大 ×(ω1×Z 性能 +ω2×Z 电池 ), ω1 and ω2 are weights for adjusting the performance coefficient and battery coefficient respectively;

[0014] It should be added that the performance coefficient Z of the embedded device is obtained 性能 and the battery factor Z 电池 The specific acquisition process is as follows:

[0015] Get the current idle CPU capacity of the embedded device.

[0016] Get the remaining battery power of the embedded device, then

[0017] Compare the target frame rate of the embedded device with the basic frame rate of the animation. If the target frame rate of the embedded device is greater than the basic frame rate of the animation, delete frames from the animation. The specific operation process is as follows:

[0018] Calculate the frame rate of embedded devices and animations In the animation every Z 调整 Insert a frame between the base frames;

[0019] If the target frame rate of the embedded device is lower than the base frame rate of the animation, the animation is interpolated. The specific operation process is as follows:

[0020] Similarly, calculate the frame rate adjustment frame number of embedded devices and animations In the animation Delete one frame between base frames;

[0021] If the target frame rate of the embedded device is equal to the base frame rate of the animation, the animation frame rate is not adjusted.

[0022] Exemplarily, the specific operation logic of the rendering optimization submodule is:

[0023] The smoothness parameters include a resolution scaling factor, a texture quality factor and a LOD factor;

[0024] Among them, embedded devices correspond to different hardware conditions and are specifically divided into GPU performance coefficient, memory and maximum frame rate;

[0025] Analyze the resolution scaling factor corresponding to embedded devices

[0026] Analyze texture quality factors for embedded devices ε 设备内存 , ε 动画内存 They represent the memory corresponding to the embedded device and the standard required memory for the animation stored in the configuration storage module respectively;

[0027] Analyze the LOD factors corresponding to embedded devices ε GPU性能系数 is the GPU performance coefficient corresponding to the embedded device, ε 参考 Indicates the set reference GPU performance coefficient;

[0028] According to the smooth parameters of the animation under different hardware conditions of the embedded device, the resolution scaling factor corresponding to the embedded device is compared with the value 1. If ε1<1, the resolution of the embedded device is reduced; if ε1=1, the original resolution of the embedded device is kept unchanged; if ε1>1, the resolution of the embedded device is increased or kept unchanged;

[0029] Compare the texture quality factor corresponding to the embedded device with the value 1. If ε2<1, use low-quality textures for the embedded device. If ε2=1, use standard-quality textures for the embedded device. If ε2>1, use high-quality textures for the embedded device.

[0030] The LOD factor corresponding to the embedded device is compared with the value 1. If ε3<1, the detail level of the embedded device is reduced. If ε3=1, the original detail level of the embedded device is kept unchanged. If ε3>1, the detail level of the embedded device is increased.

[0031] Exemplarily, the animation parameters specifically include animation frame rate, animation playback speed, animation resolution and animation detail level.

[0032] Exemplarily, the specific operation logic of the user interaction detection submodule is:

[0033] Through the hardware device of the embedded device, the user's operating frequency corresponding to the embedded device is monitored. freq , and get the basic frame rate Z of the animation 基础 , the corresponding animation frame rate F of the embedded device in the user interaction detection submodule is obtained by calculation current =Z 基础 ×(1+k f ×O freq );

[0034] Through the hardware device of the embedded device, the user's operation speed corresponding to the embedded device is monitored. speed , according to the reference playback speed S of the animation stored in the configuration storage module 基础 , the animation playback speed S corresponding to the embedded device in the user interaction detection submodule is obtained by calculation current =S 基础 ×(1+k s ×O speed );

[0035] Through the hardware device of the embedded device, the user's operation complexity of the corresponding embedded device is monitored. complexity , according to the base resolution R of the animation stored in the configuration storage module 基础 , the corresponding animation resolution R of the embedded device in the user interaction detection submodule is obtained by calculation current =R 基础 ×(1-k r ×O complexity );

[0036] Through the hardware device of the embedded device, the precision of the user's operation on the corresponding embedded device is monitored. precision , according to the baseline level of detail D of the animation stored in the configuration storage module 基础 , the animation detail level D corresponding to the embedded device in the user interaction detection submodule is obtained by calculation current =D 基础 ×(1+k d ×O precision );

[0037] where k f , k s , k r , k d They represent the adjustment coefficients of the predefined operation frequency, speed, complexity and precision respectively.

