A frame rate control switching method and device for a game display picture and electronic equipment

Abstract

A frame rate control switching method and device for a game display picture and electronic equipment, and relate to the field of data processing. In the method, a scene complexity parameter of a game application is obtained, the scene complexity parameter including the number, type and size of rendering objects in a game scene; a scene complexity change trend of the game application in a preset time period is predicted according to the scene complexity parameter, and a scene complexity prediction value is obtained; a current performance parameter of a game device is obtained, and a target adjustment factor is calculated based on the scene complexity prediction value and the current performance parameter, the target adjustment factor being used to adjust a game frame rate; a target frame rate is determined from a preset frame rate group according to the target adjustment factor, the preset frame rate group including multiple frame rates; and a current frame rate of the game application is smoothly transitioned to the target frame rate. The technical solution provided in the application improves the experience of game players.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, device and electronic device for controlling and switching the frame rate of a game display screen. Background Art

[0002] With the rapid development of the gaming industry and players' ever-increasing expectations for gaming experience, high-quality visuals and smooth gameplay have become key factors in evaluating a game's quality. Modern games feature increasingly complex scenes, from vast open worlds to high-intensity combat scenarios, placing extremely high demands on graphics processing capabilities. To meet these demands, game developers and hardware manufacturers are constantly introducing new technologies and more powerful equipment.

[0003] Currently, most games use a static frame rate control strategy, setting a fixed frame rate target before the game starts, such as 30 or 60 frames per second. While simple, this approach lacks flexibility and adaptability. In actual gameplay, when scene complexity increases suddenly, maintaining a fixed frame rate is often difficult, resulting in game freezes or delays, impacting the player experience.

[0004] Therefore, there is an urgent need for a method, device and electronic device for controlling and switching the frame rate of a game display screen. Summary of the Invention

[0005] The present application provides a method, device and electronic device for controlling and switching the frame rate of a game display screen, thereby improving the player's experience.

[0006] In a first aspect of the present application, a method for frame rate control switching of a game display screen is provided, the method comprising: obtaining a scene complexity parameter of a game application, the scene complexity parameter including the number, type and size of rendered objects in the game scene; predicting a scene complexity change trend of the game application within a preset time period based on the scene complexity parameter, and obtaining a scene complexity prediction value; obtaining current performance parameters of a gaming device, and calculating a target adjustment factor based on the scene complexity prediction value and the current performance parameters, the target adjustment factor being used to adjust the game frame rate; determining a target frame rate from a preset frame rate group based on the target adjustment factor, the preset frame rate group including multiple frame rates; and smoothly transitioning the current frame rate of the game application to the target frame rate.

[0007] By adopting the above technical solution, by obtaining the scene complexity parameters of the game application, predicting the scene complexity change trend of the game application within a preset time period, and obtaining the scene complexity prediction value, the complexity of the game scene can be understood in advance, providing a basis for subsequent frame rate control. At the same time, combined with the current performance parameters of the game device, the target adjustment factor is calculated, and the game frame rate can be dynamically adjusted taking into account the complexity of the game scene and the performance of the device, avoiding problems such as device overheating caused by too high a frame rate or screen freeze caused by too low a frame rate. Determining the target frame rate from the preset frame rate group based on the target adjustment factor can reduce frame rate fluctuations while ensuring smooth game operation and improve user experience. Finally, by adjusting the current frame rate to the target frame rate in a smooth transition manner, screen flickering or freeze caused by sudden frame rate changes can be avoided, making the frame rate switch more natural and smooth, and improving the player's experience.

[0008] Optionally, predicting the scene complexity change trend of the game application within a preset time period based on the scene complexity parameters specifically includes: obtaining historical scene complexity data of the game application within a historical time period, the historical scene complexity data including scene complexity parameters corresponding to each preset time granularity within the historical time period; establishing a scene complexity prediction model based on the historical scene complexity data, the scene complexity prediction model including multiple prediction sub-models, each prediction sub-model corresponding to a scene complexity parameter; using the scene complexity prediction model, combined with the current scene complexity parameters of the game application, to predict the scene complexity parameters within the preset time period, and generate the scene complexity prediction value.

[0009] By adopting the above technical solution and obtaining historical scene complexity data for game applications over historical time periods, we can understand the changing patterns of game scene complexity and provide a data foundation for subsequent scene complexity predictions. By establishing a scene complexity prediction model based on historical scene complexity data and employing multiple prediction sub-models corresponding to different scene complexity parameters, we can improve the accuracy and comprehensiveness of the predictions. During the prediction process, by combining the current scene complexity parameters of the game application and using the prediction model to predict the changing trends of scene complexity over a period of time, we can dynamically adjust the frame rate control strategy, proactively respond to possible complex scenarios, and ensure smooth game operation.

[0010] Optionally, the scene complexity prediction model is established based on the historical scene complexity data, specifically including: extracting features from the historical scene complexity data to obtain scene complexity feature vectors; selecting a preset number of scene complexity feature vectors as training samples; using the training samples to generate a regression prediction model through supervised learning algorithm training, and the regression prediction model is used to characterize the correspondence between scene complexity parameters and time; using a cross-validation method to evaluate the regression prediction model to obtain model accuracy; judging whether the model accuracy reaches a preset threshold; if the model accuracy is greater than or equal to the preset threshold, using the regression prediction model as the scene complexity prediction model; if the model accuracy is less than the preset threshold, adjusting the training parameters and returning to execute the step of using the training samples to generate a regression prediction model through supervised learning algorithm training.

[0011] By adopting the above technical solution, by extracting features from historical scene complexity data and obtaining scene complexity feature vectors, high-dimensional scene complexity data can be converted into low-dimensional feature representations, reducing the complexity of data processing. A preset number of scene complexity feature vectors are selected as training samples, and a supervised learning algorithm is used to train and generate a regression prediction model. A correspondence between scene complexity parameters and time can be established, enabling prediction of future scene complexity. The regression prediction model is evaluated using a cross-validation method to obtain model accuracy, which can verify the model's generalization ability and predictive performance. Based on the comparison results of the model accuracy with the preset threshold, it is determined whether the regression prediction model should be used as the scene complexity prediction model to ensure the reliability and effectiveness of the prediction model. If the model accuracy does not meet the standard, the training parameters are adjusted, the model is retrained, and the prediction performance is continuously optimized.

