Graphics card performance optimization method and device, storage medium and computer equipment

Abstract

The invention discloses a graphics card performance optimization method and device, a storage medium and computer equipment, and the method comprises the steps: responding to a starting instruction of any installed software, reading a preset graphics card configuration parameter corresponding to the any installed software from a preset graphics card configuration file, the preset graphics card configuration parameter is determined based on the equipment model of the current computer equipment, the graphics card model, the unique identifier of any installed software and the expected performance of any installed software; and based on the preset graphics card configuration parameters, starting a graphics card of the current computer equipment, so that the graphics card performs data processing based on the preset graphics card configuration parameters. According to the method and the device, the automation of graphics card configuration is realized, the graphics card configuration parameters can be automatically adjusted without perception of a user, and the optimization of graphics card performance can be easily realized without professional graphics card knowledge or skills of the user.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method and device for optimizing the performance of a graphics card, a storage medium, and a computer device. Background Art

[0002] With the continuous development of computer technology, the optimization of graphics card performance has become one of the key factors in improving the overall performance of a computer. Existing methods for optimizing graphics card performance mainly focus on two aspects: hardware and software, but both have certain defects and deficiencies.

[0003] In terms of hardware, although manufacturers continuously improve the performance of graphics cards by increasing the core frequency, memory frequency, number of stream processors, etc. of the graphics card, and by adopting advanced technologies such as multi-GPU configuration and real-time ray tracing, these methods are often limited by the physical characteristics of the hardware itself, and may bring problems such as increased power consumption and shortened hardware lifespan.

[0004] In terms of software, manufacturers and third-party developers optimize the code of the driver, game engine, and application programs to improve the performance of the graphics card. However, existing software optimization methods usually require users to have certain technical knowledge to perform effective optimization. For example, users need to know how to adjust the voltage and frequency of the graphics card for overclocking, or need to know the specific meaning and impact of various graphics settings options, in order to manually perform graphics card settings and driver adjustments according to their own needs and hardware configuration. This not only increases the operation burden on users, but may also lead to a decrease or instability in system performance due to incorrect operations. Summary of the Invention

[0005] In view of this, this application provides a method and device for optimizing the performance of a graphics card, a storage medium, and a computer device, which realizes the automation of graphics card configuration. Users can make the software obtain the expected performance without manually adjusting the graphics card settings; the preset graphics card configuration file is generated according to the specific conditions of each software, computer device, and graphics card, so it can provide different performance optimization solutions for different software; through the pre-calculated graphics card configuration parameters, the adjustment of graphics card configuration parameters can be automatically performed without the user's awareness, and users can easily optimize the performance of the graphics card without the need to have professional graphics card knowledge or skills.

[0006] According to one aspect of this application, a method for optimizing the performance of a graphics card is provided, including:

[0007] In response to a startup instruction of any installed software, read the preset graphics card configuration parameters corresponding to the any installed software from a preset graphics card configuration file, where the preset graphics card configuration parameters are determined based on the device model of the current computer device, the graphics card model, the unique identifier of the any installed software, and the expected performance of the any installed software;

[0008] Based on the preset graphics card configuration parameters, start the graphics card of the current computer device so that the graphics card processes data based on the preset graphics card configuration parameters.

[0009] According to another aspect of the present application, there is provided an optimization device for graphics card performance, including:

[0010] A configuration parameter reading module, configured to, in response to a startup instruction of any installed software, read the preset graphics card configuration parameters corresponding to the any installed software from a preset graphics card configuration file, where the preset graphics card configuration parameters are determined based on the device model of the current computer device, the graphics card model, the unique identifier of the any installed software, and the expected performance of the any installed software;

[0011] A graphics card startup module, configured to start the graphics card of the current computer device based on the preset graphics card configuration parameters so that the graphics card processes data based on the preset graphics card configuration parameters.

[0012] According to yet another aspect of the present application, there is provided a storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned optimization method for graphics card performance is implemented.

[0013] According to still another aspect of the present application, there is provided a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, and when the processor executes the program, the above-mentioned optimization method for graphics card performance is implemented.

