Control method and system of intelligent handheld game console

Abstract

The invention discloses a control method and system for an intelligent handheld device, and the method specifically comprises the steps: obtaining the real-time power consumption data and the calorific value data of the intelligent handheld device, and adjusting the real-time power consumption data and the calorific value data based on a pre-constructed frequency adjustment table, dynamically adjusting the processor frequency and the display screen refreshing frequency of the intelligent handheld device according to the real-time power consumption data and the calorific value data; user interaction behavior data are analyzed in real time through a pre-constructed function mode recognition model, a function mode recognition result of the intelligent handheld device is obtained, and optimal display parameter configuration is determined according to the function mode recognition result; and dynamically calculating the optimal display screen brightness and the optimal color parameter of the intelligent handheld device by adopting a self-adaptive mapping algorithm according to the environment illumination intensity data, the user watching distance information and the user watching area information. According to the method, the optimal visual experience can be provided in different use scenes of the intelligent handheld device, meanwhile, the equipment endurance time of the intelligent handheld device is prolonged, and the user satisfaction degree is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent handheld devices, and particularly to a control method and system for an intelligent handheld device. Background Art

[0002] Today, with the increasing popularity of intelligent handheld devices, users have increasingly strict requirements for their performance, power consumption, display effects, and comfort. However, there are still many problems to be solved in the control and display of existing intelligent handheld devices, which seriously affect the user experience.

[0003] First of all, most traditional intelligent handheld devices adopt fixed processor frequencies and display screen refresh frequencies. This design causes the handheld device to be unable to effectively adjust power consumption and heat generation in different application scenarios, resulting in problems such as shortened battery life and overheating of the device. Especially when dealing with complex tasks, excessive power consumption and heat generation not only accelerate battery consumption but may also damage the device hardware, reducing the stability and service life of the device.

[0004] Secondly, there are deficiencies in the display parameter adjustment of existing intelligent handheld devices. Traditional handheld devices usually cannot adjust display parameters in real time according to user interaction behaviors, resulting in unsatisfactory display effects in different function modes. For example, in the game mode, users may require a higher refresh rate and more vivid colors, while in the reading mode, users pay more attention to the brightness and color accuracy of the display screen. However, existing handheld devices often cannot make real-time adjustments according to these requirements, thus affecting the user's visual experience.

[0005] In addition, there is another problem in the display parameter adjustment of traditional intelligent handheld devices, that is, they cannot be dynamically adjusted in real time according to the ambient light intensity, user usage status, etc. This leads to poor display effects and may even cause problems such as eye fatigue. Especially in a dim environment, too high a display screen brightness will irritate the user's eyes, while in strong light, too low a display screen brightness will cause the user to be unable to see the screen content clearly.

[0006] In addition, existing intelligent handheld devices are prone to problems such as screen stuttering and blurring during fast movement or screen switching. These problems are mainly caused by the response time limit of the display screen and too long pixel lighting time. Traditional handheld devices usually use simple image processing algorithms for smoothing, but the effect is not good and cannot effectively improve the screen quality. Summary of the Invention

[0007] The object of the present invention is to provide a control method and system for an intelligent handheld device, which can provide the best visual experience in different usage scenarios of the intelligent handheld device, while extending the battery life of the intelligent handheld device and improving user satisfaction, so as to solve at least one of the above-mentioned prior art problems.

[0008] In a first aspect, the present invention provides a control method for an intelligent handheld device, and the method specifically includes:

[0009] Obtain the real-time power consumption data and heat generation data of the intelligent handheld device, and dynamically adjust the processor frequency and display screen refresh frequency of the intelligent handheld device based on a pre-constructed frequency adjustment table according to the real-time power consumption data and the heat generation data;

[0010] Real-time analyze the user interaction behavior data through a pre-constructed function mode recognition model, obtain the function mode recognition result of the intelligent handheld device, and determine the optimal display parameter configuration according to the function mode recognition result;

[0011] According to the environmental light intensity data, user viewing distance information, and user gaze area information, dynamically calculate the optimal display screen brightness and optimal color parameters of the intelligent handheld device by using an adaptive mapping algorithm;

[0012] Obtain the user prediction behavior data according to the user holding posture data and user eye movement data, and dynamically adjust the display performance data of the intelligent handheld device through the user prediction behavior data;

[0013] When the intelligent handheld device is in fast motion or screen switching, smooth the front and rear frame images by using a frame interpolation algorithm through analyzing the motion vectors of the front and rear frame images.

[0014] In a second aspect, the present invention provides a control system for an intelligent handheld device, and the system specifically includes:

[0015] A first control module, configured to obtain the real-time power consumption data and heat generation data of the intelligent handheld device, and dynamically adjust the processor frequency and display screen refresh frequency of the intelligent handheld device based on a pre-constructed frequency adjustment table according to the real-time power consumption data and the heat generation data;

[0016] A second control module, configured to real-time analyze the user interaction behavior data through a pre-constructed function mode recognition model, obtain the function mode recognition result of the intelligent handheld device, and determine the optimal display parameter configuration according to the function mode recognition result;

[0017] A third control module, configured to dynamically calculate the optimal display screen brightness and optimal color parameters of the intelligent handheld device by using an adaptive mapping algorithm according to the environmental light intensity data, user viewing distance information, and user gaze area information;

[0018] a fourth control module, configured to obtain user predicted behavior data according to the user holding posture data and the user line of sight movement data, and dynamically adjust the display performance data of the intelligent handheld game console according to the user predicted behavior data;

[0019] The fifth control module is used to analyze the motion vectors of the previous and next frame images and use the frame interpolation algorithm to smooth the previous and next frame images when the intelligent handheld game console is in fast motion or screen switching.

[0020] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor and a computer program stored in the memory, and when the computer program is executed on the processor, a method for controlling an intelligent handheld game console as described in any one of the above methods is implemented.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for controlling an intelligent handheld game console as described in any one of the above methods is implemented.

[0022] Compared with the prior art, the present invention has at least one of the following technical effects:

[0023] 1. The present invention can provide the best visual experience in different usage scenarios of the intelligent handheld game console, while extending the device life of the intelligent handheld game console and improving user satisfaction.

[0024] 2. Through dynamic adjustment, the present invention can intelligently balance power consumption and performance according to current usage, effectively reduce the real-time power consumption and heat generation of the intelligent handheld game console, extend battery life, and at the same time ensure stable operation of the device to avoid overheating problems.

[0025] 3. The reinforcement learning algorithm of the present invention can automatically learn and optimize the adjustment strategy of the processor frequency and the display refresh frequency to adapt to different usage scenarios and needs, improve the accuracy and efficiency of the adjustment, and thus enhance the user experience.

[0026] 4. The present invention analyzes user interaction behaviors in real time, intelligently identifies the functional modes of the handheld game console, and automatically adjusts display parameters according to the requirements of different modes to improve display effects and meet the visual needs of users in different scenarios.

[0027] 5. The present invention dynamically adjusts the brightness and color parameters of the display screen according to information such as ambient light intensity, user viewing distance, and user gaze area, to achieve personalized display effects and improve visual comfort and user experience.

[0028] 6. The present invention predicts user behavior and adjusts display performance data in advance, such as brightness, contrast, etc., to meet the upcoming usage requirements of users, improving the response speed and user experience.

