A homepage window optimization system for embedded devices

Abstract

The present invention relates to the technical field of device homepage window optimization, specifically to an embedded device homepage window optimization system. The system interacts resources between the window optimization system and the hardware devices of the embedded device, generates and manages animation effects through an animation engine module, and includes a frame rate control submodule for dynamically adjusting the frame rate of the animation. A rendering optimization submodule analyzes the smoothness parameters of the animation under different hardware conditions corresponding to the embedded device. The system also detects user operation behavior and adjusts animation parameters accordingly. Furthermore, the system monitors the hardware status of the embedded device in real time and adjusts animation parameters accordingly. Through resource interaction and an intelligent adjustment mechanism between the animation engine module and the embedded device hardware, the system not only improves user experience but also optimizes resource utilization and energy consumption control while ensuring stable device operation. This method is crucial for improving the market competitiveness and user satisfaction of embedded devices.

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] Against the backdrop of current social development, the rapid growth of the Internet of Things (IoT) is well known. As a crucial component of next-generation information technology, the IoT is a strategic emerging industry receiving significant development, ushering in the third wave of the global information industry revolution. With the significant advancement of IoT technology, IoT devices are becoming increasingly intelligent and user-friendly, and the demand for human-computer interaction is gradually emerging. In the IoT era, edge devices require a user-friendly, intuitive, stable, and diverse human-computer interaction interface display system. This refers to embedded graphics display systems for edge computing, which are widely used in automotive electronics, smart homes, industrial control, and other fields.

[0003] With the widespread adoption of embedded devices, users are increasingly demanding interface animation effects. However, due to the limited hardware resources of embedded devices, achieving smooth and diverse animation effects on these devices has become a challenge. Traditional embedded device homepage window animation effects are monotonous and prone to lag during animation transitions, which affects the user experience and makes it difficult to meet users' personalized and diverse needs. Therefore, designing an optimization system that can achieve flexible and diverse animation display effects is particularly important. Summary of the Invention

[0004] In view of the above problems 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 shortcomings of the prior art.

[0005] In order to achieve the above-mentioned and other purposes, the technical solutions adopted by the present invention are 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 modules are connected via wired and / or wireless connections to achieve data transmission between the modules;

[0007] A hardware adaptation module is used to exchange resources between the window optimization system and the hardware devices of the embedded device, where the hardware devices are divided into processors, memory, image processing units and storage units;

[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] An adaptive adjustment module, configured to automatically adjust animation parameters based on user operation behavior and embedded device status, includes a user interaction detection submodule and a device status monitoring submodule. The user interaction detection submodule is configured to detect user operation behavior through sensors and input devices included in the embedded device and adjust animation parameters based on the operation behavior. The device status monitoring submodule is configured to monitor the hardware status of the embedded device in real time and adjust animation parameters based on the device status.

[0010] Configure the storage module 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] For example, the specific operation logic of the frame rate control submodule is:

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

[0013] And get the performance coefficient of the embedded device and battery factor , the target frame rate of the embedded device is obtained by calculation , They are used to adjust the weights of the performance coefficient and battery coefficient respectively;

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

[0015] Get the current idle CPU capacity of the embedded device, then ;

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

[0017] Compare the target frame rate of the embedded device with the base frame rate of the animation. If the target frame rate of the embedded device is greater than the base frame rate of the animation, perform interpolation on the animation. The specific operation process is as follows:

[0018] Calculate the frame rate of embedded devices and animations , in the animation Insert a frame between the basic frames;

[0019] If the target frame rate of the embedded device is lower than the base frame rate of the animation, the animation will be deleted. 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] For example, the specific operation logic of the rendering optimization submodule is as follows:

[0023] The smoothness parameters include a resolution scaling factor, a texture quality factor, and an 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 the texture quality factor corresponding to embedded devices , 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 , is the GPU performance coefficient corresponding to the embedded device, 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 , then reduce the resolution of the embedded device. If , then the embedded device is operated to keep the original resolution unchanged. If , then the embedded device will be operated to increase the resolution or keep the original resolution unchanged;

[0029] Compare the texture quality factor corresponding to the embedded device with the value 1. If , low-quality textures are used for embedded devices. , standard quality textures are used for embedded devices, if , high-quality textures are used for embedded devices;

[0030] Compare the LOD factor corresponding to the embedded device with the value 1. If , then reduce the level of detail of the embedded device, if , then the original level of detail of the embedded device remains unchanged. If , then increase the level of detail of the embedded device.