[0038] Exemplarily, the adjustment coefficients of the predefined operating frequency, speed, complexity and precision are specifically calculated including the following steps:

[0039] Obtain historical test data, including the operation frequency corresponding to each historical test Animation base frame rate And test the best frame rate i is the number of each historical test, i = 1, 2, ... N;

[0040] Through the linear regression equation Bring the historical test data back into the linear regression equation to solve k f ,

[0041] According to k f The calculation equation of k can be solved by the same method. s , k r , k d .

[0042] Exemplarily, the specific operation logic of the equipment status monitoring submodule is:

[0043] Through the hardware device of the embedded device, the CPU usage rate of the embedded device is monitored, and the corresponding animation frame rate F of the embedded device in the device status monitoring submodule is obtained by calculation. c ' urrent =Z 基础 ×(1-CPU use ), CPU use Indicates the CPU usage of the embedded device;

[0044] The device temperature T of the embedded device is monitored through the hardware device of the embedded device, and the corresponding animation playback speed of the embedded device in the device status monitoring submodule is obtained by calculation. T max , T min These are the maximum and minimum safe temperatures for embedded devices, respectively;

[0045] The memory usage rate of the embedded device is monitored through the hardware device of the embedded device, and the corresponding animation resolution R′ of the embedded device in the device status monitoring submodule is obtained by calculation. current =R 基础 ×(1-NCuse ), NC use Indicates the memory usage of the embedded device;

[0046] The system load value of the embedded device is monitored through the hardware device of the embedded device, and the animation detail level D′ corresponding to the embedded device in the device status monitoring submodule is obtained by calculation. current =D 基础 ×(1-System_Load), where System_Load is the system load value of the embedded device.

[0047] Exemplarily, the specific operation logic of the adaptive adjustment module is:

[0048] Step 1: If the user interaction detection submodule and the device status monitoring submodule simultaneously feed back different values ​​of the same animation parameter in the adaptive adjustment module, then the two values ​​of the same animation parameter are respectively compared with the maximum tolerance threshold of the animation parameter of the embedded device; if the two values ​​of the same animation parameter are both greater than the maximum tolerance threshold of the animation parameter of the embedded device, then the maximum tolerance threshold of the animation parameter of the embedded device is recorded as the set value of the animation parameter; if the two values ​​of the same animation parameter are both less than the maximum tolerance threshold of the animation parameter of the embedded device, then the larger value is selected from the two values ​​as the set value of the animation parameter; if the two values ​​of the same animation parameter simultaneously satisfy that one is greater than the maximum tolerance threshold of the animation parameter of the embedded device and the other is less than the maximum tolerance threshold of the animation parameter of the embedded device, then the value less than the maximum tolerance threshold of the animation parameter of the embedded device is used as the set value of the animation parameter;

[0049] Step 2: If there is a submodule that first feeds back the value of a certain animation parameter to the adaptive adjustment module, the value of the animation parameter is compared with the maximum tolerance threshold of the animation parameter of the embedded device. If the former is greater than or equal to the latter, the maximum tolerance threshold of the animation parameter of the embedded device is defined as the set value of the animation parameter. If the former is less than the latter, the value of the animation parameter is defined as the set value of the animation parameter.

[0050] Step 3: If a subsequent sub-module feeds back the value of the animation parameter to the adaptive adjustment module, analysis and replacement are performed according to the steps in step 2.

[0051] In a second aspect, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0052] The processor calls the computer program stored in the memory to implement a homepage window optimization system for embedded devices.

[0053] In a third aspect, the present invention provides a computer program product stored on a computer-readable medium, including a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the embedded device homepage window optimization system.

[0054] As described above, the embedded device homepage window optimization system provided by the present invention has at least the following beneficial effects:

[0055] (1) The present invention provides an embedded device homepage window optimization system, which interacts resources between the window optimization system and the hardware device of the embedded device, generates and manages animation effects through an animation engine module, and a frame rate control submodule is used to dynamically adjust the frame rate of the animation; the rendering optimization submodule is used to analyze the smoothness parameters of the animation under different hardware conditions corresponding to the embedded device; and detects the user's operation behavior and adjusts the animation parameters according to the operation behavior; at the same time, the hardware status of the embedded device is monitored in real time, and the animation parameters are adjusted according to the device status. By dynamically adjusting the animation frame rate and optimizing the rendering process, the best user experience can be provided under different hardware conditions.