[0029] 7. When the intelligent handheld device is in fast motion or screen switching, the present invention reduces screen stuttering and blurring phenomena through a frame interpolation algorithm, improving the screen smoothness and clarity, and enhancing the visual experience.

[0030] 8. The present invention collects and analyzes the experience data of users in different usage scenarios, establishes a user experience evaluation model, and adjusts the display parameter combination according to the evaluation results to optimize the user experience. This method can ensure that the display parameters of the intelligent handheld device always meet the needs and preferences of users, improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 is a flowchart of a control method for an intelligent handheld device provided by an embodiment of the present invention;

[0033] Figure 2 is a structural diagram of a control system for an intelligent handheld device provided by an embodiment of the present invention;

[0034] Figure 3 is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0036] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0037] It should also be understood that the term "and / or" as used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0038] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrases "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" depending on the context.

[0039] In addition, in the description of the specification and appended claims of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0040] Reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0041] In the embodiments of this application, the execution subject of the process includes a terminal device. The terminal device includes but is not limited to: devices such as servers, computers, smartphones, and tablet computers that can execute the methods disclosed in this application. Figure 1 The flowchart of the control method of the intelligent handheld device disclosed in an embodiment of the present invention is shown and described in detail as follows:

[0042] S101, obtain the real-time power consumption data and heat generation data of the intelligent handheld device, and dynamically adjust the processor frequency and display screen refresh frequency of the intelligent handheld device based on a pre-constructed frequency adjustment table according to the real-time power consumption data and the heat generation data.

[0043] In this embodiment, a processor that supports dynamic voltage and frequency scaling (DVFS) is selected. This processor can adjust its frequency according to a preset frequency adjustment table, and is equipped with high-precision power consumption sensors and temperature sensors for real-time monitoring of the power consumption and heat generation of the handheld console. The display screen uses a panel that supports multiple refresh rates to enable dynamic adjustment according to requirements. The operating system or firmware of the handheld console is developed, integrating a power consumption and temperature monitoring module, a frequency adjustment module, and a display screen refresh rate adjustment module. The power consumption and temperature monitoring module is responsible for reading the sensor data and transmitting it to the frequency adjustment module. The frequency adjustment module dynamically adjusts the frequency of the processor based on the pre-constructed frequency adjustment table, combined with the real-time power consumption data and heat generation data. The display screen refresh rate adjustment module dynamically adjusts the refresh rate of the display screen according to the frequency of the processor, the currently running application, and the user's settings.

[0044] The frequency adjustment table is a preset two-dimensional array that contains the processor frequency and the display screen refresh rate corresponding to different power consumption and temperature ranges. For example, when the power consumption is low and the temperature is moderate, the processor frequency and the display screen refresh rate can be set to lower values to save energy; when the power consumption is high and the temperature is close to the critical value, the processor frequency and the display screen refresh rate need to be reduced to prevent overheating.

[0045] The power consumption sensors and temperature sensors continuously monitor the power consumption and temperature of the handheld console and transmit the data to the operating system. The operating system determines the power consumption and temperature range in which the current handheld console is located based on the received data. According to the judgment result, the corresponding processor frequency and display screen refresh rate are found in the frequency adjustment table. The operating system sends a frequency adjustment instruction to the processor to adjust the processor frequency to the found value. At the same time, the operating system also adjusts the refresh rate of the display screen to match the frequency change of the processor.

[0046] In this embodiment, by dynamically adjusting the processor frequency and the display screen refresh rate, the handheld console can minimize power consumption while ensuring performance, and extend the battery life. Adjusting the frequency according to the real-time temperature data helps prevent the handheld console from overheating and improves the stability and reliability of the system. Dynamically adjusting the display screen refresh rate according to the currently running application and user settings can provide a smoother and more comfortable visual experience.

[0047] S102. Real-time analyze the user interaction behavior data through a pre-constructed function mode recognition model to obtain the function mode recognition result of the intelligent handheld console, and determine the optimal display parameter configuration according to the function mode recognition result.

[0048] In this embodiment, the intelligent handheld device is equipped with multiple sensors for capturing user interaction behavior data, such as the position, frequency, and intensity of touching the screen, as well as the user's voice commands, etc. At the same time, the system records the display performance data of the handheld device in different functional modes, such as brightness, color saturation, contrast, etc. Machine learning algorithms, such as support vector machines (SVM) or deep learning models, are used to build a functional mode recognition model. Using historical user interaction behavior data and corresponding display performance data as the training set, the model is trained to accurately identify the functional mode of the handheld device. During the operation of the intelligent handheld device, it collects user interaction behavior data in real time and inputs it into the functional mode recognition model for analysis. Based on the input data, the model quickly identifies the current functional mode of the handheld device, such as game mode, reading mode, video mode, etc. According to the identified functional mode, the system selects the optimal configuration from the preset display parameter configuration library. For example, in game mode, the system may select a higher refresh rate and brightness to provide a smoother gaming experience; while in reading mode, it may select a lower brightness and more comfortable color saturation to reduce eye fatigue.

[0049] In this embodiment, by analyzing user interaction behavior data in real time, the intelligent handheld device can accurately identify the user's functional requirements and provide personalized display parameter configurations, thereby enhancing the user experience. According to different functional modes, the system can automatically adjust parameters such as the brightness and color saturation of the display screen to provide a display effect that better meets the user's needs. In scenarios where high brightness or high refresh rate is not required, the system can automatically reduce these parameters, thereby saving energy and reducing carbon emissions.

[0050] S103, according to the environmental light intensity data, user viewing distance information, and user gaze area information, dynamically calculate the optimal display screen brightness and optimal color parameters of the intelligent handheld device using an adaptive mapping algorithm.

[0051] In this embodiment, the intelligent handheld device is equipped with a light sensor, a distance sensor, and an eye tracking sensor, which are used to obtain environmental light intensity data, user viewing distance information, and user gaze area information respectively. The handheld device is built-in with an adaptive mapping algorithm module for processing sensor data and calculating the optimal display screen brightness and color parameters. The sensors collect data on environmental light intensity, user viewing distance, and user gaze area in real time. The adaptive mapping algorithm module receives the sensor data, performs preprocessing and feature extraction. The algorithm module applies the adaptive mapping algorithm based on the preprocessed data to calculate the optimal display screen brightness and color parameters. The display screen of the handheld device dynamically adjusts the brightness and color parameters according to the calculation results to provide the most comfortable viewing experience.

[0052] Preprocess the environmental light intensity data, user viewing distance information, and user fixation area information, including steps such as data cleaning, denoising, and normalization, to construct a feature vector for describing the current environment and user state. Apply an adaptive mapping algorithm to map the feature vector onto the space of optimal display screen brightness and color parameters. The algorithm uses machine learning or deep learning techniques to optimize the mapping relationship through training historical data. Calculate the most comfortable display screen brightness based on the environmental light intensity and user viewing distance. Calculate parameters such as the optimal color saturation, contrast, and hue based on the user fixation area and the current state of the display screen.