[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] Monitor the user's operating frequency of the corresponding embedded device through the hardware device of the embedded device , and get the basic frame rate of the animation , by calculating the corresponding animation frame rate of the embedded device in the user interaction detection submodule ;

[0034] Monitor the user's operating speed of the corresponding embedded device through the hardware device of the embedded device , based on the baseline playback speed of the animation stored in the configuration storage module , by calculating the corresponding animation playback speed of the embedded device in the user interaction detection submodule ;

[0035] Through the hardware device of the embedded device, the user's operation complexity of the corresponding embedded device is monitored , based on the base resolution of the animation stored in the configuration storage module , the animation resolution corresponding to the embedded device in the user interaction detection submodule is obtained by calculation ;

[0036] Through the hardware device of the embedded device, the user's operation precision of the corresponding embedded device is monitored , based on the baseline level of detail of the animation stored in the configuration storage module , by calculating the animation detail level of the embedded device corresponding to the user interaction detection submodule ;

[0037] in They represent the adjustment coefficients for predefined operation frequency, speed, complexity, and precision, respectively.

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

[0039] Obtain historical test data, including the operation frequency corresponding to each historical test , animation basic 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 , ;

[0041] in accordance with The calculation equation of is solved in the same way to get .

[0042] For example, the specific operation logic of the equipment status monitoring submodule is as follows:

[0043] Through the hardware device of the embedded device, the CPU usage of the embedded device is monitored, and the corresponding animation frame rate of the embedded device in the device status monitoring submodule is obtained by calculation. , 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. , These are the maximum and minimum safe temperatures for embedded devices, respectively;

[0045] Through the hardware device of the embedded device, the memory usage of the embedded device is monitored, and the corresponding animation resolution of the embedded device in the device status monitoring submodule is obtained by calculation. , Indicates the memory usage of the embedded device;

[0046] Through the hardware device of the embedded device, the system load value of the embedded device is monitored, and the animation detail level of the embedded device corresponding to the device status monitoring submodule is obtained by calculation. , The system load value of the embedded device.

[0047] For example, 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 to 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 meet the conditions 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 a submodule 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 the value of the animation parameter back 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 an embedded device.

[0053] In a third aspect, the present invention provides a computer program product stored on a computer-readable medium, comprising 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 the animation engine module, and 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 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 draining the battery 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 operating behavior and adjusting 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. This can reduce user waiting time and improve the immediate feedback of the interaction. Real-time monitoring of the hardware status of the embedded device and adjusting the animation parameters accordingly can prevent lag or animation frame skipping when the device is under high load. 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 likelihood 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 resource interaction and intelligent adjustment mechanisms between the animation engine module and the embedded device hardware, not only can the user experience be improved, but resource usage and energy consumption can also be optimized while ensuring stable device operation. 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 following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

[0062] The above contents described below in conjunction with the implementation of the present invention are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments 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 fall within the scope of protection 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. The above modules are connected through wired and / or wireless connections to achieve 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 is used to exchange resources between the window optimization system and the hardware devices of the embedded device, where the hardware devices are divided into processors, memory, image processing units and storage units;

[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 of the embedded device from the configuration storage module , and synchronously capture the basic frame rate of the animation ;

[0070] And get the performance coefficient of the embedded device and battery factor , the target frame rate of the embedded device is obtained by calculation , They are used to adjust the weights of the performance coefficient and battery coefficient respectively;

[0071] It should be added that the performance coefficient of the embedded device is obtained and battery factor 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 needs to be explained that the weight of adjusting the performance coefficient and battery coefficient The specific acquisition process is as follows:

[0075] Obtain historical test data of embedded devices from the configuration storage module, where the historical test data includes the performance coefficient under various load levels , battery coefficient and target frame rate , j is the number of each load level, j=1,2,...n;

[0076] Use the least squares method to find Cost function , n is the total number of load levels;

[0077] respectively Find the partial derivatives and set them to 0, and we get:

[0078] , and solve them together to get The value of .