[0056] (2) The animation engine module can intelligently allocate these resources to ensure the smoothness of the animation without affecting other functions of the device. For example, reducing the complexity of the animation or the frame rate when resources are tight can prevent the device from overheating or the battery from running out too quickly; the rendering optimization submodule can analyze the performance of the animation under various hardware conditions and automatically adjust the parameters to adapt to different hardware, thereby ensuring good animation effects even on devices with lower performance.

[0057] (3) By detecting the user's operation behavior and adjusting the animation parameters, the system can provide a more natural and intuitive interactive experience. For example, if the user slides the screen quickly, the animation can be accelerated to match the user's movement speed, which can reduce the user's waiting time and improve the instant feedback of the interaction; real-time monitoring of the hardware status of the embedded device and adjusting the animation parameters according to the status can prevent freezes or animation frame skipping when the device load is high. This adaptive adjustment mechanism ensures that the device can maintain a certain user experience standard under different workloads.

[0058] (4) By optimizing the animation rendering process, the possibility of system crashes and application errors can be reduced. Resource management during the animation process must be cautious to avoid memory leaks or excessive CPU / GPU usage; in short, through the resource interaction and intelligent adjustment mechanism between the animation engine module and the embedded device hardware, not only can the user experience be improved, but also resource usage and energy consumption control can be optimized while ensuring stable operation of the device. This approach is crucial to improving the market competitiveness and user satisfaction of embedded devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0060] Figure 1 It is a schematic diagram of the connection of each step of the method of the present invention.

[0061] Figure 2 The figure is a schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0062] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technical personnel in the relevant technical field may make various modifications or supplements to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0063] Example 1

[0064] See also Figure 1 As shown, a homepage window optimization system for embedded devices includes a hardware adaptation module, an animation engine module, an adaptive adjustment module and a configuration storage module, wherein the above modules are connected by wired and / or wireless connection to realize data transmission between the modules;

[0065] The animation engine module is connected to the hardware adaptation module and the self-adaptation adjustment module respectively, and the configuration storage module is connected to the animation engine module and the self-adaptation adjustment module respectively.

[0066] A hardware adaptation module, used for interacting resources between the window optimization system and the hardware device of the embedded device, wherein the hardware device is divided into a processor, a memory, an image processing unit and a storage unit;

[0067] The animation engine module is used to generate and manage animation effects, including a frame rate control submodule and a rendering optimization submodule; the frame rate control submodule is used to dynamically adjust the frame rate of the animation; the rendering optimization submodule is used to analyze the smoothness parameters of the animation under different hardware conditions of the embedded device;

[0068] Based on the above embodiment, the specific operation logic of the frame rate control submodule is as follows:

[0069] Get the maximum frame rate Z of the embedded device from the configuration storage module 最大 , and synchronously collect the basic frame rate Z of the animation 基础 ;

[0070] And get the performance coefficient Z of the embedded device 性能 and the battery factor Z 电池 , the target frame rate Z of the embedded device is obtained by calculation 目标 =Z 最大 ×(ω1×Z 性能 +ω2×Z 电池 ), ω1 and ω2 are weights for adjusting the performance coefficient and battery coefficient respectively;

[0071] It should be added that the performance coefficient Z of the embedded device is obtained 性能 and the battery factor Z 电池 The specific acquisition process is as follows:

[0072] Get the current idle CPU capacity of the embedded device, then

[0073] Get the remaining battery power of the embedded device, then

[0074] It should be explained that the specific process of obtaining the weights ω1 and ω2 for adjusting the performance coefficient and the battery coefficient is as follows:

[0075] Obtain historical test data of the embedded device from the configuration storage module, where the historical test data includes the performance coefficient α at each load level j 1. Battery factor α j 2 and target frame rate α j 3, j is the number of each load level, j = 1, 2, ... n;

[0076] Use the least squares method to find the cost function of ω1 and ω2 n is the total number of load levels;

[0077] Taking partial derivatives of ω1 and ω2 respectively and setting them to 0, we get:

[0078] The values ​​of ω1 and ω2 are obtained by simultaneous solution.