[0053] In this embodiment, by adjusting the brightness and color parameters of the display screen in real time, the display effect of the handheld console is made more in line with the current environment and user state, improving the user's viewing comfort. When the environmental light intensity is low or the user viewing distance is far, automatically reduce the brightness of the display screen to reduce energy consumption. By optimizing the color parameters, reduce the stimulation of the display screen to the eyes and reduce the impact on vision caused by long-term use of the handheld console.

[0054] S104, obtain user predicted behavior data according to the user holding posture data and user eye movement data, and dynamically adjust the display performance data of the intelligent handheld console through the user predicted behavior data.

[0055] In this embodiment, the intelligent handheld game console has a built-in sensor array, including a posture sensor (such as a gyroscope, an accelerometer) and an eye tracking sensor, which is used to capture the user's holding posture data and line of sight movement data in real time. The handheld game console is also equipped with a high-performance processor and an advanced machine learning algorithm module for processing sensor data, predicting user behavior and dynamically adjusting display performance. Among them, the sensor collects the user's holding posture and line of sight movement data in real time, the processor receives the sensor data, performs preprocessing and feature extraction, and the machine learning algorithm module predicts the user's future behavior (such as game operation, reading page turning, etc.) based on the preprocessed data. According to the predicted behavior data, the processor dynamically adjusts the display performance data of the handheld game console (such as brightness, contrast, color saturation, etc.) to provide a visual experience that better meets user needs. The posture sensor captures the user's holding angle, direction and other posture information. The eye tracking sensor tracks the user's line of sight movement trajectory in real time to determine the area and focus of the user's current gaze. The machine learning algorithm module combines historical user behavior and current sensor data, and predicts the user's future behavior through techniques such as pattern recognition and regression analysis. For example, when the user's line of sight stays on a certain game button for a long time, the algorithm may predict that the user is about to click the button to operate the game. Based on the predicted behavior data, the processor dynamically adjusts the display performance of the handheld console. If it predicts that the user will perform gaming operations, the refresh rate and color saturation of the display will be increased to enhance the smoothness and visual effects of the game screen. If it predicts that the user will read, the brightness and contrast of the display will be reduced to reduce eye fatigue.

[0056] In this embodiment, by capturing and analyzing the user's holding posture and eye movement data in real time, the intelligent handheld game console can more accurately predict user behavior and adjust the display performance accordingly, thereby providing a visual experience that better meets user needs. Based on the user's predicted behavior data, the display screen's brightness, contrast, color saturation and other parameters are dynamically adjusted to make the display effect more in line with the current scene and user needs.

[0057] S105, when the intelligent handheld game console is in fast motion or screen switching, the motion vectors of the previous and next frame images are analyzed and the previous and next frame images are smoothed by using a frame interpolation algorithm.

[0058] In this embodiment, the intelligent handheld console is built-in with a high-performance image processor and a storage unit, which are used to capture, store, and process video frame images in real time. The handheld console is also equipped with a motion vector analysis module and a frame interpolation algorithm module, which are used to analyze the motion vectors of the front and rear frame images and apply the frame interpolation algorithm for smoothing processing. When the handheld console is in fast motion or the screen is switched, the image processor captures the video frame images in real time and stores them in the storage unit. The motion vector analysis module reads the front and rear frame images in the storage unit, analyzes and calculates the motion vectors between them. The frame interpolation algorithm module generates intermediate frame images according to the motion vector information by applying the frame interpolation algorithm to smooth the changes between the front and rear frame images. The processed images are presented to the user through the display screen, providing a smoother visual experience. Specifically, the motion vector analysis module calculates the motion vectors between the front and rear frame images by comparing the position changes of the same object in the front and rear frame images. The motion vector includes two attributes, direction and magnitude, which respectively represent the moving direction and distance of the object. According to the motion vector information, the frame interpolation algorithm module generates intermediate frame images. The algorithm can adopt linear interpolation, quadratic interpolation, or higher-order interpolation methods to calculate the pixel values of the corresponding pixels in the intermediate frame images according to the pixel values and motion vector information in the front and rear frame images. In particular, quadratic interpolation or higher-order interpolation methods can sense the acceleration of the motion in the video and generate more accurate intermediate frame images, thus providing a smoother visual transition effect. By applying the frame interpolation algorithm, the intelligent handheld console can generate a smoother video transition effect during fast motion or screen switching. The insertion of intermediate frame images reduces the sense of jump between the front and rear frame images, making the video screen smoother and more natural.

[0059] In this embodiment, by applying the frame interpolation algorithm, the intelligent handheld console can generate a smoother video transition effect during fast motion or screen switching, improving the visual fluency. The insertion of intermediate frame images reduces the sense of jump between the front and rear frame images, making the video screen more coherent and natural. The smooth video transition effect enhances the user experience in scenarios such as watching videos or playing games using the handheld console.

[0060] In some embodiments, before the step S101 of dynamically adjusting the processor frequency and the display screen refresh frequency of the intelligent handheld console according to the real-time power consumption data and the heat generation data based on the pre-constructed frequency adjustment table, it further includes:

[0061] Taking the real-time power consumption data, heat generation data, processor frequency, and display screen refresh frequency of the intelligent handheld console as the state space, and taking the various adjustment operations of the processor frequency and the display screen refresh frequency as the action space, and using reducing the real-time power consumption and heat generation of the intelligent handheld console as the reward function;

[0062] Based on the state space, the action space, and the reward function, a reinforcement learning algorithm is used for modeling and training to obtain a frequency adjustment model, which is used to output a frequency adjustment table. The frequency adjustment table includes the data association relationship among the real-time power consumption, the heat generation, the processor frequency, and the display screen refresh frequency.

[0063] In this embodiment, the real-time power consumption data, the heat generation data, the processor frequency, and the display screen refresh frequency of the intelligent handheld device are obtained as the state space of the reinforcement learning algorithm. Each adjustment combination of the processor frequency and the display screen refresh frequency is determined as the action space of the reinforcement learning algorithm. With the goal of reducing the real-time power consumption and heat generation of the intelligent handheld device, a reward function of the reinforcement learning algorithm is constructed. According to the determined state space, action space, and reward function, the Q-learning reinforcement learning algorithm is used to model and train the frequency adjustment strategy. Through multiple rounds of iterative training, the Q-value table is continuously optimized and updated, and finally the optimal frequency adjustment strategy that can achieve the reduction of power consumption and heat generation is obtained. The trained optimal frequency adjustment strategy is converted into a frequency adjustment model for subsequent online frequency dynamic adjustment. The frequency adjustment model outputs a frequency adjustment table containing the adjustment values of the processor frequency and the display screen refresh frequency according to the real-time detected power consumption and heat generation data, guiding the intelligent handheld device to perform dynamic frequency adjustment and realizing the continuous optimization of power consumption and heat generation.

[0064] Exemplarily, the frequency adjustment model is established by a reinforcement learning algorithm aiming to optimize the power consumption and heat dissipation performance of the device. The state space of this model includes the real-time power consumption data, the heat generation data, the processor frequency, and the display screen refresh frequency, and these parameters jointly reflect the current operating state of the device. For example, when a certain handheld device is running a large game, the power consumption may reach 5W, the heat generation is 40°C, the processor frequency is 2.4GHz, and the screen refresh rate is 60Hz.