[0079] Compare the target frame rate of the embedded device with the base frame rate of the animation. If the target frame rate of the embedded device is greater than the base frame rate of the animation, perform interpolation on the animation. The specific operation process is as follows:

[0080] Calculate the frame rate of embedded devices and animations , in the animation Insert a frame between the basic frames;

[0081] If the target frame rate of the embedded device is lower than the base frame rate of the animation, the animation will be deleted. 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 If it is 2, a frame is inserted 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 If the value is 0.5, 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 an 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 the texture quality factor corresponding to embedded devices , 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 , is the GPU performance coefficient corresponding to the embedded device, 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 , then reduce the resolution of the embedded device. If , then the embedded device is operated to keep the original resolution unchanged. If , then the embedded device will be operated to increase the resolution or keep the original resolution unchanged;

[0093] Compare the texture quality factor corresponding to the embedded device with the value 1. If , low-quality textures are used for embedded devices. , standard quality textures are used for embedded devices, if , high-quality textures are used for embedded devices;

[0094] Compare the LOD factor corresponding to the embedded device with the value 1. If , then reduce the level of detail of the embedded device, if , then the original level of detail of the embedded device remains unchanged. If , then increase the level of detail of the embedded device.

[0095] An adaptive adjustment module, configured to automatically adjust animation parameters based on user operation behavior and embedded device status, includes a user interaction detection submodule and a device status monitoring submodule. The user interaction detection submodule is configured to detect user operation behavior through sensors and input devices included in the embedded device and adjust animation parameters based on the operation behavior. The device status monitoring submodule is configured to monitor the hardware status of the embedded device in real time and adjust animation parameters based on the device status.

[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] Monitor the user's operating frequency of the corresponding embedded device through the hardware device of the embedded device , and get the basic frame rate of the animation , by calculating the corresponding animation frame rate of the embedded device in the user interaction detection submodule ;

[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 interrupts 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 regularly 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] Monitor the user's operating speed of the corresponding embedded device through the hardware device of the embedded device , based on the baseline playback speed of the animation stored in the configuration storage module , by calculating the corresponding animation playback speed of the embedded device in the user interaction detection submodule ;

[0101] It should be added that the timestamps are recorded at the start and end of the user operation, thereby obtaining the user's operation speed of the 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 , based on the base resolution of the animation stored in the configuration storage module , the animation resolution corresponding to the embedded device in the user interaction detection submodule is obtained by calculation ;

[0109] It should be supplemented that the time required for users to complete specific tasks can be 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 user's operation precision of the corresponding embedded device is monitored , based on the baseline level of detail of the animation stored in the configuration storage module , by calculating the animation detail level of the embedded device corresponding to the user interaction detection submodule ;

[0114] It should be added that a touch screen, accelerometer, and force sensor are installed on the embedded device. Through timed sampling and event-driven methods, data such as the user's touch position, sliding speed, and applied force are collected. 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 precision of the operation. The simplified pseudo code is shown below:

[0115] #include<SomeTouchScreenLibrary.h>

[0116] #include<SomeAccelerometerLibrary.h>

[0117] #include<SomeForceSensorLibrary.h>

[0118] / / Initialize the touch screen, accelerometer, and force sensor

[0119] void setup() {

[0120] TouchScreen.init();

[0121] Accelerometer.init();

[0122] ForceSensor.init();

[0123] / / Set the timer, for example, sampling every 10ms

[0124] setupTimer(10ms, timerInterrupt);

[0125] / / Enable touch screen interrupt

[0126] TouchScreen.enableInterrupt(touchInterrupt);

[0127] }

[0128] / / Timer interrupt service routine

[0129] void timerInterrupt() {

[0130] / / Read accelerometer and force sensor data regularly

[0131] Accelerometer.read();

[0132] ForceSensor.read();

[0133] / / Data filtering can be performed here

[0134] }

[0135] / / Touch screen interrupt service routine

[0136] void touchInterrupt() {

[0137] / / Read touch position

[0138] Point touchPoint = TouchScreen.getTouchPosition();

[0139] / / More complex processing can be performed here as needed, such as calculating the sliding speed, etc.