[0079] Compare the target frame rate of the embedded device with the basic frame rate of the animation. If the target frame rate of the embedded device is greater than the basic frame rate of the animation, delete frames from the animation. The specific operation process is as follows:

[0080] Calculate the frame rate of embedded devices and animations In the animation every Z 调整 Insert a frame between the base frames;

[0081] If the target frame rate of the embedded device is lower than the base frame rate of the animation, the animation is interpolated. The specific operation process is as follows:

[0082] Similarly, calculate the frame rate adjustment frame number of embedded devices and animations In the animation Delete one frame between base frames;

[0083] If the target frame rate of the embedded device is equal to the base frame rate of the animation, the animation frame rate is not adjusted.

[0084] Applied to the following embodiment, assuming that the basic frame rate of the animation is 30FPS and the target frame rate of the embedded device is 60FPS, then Z 调整 is 2, inserting a frame between every two basic frames. Assuming the original frame sequence is A, B, C, D, the inserted frame sequence becomes A, A', B, B', C, C', D, D';

[0085] Assuming the base frame rate of the animation is 60FPS and the target frame rate of the embedded device is 30FPS, then Z 调整 is 0.5, and one frame is deleted between every two basic frames. Assuming the original frame sequence is A, B, C, D, E, F, it becomes A, C, E after deleting the frame;

[0086] Based on the above embodiment, the specific operation logic of the rendering optimization submodule is as follows:

[0087] The smoothness parameters include a resolution scaling factor, a texture quality factor and a LOD factor;

[0088] Among them, embedded devices correspond to different hardware conditions and are specifically divided into GPU performance coefficient, memory and maximum frame rate;

[0089] Analyze the resolution scaling factor corresponding to embedded devices

[0090] Analyze texture quality factors for embedded devices ε 设备内存 , ε 动画内存 They represent the memory corresponding to the embedded device and the standard required memory for the animation stored in the configuration storage module respectively;

[0091] Analyze the LOD factors corresponding to embedded devices ε GPU性能系数 is the GPU performance coefficient corresponding to the embedded device, ε 参考 Indicates the set reference GPU performance coefficient;

[0092] According to the smooth parameters of the animation under different hardware conditions of the embedded device, the resolution scaling factor corresponding to the embedded device is compared with the value 1. If ε1<1, the resolution of the embedded device is reduced; if ε1=1, the original resolution of the embedded device is kept unchanged; if ε1>1, the resolution of the embedded device is increased or kept unchanged;

[0093] Compare the texture quality factor corresponding to the embedded device with the value 1. If ε2<1, use low-quality textures for the embedded device. If ε2=1, use standard-quality textures for the embedded device. If ε2>1, use high-quality textures for the embedded device.

[0094] The LOD factor corresponding to the embedded device is compared with the value 1. If ε3<1, the detail level of the embedded device is reduced. If ε3=1, the original detail level of the embedded device is kept unchanged. If ε3>1, the detail level of the embedded device is increased.

[0095] The adaptive adjustment module is used to automatically adjust the animation parameters according to the user's operation behavior and the state of the embedded device, and includes a user interaction detection submodule and a device state monitoring submodule; the user interaction detection submodule is used to detect the user's operation behavior through the sensor and input device included in the embedded device, and adjust the animation parameters according to the operation behavior; the device state monitoring submodule is used to monitor the hardware state of the embedded device in real time, and adjust the animation parameters according to the device state;

[0096] Based on the above embodiment, the animation parameters specifically include animation frame rate, animation playback speed, animation resolution and animation detail level.

[0097] Based on the above embodiment, the specific operation logic of the user interaction detection submodule is as follows:

[0098] Through the hardware device of the embedded device, the user's operating frequency corresponding to the embedded device is monitored. freq , and get the basic frame rate Z of the animation 基础 , the corresponding animation frame rate F of the embedded device in the user interaction detection submodule is obtained by calculation current =Z 基础 ×(1+k f ×O freq );

[0099] It should be added that the GPIO (general purpose input and output) pin of the embedded device should be set to input mode and the interrupt should be configured. In the ISR, the current time should be read and stored as a timestamp. If the operation is not event-driven (such as button presses) but requires periodic detection (such as touch screen operations), a timer should be set to periodically detect the input status. In the main program loop or in a specific data processing module, the timestamp data should be analyzed to calculate the operating frequency of the embedded device.