[0014] With the above technical solution, an optimization method and device for graphics card performance, a storage medium, and a computer device provided by the present application respond to a startup instruction of a certain software and load a preset graphics card configuration file. Then, the preset graphics card configuration parameters corresponding to the software are read from the preset graphics card configuration file. Further, the graphics card is configured and started according to the preset graphics card configuration parameters, and subsequently, the graphics card will work according to the preset graphics card configuration parameters to provide the expected performance. The embodiments of the present application achieve the automation of graphics card configuration. Without the need for the user to manually adjust the graphics card settings, the software can obtain the performance that meets the expectations. The preset graphics card configuration file is generated according to the specific conditions of each software, computer device, and graphics card. Therefore, different performance optimization solutions can be provided for different software. Through the pre-calculated graphics card configuration parameters, the adjustment of the graphics card configuration parameters can be automatically performed without the user's awareness. Without the need for the user to have professional graphics card knowledge or skills, the optimization of the graphics card performance can be easily achieved.

[0015] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically exemplified below. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0017] Figure 1 A flowchart showing an optimization method for graphics card performance provided by an embodiment of the present application is shown;

[0018] Figure 2 A schematic structural diagram of an optimization device for graphics card performance provided by an embodiment of the present application is shown;

[0019] Figure 3 A schematic structural diagram of a device of a computer device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The present application will be described in detail below with reference to the drawings and in combination with embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0021] In this embodiment, an optimization method for graphics card performance is provided. As Figure 1 shown, the method includes:

[0022] Step 101: In response to a startup instruction for any installed software, read the preset graphics card configuration parameters corresponding to the any installed software from a preset graphics card configuration file, where the preset graphics card configuration parameters are determined based on the device model of the current computer device, the graphics card model, the unique identifier of the any installed software, and the expected performance of the any installed software.

[0023] Step 102: Based on the preset graphics card configuration parameters, start the graphics card of the current computer device so that the graphics card processes data based on the preset graphics card configuration parameters.

[0024] An optimization method for the performance of a graphics card provided by an embodiment of the present application can achieve automatic and reasonable adjustment of graphics card configuration parameters to avoid waste of graphics card resources while meeting the software operation requirements. When a user wants to start a certain installed software, a startup instruction for the software can be triggered by clicking on the software icon, double-clicking on the software executable file, or entering a corresponding command in the command line, etc. Then, the computer device can respond to the startup instruction of the software and load the preset graphics card configuration file. Among them, the preset graphics card configuration file can store the preset graphics card configuration parameters corresponding to each installed software on the computer device. These preset graphics card configuration parameters are generated through a series of calculations in advance according to factors such as the device model of the computer device, the graphics card model, the unique identifier of the software, and the expected performance of the software. These parameters include but are not limited to the clock frequency of the graphics card, video memory allocation, render queue size, texture filtering method, etc. Then, read the preset graphics card configuration parameters corresponding to the installed software from the preset graphics card configuration file, configure and start the graphics card according to the preset graphics card configuration parameters, and subsequently the graphics card will work according to the preset graphics card configuration parameters to provide the expected performance. For example, if the preset graphics card configuration parameters specify a higher clock frequency and a larger video memory allocation, the graphics card runs with these parameters and processes data from the software, which can be graphic data, video data, game data, etc., thereby accelerating the data processing speed.

[0025] By applying the technical solution of this embodiment, in response to the startup instruction of a certain software, a preset graphics card configuration file is loaded. Then, the preset graphics card configuration parameters corresponding to the software are read from the preset graphics card configuration file. Further, the graphics card is configured and started according to the preset graphics card configuration parameters, and the subsequent graphics card will work according to the preset graphics card configuration parameters to provide the expected performance. The embodiment of the present application realizes the automation of graphics card configuration. Without manual adjustment of the graphics card settings by the user, the software can obtain the performance that meets the expectations. The preset graphics card configuration file is generated according to the specific conditions of each software, computer device, and graphics card. Therefore, different performance optimization solutions can be provided for different software. Through the pre-calculated graphics card configuration parameters, the adjustment of the graphics card configuration parameters can be automatically performed without the user's awareness. Without the need for the user to have professional graphics card knowledge or skills, the optimization of the graphics card performance can be easily achieved.