[0065] The action space is composed of the adjustment operation combinations of the processor frequency and the display screen refresh frequency. The processor frequency has multiple gears, such as 1.8GHz, 2.0GHz, 2.2GHz, etc.; the display screen refresh frequency may have options such as 30Hz, 60Hz, 90Hz, etc. Different combinations will produce different performance and power consumption effects.

[0066] The reward function aims to reduce the real-time power consumption and heat generation. This is because excessive power consumption will accelerate battery consumption, and excessive heat generation may lead to a decline in device performance or safety hazards. By reasonably adjusting the frequency, the energy consumption and heat dissipation can be optimized while ensuring performance.

[0067] The reinforcement learning algorithm learns to select the optimal action in various states by continuously trying different frequency adjustment strategies. For example, when it detects that the device temperature is approaching the critical value, the algorithm may reduce the processor frequency and screen refresh rate to reduce heat generation. Conversely, when the temperature is low, it may increase the frequency to improve performance. During the training process, the algorithm explores various possible state-action combinations and continuously adjusts the strategy based on the feedback of the reward function. This process may require a large amount of simulation or actual device test data support. Eventually, the algorithm converges to an optimal strategy that balances performance and power consumption. The output of the frequency adjustment model is a frequency adjustment table, which contains the data correlation relationships among real-time power consumption, heat generation, processor frequency, and display screen refresh rate. This table may be presented in the form of a multi-dimensional matrix, with each dimension corresponding to a parameter. For example, when the power consumption is 3W and the heat generation is 35°C, the table may recommend setting the processor frequency to 2.0GHz and the display screen refresh rate to 60Hz. This frequency adjustment method based on reinforcement learning is more flexible and intelligent than the traditional fixed-threshold adjustment method. It can dynamically adjust the frequency according to the real-time state of the device, maximizing the battery life while ensuring the user experience. In addition, this method has self-adaptability and can continuously optimize the adjustment strategy as the device usage changes. By implementing this intelligent frequency adjustment, the intelligent handheld device can maintain the best performance-power balance in different usage scenarios. For example, when playing light casual games, the system may reduce the processor frequency and screen refresh rate to save power; while when running high-intensity 3D games, it will appropriately increase the frequency to ensure a smooth gaming experience. This dynamic adjustment not only improves the user experience but also extends the service life of the device.

[0068] In some embodiments, in the above step S102, before obtaining the function mode recognition result of the intelligent handheld device by analyzing the user interaction behavior data in real time through the pre-constructed function mode recognition model and determining the optimal display parameter configuration according to the function mode recognition result, it further includes:

[0069] Obtain the display performance data, historical application scenario data, and historical user interaction behavior data of the intelligent handheld device in different function modes;

[0070] Perform correlation analysis on the display performance data, historical application scenario data, and the historical user interaction behavior data to form a first training data set;

[0071] Use the first training data set as input and adopt the support vector machine algorithm for modeling training to construct a function mode recognition model.

[0072] In this embodiment, display performance data, historical application scenario data, and historical user interaction behavior data of the intelligent handheld device are obtained; data preprocessing is performed on the obtained display performance data, historical application scenario data, and historical user interaction behavior data, including operations such as data cleaning and data normalization; according to the preprocessed data, association analysis algorithms are used to mine the association rules and patterns between the data to form a first training dataset; the first training dataset is randomly divided into a training set and a test set, where the training set is used for model training and the test set is used for model evaluation; a support vector machine algorithm is used to perform modeling training on the training set, and through adjusting the algorithm parameters and iterative optimization, a function mode recognition model is obtained; the performance of the trained function mode recognition model is evaluated using the test set data, and if the model performance meets the preset threshold, it is saved as the final model; when the intelligent handheld device is working, its display performance data and user interaction behavior data are obtained in real time and input into the function mode recognition model for prediction to determine the current function mode.

[0073] Exemplarily, first, display performance data of the handheld device in different function modes is obtained, such as frame rate, resolution, color depth, etc. For example, in the game mode, the frame rate may reach 60fps, the resolution is 1920x1080, and the color depth is 32 bits; while in the reading mode, the frame rate may drop to 30fps, the resolution remains unchanged, and the color depth drops to 16 bits to save power. Historical application scenario data includes the types of applications used by the user, usage duration, usage frequency, etc. For instance, the user often uses game applications for 2 hours on weekday evenings and tends to use reading applications for 1 hour on weekend days, and these data reflect the user's usage habits and preferences. Historical user interaction behavior data involves the interaction methods between the user and the device, such as the frequency of touch screen operations, the usage of buttons, the frequency of voice commands, etc. For example, in the game mode, the user may frequently use the touch screen and physical buttons; while in the video viewing mode, user interaction is less and mainly focuses on volume adjustment and pause / play operations. These data are subjected to association analysis to form a first training dataset. For example, it may be found that there is a strong correlation between high frame rate, high resolution, and frequent touch screen operations in the game mode; while in the reading mode, low frame rate, lower color depth, and less screen interaction behavior are related.

[0074] The Support Vector Machine (SVM) algorithm is a powerful classification algorithm suitable for constructing a functional pattern recognition model. SVM separates data points of different classes by finding the optimal hyperplane in a high-dimensional space. In this example, each functional pattern (such as gaming, reading, video watching, etc.) can be regarded as a class. The advantage of SVM is that it can handle non-linear classification problems and still achieve good generalization ability in the case of small samples. During the training process, the SVM algorithm learns how to distinguish different functional patterns based on input features (display performance, application scenarios, user interaction behaviors). For example, it may learn that a feature combination of high frame rate, high resolution, and frequent touch screen operations usually corresponds to the gaming mode; while low frame rate, lower color depth, and less screen interaction may correspond to the reading mode. The functional pattern recognition model constructed in this way can analyze the current device state and user behavior in real time and accurately identify the current functional pattern. This enables the intelligent handheld device to automatically adjust device parameters, such as processor frequency, display screen refresh rate, etc., to optimize performance and power consumption, thereby enhancing the user experience and device battery life. For example, when the model recognizes that the user is playing a high-performance game, it can automatically increase the processor frequency and display refresh rate; while when it recognizes the reading mode, it can reduce these parameters to save power.

[0075] In some embodiments, in the above step S103, the dynamically calculating the optimal display screen brightness and optimal color parameters of the intelligent handheld device according to the environmental light intensity data, user viewing distance information, and user gaze area information by using an adaptive mapping algorithm specifically includes:

[0076] Obtain the environmental light intensity data and user viewing distance information, and based on a pre-constructed mapping table of environmental light intensity and user viewing distance to display parameters, determine the first display parameters of the intelligent handheld device according to the environmental light intensity data and the user viewing distance information, where the first display parameters include the first display screen brightness and the first color parameters;

[0077] Divide the display screen of the intelligent handheld device into several display areas according to the complexity of different display contents of the intelligent handheld device;

[0078] Obtain the user gaze area information, and determine the second display parameters of each display area according to the user gaze area information, where the second display parameters include the second display screen brightness and the second color parameters;

[0079] Determine the optimal display parameters of the intelligent handheld device according to the first display parameters and the second display parameters, where the optimal display parameters include the optimal display screen brightness and the optimal color parameters.