[0100] Through the hardware device of the embedded device, the user's operation speed corresponding to the embedded device is monitored. speed , according to the reference playback speed S of the animation stored in the configuration storage module 基础 , the animation playback speed S corresponding to the embedded device in the user interaction detection submodule is obtained by calculation current =S 基础 ×(1+k s ×O speed );

[0101] It should be added that the timestamps are recorded at the beginning and end of the user operation, so as to obtain the operation speed of the user's corresponding embedded device. The specific code is shown as follows:

[0102] #include<time.h>

[0103] struct timespec start,end;

[0104] clock_gettime(CLOCK_MONOTONIC,&start);

[0105] / / User operation code

[0106] clock_gettime(CLOCK_MONOTONIC,&end);

[0107] double time_taken=(end.tv_sec-start.tv_sec)+(end.tv_nsec-start.tv_nsec) / 1e9

[0108] Through the hardware device of the embedded device, the user's operation complexity of the corresponding embedded device is monitored. complexity , according to the base resolution R of the animation stored in the configuration storage module 基础 , the corresponding animation resolution R of the embedded device in the user interaction detection submodule is obtained by calculation current =R 基础 ×(1-k r ×O complexity );

[0109] It should be supplemented that the time required for users to complete specific tasks is counted to evaluate the complexity of the user's operation of the corresponding embedded device.

[0110] Define the tasks to be measured, such as starting an application, setting parameters, etc.

[0111] Record the time when users start and complete tasks.

[0112] Calculate the time required to complete the task and perform statistical analysis.

[0113] Through the hardware device of the embedded device, the precision of the user's operation on the corresponding embedded device is monitored. precision , according to the baseline level of detail D of the animation stored in the configuration storage module 基础 , the animation detail level D corresponding to the embedded device in the user interaction detection submodule is obtained by calculation current =D 基础 ×(1+k d ×O precision );

[0114] It should be added that touch screens, accelerometers and force sensors are installed on embedded devices. Data such as the user's touch position, sliding speed, and applied force are collected through timed sampling and event-driven. The collected data is filtered to extract features such as the touch position change rate and pressure change. The user's operation trajectory, speed and pressure changes are analyzed to evaluate the operation precision. The simplified pseudo code is shown below:

[0115]

[0116]

[0117] where k f , k s , k r , k d They represent the adjustment coefficients of the predefined operation frequency, speed, complexity and precision respectively.

[0118] On the basis of the above embodiment, the adjustment coefficients of the predefined operation frequency, speed, complexity and precision are specifically calculated including the following steps:

[0119] Obtain historical test data, including the operation frequency corresponding to each historical test Animation base frame rate And test the best frame rate i is the number of each historical test, i = 1, 2, ... N;

[0120] Through the linear regression equation Bring the historical test data back into the linear regression equation to solve k f , N is the total number of historical tests;

[0121] According to k f The calculation equation of k can be solved by the same method. s , k r , k d .

[0122] Get historical test data, including the operation speed SD corresponding to each historical test i , animation standard playback speed SD′ i And test the best playback speed SD′ i ′;

[0123] By linear regression equation SD′ i ′=SD′ i ×(1+k s ×SD i ), bring the historical test data back into the linear regression equation to solve k s ,

[0124] Obtain historical test data, including the operation complexity FZ corresponding to each historical test i , animation base resolution FZ′ i And test the best resolution FZ′ i ′;

[0125] Through the linear regression equation FZ′ i ′=FZ′ i ×(1+k r ×FZ i ), bring the historical test data back into the linear regression equation to solve k r ,

[0126] Get historical test data, including the operation precision JX corresponding to each historical test i , animation benchmark detail level JX′ i and test the best detail level JX′ i ′;

[0127] Through the linear regression equation JX′ i ′=JX′ i ×(1+k d ×JX i ), bring the historical test data back into the linear regression equation to solve k d ,

[0128] Based on the above embodiment, the specific operation logic of the equipment status monitoring submodule is as follows:

[0129] Through the hardware device of the embedded device, the CPU usage rate of the embedded device is monitored, and the corresponding animation frame rate F of the embedded device in the device status monitoring submodule is obtained by calculation. c ' urrent =Z 基础 ×(1-CPU use ), CPU use Indicates the CPU usage of the embedded device;

[0130] The device temperature T of the embedded device is monitored through the hardware device of the embedded device, and the corresponding animation playback speed of the embedded device in the device status monitoring submodule is obtained by calculation. T max 、T min These are the maximum and minimum safe temperatures for embedded devices, respectively;

[0131] The memory usage rate of the embedded device is monitored through the hardware device of the embedded device, and the corresponding animation resolution R′ of the embedded device in the device status monitoring submodule is obtained by calculation. current =R 基础 ×(1-NC use ), NC use Indicates the memory usage of the embedded device;

[0132] The system load value of the embedded device is monitored through the hardware device of the embedded device, and the animation detail level D′ corresponding to the embedded device in the device status monitoring submodule is obtained by calculation. current =D 基础 ×(1-System_Load), where System_Load is the system load value of the embedded device.

[0133] Based on the above embodiment, the specific operation logic of the adaptive adjustment module is:

[0134] Step 1: If the user interaction detection submodule and the device status monitoring submodule simultaneously feed back different values ​​of the same animation parameter in the adaptive adjustment module, then the two values ​​of the same animation parameter are respectively compared with the maximum tolerance threshold of the animation parameter of the embedded device; if the two values ​​of the same animation parameter are both greater than the maximum tolerance threshold of the animation parameter of the embedded device, then the maximum tolerance threshold of the animation parameter of the embedded device is recorded as the set value of the animation parameter; if the two values ​​of the same animation parameter are both less than the maximum tolerance threshold of the animation parameter of the embedded device, then the larger value is selected from the two values ​​as the set value of the animation parameter; if the two values ​​of the same animation parameter simultaneously satisfy that one is greater than the maximum tolerance threshold of the animation parameter of the embedded device and the other is less than the maximum tolerance threshold of the animation parameter of the embedded device, then the value less than the maximum tolerance threshold of the animation parameter of the embedded device is used as the set value of the animation parameter;

[0135] Step 2: If there is a submodule that first feeds back the value of a certain animation parameter to the adaptive adjustment module, the value of the animation parameter is compared with the maximum tolerance threshold of the animation parameter of the embedded device. If the former is greater than or equal to the latter, the maximum tolerance threshold of the animation parameter of the embedded device is defined as the set value of the animation parameter. If the former is less than the latter, the value of the animation parameter is defined as the set value of the animation parameter.

[0136] Step 3: If a subsequent sub-module feeds back the value of the animation parameter to the adaptive adjustment module, analysis and replacement are performed according to the steps in step 2.

[0137] The configuration storage module is used to store the maximum frame rate of the embedded device, the standard required memory for the animation, and the baseline playback speed, basic resolution, and baseline detail level of the animation.

[0138] Example 2

[0139] An electronic device according to an exemplary embodiment includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0140] The processor executes the above-mentioned embedded device homepage window optimization system by calling the computer program stored in the memory.

[0141] Figure 2It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement an embedded device homepage window optimization system provided in the above-mentioned various method embodiments.

[0142] The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for input and output. The embodiments of the present application will not be described in detail here.

[0143] This embodiment also provides a computer program product stored on a computer-readable medium, including a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the embedded device homepage window optimization system.

[0144] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0145] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0146] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0147] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A homepage window optimization system for an embedded device, characterized in that: It includes a hardware adaptation module, an animation engine module, an adaptive adjustment module and a configuration storage module, and the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; A hardware adaptation module, used for interacting resources between the window optimization system and the hardware devices of the embedded device, wherein the hardware devices are divided into processors, memory and input / output devices; Animation engine module, used to generate and manage animation effects, including frame rate control submodule and rendering optimization submodule; The frame rate control submodule is used to dynamically adjust the frame rate of the animation; The rendering optimization submodule is used to analyze the smoothness parameters of the animation under different hardware conditions of the embedded device; An adaptive adjustment module, which is used to automatically adjust animation parameters according to user operation behavior and embedded device status, including a user interaction detection submodule and a device status monitoring submodule; The user interaction detection submodule is used to detect the user's operation behavior through the sensors and input devices included in the embedded device, and adjust the animation parameters according to the operation behavior; the device status monitoring submodule is used to monitor the hardware status of the embedded device in real time, and adjust the animation parameters according to the device status; The configuration storage module is used to store the maximum frame rate of the embedded device, the standard required memory for the animation, and the baseline playback speed, basic resolution, and baseline detail level of the animation.