[0012] Optionally, obtaining the current performance parameters of the gaming device and calculating the target adjustment factor based on the scene complexity prediction value and the current performance parameters specifically includes: real-time monitoring of the CPU occupancy rate, GPU occupancy rate and memory occupancy rate of the gaming device as the current performance parameters; scoring the current performance parameters according to preset performance evaluation rules to obtain a device performance score; calculating the ratio of the device performance score to the scene complexity prediction value, and using the ratio as an initial adjustment factor; determining whether the initial adjustment factor is within a preset adjustment factor range; if the initial adjustment factor is within the preset adjustment factor range, using the initial adjustment factor as the target adjustment factor; if the initial adjustment factor is not within the preset adjustment factor range, using a preset adjustment factor correction formula to correct the initial adjustment factor to obtain the target adjustment factor.

[0013] By adopting the above technical solution, by real-time monitoring of the CPU occupancy rate, GPU occupancy rate and memory occupancy rate of the gaming device, the performance status of the device can be fully understood, providing a basis for frame rate control. According to the preset performance evaluation rules, the current performance parameters are scored to obtain the device performance score, which can quantitatively evaluate the comprehensive performance level of the device. The ratio of the device performance score to the scene complexity prediction value is calculated as the initial adjustment factor, which can balance the impact of device performance and scene complexity on the frame rate and dynamically adjust the frame rate control strategy. Determining whether the initial adjustment factor is within the preset adjustment factor range can ensure that the adjustment factor is within a reasonable range and avoid excessive or too small adjustment ranges. If the initial adjustment factor is not within the preset range, the preset adjustment factor correction formula is used to correct it to obtain the target adjustment factor to ensure the rationality and effectiveness of the adjustment factor.

[0014] Optionally, determining the target frame rate from a preset frame rate group based on the target adjustment factor specifically includes: calculating the matching degree between the adjustment factor and each frame rate value in the preset frame rate group, the matching degree being obtained by a preset matching degree calculation formula; selecting the frame rate value with the highest matching degree with the target adjustment factor as the candidate target frame rate; judging whether the difference between the candidate target frame rate and the current frame rate is within a preset range; if the difference between the candidate target frame rate and the current frame rate is within the preset range, determining the candidate target frame rate as the target frame rate; if the difference between the candidate target frame rate and the current frame rate is not within the preset range, selecting a frame rate value in the preset frame rate group that is adjacent to the candidate target frame rate and whose difference with the current frame rate is within the preset range as the target frame rate.

[0015] By employing the above technical solution, the degree of similarity between the adjustment factor and different frame rates can be quantitatively assessed by calculating the degree of match between the adjustment factor and each frame rate value in the preset frame rate group, providing a reference for selecting the target frame rate. Using the preset matching degree calculation formula to calculate the matching degree, the relationship between the adjustment factor and the frame rate can be comprehensively considered to obtain a reasonable matching degree value. Selecting the frame rate value with the highest degree of match with the target adjustment factor as the candidate target frame rate ensures the optimal match between the target frame rate and the adjustment factor, achieving precise frame rate control. Determining whether the difference between the candidate target frame rate and the current frame rate is within a preset range can avoid problems such as screen freezes or flickering caused by sudden frame rate changes. If the difference is within the preset range, the candidate target frame rate is determined as the final target frame rate. If the difference is outside the preset range, a frame rate value in the preset frame rate group that is adjacent to the candidate target frame rate and has a difference with the current frame rate within the preset range is selected as the target frame rate, ensuring smooth and stable frame rate switching.

[0016] Optionally, the smooth transition of the current frame rate of the game application to the target frame rate specifically includes: calculating the frame rate difference between the current frame rate and the target frame rate; dividing the frame rate difference into multiple sub-intervals according to preset transition rules; determining multiple sub-target frame rates according to each of the sub-intervals, and the sub-target frame rates are between the current frame rate and the target frame rate; controlling the frame rate of the game application, passing through each of the sub-target frame rates in turn, and finally reaching the target frame rate.

[0017] By adopting the above technical solution, by calculating the frame rate difference between the current frame rate and the target frame rate, the amplitude of the frame rate switching can be quantitatively evaluated to provide a basis for smooth transition. The frame rate difference is divided into multiple sub-intervals according to the preset transition rules, and the frame rate switching process can be decomposed into multiple small change processes to achieve a gradual effect of the frame rate. According to each sub-interval, multiple sub-target frame rates are determined, and the sub-target frame rates are between the current frame rate and the target frame rate, which can ensure the continuity and smoothness of the frame rate switching. By controlling the frame rate of the game application to pass through each sub-target frame rate in turn and finally reach the target frame rate, the screen freeze or flicker caused by the sudden change of the frame rate can be avoided, thereby improving the user's visual experience.

[0018] Optionally, the frame rate difference is divided into multiple sub-intervals according to a preset transition rule, specifically including: obtaining a preset transition time; determining a frame rate change rate for a smooth transition based on the preset transition time and the frame rate difference; and using the frame rate change rate to evenly divide the frame rate difference into multiple sub-intervals.

[0019] By adopting the above technical solution and obtaining the preset transition time, the time range for smooth transition of the frame rate can be determined, thus avoiding unnatural frame rate switching caused by too long or too short a transition time. By determining the frame rate change rate for smooth transition based on the preset transition time and frame rate difference, the speed of frame rate switching can be controlled to achieve a smooth and natural transition effect. By using the frame rate change rate to evenly divide the frame rate difference into multiple sub-intervals, it is possible to ensure that the frame rate change amplitude of each sub-interval is the same, thereby achieving a uniform gradual change of the frame rate. By reasonably setting the preset transition time and frame rate change rate, the speed and effect of the frame rate switching can be adjusted according to actual needs, thereby enhancing the user's visual experience.