[0026] In the embodiment of the present application, optionally, before step 101, the method further includes: in response to the installation instruction of any uninstalled software, obtaining the device model of the current computer device, the graphics card model, the unique identifier of the any uninstalled software, and the current graphics card configuration parameters; inputting the device model, the graphics card model, the unique identifier of the any uninstalled software, and the current graphics card configuration parameters into a preset performance prediction model to obtain the performance prediction value of the any uninstalled software; when the performance prediction value is greater than the expected performance, taking the current graphics card configuration parameters as the preset graphics card configuration parameters of the any uninstalled software and recording the preset graphics card configuration parameters; when the performance prediction value is less than or equal to the expected performance, obtaining the preset graphics card configuration parameters of the any uninstalled software according to the preset performance prediction model and recording the preset graphics card configuration parameters; correspondingly, after "recording the preset graphics card configuration parameters", the method further includes: when the any uninstalled software is installed, controlling the display interface to display the preset graphics card configuration parameters, and storing the preset graphics card configuration parameters in the preset graphics card configuration file when receiving the confirmation instruction of the preset graphics card configuration parameters.

[0027] In this embodiment, whenever a user installs new software, the preset graphics card configuration parameters corresponding to the software can be calculated and stored in a preset graphics card configuration file. Specifically, when a user attempts to install an uninstalled software on a computer, the computer device can respond to this installation instruction and collect the device model of the current computer device, the graphics card model, the unique identifier of the software installed by the user, and the graphics card configuration parameters at the current moment. Among them, the hardware basis can be understood through the device model of the current computer device; how to perform configuration optimization can be understood through the graphics card model; the serial number, version number of the software, or other information that can uniquely identify the software can be understood through the unique identifier of the installed software; the current setting status of the graphics card in the computer device can be understood through the current graphics card configuration parameters.

[0028] After that, the information collected above is input into a preset performance prediction model. This model, based on historical data, algorithms, and logic, can predict the performance of the software when using the current or different graphics card configuration parameters under the given hardware and software environment. Specifically, the training process of the preset performance prediction model can be as follows: Data collection: First, establish a data collection system for collecting computer devices of various models, graphics cards of various models, various software, various graphics card configuration parameters, and the software performance data corresponding to each combination (computer device model + graphics card model + software unique identifier + graphics card configuration parameters). The above data can be obtained through user feedback, automatic testing, etc. Data processing and analysis: Then, process and analyze the collected data for subsequent machine learning training. The processing and analysis include steps such as data cleaning, feature selection, and data standardization. Machine learning model training: After the data processing and analysis are completed, use this data to train a machine learning model. The goal of this model is to obtain the software performance prediction value for a combination of a computer device model, a graphics card model, a software unique identifier, and graphics card configuration parameters. In this way, the preset performance prediction model is obtained.

[0029] If the prediction result shows that using the current graphics card configuration parameters can meet or exceed the expected performance standard, then the current graphics card configuration parameters can be directly used as the preset graphics card configuration parameters for the software, and this configuration is recorded. If the prediction result shows that the current graphics card configuration parameters cannot meet the expected performance standard, then a more optimized graphics card configuration parameter can be calculated according to the preset performance prediction model and used as the preset graphics card configuration parameters for the software, and this configuration is recorded. It should be noted that the expected performance of the software can be manually input by the user, or determined according to the user's selection, or obtained through statistical analysis of the expected performance of the general users using the software, which is not limited here.

[0030] After the software installation is completed, the preset graphics card configuration parameters calculated for the software before can be displayed on the display interface for the user to confirm. If the user has no objection to these parameters, a confirmation instruction can be used to confirm these settings. Once the confirmation instruction from the user is received, the preset graphics card configuration parameters can then be stored in the preset graphics card configuration file for quick reading and application of the corresponding graphics card configuration when the software starts. In the embodiment of the present application, the preset performance prediction model is used to preset and optimize the graphics card configuration parameters of the software to improve the running performance of the software. This method can not only improve the running efficiency of the software, but also reduce the trouble of manual adjustment of the graphics card settings by the user and enhance the user experience.