[0140] }

[0141] / / Main loop

[0142] void loop() {

[0143] / / If necessary, further data processing and feature extraction can be performed in the main loop

[0144] }

[0145] in They represent the adjustment coefficients for predefined operation frequency, speed, complexity, and precision, respectively.

[0146] Based on the above embodiment, the adjustment coefficients of the predefined operating frequency, speed, complexity and fineness are specifically calculated including the following steps:

[0147] Obtain historical test data, including the operation frequency corresponding to each historical test , animation basic frame rate and test the best frame rate , i is the number of each historical test, i=1,2,...N;

[0148] Through the linear regression equation , bring the historical test data back into the linear regression equation to solve , , N is the total number of historical tests;

[0149] in accordance with The calculation equation of is solved in the same way to get .

[0150] Obtain historical test data, including the operation speed corresponding to each historical test , animation baseline playback speed and test the optimal playback speed ;

[0151] Through the linear regression equation , bring the historical test data back into the linear regression equation to solve , .

[0152] Obtain historical test data, including the operation complexity corresponding to each historical test , animation base resolution and test the best resolution ;

[0153] Through the linear regression equation , bring the historical test data back into the linear regression equation to solve , .

[0154] Obtain historical test data, including the operation precision corresponding to each historical test , animation baseline level of detail and test the optimal level of detail ;

[0155] Through the linear regression equation , bring the historical test data back into the linear regression equation to solve , .

[0156] Based on the above embodiment, the specific operation logic of the equipment status monitoring submodule is as follows:

[0157] Through the hardware device of the embedded device, the CPU usage of the embedded device is monitored, and the corresponding animation frame rate of the embedded device in the device status monitoring submodule is obtained by calculation. , Indicates the CPU usage of the embedded device;

[0158] 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. , These are the maximum and minimum safe temperatures for embedded devices, respectively;

[0159] Through the hardware device of the embedded device, the memory usage of the embedded device is monitored, and the corresponding animation resolution of the embedded device in the device status monitoring submodule is obtained by calculation. , Indicates the memory usage of the embedded device;

[0160] Through the hardware device of the embedded device, the system load value of the embedded device is monitored, and the animation detail level of the embedded device corresponding to the device status monitoring submodule is obtained by calculation. , The system load value of the embedded device.

[0161] Based on the above embodiment, the specific operation logic of the adaptive adjustment module is as follows:

[0162] Step 1: If the user interaction detection submodule and the device status monitoring submodule simultaneously feed back different values ​​of the same animation parameter to 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 meet the conditions 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;

[0163] Step 2: If a submodule 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;

[0164] Step 3: If a subsequent sub-module feeds the value of the animation parameter back to the adaptive adjustment module, analysis and replacement are performed according to the steps in step 2.

[0165] Configure the storage module 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.

[0166] Example 2

[0167] According to an exemplary embodiment, an electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0168] The processor executes the above-mentioned embedded device homepage window optimization system by calling the computer program stored in the memory.

[0169] Figure 2 This is a structural 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 can 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.

[0170] The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also include components such as a wired or wireless network interface and an input / output interface for input and output.

[0171] 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.

[0172] 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.

[0173] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0174] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0175] 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 scope of protection of the present invention.