[0020] In a second aspect of the present application, a frame rate control switching device for a game display screen is provided, the device comprising an acquisition module and a processing module, wherein: the acquisition module is used to acquire scene complexity parameters of a game application, the scene complexity parameters including the number, type and size of rendered objects in the game scene; the processing module is used to predict the scene complexity change trend of the game application within a preset time period based on the scene complexity parameters, and obtain a scene complexity prediction value; the acquisition module is also used to acquire current performance parameters of a gaming device, and calculate a target adjustment factor based on the scene complexity prediction value and the current performance parameters, the target adjustment factor being used to adjust the game frame rate; the processing module is also used to determine a target frame rate from a preset frame rate group based on the target adjustment factor, the preset frame rate group including multiple frame rates; the processing module is also used to smoothly transition the current frame rate of the game application to the target frame rate.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. By obtaining the scene complexity parameters of the game application, predicting the scene complexity change trend of the game application within a preset time period, and obtaining the scene complexity prediction value, the complexity of the game scene can be understood in advance, providing a basis for subsequent frame rate control. At the same time, combined with the current performance parameters of the gaming device, the target adjustment factor is calculated. This can comprehensively consider the game scene complexity and device performance, dynamically adjust the game frame rate, and avoid problems such as device overheating due to excessively high frame rates or screen freezes due to excessively low frame rates. Determining the target frame rate from the preset frame rate group based on the target adjustment factor can reduce frame rate fluctuations while ensuring smooth game operation and improve user experience. Finally, by adjusting the current frame rate to the target frame rate through a smooth transition, screen flickering or freezes caused by sudden frame rate changes can be avoided, making frame rate switching more natural and smooth.

[0025] 2. By acquiring historical scene complexity data for game applications over historical time periods, we can understand the changing patterns of game scene complexity and provide a data foundation for subsequent scene complexity predictions. Building a scene complexity prediction model based on historical scene complexity data and employing multiple prediction sub-models corresponding to different scene complexity parameters can improve the accuracy and comprehensiveness of predictions. During predictions, combined with the current scene complexity parameters of the game application, the prediction model is used to predict the changing trends of scene complexity over a period of time. This allows for dynamic adjustment of the frame rate control strategy, proactively addressing possible complex scenarios, and ensuring smooth game operation.

[0026] 3. By extracting features from historical scene complexity data and obtaining scene complexity feature vectors, high-dimensional scene complexity data can be converted into low-dimensional feature representations, reducing the complexity of data processing. A preset number of scene complexity feature vectors are selected as training samples, and a supervised learning algorithm is used to train and generate a regression prediction model. This establishes a correspondence between scene complexity parameters and time, enabling predictions of future scene complexity. The regression prediction model is evaluated using a cross-validation method to obtain model accuracy, which can verify the model's generalization ability and predictive performance. Based on the comparison of the model accuracy with the preset threshold, it is determined whether the regression prediction model should be used as the scene complexity prediction model to ensure the reliability and effectiveness of the prediction model. If the model accuracy does not meet the standard, the training parameters are adjusted, the model is retrained, and the prediction performance is continuously optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a method for controlling and switching the frame rate of a game display screen disclosed in an embodiment of the present application;

[0028] Figure 2 This is a module diagram of a frame rate control switching device for a game display screen disclosed in an embodiment of the present application;

[0029] Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0030] Description of the accompanying drawings: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0032] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0033] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0034] This application provides a method for controlling the frame rate of a game display screen. Figure 1 , Figure 1 This is a flow chart of a method for controlling and switching the frame rate of a game display screen provided in an embodiment of the present application. The method is applied to a server, which is a server that executes a program for controlling and switching the frame rate of a game display screen. The server can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center. The method includes steps S101 to S105, which are as follows:

[0035] Step S101: Obtain scene complexity parameters of the game application, where the scene complexity parameters include the number, type, and size of rendering objects in the game scene.

[0036] Specifically, the server first obtains the scene complexity parameter of the game application. The scene complexity parameter is an important indicator for evaluating the complexity of the game scene, which directly affects the rendering performance and frame rate of the game screen. In this application, the scene complexity parameter includes three aspects: the number, type, and size of the rendered objects in the game scene.

[0037] To obtain these parameters, the server communicates with the game client. When the game client loads and runs the game application, it collects real-time statistics on the render objects in the current game scene. This information includes the number of render objects in the scene, their categories (e.g., characters, buildings, special effects, UI, etc.), and data representing each render object's complexity, such as the number of vertices, number of faces, and texture size. The game client periodically sends this statistically collected render object information to the server. Upon receiving this data, the server can parse and extract it to obtain scene complexity parameters.

[0038] Step S102: predicting the scene complexity change trend of the game application within a preset time period based on the scene complexity parameter to obtain a scene complexity prediction value.

[0039] In step S102, based on the scene complexity parameters, the scene complexity change trend of the game application within a preset time period is predicted, specifically including: obtaining historical scene complexity data of the game application within the historical time period, the historical scene complexity data including the scene complexity parameters corresponding to each preset time granularity within the historical time period; based on the historical scene complexity data, establishing a scene complexity prediction model, the scene complexity prediction model including multiple prediction sub-models, each prediction sub-model corresponding to a scene complexity parameter; using the scene complexity prediction model, combined with the current scene complexity parameters of the game application, predicting the scene complexity parameters within the preset time period, and generating a scene complexity prediction value.

[0040] Specifically, the server obtains historical scene complexity data for the game application within a historical time period. This data can be continuously collected and stored by the server over a period of time, or it can be uploaded periodically from the game client. The historical scene complexity data includes scene complexity parameters corresponding to each preset time granularity (e.g., every minute, every 5 minutes, or every 10 minutes) within the historical time period. These parameters are similar to those obtained in step S101, including the number, type, and size of rendered objects.

[0041] For example, the server can obtain scene complexity parameters every 5 minutes in the last hour, a total of 12 sets of data. Each set of data contains the number, type, and size of rendered objects in the game scene at that point in time.

[0042] Next, the server establishes a scene complexity prediction model based on historical scene complexity data. The scene complexity prediction model can use a machine learning algorithm to summarize the patterns and trends of scene complexity changes by training and learning from historical data. Because scene complexity parameters include multiple dimensions (quantity, type, size), the scene complexity prediction model can include multiple prediction sub-models, each corresponding to a complexity parameter. For example, three prediction sub-models can be established for the number, type, and size of rendered objects, respectively. Each sub-model can select a different machine learning algorithm, such as linear regression, decision tree, neural network, etc. Through optimization and training, each sub-model can be able to more accurately predict the changing trend of the corresponding complexity parameter over a period of time in the future based on historical data.