[0031] In the embodiment of the present application, optionally, the step of "obtaining the preset graphics card configuration parameters of any uninstalled software according to the preset performance prediction model" includes: adjusting the current graphics card configuration parameters according to a preset rule, and inputting the device model, the graphics card model, the unique identifier of any uninstalled software, and the adjusted current graphics card configuration parameters into the preset performance prediction model to obtain a new performance prediction value of any uninstalled software again; judging the magnitude relationship between the newly obtained performance prediction value and the expected performance. When the newly obtained performance prediction value is less than or equal to the expected performance, adjust the current graphics card configuration parameters again according to the preset rule, and obtain the performance prediction value of any uninstalled software again until the newly obtained performance prediction value is greater than the expected performance, and then end. The current graphics card configuration parameters when the value is greater than the expected performance are used as the preset graphics card configuration parameters of any uninstalled software.

[0032] In this embodiment, first, the current graphics card configuration parameters can be adjusted according to a preset rule. The preset rule may include gradually increasing the video memory allocation, increasing the core frequency, etc., aiming to tentatively find the configuration parameters that meet the software performance requirements. After each adjustment of the current graphics card configuration parameters, the adjusted graphics card configuration parameters, together with the computer device model, the graphics card model, and the software unique identifier, can be input into the preset performance prediction model again, and the model can correspondingly output a new performance prediction value. Compare the magnitude relationship between the current performance prediction value and the expected performance. If the performance prediction value is less than or equal to the expected performance, it means that the current configuration is not sufficient to meet the software performance requirements, and the graphics card configuration parameters need to be adjusted again according to the preset rule, and the above process of inputting the model for prediction is repeated. This process will be iterated until a graphics card configuration parameter is found such that the performance prediction value exceeds the expected performance. When the graphics card configuration parameter that meets the conditions is found, this graphics card configuration parameter is used as the recommended graphics card configuration parameter for the uninstalled software to run on this computer device.

[0033] In the embodiments of the present application, by continuously iteratively adjusting the graphics card configuration parameters and using a preset performance prediction model to evaluate the performance after each adjustment until a graphics card configuration scheme that can meet (or even exceed) the expected performance requirements of the software and is as optimized as possible is found, the flexibility of the graphics card configuration can be increased and the waste of graphics card performance can be avoided.

[0034] In the embodiments of the present application, optionally, before step 101, the method further includes: in response to an update verification instruction for the graphics card configuration parameters, obtaining the target software in the current computer device that has not updated the graphics card configuration parameters; through the preset performance prediction model, obtaining the preset graphics card configuration parameters of the target software, controlling the display interface to display the preset graphics card configuration parameters of the target software, and after receiving a confirmation instruction for the preset graphics card configuration parameters of the target software, storing the preset graphics card configuration parameters of the target software in the preset graphics card configuration file.

[0035] In this embodiment, software that has not set the graphics card configuration parameters according to the above method can also be supplemented with settings. Specifically, first, the computer device can, in response to an update verification instruction for the graphics card configuration parameters, scan all the software installed in the current computer device, identify the software that has not updated the graphics card configuration parameters according to the foregoing method, and mark these software as target software. Among them, the update verification instruction for the graphics card configuration parameters can be an operation initiated by the user through a certain interface (such as the control panel, command line tool, etc.), or can be automatically executed according to a preset rule (such as automatically triggered after software update). The purpose of this instruction is to check whether there is software in the current computer device that needs to update the graphics card configuration parameters.

[0036] After determining the target software, for each target software, use the preset performance prediction model to predict the performance prediction value of the target software through the model of the current computer device, the graphics card model, the unique identifier of the target software, and the current graphics card configuration parameters. Similarly, the size relationship between the performance prediction value and the expected performance of the target software can be compared, and the graphics card configuration parameters can be continuously adjusted according to the size relationship and the preset rules until the preset graphics card configuration parameters that meet the expected performance of the target software are finally obtained. Then, the preset graphics card configuration parameters of each target software are displayed to the user through a user interface (such as a pop-up window, a setting page, etc.). The user can confirm whether to accept these preset graphics card configuration parameters through buttons or options on the user interface. If the user selects to confirm, then these parameters can be stored in the preset graphics card configuration file next.

[0037] Embodiments of the present application simplify the management process of graphics card configuration parameters in an automated and intelligent manner. Embodiments of the present application can not only recommend graphics card configuration parameters that meet the software performance expectations according to the characteristics of the software and the hardware environment, but also enable users to easily confirm and apply these configuration parameters through the interaction of the user interface, improving the running efficiency of the software and enhancing the overall user experience at the same time.