2. The embedded device homepage window optimization system according to claim 1, characterized in that: The specific operation logic of the frame rate control submodule is: Get the maximum frame rate Z of the embedded device from the configuration storage module 最大 , and synchronously collect the basic frame rate Z of the animation 基础 ; And get the performance coefficient Z of the embedded device 性能 and the battery factor Z 电池 , the target frame rate Z of the embedded device is obtained by calculation 目标 =Z 最大 ×(ω1×Z 性能 +ω2×Z 电池 ), ω1 and ω2 are weights for adjusting the performance coefficient and battery coefficient respectively; Compare the target frame rate of the embedded device with the basic frame rate of the animation. If the target frame rate of the embedded device is greater than the basic frame rate of the animation, delete frames from the animation. The specific operation process is as follows: Calculate the frame rate of embedded devices and animations In the animation every Z 调整 Insert a frame between the base frames; If the target frame rate of the embedded device is lower than the base frame rate of the animation, the animation is interpolated. The specific operation process is as follows: Similarly, calculate the frame rate adjustment frame number of embedded devices and animations In the animation Delete one frame between base frames; If the target frame rate of the embedded device is equal to the base frame rate of the animation, the animation frame rate is not adjusted.

3. The embedded device homepage window optimization system according to claim 1, characterized in that: The specific operation logic of the rendering optimization submodule is: The smoothness parameters include a resolution scaling factor, a texture quality factor and a LOD factor; Among them, embedded devices correspond to different hardware conditions and are specifically divided into GPU performance coefficient, memory and maximum frame rate; Analyze the resolution scaling factor corresponding to embedded devices Analyze texture quality factors for embedded devices ε 设备内存 , ε 动画内存 They represent the memory corresponding to the embedded device and the standard required memory for the animation stored in the configuration storage module respectively; Analyze the LOD factors corresponding to embedded devices ε GPU性能系数 is the GPU performance coefficient corresponding to the embedded device, ε 参考 Indicates the set reference GPU performance coefficient; According to the smooth parameters of the animation under different hardware conditions of the embedded device, the resolution scaling factor corresponding to the embedded device is compared with the value 1. If ε1<1, the resolution of the embedded device is reduced; if ε1=1, the original resolution of the embedded device is kept unchanged; if ε1>1, the resolution of the embedded device is increased or kept unchanged; Compare the texture quality factor corresponding to the embedded device with the value 1. If ε2<1, use low-quality textures for the embedded device. If ε2=1, use standard-quality textures for the embedded device. If ε2>1, use high-quality textures for the embedded device. The LOD factor corresponding to the embedded device is compared with the value 1. If ε3<1, the detail level of the embedded device is reduced. If ε3=1, the original detail level of the embedded device is kept unchanged. If ε3>1, the detail level of the embedded device is increased.

4. The embedded device homepage window optimization system according to claim 1, characterized in that: The animation parameters specifically include animation frame rate, animation play speed, animation resolution and animation detail level.

5. The embedded device homepage window optimization system according to claim 4, characterized in that: The specific operation logic of the user interaction detection submodule is as follows: Through the hardware device of the embedded device, the user's operating frequency corresponding to the embedded device is monitored. freq , and get the basic frame rate Z of the animation 基础 , the corresponding animation frame rate F of the embedded device in the user interaction detection submodule is obtained by calculation current =Z 基础 ×(1+k f ×O freq ); Through the hardware device of the embedded device, the user's operation speed corresponding to the embedded device is monitored. speed , according to the reference playback speed S of the animation stored in the configuration storage module 基础 , the animation playback speed S corresponding to the embedded device in the user interaction detection submodule is obtained by calculation current =S 基础 ×(1+k s ×O speed ); Through the hardware device of the embedded device, the user's operation complexity of the corresponding embedded device is monitored. complexity , according to the base resolution R of the animation stored in the configuration storage module 基础 , the corresponding animation resolution R of the embedded device in the user interaction detection submodule is obtained by calculation current =R 基础 ×(1-k r ×O complexity ); Through the hardware device of the embedded device, the precision of the user's operation on the corresponding embedded device is monitored. precision , according to the baseline level of detail D of the animation stored in the configuration storage module 基础 , the animation detail level D corresponding to the embedded device in the user interaction detection submodule is obtained by calculation current =D 基础 ×(1+k d ×O precision ); where k f , k s , k r , k d They represent the adjustment coefficients of the predefined operation frequency, speed, complexity and precision respectively.