[0043] Finally, the server can use the established scene complexity prediction model, combined with the current scene complexity parameters of the game application, to predict the scene complexity parameters within a preset time period in the future and generate a scene complexity prediction value. The preset time period can be the next 5 minutes, 10 minutes, or longer, depending on the frame rate control strategy, which is not limited by this application.

[0044] In one possible implementation, a scene complexity prediction model is established based on historical scene complexity data, specifically including: extracting features from historical scene complexity data to obtain scene complexity feature vectors; selecting a preset number of scene complexity feature vectors as training samples; using the training samples to generate a regression prediction model through supervised learning algorithm training, and the regression prediction model is used to characterize the correspondence between scene complexity parameters and time; using a cross-validation method to evaluate the regression prediction model to obtain model accuracy; judging whether the model accuracy reaches a preset threshold; if the model accuracy is greater than or equal to the preset threshold, using the regression prediction model as the scene complexity prediction model; if the model accuracy is less than the preset threshold, adjusting the training parameters and returning to execute the step of using the training samples to generate a regression prediction model through supervised learning algorithm training.

[0045] Specifically, the server performs feature extraction on the scene complexity data to obtain a scene complexity feature vector. Feature extraction is the process of converting raw complexity parameter data into a more concise and informative feature representation. For example, statistical analysis can be performed on parameters such as the number, type, and size of rendered objects to calculate statistical quantities such as their mean, variance, and peak, or to extract temporal features such as their rate of change and trend. The extracted scene complexity feature vector can better reflect the changing pattern of scene complexity.

[0046] Next, the server selects a preset number of historical scene complexity feature vectors from the extracted scene complexity feature vectors as training samples. Training samples are datasets used to train the prediction model. They contain feature vectors and their corresponding true complexity parameter values. Generally, 70% to 80% of the total number of feature vectors can be selected as training samples.

[0047] Then, the server uses the selected training samples to generate a regression prediction model through supervised learning algorithm training. Supervised learning is a commonly used machine learning method that uses labeled training samples to train the model to fit the mapping relationship between sample features and labels. The regression prediction model is a supervised learning model used to predict continuous-valued target variables. In this application, the regression prediction model is used to characterize the correspondence between scene complexity parameters and time, that is, the predicted value of the scene complexity parameters at a given time point. Regression prediction algorithms include linear regression, polynomial regression, decision tree regression, support vector regression, etc. The server can select a suitable algorithm for training based on the data characteristics and requirements.

[0048] After training is complete, the server evaluates the regression prediction model using cross-validation to determine model accuracy. Cross-validation is a model evaluation method that divides a dataset into several subsets, selecting one subset at a time as the validation set and the others as the training set. Multiple models are trained and their performance on the validation set is evaluated, with the average result taken as an estimate of the overall model accuracy. This method can effectively reduce the risk of model overfitting and improve the model's generalization ability.

[0049] Finally, the server determines whether the model accuracy of the regression prediction model meets the preset threshold. The preset threshold is set based on actual needs and experience and represents the minimum requirement for model performance. If the model accuracy is greater than or equal to the preset threshold, the regression prediction model is used as the final scene complexity prediction model for subsequent complexity prediction tasks. If the model accuracy is less than the preset threshold, it indicates that the current model performance is not ideal and requires adjustment of training parameters, such as increasing the number of training samples, trying other algorithms, and optimizing model hyperparameters. The server then returns to the training step until a model that meets the requirements is obtained.

[0050] For example, the server extracts 100 feature vectors from historical scene complexity data, each of which contains 20 statistical features. Then 80 of the feature vectors are selected as training samples and trained using the support vector regression algorithm to obtain a regression prediction model. Then, the 5-fold cross-validation method is used to divide the data set into 5 parts, and 1 part is selected as the validation set each time, and the remaining 4 parts are used as training sets. 5 models are trained and evaluated on the validation set, and the average accuracy is taken as the overall model accuracy. If the model accuracy reaches the preset threshold (90%), the model is used as a scene complexity prediction model; if the accuracy is only 85%, the training parameters are adjusted, such as increasing the training samples to 90, trying to use the decision tree regression algorithm, optimizing the model's tree depth and number of leaf nodes and other hyperparameters, and then retraining and evaluating until the model accuracy meets the requirements.

[0051] Step S103: obtaining the current performance parameters of the gaming device, and calculating a target adjustment factor based on the scene complexity prediction value and the current performance parameters, where the target adjustment factor is used to adjust the game frame rate.

[0052] In step S103, the current performance parameters of the gaming device are obtained, and the target adjustment factor is calculated based on the scene complexity prediction value and the current performance parameters, specifically including: real-time monitoring of the CPU occupancy rate, GPU occupancy rate and memory occupancy rate of the gaming device as the current performance parameters; scoring the current performance parameters according to preset performance evaluation rules to obtain a device performance score; calculating the ratio of the device performance score to the scene complexity prediction value, and using the ratio as the initial adjustment factor; judging whether the initial adjustment factor is within the preset adjustment factor range; if the initial adjustment factor is within the preset adjustment factor range, using the initial adjustment factor as the target adjustment factor; if the initial adjustment factor is not within the preset adjustment factor range, using the preset adjustment factor correction formula to correct the initial adjustment factor to obtain the target adjustment factor.

[0053] Specifically, the server monitors the gaming device's CPU, GPU, and memory utilization in real time as current performance parameters. These parameters reflect the gaming device's current hardware resource usage and load level. To obtain these parameters, the server establishes a communication connection with the game client and regularly receives performance data reported by the client. The client can use the performance monitoring interface provided by the operating system or a third-party performance monitoring tool to collect real-time utilization data for hardware resources such as the CPU, GPU, and memory, and send it to the server at regular intervals (e.g., every second or every minute).