[0038] In an embodiment of the present application, optionally, after "starting the graphics card of the current computer device" in step 103, the method further includes: real-time monitoring the running parameters of the current computer device, and when any one of the running parameters indicates abnormal operation, ending the software running process, re-determining the preset graphics card configuration parameters of any one of the installed software according to the preset performance prediction model, and simulating the running process of any one of the installed software in the virtual device according to the device model, the graphics card model, the unique identifier of any one of the installed software, and the re-determined preset graphics card configuration parameters, recording the running parameters of the virtual device, and when the running parameters of the virtual device do not indicate abnormality within a preset time period, updating the preset graphics card configuration file based on the re-determined preset graphics card configuration parameters.

[0039] In this embodiment, after starting the graphics card according to the preset graphics card configuration parameters and making the graphics card start to work, continuous monitoring of various operating parameters of the computer device is started, including but not limited to CPU usage, memory occupancy, graphics card temperature, fan speed, power status, etc. These parameters can reflect the health status and performance of the computer device. When it is detected that any one of the operating parameters exceeds the normal range or reaches the preset threshold, it is considered that an abnormality occurs in the operation of the computer device or software. To prevent the abnormality from further spreading or causing more serious consequences, the software operation process that may cause the abnormality can be terminated immediately. Then, using the preset performance prediction model, combined with the hardware information of the current device (such as device model, graphics card model, etc.) and the unique identifier of the software, a new set of graphics card configuration parameters is recalculated and determined. These parameters are designed to optimize the operation of the software while avoiding similar abnormalities from occurring again. Specifically, the existing preset graphics card configuration parameters can be adjusted again according to the preset rules, and a new graphics card configuration parameter that can make the software meet the expected performance is calculated again. To verify whether the re-determined graphics card configuration parameters are effective, a simulation test can be carried out in a virtual device environment. This virtual device can simulate the hardware and software environment of a real device, including device model, graphics card model, operating system, and the software that causes the abnormality, etc. Here, the virtual device refers to a computing environment simulated at the computer software level with specific hardware and software configurations, such as a virtual machine, etc. During the simulation test, the operating parameters of the virtual device are continuously recorded to monitor whether there are still abnormalities. If the operating parameters of the virtual device remain within the normal range within the preset time period (such as several hours, several days, etc.) and no abnormalities occur again, then it can be considered that the re-determined graphics card configuration parameters are effective. At this time, these effective graphics card configuration parameters can be updated to the preset graphics card configuration file. In this way, when the software is started again, these verified and optimized graphics card configuration parameters can be used, thereby improving the operation efficiency and stability of the software.

[0040] Through a series of steps such as real-time monitoring, anomaly detection, re-determination of configuration parameters, virtual device simulation testing, and configuration update, the embodiment of this application forms a closed-loop and automated anomaly handling and optimization process, which can not only timely detect and handle anomaly problems in the operation of computer devices, but also continuously optimize the graphics card configuration parameters of the software through intelligent means, improving the overall performance of the device and the user experience.

[0041] In an embodiment of the present application, optionally, after "starting the graphics card of the current computer device" in step 103, the method further includes: in response to an exit instruction of any installed software, outputting a software usage satisfaction inquiry interface and identifying feedback data of the software usage satisfaction inquiry interface; when the feedback data indicates that the software usage satisfaction is lower than a preset satisfaction threshold, updating the dissatisfaction count and determining the relationship between the updated dissatisfaction count and a preset number of times; when the updated dissatisfaction count is greater than the preset number of times, generating a feedback report and sending the feedback report to a preset communication address.