[0080] In this embodiment, environmental light intensity data and user viewing distance information are obtained and used as input data for adjusting the display parameters of the intelligent handheld device. According to a pre-established mapping table of environmental light intensity, user viewing distance, and display parameters, the first display screen brightness and the first color parameters corresponding to the current input data are determined. It is judged whether the first display screen brightness exceeds a preset display screen brightness threshold range. If it exceeds, the first display screen brightness is adjusted to within the threshold range. It is judged whether the first color parameters exceed a preset color parameter threshold range. If they exceed, the first color parameters are adjusted to within the threshold range.

[0081] The display content of the intelligent handheld device is obtained, the complexity of the display content is analyzed, and the display screen is divided into several display areas according to the complexity. The eye tracking technology is used to obtain the user's gaze area information, and the display area that the user is currently focusing on is judged. According to the user's gaze area information, the second display parameters of each display area are determined, including the second display screen brightness and the second color parameters. For the display area that the user is gazing at, its second display screen brightness is set to a higher value, and the second display screen brightness of the remaining display areas is set to a lower value, guiding the user's line of sight to focus on the key area through the differential screen brightness. For the display area that the user is gazing at, its second color parameters are set to values with higher saturation and larger contrast, and the second color parameters of the remaining display areas are set to values with lower saturation and smaller contrast, highlighting the key display content through the differential color effect. The change of the user's gaze area is continuously tracked, and the second display parameters of each display area are dynamically adjusted in real time to provide a personalized display effect, reduce visual fatigue, and improve the user experience.

[0082] Exemplarily, first, environmental light intensity and user viewing distance information are obtained, and these two factors directly affect the display effect. For example, in a bright outdoor environment, the screen brightness needs to be increased to ensure clarity; while in a dim indoor environment, too high brightness will cause visual fatigue. Similarly, the distance between the user and the device also affects the optimal display effect. Through the pre-established mapping table, the first display parameters suitable for the current environment can be quickly determined. Specifically, assume that in a sunny outdoor environment, the light intensity is 50,000 lux and the user viewing distance is about 30 cm. According to the mapping table, the system may adjust the screen brightness to the maximum value (such as 500 nits) and increase the color saturation and contrast to ensure visibility in strong light. On the contrary, in an indoor environment with a light intensity of only 100 lux and a user viewing distance of 50 cm, the system may reduce the brightness to 100 nits and adjust the color temperature to a warmer 3000K to reduce the stimulation of blue light to the eyes.

[0083] Next, dividing the display area according to the complexity of the displayed content is an intelligent optimization method. For example, in the game interface, the complexity of the character action area and the status bar is quite different. The system may divide the screen into three complexity areas: high, medium, and low. High-complexity areas (such as character action areas) may occupy 60% of the area in the center of the screen, medium-complexity areas (such as background elements) occupy 20%, and low-complexity areas (such as status bars) occupy the remaining 20%. Obtaining information about the user's gaze area is a key step in optimizing the display effect. Through the front camera or eye tracking technology, the system can detect the user's line of sight in real time. For example, in a reading application, if it is detected that the user is looking at the text area in the upper left corner of the screen, the system will give priority to improving the display parameters of this area. Specifically, the brightness of the area may be increased by 10%, the contrast may be increased, and the color temperature may be fine-tuned to improve the clarity of the text. For areas that the user is not currently looking at, the display parameters can be appropriately reduced to save energy.

[0084] Finally, the system needs to comprehensively consider the first display parameter and the second display parameter to determine the optimal display effect. This process involves the trade-off of multiple factors. For example, if the first display parameter recommends an overall brightness of 300 nits, and the second display parameter of the user's gaze area recommends a brightness of 350 nits, the system may take a compromise solution and set the brightness of the gaze area to 325 nits and the non-gaze area to 275 nits. This not only ensures the clarity of the gaze area, but also avoids visual discomfort caused by excessive brightness differences in various areas of the screen. The optimization of color parameters is equally important. If the color temperature recommended by the first display parameter is 6500K (cold tones), and the content of the user's gaze area (such as a warm indoor scene) is more suitable for warm tones, the system may adjust the color temperature of the gaze area to 5500K, while slightly increasing the saturation to present a more comfortable and immersive visual effect. Through this dynamic and intelligent display parameter adjustment, the intelligent handheld game console can provide users with the best visual experience in different environments and usage scenarios, while optimizing energy consumption and extending the use time of the device. This technology not only improves user satisfaction, but also reflects the intelligence level of the equipment, laying the foundation for more personalized and scenario-based human-computer interaction in the future.

[0085] In some embodiments, in the above step S104, obtaining user predicted behavior data according to the user holding posture data and the user line of sight movement data, and dynamically adjusting the display performance data of the intelligent handheld game console according to the user predicted behavior data specifically includes:

[0086] Obtain user holding posture data and user gaze movement data, as well as corresponding user behavior results;

[0087] Preprocessing and feature extraction of the user's holding posture data and the user's line of sight movement data to obtain key behavior features related to the user's behavior results;

[0088] Inputting the key behavior features into a pre-trained user behavior recognition model to obtain user predicted behavior data, wherein the user predicted behavior data includes user usage intention and user predicted usage status;

[0089] Based on a preset mapping table of user behaviors and display performance requirements, determining the display performance requirement information of the user for the intelligent handheld game console in a preset future time period according to the user predicted behavior data;

[0090] According to the display performance requirement information, the display performance data of the intelligent handheld game console is dynamically adjusted.

[0091] In this embodiment, user holding posture data and line of sight movement data, as well as corresponding user behavior result data, are obtained to construct a user behavior data set. The user behavior data set is cleaned and preprocessed to remove abnormal data and noise data, and the data is normalized. Key behavior features are extracted from the preprocessed user behavior data, including holding posture features, line of sight movement features, and behavior result features, to construct a key behavior feature vector. Based on the key behavior feature vector, an unsupervised learning algorithm is used to perform user behavior clustering analysis to obtain different types of user behavior patterns. For each user behavior pattern, a supervised learning algorithm is used to train a user behavior recognition model to obtain a recognition model that predicts the user's usage intention and usage status. The user's holding posture data and line of sight movement data obtained in real time are input into the corresponding user behavior recognition model to predict the user's current usage intention and usage status in real time.

[0092] According to the user behavior prediction data, by querying the preset mapping table of user behavior and display performance requirements, determine the user's demand information for the display performance of the smartphone during the prediction period. The demand information includes parameters such as display brightness, display frame rate, and display resolution. According to the determined user display performance demand information, judge whether the current display performance parameter settings of the smartphone meet the requirements. If not, trigger the dynamic adjustment process of the display performance parameters. When dynamically adjusting the display performance parameters, obtain the performance parameter range of the display screen from the mobile phone hardware configuration information, determine the target performance parameter value according to the demand information, and calculate the adjustment step and adjustment time interval from the current parameter to the target parameter through an intelligent algorithm. According to the calculated adjustment step and time interval, dynamically and progressively adjust the display brightness, frame rate, resolution, etc. of the smartphone until the target demand value is reached to achieve dynamic optimization of the display performance. During the process of adjusting the display performance parameters, continuously monitor the usage of hardware resources such as the mobile phone CPU, GPU, memory, and battery. If it is detected that the resource occupancy is too high or the battery power is insufficient, trigger the dynamic downgrade adjustment of the performance parameters to ensure the smooth operation and battery life of the mobile phone. Continuously track the behavior changes of users at different times and in different scenarios, and regularly update the user behavior prediction model and the behavior-performance mapping table to adapt to the dynamic changes of user needs and achieve continuous optimization of the display performance of the smartphone.