[0054] Next, the server scores the current performance parameters according to preset performance evaluation rules to obtain a device performance score. A preset performance evaluation rule is an algorithm or formula that comprehensively considers multiple performance parameters and quantifies them into a single performance score. In this embodiment of the present application, a performance score based on a percentage system is preferably set, with CPU utilization, GPU utilization, and memory utilization weighted at 30%, 50%, and 20%, respectively. Based on the difference between the actual value of each parameter and the preset parameter threshold, the server calculates the score for each parameter and finally takes the weighted average to obtain the overall performance score. The server then calculates the ratio of the device performance score to the predicted scene complexity value and uses this ratio as the initial adjustment factor. The predicted scene complexity value reflects the complexity of the game scene, while the device performance score reflects the performance level of the device. The ratio between the two can reflect the relative relationship between device performance and scene complexity, namely, whether the device has sufficient performance to cope with changes in scene complexity. A larger ratio indicates higher device performance and lower scene complexity, which can appropriately increase the frame rate; a smaller ratio indicates lower device performance and higher scene complexity, which requires an appropriate reduction in the frame rate. For example, if the device performance score is 80 and the scene complexity prediction value is 100, the initial adjustment factor is 80 / 100=0.8.

[0055] Next, the server determines whether the initial adjustment factor is within a preset adjustment factor range. This range is a reasonable range determined based on experience and testing. For example, [0.5, 1.5] indicates that the minimum adjustment factor is 0.5 and the maximum is 1.5. If the initial adjustment factor is within this range, it is directly used as the target adjustment factor for subsequent frame rate adjustments. If the initial adjustment factor is not within this range, it needs to be corrected.

[0056] If the initial adjustment factor is less than the lower limit of the interval, it means that the gap between the device performance and the scene complexity is too large, and the frame rate needs to be further reduced. The server can use the preset adjustment factor correction formula to correct the initial adjustment factor. The preset adjustment factor interval is a reasonable range based on experience and testing, for example [0.5, 1.5], which means that the minimum value of the adjustment factor is 0.5 and the maximum value is 1.5. If the initial adjustment factor is within this interval, it is directly used as the target adjustment factor for subsequent frame rate adjustment. If the initial adjustment factor is not within this interval, it needs to be corrected. The preset adjustment factor correction formula can be flexibly designed according to actual conditions. Here, the piecewise function is taken as an example: if the initial adjustment factor <0.5, then the target adjustment factor = (initial adjustment factor) 2 ; If the initial adjustment factor is > 1.5, the target adjustment factor = sqrt(initial adjustment factor); otherwise, the target adjustment factor = initial adjustment factor.

[0057] For example, if the initial adjustment factor is 0.3, then the target adjustment factor = 0.3 2 =0.09; if the initial adjustment factor is 2.0, the target adjustment factor = sqrt(2.0) = 1.41; if the initial adjustment factor is 1.2, the target adjustment factor = 1.2.

[0058] Finally, the server uses the target adjustment factor to guide the game frame rate adjustment. A larger target adjustment factor indicates an appropriate increase in the frame rate; a smaller target adjustment factor indicates a decrease in the frame rate. The specific adjustment method can be: target frame rate = base frame rate * target adjustment factor. The base frame rate can be the game's default frame rate or the maximum frame rate supported by the device. For example, if the game's base frame rate is 60 FPS and the target adjustment factor is 0.8, then the target frame rate = 60 * 0.8 = 48 FPS.

[0059] In this way, the server monitors the performance of the gaming device in real time, combines it with the predicted value of scene complexity, uses a formula to calculate a reasonable target adjustment factor, and uses it to obtain the target frame rate, thereby realizing dynamic adjustment of the game frame rate. While ensuring the smoothness of the game, it also takes into account the load of the device performance.

[0060] Step S104: determining a target frame rate from a preset frame rate group according to the target adjustment factor, where the preset frame rate group includes a plurality of frame rates.

[0061] In step S104, the target frame rate is determined from the preset frame rate group according to the target adjustment factor, specifically including: calculating the matching degree between the adjustment factor and each frame rate value in the preset frame rate group, the matching degree being obtained by a preset matching degree calculation formula; selecting the frame rate value with the highest matching degree with the target adjustment factor as the candidate target frame rate; judging whether the difference between the candidate target frame rate and the current frame rate is within a preset range; if the difference between the candidate target frame rate and the current frame rate is within the preset range, determining the candidate target frame rate as the target frame rate; if the difference between the candidate target frame rate and the current frame rate is not within the preset range, selecting a frame rate value in the preset frame rate group that is adjacent to the candidate target frame rate and whose difference with the current frame rate is within the preset range as the target frame rate.

[0062] The preset matching degree calculation formula is: M(K, F)=e^(-α|KF / Fmax|);

[0063] Among them, M(K, F) is the matching degree between the adjustment factor K and the frame rate F, Fmax is the maximum frame rate value in the preset frame rate group, α is a preset parameter, α>0; the value range of the adjustment factor is [0, 1], and the closer the frame rate value is to Fmax*K, the higher the matching degree.

[0064] Specifically, the server determines the most appropriate target frame rate from a preset frame rate group based on the calculated target adjustment factor. A preset frame rate group is a set of predefined frame rate values, such as {30, 45, 60, 90, 120}. These frame rate values ​​are typically pre-set based on factors such as game type and device performance. The server's task is to select a frame rate from the preset frame rate group that best matches the target adjustment factor and falls within a preset range from the current frame rate as the final target frame rate.

[0065] First, the server calculates the matching degree between the target adjustment factor and each frame rate value in the preset frame rate group. Matching degree is an indicator to measure the matching degree between the target adjustment factor and the frame rate value, which can be obtained by the preset matching degree calculation formula. The matching degree calculation formula provided in this application is: M(K, F)=e^(-α|KF / Fmax|); wherein, M(K, F) represents the matching degree between the adjustment factor K and the frame rate F, Fmax is the maximum frame rate value in the preset frame rate group, α is a preset parameter, and α>0. The value range of the adjustment factor K is [0, 1], which represents the expected frame rate level. The physical meaning of this formula is that the closer the frame rate F is to Fmax*K, the higher the matching degree, that is, the more the target adjustment factor K matches the frame rate F. The parameter α controls the decay rate of the matching degree. The larger α is, the faster the matching degree decays as the gap between K and F / Fmax increases.