[0042] In this embodiment, after the software is started normally, the graphics card begins to work according to the corresponding preset graphics card configuration parameters. After the user exits the software, a software usage satisfaction inquiry interface can be displayed on the computer device at this time. This interface can be a pop-up window or dialog box, asking the user to evaluate the satisfaction of the just-used software. Specifically, the user can express their satisfaction through the options provided by the interface (such as very satisfied, satisfied, average, dissatisfied, etc.) or free text input. Then, the feedback data can be identified, and the size relationship between the software usage satisfaction indicated by the feedback data and the preset satisfaction threshold can be determined. Among them, the preset satisfaction threshold is used to judge whether the user's satisfaction is high enough. When the feedback data of the user indicates that the software usage satisfaction is lower than the preset satisfaction threshold, the current dissatisfaction count can be incremented by one at this time, and this count records the number of times the user expresses dissatisfaction with the software. In addition, a preset number of times is also set to judge whether a feedback report needs to be generated and sent. When the updated dissatisfaction count is greater than the preset number of times, a feedback report can be generated. This report can include information such as the user's satisfaction data and the specific reasons for dissatisfaction (if provided by the user). After that, the generated feedback report is sent to the preset communication address. This address can be the email or server address of the developer of the preset performance prediction model, the technical support team, or the product management team, so that they can make improvements based on the feedback.

[0043] The embodiment of the present application provides a way for the developer of the preset performance prediction model to understand user needs and improve graphics card configuration parameters by monitoring the software exit process and collecting user satisfaction feedback. Through the preset satisfaction threshold and the preset number of times, it can be automatically judged when a feedback report needs to be generated and sent, thus improving the efficiency and accuracy of feedback collection.

[0044] In an embodiment of the present application, optionally, the expected performance corresponding to each software is determined based on the first user usage preference of the user corresponding to the computer device and / or the second user usage preference of the user group corresponding to the software.

[0045] In this embodiment, the first user refers to the user who uses the current computer device. The usage preferences of the first user may include personal preferences or requirements regarding the software interface, functions, response speed, etc. These preferences can be obtained through user surveys, analysis of historical data generated when the first user used the software in the past, and other means. The second user refers to all users corresponding to the software. By analyzing the usage preferences of this user group, the general requirements or expectations of most users for software performance can be understood, thereby formulating software expected performance that better meets market demands.

[0046] When determining the expected performance of a certain software, the usage preferences of the first user or the second user can be considered alone, or the usage preferences of the first user and the second user can be considered comprehensively.

[0047] Furthermore, as Figure 1 a specific implementation of the method, an embodiment of the present application provides an optimization device for the performance of a graphics card, as Figure 2 shown. The device includes:

[0048] A configuration parameter reading module, configured to, in response to a startup instruction of any installed software, read the preset graphics card configuration parameters corresponding to the any installed software from a preset graphics card configuration file, where the preset graphics card configuration parameters are determined based on the device model of the current computer device, the graphics card model, the unique identifier of the any installed software, and the expected performance of the any installed software;

[0049] A graphics card startup module, configured to start the graphics card of the current computer device based on the preset graphics card configuration parameters, so that the graphics card processes data based on the preset graphics card configuration parameters.

[0050] Optionally, the device further includes a recording module; the recording module is configured to:

[0051] Before obtaining the preset graphics card configuration parameters corresponding to the any installed software from the preset graphics card configuration file in response to a startup instruction of any installed software, in response to an installation instruction of any uninstalled software, obtain the device model of the current computer device, the graphics card model, the unique identifier of the any uninstalled software, and the current graphics card configuration parameters;

[0052] Input the device model, the graphics card model, the unique identifier of the any uninstalled software, and the current graphics card configuration parameters into a preset performance prediction model to obtain a performance prediction value of the any uninstalled software;

[0053] When the performance prediction value is greater than the expected performance, use the current graphics card configuration parameters as the preset graphics card configuration parameters for the any uninstalled software, and record the preset graphics card configuration parameters;

[0054] When the performance prediction value is less than or equal to the expected performance, according to the preset performance prediction model, obtain the preset graphics card configuration parameters of any uninstalled software, and record the preset graphics card configuration parameters;

[0055] Correspondingly, the device further includes a parameter confirmation module; the parameter confirmation module is used for:

[0056] After recording the preset graphics card configuration parameters, when any uninstalled software is installed, control the display interface to display the preset graphics card configuration parameters, and when receiving the confirmation instruction of the preset graphics card configuration parameters, store the preset graphics card configuration parameters in the preset graphics card configuration file.