[0093] Exemplarily, first, user holding postures and eye movement data are obtained through sensors and cameras. For example, when the user holds the device with both hands and frequently moves their eyes, it may indicate intense gaming operations. These raw data are preprocessed and feature-extracted to be transformed into meaningful key behavior features. For instance, the holding posture can be transformed into finger positions and pressure distributions, and eye movement can be transformed into fixation point trajectories and dwell times. These key behavior features are then input into a pre-trained user behavior recognition model. This model may adopt deep learning algorithms such as Long Short-Term Memory Networks (LSTM), which can effectively capture the temporal features of user behavior. The model outputs user predicted behavior data, including usage intent and predicted usage status. For example, the model may predict that the user is about to start a long gaming session or prepare to switch to reading mode. Based on a preset mapping table, the system transforms the user predicted behavior data into display performance requirement information. This mapping table may be optimized through a large number of user tests and feedback. For example, for a predicted gaming session, the system may increase the refresh rate and color saturation; while for the reading mode, it may reduce the brightness and adjust the color temperature to a warm tone. Finally, the system dynamically adjusts the display parameters of the handheld console according to the display performance requirement information. This adjustment is real-time and gradual to avoid abrupt changes affecting the user experience. For example, if it is predicted that the user will play games in the next 30 minutes, the system may gradually increase the refresh rate from 60Hz to 120Hz within 5 minutes and increase the color saturation by 10% simultaneously. This intelligent display performance adjustment not only enhances the user experience but also optimizes energy usage. For example, when it is predicted that the user is about to end the usage session, the system can reduce unnecessary high-performance display settings in advance, thus extending the battery life. In addition, by analyzing user behavior patterns, the system can also learn personalized display preferences. For example, for users who often use the device at night, the system may be more inclined to automatically enable the low blue light mode at night. The implementation of this technology needs to consider privacy protection. The collection and processing of user behavior data should be carried out locally on the device to avoid the leakage of sensitive information. At the same time, the system should also provide manual adjustment options to allow users to override the automatic settings when needed to meet personalized requirements in specific scenarios. Through this intelligent display performance optimization, the intelligent handheld console can better adapt to the usage habits of different users and environmental changes, providing a more personalized and efficient usage experience.

[0094] In some embodiments, in the above step S105, when smoothing the front and rear frame images by analyzing the motion vectors of the front and rear frame images and adopting a frame interpolation algorithm, it specifically includes:

[0095] Obtain every two adjacent frames of images of the intelligent handheld console, and divide every two adjacent frames of images into a first frame image and a second frame image;

[0096] Using a motion estimation algorithm based on block matching, perform motion estimation on the first frame image and the second frame image to obtain a motion vector field between the first frame image and the second frame image;

[0097] Based on the motion vector field, use a motion compensation frame interpolation algorithm to insert at least one intermediate frame image between the first frame image and the second frame image;

[0098] Perform weighted fusion on the intermediate frame image, the first frame image, and the second frame image to obtain an image sequence.

[0099] In this embodiment, obtain the video sequence collected by the intelligent handheld device, and select two adjacent frames of images therefrom as the first frame image and the second frame image to be processed; divide the first frame image into a number of non-overlapping image blocks of the same size, and for each image block, determine a search window in the second frame image; calculate the similarity between the image block and each candidate block within the search window, and select the candidate block with the maximum similarity as the best matching block of the image block in the second frame image; according to the position of the image block in the first frame image and the position of the best matching block in the second frame image, calculate the motion vector of the image block; repeat the above steps for all image blocks to obtain a motion vector field between the first frame image and the second frame image; use a median filtering algorithm to smooth the motion vector field, and eliminate isolated incorrect vectors to obtain a smooth motion vector field. According to the motion vector field, use a motion compensation frame interpolation algorithm to insert at least one intermediate frame image between the first frame image and the second frame image. The pixel point positions in the intermediate frame image are obtained by interpolation calculation based on the motion vectors between the corresponding pixel points of the first frame image and the second frame image. For each pixel point in the intermediate frame image, calculate its weight coefficients with respect to the corresponding pixel points in the first frame image and the second frame image according to the time intervals between its position and the first frame image and the second frame image. According to the weight coefficients, perform weighted averaging on the pixel values of each pixel point in the intermediate frame image and the corresponding pixel points in the first frame image and the second frame image to obtain the final pixel value of each pixel point in the intermediate frame image. Concatenate all the interpolated intermediate frame images, the first frame image, and the second frame image in chronological order to obtain the image sequence after image interpolation. Perform motion edge detection on each frame image in the image sequence, extract the contour information of the moving object, and judge whether the motion trajectory of the moving object is coherent and smooth according to the contour information. If the motion trajectory shows sudden changes or incoherence, then return to adjust the motion vector field and perform frame interpolation again; otherwise, output the finally generated image sequence.

[0100] Exemplarily, first, the system acquires two adjacent frames of images, divides them into the first frame and the second frame, and then analyzes these two frames of images using a block-matching based motion estimation algorithm. This algorithm divides the image into multiple small blocks and estimates the motion by comparing the position differences of the corresponding blocks in adjacent frames. For example, in a racing game, the background may remain relatively stationary while the racing car moves rapidly. The algorithm will identify that the blocks in the area where the racing car is located have a large displacement, while the blocks in the background area have a small displacement. This method can effectively capture the local motion features in complex scenes. The result of motion estimation is a motion vector field that describes the motion direction and amplitude of each part of the image. In the example of the racing game, the motion vector field may show a small backward motion at the edge of the track and a large forward motion vector in the area of the racing car. Based on the obtained motion vector field, the system uses a motion compensation frame interpolation algorithm to insert intermediate frames between the original two frames. For example, if the original frame rate is 30 frames per second, by inserting one intermediate frame, the frame rate can be increased to 60 frames per second. The interpolation algorithm will calculate the positions and colors of each pixel in the intermediate frame according to the information in the motion vector field. In the racing game, this means that the position of the racing car will be accurately interpolated to a certain position between the two original frames, creating a smoother motion effect. The last step is to perform weighted fusion of the interpolated intermediate frame and the original two frames. This process aims to further improve the image quality and reduce the possible interpolation artifacts. The fusion weights can be determined according to the time distance between the intermediate frame and the original frames. For example, if the intermediate frame is exactly at the midpoint between the two original frames, then equal weights can be assigned to the three frames. This fusion technique can achieve a smooth transition and reduce the sense of screen tearing or jumping. The benefits brought by this frame rate improvement technique are manifold. First, it can significantly improve the smoothness of the animation, reduce motion blur, and make the outlines of fast-moving objects (such as racing cars) clearer. Second, it can enhance the visual quality of the game or video without increasing the production cost of the original content. In addition, this technique can also make up for the deficiencies in hardware performance to a certain extent, enabling devices with weaker performance to present higher-quality images.