Claims

1. A homepage window optimization system for embedded devices, characterized in that: It includes a hardware adaptation module, an animation engine module, an adaptive adjustment module and a configuration storage module. The above modules are connected by wired and / or wireless connections to achieve data transmission between the modules; A hardware adaptation module, used to enable resource interaction between the window optimization system and the hardware devices of the embedded device, where 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; Adaptive adjustment module, used to automatically adjust animation parameters according to user operation behavior and embedded device status, including user interaction detection submodule and device status monitoring submodule; The user interaction detection submodule is used to detect user operation behaviors through sensors and input devices included in the embedded device, and adjust animation parameters according to the operation behaviors; the device status monitoring submodule is used to monitor the hardware status of the embedded device in real time and adjust animation parameters according to the device status. The specific operation logic is as follows: Step 1: If the user interaction detection submodule and the device status monitoring submodule simultaneously feed back different values ​​of the same animation parameter to 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 meet the conditions 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 a submodule 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; Configure the storage module 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 of the embedded device from the configuration storage module , and synchronously capture the basic frame rate of the animation ; And get the performance coefficient of the embedded device and battery factor , the target frame rate of the embedded device is obtained by calculation , They are used to adjust the weights of the performance coefficient and battery coefficient respectively; Compare the target frame rate of the embedded device with the base frame rate of the animation. If the target frame rate of the embedded device is greater than the base frame rate of the animation, perform interpolation on the animation. The specific operation process is as follows: Calculate the frame rate of embedded devices and animations , in the animation Insert a frame between the basic frames; If the target frame rate of the embedded device is lower than the base frame rate of the animation, the animation will be deleted. 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 as follows: The smoothness parameters include a resolution scaling factor, a texture quality factor, and an 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 the texture quality factor corresponding to embedded devices , 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 , is the GPU performance coefficient corresponding to the embedded device, 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 , then reduce the resolution of the embedded device. If , then the embedded device is operated to keep the original resolution unchanged. If , then the embedded device will be operated to increase the resolution or keep the original resolution unchanged; Compare the texture quality factor corresponding to the embedded device with the value 1. If , low-quality textures are used for embedded devices. , standard quality textures are used for embedded devices, if , high-quality textures are used for embedded devices; Compare the LOD factor corresponding to the embedded device with the value 1. If , then reduce the level of detail of the embedded device, if , then the original level of detail of the embedded device remains unchanged. If , then increase the level of detail of the embedded device.

4. The embedded device homepage window optimization system according to claim 1, characterized in that: The animation parameters specifically include animation frame rate, animation playback 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: Monitor the user's operating frequency of the corresponding embedded device through the hardware device of the embedded device , and get the basic frame rate of the animation , by calculating the corresponding animation frame rate of the embedded device in the user interaction detection submodule ; Monitor the user's operating speed of the corresponding embedded device through the hardware device of the embedded device , based on the baseline playback speed of the animation stored in the configuration storage module , by calculating the corresponding animation playback speed of the embedded device in the user interaction detection submodule ; Through the hardware device of the embedded device, the user's operation complexity of the corresponding embedded device is monitored , based on the base resolution of the animation stored in the configuration storage module , the animation resolution corresponding to the embedded device in the user interaction detection submodule is obtained by calculation ; Through the hardware device of the embedded device, the user's operation precision of the corresponding embedded device is monitored , based on the baseline level of detail of the animation stored in the configuration storage module , by calculating the animation detail level of the embedded device corresponding to the user interaction detection submodule ; in They represent the adjustment coefficients for 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 for predefined operation frequency, speed, complexity, and precision are calculated as follows: Obtain historical test data, including the operation frequency corresponding to each historical test , animation basic 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 , ; in accordance with The calculation equation of is solved in the same way to get .

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 as follows: Through the hardware device of the embedded device, the CPU usage of the embedded device is monitored, and the corresponding animation frame rate of the embedded device in the device status monitoring submodule is obtained by calculation. , 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. , These are the maximum and minimum safe temperatures for embedded devices, respectively; Through the hardware device of the embedded device, the memory usage of the embedded device is monitored, and the corresponding animation resolution of the embedded device in the device status monitoring submodule is obtained by calculation. , Indicates the memory usage of the embedded device; Through the hardware device of the embedded device, the system load value of the embedded device is monitored, and the animation detail level of the embedded device corresponding to the device status monitoring submodule is obtained by calculation. , The system load value of the embedded device.

8. 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 calls the computer program stored in the memory to execute and implement the embedded device homepage window optimization system according to any one of claims 1 to 7.

9. A computer program product stored on a computer-readable medium, characterized in that: It comprises 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 1-7.

Images