[0057] Optionally, the recording module is further used for:

[0058] Adjust the current graphics card configuration parameters according to a preset rule, and input the device model, the graphics card model, the unique identifier of any uninstalled software, and the adjusted current graphics card configuration parameters into the preset performance prediction model to obtain the performance prediction value of any uninstalled software again;

[0059] Judge the magnitude relationship between the newly obtained performance prediction value and the expected performance. When the newly obtained performance prediction value is less than or equal to the expected performance, adjust the current graphics card configuration parameters again according to the preset rule, and obtain the performance prediction value of any uninstalled software again until the newly obtained performance prediction value is greater than the expected performance and then end. Use the current graphics card configuration parameters when it is greater than the expected performance as the preset graphics card configuration parameters of any uninstalled software.

[0060] Optionally, the device further includes a verification module; the verification module is used for:

[0061] Before reading the preset graphics card configuration parameters corresponding to any installed software from the preset graphics card configuration file in response to the startup instruction of any installed software, in response to the update verification instruction of the graphics card configuration parameters, obtain the target software in the current computer device whose graphics card configuration parameters have not been updated;

[0062] Obtain the preset graphics card configuration parameters of the target software through the preset performance prediction model, control the display interface to display the preset graphics card configuration parameters of the target software, and when receiving the confirmation instruction of the preset graphics card configuration parameters of the target software, store the preset graphics card configuration parameters of the target software in the preset graphics card configuration file.

[0063] Optionally, the device further includes an update module; the update module is used for:

[0064] After starting the graphics card of the current computer device, the running parameters of the current computer device are monitored in real time. When any one of the running parameters indicates abnormal operation, the software running process is ended, the preset graphics card configuration parameters of any installed software are re-determined according to the preset performance prediction model, and in the virtual device, according to the device model, the graphics card model, the unique identifier of any installed software, and the re-determined preset graphics card configuration parameters, the running process of any installed software is simulated, the running parameters of the virtual device are recorded, and when the running parameters of the virtual device do not indicate abnormality within a preset time period, the preset graphics card configuration file is updated based on the re-determined preset graphics card configuration parameters.

[0065] Optionally, the device further includes a feedback module; the feedback module is used for:

[0066] After starting the graphics card of the current computer device, in response to the exit instruction of any installed software, a software usage satisfaction inquiry interface is output, and the feedback data of the software usage satisfaction inquiry interface is identified;

[0067] When the feedback data indicates that the software usage satisfaction is lower than the preset satisfaction threshold, the dissatisfaction count is updated, and the relationship between the updated dissatisfaction count and the preset number of times is judged;

[0068] When the updated dissatisfaction count is greater than the preset number of times, a feedback report is generated and sent to the preset communication address.

[0069] Optionally, the expected performance corresponding to each software is determined based on the first user usage preference of the user corresponding to the computer device and / or the second user usage preference of the user group corresponding to the software.

[0070] It should be noted that for other corresponding descriptions of each functional unit involved in an optimization device for graphics card performance provided in an embodiment of the present application, reference can be made to Figure 1 the corresponding description in the method, which will not be elaborated here.

[0071] An embodiment of the present application further provides a computer device, which can specifically be a personal computer, a server, a network device, etc., such as Figure 3As shown in the figure, the computer device includes a bus, a processor, a memory, and a communication interface, and may further include an input / output interface and a display device. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the method embodiments are implemented.

[0072] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0073] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium may be non-volatile or volatile, and stores a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0074] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0075] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0076] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0077] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0078] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for optimizing graphics card performance, characterized in that: include: In response to a startup instruction of any installed software, read preset graphics card configuration parameters corresponding to any installed software from a preset graphics card configuration file, wherein the preset graphics card configuration parameters are determined based on a device model of a current computer device, a graphics card model, a unique identifier of any installed software, and an expected performance of any installed software; Based on the preset graphics card configuration parameters, the graphics card of the current computer device is started, so that the graphics card performs data processing based on the preset graphics card configuration parameters.