[0066] Next, the server determines whether the difference between the candidate target frame rate and the current frame rate is within a preset range. This range is a predefined interval of frame rate variation based on factors such as user experience and device performance. For example, [-15, 15] means the difference between the target frame rate and the current frame rate cannot exceed plus or minus 15 FPS. Otherwise, the frame rate change will be too abrupt, affecting the gaming experience.

[0067] If the difference between the candidate target frame rate and the current frame rate is within the preset range, the candidate target frame rate is determined as the final target frame rate. For example, if the current frame rate is 60FPS, the candidate target frame rate is 90FPS, and the preset range is [-15, 15], the difference between 90 and 60 is 30, which exceeds the preset range, and 90 cannot be directly determined as the target frame rate. In this case, the server needs to select a frame rate value in the preset frame rate group that is adjacent to the candidate target frame rate and whose difference with the current frame rate is within the preset range as the target frame rate. The specific strategy is to find the first frame rate value on the left and right sides of the candidate target frame rate whose difference with the current frame rate is within the preset range, and then compare the matching degree of these two frame rate values ​​with the target adjustment factor, and select the one with the higher matching degree as the target frame rate.

[0068] The server then determines the most appropriate target frame rate from the preset frame rate group based on the target adjustment factor, by calculating the matching degree and taking into account factors such as the current frame rate. In actual applications, parameters such as the matching degree calculation formula and the preset range can be set and optimized according to specific needs to achieve intelligent, smooth, and efficient dynamic adjustment of the game frame rate.

[0069] Step S105: smoothly transition the current frame rate of the game application to the target frame rate.

[0070] In step S105, the current frame rate of the game application is smoothly transitioned to the target frame rate, specifically including: calculating the frame rate difference between the current frame rate and the target frame rate; dividing the frame rate difference into multiple sub-intervals according to preset transition rules; determining multiple sub-target frame rates according to each sub-interval, and the sub-target frame rates are between the current frame rate and the target frame rate; controlling the frame rate of the game application, passing through each sub-target frame rate in turn, and finally reaching the target frame rate.

[0071] Specifically, the server smoothly transitions the game application's current frame rate to the target frame rate. This process avoids sudden frame rate changes that can cause stuttering or jumpy gameplay, providing a smoother and more natural gaming experience. This smooth transition is achieved by dividing the difference between the current and target frame rates into multiple subranges. The server then sequentially passes through the subtarget frame rates corresponding to these subranges until the target frame rate is reached. First, the server calculates the frame rate difference between the current and target frame rates. For example, if the current frame rate is 30 FPS and the target frame rate is 60 FPS, the frame rate difference is 60 - 30 = 30 FPS. Next, the server divides the frame rate difference into multiple subranges based on a preset transition rule. This preset transition rule can be set based on actual needs and experience. For example, an equal division rule divides the frame rate difference into several subranges, with each subrange having an equal frame rate change. For example, a 30 FPS difference can be divided into three subranges, with each subrange having a frame rate change of 10 FPS. Acceleration rule: The frame rate change is small in the first few subintervals, and gradually increases in the following subintervals. This maintains a relatively stable frame rate change during the initial transition, accelerating to the target frame rate later. For example, a 30 FPS difference is divided into three subintervals with frame rate changes of 5 FPS, 10 FPS, and 15 FPS, respectively. Assume the server uses an equal division rule, dividing the 30 FPS difference into three subintervals, each with a frame rate change of 10 FPS. The server then determines the corresponding subtarget frame rate based on these subintervals. These subtarget frame rates are intermediate frame rates between the current frame rate and the target frame rate, representing the intermediate frame rates passed through during the transition. In the above example, the subtarget frame rates corresponding to the three subintervals are 40 FPS, 50 FPS, and 60 FPS, respectively. Finally, the server controls the frame rate of the game application, smoothly transitioning according to the determined subtarget frame rate sequence. A specific control method can be to set a frame rate cap parameter in the game engine or graphics API to limit the game's maximum frame rate to the current subtarget frame rate.

[0072] In one possible implementation, the frame rate difference is divided into multiple sub-intervals according to a preset transition rule, specifically including: obtaining a preset transition time; determining a frame rate change rate for a smooth transition based on the preset transition time and the frame rate difference; and using the frame rate change rate to evenly divide the frame rate difference into multiple sub-intervals.

[0073] Specifically, the server obtains a preset transition time. The preset transition time can be set based on factors such as the game type, user preferences, and network conditions. For example, for a fast-paced action game, the transition time can be set shorter to ensure smooth graphics; while for an adventure game with beautiful graphics, the transition time can be set longer to create a more soothing visual effect.

[0074] After obtaining the preset transition time, the server determines the frame rate change rate for a smooth transition based on the time and the frame rate difference. The frame rate change rate represents the speed at which the frame rate needs to change during the transition time. This rate is calculated by dividing the frame rate difference by the preset transition time.

[0075] For example, suppose the current frame rate is 30 fps, the target frame rate is 60 fps, the frame rate delta is 30 fps, and the preset transition time is 3 seconds. The frame rate change rate is 10 fps / s, meaning the frame rate needs to increase by 10 frames per second. Once the frame rate change rate is determined, the server can use it to evenly divide the frame rate delta into multiple subranges. This subrange division allows for a gradual frame rate transition and avoids issues like lag or flickering caused by sudden frame rate changes.

[0076] Specifically, the server calculates the number of subintervals required based on the frame rate change rate and the preset transition time. The number of subintervals is calculated by dividing the frame rate difference by the frame rate change rate. In the example above, the number of subintervals is 3, meaning the 30fps frame rate difference is evenly divided into three subintervals, each corresponding to a 10fps frame rate change.

[0077] After the sub-intervals are divided, the server can determine the sub-target frame rate for each sub-interval based on the sub-interval boundary values. The sub-target frame rate can be calculated by adding the frame rate change within the sub-interval to the current frame rate. For example, in the example above, the sub-target frame rate for the first sub-interval is 40fps, the sub-target frame rate for the second sub-interval is 50fps, and the sub-target frame rate for the third sub-interval is 60fps. After determining the sub-target frame rates, the server can begin controlling the game application's frame rate. Specifically, the server will sequentially set the game application's frame rate to each sub-target frame rate and maintain it at each sub-target frame rate for a period of time to achieve a smooth transition. This process can be achieved by setting a timer or listening for frame rate change events. During the frame rate transition process, the server monitors the game application's performance indicators in real time, such as CPU utilization, GPU utilization, and memory utilization. If any performance indicators are detected, the server can adjust the sub-target frame rate or transition speed appropriately to ensure smooth game operation.