[0101] In some embodiments, in the above steps S101 to S105, the method further includes:

[0102] Collect experience data of users in different usage scenarios of the intelligent handheld device, where the experience data includes eye fatigue degree, hand comfort level, and operation smoothness;

[0103] Obtain the historical display data of the intelligent handheld device in different usage scenarios, and perform correlation analysis on the historical display data and the experience data to form a second training dataset;

[0104] According to the second training dataset, use the support vector machine algorithm for modeling training to establish a user experience evaluation model;

[0105] Obtain the experience score of the user in any usage scenario of the intelligent handheld device according to the user experience evaluation model, and determine whether to adjust the display parameter combination of the intelligent handheld device by comparing the experience score with a preset experience score threshold.

[0106] In this embodiment, obtain the historical display data of the intelligent handheld device in different usage scenarios. For parameters such as brightness, contrast, and color temperature in the historical display data, determine whether they exceed the preset threshold range. If they exceed, mark them as abnormal data that may cause eye fatigue. Collect the eye fatigue degree data of the user in different usage scenarios, obtain the marked abnormal historical display data, and perform correlation analysis on the two to obtain the display parameter threshold that causes eye fatigue. Obtain the hand operation data of the user in different usage scenarios, including button size, key position distribution, holding method, etc., and evaluate its comfort according to ergonomic parameters to obtain the comfort values under different hand operation parameters. Collect the operation fluency data of the user in different usage scenarios, obtain the corresponding handheld device hardware configuration parameters and software performance parameters, and establish a relationship model between hardware configuration, software performance, and operation fluency through multiple regression analysis. Use the eye fatigue threshold, hand comfort value, and operation fluency relationship model as training features, and combine with the user's subjective score to construct a support vector machine classification model to achieve the comprehensive evaluation of user experience. In the new usage scenario of the intelligent handheld device, collect its hardware configuration, software performance, display parameters, and user operation parameters in real time, and input them into the evaluation model to predict the user experience degree in this scenario in real time. For each usage scenario, calculate the user's experience score through the user experience evaluation model, and compare the score result with the preset experience score threshold. If the user experience score is lower than the preset threshold, it is determined that the display parameter combination of the smartphone needs to be adjusted and optimized; if the user experience score is higher than or equal to the preset threshold, the current display parameter combination remains unchanged. According to the usage scenario that needs to be adjusted, determine the display parameters related to this scenario, such as screen brightness, color temperature, contrast, resolution, etc., to form a candidate set for parameter adjustment. Through intelligent optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, search for the optimal parameter combination in the candidate parameter set to make the user experience score the highest under this combination. Apply the optimized display parameter combination to the display settings of the smartphone and display with the new parameter combination in the corresponding usage scenario. Continuously monitor the experience score of the user under the new parameter combination, and dynamically judge the optimization effect of the parameter combination through the user experience evaluation model. When necessary, trigger the parameter adjustment process again to achieve the closed-loop control of display optimization.

[0107] Exemplarily, first, collect the user experience data in different scenarios, including eye fatigue, hand comfort, and operation smoothness. For example, playing action games for a long time in a bright environment may exacerbate eye fatigue, while watching videos in a dim environment may relieve eye pressure. Hand comfort may be affected by the type of game. For example, strategy games may require long-term holding of the device, while casual games allow intermittent relaxation. Operation smoothness is closely related to game frame rate and touch response. Obtain historical display data and perform correlation analysis with the experience data to form a training data set. For example, a high refresh rate may improve operation smoothness in fast-paced games but may not be obvious when watching movies, and color temperature adjustment may affect eye comfort under different lighting conditions. Use the support vector machine algorithm to establish a user experience evaluation model. The support vector machine can effectively handle high-dimensional feature spaces and is suitable for processing complex user experience data. During the model training process, it may be found that certain combinations of display parameters can significantly improve the user experience in specific scenarios. For example, in strong outdoor light, increasing the screen brightness and contrast may be more effective; while in a weak indoor light environment, reducing the blue light output may be more beneficial to protecting eyesight. According to the trained model, the user experience score can be predicted for any usage scenario. Assume that the preset experience score threshold is 85 points (out of 100). When the predicted score is lower than this threshold, the system will automatically adjust the display parameters. For example, when it is detected that the user is using the device for a long time in a dark environment and the predicted eye fatigue score may drop to 80 points, the system may automatically reduce the screen brightness, activate the eye protection mode, and remind the user to rest appropriately. This dynamic adjustment mechanism can not only improve the user experience but also extend the service life of the device. By responding to user needs in real time, the intelligent handheld can always maintain the best state in different scenarios, protecting the user's health and ensuring the entertainment experience. This data-driven intelligent optimization method reflects the trend of modern consumer electronics products towards personalization and intelligence, providing users with a more considerate and intelligent usage experience.

[0108] Referring to Figure 2 , an embodiment of the present invention provides a control system 2 for an intelligent handheld, and the system 2 specifically includes:

[0109] A first control module 201, configured to obtain the real-time power consumption data and heat generation data of the intelligent handheld, and dynamically adjust the processor frequency and display screen refresh frequency of the intelligent handheld based on a pre-constructed frequency adjustment table according to the real-time power consumption data and the heat generation data;

[0110] A second control module 202, configured to analyze the user interaction behavior data in real time through a pre-constructed function mode recognition model, obtain the function mode recognition result of the intelligent handheld, and determine the optimal display parameter configuration according to the function mode recognition result;

[0111] The third control module 203 is used to dynamically calculate the optimal display screen brightness and optimal color parameters of the intelligent handheld game console using an adaptive mapping algorithm according to the ambient light intensity data, the user viewing distance information and the user gaze area information;

[0112] The fourth control module 204 is used to obtain user predicted behavior data according to the user holding posture data and the user line of sight movement data, and dynamically adjust the display performance data of the intelligent handheld game console according to the user predicted behavior data;

[0113] The fifth control module 205 is used to analyze the motion vectors of the previous and next frame images and use the frame interpolation algorithm to smooth the previous and next frame images when the intelligent handheld game console is in fast motion or screen switching.

[0114] It is understandable that if Figure 1 The contents of the control method embodiment of the intelligent handheld game console shown in the figure are applicable to the control system embodiment of the intelligent handheld game console. The functions specifically implemented by the control system embodiment of the intelligent handheld game console are similar to those of the embodiment of the intelligent handheld game console shown in the figure. Figure 1 The control method of the intelligent handheld game console shown in the embodiment is the same as that of the embodiment shown in the embodiment, and the beneficial effects achieved are the same as those of the embodiment shown in the embodiment. Figure 1 The beneficial effects achieved by the embodiment of the control method of the intelligent handheld game console shown are also the same.

[0115] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0116] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0117] Reference Figure 3, An embodiment of the present invention further provides a computer device 3, including: a memory 302, a processor 301, and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements the control method of the intelligent handheld device as described in any one of the above methods.

[0118] The computer device 3 may be a computing device such as a desktop computer, a notebook, a handheld computer, and a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that Figure 3 merely examples of the computer device 3, which do not constitute a limitation on the computer device 3, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0119] The so-called processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0120] The memory 302 may be an internal storage unit of the computer device 3 in some embodiments, such as the hard disk or memory of the computer device 3. The memory 302 may also be an external storage device of the computer device 3 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 3. Further, the memory 302 may also include both the internal storage unit and the external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or will be output.