2. The method according to claim 1, characterized in that Before obtaining preset graphics card configuration parameters corresponding to any installed software from a preset graphics card configuration file in response to a startup instruction of any installed software, the method further includes: In response to an installation instruction of any software not installed, obtaining a device model of a current computer device, a graphics card model, a unique identifier of any software not installed, and a current graphics card configuration parameter; Input the device model, the graphics card model, the unique identifier of any non-installed software, and the current graphics card configuration parameters into a preset performance prediction model to obtain a performance prediction value of any non-installed software; When the performance prediction value is greater than the expected performance, using the current graphics card configuration parameter as the preset graphics card configuration parameter of any non-installed software, and recording the preset graphics card configuration parameter; When the performance prediction value is less than or equal to the expected performance, obtaining preset graphics card configuration parameters of any non-installed software according to the preset performance prediction model, and recording the preset graphics card configuration parameters; Correspondingly, after recording the preset graphics card configuration parameters, the method further includes: When any of the uninstalled software is installed, the display interface is controlled to display the preset graphics card configuration parameters, and when a confirmation instruction of the preset graphics card configuration parameters is received, the preset graphics card configuration parameters are stored in the preset graphics card configuration file.

3. The method according to claim 2, characterized in that The obtaining, according to the preset performance prediction model, preset graphics card configuration parameters of any non-installed software includes: Adjusting the current graphics card configuration parameters according to preset rules, and inputting the device model, the graphics card model, the unique identifier of any non-installed software, and the adjusted current graphics card configuration parameters into the preset performance prediction model to obtain the performance prediction value of any non-installed software again; Determine the relationship between the re-obtained performance prediction value and the expected performance; when the re-obtained performance prediction value is less than or equal to the expected performance, adjust the current graphics card configuration parameters again according to the preset rules, and obtain the performance prediction value of any non-installed software again, until the re-obtained performance prediction value is greater than the expected performance, and use the current graphics card configuration parameters when they are greater than the expected performance as the preset graphics card configuration parameters of any non-installed software.

4. The method according to claim 1, characterized in that: Before responding to the startup instruction of any installed software and reading the preset graphics card configuration parameters corresponding to any installed software from the preset graphics card configuration file, the method further includes: In response to the update verification instruction of the graphics card configuration parameters, obtaining the target software in the current computer device for which the graphics card configuration parameters have not been updated; The preset graphics card configuration parameters of the target software are obtained through the preset performance prediction model, the display interface is controlled to display the preset graphics card configuration parameters of the target software, and after receiving a confirmation instruction of the preset graphics card configuration parameters of the target software, the preset graphics card configuration parameters of the target software are stored in the preset graphics card configuration file.

5. The method according to claim 1, characterized in that After starting the graphics card of the current computer device, the method further includes: The operating parameters of the current computer device are monitored in real time. When any one of the operating parameters indicates an abnormal operation, the software operation process is terminated, the preset graphics card configuration parameters of any installed software are re-determined according to the preset performance prediction model, and the operation process of any installed software is simulated in the virtual device according to the device model, the graphics card model, the unique identifier of any installed software and the re-determined preset graphics card configuration parameters, and the operating parameters of the virtual device are recorded. When the operating parameters of the virtual device do not indicate an abnormality within a preset time period, the preset graphics card configuration file is updated based on the re-determined preset graphics card configuration parameters.

6. The method according to claim 1, characterized in that After starting the graphics card of the current computer device, the method further includes: In response to an exit instruction of any of the installed software, output a software usage satisfaction inquiry interface, and identify feedback data of the software usage satisfaction inquiry interface; When the feedback data indicates that the software usage satisfaction is lower than a preset satisfaction threshold, updating the dissatisfaction count, and determining the relationship between the updated dissatisfaction count and the preset number of times; When the updated dissatisfaction count is greater than the preset number of times, a feedback report is generated and sent to a preset communication address.

7. The method according to any one of claims 1 to 6, characterized in that The expected performance corresponding to each software is determined based on a first user usage preference of a user corresponding to the computer device and / or a second user usage preference of a user group corresponding to the software.

8. A device for optimizing graphics card performance, characterized in that: include: a configuration parameter reading module, configured to read, in response to a startup instruction of any installed software, preset graphics card configuration parameters corresponding to any installed software from a preset graphics card configuration file, wherein the preset graphics card configuration parameters are determined based on a device model of a current computer device, a graphics card model, a unique identifier of any installed software, and an expected performance of any installed software; The graphics card startup module is used to start the graphics card of the current computer device based on the preset graphics card configuration parameters, so that the graphics card performs data processing based on the preset graphics card configuration parameters.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

Images