[0078] Reference Figure 2The present application also provides a frame rate control switching device for a game display screen, which is a server. The server includes an acquisition module 201 and a processing module 202, wherein: the acquisition module 201 is used to obtain the scene complexity parameters of the game application, and the scene complexity parameters include the number, type and size of the rendering objects in the game scene; the processing module 202 is used to predict the scene complexity change trend of the game application within a preset time period based on the scene complexity parameters, and obtain a scene complexity prediction value; the acquisition module 201 is also used to obtain the current performance parameters of the game device, and calculate the target adjustment factor based on the scene complexity prediction value and the current performance parameters, and the target adjustment factor is used to adjust the game frame rate; the processing module 202 is also used to determine the target frame rate from the preset frame rate group according to the target adjustment factor, and the preset frame rate group includes multiple frame rates; the processing module 202 is also used to smoothly transition the current frame rate of the game application to the target frame rate.

[0079] In one possible implementation, the processing module 202 predicts the scene complexity change trend of the game application within a preset time period based on the scene complexity parameters, specifically including: obtaining historical scene complexity data of the game application within the historical time period, the historical scene complexity data including the scene complexity parameters corresponding to each preset time granularity within the historical time period; establishing a scene complexity prediction model based on the historical scene complexity data, the scene complexity prediction model including multiple prediction sub-models, each prediction sub-model corresponding to a scene complexity parameter; using the scene complexity prediction model, combined with the current scene complexity parameters of the game application, predicting the scene complexity parameters within the preset time period, and generating a scene complexity prediction value.

[0080] In one possible implementation, the processing module 202 establishes a scene complexity prediction model based on the historical scene complexity data, specifically including: the processing module 202 performs feature extraction on the historical scene complexity data to obtain a scene complexity feature vector; the processing module 202 selects a preset number of scene complexity feature vectors as training samples; the processing module 202 uses the training samples to generate a regression prediction model through supervised learning algorithm training, and the regression prediction model is used to characterize the correspondence between scene complexity parameters and time; the processing module 202 evaluates the regression prediction model using a cross-validation method to obtain model accuracy; determines whether the model accuracy reaches a preset threshold; if the model accuracy is greater than or equal to the preset threshold, the processing module 202 uses the regression prediction model as the scene complexity prediction model; if the model accuracy is less than the preset threshold, the processing module 202 adjusts the training parameters and returns to execute the step of using the training samples to generate a regression prediction model through supervised learning algorithm training.

[0081] In one possible implementation, the acquisition module 201 acquires the current performance parameters of the gaming device, and calculates the target adjustment factor based on the scene complexity prediction value and the current performance parameters, specifically including: the acquisition module 201 monitors the CPU occupancy, GPU occupancy, and memory occupancy of the gaming device in real time as current performance parameters; the acquisition module 201 scores the current performance parameters according to preset performance evaluation rules to obtain a device performance score; the acquisition module 201 calculates the ratio of the device performance score to the scene complexity prediction value, and uses the ratio as an initial adjustment factor; the acquisition module 201 determines whether the initial adjustment factor is within a preset adjustment factor range; if the initial adjustment factor is within the preset adjustment factor range, the acquisition module 201 uses the initial adjustment factor as the target adjustment factor; if the initial adjustment factor is not within the preset adjustment factor range, the acquisition module 201 uses a preset adjustment factor correction formula to correct the initial adjustment factor to obtain the target adjustment factor.

[0082] In one possible embodiment, the processing module 202 determines the target frame rate from the preset frame rate group based on the target adjustment factor, specifically including: the processing module 202 calculates the matching degree between the adjustment factor and each frame rate value in the preset frame rate group, and the matching degree is obtained by a preset matching degree calculation formula; the processing module 202 selects the frame rate value with the highest matching degree with the target adjustment factor as the candidate target frame rate; the processing module 202 determines whether the difference between the candidate target frame rate and the current frame rate is within a preset range; if the difference between the candidate target frame rate and the current frame rate is within the preset range, the processing module 202 determines the candidate target frame rate as the target frame rate; if the difference between the candidate target frame rate and the current frame rate is not within the preset range, the processing module 202 selects a frame rate value in the preset frame rate group that is adjacent to the candidate target frame rate and whose difference with the current frame rate is within the preset range as the target frame rate.

[0083] In one possible implementation, the processing module 202 smoothly transitions the current frame rate of the game application to the target frame rate, specifically including: the processing module 202 calculates the frame rate difference between the current frame rate and the target frame rate; the processing module 202 divides the frame rate difference into multiple sub-intervals according to a preset transition rule; based on each sub-interval, determines multiple sub-target frame rates, and the sub-target frame rates are between the current frame rate and the target frame rate; the processing module 202 controls the frame rate of the game application, passes through each sub-target frame rate in turn, and finally reaches the target frame rate.

[0084] In one possible implementation, the processing module 202 divides the frame rate difference into multiple sub-intervals according to a preset transition rule, specifically including: the acquisition module 201 obtains the preset transition time; the processing module 202 determines the frame rate change rate for smooth transition based on the preset transition time and the frame rate difference; the processing module 202 uses the frame rate change rate to evenly divide the frame rate difference into multiple sub-intervals.

[0085] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0086] This application also provides an electronic device. Figure 3 , Figure 3 3. This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0087] The communication bus 302 is used to implement the connection and communication between these components.

[0088] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0089] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0090] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0091] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , as a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and an application program of a method for controlling and switching the frame rate of a game display screen.

[0092] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for a frame rate control switching method of a game display screen. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0093] The present application further provides a computer-readable storage medium storing instructions, which, when executed by one or more processors 301 , enable the electronic device 300 to perform one or more of the methods described in the above embodiments.