[0121] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the control method of the intelligent handheld device as described in any one of the above methods.

[0122] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0123] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0125] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the apparatus or unit can be in electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for controlling an intelligent handheld game console, characterized in that: The method specifically comprises: Acquire real-time power consumption data and heat generation data of the intelligent handheld game console, and dynamically adjust the processor frequency and display screen refresh frequency of the intelligent handheld game console according to the real-time power consumption data and the heat generation data based on a pre-built frequency adjustment table; Analyze user interaction behavior data in real time through a pre-built functional pattern recognition model to obtain a functional pattern recognition result of the intelligent handheld game console, and determine an optimal display parameter configuration according to the functional pattern recognition result; According to the ambient light intensity data, the user's viewing distance information and the user's gaze area information, an adaptive mapping algorithm is used to dynamically calculate the optimal display brightness and optimal color parameters of the intelligent handheld game console; Acquire user predicted behavior data according to the user holding posture data and the user line of sight movement data, and dynamically adjust the display performance data of the intelligent handheld game console according to the user predicted behavior data; When the intelligent handheld game console is in fast motion or image switching, the frame interpolation algorithm is used to smooth the frame images by analyzing the motion vectors of the frame images.

2. The method according to claim 1, characterized in that The method of dynamically adjusting the processor frequency and the display screen refresh frequency of the intelligent handheld game console based on the pre-built frequency adjustment table according to the real-time power consumption data and the heat generation data also includes: The real-time power consumption data, heat generation data, processor frequency and display screen refresh frequency of the intelligent handheld game console are used as the state space, and various adjustment operation combinations of the processor frequency and the display screen refresh frequency are used as the action space, so as to reduce the real-time power consumption and heat generation of the intelligent handheld game console as the reward function; Based on the state space, the action space and the reward function, a reinforcement learning algorithm is used to perform modeling training to obtain a frequency regulation model. The frequency regulation model is used to output a frequency regulation table. The frequency regulation table includes data associations between real-time power consumption, heat generation, processor frequency and display screen refresh frequency.

3. The method according to claim 1, characterized in that The method further comprises: analyzing the user interaction behavior data in real time through the pre-built functional pattern recognition model to obtain the functional pattern recognition result of the intelligent handheld game console, and determining the optimal display parameter configuration according to the functional pattern recognition result. Acquire display performance data, historical application scenario data, and historical user interaction behavior data of the intelligent handheld game console in different functional modes; Performing correlation analysis on the display performance data, the historical application scenario data, and the historical user interaction behavior data to form a first training data set; The first training data set is used as input, and a support vector machine algorithm is used for modeling training to construct a functional pattern recognition model.

4. The method according to claim 1, characterized in that The method of dynamically calculating the optimal display screen brightness and optimal color parameters of the intelligent handheld game console using an adaptive mapping algorithm according to the ambient light intensity data, the user viewing distance information, and the user gaze area information specifically includes: Acquire ambient light intensity data and user viewing distance information, and determine a first display parameter of the intelligent handheld game console according to the ambient light intensity data and the user viewing distance information based on a pre-constructed mapping table of ambient light intensity, user viewing distance and display parameters, wherein the first display parameter includes a first display screen brightness and a first color parameter; Dividing the display screen of the intelligent handheld game console into a plurality of display areas according to the complexity of different display contents of the intelligent handheld game console; Acquire user gaze area information, and determine second display parameters of each display area according to the user gaze area information, wherein the second display parameters include second display screen brightness and second color parameters; The optimal display parameters of the intelligent handheld game console are determined according to the first display parameters and the second display parameters, and the optimal display parameters include optimal display screen brightness and optimal color parameters.

5. The method according to claim 1, characterized in that The method of acquiring user predicted behavior data according to the user holding posture data and the user sight movement data, and dynamically adjusting the display performance data of the intelligent handheld game console according to the user predicted behavior data, specifically includes: Obtain user holding posture data and user gaze movement data, as well as corresponding user behavior results; Preprocessing and feature extraction of the user's holding posture data and the user's line of sight movement data to obtain key behavior features related to the user's behavior results; Inputting the key behavior features into a pre-trained user behavior recognition model to obtain user predicted behavior data, wherein the user predicted behavior data includes user usage intention and user predicted usage status; Based on a preset mapping table of user behaviors and display performance requirements, determining the display performance requirement information of the user for the intelligent handheld game console in a preset future time period according to the user predicted behavior data; According to the display performance requirement information, the display performance data of the intelligent handheld game console is dynamically adjusted.

6. The method according to claim 1, characterized in that The method of analyzing the motion vectors of the front and back frame images and smoothing the front and back frame images using a frame interpolation algorithm specifically includes: Acquire every two adjacent frames of the intelligent handheld game console, and divide every two adjacent frames of the image into a first frame of image and a second frame of image; Using a motion estimation algorithm based on block matching, performing motion estimation on the first frame image and the second frame image to obtain a motion vector field between the first frame image and the second frame image; Based on the motion vector field, a motion compensation frame interpolation algorithm is used to insert at least one intermediate frame image between the first frame image and the second frame image; The intermediate frame image is weightedly fused with the first frame image and the second frame image to obtain an image sequence.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: Collecting user experience data in different usage scenarios of the intelligent handheld game console, including eye fatigue, hand comfort and operation fluency; Acquire historical display data of the intelligent handheld game console in different usage scenarios, and perform correlation analysis on the historical display data and the experience data to form a second training data set; Based on the second training data set, a support vector machine algorithm is used to perform modeling training to establish a user experience evaluation model; The user experience score of the user in any usage scenario of the intelligent handheld game console is obtained according to the user experience evaluation model, and whether to adjust the display parameter combination of the intelligent handheld game console is determined by comparing the experience score with a preset experience score threshold.

8. An intelligent handheld game console control system, characterized in that: The system specifically comprises: A first control module is used to obtain real-time power consumption data and heat generation data of the intelligent handheld game console, and dynamically adjust the processor frequency and display screen refresh frequency of the intelligent handheld game console according to the real-time power consumption data and the heat generation data based on a pre-built frequency adjustment table; The second control module is used to analyze the user interaction behavior data in real time through a pre-built function pattern recognition model, obtain the function pattern recognition result of the intelligent handheld game console, and determine the optimal display parameter configuration according to the function pattern recognition result; The third control module is used to dynamically calculate the optimal display screen brightness and optimal color parameters of the intelligent handheld game console using an adaptive mapping algorithm according to the ambient light intensity data, the user's viewing distance information and the user's gaze area information; a fourth control module, configured to obtain user predicted behavior data according to the user holding posture data and the user line of sight movement data, and dynamically adjust the display performance data of the intelligent handheld game console according to the user predicted behavior data; The fifth control module is used to analyze the motion vectors of the previous and next frame images and use the frame interpolation algorithm to smooth the previous and next frame images when the intelligent handheld game console is in fast motion or screen switching.

9. A computer device, characterized in that: include: A memory, a processor and a computer program stored in the memory, when the computer program is executed on the processor, implements the control method of the intelligent handheld game console as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the control method of the intelligent handheld game console as claimed in any one of claims 1 to 7 is implemented.

Images