6. The embedded device homepage window optimization system according to claim 5, characterized in that: The adjustment coefficients of predefined operation frequency, speed, complexity and precision are calculated in the following steps: Obtain historical test data, including the operation frequency corresponding to each historical test Animation base frame rate And test the best frame rate i is the number of each historical test, i = 1, 2, ... N; Through the linear regression equation Bring the historical test data back into the linear regression equation to solve k f , According to k f The calculation equation of k can be solved by the same method. s , k r , k d .

7. The embedded device homepage window optimization system according to claim 5, characterized in that: The specific operation logic of the equipment status monitoring submodule is: Through the hardware device of the embedded device, the CPU usage rate of the embedded device is monitored, and the corresponding animation frame rate F of the embedded device in the device status monitoring submodule is obtained by calculation. c ' urrent =Z 基础 ×(1-CPU use ), CPU use Indicates the CPU usage of the embedded device; The device temperature T of the embedded device is monitored through the hardware device of the embedded device, and the corresponding animation playback speed of the embedded device in the device status monitoring submodule is obtained by calculation. T max 、T min These are the maximum and minimum safe temperatures for embedded devices, respectively; The memory usage rate of the embedded device is monitored through the hardware device of the embedded device, and the corresponding animation resolution R′ of the embedded device in the device status monitoring submodule is obtained by calculation. current =R 基础 ×(1-NC use ), NC use Indicates the memory usage of the embedded device; The system load value of the embedded device is monitored through the hardware device of the embedded device, and the animation detail level D′ corresponding to the embedded device in the device status monitoring submodule is obtained by calculation. current =D 基础 ×(1-System_Load), where System_Load is the system load value of the embedded device.

8. The embedded device homepage window optimization system according to claim 7, characterized in that: The specific operation logic of the adaptive adjustment module is: Step 1: If the user interaction detection submodule and the device status monitoring submodule simultaneously feed back different values ​​of the same animation parameter in the adaptive adjustment module, then the two values ​​of the same animation parameter are respectively compared with the maximum tolerance threshold of the animation parameter of the embedded device; if the two values ​​of the same animation parameter are both greater than the maximum tolerance threshold of the animation parameter of the embedded device, then the maximum tolerance threshold of the animation parameter of the embedded device is recorded as the set value of the animation parameter; if the two values ​​of the same animation parameter are both less than the maximum tolerance threshold of the animation parameter of the embedded device, then the larger value is selected from the two values ​​as the set value of the animation parameter; if the two values ​​of the same animation parameter simultaneously satisfy that one is greater than the maximum tolerance threshold of the animation parameter of the embedded device and the other is less than the maximum tolerance threshold of the animation parameter of the embedded device, then the value less than the maximum tolerance threshold of the animation parameter of the embedded device is used as the set value of the animation parameter; Step 2: If there is a submodule that first feeds back the value of a certain animation parameter to the adaptive adjustment module, the value of the animation parameter is compared with the maximum tolerance threshold of the animation parameter of the embedded device. If the former is greater than or equal to the latter, the maximum tolerance threshold of the animation parameter of the embedded device is defined as the set value of the animation parameter. If the former is less than the latter, the value of the animation parameter is defined as the set value of the animation parameter. Step 3: If a subsequent sub-module feeds back the value of the animation parameter to the adaptive adjustment module, analysis and replacement are performed according to the steps in step 2.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the embedded device homepage window optimization system as described in any one of claims 1 to 8 by calling the computer program stored in the memory.

10. A computer program product stored on a computer readable medium, characterized in that: It includes a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement an embedded device homepage window optimization system as described in any one of claims 1-8.

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