[0094] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0096] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0097] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0098] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0099] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0100] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for controlling and switching the frame rate of a game display screen, characterized in that: The method comprises: Obtaining scene complexity parameters of the game application, wherein the scene complexity parameters include the number, type, and size of rendered objects in the game scene; Predicting a scene complexity change trend of the game application within a preset time period based on the scene complexity parameter to obtain a scene complexity prediction value; Obtaining current performance parameters of the gaming device, and calculating a target adjustment factor based on the scene complexity prediction value and the current performance parameters, wherein the target adjustment factor is used to adjust the game frame rate; determining a target frame rate from a preset frame rate group according to the target adjustment factor, the preset frame rate group including a plurality of frame rates; Smoothly transition the current frame rate of the game application to the target frame rate, wherein: The obtaining of the current performance parameters of the gaming device and calculating the target adjustment factor based on the scene complexity prediction value and the current performance parameters specifically includes: Real-time monitoring of the CPU occupancy rate, GPU occupancy rate, and memory occupancy rate of the gaming device as the current performance parameters; Scoring the current performance parameters according to preset performance evaluation rules to obtain a device performance score; Calculating a ratio of the device performance score to the predicted value of the scene complexity, and using the ratio as an initial adjustment factor; Determining whether the initial adjustment factor is within a preset adjustment factor range; If the initial adjustment factor is within the preset adjustment factor range, the initial adjustment factor is used as the target adjustment factor; If the initial adjustment factor is not within the preset adjustment factor range, the initial adjustment factor is corrected using a preset adjustment factor correction formula to obtain the target adjustment factor.

2. The method according to claim 1, characterized in that The predicting, based on the scene complexity parameter, a scene complexity change trend of the game application within a preset time period, specifically includes: Acquire historical scene complexity data of the game application within a historical time period, wherein the historical scene complexity data includes scene complexity parameters corresponding to each preset time granularity within the historical time period; Based on the historical scene complexity data, establishing a scene complexity prediction model, wherein the scene complexity prediction model includes a plurality of prediction sub-models, each of the prediction sub-models corresponding to a scene complexity parameter; The scene complexity prediction model is used in combination with the current scene complexity parameters of the game application to predict the scene complexity parameters within the preset time period and generate the scene complexity prediction value.

3. The method according to claim 2, characterized in that The step of establishing a scene complexity prediction model based on the historical scene complexity data specifically includes: Performing feature extraction on the historical scene complexity data to obtain a scene complexity feature vector; Select a preset number of scene complexity feature vectors as training samples; Using the training samples, a regression prediction model is generated through supervised learning algorithm training, wherein the regression prediction model is used to characterize the corresponding relationship between scene complexity parameters and time; The regression prediction model is evaluated using a cross-validation method to obtain the model accuracy; Determining whether the accuracy of the model reaches a preset threshold; If the model accuracy is greater than or equal to the preset threshold, the regression prediction model is used as the scene complexity prediction model; If the model accuracy is less than the preset threshold, the training parameters are adjusted and the process returns to the step of using the training samples to generate a regression prediction model through supervised learning algorithm training.

4. The method according to claim 1, wherein Determining a target frame rate from a preset frame rate group according to the target adjustment factor specifically includes: Calculating a matching degree between the adjustment factor and each frame rate value in the preset frame rate group, wherein the matching degree is obtained by a preset matching degree calculation formula; Selecting the frame rate value with the highest matching degree with the target adjustment factor as the candidate target frame rate; Determining whether a difference between the candidate target frame rate and the current frame rate is within a preset range; If the difference between the candidate target frame rate and the current frame rate is within a preset range, determining the candidate target frame rate as the target frame rate; If the difference between the candidate target frame rate and the current frame rate is not within a preset range, a frame rate value adjacent to the candidate target frame rate and having a difference with the current frame rate within a preset range is selected from the preset frame rate group as the target frame rate.

5. The method according to claim 1, wherein The step of smoothly transitioning the current frame rate of the game application to the target frame rate specifically includes: Calculating a frame rate difference between the current frame rate and the target frame rate; Dividing the frame rate difference into a plurality of sub-intervals according to a preset transition rule; Determining a plurality of sub-target frame rates according to each of the sub-intervals, wherein the sub-target frame rates are between the current frame rate and the target frame rate; The frame rate of the game application is controlled to pass through each of the sub-target frame rates in sequence and finally reach the target frame rate.

6. The method according to claim 5, characterized in that The frame rate difference is divided into a plurality of sub-intervals according to a preset transition rule, specifically including: Get the preset transition time; Determining a frame rate change rate for smooth transition according to the preset transition time and the frame rate difference; The frame rate difference is evenly divided into a plurality of sub-intervals using the frame rate change rate.

7. A frame rate control switching device for a game display screen, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire scene complexity parameters of the game application, wherein the scene complexity parameters include the number, type and size of rendering objects in the game scene; The processing module (202) is used to predict the scene complexity change trend of the game application within a preset time period based on the scene complexity parameter to obtain a scene complexity prediction value; The acquisition module (201) is further used to acquire current performance parameters of the gaming device and calculate a target adjustment factor based on the scene complexity prediction value and the current performance parameters. The target adjustment factor is used to adjust the game frame rate, wherein: The obtaining of the current performance parameters of the gaming device and calculating the target adjustment factor based on the scene complexity prediction value and the current performance parameters specifically includes: Real-time monitoring of the CPU occupancy rate, GPU occupancy rate, and memory occupancy rate of the gaming device as the current performance parameters; Scoring the current performance parameters according to preset performance evaluation rules to obtain a device performance score; Calculating a ratio of the device performance score to the predicted value of the scene complexity, and using the ratio as an initial adjustment factor; Determining whether the initial adjustment factor is within a preset adjustment factor range; If the initial adjustment factor is within the preset adjustment factor range, the initial adjustment factor is used as the target adjustment factor; If the initial adjustment factor is not within the preset adjustment factor range, the initial adjustment factor is corrected using a preset adjustment factor correction formula to obtain the target adjustment factor; The processing module (202) is further configured to determine a target frame rate from a preset frame rate group according to the target adjustment factor, wherein the preset frame rate group includes a plurality of frame rates; The processing module (202) is further configured to smoothly transition the current frame rate of the game application to the target frame rate.

8. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 6 is executed.

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